Characterization of cactus pear germplasm in South Africa BY BARBARA KEITUMETSE MASHOPE A thesis submitted in fulfilment of the requirements for the degree of Philosophiae Doctor May 2007 In the Faculty of Natural and Agricultural Sciences Department of Plant Sciences (Plant Breeding Division) University of the Free State Promoter: Prof. M. T. Labuschagne Co-promoters: Prof. W. J. Swart Dr. L. Herselman TABLE OF CONTENTS PAGE DECLARATION v ACKNOWLEDGEMENTS vi ABBREVIATIONS AND ACRONYMS vii LIST OF TABLES xi LIST OF FIGURES xii GENERAL INTRODUCTION 1 CHAPTER 1 Characterisation and evaluation of Opuntia spp. 1.1 Introduction 5 1.2 General background 6 1.3 Germplasm characterisation 8 1.3.1 Morphological markers 8 1.3.2 Isozymes 11 1.3.3 DNA markers 12 1.4 Germplasm evaluation 14 1.4.1 Evaluation for fruit quality 14 1.4.2 Evaluation for fodder quality 16 1.4.3 Evaluation for resistance to fungal disease 18 1.5 Conclusions 21 References 24 CHAPTER 2 Genotyping South African cactus pear (Opuntia spp.) varieties using AFLP markers Abstract 37 2.1 Introduction 38 2.2 Materials and Methods 40 2.2.1 Plant Material 40 2.2.2 DNA isolation 40 2.2.3 AFLP analysis 42 2.2.4 Restriction endonuclease digestion and ligation 42 2.2.5 Pre-selective amplification 43 2.2.6 Selective amplification 44 2.2.7 Polyacrylamide gel electrophoresis 44 2.2.8 Silver staining 44 2.2.9 Statistical analysis 44 i 2.3 Results and Discussion 46 2.4 Conclusions 58 References 60 CHAPTER 3 Fruit quality of South African cactus pear (Opuntia spp.) varieties Abstract 66 3.1 Introduction 67 3.2 Materials and Methods 69 3.2.1 Trial site and layout 69 3.2.2 Climatic data 70 3.2.3 Cultural practices 71 3.2.4 Data collection and statistical analysis 74 3.3 Results and Discussion 75 3.3.1 Fruit quality : Season 1 75 3.3.1.1 Peel thickness 75 3.3.1.2 Fruit shape 77 3.3.1.3 Fruit mass 77 3.3.1.4 Total soluble solids content 78 3.3.1.5 Percentage pulp 78 3.3.1.6 Number of fruit 78 3.3.1.7 Peelability 79 3.3.1.8 Fruit width 79 3.3.1.9 Fruit length 79 3.3.1.10 Pulp colour 79 3.3.2 Fruit quality : Season 2 80 3.3.2.1 Peel thickness 80 3.3.2.2 Fruit shape 80 3.3.2.3 Fruit mass 80 3.3.2.4 Total soluble solids content 82 3.3.2.5 Percentage pulp 82 3.3.2.6 Number of fruit 82 3.3.2.7 Peelability 82 3.3.2.8 Fruit width 82 3.3.2.9 Fruit length 82 3.3.2.10 Pulp colour 83 3.3.3 Phenological and qualitative traits 83 ii 3.3.4 Effect of microclimatic conditions during fruit development on fruit 86 quality 3.3.5 Combined analysis 89 3.4 Conclusions 93 References 95 CHAPTER 4 Evaluation of South African cactus pear (Opuntia spp.) varieties for specific use as fodder Abstract 102 4.1 Introduction 103 4.2 Materials and Methods 104 4.2.1 Nutritional quality analysis 104 4.2.1.1 Trial site 1 104 4.2.1.2 Climatic data 104 4.2.1.3 Dry matter content 104 4.2.1.4 Organic matter content 105 4.2.1.5 Crude protein content 105 4.2.2 Evaluation of vegetative growth 105 4.2.2.1 Trial site 2 105 4.2.3 Statistical analysis 106 4.3 Results and Discussion 107 4.3.1 Nutritional quality 107 4.3.1.1 Dry matter content (DM) 107 4.3.1.2 Crude protein content (CP) 108 4.3.1.3 Organic matter content (OM) 108 4.3.2 Vegetative growth over combined seasons 110 4.3.2.1 Number of cladodes removed with pruning 110 4.3.2.2 Number of cladodes remaining after pruning 110 4.3.2.3 Mass of cladodes 112 4.3.2.4 Cladode yield 112 4.3.3 Cluster analysis 113 4.4 Conclusions 114 References 116 iii CHAPTER 5 Resistance of cactus pear varieties to three fungal pathogens and an option for biocontrol using yeasts Abstract 120 5.1 Introduction 121 5.2 Materials and Methods 123 5.2.1 Trial site and layout 123 5.2.2 Pathogenicity studies 123 5.2.2.1 Inoculum preparation 123 5.2.2.2 Cladode inoculation 123 5.2.3 Statistical analysis 124 5.2.4 In vitro inhibition studies 124 5.2.4.1 Yeast isolation 124 5.2.4.2 In vitro inhibition screening 124 5.2.4.3 Molecular identification of yeast isolates 125 5.2.4.4 Statistical analysis 125 5.3 Results and Discussion 125 5.3.1 Pathogenicity studies 125 5.3.1.1 Susceptibility of cactus pear varieties to Fusarium oxysporum 125 5.3.1.2 Susceptibility of cactus pear varieties to Fusarium proliferatum 128 5.3.1.3 Susceptibility of cactus pear varieties to Phialocephala virens 131 5.3.1.4 Overall susceptibility of cactus pear varieties to fungal pathogens 133 5.3.2 In vitro inhibition studies 135 5.3.2.1 Yeast isolate identification 135 5.3.2.2 In vitro inhibition screening 136 5.4 Conclusions 139 References 141 GENERAL CONCLUSIONS AND RECOMMENDATIONS 146 SUMMARY 150 OPSOMMING 151 APPENDICES 152 iv DECLARATION “I hereby declare that the thesis submitted by me for the degree of Philosophiae Doctor at the University of the Free State is my own independent work and has not previously been submitted by me at another University/Faculty. …………………………………. Barbara Keitumetse Mashope v ACKNOWLEDGEMENTS I would like to express and convey my sincere gratitude to all who assisted and contributed to the successful completion of this study. In particular, I would like to thank the following persons: To my parents Mr. and Mrs. G. M. Mashope for their tireless support and assistance throughout my studies. Many thanks to my promoter Prof. M.T. Labuschagne for her continuous support, encouragement and enthusiasm. I am also grateful to my co-promotor Dr. L. Herselman for her meticulous scrutiny of the many drafts that have led to the completion of this thesis and Prof. W.J. Swart for his expert advice and insightful comments. The Limpopo Department of Agriculture for the research and weather data used in this study. I am also grateful to Mr. J. Potgieter for his profound insight, support and lively discussion in addition to supplying photos, data and articles used in this study. My friends Drs. P.D. Gqola, E. Koen, M. Tesfaendrias, Mrs K. Lehloenya, and Ms. L. Chetty for support and encouragement throughout this study. Mr. S. Sithole and Dr. S.G. Mhlongo for collection of data for disease screening trials and Mr. L.L. Sehurutshi for his assistance. Ms. K. Ngesi and Dr. J Bahta for their assistance with the statistical analysis. Many thanks also to my dear friend Mr. O. Philippou for his gracious support and enthusiastic encouragement. Dr. Patrick Griffith from the Rancho Santa Anna Botanic Garden, Claremont, CA and Dr. C. Mondragón-Jacobo from the Instituto Nacional de Investigaciones Forestales y Agricolas y Pecuarias, Queretaro, MEXICO for their rich discussions and assistance in obtaining journal articles on the molecular aspects of this study. Dr. H. Fouche and Mr. P. Avenant from the Agricultural Research Council Pasture and Rangeland Institute, Department of Soil-, Crop- and Climate Sciences, UFS, Bloemfontein for supplying nutritional quality and weather data used in Chapter 4. Prof H.O. de Waal for his rich discussions on nutritional quality of fodder and encouraging comments and Mr. W. Combrink for his technical assistance. Dr. M. de Wit from the Department of Microbial Biochemical and Food Biotechnology for supplying photos of the pulp of the cactus pear varieties. Mr. T Unterpertinger, Consolata Estates, Limpopo Province, for supplying the cactus pear fruit used for the biocontrol studies. vi Finally I would like to thank the Andrew Mellon Foundation (UFS) and the NRF for funding this resesarch. ABBREVIATIONS AND ACRONYMS °C Degree Celsius µg Microgram(s) µl Microlitre(s) µM Micrometer(s) ADF Acid detergent fibre AFLP/s Amplified Fragment Length Polymorphism/s AIDS Acquired Immuno Deficiency Syndrome AMMI Additive Main Effects and Multiplicative Interactions Analysis ANOVA Analysis of variance ARC-ISCW Agricultural Research Council Institute for Soil, Climate and Water ATP Adenine triphosphate bp Base pair(s) BSA Bovine serum albumin CACTUSNET-FAO FAO International Technical Co-operation Network on Cactus Pear CAM Crassulacean acid metabolism cm Centimetre(s) CP Crude protein cpDNA Chloroplast DNA cpSSR Chloroplast simple sequence repeat CTAB Hexadecyltrimethylammonium bromide CU Chill Units DM Dry matter DNA Deoxyribonucleic acid dNTP 2’-deoxynucleoside 5’-triphosphate DTT 1,4 dithiothreitol EDTA Ethylene-diaminetetraacetate ESTs Expressed sequence tags ETo Evapotranspiration FAO Food and Agricultural organisation of the United Nations FDP Fruit Development Period FFR 50% fruit ripening vii FM Fresh matter g Gram (s) g Centrifugal force G X E Genotype x environment interaction GSF Genotype specific fragments GTM Gene Targeted Markers ha Hectare hr Hour(s) HU Heat units INIFAP Instituto Nacíonal de Investígaciones Forestales, Agríocola y Pecuarías IPGRI International Plant Genetic Resources Institute ISSR Inter simple sequence repeat kg Kilogram(s) km Kilometre(s) LSU Large Subunit m Metre(s) M Molar MDH Malate dehydrogenase mg Milligram(s) MJ/m2/s Mega Joules/Square Metre/Second ml Millilitre(s) mM Millimolar(s) mm Millimetre(s) Mt Metric tonne(s) mtDNA Mitochondrial DNA NA Nutrient agar NDF Neutral detergent fibre ng Nanogram(s) NIH National institute of Health nm Nanometre(s) NPF Number of polymorphic fragments NRF National Research Foundation nr ITS Nuclear ribosomal internal transcribed spacers nt Nucleotide NTYSYS Numerical taxonomy and multivariate analysis system OM Organic matter viii PCR Polymerase chain reaction PDA Potato dextrose agar PGI Phosphoglucoisomerase PGM Phosphoglucomutase PIC Polymorphic Information Content pmol Picomole(s) QTL Quantitative Trait Locus RAPD Random Amplified Polymorphic DNA RBB Reproductive Bud Break RBC Rose Bengal Chloramphenicol RDM Random DNA markers rDNA Ribosomal DNA REGW Ryan Einot Gabriel and Welsch Test RFLP Restriction fragment length polymorphism RH Relative humidity RNA Ribonucleic acid Rs Solar Radiation RUE Rain-Use Efficiency s Second(s) SNP Single Nucleotide Polymorphisms sp. Species spp. Plural abbreviation of species SPSS Statistical Package for the Social Sciences SSM Simple Matching coefficient SSR Simple sequence repeat STS Sequence tagged sites Taq Thermus Aquaticus TBE Tris-borate/EDTA TE Tris-HCl/EDTA TFPGA Tools for Population Genetic Analyses TNF Total Number of Fragments Tris-HCl Tris (hydroxymethyl) aminomethane Hydrochloride TSS Total Soluble Solids TTA Total titratable acidity U Unit(s) UNCCD United Nations Convention to Combat Desertification UPGMA Unweighted Pair-Group Method of Arithmetic Averages ix v Volume V Volt v/v Volume/volume W Watt w/v Weight/volume YM Yeast malt yr Year(s) x LIST OF TABLES Table 2.1 Cactus pear varieties used in this study 41 Table 2.2 Nucleotide sequences of EcoRI- and MseI- adaptors and primers 43 Table 2.3 Similarity coefficients for allelic non-informative marker data 46 Table 2.4 Summary statistics of the nine EcoRI/MseI primer combinations 48 used for selective amplification Table 2.5 Uniquely identified cactus pear varieties 51 Table 3.1 Desirable characteristics of cactus pear varieties in South Africa 68 Table 3.2 Climatic and soil characteristics of the Gillemberg cactus pear 69 germplasm block Table 3.3 Cactus pear varieties evaluated for fruit quality 71 Table 3.4 List of fruit quality traits and their descriptor states 72 Table 3.5 List of phenological and qualitative traits used for clustering of 73 cactus pear varieties Table 3.6 Soil analysis results for Gillemberg germplasm block (1999-2001) 73 Table 3.7 Fertilisation recommendations and application for Gillemberg 74 germplasm block Table 3.8 Fruit quality traits of cactus pear varieties (Season 1) 76 Table 3.9 Fruit quality traits of cactus pear varieties (Season 2) 81 Table 3.10 Reproductive bud break, fifty percent fruit ripening and fruit 84 development period for season 1 Table 3.11 Reproductive bud break, fifty percent fruit ripening and fruit 85 development period for season 2 Table 3.12 Mean climatic conditions over two seasons 87 Table 3.13 Mean fruit quality traits over combined seasons 88 Table 3.14 Fruit quality traits over combined seasons 90 Table 3.15 Mean fruit quality traits for dendrogram clusters 92 Table 4.1 Morpho-agronomic traits and short descriptions 106 Table 4.2 Mean climatic conditions prior to nutritional quality assessment 107 Table 4.3 Nutrient composition of different cactus pear varieties (dry matter 109 basis) Table 5.1 Mean lesion diameter of cactus pear cladodes 52 days post- 126 inoculation Table 5.2 Yeast isolate, species names and number of nucleotides of the 136 sequenced D1/D2 domain Table 5.3 Mean colony diameter (mm) and percentage inhibition of fungal 138 pathogens on dual cultures seeded with various yeast isolates xi LIST OF FIGURES Figure 1.1 A spine-less cactus pear plant with cladodes that have reverted 9 to spineness Figure 2.1 Photograph of a silver stained 5% denaturing polyacrylamide 49 gel Figure 2.2 Distribution of the Polymorphic Information Content of 50 polymorphic AFLP fragments Figure 2.3 Dendrogram for 38 South African cactus pear varieties based 54 on cluster analysis (UPGMA) of genetic similarity estimates using the Jaccard similarity coefficient Figure 2.4 Dendrogram for 38 South African cactus pear varieties based 55 on cluster analysis (UPGMA) of genetic similarity estimates using the Simple Matching coefficient Figure 2.5 Cophenetic correlation matrix for Simple Matching coefficent 56 data Figure 2.6 Cophenetic correlation matrix for Jaccard coefficient data 57 Figure 3.1 Dendrogram constructed from fruit quality and morphological 92 traits using the Gower dissimilarity coefficient Figure 4.1 Number of cladodes remaining on cactus pear varieties after 111 pruning over combined seasons Figure 4.2 Average mass (kg) of cladodes of each cactus pear varieties 111 over combined seasons Figure 4.3 Mean cladode yield (kg) for cactus pear varieties measured 112 over combined seasons Figure 4.4 Dendrogram constructed from vegetative and morphological 113 traits of 23 cactus pear varieties based on the Gower dissimilarity coefficient over combined seasons Figure 5.1 Mean lesion diameter of cactus pear varities 52 days after 127 inoculation with Fusarium oxysporum Figure 5.2 Dendrogram of 38 cactus pear varieties constructed on the 128 basis of susceptibility to Fusarium oxysporum Figure 5.3 Mean lesion diameter of cactus pear varieties 52 days after 130 inoculation with Fusarium proliferatum Figure 5.4 Dendrogram of 38 cactus pear varieties constructed on the 131 basis of susceptibility to Fusarium proliferatum Figure 5.5 Mean lesion diameter of cactus pear varieties 52 days after 132 inoculation with Phialocephala virens Figure 5.6 Dendrogram of 38 cactus pear varieties constructed on the 133 basis of susceptibility to Phialocephala virens Figure 5.7 Dendrogram of 38 cactus pear varieties constructed on the 134 basis of overall susceptibility to fungal pathogens Figure 5.8 In vitro growth inhibition 137 xii GENERAL INTRODUCTION Semi-arid and arid regions are a challenge to conventional cropping systems because of limited or erratic rainfall, poor soils, and high temperatures (Le Houérou, 1996). Hence, the cultivation of conventional crops such as maize, rice, and wheat in these areas has proven to be agriculturally unproductive. However, productivity in these areas can be increased by the cultivation of adapted crops such as Opuntia species, especially cactus pear (Pimienta-Barrios and Muñoz-Urias, 1995). Opuntias can tolerate water-limited conditions, high temperatures, and poor soils. Consequently, cactus pear (Opuntia spp.) is increasingly being cultivated in semi-arid areas around the world, including South Africa, which according to the United Nations Convention to Combat Desertification (UNCCD) index for the classification of dry lands, is 80% semi-arid to arid (FAO, 2005). Opuntia species are crassulacean acid metabolism (CAM) plants that convert water to biomass four fold more efficiently than either C4 or C3 plants. They are a source of dry matter in water-limited areas when fed to animals as green feed, hay, or silage. Opuntias meet the most important criteria for fodder crops in drought prone regions, drought tolerance and palatability (Tegegne, 2001). However, on its own as feed, cactus pear does not fill the dietary requirements of livestock since cladodes are low in crude protein and should be supplemented (Nefzaoui and Ben Salem, 2001). In South Africa, Opuntia species were first reported in the 18th century, and grown in the Western Cape Province as a fodder crop (Van der Merwe, 1931). In 1914, 22 spine-less varieties were imported from the Burbank nursery (Wessels, 1988) and established at the Grootfontein Agricultural College, Middelburg, Eastern Cape Province. Plant material from Grootfontein was distributed to farmers in the Karoo area to be used as a drought tolerant fodder crop (Potgieter, 2002). In cactus pear fruit plantations in South Africa terminal cladodes are used to vegetatively propagate varieties. However, the varieties have not been fully characterised, hampering research and breeding efforts directed at the development of improved varieties. In addition, few published records of varietal fruit quality traits are readily available. Commercially, cactus pear is mainly cultivated in summer rainfall areas, most of which are prone to hail. Physical damage caused by hail facilitates the entry of pathogenic fungi. Varieties currently being cultivated have not been screened for resistance to 1 fungal diseases. Reports of new diseases and associated financial losses due to post- harvest fruit rot are thus increasing (Swart et al., 2003). Post-harvest problems of fruit are directly related to physical damage at harvest that facilitates decay at the stem-end caused by Fusarium spp., Alternaria spp., Chlamydomices spp., and Penicillium spp. (Rodriguez-Felix, 2002). Although fungicides are the conventional method of controlling post-harvest disease, public concern over food safety and the development of fungicide resistant pathogens has increased the search for less harmful alternative methods (Spotts and Cervantes, 1986). Biological control (biocontrol) using antagonistic microorganisms is amongst the methods being explored to replace and/or reduce the use of fungicides. Biocontrol has been endorsed as the preferred alternative to synthetic fungicides with considerable success. In particular, a host of yeast genera have been extensively used for the biological control of post-harvest diseases of fruits and vegetables (Wilson and Wisniewski, 1989; Punja, 1997). Given the aforementioned problems confronting the rapidly expanding cactus pear industry in South Africa, a study was undertaken to investigate the specific goals presented in this thesis. 2 REFERENCES FAO, 2005. Fertilizer use by crop in South Africa, Rome, Italy. http://www.fao.org/docrep/008/y5998e/y5998e06.htm#bm06 Le Houérou, H.N., 1996. Climate change, drought and desertification. Journal of Arid Environments 34: 133-185. Nefzaoui, A. and H. Ben Salem, 2001. Opuntia: A strategic fodder and efficient tool to combat desertification in the WANA region. In : Mondragón-Jacobo, C. and S. Pérez- Gonzalez (Eds.), Cactus (Opuntia spp.) as forage, pp 73-89. FAO, Rome, Italy. Pimienta-Barrios E. and A. Muñoz-Urias, 1995. Domestication of Opuntias and cultivated varieties. In: Barbera, G., P. Inglese and B.E. Pimienta (Eds.), Agroecology, cultivation and uses of cactus pear, pp 58-63. FAO Plant production and protection paper 132. Rome, Italy. Potgieter, J.P., 2002. Conservation of cactus pear germplasm in South Africa. Cactus Pear News 1: 5-7. Punja, Z.K., 1997. Comparative efficacy of bacteria, fungi, and yeasts as biological control agents for diseases of vegetable crops. Canadian Journal of Plant Pathology 19: 315-323. Rodriguez-Felix, A., 2002. Post-harvest physiology and technology of cactus pear fruits and cactus leaves. In: Nefzaoui, A. and P. Inglese (Eds.), Proceedings of the Fourth International Congress on Cactus Pear and Cochineal. Acta Horticulturae 581: 191-199. Spotts, R.A. and L.A. Cervantes, 1986. Population pathogenicity and benomyl resistance of botrytis spp., Penicillium spp. and Mucor piriformis in packinghouse. Plant Disease 70: 106-108. Swart, W.J., M.R. Oelofse and M.T. Labuschagne, 2003. Susceptibility of South African cactus pear varieties to four fungi commonly associated with disease symptoms. Journal of the Professional Association for Cactus Development 5: 86-97. Tegegne F., 2001. Nutritional value of Opuntia ficus-indica as a ruminant feed in Ethiopia. In: Mondragón-Jacobo, C. and S. Pérez-Gonzalez (Eds.), Cactus (Opuntia spp.) as forage. FAO, CACTUSNET. Rome. 3 Van der Merwe, C.R., 1931. Prickly pear and its eradication. Department of Agriculture Science Bulletin 93: 5-32. Wessels, A.B., 1988. Spine-less Prickly Pear. First Perskor Publishers, Johannesburg. Wilson, C.L. and M.E. Wisniewski, 1989. Biological control of post-harvest diseases of fruits and vegetables: An emerging technology. Annual Review of Phytopathology 27: 425-441. 4 Chapter 1 Characterisation and evaluation of Opuntia spp. 1.1 INTRODUCTION Numerous crops previously deemed of little importance, and thus not collected and researched, are being recognised by international research organisations as necessary for agricultural sustainability and food security. The increased interest in these crops stems from the recognition of their potential contribution to agricultural diversification, their application to the exploitation of marginal lands and changing environments, and their utility as additional income sources for farmers (Padulosi, 1998). New crops being introduced to arid and semi-arid areas include Opuntia spp. and the apple cactus Cereus peruvianus (L.) Mill (Weiss et al., 1993). Opuntias, in particular, have developed phenological, physiological, and structural adaptations that have enabled them to thrive in arid areas characterised by drought, erratic rainfall and poor soils. Asynchronous reproduction (Nerd and Mizrahi, 1995), CAM, structural adaptations typified by increments in water-storage tissues, and thickened cuticles (Salgado and Mauseth, 2002) have enabled the highly efficient growth of cacti under water-limited conditions (Nobel, 1995). Furthermore, the development of rhizosheaths reduces water loss to dry soil and a shallow root system assists cacti to absorb limited rainfall (Dubrovsky and North, 2002). Opuntia ficus-indica (L.) Miller (cactus pear), a member of the Opuntia genus has been introduced and used in developing countries for various purposes. This crop serves as an emergency source of feed for animals. It is an efficient water utilising xerophyte, and both the young cladodes (nopalitos), and fruits (tunas) are suitable for human consumption. The multi-functionality of this crop identifies it as a plant that developing countries in arid and semi-arid regions will benefit from. If developed further, this crop could contribute to sustainable food production in countries with large areas of semi- arid and arid land (Felker and Inglese, 2003). However, one of the major obstacles in the development of cactus pear fruit and fodder varieties is the lack of adequate characterisation and evaluation of the available germplasm. 5 Characterisation and evaluation of germplasm accessions are the two main functions of genebanks (germplasm collections). Firstly, germplasm accessions representative of the available genetic diversity of a particular crop are collected, conserved and characterised. Secondly, germplasm material is evaluated for agronomically useful traits required by breeders. These traits are often subject to strong genotype by environment (G x E) interactions. While a few germplasm collections of cactus pear are maintained at several locations around the world (Chapman et al., 2002), their maintenance is difficult and costly because of its perennial habit and large plant size. Additionally, the difficulty in genotype identification hinders the systematic collection and evaluation of Opuntia germplasm material (Chessa and Nieddu, 1997). This is evidenced by the scarcity of published accounts of the breeding history, characterisation and evaluation data of this crop (Chapman et al., 2002). Characterisation and evaluation of the available cactus pear gene pool is, however, essential for future breeding programmes. This review focuses on the advancements made in the application of molecular markers in germplasm characterisation. The potential for the application of functional marker based molecular tools in the evaluation of germplasm for agronomically important traits will also be reviewed. In addition, the use of yeasts as biological control (biocontrol) agents to lengthen the post-harvest life of fruits will be highlighted briefly. 1.2 GENERAL BACKGROUND Although cactus pear originates from arid and semi-arid areas in Mexico, it is presently cultivated worldwide, specifically O. ficus-indica which is cultivated in over 20 countries for its fruit (Inglese et al., 2002 ). Its dispersal around the world was facilitated by the inclusion of fresh cladodes on European ships in the late 15th century (Casas and Barbera, 2002). Early European botanists called this cactus Ficus indica, because of its resemblance to the then already known Indian fig (possibly Ficus bengalensis L.) (Anderson, 2001). Linnaeus published it under a new name, Cactus ficus-indica, in the group Cactus opuntia in Species Plantarum. In 1978 Miller combined the above mentioned names into Opuntia ficus-indica (Griffith, 2004). Currently, cactus pear is grouped in the genus Opuntia in the Cactaceae family (Gibson and Nobel, 1986). The classification of cactus pear is briefly summarised below: 6 Order: Caryophyllales Suborder: Portulacineae Family: Cactaceae Subfamily: Opuntioideae Genus: Opuntia Subgenus: Opuntia Species: ficus-indica (L.) Mill., Gard. Dict. Abr. ed. 8. No. 2. 1768 (Scheinvar, 1995). The taxonomic evaluation of Opuntias is complicated by variations in phenotype with changing ecological conditions, polyploidy, vegetative and sexual reproduction, and the occurrence of many hybrids between species (Scheinvar, 1995). Phenotypic variability is most frequently observed in fruit size and colour, cladode size, morphology, and phenology (fruit ripening time) (Pimienta-Barrios and Muñoz-Urias, 1995). Variability of both wild and domesticated cactus pear populations is thought to have occurred via natural hybridisation associated with polyploidy and geographic isolation (Gibson and Nobel, 1986). Natural hybrids are hypothesised to have arisen via natural crossing between different Opuntia species and F1 hybrid progeny. Hybridisation was encouraged by artificial sympatric conditions in Mexican backyards where diverse species were grown in close proximity creating an environment conducive to increased gene flow between cultivars (Grant et al., 1979; Pimienta- Barrios and Muñoz-Urias, 1995). Variation in ploidy level has played an important role in the domestication of cactus pear as Mexican residents preferentially selected, and vegetatively propagated cultivars with larger fruit and cladodes. High ploidy levels are phenotypically expressed as increased vegetative (cladode size), and reproductive vigour. Different ploidy levels of 2x, 3x, 4x, 5x, 6x, 8x, 10x, 11x, 12x, 13x, 19x, and 20x have been reported amongst wild and cultivated cactus pear populations (Yuasa et al., 1974; Pinkava et al., 1992). Varieties with the high chromosome numbers of 2n = 6x = 66 and 2n = 8x = 88 are mostly found within cultivated populations, with the exception of wild populations of O. streptacantha Lemaire. Cultivars with lower chromosome numbers of 2n = 2x = 22 and 2n = 4x = 44 occur mostly in wild populations (Pinkava et al., 1992). The species O. ficus-indica has diffused into Argentina, California, Chile, Israel, and South Africa where naturalised stands and commercial plantations for fruit occur. Plantations of cactus pear also occur in Brazil, Colombia, Peru, Spain, Greece, Turkey, Italy, Jordan, Egypt, Tunisia, Algeria, and Morocco (Inglese et al., 2002). To 7 develop improved cultivars from the varieties being grown in these countries, accurate germplasm characterisation is required. 1.3 GERMPLASM CHARACTERISATION Germplasm characterisation involves the compilation and maintenance of accurate records of the identifying traits of accessions. Characterisation facilitates the classification of accessions and the estimation of the genetic diversity within a collection. To facilitate and standardise characterisation of genebank accessions globally, the International Plant Genetic Resources Institute (IPGRI) published descriptor lists for various crop species (FAO, 1996). As such, a descriptor for cactus pear was developed by scientists who participated in the Food and Agricultural Organisation of the United Nations' International Technical Co-operation Network on Cactus Pear (CACTUSNET-FAO), specifically by members of the working group for Plant Genetic Resources Collection, Evaluation and Conservation. The cactus pear descriptor follows the international format currently endorsed by the IPGRI (Chessa and Nieddu, 1997). Mexico hosts the greatest genetic diversity of edible Opuntias and is the main source of cactus pear germplasm in the world. The largest number of entries is held at Instituto Nacíonal de Investígaciones Forestales Agrícolas y Pecuarías (INIFAP) in Mexico, and other germplasm collections are maintained at several locations around the world (Chapman et al., 2002). Mexican institutions engaged in cactus pear research are involved in germplasm collection and characterisation, a very costly effort. Collection of accessions is largely based on morphological traits, and often leads to duplication (Chapman et al., 2002). 1.3.1 Morphological markers Morphological markers/traits are the oldest and most widely used genetic markers for germplasm characterisation. Their popularity stems from their simplicity, speed and inexpensive nature (Bretting and Widrlechner, 1995). Previously, morphological descriptors for characters that are highly heritable, easily observable, and expressed in all environments formed the core constituents of characterisation data. The cactus pear plant is unique in morphology with cladodes (pads), modified photosynthetic stems, that resemble leaves. Cladodes have numerous aereoles with glochids, short leaf spines that are easily dislodged. The descriptor for cactus pear examines plant, growth, cladode, flower, and fruit descriptors (Chessa and Nieddu, 1997). 8 However, Weniger observed that spininess, cladode shape and size, fruit characteristics, and plant productivity were influenced by the environment (Chapman et al., 2002). These characters constitute a major portion of the data collected following the descriptor format. In contrast, Chessa et al. (1995) found that the number of spines allowed the classification of biotypes of cactus pear according to their territorial distribution. Plants with an average or high number of thorns were concentrated in areas that were ecologically different from areas where thornless plants grew. In South Africa there are, however reports of the reversion to spininess of commercially cultivated spine-less cactus pear varieties (Figure 1.1). The classification of commercial O. ficus-indica fruit types based on traditional, phenotypic taxonomic approaches is being contested by findings from molecular data. Previously, Sheinvar used spines to group taxa as either, spine-less O. ficus-indica; or spiny O. hyptiacantha Web, O. streptacantha, and O. megacantha Salm-Dick (Sheinvar, 1995). In contrast, random amplified polymorphic DNA (RAPD) patterns grouped a spiny O. hyptiacantha clone (1287) as similar to a spine-less O. ficus-indica clone (1281). The O. ficus-indica clone (1281) showed a greater genetic similarity to the spiny O. hyptiacantha clone (1287) than to other spine-less O. ficus-indica clones (Wang et al., 1999). PHOTO: J. P. POTGIETER FIGURE 1.1 A SPINE-LESS CACTUS PEAR PLANT WITH CLADODES THAT HAVE REVERTED TO SPININESS 9 Although morphological markers are easily monitored, they are inadequate in characterising germplasm, since they can be influenced by the environment, and some markers such as flower colour, appear late in plant development (Andersen and Lübberstedt, 2003). In addition, the exclusive use of morphological traits for the collection of accessions has often led to duplication, complicating subsequent evaluation and utilisation (Chapman et al., 2002). As a result, generally, germplasm characterisation has advanced with the evolution of genetic markers from morphological traits, through isozyme to DNA markers (Bretting and Widrlechner, 1995; Andersen and Lübberstedt, 2003). Confusion regarding species classification within the Opuntia genus has hindered the characterisation of germplasm accessions. The delineation of the 250 species of the Opuntioidae subfamily based on morphology alone has resulted in taxonomic confusion because of the high level of phenotypic plasticity within its members (Wallace and Gibson, 2002). The large morphological variation of the 181 species has led Labra et al. (2003) to the conclusion that phenotypic traits alone will not allow a stable classification within the Opuntia genus. Consequently, molecular techniques are being used to clarify classification within the Opuntia genus. DNA sequences of the nuclear ribosomal internal transcribed spacers (nrITS) were phylogenetically analysed, and demonstrated that the taxonomic concept of O. ficus-indica should be considered as polyphyletic, deriving from multiple lineages (Griffith, 2004). Labra et al. (2003) have suggested that the Opuntia genus be re- classified with the inclusion of molecular data. Their findings based on molecular data [chloroplast simple sequence repeat (cpSSR) and amplified fragment length polymorphism (AFLP)], morphological traits and biogeographic distribution, suggest that O. ficus-indica be considered as a domesticated form of O. megacantha. Resolution of the taxonomic classification of Opuntia species using molecular markers will facilitate the characterisation of germplasm accessions, especially of the hybrid Burbank varieties used for commercial fruit production in South Africa. The classification of Opuntia x rooneyi M.P.Griffith and Opuntia x spinosibacca M.S. Anthony as hybrids of O. aureispina(S.Brack & K.D.Heil) and O. macrocentraEngelm, and O. camanchica Engelm and O. aureispina, respectively, was achieved using RAPD markers (Griffith and Porter, 2003). 10 1.3.2 Isozymes Isozymes are the earliest molecular markers developed. They occur as a result of variations in nucleotide sequence that result in the substitution of one amino acid for another. Such a substitution may result in the alteration of the net electrical charge on a protein. The charge difference is subsequently detected as an alteration in the migration rate of a protein through an electrical field. Electrophoretic separation is then used to measure protein mobility variation within a population (Klug and Cummings, 2000). Thus, electrophoretically distinct forms of a protein (isozymes) could imply that they are encoded by different alleles, i.e., genetic variation. The first molecular marker technique used in cactus pear to investigate genetic diversity was isozymes (Uzun, 1997). An investigation of seven enzyme systems in three Italian cultivars, and 15 Turkish cactus pear ecotypes showed no variation in isozyme banding patterns for a given enzyme system in the same plant organ. However, differences were observed between fruit and cladode isozymes for a given cultivar (Chessa et al., 1997; Uzun, 1997). In 1997, Chessa et al. demonstrated that isozyme analysis of pollen produced the best results compared to root, cladode, and petal tissues. Malate dehydrogenase (MDH), phosphoglucoisomerase (PGI), and phosphoglucomutase (PGM) isozyme banding patterns allowed grouping of different varieties and biotypes. However, unique cultivar identification using isozymes was not possible (Chessa et al., 1997). Although isozymes were used in the past in various other fruit species, for example for identification of apple cultivars (Weeden and Lamb, 1985), to verify the parentage of presumed peach x almond hybrids (Carter and Brock, 1980), and as genetic markers in peach (Durham et al., 1987), they have been surpassed by DNA markers because of the low number of markers they generate. Additionally, because isozymes are the products of gene expression they are often affected by environmental conditions, tissue type and the developmental stage of a plant. Proteins are also subject to post- translational modifications that may alter their electrophoretic mobility (Kumar, 1999). In addition, since not all substitutions change the net electrical charge on the molecule, approximately 30% of the actual variation due to amino acid substitutions are electrophoretically detected (Klug and Cummings, 2000). 11 1.3.3 DNA markers DNA polymorphisms represent differences in the DNA sequence of two individuals and are the desired markers for the identification and characterisation of plants. Given that DNA is an integral part of plants and is not subject to environmental modification (Bachmann et al., 2001), nuclear and cytoplasmic (chloroplast DNA [cpDNA], and mitochondrial DNA [mtDNA]) DNA can be analysed for polymorphisms using various techniques. DNA marker techniques have progressed from hybridisation-based methods such as restriction fragment length polymorphisms (RFLPs), to more rapid polymerase chain reaction (PCR)-based DNA methods such as RAPDs, simple sequence repeats (SSRs) or microsatellites, sequence-tagged sites (STS), AFLPs, inter-simple sequence repeat amplifications (ISSR) and single nucleotide polymorphisms (SNPs) (Gupta et al., 1999). RAPD markers have been used widely in fruit crops. RAPD patterns are PCR derived markers obtained by the random amplification of DNA using short nucleotide primers of arbitrary nucleotide sequence (Williams et al., 1990). They have been used for the characterisation of peach species and cultivars (Sharifani and Jackson, 2000), to estimate the genetic diversity of apricot (Zhebentyayeva and Sivolap, 2000) and to classify jujube cultivars (Mengjun and Zhao, 2003). Initially, the application of molecular marker techniques was hampered by the difficulty in extracting genomic DNA from mucilaginous tissues (De La Cruz et al., 1997; Wang et al., 1998b; Tel-Zur et al., 1999; Griffith and Porter, 2003). However, researchers have demonstrated that RAPD patterns can be obtained from cacti using primers OPA-11 (De La Cruz et al., 1997), and OPA-12 (Tel-Zur et al., 1999). RAPD profiles have been used to verify the maternal origin of apomictic seedlings in cactus pear (Mondragón-Jacobo , 2002). In South Africa, preliminary studies by Potgieter and Carstens (1996) employed six RAPD primers that produced specific banding profiles for 18 accessions tested. Arnholdt-Schmitt et al. (2001) also found that RAPD patterns for the cactus pear cultivars tested provided reproducible banding patterns. Amongst the eight clones tested using RAPDs, reproducible and distinct differences were observed. Of the detected bands, 75% were polymorphic, and allowed for unique cultivar identification. The fruit accessions tested were closely related to each other, and the groupings 12 based on RAPD banding profiles agreed with those obtained from morphological and physiological data (Arnholdt-Schmitt et al., 2001). Although RAPDs have the advantage of generating numerous markers, the resolution of RAPD profiles on agarose gels is poor (Gupta et al., 1999). This shortcoming has been circumvented by coupling RAPDs to denaturing gel electrophoresis (Dweikat et al., 1994), and temperature sweep gel electrophoresis (Penner and Bezte, 1994). AFLP is another DNA-based marker technique that has been used in fruit crops for genetic diversity analysis (Hagen et al., 2001), and cultivar identification (Boritzki et al., 1999; Geuna et al., 2003). This technique involves the digestion of genomic DNA with two endonucleases, followed by the ligation of site specific adaptors to the DNA fragments. Primers designed with selective nucleotides added at the 3’ ends and complementary to the adaptors and the restriction sites are used for amplification. Thereafter DNA fragments are resolved on standard sequencing gels (Vos et al., 1995). This technique has the advantages of being highly sensitive, reproducible and widely applicable. Its limitations, however, are that it is relatively expensive, technically demanding, and a dominant marker system (IPGRI, 1996). DNA-based marker analysis techniques such as AFLP, RAPD, and RFLP are dependent on gel electrophoresis and associated with difficulties in correlating fragments on gels with allelic variants (Jaccoud et al., 2001), and thereby characterised as low-throughput. As a result high-throughput hybridisation techniques of nucleic acids immobilised on solid states (DNA chips) were developed to replace gel-based analysis systems. Non-gel based high-throughput genotyping technologies such as DNA microarrays (Chee et al., 1996; Lipshutz et al., 1999) allow the simultaneous analysis of many hundreds of thousands of oligonucleotides attached to a solid silicon surface in an ordered array to create a microarray. The DNA or RNA sample of interest is PCR amplified to incorporate fluorescently labelled nucleotides and subsequently hybridised to the array. Each oligonucleotide or cDNA on the array acts as an allele specific probe. Perfectly matched sequences hybridise more efficiently, giving off a stronger fluorescent signal than mismatched oligonucleotide-target combinations. The fluorescent signals are quantified by high resolution fluorescent scanning and analysed electronically This allows the identification of heterozygous base pair mutations, insertions and deletions (Chee et al., 1996; Lipshutz et al., 1999). 13 DNA chips (microarrays) have been developed to genotype SNPs in germplasm (Wang et al., 1998a). SNPs are single base variations in the nucleotide sequence at a unique physical location. SNPs have the advantage of ease of automation because they can be screened in a digital format analysing the presence or absence of a sequence, enabling high-throughput analysis (Wang et al., 1998a). Initially, DNA chips developed to analyse SNPs, required prior DNA sequencing. To circumvent sequencing, Diversity arrays (DArT ) have been developed for the detection of specific DNA fragments derived from the total genomic DNA of an organism or a population of organisms (Jaccoud et al., 2001). Given the progress made in other fruit crops, and a proposal for the development of a genetic map for O. ficus-indica using molecular sequence data (Chapman and Paterson, 2000), modest progress has been made in the application of molecular marker techniques to cactus pear germplasm characterisation. 1.4 GERMPLASM EVALUATION The evaluation of germplasm for useful traits is the stage where the most value is added to germplasm collections. It is at this stage when it is determined whether an accession harbours genes of utility to breeders and to agriculture in general (FAO, 1996). Agronomic traits required by breeders are too genetically complex to be screened in the preliminary characterisation stages, as they may be subject to strong G x E interactions. In order to exploit the genetic variability in the different cactus pear-producing countries it was recognised that a thorough understanding of the characteristics of Opuntia germplasm, and of the variability in its horticultural and pomological traits, was necessary. Consistency in the methodology used for data collection and terminology would be essential to meet this goal, as it would allow better utilisation of germplasm within and between countries for agronomic purposes, and to develop programmes for genetic improvement (Chessa et al., 1995). 1.4.1 Evaluation for fruit quality Fruit quality is complex, but the simplest definition thereof is, 'whatever the consumer desires' (Barritt, 2001). In general, the consumer assesses quality on the appearance of the fruit at the point of sale, and thereafter by its taste (Kader, 2002). Appearance, in turn, is determined by fruit size and colour (Callahan, 2003). In cactus pear, fruit 14 quality is based on sugar content, peel colour, fruit weight, pulp weight, and seed content (Cantwell, 1991). The cactus pear fruit is an oval shaped berry fruit with an average weight of 100-200 g. Cactus pear fruits are appreciated for their characteristic taste and aroma, and dietetic properties. Fruits have a thick fleshy skin that contributes 30-40% of the total fruit weight. The juicy pulp contributes 60-70% of the total fruit weight, and contains many hard-coated seeds that contribute 5-10% of the pulp weight. Each variety produces fruits of different shapes, colours and flavours. The main components of the fruit pulp are water (85%), carbohydrates (10-15%) and vitamin C (25-30 mg/100g) (Cantwell, 1995). In general, high ploidy levels are phenotypically expressed as increased reproductive vigour (fruit size). Similarly, variation in ploidy level has played an important role in the domestication of cactus pear. Mexican people preferentially selected and vegetatively propagated cultivars with larger fruit. Different ploidy levels have been reported amongst wild and cultivated cactus pear populations from cytogenetic studies (Yuasa et al., 1974; Pinkava et al., 1992). Varieties with the high chromosome numbers of 2n = 6x = 66 and 2n = 8x = 88 are mostly found within cultivated populations (Pinkava et al., 1992). Currently, cactus pear fruit size is evaluated based on fruit mass, length and equatorial width (Chessa and Nieddu, 1997), and edible and skin fresh matter content. Italian germplasm was evaluated using an abridged version of the descriptor list. Six accessions with high yield and fruit qualities were selected as parental types for the development of new varieties (Nieddu et al., 2002). In South Africa, varietal evaluation for fruit production is based on the following minimum criteria: fruit mass > 140.0 g, total soluble solids (TSS) > 13°Brix, %pulp > 50% and peel thickness < 6 mm (Potgieter and Mkhari, 2002). The cactus pear fruit contains many hard coated seeds that are completely wrapped by a stalk that becomes hard and bony (Rebman and Pinkava, 2001) and contribute 5- 10% of the pulp weight (Cantwell, 1995). The seed content in cactus pear fruits varies from 2.8-7.5 g per fruit depending on cultivar and size (Mondragón-Jacobo and Perez, 1995). The high seed content is an apparent deterrent to its introduction into new markets. High seed content is however, positively correlated with fruit size. It has therefore been suggested that a fruit of ideal size should have a high ratio of aborted to normal seed (Mondragón-Jacobo and Bordelon, 1996). Additionally, normal seed 15 number and matter were found to be positively inter-correlated and found to account for 57.4% of the variation in fruit size. This variation mainly affects fruit weight and size variation, suggesting that normal seed number and matter controlled fruit weight, and size (Gutiérrez-Acosta et al., 2002). In general, actual fruit size is governed by G x E interactions whilst potential fruit size is genetically determined (Zhang et al., 2006). Fruit size is a function of cell number, volume, and density (Scorza et al., 1991), and is largely genetically controlled (Janick and Moore, 1996). Similarly, cactus pear researchers are reporting that fruit size is not exclusively determined by environmental or edaphic factors and that genetic factors are important determinants of fruit size (Felker et al., 2005). Little is known about the molecular properties of the genes that determine fruit size. Fruit size is a complex trait governed by a number of genes or quantitative trait loci (QTL) as well as by environmental factors (Nesbitt and Tanksley, 2001). A fruit size QTL fw2.2 responsible for a 30% difference in fruit size between large domesticated tomatoes (Lycopersicon esculentum Mill.) and their small-fruited wild relatives has been described. The gene underlying this QTL was cloned and found to be associated with fruit size and altered cell division in ovaries (Frary et al., 2000). In permanent crops, with a medium length of juvenility such as cactus pear, evaluation for desired fruit quality traits is only possible after a few years. It is at this point that accessions to be used as parental types in breeding programmes can be selected. 1.4.2 Evaluation for fodder quality When cactus pears plants begin fruiting, they are pruned to facilitate cultural practices and to renew fertile cladodes (Inglese, 1995). Pruning generates huge amounts of cladode waste material. Cladodes, are however very nutritious and can be used as fodder. In addition, cladodes are highly digestible and contain sufficient water and minerals that in combination with a protein source constitute a complete feed for livestock (Kueneman, 2001). It is well established that Opuntias meet most of the requirements for fodder crops in drought prone regions (Nefzaoui and Ben Salem, 2002). Drought-tolerance of O. ficus-indica in the Mediterranean basin is comparable to that of olive, almond, pistachio, pomegranate, and fig tree. Yields of between 20-60 metric tons (Mt) fresh matter (FM)/ha/yr (equivalent to 3-9 Mt dry matter (DM)/ha/yr) on arid lands with a mean annual rainfall of 200-400 mm, under poor cultivation practises and no fertilization were recorded (Le Houérou, 2002). Under a mean annual rainfall of 400- 16 600 mm the yield in extensively managed conditions rose to 60-100 Mt FM/ha/yr (i.e., 9-15 Mt DM/ha/yr) (Le Houérou, 2002). These yields correspond to Rain-Use Efficiency (RUE) of 15-25 kg of above ground DM/ha/yr/mm. These RUEs are 3-5 times higher than the best rangelands under good management in the same areas where the RUE is seldom above 5 kg of above ground DM/ha/yr/mm (Felker, 1995). The nutrient content of Opuntia spp. depends on the genetic characteristics of the species or clones, the cladode’s age, the cladode sampling location, the pad harvesting season and the growing conditions such as soil fertility and climate (Nefzaoui and Ben Salem, 2001). DM content, the component in feed after drying, depends on the season in which cladodes are harvested. Significant differences in DM content among clones of O. ficus-indica (L) f. inermis Weber, O. robusta Wend., O. paraguayensis K. Schum., and O. spinulifera Salm-Dyck have been reported for Opuntia spp. In addition, a positive linear relationship between DM content and age (p<0.05) was established for these clones (Guevara et al., 2004). Season affects the chemical composition of cladodes. The DM content of one to three year old cladodes ranged from 10-15% in the rainy season to 15-25% in the dry season (Le Houérou, 2002). Organic matter (OM) content among Opuntia spp. clones varied significantly, but was not considerably different for clones of different ages. Different researchers have reported different values for OM content of cladodes, ranging from 74.6-86.9% (Guevara et al., 2004). Cladode crude protein (CP) content varied amongst clones and between cladodes of different ages and it is thought to be sensitive to changes in soil N (Guevara et al., 2004), which may explain high CP contents of 8.5% previously reported by other researchers (Gregory and Felker, 1992). A negative linear relationship exists between CP content and age (Guevara et al., 2004) although the rate of decrease in CP content differs between clones (Nefzaoui and Ben Salem, 2001). Crude protein content during flowering decreased from the basal to the apical area of cladodes (Gugliuzza et al., 2002). With regard to sampling location, it has been shown that the central-basal zone of a cladode comprised of 40 sampling locations grouped in a rectangular manner, represented the average CP content of the entire cladode (Guevara et al., 2006). Neutral detergent fibre (NDF) denotes the insoluble portion of fodder and typically contains cellulose, hemicellulose, lignin and silica. NDF is negatively correlated with DM intake. Therefore, livestock will consume less forage, with increasing NDF 17 content. In addition to a positive linear relationship (p < 0.05) between NDF content and age, significant differences in NDF content amongst clones of O. ficus-indica f. inermis, O. robusta, O. paraguayensis, and O. spinulifera, have been reported (Guevara et al., 2004). The NDF values reported by Guevara et al. (2004) were in the range previously reported as 21.8%, and 25.5% by Ben Salem et al. (2002). Higher NDF values of 33.8% have also been reported (Ben Salem et al., 2004). The acid detergent fibre (ADF) fraction of fodder includes cellulose, lignin, and silica. ADF is an important indicator of fodder digestibility, and is negatively correlated with digestibility. A positive linear relationship was found between ADF content and cladode age. Significant differences were observed amongst ADF content of different clones of O. fiucs-indica f. inermis, O. robusta, O. paraguayensis, and O. spinulifera (Guevara et al., 2004). ADF contents reported for these clones (14.3-16.0%) were consistent with those previously reported as 14.7% and 16.8% by Ben Salem et al. (2004). On its own as feed, cactus pear does not fill the dietary requirements of livestock. Cladodes are low in crude protein and supplementation with a protein source is recommended. The nutritional value of cladodes of different varieties (genetic characteristics), ages, at different locations, during different seasons, and under diverse growing conditions such as soil fertility and climate have been studied by various authors (Nefzaoui and Ben Salem, 2001). These factors influence the nutritional content of cladodes resulting in incomparable literature reports (Felker et al., 2006). The nutritional value of cactus pear cladodes pruned annually in commercial orchards for use as fodder has however not been researched that extensively. 1.4.3 Evaluation for resistance to fungal disease Evaluation of fruit tree germplasm for disease resistance is conventionally done with bioassays, where plants are cultured seven to ten years without fungicide application. Infected material is often brought into the orchard to increase infection pressure (Kemp and van Dieren, 2000; Kirby et al., 2001). Fungal pathogens penetrate the host tissue via mechanical perforation of the cuticle and underlying cell wall, or through enzymatic activity (Granata, 1995). However, the structural nature of cladodes limits pathogen entry. The artificial inoculation of cladodes using colonised toothpicks has been described (Swart et al., 2003) for bioassays in cactus pear. Fungal pathogens naturally gain entry to cacti through wounds such as those sustained during hailstorms. Cactus pear fungal pathogens belong to the genera 18 Armillaria, Dothiorella, Phytophthora, Alternaria, Fusarium, Phyllosticta, Sclerotina, and to a lesser extent to the genera Colletotricum, Capnodium, Macrophomina, Cercospora, Aecidium, Phoma, Cytospora, Gleosporium, Mycospherella, and Pleospora (Granata, 1995). Reports on the screening of cacti for resistance to fungal diseases have not been widely published (Kim and Kim, 2002; Swart et al., 2003). Abscission layer formation in stem disk cells from a two year old resistant Cereus peruvianus plant limited colonisation of Glomerella cingulata (Stoneman) Spauld. & H. Schrenk whilst the susceptible C. tetragonus (L.) Miller became extensively colonised (Kim and Kim, 2002). Glasshouse and field evaluation of the susceptibility of ten commercially important South Africa cactus pear varieties to four fungal pathogens [Phialocephala virens Siegfried and Siefert, Lasiodiplodia theobromae (Pat.) Griffon & Maubl, Fusarium oxysporum (Schltdl), and F. proliferatum (Matsush) Nirenberg ex Gerlach and Nirenberg] showed variations in susceptibility to fungal colonisation. The varieties Nudosa, and Algerian were the most susceptible, whilst Gymno Carpo, Zastron, and Malta were the most resistant to fungal disease (Swart et al., 2003). Although cladodes are not highly susceptible to fungal pathogen attack, the cactus pear fruit is. As fresh produce, cactus pears are susceptible to damage in the period between harvest and consumption (Rodriguez-Felix, 2002). In general, the deterioration rate of harvested produce is proportional to respiration rate. However, cactus pears are non-climacteric fruits with low respiration rates (20 ml CO2/kg/hr) and low ethylene production (0.2 µl C2H4/kg/hr) at 20°C (Rodriguez-Felix, 2002). Although cactus pear fruits produce very little ethylene, the application of Ethrel to fruits has been used experimentally to hasten abscission zone formation, reducing harvest injury at the stem end (Cantwell, 1986). Cactus pears are highly perishable, and under marketing conditions [20°C, 60– 70% relative humidity (RH)] have a shelf life of only a few days (Rodriguez-Felix, 2002). The main post-harvest problems are directly related to physical damage incurred at harvesting. This leads to water loss and stem end rots, or both (Cantwell, 1986). Post-harvest losses vary depending on cultivar, stage of maturity, environmental conditions, and harvesting method (Schirra et al., 1999). Peel thickness and toughness affect shelf life as some cactus pear varieties have been reported to be 19 more resilient to handling than others (Mondragón-Jacobo and Bordelon, 1996). Other factors that influence shelf life include decay at the stem end caused by Fusarium spp., Alternaria spp., Chlamydomices spp., and Penicillium spp. (Rodriguez- Felix, 2002). Stem end rots are highly prevalent in cactus pear. Pathogenic fungi have resulted in huge losses in the fresh fruit industry (Sommer, 1985). Previous studies by Swart and Swart (2003) found fungi from the following genera associated with healthy cactus pear fruits (cv. Algerian) in South Africa; Rhizopus sp., Mucor sp., Epicoccum spp., Cladosporum sp., Fusarium spp., Phoma sp., Aspergillus spp., Stemphyllium sp., Alternaria spp., Rhizoctonia sp. Rhizopus spp., and Penicillium spp. Some bacteria were associated with post-harvest rot of cactus pear fruit (cv. Algerian) in South Africa. In addition, the yeasts Hanseniaspora ovarum (Niehaus) Shehata, Mrak & Phaff, Pichia kluyveri Bedford ex Kudryavtsev, P. membranaefaciens E.C. Hansen, and various Candida spp. were associated with diseased fruits (Swart and Swart, 2003). Fruit shape affects harvesting as oval or barrel-shaped fruits are easier to harvest than elongated fruits and therefore undergo less harvest damage to the stem end (Cantwell, 1991). Farmers are advised to cut off a small piece of the mother cladode with the fruit to reduce damage during harvesting and thus limiting possible decay. Holding the crop at ambient conditions for one or two days at increased airflow is subsequently used to dry up the cladode piece (Rodriguez-Felix, 2002). Cold storage increases post-harvest life of most horticultural crops (Wang, 1994) by retarding respiration, ethylene production, ripening, senescence, undesirable metabolic changes, and decay (Rodriguez-Felix, 2002). However, Chessa and Barbera reported that cactus pears are susceptible to chilling injury when stored at temperatures below 9°C or 10°C, depending on the cultivar (Inglese et al., 2002). Due to its sensitivity to chilling injury, various innovative techniques aimed at increasing shelf life have been developed for cactus pear. These include intermittent warming, controlled atmospheres, film wrapping, and heat treatments with hot air or water (Rodriguez-Felix, 2002). Fungicides have been principally used to control post-harvest decay of fruits and vegetables (Sommer, 1985). However, public concern over food safety and the development of fungicide resistant pathogens has increased the search for less harmful alternative methods. Biological control using antagonistic microorganisms has 20 been popularised as an alternative to the use of synthetic fungicides with considerable success. Numerous studies have demonstrated the potential of biological control of post-harvest diseases using microbial antagonists (Sugar, 1999; Leverentz et al., 2000; Tian et al., 2002; Yu et al., 2006). In particular, a variety of yeast genera have been extensively used for the biological control of post-harvest diseases of fruits and vegetables (Wilson and Wisniewski, 1989; Punja, 1997), to protect moulding of stored grains (Petersson et al., 1999), and to control foliar diseases (Urquhart and Punja, 1997). Decay caused by Botrytis cinerea Pers. and Penicillium expansum Link on pome fruits has been controlled at laboratory and pilot stage trial by bacterial and yeast antagonists (Roberts, 1990; Janisiewicz and Marchi, 1992; Janisiewicz et al., 1994; Chand-Goyal and Spotts, 1996). Furthermore, formulated biocontrol product such as Aspire and Bio-Save 11 are available internationally. 1.5 CONCLUSIONS South Africa hosts one of the largest collections of genetic diversity of cultivated Opuntia spp. in the world, and various initiatives are now in place to facilitate a consolidated effort towards cultivar development. However, cultivar development requires accurate genotype identification that cannot be confidently achieved using phenotypic traits since cactus pear expresses significant G x E interactions. Thus, DNA marker techniques such as RAPDs, AFLPs and SSRs can be used in combination with phenotypic characterisation to increase the accuracy of genotype identification. This approach will support the identification of cactus pear varieties that can be used as parental types in future breeding programmes. Many challenges remain in conventional breeding and the application of marker- assisted selection in cactus pear. Breeding requires the production of seeds, and cactus pear is renowned for slow seed germination (Bregman and Bouman, 1983) and apomixis (Mondragón-Jacobo and Pimienta, 1995). However, chemical scarification of seeds in concentrated H2SO4 or with Schweizer reagent followed by incubation in H2O2 under photoperiodic conditions has been shown to increase the percentage of germinated seeds in the shortest time (Altare et al., 2006). Apomixis, the asexual production of seeds from maternal tissues (Koltunow, 1993), complicates breeding as it hinders the screening of progeny from crosses. Additionally, in cactus pear, it has been shown that artificial crossing in species naturally prone to this phenomenon, and the germination of seeds in the greenhouse increases apomixis (Mondragón-Jacobo, 2001a). Late emergent seedlings have been 21 shown to display RAPD patterns similar to that of the maternal entries (Mondragón- Jacobo, 2001b). Subsequent to parental type selection and crossings, individuals from crosses in cactus pear are presently selected based on morpho-agronomic traits. This selection process is time consuming, especially in cactus pear due to its long juvenile phase, estimated to be between four to six years (Mondragón-Jacobo, 2001a). Currently, however, functional markers (Andersen and Lübberstedt, 2003) can be developed to screen for genes of agronomic importance before they are expressed in the mature plant, hence shortening the time required to select progeny with desirable traits and ultimately produce new cultivars. Unlike DNA-based marker techniques such as AFLP, RFLP, SSR, and RAPD that generate markers derived from arbitrary regions of the genome, and as such are described as random DNA markers (RDMs) (Andersen and Lübberstedt, 2003), molecular markers from the transcribed region of the genome, known as gene targeted markers (GTMs) (Andersen and Lübberstedt, 2003; Gupta and Rustgi, 2004) and functional markers derive from polymorphic sites within genes responsible for phenotypic trait variation (Andersen and Lübberstedt, 2003). The development of functional markers however, requires functionally characterised genes, allele sequences from these genes, the identification of polymorphic, functional motifs that affect plant phenotype within the genes and the corroboration of the association between DNA polymorphisms and trait variation (Lübberstedt et al., 2005). The progress made in genetics and genomics has improved the understanding of structural and functional aspects of plant genomes in ways that can increase the ability to improve crop plants. The complete genome sequences of Arabidopsis thaliana (L.) Heynh., poplar, and rice, and an enormous number of expressed sequence tags in plants (ESTs) are now available. This has made available many strategies for developing functional molecular markers such as SNP (Rafalski, 2002), SSRs (Varshney et al., 2005a), conserved orthologous sets of markers (Rudd et al., 2005), and comserved intron scanning primers (Feltus et al., 2006). The transfer of QTLs of agronomically important traits from wild species into crop varieties can now be achieved via advanced backcross QTL analysis (Tanksley and Nelson, 1996). In addition, allele mining can be performed to gather information for all the alleles of a fully characterised gene in a germplasm collection. 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Acta Horticulturae 538: 525-530. 36 Chapter 2 Genotyping South African cactus pear (Opuntia spp.) varieties using AFLP markers ABSTRACT Cactus pear (O. ficus-indica) is increasingly being utilised in South Africa for fodder and fruit production. However, breeding efforts to increase productivity and fruit quality are hampered by the difficulty of varietal identification. AFLP markers were used to estimate the genetic diversity within the South African cactus pear germplasm. Estimates of genetic diversity are useful in plant breeding for organising germplasm, identification of varieties, and assisting in the selection of parents for crossings. Nine primer combinations used during AFLP analysis generated 346 fragments (per sample), of which 168 were polymorphic. A large number of the markers produced had a polymorphic information content (PIC) value between 0.3-0.5, indicative of good discriminatory value. The majority of the accessions grouped into four clusters using both the Jaccard and Simple Matching similarity coefficients. Cultivated varieties were evenly dispersed within the different clusters, with the greatest percentage clustered in group III. Varieties that originated from Botswana (R1251, R1259, and R1260) clustered together, whilst those from Israel (Sharsheret, Ofer, and Messina) were dispersed amongst groups II and III. Genotype specific fragments (GSF) were generated with the use of six primer combinations (E-AGG + M-CAT, E-ACT + M- CAG, E-ACT + M-CAT, E-ACA + M-CAT, E-ACA + M-CTT, and E-ACA + M-CAG). Genotype specific fragments allowed the unique identification of nine varieties, three of which are commercially cultivated (Meyers, Roedtan, and Santa Rosa). 37 2.1 INTRODUCTION Characterisation of Opuntia is complicated by G x E interaction, polyploidy, the presence of vegetative and sexual reproduction, and the occurrence of many hybrids between species (Scheinvar, 1995). This complexity obstructs breeding efforts aimed at increasing productivity and fruit quality, since they require accurate and consistent cultivar identification. The difficulty experienced in identifying different Opuntia spp. accessions has hindered breeding and germplasm evaluation (Chessa and Nieddu, 1997). Consequently, as with many other crops (Pejic et al., 1998; Prevost and Wilkinson, 1999; Smith et al., 2000; Grzebelus et al., 2001; Singh et al., 2002; Yue et al., 2002; Ferriol et al., 2003; Vijayan et al., 2004; Zacarias et al., 2004; Mba and Tohme, 2005; Wang et al., 2005; Zoghlami et al., 2007), fingerprinting data based on molecular markers is being explored to either complement or replace morphological characters in assessing genetic diversity. Molecular markers have, for example, been investigated for varietal identification of sugar beet (De Riek et al., 2001), peach (Aranzana et al., 2001), strawberry (Arnau et al., 2001), and grapevine (Regner et al., 2001) varieties. The AFLP technique is one of many molecular marker techniques being used to characterise germplasm (Ude et al., 2003; Nguyen et al., 2004; Genet et al., 2005). It involves the digestion of genomic DNA with two restriction enzymes, a frequent cutter such as MseI, and a rare cutter such as EcoRI (Vos et al., 1995). Digestion is followed by ligation of double stranded adaptors consisting of a core sequence, and the restriction enzyme-specific sequence. A two-step procedure is subsequently used to reduce the number of fragments. The first, a PCR reaction known as pre-selective amplification, employs a primer that incorporates the adaptor sequence, the enzyme specific sequence, and an additional pre-selective single base at the 3’ end for amplification, resulting in a 16-fold decrease in the number of fragments generated. The second reduction step, selective PCR amplification, uses a primer identical in sequence to the pre-selective primer with two additional nucleotide sequences at the 3’ end. Amplicons are electrophoretically separated on a denaturing polyacrylamide gel, and visualised using either radio-activity or with silver staining (Vos et al., 1995). More recently, high- throughput genotyping has been facilitated by the use of automated sequencers and dye-labelled PCR-primers (Applied Biosystems, 2000). Scoring digital AFLP gel images, using the specifically developed software AFLP-Quantar, is now possible (Keygene, 2000). 38 AFLP is a highly sensitive technique that can detect polymorphisms in an entire genome, allowing the variability of unknown DNA fragments to be assayed. It has therefore been used to assess the genetic diversity in, for example, rapeseed (Lombard et al., 2000), globe artichoke (Lanteri et al., 2004), African daisy (Berio et al., 2001), apricot (Hagen et al., 2001), and the common bean (Métais et al., 2001). The AFLP technique is highly sensitive, reproducible and widely applicable. Its limitations, however, are that it is a dominant marker technique, not able to discriminate between homozygous and heterozygous individuals. It is also relatively expensive and technically demanding (IPGRI, 1996). AFLP analysis is reliable at generating hundreds of genetic markers. These markers have found the widest application in analysing genetic variation below the species level, especially in investigations into population structure and differentiation (Mueller and Wolfenbarger, 1999). Molecular markers are popularly being used for the description of genetic relationships among different germplasm in seed banks and breeding programmes, and for assessing the level of genetic diversity present in germplasm pools (Reif et al., 2005). It is thus imperative with the increasing importance and popularity of cactus pear, that its genetic diversity be evaluated to inform decision makers on crop improvement strategies, and to elucidate whether a need exists to increase South Africa’s cactus pear gene pool. The AFLP technique was selected to (1) examine the level of genetic variation within the South African cactus pear germplasm, (2) determine whether AFLP markers can be used for variety identification, (3) establish genetic distances between different varieties, using two similarity coefficients, (4) compare the efficiency of different AFLP primer combinations in detecting genetic variation, and (5) compare dendrograms constructed using the Jaccard, and Simple Matching similarity coefficients. 39 2.2 MATERIALS AND METHODS 2.2.1 Plant material Plant material (Table 2.1) was obtained from a field genebank near CLINVET, 20 km west of Bloemfontein in the Free State Province. This germplasm was established in 2001 by the vegetative propagation of accessions held at the Limpopo Department of Agriculture, Mara germplasm block. Thin sections of cladodes from each variety were freeze-dried (Freeze Mobile II, Virtis Inc), and stored at -20°C until further use. 2.2.2 DNA isolation Freeze-dried material was ground to a fine powder after adding silica beads. Genomic DNA was isolated using the CTAB (hexadecyltrimethylammonium bromide) method (Saghai-Maroof et al., 1984). Subsequently a 250 µ aliquot of powder was incubated in 750 µ CTAB buffer, pH 8.0 [100 mM Tris-HCl [tris (hydroxymethyl) aminomethane], 1.4 M NaCl, 20 mM EDTA (ethylene-diaminetetraacetate), 2% (w/v) CTAB, and 0.2% (v/v) -mercapthoethanol)] at 65°C for one hour. A 500 µ aliquot of chloroform- isoamylalcohol [24:1 (v/v)] was added prior to phase separation by centrifugation at 12 000 g for three minutes. DNA was precipitated from the aqueous phase at room temperature for 20 minutes by addition of 500 µ isopropanol. The pellet was collected by centrifugation at 12 000 g for five minutes, and washed with 500 µ ice-cold 70% (v/v) ethanol for 20 minutes at room temperature. After centrifugation at 12 000 g for five minutes, the ethanol was discarded and the pellet was air-dried at room temperature for one hour and re-suspended in TE buffer pH 8.0 (10 mM Tris-HCl, 1 mM EDTA). DNA was treated with 0.4 mg/m DNase-free RNase for two hours at 37°C. DNA was further treated with 0.75 M ammonium acetate and an equal volume of chloroform-isoamylalcohol [24:1 (v/v)]. DNA was collected from the upper phase after centrifugation at 12 000 g for three minutes, and precipitated overnight at -20°C from the aqueous phase with two volumes of ice-cold absolute ethanol. 40 TABLE 2.1 CACTUS PEAR VARIETIES USED IN THIS STUDY Variety Variety name Commercially cultivated number varieties Country of origin 1 Direkteur X South Africa 2 Skinners Court X South Africa 3 Fusicaulis X South Africa 4 Nudosa X South Africa 5 Gymno Carpo X South Africa 6 American Giant X South Africa 7 Blue motto X South Africa 8 Morado X South Africa 10 Zastron X South Africa 11 Malta X South Africa 12 Algerian X South Africa 13 Turpin South Africa 14 Roly Poly South Africa 15 Meyers X South Africa 16 Roedtan X South Africa 17 Arbiter South Africa 18 Ofer Israel 20 Messina Israel 21 Fresno South Africa 22 Muscatel South Africa 23 Tormentosa South Africa 24 X 28 (Robusta x Castillo) South Africa 25 Corfu South Africa 26 Ficus-Indice South Africa 27 Vryheid South Africa 28 Mexican X South Africa 29 Nepgen South Africa 30 Amersfoort South Africa 31 Silician Indian Fig South Africa 32 R1260 Botswana 33 R1259 Botswana 34 R1251 Botswana 35 Sharsheret Israel 36 Rossa Italy 37 Unknown South Africa 38 Van As X South Africa 39 Cross X South Africa 40 Berg x Mexican South Africa 41 Santa Rosa X South Africa 42 Schagen South Africa A list of the different Opuntia spp. varieties used in this study, with the accompanying variety numbers. Varieties depicted in the blue font were reported by Brutsch (1979) as being of good potential for commercial fruit production 41 Following overnight incubation, DNA was collected by centrifugation at 12 000 g for 15 minutes, and washed twice with ice-cold 70% (v/v) ethanol. The pellet was air-dried at room temperature, and thereafter re-suspended in 50 µ TE buffer pH 8.0. DNA quantity and quality were estimated by measuring absorbencies at A260nm and A280nm using a spectrophotometer. The quality of the extracted DNA was verified by electrophoretic separation through a 0.8% (w/v) agarose gel in 1 X UNTAN (40 mM Tris-HCl, 2 mM EDTA, pH adjusted to 7.4 with acetic acid) buffer at 60 V for 45 minutes. DNA samples were diluted to working solutions of 200 ng/µ, and stored at -4°C until further use. 2.2.3 AFLP analysis AFLP analysis was performed using MseI- and EcoRI-primer pair combinations. EcoRI- and MseI-primers were given names beginning with E and M respectively. The code following the E or M refers to the selective nucleotides at the 3’-end of the primer. This coding system will be used throughout the thesis. Different MseI- and EcoRI-primer combinations were screened (Table 2.2). Primers and adaptors were synthesised by Integrated DNA Technologies, Inc, USA. Adaptors were prepared by the addition of equimolar amounts of both strands, heating for 10 minutes at 65 °C in a water bath, and leaving the mixture to cool down to room temperature. AFLP analysis was performed according to Vos et al. (1995), with minor modifications as described by Herselman (2003). 2.2.4 Restriction endonuclease digestion and ligation Genomic DNA (1.0 µg) was digested with 4 U MseI at 37°C for five hours. Thereafter, restriction fragments were further digested with 5 U EcoRI, in the presence of 100 mM NaCl at 37°C overnight. Ligation to adaptors was performed overnight in the presence of 0.4 mM Adenosine triphosphate (ATP), 50 pmol MseI-adaptor, 5 pmol EcoRI-adaptor, 1 X T4 DNA Ligase buffer [(66 mM Tris-HCl, pH 7.6, 6.6 mM MgCl2, 10 mM 1,4 dithiothreitol (DTT), 66 mM ATP)] and 1 U T4 DNA ligase, at 16°C. 42 TABLE 2.2 NUCLEOTIDE SEQUENCES OF EcoRI- AND Msel-ADAPTORS AND PRIMERS Enzyme Type Sequence (5'-3') EcoRI Adaptor-F CTCGTAGACTGCGTACC Adaptor-R AATTGGTACGCAGTCTAC MseI Adaptor-F GACGATGAGTCCTGAG Adaptor-R TACTCAGGACTCAT EcoRI Primer +1 GACTGCGTACCAATTCA MseI Primer +1 GATGAGTCCTGAGTAAC EcoRI Primer +3 GACTGCGTACCAATTCANN E-ANN: ACA, ACC, ACT, AGG, AAG MseI Primer +3 GATGAGTCCTGAGTAACNN M-CNN: CAG, CTC, CAT, CTT, CAC Primer+1 used for pre-selective amplification reactions, and primer+3 used for selective amplification reactions 2.2.5 Pre-selective amplification Pre-selective reactions were performed in a total volume of 50 µ by the addition of 1 X Promega polymerase buffer, 2 mM MgCl2, 200 µM of each dNTP, 30 ng of each pre- selective primer [EcoRI- and MseI-primer +1 (Table 2.2)] and 0.02 U Taq DNA Polymerase (Promega, Madison, WI, USA), to 5 µ DNA template (ligation mixture). Pre- selective amplification was performed (DNA Engine DYAD, BIO-RAD, USA) with an initial denaturation step at 94°C, followed by 30 cycles at 94°C for 30 seconds, 56°C for 60 seconds, and 72°C for 60 seconds. Final elongation was performed at 72°C for five minutes. The quality and quantity of pre-selective amplification products were determined by separation through a 1.5% (w/v) agarose gel. Appropriate dilutions (1:5, 1:10 or 1:20) thereof were made in 1 X TE buffer pH 8.0 prior to selective amplification. 43 2.2.6 Selective amplification Amplification reactions were performed in 20 µ reaction volumes containing 1 x Promega polymerase buffer, 2 mM MgCl2, 200 µM of each dNTP, 30 ng MseI-primer+3, 30 ng EcoRI+3, 0.75 U Taq DNA polymerase (Promega), 5 µ of diluted pre-selective amplification product and 100 µg/m Bovine serum albumin (BSA). The cycling conditions used for amplification were initiated with denaturation at 94°C for five minutes, followed by 10 cycles of touchdown (1°C per cycle) PCR at 94°C for 30 seconds, 65°C for 30 seconds, and 72°C for one minute, followed by 25 cycles at 94°C for 30 seconds, 56°C for 30 seconds, and 72°C for one minute, with a final extension step at 72°C for two minutes. Primers used for selective amplification were randomly selected. 2.2.7 Polyacrylamide gel electrophoresis Amplification products were mixed with an equal volume of formamide loading buffer [98% (v/v) de-ionised formamide, 10 mM EDTA pH 8.0, 0.05% (w/v) bromophenol blue, and 0.05% (w/v) xylene cyanol], and denatured at 95°C for five minutes. The mixtures were immediately placed on ice. Aliquots of 5 µ of each sample were separated through a 5% denaturing polyacrylamide gel [19:1 acrylamide: bis-acrylamide, 7 M urea, and 1 X TBE buffer (89 mM Tris-HCl, 89 mM Boric acid, 20 mM EDTA)] at a constant power of 80 W for two hours. 2.2.8 Silver staining AFLP gels were silver stained according to the protocol of the Silver Sequence™ DNA Sequencing System supplied by Promega (Madison, WI, USA). Gels were left to air-dry overnight, and photographed by exposing photographic paper (Kodak Polymax II) positioned under the gel, to dim light for approximately 20 seconds. This produced a negative image of the same size as the gel. 2.2.9 Statistical analysis A binary variety x marker matrix recording AFLP fragments as present (1) or absent (0) was compiled for all primer combinations used in the study. Only reliable fragments of between 150 and 700 bp were considered. PIC measures the informativeness of genetic markers (Botstein et al., 1980). PIC for dominant markers was calculated using the equation: 44 PIC = 1 − [ f 2 + (1 − f )2 ] (De Riek et al., 2001) In the equation above, "f" is the frequency of the marker in the data set. Only polymorphic markers were used to display PIC distribution. Allele frequency for dominant markers was estimated using the method outlined by Lynch and Milligan (1994) in the programme Tools for Population Genetic Analyses (TFPGA) (Miller, 1997). PIC-values of all polymorphic fragments for a primer pair were averaged to give the PIC- value for the primer pair. The variety x marker binary matrix was used to estimate the genetic similarity between genotypes, using the Jaccard similarity coefficient (SJ) (Jaccard, 1908) and the Simple Matching coefficient (SSM) (Sokal and Michener, 1958) (Table 2.3). The NTSYS-pc programme (Version 2.02i, Rohlf, 1998) was used to calculate similarities between pairs of individuals. The unweighted pair-group method of arithmetic averages (UPGMA) was used to construct dendrograms, depicting the relationships among accessions in the germplasm. Correlation between cophenetic distances obtained from the dendrograms and similarities calculated, using each of the similarity coefficients, was measured, using the cophenetic correlation coefficient. The size of the cophenetic correlation coefficient should be very close to one, for high quality resolution. This measure was used to compare alternative cluster resolutions. 45 TABLE 2.3 SIMILARITY COEFFICIENTS FOR ALLELIC NON- INFORMATIVE MARKER DATA Variable Similarity coefficient Name Range a + d Simple matching (Sokal SSM a + b + c + d 0,1 and Michener, 1958) a SJ a + b + c Jaccard (1908) 0,1 2a SD 2a + b + c Dice (1945) 0,1 a = number of fragments in common between two operational taxonomic units i1 and i2, b = number of fragments present in i1 and absent in i2, c = number of fragments present in i2 and absent in i1, d =number of shared absences between two operational taxonomic units i1 and i2 , (Kosman and Leonard, 2005; Reif et al., 2005) 2.3 RESULTS AND DISCUSSION It was possible to obtain total genomic DNA of good quality from freeze-dried cladode sections. However, DNA could not be extracted from Zastron (variety number 10) and Cross X (variety number 39), thus they were not analysed further. Various protocols for the extraction of genomic DNA from mucilaginous cactus tissue have been developed (De La Cruz et al., 1997; Arnholdt-Schmitt et al., 2001; Griffith and Porter, 2003) in order to circumvent problems associated with mucilage and other secondary metabolites. Mucilage is a water-soluble, pectin-like polysaccharide that forms large macromolecular aggregates in solution (Cárdenas et al., 1997). The above-mentioned methods were not used, as they required the use of multiple extraction buffers (De La Cruz et al., 1997), and the use of an expensive commercially available extraction kit (Arnholdt-Schmitt et al., 2001). The use of thin epidermal cladode sections as reported by Arnholdt-Schmitt et al. (2001) and Griffith and Porter (2003), increased the yield of DNA extracted. The use of comparatively larger amounts of plant material 30–50 mg (Griffith and Porter, 2003) or 3 g (De La Cruz et al., 1997), requires larger volumes of extraction buffer than the method described here. In this study freeze-drying thin epidermal cladode sections, with subsequent grinding after the addition of silica beads, enabled DNA extraction from 46 250 µg plant material in 750 µ CTAB buffer. This is a substantial decrease in the initial amount of plant material used for the extraction of genomic DNA from cacti. Larger quantities of plant tissue have been reported in literature. The reduction in the quantity of starting material enabled the processing of more samples in one sitting since extraction could be performed in smaller microcentrifuge tubes. In addition, freeze- drying appears to have assisted in reducing the amount of mucilage co-extracted with DNA. The nine primer combinations used for selective amplification (Table 2.4) generated 346 fragments (per sample) in total, of which 168 (48.6%) were polymorphic between samples. Five different EcoRI- and five different MseI-primers were used in nine combinations. The average number of polymorphic fragments generated per primer combination was 19. Figure 2.1 displays AFLP markers generated using the primer combination E-ACA + M-CAT. Primer pairs that generated the highest percentage polymorphic fragments were E-AGG + M-CAT (75%), E-ACA + M-CAG (58%), and E-ACT + M-CAT (55%) (Table 2.4). The above mentioned primer combinations can be used in future fingerprinting studies for cactus pear. They generated the highest amounts of polymorphic markers, which is essential in the assessment of genetic diversity. Polymorphic AFLP fragments were analysed to determine PIC (Figure 2.2). PIC measures the relative discriminatory value of a locus. It is a measure of the information content as a function of a marker system’s ability to differentiate between genotypes (Weir, 1996). Monomorphic fragments have a low PIC value and thus no discriminatory power and were not included in the computation of the distribution profile. 47 TABLE 2.4 SUMMARY STATISTICS OF THE NINE EcoRI/MseI-PRIMER COMBINATIONS USED FOR SELECTIVE AMPLIFICATION Primer combination NPF %P TNF GSF PIC E-ACA + M-CAG 31 58 53 4 0.33 E-ACA + M-CTC 12 46 26 0 0.32 E-ACA + M-CTT 15 52 29 1 0.31 E-ACA + M-CAT 18 37 42 1 0.34 E-ACT + M-CAT 23 55 42 1 0.25 E-ACT + M-CAG 12 28 43 3 0.18 E-ACC + M-CTC 19 49 39 0 0.32 E-AGG + M-CAT 27 75 36 1 0.33 E-AAG + M-CAT 11 31 36 0 0.27 TOTAL 168 346 11 Mean 19 48 38 1 0.29 NPF = number of polymorphic fragments; %P = percentage of polymorphic fragments; TNF = total number of fragments; GSF = genotype specific fragments; PIC = polymorphic information content PIC for dominant markers has a maximum value of 0.5 for "f" = 0.5 (De Riek et al., 2001). The majority of the fragments generated had a PIC value between 0.3-0.5, indicative of good discriminatory ability. Polymorphic markers with good discriminatory value occurred at a higher frequency than those with lower discriminatory value (PIC values <0.3) (Figure 2.2). 48 7 00bp M 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 6 00bp 5 00bp Lane Description M 100 bp DNA Ladder 1 DIREKTEUR 2 SKINNERS COURT 3 FUSICAULIS 400bp 4 NUDOSA 5 GYMNO CARPO 6 AMERICAN GIANT 7 BLUE MOTTO 8 MORADO 9 MALTA 10 ALGERIAN 11 TURPIN 3 00bp 12 ROLY POLY 13 MEYERS 14 ROEDTAN 15 ARBITER 16 OFER 17 MESSINA 18 FRESNO 19 MUSCATEL 20 TORMENTOSA 21 X 28 22 CORFU 23 FICUS-INDICE 24 VRYHEID 25 MEXICAN 26 NEPGEN 200bp 27 AMERSFOORT 28 SICILIAN INDIAN FIG 29 R 1260 30 R 1259 31 R 1251 32 SHARSHERET 33 ROSSA 34 UNKNOWN 35 VAN AS 36 BERG x MEXICAN 37 SANTA ROSA 38 SCHAGEN FIGURE 2.1 PHOTOGRAPH OF A SILVER STAINED 5% DENATURING POLYACRYLAMIDE GEL AFLP fragments were amplified using the primer combination E-ACA + M-CAT 49 30.0 25.0 20.0 F requency 15.0 10.0 5.0 0.0 0.2 0.4 0.6 Polymorphic Information Content FIGURE 2.2 DISTRIBUTION OF THE POLYMORPHIC INFORMATION CONTENT OF POLYMORPHIC AFLP FRAGMENTS The average PIC for each primer combination was computed from the PIC values generated for every polymorphic marker (Table 2.4). Six of the nine primer combinations used displayed PIC values greater than 0.3. Primer combinations that gave the highest PIC were E-ACA + M-CAT (0.34), E-AGG + M-CAT (0.33), and E-ACA + M-CAG (0.33) (Table 2.4). The average PIC (0.29) for all primer pairs used, compared well with those previously reported (Rana and Bhat, 2004). Eleven GSF (Table 2.5) enabled the differentiation of nine varieties. Primer pairs that gave the highest GSF values were E-ACA + M-CAG (4) and E-ACT + M-CAG (3). Primer pair E-ACT + M-CAG amplified three GSF, had the lowest percentage polymorphic fragments (28), and the lowest PIC (18). 50 TABLE 2.5 UNIQUELY IDENTIFIED CACTUS PEAR VARIETIES Primer combination GSF Variety NUF Roedtan 2 E-ACA + M-CAG 4 Meyers 1 Corfu 1 E-ACA + M-CTT 1 Roedtan 1 E-ACA + M-CAT 1 Unknown 1 E-ACT + M-CAT 1 Berg x Mexican 1 X 28 (Robusta x Castillo) 1 E-ACT + M-CAG 3 Santa Rosa 1 Roly Poly 1 E-AGG + M-CAT 1 Nepgen 1 TOTAL 11 11 GSF= genotype specific fragments; NUF = number of unique fragments E-ACA + M-CAG, and E-ACA + M-CTT generated three specific markers in total that can be used to identify Roedtan. E-ACT + M-CAG, and E-ACA + M-CAG enabled the unique identification of five cactus pear varieties (Roedtan, Corfu, Meyers, Santa Rosa, and Roly Poly), three of which (Meyers, Roedtan, and Santa Rosa) are commercially cultivated (Tables 2.4 and 2.5). With further research, these fragments can be converted to STS markers that can be used for variety identification or to detect the presence of agronomically important traits. STS markers developed from AFLP-markers have been used by Seo et al. (2001) for the identification of wheat lines carrying the 2RL resistance gene, and for genotype identification during marker-assisted breeding for resistance to cyst nematode in soybean (Meksem et al., 2001). Commentary on the subjective choice of similarity coefficients for use as measures of genetic distance between genotypes based on molecular data is well documented in literature (Jackson et al., 1989; Duarte et al., 1999; Da Silva Meyer et al., 2004; Kosman and Leonard, 2005). Although the coefficients under discussion are mathematically different (Table 2.3) and may give different quantitative and qualitative results of the relationship between individuals (Jackson et al., 1989; Duarte et al., 1999), most researchers do not offer any reasons to support their choice of coefficient (Da Silva Meyer et al., 2004) in relation to the type of markers evaluated, ploidy and mating system of the organism being studied (Kosman and Leonard, 2005). 51 In addition, the same coefficients have been used for both dominant (RAPD and AFLP) and co-dominant (allozymes, RFLP and SSR) markers without regard for whether the species being studied are haploid, diploid or polyploid, or the degree of genetic recombination or heterozygosity expected from its mating system (Kosman and Leonard, 2005). In response, a number of comparative studies where two or more similarity coefficients were used for data analysis (Duarte et al., 1999; Da Silva Meyer et al., 2004) have been published. In this study two different similarity coefficients, the Jaccard and Simple Matching coefficients, were used to estimate the genetic diversity of varieties within the cactus pear germplasm. Coefficient measures of similarity are commonly used to analyse similarity between individuals when knowledge of ancestry of all individuals in the population is not known (Kosman and Leonard, 2005). This was the case in this study as a large number of the varieties are of unknown pedigree (Table 2.1). Berg x Mexican and X 28 (Robusta x Castillo) are the only varieties of known pedigree, thus necessitating the use of similarity coefficients to estimate genetic diversity. AFLP markers are dominant and therefore do not allow the exact determination of the genetic similarity between individuals that share a fragment at the same position (Kosman and Leonard, 2005). When using dominant markers to assess genetic diversity in diploid or polyploid individuals, one cannot distinguish fragments that represent two alleles at a homozygous locus from fragments that represent only one allele. The Jaccard coefficient was therefore used to measure genetic similarity instead of the commonly used Dice similarity coefficient (Table 2.3) which attaches more weight to shared fragments. The Simple Matching coefficient was chosen to compare with the genetic distances generated using the Jaccard similarity coefficient. The Simple Matching coefficient computes genetic similarities by the inclusion of shared fragment absences (Table 2.3). The inclusion of joint absences has been demonstrated to give equal importance to species (fragment) presence and absence, thus giving equal importance to rare and ubiquitous species (fragments) in cluster formation (Jackson et al., 1989). All 38 varieties in this study could be separated based on AFLP fingerprints, using both similarity measures. Previously, researchers have shown that AFLP fingerprinting can be used to distinguish between Opuntia species (Labra et al., 2003, Nilsen et al., 2005). However, in contrast to our findings, Nilsen and co-workers reported that AFLP 52 fingerprinting failed to distinguish traditionally classified forms of O. pilifera F.A.C. Weber. (Nilsen et al., 2005). Previously, isozyme studies employing 13 enzyme systems on root, cladode, petal, and pollen material, failed to identify individual genotypes (Chessa et al., 1997). In addition, Uzun (1997) detected no differences between varieties, for the same enzyme system in the same plant organ. Using RAPD-markers, Opuntia spp. accessions were separated into fruit and ornamental types, but very few differences amongst fruit clones were reported (Wang et al., 1998). RAPD markers have also been used to investigate the genetic diversity in the Tunisian cactus pear germplasm collection (Zoghlami et al., 2007). In this study, the different accessions grouped into four main clusters (Figures 2.3 and 2.4), using both similarity coefficients. In addition, varieties grouped into the same clusters using both coefficients, with the exception of Roly Poly and Schagen (Figures 2.3 and 2.4). Using the Jaccard similarity coefficient, Roly Poly clustered with Santa Rosa in cluster IV. In contrast, Roly Poly remained ungrouped, using the Simple Matching coefficient, and Santa Rosa grouped into cluster IV with Schagen. 53 Direkteur I Gymno Carpo Blue Motto Fusicaulis Fresno Corfu Mexican Skinners Court Messina II Muscatel Amersfoort Vryheid Nepgen American Giant Morado Malta Algerian Turpin Sicilian Indian Fig R1260 R1259 R1251 III Sharsheret Ficus-Indice Van As Rossa Meyers Arbiter Tormentosa Berg x Mexican Ofer Nudosa Unknown Roedtan X 28 IV Roly Poly Santa Rosa Schagen 0.75 0.80 0.85 0.90 0.95 1.00 Jaccard Similarity Coefficient FIGURE 2.3 DENDROGRAM OF 38 SOUTH AFRICAN CACTUS PEAR VARIETIES BASED ON CLUSTER ANALYSIS (UPGMA) OF GENETIC SIMILARITY ESTIMATES USING THE JACCARD SIMILARITY COEFFICIENT Varieties in blue are those cultivated for fruit in South Africa (Brutsch, 1979) Cultivated varieties were dispersed amongst the different clusters, of which the highest percentage clustered in group III (Figures 2.3 and 2.4). This finding is important for both cactus pear breeders and farmers, in that it indicated that commercially cultivated varieties represent the genetic diversity present within the germplasm. Therefore, the risk of genetic homogeneity within commercially cultivated varieties in this germplasm is low. 54 Direkteur I Gymno Carpo Blue Motto Fusicaulis Fresno Corfu Mexican Skinners Court Messina II Muscatel Amersfoort Vryheid Nepgen American Giant Morado Malta Algerian Turpin Sicilian Indian Fig R 1260 R 1259 R 1251 III Sharsheret Ficus-Indice Van As Rossa Meyers Arbiter Tormentosa Berg x Mexican Ofer Nudosa Unknown Roedtan IV X 28 Santa Rosa Schagen Roly Poly 0.80 0.84 0.88 0.92 0.96 1.00 Simple Matching Similarity FIGURE 2.4 DENDROGRAM FOR 38 SOUTH AFRICAN CACTUS PEAR VARIETIES BASED ON CLUSTER ANALYSIS (UPGMA) OF GENETIC SIMILARITY ESTIMATES USING THE SIMPLE MATCHING COEFFICIENT Varieties in blue are those cultivated for fruit production in South Africa (Brutsch, 1979) Sharsheret and R1251, Malta and Algerian, and R1260 and R1259 were genotypically very similar. The three varieties from Botswana (R1260, R1259 and R1251) clustered together, whilst those from Israel (Sharsheret, Ofer and Messina) were spread amongst groups II and III (Figures 2.3 and 2.4). However, Sharsheret was genotypically very similar to R1251 although from Israel and Botswana, respectively. Furthermore, in a study by Wang et al. (1998), cactus pear varieties used for fruit, did not group according to species or geographic origin using RAPD markers. Although one would expect accessions from different countries to differ substantially, they may have the same origin. It has been reported by Dreyer that although the majority of the South African cactus pear varieties are Burbank types obtained from the USA they were originally collected from many regions such as Mexico, Africa and Australia (Chapman et al., 2002). Therefore, the origin of many of these varieties is unknown. This may also be true for Sharsheret and R1251, which are genotypically very similar even though collected from different geographic regions. 55 The different accessions in the germplasm were genetically similar with the greatest distance between them at 0.220 (Jaccard), or 0.195 (Simple Matching). The fact that cactus pear is commonly propagated by cloning, could be a possible explanation for the narrow genetic base observed. Plants are rarely commercially grown from seeds, limiting genetic recombination and increasing genetic homogeneity. Clustering methods will always cluster data, whether or not clusters are present in the original data (Sneath and Sokal, 1973). Cophenetic analysis of a dendrogram computes a linear correlation coefficient between the cophenetic distances from the tree, and the original distances (similarities) used to construct the dendrogram. It verifies how accurately the dendrogram reflects the original distances (Sokal and Rohlf, 1962). It is therefore important that one confirms the existence of clusters. In this study the cophenetic correlation coefficient for the dendrogram based on the the Simple Matching coefficient was r = 0.954 (Figure 2.5) and r = 0.953 (Figure 2.6) for the Jaccard similarity coefficient. Clusters generated thus accurately represented the distances between the accessions as determined by the similarity coefficients. 1.00 0.95 Y Label 0.90 0.85 0.80 0.76 0.82 0.88 0.94 1.00 X Label FIGURE 2.5 COPHENETIC CORRELATION MATRIX FOR SIMPLE MATCHING COEFFICENT DATA 56 1.00 0.94 Y Label 0.89 0.83 0.78 0.74 0.80 0.87 0.93 1.00 X Label FIGURE 2.6 COPHENETIC CORRELATION MATRIX FOR JACCARD COEFFICIENT DATA The dendrograms and clusters (Figures 2.3 and 2.4) generated by the two similarity coefficients were almost identical. This is in contradiction with various arguments, that given the different mathematical formulas used for each coefficient, different clustering patterns should result. In addition, the Simple Matching coefficient is computed using fragment absences; thus, one would expect a greater difference between the two dendrograms. The similarity matrices obtained using the Jaccard and Simple Matching coefficients, were compared by the Mantel test statistic for matrix comparison. Mantel (1967) developed a test that enables one to compare two matrices. The test gives a product-moment correlation (r), and a statistic (Z) to measure the degree of relatedness between two matrices. Rohlf (1990) further suggests that the degree of fit can be subjectively interpreted as being very good when r ≥ 0.9. The matrices generated by the Jaccard and Simple Matching coefficients were highly correlated at r = 0.929. This furthermore confirmed the observed similarity of the dendrograms generated, using the different similarity coefficients. This finding suggested that these similarity coefficients give similar estimates of genetic relationships among these accessions of cactus pear. What is known of the ancestry of the 39 South African cactus pear varieties is that they were developed either as clones or as natural or artificial hybrids from 21 spine-less types from the Burbank nursery imported into the country. These varieties were collected by researchers at the Limpopo Department of Agriculture and distributed to commercial and emerging farmers. Investigations into the reticulate evolution, (occasional hybridisation and combination of two species), in Opuntia spp. using 57 molecular markers can be applied to elucidate the ancestry of these varieties. RAPD markers were used by Griffith (2003) to complement morphological data (Griffith, 2001a) and observed interfertility of parental taxa (Griffith, 2001b) to phylogenetically identify putative parental taxa of two hybrid Opuntia taxa, O. x rooneyi and O. x spinosibacca. Varieties classified as O. fusicaulis (Direkteur, Blue Motto, and Fusicaulis) grouped into cluster I with the exception of Gymno Carpo, which also grouped in this cluster, but is classified as an O. ficus-indica type. The rest of the varieties classified as O. ficus-indica (Morado, Malta, Algerian) grouped into cluster III. Varieties classified as hybrids between the different Opuntia species in South Africa, and hence denoted as Opuntia spp. (Nudosa, American Giant, and Skinners Court) were dispersed over clusters II and III (Figures 2.3 and 2.4). Variety 37, an unknown accession, was shown not to be identical to any other variety even though it was thought to be a duplicate. 2.4 CONCLUSIONS AFLP markers were successfully applied to genotype South African cactus pear germplasm. Primer combinations that resulted in the highest percentage polymorphic fragments were described and are recommended for future fingerprinting efforts of cactus pear. Genotype specific fragments were generated with the use of six primer combinations (E-AGG + M-CAT, E-ACT + M-CAG, E-ACT + M-CAT, E-ACA + M-CAT, E-ACA + M-CTT, and E-ACA + M-CAG). These GSF allowed the unique identification of nine varieties, three of which are commercially cultivated (Meyers, Roedtan, and Santa Rosa). These fragments can be converted into STS markers for more rapid identification of cultivated varieties. A large number of the varieties are of unknown pedigree (Table 2.1). Berg x Mexican, and X 28 (Robusta x Castillo) are the only varieties of known pedigree. Berg x Mexican clustered in group III and did not cluster in the same group as one of its progenitors, Mexican, which was classified in cluster I. For future research, the various known progenitors of such varieties should be included into AFLP marker studies in order to assist in the elucidation of their pedigrees. Comparative analysis of dendrograms constructed, based on the Simple Matching and Jaccard similarity coefficients displayed negligible differences. The widest genetic distance within the germplasm based on the Jaccard similarity coefficient was 0.220. This value was found to be comparable to that deduced from the dendrogram based on the Simple Matching coefficient (0.195). The only differences in clusters based on the 58 two similarity coefficients were the grouping of Roly Poly with Santa Rosa in cluster IV, using the Jaccard similarity coefficient. In contrast, Roly Poly remained ungrouped using the Simple Matching coefficient, and Santa Rosa grouped into cluster IV with Schagen. Cophenetic correlation analysis confirmed the goodness of fit between cophenetic values and the original similarity estimates as being high for both dendrograms. Visual similarity between the dendrograms was confirmed using the Mantel test. 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Characterization and genetic distance analysis of cassava (Manihot esculenta Crantz) germplasm from Mozambique using RAPD fingerprinting. Euphytica 138: 49-53. Zoghlami, N., I. Chrita, B. Bouamama, M. Gargouri, H. Zemni, A. Ghorbel and A. Mliki, 2007. Molecular based assessment of genetic diversity within the Barbary fig (Opuntia ficus indica (L.) Mill.) in Tunisia. Scientia Horticulturae (in press). 65 Chapter 3 Fruit quality of South African cactus pear (Opuntia spp.) varieties ABSTRACT South Africa hosts one of the largest cactus pear germplasm collections in the world at the Mara Germplasm bank, Limpopo. However, the available gene pool within the conserved accessions is not fully exploited. A study was undertaken to evaluate 23 cactus pear varieties for use in fruit production in the Mokopane district of the Limpopo Province. Data were collected over two seasons (1999-2000, 2000-2001), and subjected to analysis of variance (ANOVA) using the general linear model. The Gower distance coefficient was used as a measure of diversity, and the UPGMA for cluster analysis. Varieties grouped into four main clusters. Commercially cultivated varieties were evenly dispersed among the different clusters, with the greatest percentage grouped into cluster IIa. Varieties from Botswana (R1251, R1259) clustered separately. Varieties recommended for use as fruit crops grouped into cluster IIa. Gymno Carpo, Malta, and Algerian grouped into cluster IIa. Cluster I varieties had the highest TSS content (14.26°Brix) with a pulp content of 55.36%. The majority of the varieties had a fruit development period (FDP) within the 120–130 days range. Varieties with the longest FDP over both seasons were Nepgen (148 days for season 1, and 193 days for season 2), Skinners Court (141 days in season 1, and 148 days in season 2), and Zastron (148 days in season 1, and 162 days in season 2). All the varieties underwent an extended FDP during the second season as a result of chillier conditions. 66 3.1 INTRODUCTION The potential of cactus pear as a commercial fruit crop in South Africa is increasingly being exploited by farmers. In South Africa, cactus pear is usually cultivated under dry- land conditions. Commercial fruit plantations make use of spine-less Burbank varieties that are clonally propagated using terminal cladodes (Brutsch, 1979; Wessels, 1988). A number of plantations were established in the middle eighties and are increasing in number (Wessels et al., 1997). Commercial plantations of spine-less cactus pear are well established and the Limpopo Province contains the largest cactus pear plantations for fruit production in South Africa (Potgieter, 2002). The cactus pear is an oval shaped “false” berry (Hills, 1995) with an average weight of between 100-200 g. Cactus pear fruits are appreciated for their characteristic taste and aroma as well as their dietetic properties. It has a thick fleshy skin that contributes 30- 40% of the total fruit weight. The juicy pulp contributes 60-70% of the total fruit weight, and contains many hard coated seeds that contribute 5-10% of the pulp weight (Griffiths and Hare, 1906; Cantwell, 1991; Barbera, 1995). Each variety produces fruits of different shapes, colours, and flavours. The primary components of the fruit pulp are water (85%), carbohydrates (10-15%), and vitamin C (25-30 mg/100 g) (Cantwell, 1995). Fruits are mainly produced on mature terminal cladodes, and require 110-120 days to develop (Cantwell, 1986). The choice of variety is primarily governed by a variety's suitability to the climatic conditions of the region chosen for cultivation, and by the intended market to be supplied (i.e. local or international). Other factors that influence choice of variety include the cultivar’s yield potential, ripening time, and quality characteristics (Potgieter, 1997). Desirable characteristics of varieties to be used for fruit production in South Africa have been described (Table 3.1). 67 TABLE 3.1 DESIRABLE CHARACTERISTICS OF CACTUS PEAR VARIETIES IN SOUTH AFRICA Plant characteristics Fruit characteristics High yield potential Large fruit size (>140.0 g) Short juvenile phase (early bearing) Attractive internal and external colour Consistent good yield Long shelf-life Moderate vegetative vigour Low seed content Require little pruning and fruit-thinning Seeds should be small High tolerance to pests and diseases Fruit should not bruise easily Wide climatic adaptability Acceptable peel thickness (< 6 mm) Natural tendency to bear out of season High TSS content (>13°Brix) Few thorns and glochids Pleasant taste and aroma/flavour Easily manipulated (winter production and High juice content scozzollatura) High percentage pulp (> 50%) Crack resistance Easy peeling Suitability to processing TSS = Total soluble solids (Potgieter and Mkhari, 2000) For commercial handling, the maturity or ripeness stage of fruit at harvest is important (Cantwell, 1995). Fruit quality characteristics for cactus pear include percentage pulp, thickness and ease of removal of the peel, and peel resistance to physical handling (Wessels, 1988). Kader (2000) included uniformity and intensity of colour, size, and freedom from defects and decay as indices for grading cactus pear fruits. Large differences occur among cultivars in TSS (12-17°Brix), titratable acidity (0.03-0.12%), pH (6.0-6.6), and ascorbic acid content (20-40 mg/100g fresh weight) (Kader, 2000). Internationally a few studies have reported on the characterisation of cactus pear varieties for fruit production (Chessa and Nieddu, 1997; Arba et al., 2002; Felker et al., 2002a; Nieddu et al., 2002; Valdez et al., 2002). Characterisation of cactus pear varieties is further complicated because cactus pear, unlike most fruit crops, is not monospecific. It derives from a number of species from the genus Opuntia (Chessa et al., 1995), hence many researchers refer to commonly cultivated varieties as Opuntia spp. Although South Africa hosts one of the largest germplasm collections of cactus pear in the world (Chapman et al., 2002) limited research into this emerging crop has been published. Of the work being done few publications have reported on the evaluation of the fruit quality of different varieties that occur in South Africa. It thus became the aims 68 of this study to (1) examine the differences in fruit quality of 23 South African cactus pear varieties and, (2) determine which varieties produce fruit of a higher quality. 3.2 MATERIALS AND METHODS 3.2.1 Trial site and layout Trial site Evaluation was carried out at the Gillemberg cactus pear germplasm block in the Mokopane (previously Potgietersrus) district of the Limpopo Province. This area is characterised by warm summers, cool winters and a mean annual rainfall of 450 mm that predominantly falls in summer. A brief list describing some of the climatic and soil characteristics of the trial site is provided in Table 3.2. TABLE 3.2 CLIMATIC AND SOIL CHARACTERISTICS OF THE GILLEMBERG CACTUS PEAR GERMPLASM BLOCK Character Name/ Value Farm name Gillemberg Magisterial district Potgietersrus Latitude 23º 50 ’S Longitude 28º 58 ’E Altitude (m) 1 100 Average annual rainfall (mm) 450 Average daily maximum air temperature in December 27.92 (°C) Average daily minimum air temperature in June (°C) 6.04 Accumulated positive C.U. (May-Aug) (°C) 245.5 Accumulated H.U. (Oct-Mar) Growth Degree Day (°C) 2 367 Average daily solar radiation (MJ/m2/s) 19.10 Average wind speed (m/s) 2.38 Average daily evaporation (mm) 6.04 Average daily maximum R.H. (%) 84.18 Average daily minimum R.H. (%) 41.98 Soil texture Loamy sand Clay percentage 15 Silt percentage 5 Sand percentage 80 Veldt type (Acocks, 1952) Mixed bushveld C.U. = chill units H.U. = heat units R.H. = relative humidity 69 Trial layout Data gathered for this study were collected from the Gillemberg cactus pear germplasm block. The trial was thus not statistically laid out for the purposes of this study. Only those varieties that showed signs of fruit bearing were evaluated and those that formed few or no flowers, were excluded from this study. Data was gathered for 23 of the varieties (Table 3.3) that were deemed promising for commercialisation (Brutsch, 1979). The orchard consisted of 20 plants per variety planted in a single row orientated in an East/West direction. Plants were spaced 5 m between rows and 2 m in a row (1000 plants/ha). Three plants on each end of each row, and four in the middle of the row were used as border plants. The remaining 10 were used as data plants. Data collected over two seasons (Season 1: 1999-2000, Season 2: 2000-2001) were used for fruit quality evaluation. Data for 10 fruit quality traits were captured for each variety (Table 3.4). Quantitative characters were collected as an average value of the 10 central plants per variety at two harvest times. Two harvesting times were used, one at 30-40% total fruit ripening and again at 50-60% total fruit ripening. Two harvesting times were used because flower bud burst was unsynchronised, therefore harvesting twice during fruit development allowed for a more representative sample of a particular variety. The method for quantitative trait data collection is given in Table 3.4. Phenological stages were recorded as described in Table 3.5. The fruit development period was deduced as the time period between reproductive bud break (RBB) and 50% fruit ripening (FFR). 3.2.2 Climatic data Climatic data was captured via an automatic weather station (Mike Cotton Systems) installed 50 m from the site (Appendix I). Mean daily values for temperature (°C), rainfall (mm), heat units (HU), chill units (CU), evapotranspiration (ETo), and solar radiation (Rs) were summarised to mean monthly values. 70 TABLE 3.3 CACTUS PEAR VARIETIES EVALUATED FOR FRUIT QUALITY Variety number Variety name Commercially cultivated Country of origin varieties 12 Algerian X South Africa 40 Berg x Mexican South Africa 39 Cross X South Africa 26 Ficus-Indice South Africa 5 Gymno Carpo X South Africa 11 Malta X South Africa 15 Meyers X South Africa 8 Morado X South Africa 29 Nepgen South Africa 4 Nudosa X South Africa 18 Ofer Israel 34 R1251 Botswana 33 R1259 Botswana 16 Roedtan X South Africa 41 Santa Rosa X South Africa 42 Schagen South Africa 31 Silician Indian Fig South Africa 2 Skinners Court X South Africa 23 Tormentosa South Africa 13 Turpin South Africa 38 Van As X South Africa 24 X 28 (Robusta x Castillo) South Africa 10 Zastron X South Africa A list of the different Opuntia spp. varieties used in this study, with the accompanying variety numbers. The cultivated varieties, depicted in the blue font, were reported by Brutsch (1979) as being of good potential for commercial fruit production 3.2.3 Cultural practices The germplasm block was maintained as a commercial fruit orchard and generally accepted orchard practises such as pruning and pad thinning were performed. No supplementary irrigation was given, and orchard practices followed were as described in Potgieter (1997) with the following modifications: Pruning Varieties were pruned more severely than outlined in Potgieter (1997). Additional terminal cladodes were removed and distributed to farmers to use as planting material, especially of varieties number 13 to 42 as they were considered new varieties at the time. 71 TABLE 3.4 LIST OF FRUIT QUALITY TRAITS AND THEIR DESCRIPTOR STATES Character name Fruit quality trait and descriptive value PEEL THICKNESS Two measurements were taken of peel thickness at 180 degrees (mm) from one another for 20 fruits of the same variety FRUITSHAPE Index indicative of fruit shape derived as, fruitshape = fwidth / flength: 0.45-0.55 = oblong, 0.56- 0.60 = elliptic, 0.70-0.79 = ovoid 0.80-0.89 = round FMASS (g) Fruit mass of 20 plants per variety TSS (°Brix) Total soluble solid content, was determined for 20 fruits of the same variety %PULP The edible portion of the fruit expressed as a percentage of the whole fruit for 20 fruits of the same variety FRUITNO (n) Number of fruits per plant was deduced from the number of reproductive buds counted subsequent to fruit thinning to allow 40-50 mm between buds PEELABILITY Peelability index, the ease with which the peel is removed from the pulp, given an arbitrary value from 1 to 5 depending on the ease of removal; 1 = difficult to remove, 5 = easy to remove FWIDTH (mm) Equatorial diameter of 20 fruits per variety FLENGTH (mm) Longitudinal length of 20 fruits per variety PC Pulp colour: 1 = khaki/yellow, 2 = green/white, 3 = white, 4 = orange/red, 5 = orange, 6 = pink, 7 = white/red, 8 = purple 9 = pink/purple, 10 = red, 11 = unknown (Appendix III) Fertilisation Fertilisation was carried out based on soil analysis results obtained from the Agricultural Research Council Institute for Soil, Climate and Water (ARC-ISCW) laboratory, Tshwane (Tables 3.6 and 3.7). Top soil samples (0-300 mm) were taken from within the drip area of the plants during late winter/spring. Five sub-samples were taken over the entire orchard. 72 TABLE 3.5 LIST OF PHENOLOGICAL AND QUALITATIVE TRAITS USED FOR CLUSTERING OF CACTUS PEAR VARIETIES Character Phenological trait descriptive value name RBB Reproductive bud break: week of the month when reproductive buds are clearly visible FFO 50% flower opening (anthesis): week of the month when 50% of all flower buds are showing petals FFR 50% fruit-ripening: week of the month during which 50% of all fruit on a variety are ripe FDP Fruit development period: total number of days from the first working day of the week during which reproductive bud break was recorded until and inclusive of the first day of the week during which 50% fruit ripening was recorded PULPMASS (g) Pulp mass: measured for ten fruits of the same variety FPC Flower petal colour: 1 = dark yellow, 2 = yellow, 3 = orange, 4 = unknown (Appendix II) CLADSHAPE Cladode shape; 1 = elliptic, 2 = ovate, 3 = large diamond, 4 = round (Appendix V) PH Plant habitus: 1 = bush/shrubby, 2 = spreading, 3 = upright, 4 = arborescent (Apppendix V) PA (yrs) Plant age: number of years since plant was established TABLE 3.6 SOIL ANALYSIS RESULTS FOR GILLEMBERG GERMPLASM BLOCK (1999-2001) Season P K Ca Mg Na Resistance pH TTA (cmol (mg/kg) (mg/kg) (mg/kg) (mg/kg) (mg/kg) (ohm) (H2O) (+)/kg) 1999-2000 37.3 93 732 115 12 1400 5.76 0 2000-2001 20.9 68 519 100 14 2400 5.74 0 TTA = Total titratable acidity Soil analysis was determined for exchangeable and water soluble nutrients using the following techniques: P = Bray 1; K, Ca, Mg, and Na = ammonium acetate method, and electrical resistance = soil paste technique. 73 TABLE 3.7 FERTILISATION RECOMMENDATIONS AND APPLICATION FOR GILLEMBERG GERMPLASM BLOCK Season Element Product Quantity Time of application *Method 1999/2000 N (100 kg/ha) LAN (28%N) 375 kg/ha Two equal split dressings By hand (November, February) K (60 kg/ha) Potassium chloride 120 kg/ha December By hand (50% K) 2000/2001 N (100 kg/ha) LAN (28%N) 375 kg/ha Two equal split dressings By hand (November, February) K (100 kg/ha) Potassium chloride 200 kg/ha December By hand (50% K) Lime Dolomite lime 2000 kg/ha December By hand *Fertiliser was applied by hand in drip area 3.2.4 Data collection and statistical analysis In assessing fruit quality traits of cactus pear varieties a fully randomised experimental design with random sampling of all data points was carried out. During the course of the study, data for fruit quality traits were collected for each variety, and entered into the Statistical Package for the Social Sciences (SPSS Inc, 1997). Mean values for each of the traits were calculated for each variety for both seasons. Data for quantitative characters were subjected to analysis of variance using the general linear model in SPSS. The Tukey multiple range test was used to detect significant differences between means at p ≤ 0.05. The Gower distance was used as a measure of diversity between different varieties. Gower similarity measure between the i th and j th individual, Sij, can be used with continuous, ordinal, binary, and nominal variables. This similarity measure was used to compute distances between varieties (Gower, 1971). Gower distance [dij = (1 – Sij) ½] between two individuals is Euclidean metric. For k variables (k = 1,2,….,p), Gower similarity measure between two individuals i and j is: p Wijk S ijk S ij = k=1 p Wijk k =1 74 Where: wijk : is a weight given to the ijk th comparison, 1 is assigned to valid comparisons, and 0 for invalid comparisons (when the value of the variable is missing in one or both individuals) Sijk is the contribution of the k th variable to the total similarity between individuals i and j, and it takes values between 0 and 1 for nominal variables, if the value of the k th variable is the same for both individuals i and j then Sijk = 1; otherwise it equals 0 for a continuous variable Sijk = 1 – |xik - xjk| /Rk where xik and xjk are the values of the k th variable for the i and j individuals respectively, and Rk is the range of the k th variable in the sample (Franco et al., 2005). Gower distances were used to compute a dissimilarity matrix, and the UPGMA used for dendrogram construction using the NTYSYS-pc programme (Version 2.02i, Rohlf, 1998). 3.3 RESULTS AND DISCUSSION 3.3.1 Fruit quality: Season1 Varieties from Botswana (R1251 and R1259) did not produce any fruit during the first season of the trial, as they had not reached the productive age for fruit production. 3.3.1.1 Peel thickness The peel of the cactus pear fruit develops from the receptacle that surrounds the ovary (Gibson and Nobel, 1986). The peel is thick and must be removed before the tasty pulp can be consumed. Varieties that had the highest peel thickness were Nepgen (7.11 mm), Ficus-Indice (5.79 mm) and Skinners Court (5.43 mm). Varieties that had a low peel thickness were Malta (3.86 mm), Gymno Carpo (4.02 mm) and Morado (4.05 mm) (Table 3.8). Potgieter and Mkhari (2002) recommended a peel thickness of less than 6 mm for cactus pear fruit. All varieties evaluated had a peel thickness less than 6 mm, except for Nepgen which had a peel thickness of 7.11 mm. With regards to peel thickness, all varieties evaluated, except for Nepgen, meet the requirements for fruit production in South Africa. 75 76 TABLE 3.8 FRUIT QUALITY OF CACTUS PEAR VARIETIES (SEASON 1) Fruit Fruit Pulp Variety name Peelthick F shape F mass TSS %Pulp Fruit no Peelability Width Length colour Algerian 4.21abcdefh 0.77efghi 160.85abcdefg 12.89bcde 60.59defg 155.30ghi 3.20defg 59.24cdef 77.73abc Dark pink Berg x Mexican 4.18abcde 0.72abcdefg 158.38abcdefg 12.58abcde 59.37bcdefg 42.30bcd 4.20hij 58.70cdef 81.50abcde Dark pink Cross X 4.26abcdef 0.75cdefghi 159.85abcdefg 12.49abcde 60.70efg 36.60abc 4.75ij 59.41cdef 80.00abcd Orange Ficus-Indice 5.79j 0.68abc 173.55cdefg 12.93bcde 55.00ab 60.60bcde 2.95cde 59.80cdef 87.75def Orange Gymno Carpo 4.02abc 0.77ghi 173.79cdefg 11.37a 61.55fg 176.50hi 2.85cde 61.81f 80.35abcde Orange Malta 3.86ab 0.79hi 165.48bcdefg 11.94abc 63.62g 140.30gh 2.85cde 60.40def 77.09abc Orange Meyers 4.42bcdefgh 0.77fghi 170.61bcdefg 12.55abcde 61.70fg 156.60ghi 3.80gh 61.64ef 80.13abcd Dark pink Morado 4.05abcd 0.77ghi 146.46abcde 13.15bcde 60.38defg 133.80g 2.40bc 57.81abcde 75.01a White Nepgen 7.11k 0.68ab 141.31ab 12.80bcde 50.35a 77.90cde 2.60cd 54.02a 79.90abcd White Nudosa 4.98ghi 0.70abcdef 231.10h 11.34a 58.79bcdef 63.10bcde 1.55a 66.00g 94.53f Red/Orange Ofer 4.74defghi 0.80i 153.36abcdefg 13.77e 58.97bcdefg 183.00i 4.20hij 60.64def 76.05ab Orange R1251 0.00 0.00 0.00 0.00 0.00 0.00a 0.00 0.00 0.00 - R1259 0.00 0.00 0.00 0.00 0.00 0.00a 0.00 0.00 0.00 - Roedtan 4.57cdefgh 0.75defghi 163.75bcdefg 12.91bcde 61.18fg 144.50ghi 4.85j 60.08def 80.33abcde Orange Santa Rosa 4.06abcd 0.69abcd 157.34abcdefg 12.93bcde 59.49bcdefg 40.50abcd 3.75fgh 56.91abcd 82.96abcde Orange Schagen 4.43bcdefgh 0.66a 174.04cdefg 12.32abcd 57.19bcdefg 37.40abc 4.55ij 58.30bcdef 88.89ef White Sicilian Indian Fig 4.86efghi 0.73bcdefgh 167.60bcdefg 13.46de 56.28bcde 81.20de 3.10def 59.47cdef 81.95abcde Dark pink Skinners Court 5.43ij 0.73bcdefghi 186.91g 13.67de 55.37bc 26.60ab 1.50a 62.21gf 85.61cde White/ green Tormentosa 4.58cdefgh 0.71abcdefg 183.55fg 12.79bcde 58.98bcdefg 66.80bcde 2.80cde 60.44def 85.53cde Orange Turpin 4.89fgh 0.69abcd 177.96efg 12.66abcde 55.94bcd 161.10ghi 3.00cde 60.48def 87.54def Orange Van As 4.30bcdefg 0.70abcde 164.19bcdefg 12.83bcde 59.87cdefg 32.50ab 4.65ij 58.36bcdef 83.89bcde White X 28 5.09hij 0.73bcdefghi 176.40defg 11.98abc 60.49defg 91.80ef 2.70cde 60.90ef 83.50abcde Orange Zastron 4.82efghi 0.74bcdefghi 142.88abc 13.15bcde 56.27bcde 125.70fg 1.85ab 56.09abc 75.69ab White Grand mean 4.64 0.73 165.51 12.68 58.78 88.44 3.29 59.30 81.80 Within column values with the same letter are not significantly different at p≤ 0.05 according to Tukey multiple range test. Peelthick = peelthickness, F shape = fruit shape, F mass = fruit mass, TSS = Total soluble solids content, %Pulp = percentage pulp content, Fruit no = number of reproductive buds remaining per plant after thinning. The cultivated varieties, depicted in the blue font, were reported by Brutsch (1979) as being of good potential for commercial fruit production 3.3.1.2 Fruit shape In cactus pear, fruit shape index is deduced as the ratio of equatorial fruit width and longitudinal fruit length. This index is used to determine fruit shape and did not show large variation between varieties (Table 3.8). The majority of fruits had a fruit shape index in the range of 0.70-0.79, indicative of ovoid shaped fruits. Cactus pear fruits are classified according to four shapes namely, round, elliptic, ovoid, and oblong (Chessa and Nieddu, 1997; Ochoa, 1997). Fruit size and shape are important considerations when choosing a variety for cultivation because oval or barrel-shaped fruits are easier to handle than elongated fruits. In addition, oval shaped fruits undergo less damage to the stem end during harvesting (Cantwell, 1991). Therefore, in terms of shape, the majority of the varieties would qualify for commercialisation. Recent findings have shown that shape attributes have a genetic basis (Van der Knaap and Tanksley, 2003). Additionally, trait terms and mathematical descriptors of shape attributes have been developed to improve phenotypic analyses. A software programme, Tomato analyser, which performs semi-automated, objective and quantitative measurements of fruit shape has been developed. It is envisioned that this programme will accelerate phenotypic characterisation, and eliminate subjective scoring of many fruit shape traits (Brewer et al., 2006). This development will be of great value for cactus pear researchers since evaluating fruits for shape attributes is time consuming and requires manual measurement of widths and lengths of countless fruits over many seasons. 3.3.1.3 Fruit mass Significant differences in fruit mass were observed between varieties at p ≤ 0.05 (Table 3.8). Cactus pear fruit mass is affected by the number of seeds (Barbera et al., 1994), cladode load (Wessels, 1988; Brutsch, 1992; Inglese et al., 1995b), water availability (Barbera, 1984) and ripening time (Brutsch and Scott, 1991; Nerd et al., 1991, Barbera et al., 1994). Nudosa (231.10 g), Skinners Court (186.92 g), and Tormentosa (183.56 g) produced the heaviest fruits during season 1. The mean fruit mass of the varieties evaluated was 165.51 g, which is higher than the minimum acceptable mass for cactus pears destined for exportation (120.00 g) (Inglese et al., 2002) and that of 140.0 g recommended for commercial fruit production in South Africa (Potgieter and Mkhari, 2002). The fruit mass of Nudosa (231.10 g) and Algerian (160.58 g) were higher than previously reported as 180 g and 100 g, respectively (Wessels, 1988). 77 'Scozzolatura' (the removal of initial blooms in order to delay fruit ripening and harvest) has been shown to increase fruit mass (Barbera et al., 1990; 1991; Mulas, 1997), and induce fruit of a better quality (Nieddu et al., 1997). Scozzolatura can be used in conjunction with the recommended cultural practices for fruit production to enhance fruit size for varieties with low fruit mass, however, some negative effects are associated with this practise, such as lower TSS, acids, and sugar content (Mulas, 1997). 3.3.1.4 Total soluble solids content TSS measured as °Brix, is an indication of sugar content. Sugar content is an important criterion of fruit quality for consumers since they prefer sweet fruits (Inglese et al., 1995a). In general, as fruits ripen, levels of soluble solids in the cell vacuoles increase as acidity decreases. Ofer (13.77 °Brix), Skinners Court (13.67 °Brix), and Morado (13.16 °Brix) were found to have high TSS content (Table 3.8). The variety Morado is amongst the sweeter varieties and is commercially cultivated in South Africa. Nudosa (11.34 °Brix), Gymno Carpo (11.37 °Brix), and X 28 (11.98 °Brix) had the lowest recorded TSS content. The mean TSS content for the varieties tested was 12.68 °Brix. This TSS level compares well with that recommended for cactus pear fruits (13-15 °Brix) (Barbera et al., 1992; Kuti, 1992). 3.3.1.5 Percentage pulp Cactus pear fruits are of the berry type with a juicy pulp that contains many hard-coated seeds (Barbera et al., 1992). The percentage pulp should not be lower than 55-60% in fruits destined for export markets (Inglese et al. 1995a). Varieties with the highest percentage pulp content were Malta (63.62%), Meyers (61.70%), and Gymno Carpo (61.55%). Nepgen, Ficus-Indice, and Skinners Court produced fruit with the lowest percentage pulp at 50.35%, 54.97%, and 55.37%, respectively (Table 3.8). The range of percentage pulp content within all varieties tested (63.62-50.35%) is higher than that previously reported (30-60%) for South African cactus pear varieties (Wessels, 1988). Nepgen was the only variety with a percentage pulp content lower than required for commercial cultivation in South Africa. 3.3.1.6 Number of fruit Significant differences were observed in the number of fruit remaining after thinning, in September, within varieties (Table 3.8). The number of fruit remaining after thinning is an indication of the fertility of a particular variety in a specific area. Varieties that had the 78 highest number of fruit after thinning were Ofer (183.00 fruit/plant), Gymno Carpo (176.50 fruit/plant), Turpin (161.10 fruit/plant) and Algerian (155.30 fruit/plant). Skinners Court had a low fertility in this area and produced 26.20 fruit/plant. The ability to produce fruit is influenced by cladode position and orientation and can be related to dry matter accumulation relative to cladode surface area (Garcia de Cortazar and Nobel, 1992). The average fertility of the varieties evaluated was 88.44 fruit/plant for the first season. 3.3.1.7 Peelability Peelability, the ease with which the peel is removed from the pulp, varied significantly between varieties (Table 3.8). Skinners Court, Nudosa, and Zastron were varieties that were difficult to peel as reflected by very low peelability indices of 1.50, 1.55, and 1.85 respectively. Varieties that allowed easy removal of the peel, were Roedtan (4.85), Cross X (4.75) and Van As (4.65). 3.3.1.8 Fruit width Fruit width had a low variability within varieties tested (Table 3.8). Varieties that had the widest equatorial diameter were Nudosa (66.00 mm), Skinners Court (62.21 mm), Gymno Carpo (61.81 mm), and Meyers (61.64 mm). Varieties that had the lowest diameter were mainly of the white pulp colour type, namely Nepgen (54.02 mm), Zastron (56.09 mm), Santa Rosa (56.91 mm), and Morado (57.81 mm). 3.3.1.9 Fruit length There was a low variability of fruit length within varieties tested. Varieties that had the highest fruit length were Nudosa (94.53 mm), Schagen (88.89 mm), and Ficus-Indice (87.75 mm). Varieties with the lowest length were Morado (75.01 mm), Zastron (75.69 mm), and Ofer (76.05 mm). 3.3.1.10 Pulp colour Varieties within the germplasm block had a wide array of pulp colour (Appendix III). Pulp colour is a determinant of the market to be supplied. Local consumers prefer a white/green pulp whilst overseas consumers prefer a red/orange or purple coloured pulp (Inglese et al., 2002). The majority of the varieties had a dark pink or orange pulp colour, and would thus be suitable to be sold overseas (Table 3.8). Varieties with a white or white/green pulp colour that would suite the preference of the local market were Meyers, Morado, Schagen, Skinners Court, Van As, and Zastron (Table 3.8). 79 3.3.2 Fruit quality: Season 2 3.3.2.1 Peel thickness Varieties that had the thickest peels were Nudosa (5.54 mm), Roedtan (5.22 mm), and Van As (5.21 mm) (Table 3.9). Varieties that had the thinnest peels were Cross X (4.13 mm), R1251 (4.28 mm), and Sicilian Indian Fig (4.46 mm). The overall peel thickness of varieties during the second season (4.80 mm) did not differ significantly from that recorded for all varieties in the first season (4.64 mm). Thick peeled dark purple fruit varieties have been found to have low percentage pulp content (Felker et al., 2005). Similarly, in this evaluation varieties that had the thickest peels (Nudosa, Roedtan, and Van As) had low percentage pulp content. 3.3.2.2 Fruit shape Varieties that had the highest values for fruit shape index were Gymno Carpo (0.73), Malta (0.72), and Algerian (0.72) (Table 3.9). This shape index is indicative of an ovoid shape, which is the preferred shape for cactus pear fruit. Varieties with the lowest shape indices were Nepgen (0.60), Zastron (0.62), and Ficus-Indice (0.64). One of the attributes of the perfect cactus pear fruit is glochids that are easily removable by mechanical brushing (Felker et al., 2005). Glochids located in the receptacle area are difficult to remove with mechanical brushing techniques. The degree of difficulty in removing these glochids could be influenced by fruit shape. Ovoid shaped fruits are preferred since glochid removal from the receptacle area is easier than for elliptical fruits. 3.3.2.3 Fruit mass Varieties that had the highest fruit mass were Nudosa (223.10 g), Tormentosa (186.68 g), and X 28 (181.95 g) (Table 3.9). Varieties that had the lowest fruit mass were Nepgen (138.07 g), Ficus-Indice (141.78 g), and Malta (145.85 g). In South Africa fruits meant for the export market must exceed 120 g (Wessels, 1988), thus even these low ranking varieties would still be suitable for exportation based on fruit mass. Using the cultural practices recommended for commercial fruit cultivation (Potgieter, 1997) and under rain fed conditions, all varieties in this genebank produced fruits of an adequate mass for exportation. 80 81 TABLE 3.9 FRUIT QUALITY TRAITS OF CACTUS PEAR VARIETIES (SEASON 2) Fruit Fruit Pulp Variety name Peelthick F shape Fmass TSS %Pulp Fruit no Peelability width length colour Algerian 5.11cde 0.72de 155.24abc 12.50a 52.87 abcd 45.50cde 4.35defg 58.38abcde 81.65ab Dark pink Berg x Mexican 4.59abcd 0.69cde 170.46bcd 14.29bcd 56.47 cde 29.10abcd 4.00cde 59.28bcde 85.92abcd Dark pink Cross X 4.13a 0.67abcde 162.24abcd 12.25a 53.56 abcde 19.30abcd 4.50efg 58.24abcde 87.99abcd Orange Ficus-Indice 4.91bcde 0.64abc 141.78ab 14.63cde 49.96 a 46.60cde 4.90g 55.09a 86.39abcd Orange Gymno Carpo 4.48abcd 0.72e 158.00abcd 13.09ab 57.69 de 4.20ab 4.70efg 59.86cde 82.84abc Orange Malta 4.56abcd 0.72de 145.85ab 15.00cde 57.60 de 18.60abc 4.65efg 56.67abcd 78.99a Orange Meyers 4.81abcde 0.69cde 153.80abc 14.12bc 57.39 de 12.30abc 4.90g 57.77abcde 83.39abc Dark pink Morado 4.52abcd 0.69bcde 148.60ab 14.51cde 58.63 e 11.10abc 4.50efg 56.75abcd 82.81abc White Nepgen 4.67abcd 0.60a 138.07a 15.53de 52.18 abc 42.30cde 1.60a 54.58a 91.61cde White Nudosa 5.54e 0.69cde 223.09e 12.00a 53.94 abcde 2.90a 2.70b 66.49f 96.26e Red/Orange Ofer 5.05cde 0.68bcde 158.53abcd 15.48de 53.73 abcde 38.50bcde 4.15cdef 58.67abcde 86.19abcd Orange R1251 4.28ab 0.69cde 147.17ab 12.77a 54.02 abcde 18.10abc 3.75cd 56.99abcd 82.99abc Orange R1259 4.95bcde 0.66abcde 148.84ab 14.68cde 52.14 abc 13.10abc 4.25cdefg 55.58ab 84.73abcd Orange Roedtan 5.22de 0.70cde 161.81abcd 14.31bcd 55.89 cde 24.90abcd 4.45defg 58.55 84.13abc Orange Santa Rosa 4.56abcd 0.65abcd 157.58abcd 15.06cde 56.28 cde 32.70abcde 4.15cdef 56.54abcd 87.30abcde Orange Schagen 5.16cde 0.67abcde 169.28bcd 15.21cde 52.88 abcd 16.50abc 4.75fg 58.62abcde 88.38bcde White Sicilian Indian Fig 4.46abc 0.66abcde 152.50abc 15.64e 55.57 bcde 66.00e 3.60c 56.37abc 85.72abcd Dark pink Skinners Court 4.81abcde 0.70cde 157.53abcd 15.26cde 53.79 abcde 21.60abcd 1.75a 58.45abcde 86.06abcd White/ green Tormentosa 4.69abcd 0.66abcde 186.67d 15.17cde 57.17 cde 54.20de 3.75cd 61.43e 93.71de Orange Turpin 5.06cde 0.68bcde 156.39abc 14.28bcd 54.29 abcde 23.90abcd 4.40defg 57.75abcde 85.57abcd Orange Van As 5.21cde 0.65abcde 153.40abc 14.99cde 50.45 ab 18.30abc 4.80fg 56.23abc 86.34abcd White X 28 4.91bcde 0.67abcde 181.94d 15.08cde 57.09 cde 30.80abcde 4.60efg 60.52de 91.73cde Orange Zastron 4.78abcd 0.62ab 153.82abc 14.38cde 54.85 abcde 116.90f 1.70a 56.22abc 91.73cde White Grand mean 4.80 0.67 160.11 14.36 54.72 30.76 3.95 58.04 86.55 Within column values with the same letter are not significantly different at p≤ 0.05 according to Tukey multiple range test. Peelthick = peelthickness, F shape = fruit shape, F mass = fruit mass, TSS = Total soluble solids content, %Pulp = percentage pulp content, Fruit no = number of reproductive buds remaining per plant after thinning. The cultivated varieties, depicted in the blue font, were reported by Brutsch (1979) as being of good potential for commercial fruit production 3.3.2.4 Total soluble solids content Varieties that had the highest TSS content as measured in °Brix were Sicilian Indian Fig (15.65), Nepgen (15.54), and Ofer (15.48). Varieties that had the lowest TSS content were Nudosa (12.01), Cross X (12.26), and Algerian (12.50) (Table 3.9). The vast majority of the varieties had a higher TSS content over the second season (Table 3.9) compared to the first season (Table 3.8). TSS content, an indication of the sugar content, increased from 12.68°Brix during the first season to 14.36°Brix in the second season (Table 3.9). Similar findings of year-to-year variation in the mean TSS of cactus pear clones had been reported (Wang et al., 1997). 3.3.2.5 Percentage pulp Varieties that had the highest percentage pulp content were Gymno Carpo (57.70), Malta (57.60), and Meyers (57.40). Varieties with the lowest percentage pulp content were Ficus-Indice (49.96), Van As (50.46), and R1259 (52.14) (Table 3.9). 3.3.2.6 Number of fruit Varieties with the highest number of fruit were Zastron (116.90 fruit/plant), Sicilian Indian Fig (66.00 fruit/plant), and Tormentosa (54.20 fruit/plant). Varieties that produced the lowest number of fruit in the second season (Table 3.9) were Nudosa (2.90 fruit/plant), Gymno Carpo (4.20 fruit/plant), and Morado (11.10 fruit/plant). 3.3.2.7 Peelability Varieties that were difficult to peel as indicated by the peelability index, were Nepgen (1.60), Zastron (1.70), and Skinners Court (1.75). Of the varieties evaluated (Table 3.9) those that were easy to peel were Ficus-Indice (4.90), Meyers (4.90), and Van As (4.80). 3.3.2.8 Fruit width Varieties that had the widest diameter (Table 3.9) were Nudosa (66.49 mm), Tormentosa (61.43 mm), and X 28 (60.52 mm). Varieties that had the narrowest diameter were Nepgen (54.58 mm), Ficus-Indice (55.09 mm), and R1259 (55.58 mm). 3.3.2.9 Fruit length Varieties that had the longest length (Table 3.9) were Nudosa (96.26 mm), Tormentosa (93.71 mm), and X 28 and Zastron (91.73 mm). Varieties that had the shortest lengths were Malta (78.99 mm), Algerian (81.66 mm) and Morado (82.81 mm). Varieties that were the longest also had the widest diameters. 82 3.3.2.10 Pulp colour Varieties from Botswana, R1251 and R1259, that did not produce fruit during the first season produced fruits with an orange coloured pulp in the second season (Table 3.9). Pulp colour of the remainder of the varieties remained the same as for season 1. 3.3.3 Phenological and qualitative traits SEASON 1 The length of the FDP and the ripening time in cactus pear are cultivar dependant, but show large within-plant variability (Inglese et al., 1995a). Varieties that had the longest FDP during the first season were Zastron (148 days), Nepgen (148 days), and Skinners Court (141 days) (Table 3.10). The variety with the shortest FDP for this season was Cross X at 113 days. The majority of varieties had a FDP within the 120-130 day range (Table 3.10). Zastron, Nepgen, and Skinners court had the longest FDP as a result of early bud burst. Reproductive bud break (RBB) of all the varieties was spread over a period of several weeks as has been reported for cactus pear varieties (Wessels and Swart, 1990). The varieties that displayed early reproductive bud break were Zastron and Nepgen during the 2nd week of August, and Skinners Court during the 4th week of August (Table 3.10). The ripening period was more concentrated in the 1st, 2nd and 3rd weeks of January for all the varieties, except for Nudosa which reached FFR during the 4th week of February (Table 3.10). Fruit mass is influenced by the time of bud emergence, cladode fruit load and environment. Early flush buds (and thereby a longer FDP) were found to produce heavier fruits than late flush buds (Wessels and Swart, 1990). However, Zastron and Nepgen (FDP of 148 days) did not produce the heaviest fruits during the first season (Table 3.8). On the contrary Nepgen, which had a FDP of 148 days (Table 3.10), produced fruits of low mass (141.31 g) within the varieties tested (Table 3.8). Skinners Court, however, had a long FDP (141 days), but produced fruits of high mass (186.92 g). These findings could indicate that some aspects of fruit mass are genetically controlled. 83 TABLE 3.10 REPRODUCTIVE BUD BREAK, FIFTY PERCENT FRUIT RIPENING AND FRUIT DEVELOPMENT PERIOD FOR SEASON 1 VARIETY RBB FFR FDP Algerian 1/9 2/1 127 Berg x Mexican 2/9 2/1 120 Cross X 3/9 2/1 113 Ficus-Indice 2/9 3/1 127 Gymno Carpo 2/9 3/1 127 Malta 1/9 2/1 127 Meyers 3/9 3/1 120 Morado 2/9 3/1 127 Nepgen 2/8 1/1 148 Nudosa 2/9 4/1 134 Ofer 1/9 2/1 127 R1251 - - - R1259 - - - Roedtan 2/9 3/1 127 Santa Rosa 2/9 2/1 120 Schagen 2/9 3/1 127 Sicilian Indian Fig 3/9 3/1 120 Skinners Court 4/8 2/1 141 Tormentosa 1/9 3/1 134 Turpin 1/9 2/1 127 Van As 2/9 3/1 127 X 28 1/9 3/1 134 Zastron 2/8 1/1 148 Mean FDP 128 RBB: reproductive bud break FFR: 50 % fruit ripening FDP: Fruit development period Cultivated varieties, depicted in the blue font, were reported by Brutsch (1979) as being of good potential for commercial fruit production - : Varieties did not produce fruit during this season SEASON 2 The same varieties that had the longest FDP over the first season (Table 3.10) had the longest FDP in the second season: Nepgen (193 days), Zastron (162 days), and Skinners Court (148 days) (Table 3.11). The FDP were longer for these three varieties during the second season. Similar findings were observed with the effect of time of bud emergence and fruit mass. Contrary to the findings of Wessels and Swart (1990), varieties with shorter FDP, Nudosa (134 days) and Tormentosa (120 days), produced 84 heavier fruits, 223.09 g and 186.67 g respectively, than those with a longer FDP, Nepgen (138.10 g) and Zastron (153.82 g). TABLE 3.11 REPRODUCTIVE BUD BREAK, FIFTY PERCENT FRUIT RIPENING AND FRUIT DEVELOPMENT PERIOD FOR SEASON 2 VARIETY RBB FFR FDP PA Algerian 3/9 2/1 113 9 Berg x Mexican 2/9 3/1 127 5 Cross X 2/9 3/1 127 5 Ficus-Indice 2/9 2/1 120 5 Gymno Carpo 3/9 2/1 113 9 Malta 3/9 3/1 113 9 Meyers 3/9 2/1 113 8 Morado 3/9 3/1 120 9 Nepgen 4/7 4/2 193 5 Nudosa 4/9 1/2 134 9 Ofer 3/9 2/1 113 7 R1251 1/9 3/1 134 5 R1259 1/9 3/1 134 5 Roedtan 3/9 3/1 120 8 Santa Rosa 4/8 3/1 141 5 Schagen 2/9 3/1 127 5 Sicilian Indian Fig 4/8 2/1 134 5 Skinners Court 1/8 1/1 148 9 Tormentosa 2/9 2/1 120 5 Turpin 2/9 2/1 120 8 Van As 4/8 2/1 127 4 X 28 2/9 3/1 127 5 Zastron 4/7 1/1 162 9 Mean FDP 131 RBB: Reproductive bud break FFR: 50% fruit ripening FDP: Fruit development period PA : Plant age in 2001 (yrs) Cultivated varieties, depicted in the blue font, were reported by Brutsch (1979) as being of good potential for commercial fruit production Cladode load influences fruit mass (Nerd and Mizrahi, 1995), thus one would expect that cladodes with fewer fruit buds will have heavier fruit. This has not been consistent with the present study. This study was carried out in a low-rainfall area therefore not more than eight fruits per cladode were left on a cladode after fruit thinning (Potgieter, 1997). During the first season Cross X had fewer fruit buds per plant (36.60 fruit/plant) than Nudosa (63.10 fruit/plant), but Nudosa produced heavier fruit (231.10 g) compared to that of Cross X (159.85 g). Similarly during the second season Morado, which had been established for nine years, produced lighter fruit (148.60 g) at 11.10 fruit/plant than Tormentosa which had only been established for five years (Table 3.11) and produced heavier fruit (186.67 g) at 54.20 fruit/plant, suggesting that certain varieties naturally 85 produced larger fruit regardless of cladode load, time of bud emergence or the number of years since establishment. However, a limitation of this study is that the number of seeds of the varieties was not determined. Fruit size also depends on the number of seeds (Barbera et al., 1994). Therefore, the number of seeds could be responsible for larger fruits despite cladode load and early bud emergence. The overall FDP during the second season (131 days) was longer than that recorded for the first season (128 days). Low temperatures during the FDP delay fruit ripening and result in an extended fruit harvest period at both plant and orchard level (Inglese et al., 2002). The accumulated heat units during the second season (484.84 HU) were higher than during the first season (461.90 HU). However, the accumulated chill units during the second season (305.50 CU) were also higher than those recorded for the first season (101.50 CU) (Table 3.12). This could indicate that the overall effect of chilling had a greater effect on the length of the FDP than increments in temperature. Varieties that had the shortest FDP of 113 days were Gymno Carpo, Malta, Algerian, Meyers and Ofer (Table 3.11). Knowledge of the FDP is important in crop forecasting, providing farmers with information for harvest planning, price policy and stock management (Moriondo et al., 2001). 3.3.4 Effect of microclimatic conditions during fruit development on fruit quality Variations in TSS content of cactus pear varieties were observed within and between seasons in this study. The mean TSS content of the varieties was 12.68 in season 1, and increased to 14.36 in season 2 (Tables 3.8 and 3.9). The mean rainfall during the FDP was lower during the second season (76.67 mm) as compared to that of the first season of 106.06 mm (Table 3.12). Similar findings have been reported by Wang et al. (1997), who reported a mean TSS of 14.0 in 1996, 11.8 in 1997, and 13.6 in 1998 for 24 clones of cactus pear. The total rainfall recorded was 250 mm in 1996, 909 mm in 1997 and 23 mm 1998. High TSS content in cactus pear seems to be associated with drier periods. Various reports of correlations between cladode mineral content and TSS content have been reported. It has been reported that TSS concentration is positively correlated with cladode Mg concentration (Karim et al., 1997). In contrast Galizzi et al. (2004) have observed a significant negative correlation between TSS and cladode P and Zn concentrations. Future studies on South African varieties in local environments should include determination of whether relationships exist between TSS and Mg, P and Zn concentrations in cladodes. 86 TABLE 3.12 MEAN CLIMATIC CONDITIONS OVER TWO SEASONS Parameter Season 1 Season 2 Average rainfall (mm) 106.06 76.67 Average maximum air temperature (Tmax, °C) 26.26 27.26 Average minimum air temperature (Tmin, °C) 15.41 15.20 Average temperature (Tave, °C) 20.54 20.90 Accumulated heat units (HU) 461.90 484.84 Accumulated positive Richardson chill units (CU) 101.50 305.50 Average maximum relative humidity (RHx,%) 88.22 89.26 Average minimum relative humidity (RHn,%) 53.49 51.50 Average daily evapotranspiration (ETo, mm) 5.18 4.20 Average solar radiation (Rs, MJ/m2/s) 1074.59 655.81 Fruit shape shifted from being ovoid (shape index 0.70-0.79) in the first season to elliptic (shape index 0.56-0.60) in the second season (Table 3.13). This change in shape can be attributed to an increase in length of the fruit from 81.80 mm in the first season to 86.54 mm in the second season, whilst the equatorial diameter remained constant (59.30 mm in the first season and 58.04 mm in the second season). These changes decreased the fruit shape index and thereby the general shape of the fruit in the second season. Fruit mass decreased over the second season, from 165.51 g in the first season to 160.11 g in the second season (Table 3.13). The decrease in fruit mass can be attributed to a significant decrease in rainfall during the FDP over the area in the second season (Table 3.12). Fruit development in this area starts with reproductive bud burst in August and ends with 50% fruit ripening the following year in February. The genebank under study was maintained under dry-land conditions, with rain as the only source of water. Previous reports stated that irrigation and thinning to six fruits per cladode during the FDP significantly increased fruit size. Since water constitutes 85% of the fruit pulp (Cantwell, 1995) periods of less water availability will cause a decrease in pulp content. Higher rainfall, particularly in the last two months of fruit maturation, has been observed to cause an increase in fruit size and higher percentage pulp content (Felker et al., 2002b). Combined thinning and irrigation were reported to increase the frequency of fruits with a mass greater than 100 g (Gugliuzza et al., 2002). However, irrigation without thinning, and vice versa, do not produce a significant increase in fruit size, but only an increase in the frequency of fruits with a mass greater than 100 g (Gugliuzza et al., 2002). In addition, early thinning, taking into consideration the natural crop load of a variety, is required to have a significant effect on fruit size (Inglese et al., 1995b). 87 TABLE 3.13 MEAN FRUIT QUALITY TRAITS OVER COMBINED SEASONS Fruit Quality Trait Season Value Peel thickness 1 4.64 mm 2 4.80 mm Fruit shape 1 0.73 2 0.68 Fruit mass 1 165.51 g 2 160.11 g %TSS 1 12.68°Brix 2 14.36°Brix %Pulp 1 58.78% 2 54.72% Fruit no 1 88.44 fruit/plant 2 30.76 fruit/plant Peelability 1 3.29 2 3.95 Fruit width 1 59.30 mm 2 58.05 mm Fruit length 1 81.80 mm 2 86.55 mm TSS = Total soluble solids content, %Pulp = percentage pulp content, Fruit no = number of reproductive buds remaining per plant after thinning During the trial period chill units registered for the second season (305.50 CU) were significantly higher than for the first season (101.50 CU) (Table 3.12). Low temperatures during the fruit development period encourage an increase in peel thickness (Nerd et al., 1993). There was a slight increase in the overall peel thickness of the varieties evaluated from 4.64 mm during the first season to 4.80 mm during the second season (Table 3.13). Barbera and Inglese (1993) reported that low temperatures led to a decrease in pulp content. Similar results were obtained during this study since overall percentage pulp content of the varieties decreased from 58.78% during the first season to 54.72% during the second season (Table 3.13). Even though there was a decrease in percentage pulp content during the second season, it remained within the recommended level of between 50-60%, required to maintain the post-harvest quality of fruit (Kader, 1999). 88 3.3.5 Combined analysis The ideal cactus pear variety would have the following traits: spine-less cladodes, glochids easily removable by mechanical brushing, tolerance to -9°C, pulp percentage > 55%, °Brix > 13, pulp firmness > 1 kg, mature yield > 20 000 kg/ha, post-harvest shelf life at 2°C > 4 weeks, and seediness < 3 g seeds per 100 g pulp (Felker et al., 2002a). In addition it should produce fruit of a variety of colours (yellow, orange, pink, and purple) (Felker et al., 2005). With the above mentioned goal in mind the means of the various fruit quality traits were combined for each variety to give the overall performance of individual varieties over both seasons (Table 3.14). In terms of peel thickness Nepgen (5.89 mm), Ficus-Indice (5.35 mm), and Nudosa (5.26 mm) ranked the highest within varieties evaluated. These peel thickness values are still within the recommended (< 6 mm) for fruit varieties in South Africa. Varieties that had the thinnest peels were Cross X (4.20 mm), Malta (4.21 mm), and Gymno Carpo (4.25 mm). In terms of varieties with high fruit mass, Nudosa (227.10 g), Tormentosa (185.11 g), and X 28 (179.17 g) performed best. Varieties that produced fruits low in fruit mass were Nepgen (139.69 g), R1251 (139.34 g), and R1259 (147.30 g). Concerning TSS content, varieties that had the highest TSS content were Ofer (14.63°Brix), Sicilian Indian Fig (14.56°Brix), and Skinners Court (14.47°Brix). Varieties that had the lowest TSS content were Nudosa (11.67°Brix), Gymno Carpo (12.23°Brix), and Cross X (12.37°Brix). In terms of percentage pulp content, varieties that had the highest pulp content were Malta (60.61), Gymno Carpo (59.62), and Meyers (59.55). Those with a low percentage pulp were Skinners Court (54.58), Ficus-Indice (52.46), and Nepgen (51.26). 89 90 TABLE 3.14 FRUIT QUALITY TRAITS OVER COMBINED SEASONS Fruit Fruit Fruit Fruit Variety Peelthickness shape mass TSS %Pulp Fruitno Peelability width length Algerian 4.66 ± 0.63 0.74 ± 0.03 158.05 ± 3.96 12.70 ± 0.28 56.73 ± 5.45 100.40 ± 77.64 3.78 ± 0.81 55.87 ± 0.61 77.75 ± 5.52 Berg x Mexican 4.39 ± 0.29 0.71 ± 0.02 164.42 ± 8.54 13.44 ± 1.21 57.92 ± 2.05 35.70 ± 9.33 4.10 ± 0.14 45.04 ± 0.41 83.81 ± 3.33 Cross X 4.20 ± 0.10 0.71 ± 0.06 161.05 ± 1.69 12.37 ± 0.17 57.13 ± 5.05 27.95 ± 12.23 4.63 ± 0.18 43.77 ± 0.82 83.27 ± 9.82 Ficus-Indice 5.35 ± 0.62 0.66 ± 0.03 157.67 ± 22.47 13.78 ± 1.20 52.46 ± 3.54 53.60 ± 9.90 3.93 ± 1.38 49.66 ± 3.33 85.32 ± 1.52 Gymno Carpo 4.25 ± 0.33 0.75 ± 0.03 165.90 ± 11.16 12.23 ± 1.22 59.62 ± 2.73 90.35 ± 121.83 3.78 ± 1.31 56.75 ± 1.38 83.24 ± 0.56 Malta 4.21 ± 0.50 0.76 ± 0.05 155.66 ± 13.88 13.47 ± 2.16 60.61 ± 4.26 79.45 ± 86.05 3.75 ± 1.27 53.92 ± 2.63 77.76 ± 1.74 Meyers 4.61 ± 0.28 0.73 ± 0.05 162.20 ± 11.89 13.34 ± 1.11 59.55 ± 3.05 84.45 ± 102.04 4.35 ± 0.78 55.50 ± 2.73 80.09 ± 4.67 Morado 4.29 ± 0.33 0.73 ± 0.06 147.53 ± 1.51 13.84 ± 0.96 59.51 ± 1.23 72.45 ± 86.76 3.45 ± 1.48 49.41 ± 0.74 82.39 ± 0.59 Nepgen 5.89 ± 1.72 0.64 ± 0.06 139.69 ± 2.29 14.17 ± 1.93 51.26 ± 1.29 60.10 ± 25.17 2.10 ± 0.71 44.93 ± 0.40 87.48 ± 5.84 Nudosa 5.26 ± 0.40 0.70 ± 0.01 227.10 ± 5.66 11.67 ± 0.47 56.36 ± 3.43 33.00 ± 42.57 2.13 ± 0.81 55.83 ± 0.38 96.22 ± 0.06 Ofer 4.90 ± 0.22 0.74 ± 0.08 155.94 ± 3.66 14.63 ± 1.21 56.35 ± 3.71 110.75 ± 102.18 4.18 ± 0.04 56.67 ± 1.40 83.06 ± 4.42 R1251 4.35 ± 0.10 0.70 ± 0.01 139.33 ± 11.08 12.99 ± 0.30 56.63 ± 3.69 9.05 ± 12.80 3.95 ± 0.28 35.80 ± 1.68 83.89 ± 1.27 R1259 4.26 ± 0.98 0.70 ± 0.05 147.30 ± 2.18 13.24 ± 2.04 56.43 ± 6.08 6.55 ± 9.26 3.80 ± 0.64 37.75 ± 0.28 80.32 ± 6.25 Roedtan 4.89 ± 0.46 0.73 ± 0.04 162.78 ±1.37 13.62 ± 0.99 58.53 ± 3.74 84.70 ± 84.57 4.65 ± 0.28 54.33 ± 1.08 80.62 ± 4.96 Santa Rosa 4.31 ± 0.36 0.67 ± 0.03 157.46 ± 0.17 14.00 ± 1.50 57.88 ± 2.27 36.60 ± 5.52 3.95 ± 0.28 45.07 ± 0.26 82.30 ± 7.07 Schagen 4.79 ± 0.52 0.66 ± 0.00 171.66 ± 3.37 13.77 ± 2.04 55.04 ± 3.05 26.95 ± 14.78 4.65 ± 0.14 45.85 ± 0.23 86.93 ± 2.04 Sicilian Indian Fig 4.66 ± 0.28 0.70 ± 0.05 160.05 ± 10.68 14.56 ± 1.54 55.93 ± 0.50 73.60 ± 10.75 3.35 ± 0.35 52.62 ± 2.19 83.20 ± 3.57 Skinners Court 5.12 ± 0.44 0.72 ± 0.02 172.22 ± 20.78 14.47 ± 1.12 54.58 ± 1.11 24.10 ± 3.54 1.63 ± 0.18 47.07 ± 2.65 87.31 ± 1.77 Tormentosa 4.64 ± 0.08 0.69 ± 0.04 185.11 ± 2.21 13.98 ± 1.68 58.08 ± 1.28 60.50 ± 8.91 3.28 ± 0.67 53.35 ± 0.70 89.11 ± 6.50 Turpin 4.98 ± 0.12 0.68 ± 0.01 167.18 ± 15.26 13.47 ± 1.14 55.12 ± 1.17 92.50 ± 97.02 3.70 ± 0.99 57.24 ± 1.93 87.29 ± 2.44 Van As 4.76 ± 0.64 0.68 ± 0.03 158.79 ± 7.63 13.91 ± 1.52 55.16 ± 6.66 25.40 ± 10.04 4.73 ± 0.11 44.01 ± 1.50 86.43 ± 0.13 X 28 5.00 ± 0.13 0.70 ± 0.04 179.17 ± 3.92 13.53 ± 2.20 58.79 ± 2.40 61.30 ± 43.13 3.65 ± 1.34 52.40 ± 0.27 88.47 ± 2.74 Zastron 4.81 ± 0.03 0.68 ± 0.09 148.35 ±7.73 13.77 ± 0.87 55.57 ± 1.01 121.30 ± 6.22 1.78 ± 0.11 56.00 ± 0.10 82.22 ± 13.44 Mean fruit quality traits over two seasons with their standard deviations. F mass = fruit mass, TSS = Total soluble solids content, %Pulp = percentage pulp content, Fruit no = number of reproductive buds remaining per plant after thinning, Fwidth = fruit width, Flength = fruit length. Cultivated varieties depicted in the blue font, were reported by Brutsch (1979) as being of good potential for commercial fruit production The overall means of fruit quality traits were combined with phenological and qualitative traits to determine the relatedness between varieties in terms of fruit quality. Varieties grouped into two main clusters (Figure 3.1). Two varieties, Nepgen, and Nudosa clustered separately (Figure 3.1). Nudosa was the most dissimilar of the varieties in terms of fruit quality. The two most similar varieties were R1251 and R1259, followed by Tormentosa and X 28. Varieties from Botswana (R1251, R1259) clustered together (cluster IId) in a group that had the lowest fruit quality traits of the deduced clusters. This cluster was characterised by a mean fruit mass of 138.63 g, TSS content of 13.11°Brix, and a 56.53% pulp content. Malta, Algerian, Morado, and Meyers grouped into cluster IIa, consistent with clustering deduced from AFLP data (Figure 2.4) where these varieties grouped together in the same cluster. The majority of varieties grouped together into cluster II, which can be subdivided into four sub-clusters (Figure 3.1). Ofer, a variety from Israel, although closely related to the South African varieties, clustered separately from them in cluster IIc. Cluster IIb varieties produced the heaviest fruits (168.42 g) with an acceptable TSS content (13.60°Brix) (Table 3.15). Cluster I varieties had the highest TSS content (14.26°Brix) with a percentage pulp content of 55.36 (Table 3.15). Gymno Carpo, Malta, and Algerian grouped into cluster IIa. These varieties were identified by Brutsch (1979) as varieties of promising potential for commercial cultivation in South Africa (Table 3.3). 91 I SkinnersCourt Zastron Sicilian Indian Fig Gymno Carpo Malta IIA Algerian Morado Meyers Roedtan Turpin Tormentosa SicilianIndianFiMW X28 IIB Ficus-indice CrossX SantaRosa Berg x Mexican IIC Van As Schagen Ofer IID R1259 R1251 Nepgen Nudosa 0.72 0.56 0.41 0.26 0.10 Gowers distance FIGURE 3.1 DENDROGRAM CONSTRUCTED FROM FRUIT QUALITY AND MORPHOLOGICAL TRAITS USING THE GOWER DISSIMILARITY COEFFICIENT TABLE 3.15 MEAN FRUIT QUALITY TRAITS FOR DENDRGOGRAM CLUSTERS Cluster Fmass Peelthickness Peelability %TSS %Pulp F shape I 165.80 4.86 2.25 14.26 55.36 0.70 IIa 165.56 4.56 3.92 13.24 58.52 0.73 IIb 168.42 4.68 4.11 13.60 56.56 0.68 IIc 138.63 4.30 3.88 13.11 56.53 0.70 F mass = fruit mass, TSS = Total soluble solids content, %Pulp = percentage pulp content, F shape = fruit shape The number of years of establishment played a significant role in the grouping of varieties into clusters. All varieties in cluster I were established for nine years except for Sicilian Indian Fig which had only been established for five years by 2001 (Table 3.11). Similarly all varieties that grouped into cluster IIa were established for eight or nine years in 2001. Other commonly cultivated varieties in South Africa (Morado, Meyers, and Roedtan) also clustered in this group. All the varieties in cluster IIb had been established for five years except for Turpin which was established for eight years, and Van As which had been established for four years in 2001. 92 Cultivated varieties (Table 3.3) were dispersed among the different clusters, with the greatest percentage grouped in cluster IIa (Figure 3.1). Other commercially cultivated varieties were dispersed throughout the germplasm collection. This finding is important for both cactus pear breeders and farmers as it signifies that commercially cultivated varieties represent the fruit quality diversity present within the germplasm. The dendrogram indicated that based on fruit quality traits, cultivated varieties did not all cluster into one group of closely related varieties. Therefore, the risk of genetic homogeneity within commercially cultivated varieties in South Africa is low. From the findings of this study it is evident that no single variety outperforms all others for all the fruit quality traits evaluated. Hybrid cactus pear varieties, classified as Opuntia spp. (Skinners Court and Zastron) grouped together in cluster I. However, another hybrid variety (Nudosa) did not cluster with the varieties evaluated in this study (Figure 3.1). Varieties classified as O. ficus- indica types (Gymno Carpo, Malta, Algerian, and Morado) grouped together in cluster IIa (Figure 3.1). Potgieter and Smith (2006) found that O. ficus-indica varieties merged into one group of yields above the mean using the Additive Main Effects and Multiplicative Interactions Analysis (AMMI). Based on the AMMI findings these varieties were recommended for cactus pear fruit production in the Lowveld and Middleveld agro-climatic zones of the Limpopo Province (Potgieter and Smith, 2006). 3.4 CONCLUSIONS The majority of fruit quality traits investigated in this study can be altered using recommended cultural practises. Adequate irrigation increases plant growth and as a result, fruit yield (Mulas and D'Hallewin, 1997). Harvest size can be increased with thinning of the number of fruits per plant. In South Africa, Wessels (1988) recommended not more that 9-12 fruits per cladode to increase fruit harvest size. Irrigation has been demonstrated to increase fruit size and percentage pulp content (Barbera, 1984; La Mantia et al., 1998). Application of nitrogen increases biomass production, fruit mass and TSS content (Potgieter and Mkhari, 2000) while application of phosphorous increases fruit production (Gathaara et al., 1989). Fruit quality traits are therefore amiable to manipulation using cultural practices. Varieties that are recommended for commercial cultivation in the Mokopane district of the Limpopo Province, South Africa, are those grouped in cluster IIa. These varieties are Gymno Carpo, Malta, Algerian, Morado, Meyers, and Roedtan. These varieties meet the minimum requirements for cactus pear fruit production in South Africa 93 (Potgieter and Mkhari, 2002). However, because most of the traits that govern fruit quality in cactus pear are influenced by environmental conditions, cultivar recommendations will have to be determined for each of the different climatic regions in South Africa. Previous studies have shown that fruit size and shape are affected by seed number and weight (Barbera, 1995). New consumers dislike seedy cactus pears and low pulp firmness (Felker et al., 2005) which have not been investigated in this study. A study investigating the seed content of the varieties in clusters IIa and IIb are recommended in order to determine the extent to which they affect fruit size. 94 REFERENCES Acocks, J.P.H., 1952. Veld types of South Africa 57, pp 146. Department of Agriculture and Water Supply, Government printer. Pretoria, South Africa. Arba, M., M.C. Benismail and M. Mimoun, 2002. The cactus pear (Opuntia spp.) in Morocco: Main species and cultivar characterization. In: Nefzaoui, A. and P. Inglese (Eds.), Proceedings of the Fourth International Conference on Cactus Pear and Cochineal. Acta Horticulturae 581: 103-109. Barbera, G., 1984. Ricerche sull’irrigazione del ficodindia. Frutticoltura 8: 49-55. Barbera, G., 1995. History, economic, and agro-ecological importance. In: Barbera G., P. Inglese and B.E. Pimienta (Eds.), Agroecology, cultivation and uses of cactus pear. FAO Plant production and protection paper 132. Rome, Italy. pp 19-35. Barbera, G. and P. Inglese, 1993. La Coltura del Ficodindia. Calderini Edagricoe, Bologna. Barbera, G., F. Carimi and P. Inglese, 1991. The re-flowering of prickly pear Opuntia ficus-indica (L.) Miller: Influence of removal time and cladode load on yield and fruit ripening. Advances in Horticultural Science 2: 77-80. Barbera, G., F. Carimi, P. Inglese and M. Panno, 1992. Physical, morphological and chemical changes during fruit development and ripening in three cultivars of prickly pear, Opuntia ficus-indica (L.) Miller. Journal of Horticultural Science 67: 307-312. Barbera, G., F. Carimi, P. Inglese and P. Sajeva, 1990. The intensive growing of prickly pear Opuntia ficus-indica Mill. for fruit production in Sicily. Actas del Congreso sobre "El nopal su conocimiento y aprovechamiento". Buenavista (Mexico), Diciembre: 127-144. Barbera, G., P. Inglese and T. La Mantia, 1994. Seed content and fruit characteristics in cactus pear (Opuntia ficus-indica Mill.). Scientia Horticulturae 58: 161-165. 95 Brewer, M.T., L. 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Schirra, 2002. Cactus pear fruit production. In: Nobel, P.S. (Ed.), Cacti: Biology and Uses, pp 163-183. University of California Press, California, USA. Inglese, P., G. Barbera and T. La Mantia, 1995a. Research strategies for the improvement of cactus pear (Opuntia ficus-indica) fruit quality and production. Journal of Arid Environments 29: 455-468. Inglese, P., G. Barbera, T. Le Mantia and S. Protoleuo, 1995b. Crop production, growth and ultimate size of cactus pear fruit following fruit thinning. Horticultural Science 30. Kader, A.A., 1999. Produce facts: Cactus (prickly) pear. Perishable Handling Quartely 100: 15-16. Kader, A.A., 2000. Cactus (prickly) pear: Recommendations for maintaining post- harvest quality. Post-harvest Technology research and Information Centre, California, U.S.A. Karim, M.R., P. Felker and R.L. Bingham, 1997. Correlations between cactus pear (Opuntia spp) cladode nutrient concentrations and fruit yield and quality. 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Fruit quality for four cactus pear (Opuntia ficus- indica Mill.) cultivars as influenced by irrigation. In: Inglese, P. and M.O. Brutsch (Eds.), Proceedings of the Third International Congress on cactus pear and Cochineal. Acta Horticulturae 438: 115-120. Nerd, A. and Y. Mizrahi, 1995. Reproductive biology. In: Barbera, G., P. Inglese and E. Pimienta-Barrios (Eds.), Agroecology, Cultivation and Uses of Cactus pear, FAO Plant Production and Protection Paper 132. FAO, Rome, pp 49-57. Nerd, A., A. Karady and Y. Mizrahi, 1991. Out-of season prickly pear: fruit characteristics and effect of fertilization and short droughts on productivity. Horticultural Science 26: 527-529. Nerd, A., R. Mesika and Y. Mizrahi, 1993. Effect of N fertilizer on the autum floral flush and cladode N in prickly pear Opuntia ficus-indica (L.) Mill. Journal of Horticultural Science 68: 337-342. Nieddu, G., I. Chessa, D. Satta, L. De La Pau and M. Pala, 2002. Description of six cactus pear (Opuntia ficus-indica Mill.) fruit cultivars from Italy. In: Nefzaoui, A. and P. Inglese (Eds.), Proceedings of the Fourth International Conference on Cactus Pear and Cochineal. Acta Horticulturae 581: 125-129. 99 Nieddu, G., L. De Pau, M. Schirra and G. D’Hallewin, 1997. Chemical composition of fruit and seeds of cactus pears during early and late-induced crop ripening. In: Inglese, P. and M. Brutsch (Eds.), Proceedings of the Third International Conference on Cactus Pear and Cochineal. Acta Horticulturae 438: 105-111. Ochoa, J., 1997. Cactus pear (Opuntia spp.) varieties main characteristics at Republica Argentina. Inglese, P. and A. Nefzaoui (Eds.). CACTUSNET-FAO. Potgieter, J.P., 1997. Guidelines for the cultivation of spine-less cactus pears for fruit production. Second Edition. Group 7 Trust Publishers, Sinoville, SA. Potgieter, J.P., 2002. Potential of cactus pear (Opuntia spp.) as a multi-purpose crop for emerging farmers in semi-arid regions of the Limpopo Province. Third SANCRA Congress, 25-28 June 2002, Nelspruit, South Africa. Potgieter, J.P. and J.J. Mkhari, 2000. The effects of N, P, K and lime on cactus pear (Opuntia spp.) fruit yield and quality. Fifth International Cactus Pear and Cochineal Congress, October 2000, Hammamet, Tunisia. Potgieter, J.P. and J.J. Mkhari, 2002. Evaluation of cactus pear (Opuntia spp.) germplasm for fruit production purposes. Combined Congress, 15-17 January 2002, Pietermaritzburg, Kwazulu/Natal. Potgieter, J. P. and M. Smith, 2006. Genotype X Environment interaction in cactus pear (Opuntia spp.), Additive Main Effects and Multiplicative interaction of fruit yield. Acta Horticulturae 728: 97-104. Rohlf, F.J., 1998. On applications of geometric morphometrics to studies of ontogeny and phylogeny. Systematic Biology 47: 147-158. SPSS for Windows, 22 December 1997. Copyright SPSS Inc. Valdez, C.R., C. Gallegos and M. Blanco, 2002. Agrupamiento Jeraquico de veintinueve genotipos de Opuntia spp. Mediante caracteristicas del fruti (tuna). In: Nefzaoui, A. and P. Inglese (Eds.), Congreso Nacional y 4 th Congreso Internacional sobre el conocimiento y Aprovechamiento del Nopal. Acta Horticulturae 581: 3-7. 100 Van der Knaap, E. and S.D. Tanksley, 2003. The making of a bell pepper-shaped tomato fruit: identification of loci controlling fruit morphology in Yellow Stuffer tomato. Theoretical and Applied Genetics 107: 139-147. Wang, X., P. Felker and A. Paterson, 1997. Environmental influences on cactus pear fruit yield, quality and cold hardiness and development of hybrids with improved cold hardiness. Journal of the Professional Association for Cactus Development 2: 48-59. Wessels, A.B., 1988. Spine-less prickly pear. Perskor Publishers, Johannesburg, pp 21-24. Wessels, A.B. and E. Swart, 1990. Morphogenesis of the reproductive bud and fruit of the prickly pear Opuntia ficus-indica (L.) Mill. cv. Morado. Acta Horticulturae 275: 245- 253. Wessels, A.B., L.L. van der Merwe and H. du Plessis, 1997. Yield variation in clonally propagated Opuntia ficus-indica (L.) Miller plants when terminal cladodes are used. In: Inglese, P. and M.O. Brutsch (Eds.), Proceedings of the Third International Congress on cactus pear and Cochineal. Acta Horticulturae 438: 73-76. 101 Chapter 4 Evaluation of South African cactus pear (Opuntia spp.) varieties for specific use as fodder ABSTRACT Spine-less cactus pear varieties were originally introduced to South Africa in 1914 for use as fodder. However, they are increasingly being cultivated for fruit, and the trees are pruned annually. Cladodes removed with pruning can be used as fodder, yet they are considered a waste product and destroyed at huge cost to the farmer. Hence, a study was undertaken to evaluate the nutritional quality of cladodes removed with pruning from a commercially maintained orchard, (Waterkloof) outside Bloemfontein. Crude protein (CP), organic matter (OM) and dry matter (DM) content were determined. In addition, vegetative yield under commercial orchard practices were evaluated for the Mokopane district of the Limpopo Province. Vegetative yield was assessed from data collected over two seasons (1999-2000, 2000-2001) at the Gillemberg germplasm block. The Gower distance was used as a measure of diversity between the different varieties. Two varieties, Turpin and Meyers, performed well in terms of vegetative vigour, with the mean number of cladodes removed by pruning being 107.60 ± 43.91 and 91.90 ± 37.12 cladodes per plant respectively. The variety Morado had a high number of cladodes removed by pruning, with 72.50 ± 25.88 cladodes removed per plant. The varieties Sicilian Indian Fig (89.35 ± 22.31 kg fresh material), Turpin (88.89 ± 25.62 kg fresh material), and Meyers (84.67 ± 24.58 kg fresh material) produced the highest biomass yield. The varieties Malta (109.24 g CP/kg DM), Gymno Carpo (108.23 g CP/kg DM), and American Giant (102.83 g CP/kg DM) had the highest CP content and are recommended for use as fodder. Analysis of variance showed significant (p ≤ 0.05) differences in CP and OM content amongst the varieties tested, indicating the presence of substantial variability. Animal performance testing to measure the digestibility and palatability of these promising varieties is recommended to assess their effectiveness under practical feeding conditions. 102 4.1 INTRODUCTION South Africa has been subject to severe droughts since the 1990s. It is predicted that the Western and Northern Cape regions will experience more droughts, whilst Limpopo, Gauteng, Mpumalanga and KwaZulu-Natal will suffer long dry spells followed by torrential rain and flooding (Kigotho, 2005). This change in climate will have a drastic negative effect on farming in the drier, drought prone regions of South Africa. This has led to an increasing number of South African livestock farmers planting cactus pear for use as feed during dry spells (Le Houérou, 1992; Martin, 1993). Internationally, cactus pear (Opuntia spp.) has found huge application as a drought tolerant feed in arid and semi-arid regions of the world (Cordeiro Dos Santos and Gonzaga De Albuquerque, 2001; De Kock, 2001; Nefzaoui and Ben Salem, 2001; Tegegne, 2001). It is more efficient at converting water to dry matter (digestible energy) than C3 grasses and C4 broadleaves (Nobel, 1995; Han and Felker, 1997). It responds well to fertilization (Nobel et al., 1987; Nobel, 1988), can withstand pruning (Inglese et al., 2002), and can be fed as forage, or stored as silage (FAO, 2000; Nefzaoui and Ben Salem, 2002). Opuntias are highly digestible (Nefzaoui and Ben Salem, 2002) and contain sufficient water and minerals that in combination with a protein source constitute a complete feed for livestock (Kueneman, 2001). Cactus pear can therefore be used to substitute grass hay for up to 20% for the maintenance of livestock live weight (Tegegne, 2002). In addition, Opuntia spp. meet most of the requirements for fodder crops in drought prone regions (De Kock, 1980), drought tolerance and palatability for animals. Other important requirements include adaptability to marginal land, ease of propagation, persistency, DM yield, digestibility and nutrient content (Tegegne, 2001). In South Africa, cactus pear trees in commercial fruit plantations are pruned annually during winter and the cladodes discarded as waste. This pruned material can alternatively be used as a valuable source feed for livestock (Oelofse et al., 2006). These varieties have however, not been evaluated for use as fodder for livestock. It thus became the aims of this study 1) to assess the nutritional quality of different cactus pear varieties, 2) to evaluate the varieties for differences in cladode yield, and 3) to determine which varieties are suitable as fodder crops with good vegetative yield and nutritional composition. 103 4.2 MATERIALS AND METHODS 4.2.1 Nutritional quality analysis 4.2.1.1 Trial site 1 Plant material of 39 cactus pear varieties was collected from a five year old cactus pear orchard (Waterkloof), outside Bloemfontein (29º 06' S, 26º 18’ E). The germplasm was maintained by weeding thrice annually. Five plants per variety were planted at a spacing of 3 m x 5 m (667 plants/ha) in single rows oriented in an East/West direction. No supplementary irrigation was given, and standard orchard practices as recommended by Potgieter (1997) were followed throughout the duration of the trial. The germplasm block was maintained as a commercial fruit orchard and all generally accepted orchard practises such as pruning, and pad thinning were performed. Cladodes were sampled in September 2006. 4.2.1.2 Climatic data Climatic data was captured via an automatic weather station (Mike Cotton Systems) installed 50 m from the site (Appendix IV). Mean daily values for temperature (°C), rainfall (mm), heat units (HU), chill units (CU), evapotranspiration (ETo), and solar radiation (Rs) were summarised to mean monthly values. 4.2.1.3 Dry matter content Three cladodes of each variety were selected; the middle third of the cladode was cut out and dried at 80°C for 48 hours. Sections were pre-cut into strips to facilitate quick drying. The weight of the fresh as well as dried samples was recorded and the dry matter content calculated. The dried cladode material was milled through a 1 mm sieve, to facilitate further analysis. The following equation was used to determine the dry matter content (g/kg fresh material): Weight before drying − Weight after drying Dry matter (g DM / kg wet weight ) = X 1000 Weight before drying 104 4.2.1.4 Organic matter content Two grams of milled cladode material was dried overnight at 100°C, cooled in a desiccator, and the weight of the crucible and dried sample recorded. Samples were incinerated at 550°C for three hours. The weight of the ash contained in the silica bowls were recorded after cooling in a desiccator for 20 minutes. Organic matter (OM) was calculated by subtracting the percentage ash from 100. The following equation was used to determine the OM content: OM (g / kg DM ) = 100 − %ASH 4.2.1.5 Crude protein content Approximately 0.2 g DM material was weighed into a foil cup and inserted into the Leco Nitrogen analyser (Leco Corporation, 2001). The total nitrogen (N) content was determined on combustion in oxygen, and a factor of 6.25 used to convert N content to CP content. All CP determinations were done in duplicate. 4.2.2 Evaluation of vegetative growth 4.2.2.1 Trial site 2 The trial site description is as given in section 3.2.1. Climate and soil characteristics can be found in Table 3.2. Trial layout and data collection methods are given in section 3.2.1. During the course of the study, data for 14 morpho-agronomic characters were captured for each variety (Table 4.1). Data for nine quantitative characters were collected as an average value of the 10 central plants per variety. The method for quantitative trait data collection is described in Table 4.1. Data for five qualitative characters were collected according to a simplified version of the cactus pear (Opuntia spp.) descriptors list (Chessa and Nieddu, 1997). Each accession was scored for the most frequent character state. 105 TABLE 4.1 MORPHO-AGRONOMIC TRAITS AND SHORT DESCRIPTIONS Character number Character and descriptive value Quantitative traits 1 Water content WC (g/kg) 2 Organic matter content OM (g/kg DM) 3 Crude protein content CP (g CP/kg DM) 4 Total number of cladodes pruned from each plant for 10 plants per variety cladno 5 Total number of cladodes remaining on 10 plants per variety after pruning cladleft 6 Cladode yield per plant: the weight of the total number of cladodes pruned from 10 plants per variety cyieldp (kg) 7 Cladode mass: derived by dividing the number of cladodes pruned by the cladode yield per variety cmass (kg) 8 Plant age: number of years since the variety had been established in 2001 pa Qualitative traits Vegetative bud break (VBB); week of a particular month during which 9 vegetative buds are clearly visible: (1) 1-7 August, (2) 1-7 September, (3) 1-7 October: 10 Flower petal colour (FPC): (1) dark yellow, (2) yellow, (3) orange, (4) unknown Country of origin (COO): (1) South Africa, (2) Botswana, (3) Israel, (4) 11 Italy 12 Cladode shape (CS): (1) elliptic, (2) ovate, (3) large diamond, (4) round 13 Plant habit (PH): (1) bush/shrubby, (2) spreading, (3) upright, (4) aborescent 4.2.3 Statistical analysis Data for quantitative nutritional traits were subjected to analysis of variance using the SPSS (SPSS Inc, 1997) statistical package. The Tukey multiple range test was used to identify varieties that were significantly different from each other. The Gower distance (Gower, 1971) was used as a measure of diversity between the different varieties as described in section 3.2.4. Gower distances were used to compute a dissimilarity matrix, and used for dendrogram construction using UPGMA and the NTSYS-pc programme (Version 2.02i, Rohlf, 1998). 106 4.3 RESULTS AND DISCUSSION 4.3.1 Nutritional quality 4.3.1.1 Dry matter content (DM) Cladode nutritional quality of varieties obtained from a commercially maintained orchard in Bloemfontein, South Africa was assessed in early September 2006. The climatic conditions prevalent during the preceeding winter months (Appendix IV) were characterised by low rainfall and chilly conditions (Table 4.2). Cladodes were sampled during this time as it is recommended that pruning be performed when the plants are dormant, during winter in South Africa (Potgieter, 1997). In this study substantial differences amongst the varieties tested for DM content (Table 4.3) were detected. DM is the component left in feed after drying and is strongly influenced by many factors including species, genotype, variety, soil, climate, and season (López-García et al., 2001). Varieties that produced the highest amount of DM were Messina (79.80 g DM/kg fresh material), Nepgen (78.62 g DM/kg fresh material), and Cross X (74.14 g DM/kg fresh material). Varieties that produced the lowest amount of DM were Gymno Carpo (52.89 g DM/kg fresh material), Ficus Indice (55.29 DM/kg fresh material), and Fusicaulis (55.97 g DM/kg fresh material) (Table 4.3). TABLE 4.2 MEAN CLIMATIC CONDITION PRIOR TO NUTRITIONAL QUALITY ASSESSMENT Parameter June July August September Solar radiation (Rs, MJ m2/s) 12.08 14.58 21.02 Average temperature (°C) 8.60 10.48 10.50 15.41 Average humidity (%) 45.74 59.72 41.29 Average rainfall (mm) 1.30 1.51 2.34 1.86 Total rainfall (mm) 0.51 0.00 86.34 0.76 Average maximum temperature (°C) 17.90 19.27 17.73 23.83 Absolute maximum temperature (°C) 23.23 25.29 25.05 29.38 Average minimum temperature (°C) 0.46 2.29 3.79 7.02 Absolute minimum temperature (°C) -4.08 -6.21 -3.24 0.81 Average evapotranspiration per day (mm) 2.52 2.76 4.38 107 4.3.1.2 Crude protein content (CP) Analysis of variance (ANOVA) results indicated that there were significant differences in CP content between varieties at p ≤ 0.05. Cactus pear alone, as feed, is not complete to fill the dietary requirements of livestock (Nefzaoui and Ben Salem, 2001). Cladodes are low in CP and should be supplemented with protein sources. Guevara et al. (2004) reported that the mean CP determined for the clones they tested was below the requirements for maintaining 40 kg goats (7.7%) and for dry pregnant mature cows (5.9%) as recommended by the National Research Council. Protein content varies with plant age, cladode age, and variety. In addition, crude protein content is strongly influenced by soil fertility and crop management (Mondragón- Jacobo and Pérez-González, 2001), but can be increased by the application of nitrogen fertiliser. The CP increased from 5.5% for unfertilised varieties up to 9.9% with the application of 224 kg N/ha (Gonzalez, 1989). Varieties with the lowest CP content were Messina (44.13 g CP/kg DM), Cross X (62.95 g CP/kg DM) and Algerian (60.97 g CP/kg DM). Interestingly varieties Messina and Cross X produced the highest dry matter amongst the varieties tested. Varieties that produced the highest CP content were Malta (109.24 g CP/kg DM), Gymno Carpo (108.23 g CP/kg DM), and American Giant (102.83 g CP/kg DM) (Table 4.3). 4.3.1.3 Organic matter content (OM) The majority of the varieties were significantly different in terms of OM content (Table 4.3). Varieties with low OM content were Gymno Carpo (746.30 g OM/kg DM), Fresno (750.50 g OM/kg DM), and Blue Motto (757.50 g OM/kg DM). Similarly, low OM content values have been reported for cactus pear by other researchers, 74.6% by Ben Salem et al. (2002) and 76.2% by Ben Salem et al. (2004). In contrast, the mean OM content for the varieties tested was 785.72 g OM/kg DM and lower than previous reports of an OM content of 84.4% (844 g OM/kg DM) for cactus pear (Guevara et al., 2004). The highest OM within the germplasm was recorded for the varieties Cross X (833.03 g OM/kg DM), Muscatel (818.07 g OM/kg DM), and Algerian (816.80 g OM/kg DM), and Sicilian Indian Fig (816.37 g OM/kg DM) (Table 4.3). 108 TABLE 4.3 NUTRIENT COMPOSITION OF DIFFERENT CACTUS PEAR VARIETIES (DRY MATTER BASIS) Dry matter content Composition (g/kg DM) VARIETY (g DM/kg fresh material) aCP aOM ALGERIAN 72.82 60.97 b 816.80 o AMERICAN GIANT 72.45 102.83 s 798.67 jkl AMERSFOORT 73.80 73.60 cdef 800.53 kl ARBITER 59.36 100.51 rs 775.93 def BERG X MEXICAN 63.83 82.54 hijk 796.03 ijkl BLUE MOTTO 63.02 85.35 ijklmn 757.50 abc CORFU 59.91 77.72 fgh 784.70 efghi CROSS X 74.14 62.95 b 833.03 o DIREKTEUR 63.30 83.43 ijkl 759.53 bc FICUS-INDICE 55.29 87.91 lmno 788.80 ghijk FRESNO 60.23 90.85 op 750.50 ab FUSICAULIS 55.97 89.01 nop 757.13 abc GYMNO CARPO 52.89 108.23 t 746.30 a MALTA 60.96 109.24 t 774.67 de MESSINA 79.80 44.13 a 804.93 lmn MEXICAN 61.42 85.49 ijklmn 760.37 bc MEYERS 63.95 85.38 ijklmn 789.33 hijk MORADO 64.47 95.86 qr 788.93 ghijk MUSCATEL 64.10 68.81 c 818.07 o NEPGEN 78.62 72.23 cde 814.63 mno NUDOSA 69.66 86.48 jklmno 772.50 d OFER 63.07 81.27 ghi 784.57 efghi R1251 64.00 83.00 ijkl 791.03 ijk R1259 67.78 91.34 opq 790.43 hijk R1260 63.29 93.16 opq 777.27 defg ROBUSTA X CASTILLO 66.32 70.22 c 786.53 fghi ROEDTAN 68.60 80.67 ghi 778.93 defgh ROLY POLY 66.92 76.81 efg 759.73 bc ROSSA 69.53 90.59 op 767.83 cd SANTA ROSA 59.59 75.37 def 787.03 fghij SCHAGEN 65.50 87.03 klmno 785.23 efghi SHARSHERET 61.00 71.61 cd 789.07 ghijk SICILIAN INDIAN FIG 64.36 62.57 b 816.37 no SKINNERS COURT 64.33 81.92 hij 794.33 ijkl TORMENTOSA 64.69 82.56 hijk 760.17 bc TURPIN 71.95 88.67 mnop 803.83 lm VAN AS 69.47 87.31 klmno 796.03 ijkl VRYHEID 58.51 83.95 ijklm 799.43 kl ZASTRON 66.50 89.23 nop 786.33 efghi a Within column values with the same letter are not significantly different at p ≤ 0.05 according to Tukey multiple range test. DM = Dry matter content; CP = Crude protein content; OM = Organic matter content 109 4.3.2 Vegetative growth over combined seasons 4.3.2.1 Number of cladodes removed with pruning It is common practise to prune cactus pear plants to maintain plant height at 1.8 m and improve fruit size (Potgieter, 1997). Cladodes that are removed in this way are usually discarded by farmers. These cladodes are however, a useful by-product of fruit production that can be used as fodder for livestock. The mean number of cladodes pruned per variety per year is a good indication of the amount of pruned material that can be used as fodder. Plants can be pruned from the first year after establishment. It is important to prune plants in order to allow sufficient sunlight into the plant for the cladodes to be productive, to facilitate the early detection and control of cochineal, and to facilitate harvesting (Potgieter, 1997). There was a higher mean number of cladodes pruned (cladno) from all the varieties in 1999–2000 (season 1) (53.02 cladodes pruned/plant) as compared to (33.83 cladodes pruned/plant) the following season 2000–2001 (season 2) (AppendixVI). Turpin (107.60 cladodes pruned/plant), Meyers (91.90 cladodes pruned/plant), and Morado (72.50 cladodes pruned/plant) had the highest mean number of cladodes pruned in season 1. The following season the highest mean number of cladodes removed with pruning was recorded for Sicilian Indian Fig (55.30 cladodes pruned/plant), Zastron (50.70 cladodes pruned/plant) and Turpin (45.50 cladodes pruned/plant). Varieties with the lowest number of cladodes removed with pruning for season 1 were Skinners Court (22.90 cladodes pruned/plant), Tormentosa (23.30 cladodes pruned/plant), and Nepgen (23.90 cladodes pruned/plant). Van As (16.30 cladodes pruned/plant), Cross X (22.00 cladodes pruned/plant), and Skinners Court (22.90 cladodes pruned/plant) had the lowest mean number of cladodes pruned for season 2 (Appendix VI). 4.3.2.2 Number of cladodes remaining after pruning The mean number of cladodes remaining per variety annually after pruning were recorded. Turpin (88.3), Gymno Carpo (87.30), and Meyers (85.60) had the highest mean cladodes left in season 1 (Appendix VI). Turpin and Gymno Carpo presented the highest number of cladodes remaining after pruning for the following season, (95.60) and (94.80) respectively (Appendix VI). The mean number of cladodes left on plants of a certain variety gives a good indication of pruning intensity. Turpin and Gymno Carpo were consistently high in cladodes remaining after pruning over the duration of the trial 110 (Figure 4.1). The production of fodder requires incomplete or total removal of the vegetative material. Thus, the ability to recover after pruning is important (Mondragón- Jacobo and Pérez-González, 2001). Turpin and Gymno Carpo consistently ranked high with regards to the number of cladodes removed, and remaining after pruning. This indicates good vegetative growth, therefore, these varieties are recommended as fodder types for the Mokopane district of the Limpopo Province. 120.00 100.00 80.00 60.00 40.00 20.00 0.00 rt sa po do on ltaou o r ra tr a ria n in s n r 8 n 9 1 s X n a n d a o s M e ur p er ey dt a e a 2 e g C e O f os X ic ge Fi 25 5 A a s e u C a g T nt d p 1 1 2 s c n e n a n i o ros ex R a g er s N o M Z Al M Ro e s- i m N R R dia V C xM nta Sc h inn ym n To r Fic u n I n rg a Sk G ilia B e S c Si Cactus pear varieties FIGURE 4.1 NUMBER OF CLADODES REMAINING ON CACTUS PEAR VARIETIES AFTER PRUNING OVER COMBINED SEASONS Bars represent the standard deviation for data recorded over two years, charts with no bars only had data for one year 3.00 2.50 2.00 1.50 1.00 0.50 0.00 ur t sa rpo do on a alt ian pi n rs n er a 8 e n ige 9 1 s X n sa nr C o o a a t r r ud C or s M e Tu ey ed ta Of nto s X2 dic gen p an F 512 12 5 A s ca o ge rs N o M Za lg M o e s-i e i R R a n sro ex i a R ha e A R mn mn or u N Ind V t c C xM n y T a n S ki G F ic n g S cil ia er S i BS Cactus pear varieties FIGURE 4.2 AVERAGE MASS (KG) OF CLADODES OF EACH CACTUS PEAR VARIETY OVER COMBINED SEASONS Bars represent the standard deviation for data recorded over two years, charts with no bars only had data for one year 111 Cladode mass (kg) Mean cladodes left (n) 4.3.2.3 Mass of cladodes Skinners Court consistently produced the heaviest cladodes over the trial period (2.80 kg, 2.71 kg) (Appendix VI, Figure 4.2). Tormentosa (1.49 kg) and Nudosa (1.36 kg) had heavy cladodes for the period 1999–2000. Schagen (2.11 kg) and Ficus Indice (2.06 kg) gave the second and third highest values for individual cladode mass for the 2000–2001 season. Turpin (1.05 kg), Roedtan (1.09 kg), and Malta (1.05 kg) gave the lowest cladode mass for the period 1999–2000. Van As (1.37 kg), Zastron (1.45 kg), and Roedtan (1.45 kg) produced the lowest cladode mass for the period 2000–2001 (Appendix VI). 4.3.2.4 Cladode yield Turpin (107.01 kg fresh material), Meyers (102.05 kg fresh material), and Morado (78.84 kg fresh material) produced the highest cladode yield during 1999–2000 period (Appendix VI). Sicilian Indian Fig (105.13 kg fresh material), Zastron (72.97 kg fresh material), and Turpin (70.77 kg fresh material) had the highest cladode yield for the period 2000–2001. Van As (24.89 kg fresh material), Cross X (44.46 kg fresh material), and Algerian (47.64 kg fresh material) gave the lowest cladode yield for the period 2000–2001 (Appendix VI). Of the varieties evaluated in the study, Turpin performed well in terms of vegetative yield (Figure 4.3). 140 120 100 80 60 40 20 0 t ou r sa a r g do ar po do n lt ra tro a ria n pin ers ta n fe sa 28 ice en i F 59 51 s X n a n C o s M e ur ey ed O nt o X nd pg an 12 12 n A s a os e s u C M a lg T M o e -i e di R R a ro s ex ic a R ha g er N no Z A R m s N V C M t c inn ym To r Fic u n n S ian I x k l er g Sa S G Sic i B Cactus pear varieties FIGURE 4.3 MEAN PRUNED CLADODE YIELD (kg) FOR CACTUS PEAR VARIETIES MEASURED OVER COMBINED SEASONS Bars represent the standard deviation for data recorded over two years, charts with no bars only had data for one year 112 Cladode yield ( t / ha) Mean vegetative measurements for the germplasm were lower during the second season (2000–2001) as compared to the first (1999-2000) (Appendix VI). This could indicate that climatic conditions during the second season limited vegetative growth. The average rainfall recorded for season 2 (76.67mm) was lower than that recorded for season 1 (106.06mm) and the average solar radiation recorded for season 2 (655.81 MJ/m2/s) was lower than that recorded for seaon 1 (1074.59 MJ/m2/s) (Table 3.12). 4.3.3 Cluster analysis Cluster analysis was used to group the 23 varieties into homogenous clusters using five qualitative and nine vegetative traits. Based on vegetative and morphological traits the 23 varieties grouped into four clusters (Figure 4.4). Skinners Court was dissimilar to the majority of the varieties analysed, and did not group into the designated clusters. The two varieties from Botswana (R1259, R1251) grouped together in cluster IV (Figure 4.4). Interestingly, these varieties grouped very closely to a variety from Israel (Ofer). The same trend was observed in the previous chapter (Figure 3.1), when the dendrogram was constructed on the basis of fruit quality and morphological traits. Skinners Court Nudosa I Morado Gymno Carpo MAalgltear ian Turpin Roedtan Meyers II Tormentosa Ficus - ndice SicilianIndianFiMW Schagen X28 Santa Rosa Cross X Van As Nepgen Berg x Mexican III Zastron Sicilian Indian Fig IV Ofer R1259 R1251 0. 7 0. 6 0.4Gower dissimilarity coe fficient 0. 3 0. 2 FIGURE 4.4 DENDROGRAM CONSTRUCTED FROM VEGETATIVE AND MORPHOLOGICAL TRAITS OF 23 CACTUS PEAR VARIETIES BASED ON THE GOWER DISSIMILARITY COEFFICIENT OVER COMBINED SEASONS 113 The majority of the varieties grouped into cluster II. This cluster is characterised by a mean number of pruned cladodes of 36.32 cladodes pruned/plant, average cladode mass of 1.63 g, cladodes remaining after pruning of 47.79, and cladode yield per plant of 54.79 kg fresh material. All the varieties in this cluster had ovate cladodes, and a shrubby plant habitus except for Nepgen that was upright (Appendix V). The varieties Turpin, Roedtan, and Meyers are recommended for use as fodder varieties in the Mokopane district of the Limpopo province and areas with similar environmental conditions because of their superior vegetative vigour in this area. The varieties Malta and Algerian, and Nudosa and Morado were not clearly separated using morphological and vegetative traits (Figure 4.4). In chapter two Malta and Algerian were illustrated to be genotypically very similar using AFLP markers. This relationship was further confirmed by these findings. Based on vegetative and morphological traits varieties classified as O. ficus-indica species (Morado, Gymno Carpo, Malta, and Algerian) grouped in cluster I (Figure 4.4). Interestingly, Nudosa, which is classified as a hybrid variety (Opuntia spp.) was closely associated with Morado, an O. ficus-indica species. The hybrid variety, Skinners Court did not cluster with the rest of the varieties evaluated in this study, whilst the other hybrid varieties, Zastron and Nudosa clustered within the same groupings with the rest of the other varieties (Figure 4.4). 4.4 CONCLUSIONS The nutritional value of cladodes is affected by variety (genetic characteristics), age, location, season and growing conditions such as soil fertility and climate. It is, however, possible to supplement nutritional components that are found to be limited in cladodes or to apply relevant cultural practices such as fertilisation to improve the nutritional value of cladodes. Chicken litter is being favourably used in South Africa to supplement the low CP content of cladodes. Recently it was shown that dried cladode material (cv Algerian) can be successfully included into a balanced feed for sheep (De Waal et al., 2006). Varieties that ranked the highest for vegetative yield fodder crops in Mokopane district of the Limpopo Province are Gymno Carpo and Turpin. It is important that further trials be performed to maximise potential productivity and nutritional quality of these varieties by manipulating cultural practises. In addition it must be highlighted that this germplasm was as per normal for orchards maintained for fruit production, and that pruning was done to maximise fruiting. 114 The most accurate measure of feed quality is animal performance. However, due to the arduous nature of animal trials, the screening of large numbers of varieties of feeds for genetic improvement trials is not common. Therefore, models to predict forage quality from feed attributes are being developed. This would, however, require considerable collaboration amongst researchers (Coleman and Moore, 2003). It has, however, been highlighted that the use of a single parameter or technique to aid plant breeders in selecting varieties for feed quality without animal-feed interaction will not provide an accurate estimate of animal performance under practical feeding situations (Mould, 2003). Further research involving measures of palatability, intake, and digestibility trials are required. Research into cactus pear cladodes harvested as pruned material for use as fodder in South Africa is in its infancy. It is hoped that the information presented in this study will aid researchers in further investigations. Many of the quality and productivity traits are influenced by the environment. If is further suggested that G X E interaction studies be explored, to determine which varieties are best suited to the vastly different agro-ecological regions of this country. 115 REFERENCES Ben Salem, H., A. Nefzaoui and L. Ben Salem, 2002. Supplementation of Acacia cyanophylla Lindl. foliage-based diets with barley or shrubs from arid areas (Opuntia ficus-indica f. inermis and Atriplex nummularia L.) on growth and digestibility in lambs. Animal Feed Science and Technology 96: 15-30. Ben Salem, H., A. Nefzaoui and L. Ben Salem, 2004. Spine-less cactus (Opunita ficus- indica f. inermis) and oldman saltbush (Atriplex nummularia L.) as alternative supplements for growing Barbarine lambs given straw-based diets. Small Ruminant Research 51: 65-73. Chessa, I. and G. Nieddu, 1997. Descriptors for cactus pear (Opuntia spp.). CACTUSNET-FAO, Universitá degli Studi, Rome, Italy. Coleman, S.W. and J.E. Moore, 2003. Feed quality and animal performance. Field Crops Research 84: 17-29. Cordeiro Dos Santos, D. and S. Gonzaga De Albuquerque, 2001. Fodder nopal use in the semi-arid Northeast of Brazil. In: Mondragón-Jacobo, J.C. and S. Pérez-González (Eds.), Cactus (Opuntia spp.) as forage, pp 37-49. CACTUSNET, FAO, Rome. De Kock, G.C., 1980. Drought resistant fodder shrub crops in South Africa. In: H.N., Le Houérou (Ed.), Browse in Africa. The current state of knowledge. International Livestock Centre for Africa, pp 399-408. Adis Ababa, Ethiopia. De Kock, G.C., 2001. The use of Opuntia as a fodder source in arid areas of Southern Africa. In: Mondragón-Jacobo, C. and S. Pérez-Gonzalez (Eds.), Cactus (Opuntia spp.) as forage, pp 101-105. CACTUSNET, FAO, Rome. De Waal, H.O., D.C. Zeeman and W.J. Combrinck, 2006. A perspective on the wet faeces produced by dried and coarsely ground Opuntia ficus-indica cladodes in sheep diets. In: Louw, S. and T. Labuschagne (Eds.), Proceedings of the International Cactus pear Conference, pp 25, Centre for Plant Health Management, SA, Bloemfontein. FAO, 2000. New on the menu for livestock. http://www.fao.org./ag/magazine/0009sp2.htm 116 Gonzalez, C.L., 1989. Potential of fertilization to improve nutritive value of prickly pear cactus (Opuntia lindheimeri Engelm.) Journal of Arid Environments 16: 87-94. Gower, J.C., 1971. A general coefficient of similarity and some of its properties. Biometrics 27: 857-874. Guevara, J.C., J.H. Silva Colomer and O.R. Estevez, 2004. Nutrient content of Opuntia forage clones in the Mendoza Plain, Argentina. Journal of the Professional Association for Cactus Development 6: 62-77. Han, H. and P.Felker, 1997. Field validation of water-use efficiency of the CAM plant Opuntia ellisiana in south Texas. Journal of Aric Environments 36:133-148. Inglese, P., F. Basile and M. Schirra, 2002. Cactus pear fruit production. In: Nobel, P.S. (Ed.), Cacti, biology and uses. pp 163-183. University of California Press, Los Angeles, USA. Kigotho, W., 2005. Decades of drought predicted for Southern Africa. All africa Global Media. Scientific Development Network: http://allafrica.com/stories/printable/200506020713.html Kueneman, E., 2001. Foreword. In: Mondragón-Jacobo, C. and S. Pérez-Gonzalez (Eds.), Cactus (Opuntia spp.) as forage. CACTUSNET, FAO, Rome. Leco Cooperation, 2001, 3000 Lakeview Ave, St Joseph, MI 49085. Le Houérou, H.N., 1992. The role of Opuntia cacti in the agricultural development of Mediterranean arid zones. Proceedings of the Second International Congress on Prickly pear and cochineal, Santiago, Chile, pp 186-198. López-García, J.J., J.M. Fuentes-Rodríguez and R.A.R. Rodríguez, 2001. Production and use of Opuntia as forage in Northern Mexico. In: Mondragón-Jacobo, C. and S. Pérez-González (Eds.), Cactus (Opuntia spp.) as forage, pp 29-36. FAO, Rome, Italy. Martin, F., 1993. Drought feed provides handy income. Farmers weekly, January 15, pp 31-32. 117 Mondragón-Jacobo, C. and S. Pérez-González, 2001. Germplasm resources and breeding Opuntia for fodder production. In: Mondragón-Jacobo, C. and S. Pérez- González (Eds.), Cactus (Opuntia spp.) as forage, pp 21-28. FAO, Rome, Italy. Mould, L.F., 2003. Predicting feed quality-chemical analysis and in vitro evaluation. Field Crops Research 84: 31-44. Nefzaoui, A. and H. Ben Salem, 2001. Opuntia: A strategic fodder and efficient tool to combat desertification in the WANA region. In : Mondragón-Jacobo, C. and S. Pérez- Gonzalez (Eds.), Cactus (Opuntia spp.) as forage, pp 73-89. CACTUSNET, FAO, Rome. Nefzaoui A. and H. Ben Salem, 2002. Cacti: efficient tool for rangeland rehabilitation, drought mitigation and to combat desertification. In: Nefzaoui, A. and P. Inglese (Eds.), Proceedings of the Fourth International Congress on Cactus Pear and Cochineal. Acta Horticulturae 581: 295-315. Nobel, P.S., 1988. Environmental Biology of Agaves and Cacti. Cambridge University Press, New York, USA. Nobel, P.S., 1995. Environmental biology. In: Barbera, G., P. Inglese and B.E. Pimienta (Eds.), Agroecology, cultivation and uses of cactus pear, pp 36-48. FAO Plant production and protection paper 132. Rome, Italy. Nobel, P.S., C.E. Russell, P. Felker, M. Galo and E. Acuna, 1987. Nutrient relations and productivity of prickly pear cacti. Agronomy Journal 79: 550-555. Oelofse, R.M., M.T. Labuschagne and J.P. Potgieter, 2006. Plant and fruit characteristics of cactus pear (Opuntia spp.) cultivars in South Africa. Journal of the Science of Food and Agriculture 86: 1921-1925. Potgieter, J.P., 1997. Guidelines for the cultivation of cactus pear for fruit production purposes. Second revised Edition. Group 7 Trust Publishers. Pretoria. Rohlf, F.J., 1998. On applications of geometric morphometrics to studies of ontogeny and phylogeny. Systematic Biology 47: 147-158. SPSS for Windows, 22 December 1997. Copyright SPSS Inc. 118 Tegegne F., 2001. Nutritional value of Opuntia ficus-indica as a ruminant feed in Ethiopia. In: Mondragón-Jacobo, C. and S. Pérez-Gonzalez (Eds.), Cactus (Opuntia spp.) as forage. CACTUSNET, FAO, Rome. Tegegne F., 2002. In vivo assessment of the nutritive value of cactus pear as a ruminant feed. In: Nefzaoui, A. and P. Inglese (Eds.), Proceedings of the Fourth International Congress on Cactus Pear and Cochineal. Acta Horticulturae 581: 323-328. 119 Chapter 5 Resistance of cactus pear varieties to three fungal pathogens and an option for biocontrol using yeasts ABSTRACT Increased cactus pear farming in South Africa has been accompanied by escalating reports of new diseases and financial losses due to post-harvest fruit rot especially in shipments destined for overseas markets. A study was designed to screen 38 South African cactus pear varieties for resistance to three important fungal pathogens (Phialocephala virens, Fusarium oxysporum and F. proliferatum) previously isolated from diseased cactus pear in South Africa. Disease progression was monitored on mature cladodes in the field over 52 days following artificial inoculation. There were no substantial differences in the virulence of the two Fusarium pathogens tested across all varieties. The varieties most susceptible to all three fungal pathogens were Roly Poly, Zastron, and Direkteur. The varieties most resistant to all three pathogens were Amersfoort, Algerian, and Meyers. In addition, yeast isolates with potential antagonistic activity against these pathogens were isolated and screened in vitro. Yeast isolates with the highest antagonistic activity against these pathogens were screened by means of dual challenge tests on agar plates. Statistically significant differences between mean colony diameters of the pathogens were determined (ANOVA) and means were separated using the Ryan Einot Gabriel and Welsch Test at p ≤ 0.05. Of the ten antagonistic yeast isolates selected, 60% belonged to the genus Cryptococcus. An isolate of Rhodotorula mucilaginosa however, displayed the greatest degree of inhibition to all three fungal pathogens in vitro. Of the three fungal pathogens challenged in vitro, P. virens was least inhibited by the antagonistic action of the yeasts. 120 5.1 INTRODUCTION The increasing interest in cactus pear as a source of fruit and fodder in arid and semi- arid regions has led to the initiation of many fruit improvement programmes (Inglese et al., 1995). The most important aim of fruit improvement programmes around the world is to increase resistance to pests and diseases by classical breeding and/or genetic engineering. Breeding disease resistant fruit cultivars involves combining the best fruit quality traits with disease resistance traits. This requires the assessment of resistance to disease as part of crop improvement programmes in breeding stocks or germplasm (Momol et al., 1996). The introduction of cactus pear into new geographic areas has resulted in the appearance of new diseases. Relatively few reports on diseases of Opuntia spp. have, however, been published (Varvaro et al., 1993; Granata, 1995; Granata and Sidoti, 2000; Zimmermann and Granata, 2002). Fungal pathogens of cactus pear usually belong to the genera Armillaria, Dothiorella, Phytophthora, Alternaria, Fusarium, Phyllosticta, Sclerotinia, and to a lesser extent Colletotricum, Capnodium, Macrophomina, Cercospora, Aecidium, Phoma, Cytospora, Gleosporium, Mycospherella, and Pleospora (Granata, 1995). Dry rot of cladodes in South Africa is associated with Alternaria tenuissima (Kunze) Wiltshire, Stemphyllium sp., Fusarium spp., and various Phoma spp. Superficial necrosis of cladodes is associated with A. alternata (Fr.) Keissl, Cylindrocarpon sp., and F. sporotrichoides Sherb (Swart and Kriel, 2002). Commercial cultivation of cactus pear for fruit production in South Africa is growing at a steady pace. However, many farmers are increasingly reporting disease-related losses of fruit. Cactus pears are highly perishable, and under marketing conditions [(20°C, 60– 70% relative humidity (RH)] have a shelf life of only a few days (Rodriguez-Felix, 2002). The main post-harvest problems experienced are directly related to physical damage incurred during harvesting. Factors affecting shelf life include decay at the stem end caused by Fusarium spp., Alternaria spp., Chlamydomices spp., and Penicillium spp. (Rodriguez-Felix, 2002). Previous studies by Swart and Swart (2003) found isolates from the following fungal genera associated with decayed cactus pear fruits (cv. Algerian) in South Africa: Rhizopus sp., Mucor sp., Epicoccum spp., Cladosporum sp., Fusarium spp., Phoma sp., Aspergillus spp., Stemphyllium sp., Alternaria spp., Rhizoctonia sp. Rhizopus spp., and Penicillium spp. 121 Cold storage increases the post-harvest life of most horticultural crops (Wang, 1994). Cactus pear however, is sensitive to chilling injury when stored at temperatures below 9° C (Chessa and Barbera, 1984) or 10° C, depending on the cultivar. Fungicides are therefore the principal method to control post-harvest disease. Although cactus pear fruits produce low levels of ethylene, Ethrel application to fruits has been used experimentally to hasten abscission zone formation, reducing harvest injury at the stem end (Cantwell, 1986). However, public concern over food safety and the development of fungicide resistant pathogens has increased the search for alternative methods less harmful to man and the environment. Biological control using antagonistic microorganisms has been endorsed as an alternative to the use of synthetic fungicides with considerable success. In particular, a host of yeast genera have been extensively used for the biological control of post- harvest diseases of fruits and vegetables (Wilson and Wisniewski, 1989; Punja, 1997) to protect moulding of stored grains (Petersson et al., 1999), and to control foliar diseases (Urquhart and Punja, 1997). Research into the pathogens causing diseases of cactus pear (Opuntia spp.) in South Africa and the relative susceptibilities of varieties of O. ficus-indica being cultivated to the most virulent pathogens is of the utmost importance. Specific attention directed towards establishing methods for the prevention of post-harvest fruit rot using suitable biological control agents will contribute greatly to the success of the emerging industry. The aims of this study were to (1) evaluate 38 South African cactus pear varieties for susceptibility to three common fungal pathogens (Phialocephala virens Siegfried and Siefert, F. proliferatum (Matsush.) Nirenberg ex Gerlach & Nirenberg and F. oxysporum Schltdl), (2) determine the relative virulence of these three pathogens to commercially important cactus pear varieties, and (3) isolate and identify yeasts displaying antagonistic activity in vitro against these fungal pathogens from the surface of cactus pear fruits. 122 5.2 MATERIALS AND METHODS 5.2.1 Trial site and layout The trial site description is as given in section 2.2.1. The 38 cactus pear varieties used in this study (Table 2.1) were not arranged in a statistical layout. Five plants per variety were planted at a spacing of 3 m x 5 m (667 plants/ha) in single rows orientated in an East/West direction. 5.2.2 Pathogenicity studies 5.2.2.1 Inoculum preparation Single spore isolates of three fungal pathogens (P. virens, F. oxysporum and F. proliferatum) were obtained from the New Crop Pathology Programme fungal collection and incubated at room temperature on potato dextrose agar (PDA)-streptomycin [0.03% (v/v)] plates until sizeable colonies were visible. Sterilised toothpicks (Swart et al., 2003) were transferred to the margins of fungal colonies and plates were further incubated at room temperature. The colonised toothpicks were used as an inoculum source. 5.2.2.2 Cladode inoculation Inoculum coated toothpicks were inserted up to 10 mm into one year old terminal cladodes at three positions, the apical, medial and basal sections, for each isolate. Insertion points were sealed with masking tape to prevent desiccation. Nine sterile toothpicks inserted at different locations (apical, median, and basal) on three cladodes per variety were used as controls. Lesion diameters were measured at various intervals (2, 7, 14, 21, 28, 38, and 52 days) using an electronic digital caliper to follow disease progression. Data were subjected to statistical analyses. Koch’s postulates were confirmed by the re-isolation, and identification of the original pathogens from 20% of the resultant lesions. 123 5.2.3 Statistical analysis Lesion diameter readings after 52 days were entered into SPSS (SPSS, 1997) and subjected to analysis of variance using the general linear model. The Tukey multiple range test was used to detect significant differences between means at p ≤ 0.05. The Gower distance (Gower, 1971) was used as a measure of diversity between different varieties as described in section 4.2.4. Gower distances were used to compute a dissimilarity matrix and the UPGMA used for dendrogram construction using the NTYSYS-pc programme (Version 2.02i, Rohlf, 1998). 5.2.4 In vitro inhibition studies 5.2.4.1 Yeast isolation Yeasts were isolated from the surfaces of fruits purchased from a commercial outlet in Bloemfontein, South Africa. The varieties used were Skinners Court, Gymno Carpo, Morado, and Fusicaulis. Fruits were rinsed in 100 ml sterile distilled water and a 2 ml aliquot of the suspension was serially diluted with 18 ml sterile 1% peptone water [1% (w/v) peptone, 0.5% (w/v) NaCl in distilled water]. A 0.1 ml aliquot of the suspension was plated in duplicate onto rose bengal chloramphenicol (RBC) agar plates and incubated at 25°C for seven days. Visually distinguishable yeast colonies were isolated from plates of the highest dilutions with between 30-300 colony forming units. Single colonies were sub-cultured onto yeast malt extract agar (0.4% yeast extract, 0.4% glucose, 1% malt extract, 2% agar) (YM) until cultures were pure. After incubation at 25ºC for five days, cultures were stored at 4°C on YM slants for further characterisation. For long-term preservation, cultures were stored in 35% glycerol at -70°C (Henry and Kirsop, 1989). 5.2.4.2 In vitro inhibition screening Fungal isolates previously associated with disease in cactus pear fruits and cladodes, P. virens, F. oxysporum, and F. proliferatum (Swart et al., 2003) were used to assess the in vitro inhibition capacity of the various yeast isolates. Dual cultures were prepared with the fungal plugs at the centre of nutrient agar (NA) plates and yeast isolates from two day old cultures streaked approximately 3 cm from the pathogen plug. Control plates containing only the fungus were similarly prepared. Control and treatment reactions were prepared in triplicate for each fungal pathogen. Plates were incubated at room 124 temperature for seven days, followed by measurement of the diameter of the fungal colonies. The experiment was performed twice. 5.2.4.3 Molecular identification of yeast isolates Yeast isolates were identified using the method of Kurtzman and Robnett (1998). Briefly, the variable D1/D2 domain of the large subunit (LSU) ribosomal DNA (rDNA) was amplified using primers NL-1 (5’-GCATATCAATAAGCGGAGGAAAAG) and NL-4 (5’- GGTCCGTGTTTCAAGACGG). Amplicons were sequenced and DNA sequences queried for nucleotide sequence alignment against all genetic sequence databases, held in GenBank® of the National institute of Health (NIH), (http://www.ncbi.nlm.gov) (Altschul et al., 1997). 5.2.4.4 Statistical analysis The percentage inhibition caused by each yeast isolate was calculated as the difference in mean colony diameter between the control (pathogen) and the mean colony diameter of the pathogen in the presence of the yeast isolate. Mean colony diameter (mm) data were analysed using ANOVA to determine significant differences between treatments. Means were separated into homogeneous groups using the Ryan Einot Gabriel and Welsch Test (REGW) (SPSS, 1997) at p ≤ 0.05. 5.3 RESULTS AND DISCUSSION 5.3.1 Pathogenicity studies 5.3.1.1 Susceptibility of cactus pear varieties to Fusarium oxysporum There were statistically significant (p ≤ 0.05) differences between varieties in terms of mean lesion diameter (Table 5.1 and Figure 5.1). The control treatments developed very small lesions as compared to inoculated tissue. Mean lesion diameters 52 days after inoculation with F. oxysporum showed that Roly Poly (8.53 mm), Direkteur (8.05 mm), and Zastron (8.03 mm) produced the largest lesions and thus, were the most susceptible varieties (Figure 5.1). Varieties that were the most resistant to F. oxysporum infection were Algerian (5.17 mm), Amersfoort (5.32 mm), and American Giant (5.35 mm). 125 TABLE 5.1 MEAN LESION DIAMETER OF CACTUS PEAR CLADODES 52 DAYS POST-INOCULATION Variety aF. oxysporum aF. proliferatum aP. virens Algerian 5.17 a 5.42 abc 5.16 a American Giant 5.35 a 5.55 abcd 5.41 abcd Amersfoort 5.32 a 4.82 a 5.11 a Arbiter 6.38 abcde 6.57 abcde 6.47 abcdefgh Blue Motto 7.61 cdef 7.16 cdefg 7.49 efghi Corfu 7.03 abcdef 6.66 abcdef 6.83 abcdefgh Cross X 6.79 abcdef 6.96 bcdef 6.67 abcdefgh Direkteur 8.05 ef 7.74 efg 7.67 ghi Ficus-Indice 6.50 abcde 6.04 abcde 6.43 abcdefgh Fresno 6.37 abcde 7.01 cdef 5.67 abcdef Fusicaulis 6.64 abcdef 6.06 abcde 6.44 abcdefgh Gymno Carpo 5.99 abc 6.66 abcdef 6.24 abcdefgh Malta 6.06 abcd 6.62 abcde 5.81 abcdefg Messina 5.83 abc 5.46 abcd 5.58 abcde Mexican 7.38 bcdef 6.37 abcde 7.23 cdefghi Meyers 5.75 abc 4.90 ab 5.31 abc Morado 6.91 abcdef 6.10 abcde 6.49 abcdefgh Muscatel 5.50 ab 5.48 abcd 5.10 a Nepgen 7.06 abcdef 6.61 abcde 7.58 fghi Nudosa 6.00 abc 6.26 abcde 6.27 abcdefgh Ofer 7.12 abcdef 6.21 abcde 6.91 abcdefgh R1251 6.09 abcde 5.85 abcde 5.72 abcdef R1259 5.78 abc 6.22 abcde 5.74 abcdefg R1260 5.81 abc 6.59 abcde 5.47 abcd Roedtan 5.98 abc 5.81 abcde 5.26 ab Roly Poly 8.53 f 8.75 fg 8.91 i Rossa 6.97 abcdef 6.22 abcde 6.49 abcdefgh Santa Rosa 6.57 abcdef 6.50 abcde 7.11 bcdefghi Schagen 5.58 ab 5.42 abc 5.46 abcd Sharsheret 6.05 abcd 6.32 abcde 6.35 abcdefgh Sicilian Indian Fig 6.45 abcde 7.53 defg 6.29 abcdefgh Skinners Court 6.13 abcde 5.69 abcde 6.18 abcdefgh Tormentosa 6.69 abcdef 6.12 abcde 5.88 abcdefg Turpin 6.61 abcdef 6.57 abcde 5.84 abcdefg Van As 7.08 abcdef 7.75 efg 7.27 defghi Vryheid 7.08 abcdef 6.50 abcde 6.10 abcdefgh X 28 6.55 abcdef 6.30 abcde 6.08 abcdefgh Zastron 8.03 def 9.14 g 7.91 hi Grand Mean 6.50 6.42 6.31 a Within column values with the same letter are not significantly different at p ≤ 0.05 according to Tukey multiple range test. 126 127 Roly Poly Direkteur Zastron Blue Motto Mexican Ofer Vryheid Van As Corfu NepgenRossa 12.00 Morado Cross X 11.00 Tormentosa Fusicaulis 10.00 Turpin Santa Rosa 9.00 X 28 8.00 Ficus-IndiceSicilian Indian Fig 7.00 Arbiter Fresno 6.00 Skinners Court R12515.00 Malta Sharsheret 4.00 Nudosa 3.00 Gymno Carpo Roedtan 2.00 Messina R1260 1.00 R1259 Meyers0.00 Schagen Cactus pear varieties MuscatelAmersfoort American Giant Algerian FIGURE 5.1 MEAN LESION DIAMETER OF CACTUS PEAR VARIETIES 52 DAYS AFTER INOCULATION WITH FUSARIUM OXYSPORUM L e s i o n d i a m e t e r ( m m ) I Direkteur Zastron RolyPoly I Skinners Court Malta Sharsheret R1251 Nudosa Gymno Carpo Roedtan Meyers IIA R1259 Messina R1260 IIA American Giant Amersfoort Algerian Muscatel Schagen Fusicaulis Turpin SicilianIndianFiMW X 28 Santa Rosa Tormento sa Cross X IIB Arbiter Fresno Ficus - Indice Sicilian Indian Fi g IIB Blue Motto Mexican Morado Rossa Ofer Vryheid Van As Nepgen Corfu 0.85 0.64 0.42 0.21 0.00 Gower Distance Coefficient F IGURE 5.2 DENDROGRAM OF 38 CACTUS PEAR VARIETIES CONSTRUCTED ON THE BASIS OF SUSCEPTIBILITY TO FUSARIUM OXYSPORUM The Gower dissimilarity coefficient was used to estimate dissimilarity between varieties Using cluster analysis, varieties Gymno Carpo and Roedtan, and Vryheid and Van As clustered closely together (Figure 5.2). Variety R1260, from Botswana clustered close to Messina from Israel. Varieties clustered into two main groups I and II. The most susceptible varieties (Direkteur, Zastron, and Roly Poly) clustered separately into cluster I (Figure 5.2). The majority of the varieties grouped into cluster II that can be sub-divided into two sub-clusters IIA and IIB (Figure 5.2). The most resistant varieties (Amersfoort, Algerian, and American Giant) clustered within a subgroup of IIA along with Muscatel and Schagen which also produced the smallest lesions with this pathogen. Commercially cultivated varieties were evenly dispersed within the dendrogram (Figure 5.2). 5.3.1.2 Susceptibility of cactus pear varieties to Fusarium proliferatum Based on mean lesion diameter, the following varieties were the most susceptible to infection with F. proliferatum: Zastron (9.14 mm), Roly Poly (8.75 mm), Van As (7.75 mm) and Direkteur (7.74 mm). Roly Poly, Direkteur, and Zastron were also amongst the most susceptible to infection with F. oxysporum (Table 5.1). Varieties that 128 were the most resistant to F. proliferatum were Amersfoort (4.82 mm), Meyers (4.90 mm), Algerian, and Schagen (5.42 mm) (Table 5.1 and Figure 5.3). Cluster analysis grouped the varieties into two main clusters I and II. Varieties that were the most similar were Algerian and Schagen, and Vryheid and Santa Rosa (Figure 5.4). Zastron and Roly Poly clustered separately in cluster II from the rest of the varieties. Zastron and Roly Poly were the varieties most susceptible to infection with F. proliferatum. Zastron and Roly Poly also grouped together in cluster I (Figure 5.2) based on resistance to F. oxysporum. 129 130 Zastron Roly PolyDirekteur Van As Sicilian Indian Fig Blue Motto 12.00 Fresno Cross X 11.00 Gymno Carpo Corfu 10.00 Malta R1260 Nepgen 9.00 Arbiter Turpin 8.00 Vryheid Santa Rosa 7.00 MexicanSharsheret X 28 6.00 Nudosa Rossa 5.00 R1259 Ofer 4.00 Tormentosa MoradoFusicaulis 3.00 Ficus-Indice R1251 2.00 Roedtan Skinners Court 1.00 American GiantMessina Muscatel 0.00 Algerian SchagenCactus pear varieties Meyers Amersfoort FIGURE 5.3 MEAN LESION DIAMETER OF CACTUS PEAR VARIETIES 52 DAYS AFTER INOCULATION WITH FUSARIUM PROLIFERATUM L e s i o n d i a m t e r ( m m ) Direkteur IA VanAs IA Sicilian Indian Fi g Blue Motto Fresno Cross X Skinners Court Roedtan R1251 American Giant IB Algerian IB Schagen Messina Muscatel Meyers Amersfoort Fusicaulis Ficus - Indice Morado SicilianIndianFiMW Tormentosa Nudosa X28 Sharsheret Mexican Ofer IICC R1259 Rossa Gymno Carpo Corfu Malta Nepgen Turpin Arbiter R1260 Vryheid II II SantaRosa II Zastron RolyPoly 0.85 0.64 0.42 0.21 0.00 Gower Distance Coefficeint FIGURE 5.4 DENDROGRAM OF 38 CACTUS PEAR VARIETIES CONSTRUCTED ON THE BASIS OF SUSCEPTIBILITY TO FUSARIUM PROLIFERATUM The Gower dissimilarity coefficient was used to estimate dissimilarity between varieties 5.3.1.3 Susceptibility of cactus pear varieties to Phialocephala virens Varieties that were the most susceptible to infection with P. virens, based on mean lesion data were Roly Poly (8.91 mm), Zastron (7.91 mm), and Direkteur (7.67 mm). The most resistant varieties to P. virens were Muscatel (5.10 mm), Amersfoort (5.11 mm), and Algerian (5.16 mm) (Figure 5.5 and Table 5.1). Based on cluster analysis, Roly Poly, the most resistant to infection with P. virens, clustered separately from the rest of the varieties in the germplasm (Figure 5.6). The remainder of the varieties grouped into two clusters I and II. The most susceptible varieties (Zastron, Direkteur, Nepgen, and Blue Motto) were grouped in cluster I (Figure 5.6). Cluster II was further sub-divided into IIA IIB, and IIC. The most resistant varieties (Algerian, Roedtan, Muscatel, and Amersfoort) grouped into cluster IIB. Varieties Morado and Rossa could not be clearly separated from one another (Figure 5.6). Two varieties from Botswana (R1259 and R1251) grouped together in cluster IIC. 131 132 Roly Poly Zastron Direkteur Nepgen Blue Motto Van As Mexican Santa Rosa Ofer Corfu 12.00 Cross X Morado 11.00 Rossa Arbiter 10.00 Fusicaulis Ficus-Indice 9.00 Sharsheret Sicilian Indian Fig 8.00 Nudosa Gymno Carpo 7.00 Skinners Court Vryheid 6.00 X 28 Tormentosa 5.00 Turpin Malta 4.00 R1259R1251 Fresno 3.00 Messina R1260 2.00 Schagen American Giant 1.00 Meyers Roedtan 0.00 Algerian Cactus pear varieties Amersfoort Muscatel FIGURE 5.5 MEAN LESION DIAMETER OF CACTUS PEAR VARIETIES 52 DAYS AFTER INOCULATION WITH PHIALOCEPHALA VIRENS L e s i o n d i a m e t e r ( m m ) Direkteur Nepgen I BlueMotto Zastron Ofer Corfu Mexican VanAs SantaRosa SkinnersCourt X28 Vryheid Nudosa SicilianIndianFi IIA GymnoCarpo Sharsheret Fusicaulis Ficus-Indice SicilianIndianFiMW Morado Rossa Arbiter CrossX AmericanGiant R1260 IIB Schagen Meyers Roedtan Algerian Muscatel Amersfoort Malta IIC Turpin Tormentosa Messina Fresno R1259 R1251 RolyPoly 0.87 0.65 0.44 0.22 0.00 Gower Distance Coefficient F IGURE 5.6 DENDROGRAM OF 38 CACTUS PEAR VARIETIES CONSTRUCTED ON THE BASIS OF SUSCEPTIBILITY TO PHIALOCEPHALA VIRENS The Gower dissimilarity coefficient was used to estimate dissimilarity between varieties 5.3.1.4 Overall susceptibility of cactus pear varieties to fungal pathogens There was no substantial difference in virulence between the different Fusarium pathogens across all varieties evaluated (Table 5.1). The overall lesion diameters across all varieties for infection with F. oxysporum were 6.50 mm and F. proliferatum 6.42 mm (Table 5.1). The overall mean lesion diameter across all the varieties for P. virens (6.31 mm) was considerably lower than that observed for the two Fusarium isolates (Table 5.1). Using cluster analysis, varieties were grouped into two distinct clusters I and II. The most susceptible varieties (Direkteur, Blue Motto, Van As, Zastron, and Roly Poly) grouped into cluster I, separate from the rest of the varieties in cluster II. Cluster II was further sub-divided into clusters IIA, IIB, and IIC (Figure 5.7). All varieties from Botswana (R1251, R1259, and R1260) grouped together in a subgroup of IIA along with Skinners Court. Varieties Rossa and Vryheid could not be separated from each other based on disease response data. The most resistant varieties (American Giant, Algerian, 133 Muscatel, Amersfoort, and Roedtan) grouped together with Schagen, Meyers and Roedtan in cluster IIC. The three varieties from Israel (Sharsheret, Ofer, and Messina) were dispersed into clusters IIA, IIB, and IIC respectively (Figure 5.7). Direkteur I BlueMotto VanAs Zastron RolyPoly SkinnersCourt R1260 R1259 R1251 Fusicaulis IIA GymnoCarpo X28 Turpin Fresno Ficus-Indice Nudosa Malta Tormentosa SicilianIndianFiMW Sharsheret Morado Arbiter Vryheid Rossa Ofer SicilianIndianFi SantaRosa IIB Corfu CrossX Mexican Nepgen AmericanGiant Schagen Algerian IIC Meyers Muscatel Roedtan Messina Amersfoort 0.75 0.56 0.38 0.19 0.00 Gower Distance Coefficient FIGURE 5.7 DENDROGRAM OF 38 CACTUS PEAR VARIETIES CONSTRUCTED ON THE BASIS OF OVERALL SUSCEPTIBILITY TO FUNGAL PATHOGENS The Gower dissimilarity coefficient was used to estimate dissimilarity between varieties Based on the overall resistance to fungal pathogens tested, varieties that are classified as O. ficus-indica and Opuntia spp. (hybrids of unknown origin) clustered in group II and its sub-groupings. Of the known species delineated South African cactus pear varieties, Fusicaulis was the only O. fusicaulis type that grouped in cluster IIb (Figure 5.7). High disease resistance is important in preventing disastrous crop losses when climatic conditions favour disease (Dayton et al., 1983). Intact cactus pear cladodes are not susceptible to fungal pathogens since they are structurally protected by a thick waxy cuticle. Nonetheless, mechanical injury sustained during hailstorms facilitates considerable access for pathogens, highlighting the importance of selecting varieties with higher disease resistance that will sustain lower crop losses than susceptible types. 134 The largest lesion diameters induced with inoculation in this study were restricted (Table 5.1), and did not exceed 10mm, even when the structural protection against fungal pathogens offered by a thick waxy cuticle was pierced with toothpicks. This could be attributed to the formation of abscission layers which led to restriction in lesion diameter. Researchers have found that resistant peruvian apple cactus (Cereus peruvianus) formed an abscission layer upon inoculation with Glomerella cingulata (Stoneman) (Spauld and H. Schrenk). No such abscission layer was formed with the susceptible Cereus tetragonus (L.) Miller (Kim and Kim, 2002). Abscission layer formation as a physical disease response mechanism in cactus pear should be investigated in future studies of cactus pear. 5.3.2 In vitro inhibition studies 5.3.2.1 Yeast isolate identification Sequence alignment allowed the unambiguous identification of nine of the 10 isolates submitted for identification (Table 5.32. One yeast isolate, 96, was identified as either Cryptococcus albidosimilis or C. liquefaceins (Table 5.2). All sequences were identified as partial sequences of the 26S/28S ribosomal gene sequence (Appendix VII). Nucleotide (nt) alignments obtained were significantly high, between 99-100%. Of the isolates identified as possible biocontrol agents, 60% belonged to the genus Cryptococcus (Table 5.2). In the present study, sequence divergence analysis of the large subunit of rDNA was used to identify yeast isolates. Divergence at the variable D1/D2 domain of the LSU rDNA is generally adequate to resolve individual species (Kurtzman and Robnett, 1998), however, isolate 96 was not clearly identified. It was identified as being either C. albidosimilis or C. liquefaciens. The sequence obtained for this isolate had three mismatched nucleotides with C. albidosimilis with a 99% sequence homology. Four mismatched nucleotides with a 99% sequence homology were found with the sequence for C. liquefaciens (APPENDIX VII). This finding is similar to that reported by other researchers that have found that some Cryptococcus species may appear identical based on sequences of the D1/D2 domain of the large subunit of the rDNA. Fell and co- workers recommend the analysis of internal transcribed spacer sequences to resolve species within the Cryptococcus genus (Fell et al., 2000). 135 5.3.2.2 In vitro inhibition screening Of the 270 yeast isolates obtained from the surfaces of cactus pear fruit, and screened for in vitro antagonistic activity, ten were chosen for further analysis using dual culture tests (Figure 5.8). All ten yeast isolates reduced hyphal growth of all three fungal pathogens, as compared to that of the control (Figure 5.8). Yeasts found on plant surfaces are thought to provide natural antagonistic protection against fungal plant diseases (Fokkema et al., 1979) via their killer activity (Starmer et al., 1987; Abranches et al., 1998; Golubev et al., 2003). Yeast isolate 26 (Rhodosporidium kratochvilovae) had the largest antagonistic activity against F. proliferatum (Table 5.3), while isolate number 25 (R. mucilaginosa) was most inhibitory towards F. oxysporum. The yeast isolate that had the highest antagonistic activity against P. virens was isolate number 25 (Rhodotorula mucilaginosa). Isolate number 22 (C. saitoi) displayed the lowest antagonistic activity against the Fusarium fungal pathogens. The yeast isolated that was least effective against P. virens was isolate number 115 (Cystofilobasidium feraegulla). As judged by percentage inhibition (Table 5.3) of the three fungal pathogens tested, P. virens was the least affected by the antagonistic activity of the yeast isolates. TABLE 5.2 YEAST ISOLATE, SPECIES NAMES AND NUMBER OF NUCLEOTIDES OF THE SEQUENCED D1/D2 DOMAIN Isolate Sequence %nt – nt aSpecies number length (nt) Alignment 22 505 100 Cryptococcus saitoi A. Fonseca, Scorzetti & Fell 25 387 100 Rhodotorula mucilaginosa (A. Jörg.) F.C. Harrison 26 529 100 Rhodosporidium kratochvilovae Hamam., Sugiy & Komag 29 500 100 Hanseniaspora clermontiae Cadez, Poot, Raspor, & M.T. Sm 72 526 100 Cryptococcus saitoi 87 499 99 Cryptococcus albidosimilis Vishniac & Kurtzman 96 515 99 Cryptococcus albidosimilis/ liquefaciens (Saito & M.Ota) Á. Fonseca, Scorzetti & Fell 109 446 100 Cryptococcus saitoi 110 520 100 Cryptococcus saitoi 115 517 100 Cystofilobasidium feraegula nt = nucleotide a Yeast isolate identification was based on nucleotide sequence divergence at the variable D1/D2 domain of the large sub-unit (LSU) ribosomal DNA 136 A B FIGURE 5.8 IN VITRO GROWTH INHIBITION (A) Inhibition of Fusarium oxysporum growth by yeast isolate number 25 as compared to growth of the yeast free control (B) Isolate 25 (Rhodotorula mucilaginosa) performed well against all three pathogens, whilst the remainder of the isolates displayed inhibition at varying intensity (Table 5.3). Treatment means (Table 5.3) of each of the isolates differed significantly from each other at p ≤ 0.05. Rhodotorula mucilaginosa gave the best all around performance against all pathogens reducing mycelial growth of F. oxysporum by 40.77%, F. proliferatum by 36.58% and P. virens by 37.13% (Table 5.3). The weakest isolate across all fungal pathogens was Cryptococcus saitoi (Isolate 22), only able to inhibit F. oxysporum mycelial growth by 15.80%, F. proliferatum by 9.45%, and P. virens by 17.40% (Table 5.3). Most of the yeasts (60%) with potential antagonistic activity belonged to the genus Cryptococcus. Yeasts belonging to this genus are basidiomycetous, colonise various habitats (De Jager et al., 2001; Valinsky et al., 2002), and have a worldwide distribution (Renker et al., 2004). In members of this genus the killer ability is linked to the production of killer toxins, or mycocins (Golubev et al., 2003). Killer yeasts produce and excrete protein toxins which are lethal to sensitive yeasts. The K1 killer toxin in yeasts is a small monomeric protein which is heat labile and only active within a pH 4.2-4.6 range (Starmer et al., 1987). It is recommended that in future in vitro inhibition studies that these culture conditions be maintained to maximise toxin activity. 137 TABLE 5.3 MEAN COLONY DIAMETER (MM) AND PERCENTAGE INHIBITION OF FUNGAL PATHOGENS ON DUAL CULTURES SEEDED WITH VARIOUS YEAST ISOLATES Yeast isolate aF. oxysporum %IHN aF. proliferaum %IHN aP. virens %IHN number 22 67.60 d 15.81 68.72 ef 9.45 64.80 cd 17.40 25 47.47 a 40.77 48.17 ab 36.58 49.38 a 37.13 26 51.09 abc 36.28 43.38 a 42.90 52.89 ab 32.64 29 60.59 bcd 24.50 56.01 cd 26.22 58.34 abc 25.67 72 47.79 a 40.36 61.94 de 18.40 64.87 cd 17.31 87 66.82 d 16.77 58.25 cd 23.27 60.55 bcd 22.83 96 48.99 a 38.88 52.78 bcd 30.49 59.50 bc 24.17 109 57.07 abcd 28.86 50.21 abc 33.88 69.49 d 11.39 110 50.60 ab 36.89 51.90 abc 31.65 68.81 cd 12.27 115 61.74 cd 23.07 53.54 bcd 29.49 62.99 bcd 19.72 Untreate d control 80.350 e 75.88 f 78.39 e a Within a column, values with the same letter are not significantly different at p ≤ 0.05 according to Ryan, Einot, Gabriel, and Welsch (REGW) F test. %IHN = Percentage inhibition In addition, members of this genus have an antibacterial capacity (C. laurentii) (McCormack et al., 1994), and some (C. neoformans, C. albidus, and C. curvatus) are pathogenic to humans, especially in AIDS patients (Kordossis et al., 1998). Further trials to determine the safety of these yeasts to humans, especially those that are immune compromised are recommended before any pilot scale experiments are attempted. Considerable treatment (yeast) x pathogen interaction was observed indicating differing responses of the fungal pathogens to the yeast antagonistic action. Antagonistic modes of action vary from the activation of host defences, to competition for space and nutrients and or antibiosis (Droby and Chalutz, 1994). Investigations to elucidate the antagonists' (yeast isolates) mode(s) of action are required in order to optimise their performance and establish a better screening procedure. It has been shown that exocellular lytic enzymes secreted by yeasts act as depolymerases of fungal cell walls and appear to have antifungal activity (Lorito et al., 1994). The level of β-1, 3-glucanase activity has been correlated with the antagonistic activity of Pichia guillermondii Wick. against Botrytis 138 cinerea Pers. (Wisniewski et al., 1991). It is recommended that future screening procedures include the examination of the presence and amount of β-1, 3-glucanases, and chitinase activity in the ten yeast isolates tested in this study. In addition, further screening trials should focus on members of the Rhodotorula genus, as they seem to be more effective against fungal pathogens of cactus pear. 5.4 CONCLUSIONS The expression of disease resistance within the 38 South African varieties surveyed in this study indicates a quantitative mode of resistance across all varieties evaluated for all three pathogens tested. Roly Poly, Direkteur, and Zastron were the more susceptible varieties. Zastron, one of the most susceptible varieties, is a commercially cultivated variety in South Africa. It is a white pulp variety suitable for the local market and has a high fruit yield of 121.30 ± 6.22 fruit/plant (Chapter 4). Although fruits produced by this variety are comparatively of low mass (148.35 ±7.73 g), it meets the minimum requirements for fruit production in South Africa (> 120 g). Zastron can in future be crossed with a more resistant variety such as Algerian which has acceptable fruit quality traits. It is recommended that prior to the assignment of resistance levels to these varieties that evaluation be replicated over a number of years and/or over diverse locations. Standard tests to quantify resistance of cactus pear varieties to fungal pathogens are required in order to continue with further evaluation of germplasm material. In addition resistance to other fungal pathogens is recommended in future studies. Roly Poly and Zastron can in the future be used in disease trials as indicator varieties that can be used to quantify resistance response of susceptible varieties when challenged with different races of the same fungal pathogens used in this study. These susceptible varieties will allow estimation of the amount of inoculum present, the effect of fluctuating seasonal conditions, and aid in confirming that they are amongst the most susceptible cactus pear varieties in South Africa. The most resistant varieties surveyed in this germplasm across all three fungal pathogens were Amersfoort, Meyers, and Algerian. Algerian is commercially cultivated for fruit production in South Africa, especially in the more humid areas in the Limpopo Province. Thus, in the event of a disease outbreak involving one of the pathogens tested in this study, farmers who have planted Algerian will sustain lower crop losses than those who have planted Zastron for example, which is more susceptible to fungal disease. However, it is recommended that disease screening trials with the pathogens tested in 139 this study be repeated in the humid areas of Limpopo Province since the climatic conditions in this area are different from the ones where this study was performed, and will influence disease progression. Fusarium spp. are considered to be important pathogens for cactus pear since they flourish in hot, humid areas, and disease development is encouraged by poor soil conditions characterised by increased acidity, low permeability, and elevated humidity (Granata, 1995). Fusarium proliferatum is listed amongst the most important pathogens of native Opuntia species in Arizona (Mildenhall et al., 1987). Fusarium oxysporum f.s. opuntiarum causes 'Fusarium wilt' in Opuntias. It affects the vascular tissues, causing wilting of cladodes and fruit, leading to a reddening of infected tissues (Zimmermann and Granata, 2002). It is recommended that further trials be performed in the Limpopo Province with the same varieties and using the pathogens tested here to augment the reliability of these findings. Post-harvest biological control in fruit is promising from a practical point of view because application sites are limited to the fruit and environmental conditions are defined and controlled in storage rooms (Jijakli et al., 1999). Products containing Pseudomonas syringae Van Hall, active against the genera Botrytis, Penicillium, Mucor, and Geotrichum are commercially available (Janisiewicz and Jeffers, 1997), whilst products containing antagonistic yeasts are still under development (Ippolito et al., 2000). The results presented in this study, are however, only preliminary. Investigations performed under field conditions or in storage facilities will be more conclusive, and are recommended to confirm these findings. In addition, the modes of antagonism of these yeast isolates against fungal pathogens of cactus pear fruit and their safety to humans require further research. 140 REFERENCES Abranches, J., P. Valente, H.N. Nóbrega, F.S.A. Fernandez, L.C. Mendonca-Hagler and A.N. Hagler, 1998. Yeast diversity and killer activity dispersed in faecal pellets from marsupials and rodents in a Brazilian tropical habitat mosaic. Federation of European Microbiological Societies Microbiology Ecology 26: 27-33. Altschul, S.F., T.L. Madden, A.A. Schaffer, J. Zhang, Z. Zhang, W. Miller and D.J. Lipman, 1997. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Research 25: 3389-3402. Cantwell, M., 1986. Post-harvest aspects of prickly pear fruits and vegetable cladodes. In: Perishables handling, post-harvest technology of fresh horticultural crops. Cooperative extension, University of California 59: 6-9. Chessa, I. and G. Barbera, 1984. Indagine sulla frigocon-servszione dei frutti dell acv. Gialla di ficodindia. Frutticoltura 46: 57-61. Dayton, D.F., R.L. Bell and E.B. Williams, 1983. Disease resistance. In: Moore, J.N. and J. Janick (Eds.), Methods in fruit breeding, pp 189-215. Purdue University Press, West Lafayette, Indiana, USA. De Jager, E.S., F.C. Wehner and L. Korsten, 2001. Microbial ecology of the mango phylloplane. Microbial Ecology 42: 201-207. Droby, S. and E. Chalutz, 1994. Mode of action of biocontrol agents of post-harvest diseases. In: Wilson, C.L. and M.E. Wisniewski (Eds.), Biological Control of Post-harvest Diseases-Theory and Practise, pp 63-75. CRC Press, Boca Raton, USA. Fell, J.W., T. Boekhout, A. Fonseca, G. Scorzetti and A. Statzell-Tallman, 2000. Biodiversity and systematics of basidiomycetous yeasts as determined by large-subunit rDNA D1/D2 domain sequence analysis. International Journal of Systematic and Evolutionary Microbiology 50: 1351-1371. Fokkema, N.J., J.G. den Houter, Y.J.C. Kosterman and A.L. Nelis, 1979. Manipulation of yeasts on field-grown wheat leaves and their antagonistic effect on Cochliobolus sativus and Septoria nodorum. Transactions of the British Mycology Society 72: 19-29. 141 Golubev, W.I., M. Gadanho, J.P. Sampaio and N.W. Golubev, 2003. Cryptococcus nemorosus sp. nov. and Cryptococcus perniciosus sp. nov., related to Papiliotrema Sampaio et al. (Tremellales). International Journal of Systematic and Evolutionary Microbiology 53: 905-911. Gower, J.C., 1971. A general coefficient of similarity and some of its properties. Biometrics 27: 857-874. Granata, G., 1995. Biotic and Abiotic diseases. In: Barbera, G., P. Inglese and E. Pimienta-Barrios (Eds.), Agro-ecology, cultivation and uses of cactus pear, pp 109-119. FAO. Plant production and protection paper 132, Rome. Granata, G. and A. Sidoti, 2000. Survey of diseases discovered on Opuntia ficus-indica in producer countries. Proceedings of the fourth International Congress on cactus pear and cochineal. Acta Horticulturae 51: 231-237. Henry, J. and B. Kirsop, 1989. Cryopreservation of yeasts in polypropylene straws. WFCC-Education Committee, UNESCO (http://www.wfcc.info/tis/info3.html). Inglese, P., G. Barbera and T. La Mantia, 1995. Research strategies for the improvement of cactus pear (Opuntia ficus-indica) fruit quality and production. Journal of Arid Environments 29: 455-468. Janisiewicz, W.J. and S.N. Jeffers, 1997. Efficacy of commercial formulation of two biofungicides for control of blue mold and gray mold of apples in cold storage. Crop Protection 16: 629-633. Ippolito, A., A. El-Ghaouth, C.L. Wilson, and M. Wisniewski, 2000. Control of postharvest decay of apple fruit by Aureobasidium pullulans and induction of defense responses. Postharvest Biological Technology 19: 265-272. Jijakli, H.M., P. Lepoivre and C. Grevesse, 1999. Yeast species for biocontrol of apple post-harvest diseases: an encouraging case study for practical use. In: Mukerji, K.G., B.P. Chamola and R.K. Upadhyay (Eds.), Biotechnological approaches in biocontrol of plant pathogens, pp 31-49. Kluwer Academic/Plenum Publishers, New York, London, UK. 142 Kim, Y.H. and K-H. Kim, 2002. Abscission layer formation as a resistance response of Peruvian apple cactus against Glomerella cingulata. Phytopathology 92: 964-969. Kordossis, T., A. Avlami, A. Velegraki, I. Stefanou, G. Georgakopoulos, C. Papalambrou and N.J. Legakis, 1998. First report of Cryptococcus laurentii meningitis and a fatal case of Cryptococcus albidus cryptococcaemia in AIDS patients. Medical Mycology 36: 335- 339. Kurtzman, C.P. and C.J. Robnett, 1998. Identification and phylogeny of ascomycetous yeasts from analysis of nuclear large subunit (26S) ribosomal DNA partial sequences. Antonie van Leeuwenhoek 73: 331-371. Lorito, M., C.K. Hayes, A. Di Pietro, S.L. Woo and G.E. Harman, 1994. Purification, characterisation, and synergistic activity of a glucan 1,3-β-glucosidase and an N-acetyl- β-glucosaminidase from Trichoderma harzianum. Phytopathology 84: 398-405. McCormack, P.J., H.G. Wildman and P. Jeffries, 1994. Production of antibacterial compounds by phylloplane-inhabiting yeasts and yeast like fungi. Applied Environmental Microbiology 60: 927-931. Mildenhall, J.P., S.M. Alcorn and W.F.O. Marasas, 1987. Pathogenicity of fungi isolated from Opuntia species in Arizona to Opuntia aurantiaoa. Phytophylactica 19: 485-489. Momol, M.T., J.L. Norelli, H.S. Aldwinckle and W. Zeller, 1996. Use of the area under the disease progress curve for quantification of resistance of apple and pear varieties and rootstocks to Erwinia amylovorsa. Acta Horticulturae 411: 373. Punja, Z.K., 1997. Comparative efficacy of bacteria, fungi, and yeasts as biological control agents for diseases of vegetable crops. Canadian Journal of Plant Pathology 19: 315-323. Renker, C., V. Blanke, B. Börstler, J. Heinrichs and F. Buscot, 2004. Diversity of Cryptococcus and Dioszegia yeasts (Basidiomycota) inhabiting arbuscular mycorrhizal roots or spores. Federation of European Microbiological Societies Yeast Research 4: 597-603. 143 Rodriguez-Felix, A., 2002. Post-harvest physiology and technology of cactus pear fruits and cactus leaves. In: Nefzaoui, A. and P. Inglese (Eds.), Proceedings of the Fourth International Congress on Cactus Pear and Cochineal. Acta Horticulturae 581: 191-199. Rohlf, F.J. 1998. On applications of geometric morphometrics to studies of ontogeny and phylogeny. Systematic Biology 47: 147-158. SPSS for Windows, 22 December 1997. Copyright SPSS Inc. Starmer, W.T., P.F. Ganter and V. Arberdeen, 1987. The ecological role of killer yeasts in natural communities of yeasts. Canadian Journal of Microbiology 33: 783-796. Swart, W.J. and V.R. Swart, 2003. The current status of research on diseases of Opuntia ficus-indica in South Africa. In: Nefzaoui, A. and P. Inglese (Eds.), Proceedings of the Fourth International Congress on Cactus Pear and Cochineal. Acta Horticulturae 581: 239-245. Swart, W.J. and W.M. Kriel, 2002. Pathogens associated with necrosis of cactus pear cladodes in South Africa. Plant Disease 86: 693. Swart, W.J., M.R. Oelofse and M.T. Labuschagne, 2003. Susceptibility of South African cactus pear varieties to four fungi commonly associated with disease symptoms. Journal of the professional Association for Cactus Development 5 : 86-97. Urquhart, E.J. and Z.K. Punja, 1997. Epiphytic growth and survival of Tilletiopsis pallescens, a potential biological control agent of Sphaerotheca fuliginea, on cucumber leaves. Canadian Journal of Botany 75: 892-901. Valinsky, L., G. Della Vedova, T. Jiang and J. Borneman, 2002. Oligonucleotide fingerprinting of rRNA genes for analysis of fungal community composition. Applied Environmental Microbiology 68: 5999-6004. Varvaro, L., G. Granata and G.M. Balestra, 1993. Severe Erwinia damage on Opuntia ficus-indica in Italy. Journal of Phytopathology 138: 325-330. Wang, C.Y., 1994. Chilling injury of tropical horticultural commodities. Horticultural Science 29: 986-988. 144 Wilson, C.L. and M.E. Wisniewski, 1989. Biological control of post-harvest diseases of fruits and vegetables: an emerging technology. Annual Review of Phytopathology 27: 425-441. Wisniewski, M., C. Biles, S. Droby, R. McLaughlin, C. Wilson and E. Chalutz, 1991. Mode of action of the post-harvest biocontrol yeast Pichia guillermondi. I. Characterisation of attachment to Botrytis cinerea. Physiological and Molecular plant Pathology 39: 245-258. Zimmermann, H.G. and G. Granata, 2002. Insect pests and diseases. In: Nobel, P.S. (Ed.), Cacti, biology and uses, pp 235-254.. University of California Press, Los Angeles. 145 GENERAL CONCLUSIONS AND RECOMMENDATIONS Commercial cactus pear fruit orchards in South Africa make use of the spine-less Burbank varieties, that are clonally propagated using terminal cladodes. Most of the planting material supplied to these farmers originated from the Limpopo Provincial Department of Agriculture, Mokopane genebank, and the Mara genebank (experimental farm near Makhado) that host more than 80 accessions of cactus pear. These varieties were developed from the original Burbank material either as clones, or as artificial or natural hybrids. Currently, it is likely that duplicated accessions occur under different common names in the collections. Cactus pear has very subtle morphological differences and it undergoes drastic changes in different environments in traits, such as cladode spininess, shape, and size, that are routinely used to identify different varieties. It therefore became important to determine whether a molecular marker technique such as AFLP can be used to characterise these varieties into homogeneous clusters in agreement with other agronomic traits of interest such as disease resistance, cladode nutritional quality, fruit quality, and vegetative yield. One of the aims of the current study was to genetically fingerprint germplasm using AFLP markers to circumvent the difficulty of doing so solely on phenotypic data. Furthermore, the varieties were evaluated for disease resistance, cladode nutritional quality and fruit quality. In addition, a search to find yeasts able to limit post-harvest rot of fruit was undertaken. Amidst the taxonomic confusion regarding the delineation of the various species within the Opuntia genus, AFLP markers were successfully used in this study to fingerprint 38 South African cactus pear (Opuntia spp.) varieties. Based on the Jaccard similarity coefficient the majority of the varieties were approximately 83% genetically similar. Phenotypic identification is unreliable for cactus pear varieties due to the high morphological similarity between cultivars, and its high plasticity. In addition, because of the possibility of partial and total hybridisation between cultivated varieties, hybrids are easily formed. In this study, cactus pear varieties were fingerprinted using AFLP markers, generating unique marker profiles for each variety. No duplicates were detected within the varieties tested. It is recommended that the AFLP technique be used to screen the remaining accessions within the South African cactus pear germplasm collection with the inclusion of more primer combinations. Genotype specific fragments generated for nine varieties can then be confirmed, since these unique markers may be present in varieties not screened in this study. 146 In South Africa, cactus pear can be used as a dual purpose crop for fruit and fodder production as annually pruned cladodes are discarded as waste at huge cost to farmers (Potgieter and Smith, 2006). The AFLP marker data generated in this study can assist breeders in the selection of varieties for fruit and fodder production. Traditional identification of fruit varieties relies on the assessment of pomological, morphological, and horticultural traits of the adult plant, which leads to significant time delays when surveying germplasm. Cactus pear breeding can be supplemented by direct selection at the genetic level using molecular markers that allow fingerprinting of plant germplasm that co-segregate with the plant genes of interest. AFLP markers have been applied in apricots for cultivar identification (Guena et al., 2003), for the identification of peach and nectarine varieties (Manubens et al., 1999). Varieties recommended for commercial fruit production in the Mokopane district of the Limpopo Province, based on cluster analysis of fruit quality and yield are Gymno Carpo, Malta, Algerian, Morado, Meyers, and Roedtan. These varieties meet the minimum requirements for cactus pear fruit production in South Africa (Potgieter and Mkhari, 2002). These varieties, except for Meyers and Roedtan, are also adapted for fruit production in the Middleveld area of the Limpopo Province (Potgieter and Smith, 2006). Multi-location yield trials are therefore recommended for these varieties (Meyers and Roedtan) to assess their fruit yield in the different agro-climatic regions of South Africa. Given that it is well established that cladodes are adequate as animal feed, provided that a protein supplement is given, the nutritional quality of annually pruned cladodes from commercial orchards for use as fodder was investigated. Malta, Gymno Carpo, American Giant, and Arbiter ranked the highest for CP content. Messina, Nepgen, and Cross X ranked the highest for DM content. Findings of this study showed that cladodes from a commercially maintained orchard had high crude protein content, and are adequate for use as fodder. In addition, since the selection of superior plants for forage production is traditionally based on plant vigour and vegetative yield, the vegetative yield was assessed. The varieties that ranked the highest for vegetative yield were Turpin and Gymno Carpo. The expression of disease resistance within the varieties surveyed indicates a quantitative mode of resistance across all varieties evaluated for all three pathogens tested. Roly Poly, Direkteur, and Zastron were the more susceptible varieties. The most resistant varieties surveyed in this germplasm across all three fungal pathogens were Amersfoort, Meyers, and Algerian. 147 The overall mean pathogen lesions previously reported by Swart et al. (2003) were smaller than those reported in this study for P. virens (4.78 mm), but bigger than those reported for F. oxysporum (12.48 mm) and F. proliferatum (7.49 mm). Variation in pathogen mean lesion diameters between the two studies could be attributed to differences in climatic conditions prevailing during field trials, as the amount and occurrence of infection can be influenced by environmental conditions (Dayton et al., 1983). In this study the disease trial was performed during summer when humidity was low, which could have limited fungal growth. Since annual variations in climatic conditions affect the host as well as the activity of the pathogen, the reliability of field testing (Dayton et al., 1983) may be reduced. It is therefore recommended that subsequent screening of these cactus pear varieties be repeated over a few years, where each season is considered as a repeat test against each pathogen. Phenotypic selection for the traits evaluated in this study (fruit quality, fruit yield, disease resistance, and vegetative yield), in future breeding studies can be altered as a result of environmental effects and by the complex genetic nature of these polygenic traits. Ideally, from the dendrograms presented in this study, individuals differing in agronomic traits of interest can be selected and hybridised to produce fertile, sexual offspring. Subsequently the offspring (mapping population) can be used to map molecular markers that are linked to agronomically important traits. These markers can then be used in future breeding programmes to shorten the time required for the selection of new varieties. The occurrence of apomixis, asexual seed production from the maternal tissues (Mondragón-Jacobo and Pimienta, 1995) is however, still an obstacle in breeding of cactus pear varieties. The short cactus pear fruit shelf life and the continuous cold chain needed to deliver attractive, high quality fruit is not always available to farmers, especially in developing countries. Thus future research into ways of increasing the post-harvest quality of fruits is essential to minimise loss incurred by farmers (Felker and Inglese, 2003). Although post-harvest biological control of fruit rot is promising, further trials to determine optimum culture conditions of yeast killer toxins and their safety to humans are required. It is hoped that the results of this study will assist in the breeding and selection of South African cactus pear varieties for increased fruit yield and quaility which in turn would result in significant improvements in productivity. 148 REFERENCES Dayton, D.F., R.L. Bell and E.B. Williams, 1983. Disease resistance. In: Moore, J.N. and J. Janick (Eds.), Methods in fruit breeding, pp 189-215. Purdue University Press, West Lafayette, Indiana, USA. Felker, P. and P. Inglese, 2003. Short-term and long-term research needs for Opuntia ficus-indica (L.) Mill. Utilization in arid areas. Journal of the Professional Association for Cactus Development 5: 131-152. Guena, F., M. Toschi and D. Bassi, 2003. The use of AFLP markers for cultivar identification in apricot. Plant Breeding 122: 526-531. Manubens, A., S. Lobos, Y. Jadue, M. Toro, R. Messina, M. Lladser and D. Seelenfreund, 1999. DNA isolation and AFLP fingerprinting of nectarine and peach varieties (Prunus persica). Plant Molecular Biology Reporter 17: 255-267. Mondragón-Jacobo, C. and E.B. Pimienta, 1995. Propagation. In: Barbera, G., P. Inglese and B.E. Pimienta (Eds.), Agroecology, cultivation and uses of cactus pear, pp 120-130. FAO Plant production and protection paper 132. Rome, Italy. Potgieter, J. P. and M. Smith, 2006. Genotype X Environment interaction in cactus pear (Opuntia spp.), Additive Main Effects and Multiplicative interaction of fruit yield. Acta Horticulturae 728: 97-104. Potgieter, J.P. and J.J. Mkhari, 2002. Evaluation of cactus pear (Opuntia spp.) germplasm for fruit production purposes. Combined Congress, 15-17 January 2002, Pietermaritzburg, Kwazulu/Natal. Swart, W.J., M.R. Oelofse and M.T. Labuschagne, 2003. Susceptibility of South African cactus pear varieties to four fungi commonly associated with disease symptoms. Journal of the professional Association for Cactus Development 5 : 86-97. 149 SUMMARY South Africa hosts one of the largest cactus pear germplasm collections in the world. However, not all the varieties have been fully characterised, and evaluated for fruit quality, nutritional quality for use as fodder, and disease resistance. In this study, 38 South African cactus pear (Opuntia ssp.) varieties were characterised using AFLP markers to circumvent G X E effect on phenotypic characterisation. With the use of nine primer combinations, the varieties were grouped into four main clusters based on 346 fragments (per sample) of which 48% were polymorphic between samples. The dendrograms generated indicated that commercially cultivated varieties were dispersed amongst the different clusters indicating that they represent the genetic diversity within the germplasm. Genotype specific fragments were generated using six primer combinations, allowing the unique identification of nine varieties, three of which are commercially cultivated (Meyers, Roedtan, and Santa Rosa). Varieties that are recommended for commercial cultivation in the Mokopane district of the Limpopo Province, based on fruit quality and yield are Gymno Carpo, Malta, Algerian, Morado, Meyers, and Roedtan. These varieties meet the minimum requirements for cactus pear fruit production in South Africa. Nutritional quality evaluation of pruned cladodes from a commercial orchard in the Free State Province indicated that the varieties, Malta, Gymno Carpo, and American Giant ranked the highest in terms of CP content. Varieties that yielded the highest DM content were Messina, Nepgen, and Cross X. Varieties that ranked the highest for OM content were Cross X, Nepgen, and Sicilian Indian Fig. Gymno Carpo and Malta are amongst the varieties recommended for cultivation for fruit, as such they can be used as dual purpose crops for the production of both fodder and fruit. Evaluation for disease resistance indicated a quantitative mode of resistance across all varieties for all three fungal pathogens tested. The most resistant varieties surveyed in this study across all three fungal pathogens were Amersfoort, Meyers, and Algerian. Roly Poly, Direkteur, and Zastron were the more susceptible varieties. Of the three fungal pathogens tested, P. virens was the least affected by the antagonistic activity of the yeast isolates. Isolate 25 (Rhodotorula mucilaginosa) performed well against all three pathogens, whilst the remainder of the isolates displayed inhibition at varying degrees. Key words: AFLP, antagonistic yeasts, biocontrol, cactus pear, disease screening, fodder, fruit quality, fungal pathogens, genotyping, inhibition, nutritional quality, Opuntia ficus-indica 150 OPSOMMING Suid-Afrika besit een van die grootste turksvy kiemplasma versamelings in die wêreld. Al die variëteite is egter nog nie ten volle vir vrugkwaliteit, voedingswaarde vir veevoer en siekteweerstand gekarakteriseer of geëvalueer nie. In hierdie studie is 38 Suid- Afrikaanse turksvy (Opuntia ssp.) variëteite suksesvol m.b.v. AFLP merkers gekarakteriseer om die effek van G X E op fenotipiese karakterisering uit te skakel. Met die gebruik van nege voorvoerder kombinasies is die variëteite op grond van 346 fragmente (per monster) waarvan 48% polimorfies was in vier groepe verdeel. Kommersiële variëteite was tussen die verskillende groepe versprei, wat aangedui het dat hulle die genetiese diversiteit binne die kiemplasma verteenwoordig. Genotipe spesifieke fragmente is met ses voorvoerder kombinasies gegenereer, wat die unieke identifikasie van nege variëteite moontlik gemaak het, insluitend drie kommersiële variëteite (Meyers, Roedtan en Santa Rosa). Variëteite wat gebaseer op vrugkwaliteit en opbrengs vir kommersiële produksie in die Mokopane distrik van die Limpopo Provinsie, aanbeveel word, is Gymno Carpo, Malta, Algerian, Morado, Meyers en Roedtan. Hierdie variëteite voldoen aan die minimum vereistes vir turksvy produksie in Suid Afrika. Voedingswaarde analise van die gesnoeide kladodes van ‘n kommersiële boord in die Vrystaat Provinsie het aangedui dat die variëteite Malta, Gymno Carpo en American Giant die beste t.o.v. ruproteïen inhoud gevaar het. Die variëteite met die hoogste droëmassa inhoud was Messina, Nepgen en Cross X. Die variëteite wat die beste vir organiese inhoud gevaar het was Cross X, Nepgen en Sicilian Indian Fig. Gymno Carpo en Malta was van die variëteite wat vir vrugproduksie aanbeveel word. As sulks kan hulle vir dubbeldoel produksie vir beide veevoer en vrugte gebruik word. Evaluasie vir siekteweerstand het ‘n kwantitatiewe model vir weerstand oor alle variëteite vir al drie fungus patogene aangedui. Die mees weerstandbiedende variëteite vir al drie patogene was Amersfoort, Meyers en Algerian. Roly Poly, Direkteur en Zastron was die mees vatbare variëteite. Van die drie fungus patogene wat getoets is, was P. virens die minste deur antagonistiese aksie van gis isolate beïnvloed. Isolaat 25 (Rhodotorula mucilaginosa) het goed teenoor al drie die patogene gereageer, terwyl die res van die isolate inhibisie van verskillende grade getoon het. Sleutelwoorde: AFLP, antagonistiese giste, biobeheer, fungus patogene, genotipering, inhibisie, Opuntia ficus-indica, siekte-evaluasie, turksvy, veevoer, voedingswaarde, vrugkwaliteit 151 APPENDICES 152 APPENDIX I: GILLEMBERG WEATHER DATA (1999-2001) CompNo Name Lat Long Alt Year M Day Rain Tmax Tmin Tave HU UCU U2 RHx RHn ETo Rs 30090 GILLEMBERG -23.8333 28.9667 1100 1999 1 1 18.4 27.9 19.1 21.7 13.5 -24.0 1.2 98.4 65.1 4.3 20.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 1 2 0.0 29.7 19.2 23.9 14.5 -24.0 1.6 96.4 58.8 4.9 21.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 1 3 0.0 27.7 17.9 22.7 12.8 -24.0 1.8 96.0 62.5 5.0 24.530090 GILLEMBERG -23.8333 28.9667 1100 1999 1 4 0.0 27.5 17.2 22.2 12.4 -23.0 1.8 95.1 56.3 5.1 24.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 1 5 8.4 29.0 16.7 21.3 12.9 -22.5 1.7 96.2 57.3 4.9 22.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 1 6 0.2 27.7 17.4 21.9 12.6 -23.0 1.7 94.3 62.0 5.6 28.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 1 7 0.0 28.2 17.5 23.1 12.9 -23.0 2.7 93.4 54.7 6.5 32.0 30090 GILLEMBERG -23.8333 28.9667 1100 1999 1 8 0.0 27.0 17.6 21.5 12.3 -23.0 2.8 94.2 51.9 6.0 28.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 1 9 0.0 28.2 16.4 21.8 12.3 -21.5 1.8 92.9 48.1 6.0 29.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 1 10 0.0 28.5 16.1 22.7 12.3 -22.0 2.3 91.9 49.6 6.4 31.0 30090 GILLEMBERG -23.8333 28.9667 1100 1999 1 11 13.4 28.0 19.3 22.0 13.7 -24.0 2.3 97.0 63.2 4.3 18.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 1 12 0.6 28.7 19.0 22.5 13.9 -24.0 2.2 95.3 61.0 5.4 25.5 30090 GILLEMBERG -23.8333 28.9667 1100 1999 1 13 0.0 28.8 17.4 22.8 13.1 -23.5 1.9 94.9 50.7 5.2 22.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 1 14 0.0 30.7 15.8 23.4 13.3 -22.5 1.9 96.3 37.9 6.5 28.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 1 15 0.0 29.1 16.0 23.0 12.6 -23.0 2.5 95.8 46.5 7.0 33.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 1 16 0.0 29.0 16.6 22.9 12.8 -22.5 2.1 93.8 42.9 6.9 33.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 1 17 0.0 32.1 15.1 24.1 13.6 -20.5 1.7 95.5 31.4 7.3 33.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 1 18 0.0 33.6 16.3 25.2 15.0 -22.5 1.4 87.2 40.5 7.2 32.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 1 19 24.2 33.4 19.0 23.9 16.2 -24.0 2.1 91.2 43.7 6.0 21.8 30090 GILLEMBERG -23.8333 28.9667 1100 1999 1 20 0.0 30.4 20.5 24.7 15.5 -24.0 2.7 87.2 51.6 6.5 27.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 1 21 7.8 28.1 19.2 21.8 13.7 -24.0 2.3 95.2 69.4 3.7 15.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 1 22 0.0 26.3 18.7 21.8 12.5 -24.0 2.4 89.7 62.2 4.8 22.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 1 23 0.0 26.1 18.3 21.2 12.2 -24.0 1.5 92.1 63.3 3.9 17.8 30090 GILLEMBERG -23.8333 28.9667 1100 1999 1 24 6.8 27.7 19.1 21.4 13.4 -24.0 1.3 94.2 63.0 4.1 19.0 30090 GILLEMBERG -23.8333 28.9667 1100 1999 1 25 0.2 28.9 18.2 22.9 13.6 -24.0 2.0 95.4 46.1 5.8 26.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 1 26 0.0 29.5 15.8 22.8 12.7 -21.5 2.0 95.4 43.2 6.3 29.0 30090 GILLEMBERG -23.8333 28.9667 1100 1999 1 27 0.4 26.8 15.7 20.7 11.3 -22.0 2.0 92.2 44.9 5.3 23.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 1 28 17.8 26.0 17.2 19.7 11.6 -20.0 1.5 94.6 59.5 4.1 19.5 30090 GILLEMBERG -23.8333 28.9667 1100 1999 1 29 0.0 28.8 18.0 22.5 13.4 -24.0 2.3 89.1 53.4 5.2 21.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 1 30 20.2 28.4 19.1 22.1 13.8 -24.0 2.3 96.2 59.7 4.2 16.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 1 31 0.0 27.7 18.7 22.6 13.2 -24.0 1.7 92.6 61.6 4.7 22.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 2 1 0.0 27.5 17.9 22.4 12.7 -24.0 2.3 96.6 62.4 5.1 24.730090 GILLEMBERG -23.8333 28.9667 1100 1999 2 2 0.8 28.3 18.7 22.3 13.5 -24.0 2.1 94.2 65.3 4.4 19.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 2 3 5.6 28.2 18.8 22.1 13.5 -24.0 1.3 94.0 67.0 4.0 18.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 2 4 0.8 29.0 19.1 22.7 14.1 -24.0 1.9 96.3 57.8 4.5 18.8 153 CompNo Name Lat Long Alt Year M Day Rain Tmax Tmin Tave HU UCU U2 RHx RHn ETo Rs 30090 GILLEMBERG -23.8333 28.9667 1100 1999 2 5 14.0 26.7 19.4 22.6 13.1 -24.0 1.6 95.5 67.1 3.4 14.8 30090 GILLEMBERG -23.8333 28.9667 1100 1999 2 6 0.0 32.1 19.6 24.4 15.9 -24.0 1.4 99.0 46.0 6.2 27.8 30090 GILLEMBERG -23.8333 28.9667 1100 1999 2 7 0.0 31.7 19.3 24.2 15.5 -24.0 2.2 97.3 49.3 6.5 28.8 30090 GILLEMBERG -23.8333 28.9667 1100 1999 2 8 0.0 27.7 18.1 21.9 12.9 -24.0 2.2 94.4 58.4 4.4 18.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 2 9 0.0 29.4 14.8 21.7 12.1 -20.0 2.3 95.2 48.7 6.4 30.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 2 10 0.0 27.7 15.4 21.0 11.6 -20.0 1.8 91.7 48.3 5.8 29.0 30090 GILLEMBERG -23.8333 28.9667 1100 1999 2 11 0.0 31.2 13.4 22.4 12.3 -17.5 1.9 92.7 42.3 6.6 30.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 2 12 0.0 29.4 17.9 23.2 13.7 -24.0 2.1 90.9 41.2 6.9 32.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 2 13 0.0 32.0 15.9 23.1 14.0 -21.0 1.8 99.9 30.1 6.9 30.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 2 14 0.0 29.6 14.3 21.8 12.0 -19.5 2.7 91.5 37.8 6.8 30.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 2 15 0.0 25.8 17.3 20.5 11.6 -23.0 2.3 89.6 65.2 4.0 17.8 30090 GILLEMBERG -23.8333 28.9667 1100 1999 2 16 0.0 30.1 16.9 22.3 13.5 -22.5 1.9 91.5 43.0 5.7 25.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 2 17 0.0 29.9 16.4 22.7 13.2 -22.5 2.4 91.5 41.7 6.4 29.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 2 18 0.0 31.4 15.3 23.6 13.4 -21.5 2.2 93.5 31.3 6.9 31.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 2 19 0.0 33.4 19.9 25.5 16.7 -24.0 1.9 91.5 36.5 6.5 27.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 2 20 0.0 29.8 20.1 22.8 15.0 -24.0 2.6 92.5 63.9 4.9 20.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 2 21 0.0 28.3 17.5 22.8 12.9 -24.0 2.3 91.5 54.8 4.8 20.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 2 22 0.0 31.5 17.7 23.6 14.6 -24.0 1.9 92.5 49.5 5.3 21.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 2 23 0.0 29.2 17.7 23.0 13.5 -24.0 2.0 91.5 48.2 4.6 18.5 30090 GILLEMBERG -23.8333 28.9667 1100 1999 2 24 0.0 31.0 14.9 22.9 13.0 -19.5 2.2 91.5 41.7 6.5 28.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 2 25 0.0 32.9 15.5 24.1 14.2 -21.0 2.5 93.5 30.0 7.5 32.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 2 26 0.0 33.5 15.8 24.5 14.7 -21.5 2.0 69.9 29.3 7.7 31.5 30090 GILLEMBERG -23.8333 28.9667 1100 1999 2 27 0.0 32.6 16.6 24.3 14.6 -23.0 2.0 91.0 24.2 7.1 28.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 2 28 0.0 35.2 14.4 24.5 14.7 -19.5 1.8 93.9 22.6 7.7 31.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 3 1 0.0 37.0 15.2 26.4 15.1 -21.5 2.7 75.7 19.8 9.4 31.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 3 2 0.0 35.7 18.0 25.2 16.5 -24.0 2.3 91.2 21.6 8.3 31.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 3 3 0.0 32.0 15.5 23.4 13.8 -21.5 2.8 90.8 28.5 7.8 31.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 3 4 0.0 34.6 14.9 24.6 14.8 -19.5 2.5 91.5 22.0 8.2 31.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 3 5 0.0 28.1 14.6 22.2 11.4 -19.5 2.3 92.6 50.7 4.4 17.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 3 6 2.0 28.0 19.5 22.5 13.8 -24.0 2.9 92.7 56.4 4.1 14.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 3 7 0.0 28.9 18.4 22.6 13.7 -24.0 2.8 93.6 38.7 6.1 25.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 3 8 0.0 28.7 16.6 22.1 12.7 -20.5 2.6 92.8 38.0 6.6 30.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 3 9 0.0 29.5 16.2 22.3 12.9 -21.5 2.8 90.9 38.3 6.8 30.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 3 10 0.0 29.7 15.9 22.6 12.8 -21.5 3.0 90.8 36.8 6.8 28.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 3 11 0.0 30.6 15.6 23.1 13.1 -21.5 2.2 93.8 34.2 6.8 30.8 30090 GILLEMBERG -23.8333 28.9667 1100 1999 3 12 0.0 33.2 16.8 24.4 15.0 -22.5 1.9 95.5 32.9 6.3 24.8 154 CompNo Name Lat Long Alt Year M Day Rain Tmax Tmin Tave HU UCU U2 RHx RHn ETo Rs 30090 GILLEMBERG -23.8333 28.9667 1100 1999 3 13 0.0 30.3 19.5 23.9 14.9 -24.0 3.1 86.7 42.7 5.8 19.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 3 14 0.0 28.1 18.4 22.5 13.3 -24.0 2.8 86.0 50.0 4.9 17.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 3 15 2.4 29.2 16.8 21.2 13.0 -22.5 2.3 92.4 50.6 5.0 21.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 3 16 5.0 26.4 18.6 20.3 12.5 -24.0 1.9 96.6 61.7 3.0 11.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 3 17 0.2 29.7 18.1 21.7 13.9 -24.0 1.9 96.6 46.3 4.5 17.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 3 18 0.2 27.9 17.9 21.3 12.9 -24.0 2.5 94.4 54.1 4.4 17.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 3 19 0.0 26.6 17.5 22.1 12.1 -18.6 2.1 97.9 53.4 4.0 17.230090 GILLEMBERG -23.8333 28.9667 1100 1999 3 20 0.0 27.3 17.8 22.5 12.5 -19.5 1.7 94.8 45.0 4.1 17.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 3 21 0.0 28.1 16.2 22.2 12.2 -18.8 2.1 96.8 53.4 4.4 19.8 30090 GILLEMBERG -23.8333 28.9667 1100 1999 3 22 2.9 25.4 19.1 22.3 12.3 -19.1 2.2 89.7 59.1 3.7 16.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 3 23 0.4 24.1 18.9 21.6 11.5 -13.0 1.5 94.9 71.6 2.9 14.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 3 24 0.0 27.7 17.6 21.7 12.7 -23.0 2.3 92.4 54.8 4.5 20.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 3 25 0.0 25.4 16.2 20.4 10.8 -21.5 2.5 88.2 52.2 4.9 23.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 3 26 0.0 28.6 14.8 21.3 11.7 -19.0 1.9 95.4 42.3 5.2 24.8 30090 GILLEMBERG -23.8333 28.9667 1100 1999 3 27 0.0 27.8 15.9 21.3 11.9 -20.0 2.9 88.6 44.8 5.7 25.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 3 28 0.0 26.6 16.7 20.5 11.7 -19.0 2.6 91.1 46.9 5.0 22.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 3 29 0.0 27.9 14.4 20.5 11.2 -16.5 2.1 94.1 40.9 5.2 24.8 30090 GILLEMBERG -23.8333 28.9667 1100 1999 3 30 0.0 25.4 12.7 19.2 9.1 -16.5 3.0 94.0 47.1 4.9 23.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 3 31 0.0 25.7 12.8 19.3 9.3 -16.0 2.8 91.0 56.7 4.2 19.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 4 1 0.0 28.1 12.9 20.3 10.5 -17.0 2.2 94.8 35.1 5.6 26.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 4 2 0.0 28.0 14.0 20.3 11.0 -17.5 2.0 93.3 41.6 4.8 22.0 30090 GILLEMBERG -23.8333 28.9667 1100 1999 4 3 0.0 27.6 14.6 20.7 11.1 -19.0 2.3 90.1 41.9 4.9 21.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 4 4 0.0 28.5 15.9 21.2 12.2 -20.5 2.1 89.3 40.0 4.9 20.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 4 5 0.0 31.1 14.7 21.8 12.9 -19.5 2.3 88.9 30.6 6.0 23.830090 GILLEMBERG -23.8333 28.9667 1100 1999 4 6 0.0 27.3 12.8 20.1 10.1 -17.0 2.1 92.8 39.9 5.1 25.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 4 7 0.0 26.8 13.5 19.9 10.2 -18.0 2.2 94.4 39.3 4.7 20.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 4 8 0.0 27.4 12.3 19.9 9.9 -17.0 2.0 95.1 43.8 4.8 24.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 4 9 0.0 30.5 13.2 21.8 11.9 -17.5 1.6 85.8 37.9 5.3 25.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 4 10 0.0 32.2 15.3 23.5 13.8 -21.0 2.0 88.4 31.7 6.0 24.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 4 11 1.0 26.7 16.5 20.4 11.6 -20.0 3.5 83.7 50.0 5.0 20.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 4 12 0.0 24.8 13.0 18.4 8.9 -14.0 2.9 82.1 49.8 4.6 21.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 4 13 0.0 24.0 11.0 17.1 7.5 -9.0 2.5 96.5 44.9 4.5 24.8 30090 GILLEMBERG -23.8333 28.9667 1100 1999 4 14 0.0 27.3 9.0 17.9 8.2 -9.5 1.8 90.5 25.9 5.2 25.5 30090 GILLEMBERG -23.8333 28.9667 1100 1999 4 15 0.0 29.3 9.6 19.3 9.5 -12.0 1.3 93.3 30.3 4.8 24.5 30090 GILLEMBERG -23.8333 28.9667 1100 1999 4 16 0.0 31.9 13.3 22.6 12.6 -18.0 1.7 58.5 25.4 5.8 24.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 4 17 0.0 31.1 15.3 23.4 13.2 -22.0 2.3 69.0 24.6 6.3 24.3 155 CompNo Name Lat Long Alt Year M Day Rain Tmax Tmin Tave HU UCU U2 RHx RHn ETo Rs 30090 GILLEMBERG -23.8333 28.9667 1100 1999 4 18 0.0 32.2 15.1 23.9 13.7 -20.5 2.3 80.2 28.2 6.1 23.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 4 19 0.0 32.9 16.7 24.2 14.8 -22.0 1.8 65.5 24.4 5.9 24.0 30090 GILLEMBERG -23.8333 28.9667 1100 1999 4 20 0.0 33.6 19.1 25.4 16.4 -24.0 2.1 72.8 29.7 5.9 19.8 30090 GILLEMBERG -23.8333 28.9667 1100 1999 4 21 1.2 30.1 19.0 23.0 14.6 -24.0 1.9 92.5 47.8 3.6 12.0 30090 GILLEMBERG -23.8333 28.9667 1100 1999 4 22 21.2 27.3 17.8 20.9 12.6 -23.5 3.6 94.1 63.7 3.4 11.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 4 23 0.0 26.7 17.6 21.0 12.2 -22.0 2.0 91.1 61.3 3.3 14.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 4 24 0.0 28.0 16.7 21.2 12.4 -20.0 2.8 90.9 57.6 4.5 21.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 4 25 0.0 25.1 16.5 19.7 10.8 -19.0 2.6 89.5 62.2 3.7 18.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 4 26 0.0 25.1 15.5 19.2 10.3 -17.0 3.4 95.3 59.1 3.7 17.0 30090 GILLEMBERG -23.8333 28.9667 1100 1999 4 27 0.0 24.0 13.4 17.7 8.7 -12.5 2.2 94.8 60.6 3.3 18.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 4 28 0.0 21.9 10.3 16.0 6.1 -7.5 2.9 95.5 59.2 3.4 19.8 30090 GILLEMBERG -23.8333 28.9667 1100 1999 4 29 0.0 21.8 9.6 14.9 5.7 -3.5 2.4 96.5 50.4 3.4 18.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 4 30 0.0 22.5 10.4 15.7 6.5 -5.0 1.9 93.1 56.5 3.1 16.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 1 0.0 25.9 9.4 16.8 7.7 -8.0 1.2 96.2 45.6 3.5 20.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 2 0.0 28.3 11.4 19.7 9.9 -13.0 1.3 79.6 40.2 4.0 21.5 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 3 0.0 29.4 12.5 21.1 11.0 -16.5 1.7 74.2 36.7 4.7 21.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 4 0.0 29.5 13.0 21.0 11.3 -16.0 1.4 73.1 36.8 4.3 21.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 5 0.0 30.6 13.2 20.3 11.9 -15.0 3.0 85.2 36.8 5.4 18.8 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 6 0.0 20.2 12.4 15.8 6.3 -7.5 3.3 88.3 66.4 3.0 17.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 7 0.0 21.7 11.7 15.8 6.7 -7.0 1.6 91.2 59.0 2.6 14.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 8 0.0 23.9 8.9 16.2 6.4 -7.5 1.5 96.5 54.9 3.1 19.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 9 0.0 24.7 10.6 17.8 7.7 -11.0 2.0 92.3 49.6 3.6 19.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 10 0.0 25.0 12.2 18.4 8.6 -13.5 1.8 90.0 50.9 3.6 20.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 11 0.0 24.6 13.4 18.0 9.0 -11.5 2.5 87.2 52.5 3.8 19.0 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 12 0.0 25.4 10.2 17.5 7.8 -9.0 1.5 93.2 46.3 3.5 20.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 13 0.0 27.7 10.3 19.2 9.0 -13.0 2.6 82.5 36.1 4.8 20.0 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 14 0.0 23.9 12.2 17.4 8.1 -12.0 2.0 95.9 56.0 3.0 15.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 15 0.0 24.6 10.2 17.6 7.4 -11.5 1.8 92.9 50.5 3.3 18.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 16 0.0 22.4 13.4 17.2 7.9 -11.0 3.7 80.7 56.1 3.6 14.5 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 17 0.0 20.8 9.8 14.9 5.3 -3.5 3.5 97.4 66.6 2.6 14.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 18 0.0 18.3 11.3 14.1 4.8 -0.5 2.6 93.7 71.4 1.9 9.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 19 0.0 22.5 8.3 15.5 5.4 -5.0 2.0 96.0 57.0 3.0 18.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 20 0.0 25.1 0.0 18.4 2.6 -14.5 2.1 92.2 53.0 3.1 13.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 21 13.6 22.9 13.2 17.6 8.1 -12.0 2.1 98.2 63.8 2.3 9.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 22 0.0 24.2 12.7 17.9 8.5 -14.5 2.1 97.6 59.5 2.9 15.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 23 0.0 22.9 12.0 17.4 7.5 -12.0 2.4 95.5 45.3 3.5 19.1 156 CompNo Name Lat Long Alt Year M Day Rain Tmax Tmin Tave HU UCU U2 RHx RHn ETo Rs 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 24 0.0 24.5 8.0 16.5 6.3 -7.5 1.8 78.7 42.7 3.5 19.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 25 0.0 23.9 11.8 17.1 7.9 -10.0 1.8 93.0 49.4 3.2 18.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 26 0.0 25.4 10.3 17.2 7.9 -8.5 1.3 91.4 47.8 3.1 18.8 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 27 0.0 28.3 11.8 19.0 10.1 -11.5 1.6 79.5 33.6 4.0 19.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 28 1.4 27.0 0.0 18.3 3.5 -9.5 2.3 81.5 29.3 4.6 18.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 29 0.0 21.7 11.9 15.5 6.8 -7.5 2.1 90.6 55.2 2.9 17.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 30 0.0 24.6 8.6 16.0 6.6 -4.5 1.3 95.8 46.2 2.9 18.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 5 31 0.0 24.4 9.6 16.4 7.0 -6.5 2.2 80.4 36.4 3.8 19.0 30090 GILLEMBERG -23.8333 28.9667 1100 1999 6 1 0.0 22.6 9.1 15.7 5.9 -6.5 2.7 92.3 46.8 3.4 18.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 6 2 0.0 19.0 7.6 13.9 3.3 1.0 2.6 94.0 57.8 2.4 12.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 6 3 0.2 16.5 11.3 14.1 3.9 0.0 2.5 91.4 76.0 1.5 6.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 6 4 0.0 22.4 8.3 14.8 5.4 -3.0 1.6 96.5 45.7 2.8 17.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 6 5 0.0 24.1 9.0 16.4 6.6 -5.5 2.0 86.8 40.4 3.4 17.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 6 6 0.0 24.5 8.0 15.9 6.3 -5.5 1.4 88.4 43.4 3.0 17.0 30090 GILLEMBERG -23.8333 28.9667 1100 1999 6 7 0.0 20.7 5.2 13.2 3.0 2.0 1.7 88.9 42.8 2.9 18.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 6 8 0.0 21.3 3.9 12.7 2.6 4.5 1.5 90.9 33.2 3.0 18.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 6 9 0.0 22.0 9.4 14.9 5.7 -3.5 2.4 67.1 35.6 3.8 18.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 6 10 0.0 21.4 4.9 13.3 3.2 1.0 1.3 81.1 40.4 2.8 18.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 6 11 0.0 23.3 6.3 14.0 4.8 0.5 1.7 75.5 31.6 3.4 18.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 6 12 0.0 24.2 5.8 14.7 5.0 -2.0 1.5 65.8 27.7 3.5 18.8 30090 GILLEMBERG -23.8333 28.9667 1100 1999 6 13 0.0 23.8 6.6 14.2 5.2 1.5 1.4 63.8 28.7 3.3 19.0 30090 GILLEMBERG -23.8333 28.9667 1100 1999 6 14 0.0 24.5 4.2 14.2 4.4 0.5 1.4 59.5 30.7 3.4 19.0 30090 GILLEMBERG -23.8333 28.9667 1100 1999 6 15 0.0 24.0 6.5 15.6 5.3 -3.5 1.4 62.8 33.7 3.3 18.5 30090 GILLEMBERG -23.8333 28.9667 1100 1999 6 16 0.0 23.5 12.3 16.5 7.9 -9.0 2.1 70.3 36.5 3.7 18.0 30090 GILLEMBERG -23.8333 28.9667 1100 1999 6 17 0.0 23.3 8.5 16.1 5.9 -8.0 2.1 75.5 37.6 3.3 13.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 6 18 0.0 24.4 11.6 17.5 8.0 -12.5 1.9 84.9 47.8 3.0 12.8 30090 GILLEMBERG -23.8333 28.9667 1100 1999 6 19 0.2 24.7 11.5 17.6 8.1 -10.5 2.3 95.4 45.0 3.3 15.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 6 20 0.0 18.4 8.6 13.2 3.5 4.0 2.6 88.6 53.1 2.7 15.8 30090 GILLEMBERG -23.8333 28.9667 1100 1999 6 21 0.0 21.3 8.7 13.5 5.0 1.0 1.7 84.2 53.5 2.7 17.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 6 22 0.0 25.6 6.0 15.8 5.8 -4.0 1.5 93.6 35.8 3.2 17.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 6 23 0.2 26.2 11.2 18.0 8.7 -9.5 1.8 68.9 34.7 3.8 17.5 30090 GILLEMBERG -23.8333 28.9667 1100 1999 6 24 0.0 26.8 9.8 18.2 8.3 -11.5 1.6 77.6 32.0 3.5 17.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 6 25 0.0 25.5 10.3 17.9 7.9 -10.5 2.3 65.9 34.0 3.9 12.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 6 26 0.0 20.0 10.0 14.8 5.0 -5.5 3.0 87.0 57.0 2.9 15.8 30090 GILLEMBERG -23.8333 28.9667 1100 1999 6 27 0.0 20.2 6.8 12.7 3.5 4.0 2.0 96.3 43.9 2.8 17.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 6 28 0.0 21.7 5.6 12.7 3.7 4.0 1.5 86.5 35.6 2.9 18.2 157 CompNo Name Lat Long Alt Year M Day Rain Tmax Tmin Tave HU UCU U2 RHx RHn ETo Rs 30090 GILLEMBERG -23.8333 28.9667 1100 1999 6 29 0.0 21.4 3.6 12.1 2.5 4.0 1.3 79.0 35.3 2.8 18.5 30090 GILLEMBERG -23.8333 28.9667 1100 1999 6 30 0.0 22.5 4.4 13.3 3.5 1.0 1.4 66.1 30.5 3.2 18.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 1 0.0 21.1 6.3 13.3 3.7 1.0 1.3 70.6 34.4 2.9 18.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 2 0.0 22.9 6.1 14.8 4.5 -2.0 2.5 54.5 28.1 4.3 18.5 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 3 0.0 20.5 9.0 13.9 4.7 0.0 2.4 94.4 37.9 3.2 16.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 4 0.0 21.6 7.6 14.1 4.6 -1.0 1.8 92.8 40.1 3.0 17.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 5 0.0 18.9 8.0 13.0 3.4 3.5 2.3 90.9 49.2 2.6 14.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 6 3.8 15.2 10.6 13.1 2.9 3.0 1.7 96.1 78.3 1.1 4.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 7 0.0 17.9 9.5 13.5 3.7 1.5 1.4 95.3 64.7 1.7 9.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 8 0.0 20.0 10.1 14.4 5.0 -2.5 2.3 92.1 50.8 2.8 16.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 9 0.0 19.0 9.0 13.4 4.0 -0.5 1.9 89.7 53.9 2.5 15.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 10 0.0 20.5 7.3 13.2 3.9 3.0 1.4 96.6 45.2 2.5 15.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 11 0.0 18.5 10.1 14.2 4.3 -1.0 2.8 82.9 49.8 2.9 13.5 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 12 0.0 17.1 8.4 12.2 2.8 5.5 2.1 94.3 59.2 2.1 12.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 13 0.0 19.9 7.8 13.2 3.8 2.5 1.6 89.5 49.6 2.7 18.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 14 0.0 21.4 7.4 14.1 4.4 1.0 1.3 97.1 44.1 2.7 18.0 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 15 0.0 23.3 11.9 16.8 7.6 -9.0 1.6 73.1 39.6 3.3 16.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 16 0.0 25.4 9.5 18.0 7.4 -10.5 2.0 72.9 30.6 4.0 18.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 17 0.0 24.7 9.8 17.0 7.2 -7.5 1.8 89.3 37.2 3.5 17.5 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 18 0.0 25.0 11.7 18.1 8.4 -10.5 2.6 85.3 42.7 3.8 15.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 19 0.0 16.0 10.1 12.8 3.1 4.5 4.2 84.7 53.6 2.7 9.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 20 0.0 17.0 4.5 10.5 0.7 11.0 2.1 93.7 47.2 2.7 19.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 21 0.0 19.3 5.4 11.8 2.3 5.5 1.7 86.6 39.4 3.0 19.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 22 0.0 22.2 5.5 13.6 3.8 1.5 1.4 91.3 39.3 3.1 19.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 23 0.0 23.2 7.0 14.6 5.1 -2.5 1.7 78.3 31.6 3.6 20.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 24 0.0 19.5 7.9 13.3 3.7 2.5 2.3 88.5 49.4 3.1 19.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 25 0.0 19.4 7.4 14.0 3.4 -0.5 2.1 94.8 51.0 2.8 17.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 26 0.0 19.6 11.2 14.8 5.4 -4.0 1.5 87.9 46.4 2.6 13.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 27 0.0 21.9 10.2 15.6 6.0 -5.5 1.4 76.0 38.5 3.2 17.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 28 0.0 21.9 7.8 15.5 4.8 -5.0 2.0 73.5 37.2 3.7 20.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 29 0.0 22.7 12.1 16.8 7.4 -8.5 3.0 64.6 38.4 4.4 18.5 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 30 0.0 24.1 9.8 17.6 7.0 -11.5 2.0 74.3 35.9 3.9 19.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 7 31 0.0 23.7 10.8 17.8 7.3 -12.0 1.9 72.0 40.2 3.6 15.5 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 1 0.0 25.6 12.9 19.0 9.2 -14.0 2.3 72.0 34.9 4.3 17.5 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 2 0.0 27.1 11.4 19.4 9.3 -14.5 2.0 81.3 29.6 4.5 20.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 3 0.0 29.1 14.7 20.7 11.9 -17.0 1.9 68.0 43.1 4.5 20.4 158 CompNo Name Lat Long Alt Year M Day Rain Tmax Tmin Tave HU UCU U2 RHx RHn ETo Rs 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 4 0.0 27.8 13.4 19.8 10.6 -13.0 2.2 70.8 45.4 4.3 18.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 5 0.0 26.8 11.2 18.7 9.0 -11.5 2.7 91.6 46.3 4.3 19.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 6 0.0 18.9 11.7 15.1 5.3 -5.5 3.5 94.3 71.3 2.6 17.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 7 0.0 21.0 10.1 14.9 5.6 -3.0 1.9 95.1 60.8 2.8 17.5 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 8 0.0 23.9 7.5 15.4 5.7 -3.0 1.5 87.7 55.7 3.4 21.8 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 9 0.0 23.7 11.6 17.4 7.6 -10.0 2.9 81.6 55.0 4.0 20.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 10 0.0 22.0 11.7 16.6 6.8 -9.0 2.6 86.6 63.2 3.4 20.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 11 0.0 22.6 12.1 16.6 7.3 -9.0 2.8 89.8 62.0 3.4 20.0 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 12 0.0 21.0 12.2 15.8 6.6 -7.0 2.8 92.0 61.3 3.1 17.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 13 0.0 23.8 9.9 16.8 6.8 -8.5 1.7 90.2 52.5 3.6 21.5 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 14 0.0 26.1 9.6 18.2 7.8 -11.5 2.2 86.8 45.5 4.3 22.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 15 0.0 27.4 11.7 19.2 9.6 -13.0 2.4 77.5 41.1 4.9 22.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 16 0.0 24.5 8.2 15.8 6.4 -3.5 2.2 93.4 41.9 4.3 23.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 17 0.0 24.7 5.6 14.7 5.2 -1.5 1.7 93.8 51.8 3.8 23.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 18 0.0 23.6 8.1 15.5 5.8 -3.0 2.2 90.7 55.6 3.9 24.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 19 0.0 22.6 8.3 15.7 5.5 -5.0 2.3 91.8 58.3 3.7 23.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 20 0.0 22.8 13.6 17.3 8.2 -10.5 1.7 84.8 63.8 3.1 16.8 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 21 0.6 25.0 11.6 17.4 8.3 -9.0 1.7 92.0 58.8 3.6 20.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 22 0.0 27.6 11.5 20.1 9.5 -15.5 1.8 83.4 52.7 4.4 23.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 23 0.0 30.3 18.2 23.7 14.2 -24.0 4.0 77.4 42.6 6.4 22.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 24 0.0 17.2 9.0 12.8 3.1 4.0 4.5 92.0 52.1 2.9 9.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 25 0.0 18.1 6.0 12.8 2.0 4.5 2.6 89.7 57.5 3.1 19.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 26 0.0 18.8 4.6 12.7 1.7 3.0 2.6 88.3 54.7 3.4 22.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 27 0.0 20.2 8.2 14.1 4.2 -1.5 2.0 93.0 70.3 3.1 22.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 28 0.0 23.1 7.8 15.9 5.4 -5.5 1.5 92.8 63.6 3.7 25.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 29 0.0 26.6 9.0 17.9 7.8 -9.0 1.6 87.2 58.9 4.2 25.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 30 0.0 28.8 11.6 18.9 10.2 -10.5 2.1 83.4 53.6 5.0 25.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 8 31 0.0 26.0 9.3 18.1 7.6 -11.0 1.9 86.5 59.4 4.3 25.0 30090 GILLEMBERG -23.8333 28.9667 1100 1999 9 1 0.0 25.5 12.2 18.5 8.9 -12.0 2.8 96.5 58.8 4.4 24.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 9 2 0.0 21.8 12.8 16.8 7.3 -10.5 3.9 95.4 69.1 3.7 23.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 9 3 0.0 22.0 12.9 16.6 7.5 -10.0 3.4 93.2 64.0 4.0 24.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 9 4 0.0 24.9 9.3 17.4 7.1 -11.0 1.8 90.5 53.4 4.3 24.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 9 5 0.0 25.9 13.5 19.4 9.7 -14.5 2.8 81.1 56.5 4.8 25.0 30090 GILLEMBERG -23.8333 28.9667 1100 1999 9 6 0.0 27.8 12.2 19.8 10.0 -14.5 2.4 90.7 50.7 5.0 25.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 9 7 0.0 24.5 13.9 18.5 9.2 -12.5 2.7 87.9 59.8 4.5 25.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 9 8 0.2 27.0 12.0 19.3 9.5 -14.0 1.7 90.3 48.3 4.8 25.9 159 CompNo Name Lat Long Alt Year M Day Rain Tmax Tmin Tave HU UCU U2 RHx RHn ETo Rs 30090 GILLEMBERG -23.8333 28.9667 1100 1999 9 9 0.0 29.5 13.9 22.0 11.7 -18.0 2.0 71.4 39.4 5.8 26.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 9 10 0.0 27.0 14.4 22.2 10.7 -21.0 3.1 55.4 42.0 5.3 15.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 9 11 0.0 16.8 8.2 12.9 2.5 4.0 3.6 62.9 37.4 4.9 28.0 30090 GILLEMBERG -23.8333 28.9667 1100 1999 9 12 0.0 22.4 7.7 14.7 5.0 -2.5 2.9 67.2 39.4 5.3 27.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 9 13 0.0 19.6 9.2 14.2 4.4 -2.0 4.2 83.3 50.6 4.6 26.5 30090 GILLEMBERG -23.8333 28.9667 1100 1999 9 14 0.0 22.5 8.8 15.7 5.7 -5.5 2.8 85.0 45.3 4.9 27.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 9 15 0.0 26.3 11.8 18.7 9.1 -11.5 1.9 77.3 37.1 5.4 27.5 30090 GILLEMBERG -23.8333 28.9667 1100 1999 9 16 0.0 29.1 12.4 21.0 10.8 -17.5 1.7 66.7 31.8 5.8 27.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 9 17 0.0 32.7 14.9 23.6 13.8 -19.5 2.0 51.8 28.0 6.8 26.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 9 18 0.0 20.3 11.5 15.7 5.9 -6.5 4.5 86.9 38.4 5.2 26.0 30090 GILLEMBERG -23.8333 28.9667 1100 1999 9 19 0.0 17.5 10.2 13.6 3.9 1.0 3.6 86.1 67.3 2.8 14.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 9 20 0.0 26.1 13.5 18.5 9.8 -13.5 1.9 78.0 43.0 5.2 25.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 9 21 0.8 29.0 13.0 21.3 11.0 -17.5 2.0 72.3 38.3 5.2 20.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 9 22 1.0 19.6 14.4 16.6 7.0 -10.0 4.7 86.5 66.4 3.5 17.5 30090 GILLEMBERG -23.8333 28.9667 1100 1999 9 23 0.0 24.2 12.6 18.0 8.4 -13.0 3.1 89.8 53.4 4.6 22.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 9 24 0.0 30.2 15.5 22.4 12.8 -18.5 3.4 81.1 39.3 6.6 24.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 9 25 0.0 34.2 18.6 26.5 16.4 -24.0 3.4 73.9 29.4 8.0 24.8 30090 GILLEMBERG -23.8333 28.9667 1100 1999 9 26 3.4 33.9 21.3 28.0 17.6 -24.0 3.4 74.1 28.0 7.1 15.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 9 27 2.2 30.0 16.6 22.9 13.3 -22.5 2.7 86.9 28.5 6.3 21.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 9 28 0.0 27.1 11.0 17.9 9.1 -9.5 3.7 82.9 30.9 6.9 28.5 30090 GILLEMBERG -23.8333 28.9667 1100 1999 9 29 0.0 17.2 10.9 13.7 4.1 2.0 3.9 82.4 52.0 3.0 7.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 9 30 2.2 15.0 9.5 11.7 2.2 8.5 2.5 83.9 59.3 2.1 6.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 1 0.0 19.5 9.7 14.1 4.6 -2.0 2.6 91.6 58.6 3.8 23.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 2 0.0 26.1 11.3 18.5 8.7 -11.5 3.3 83.9 35.3 6.5 30.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 3 0.0 19.4 12.5 16.1 6.0 -10.0 3.1 85.4 53.0 3.3 11.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 4 0.0 24.4 12.1 17.9 8.2 -11.5 3.7 83.7 45.1 6.0 30.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 5 0.0 26.2 13.2 19.1 9.7 -14.5 2.8 80.9 43.6 5.9 27.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 6 0.0 26.4 13.0 19.3 9.7 -15.0 2.8 88.0 41.4 5.8 27.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 7 0.0 23.8 11.3 17.7 7.6 -11.5 4.1 80.8 36.5 6.5 31.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 8 0.0 23.3 13.2 17.2 8.2 -10.5 2.8 79.7 33.8 6.2 32.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 9 0.0 26.8 8.9 17.7 7.9 -9.0 2.4 93.5 29.3 6.5 32.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 10 0.0 31.6 11.3 22.0 11.4 -15.5 2.5 75.2 19.6 8.1 32.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 11 0.0 32.4 14.4 23.8 13.4 -19.5 2.0 61.4 20.7 7.8 32.8 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 12 0.0 30.6 15.1 22.0 12.9 -18.0 3.7 76.9 28.1 8.4 31.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 13 0.0 22.4 12.9 16.4 7.6 -9.0 5.4 81.3 46.9 6.0 30.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 14 0.0 26.7 10.5 19.0 8.6 -12.5 2.3 89.0 40.6 6.1 31.2 160 CompNo Name Lat Long Alt Year M Day Rain Tmax Tmin Tave HU UCU U2 RHx RHn ETo Rs 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 15 0.0 30.9 14.9 23.0 12.9 -19.5 2.0 68.6 28.1 6.9 28.5 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 16 0.0 32.2 13.9 23.4 13.0 -20.0 3.2 67.1 19.4 8.9 31.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 17 0.0 23.4 13.9 18.5 8.6 -15.5 4.2 79.6 43.6 6.4 31.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 18 0.0 25.5 13.3 19.6 9.4 -16.5 3.0 86.7 38.4 6.5 32.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 19 0.0 29.3 16.2 22.3 12.7 -20.5 3.6 66.3 30.2 8.1 30.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 20 6.4 22.2 15.7 18.2 8.9 -16.5 4.8 94.7 62.6 4.3 22.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 21 1.4 23.4 14.8 18.3 9.1 -13.5 4.4 91.2 64.0 4.7 25.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 22 0.0 31.9 14.6 22.8 13.2 -18.0 2.4 93.6 31.3 7.5 32.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 23 0.0 32.1 17.0 24.8 14.5 -22.5 3.5 86.7 35.8 8.0 30.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 24 8.4 33.8 19.3 24.6 16.5 -24.0 3.7 85.0 38.7 7.8 25.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 25 1.6 26.4 15.8 20.7 11.1 -20.0 4.0 91.5 60.8 5.6 29.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 26 0.4 26.6 16.2 21.4 11.4 -20.5 3.3 90.8 58.3 5.3 25.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 27 0.0 28.7 16.5 23.2 12.6 -23.0 2.4 85.3 44.6 6.2 27.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 28 6.4 25.2 16.6 19.8 10.9 -19.5 4.0 87.6 50.5 5.8 26.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 29 0.0 24.0 14.9 19.0 9.5 -15.5 4.2 87.5 51.8 6.1 32.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 30 0.0 25.7 11.8 19.4 8.8 -15.5 2.7 85.3 32.6 6.8 33.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 10 31 0.0 29.3 16.1 22.2 12.7 -21.0 2.2 68.2 32.3 7.2 30.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 11 1 0.0 32.5 16.6 24.2 14.5 -22.0 2.6 79.3 21.7 8.4 32.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 11 2 0.0 30.6 17.9 24.1 14.2 -23.5 3.1 60.6 31.6 8.6 33.8 30090 GILLEMBERG -23.8333 28.9667 1100 1999 11 3 0.0 28.7 16.9 22.4 12.8 -23.0 3.6 78.4 38.8 7.8 34.0 30090 GILLEMBERG -23.8333 28.9667 1100 1999 11 4 0.0 34.2 16.8 25.6 15.5 -22.0 2.3 81.8 27.0 8.4 33.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 11 5 3.2 29.9 17.5 23.7 13.7 -22.5 3.0 88.9 38.2 6.6 25.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 11 6 0.2 22.4 15.6 18.6 9.0 -17.5 3.8 89.2 61.5 4.3 20.8 30090 GILLEMBERG -23.8333 28.9667 1100 1999 11 7 0.0 27.0 14.9 20.7 10.9 -18.0 2.4 91.7 47.3 5.5 25.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 11 8 0.0 31.9 17.5 24.8 14.7 -23.0 2.1 84.3 29.8 6.7 25.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 11 9 21.2 24.1 17.5 20.4 10.8 -21.5 3.6 93.3 57.2 5.5 28.8 30090 GILLEMBERG -23.8333 28.9667 1100 1999 11 10 14.6 29.1 17.4 21.7 13.2 -21.0 2.1 93.6 43.6 6.2 28.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 11 11 0.8 27.0 19.3 22.4 13.1 -24.0 3.3 81.8 43.2 6.1 23.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 11 12 0.0 29.2 16.5 22.9 12.9 -22.0 2.3 91.1 36.2 7.2 33.5 30090 GILLEMBERG -23.8333 28.9667 1100 1999 11 13 0.0 29.5 17.4 23.3 13.5 -23.0 2.7 75.7 30.2 8.0 34.5 30090 GILLEMBERG -23.8333 28.9667 1100 1999 11 14 0.0 29.8 16.5 23.4 13.2 -22.5 3.0 79.1 31.7 8.2 35.0 30090 GILLEMBERG -23.8333 28.9667 1100 1999 11 15 5.0 31.8 18.9 25.2 15.4 -24.0 2.7 65.4 29.8 8.7 34.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 11 16 0.0 30.4 19.9 25.0 15.2 -24.0 3.7 68.0 41.0 8.3 31.8 30090 GILLEMBERG -23.8333 28.9667 1100 1999 11 17 0.0 34.3 21.0 27.9 17.6 -24.0 2.7 71.3 29.7 8.6 30.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 11 18 3.4 33.4 22.0 25.9 17.7 -24.0 3.1 81.6 36.2 6.9 19.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 11 19 8.6 23.2 16.5 19.6 9.8 -20.5 2.4 95.4 63.9 2.5 7.7 161 CompNo Name Lat Long Alt Year M Day Rain Tmax Tmin Tave HU UCU U2 RHx RHn ETo Rs 30090 GILLEMBERG -23.8333 28.9667 1100 1999 11 20 9.8 24.4 16.4 19.1 10.4 -19.0 2.6 95.0 67.4 3.9 19.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 11 21 1.0 20.8 17.1 18.7 9.0 -19.0 1.4 93.2 78.8 1.8 6.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 11 22 3.0 22.1 16.3 18.8 9.2 -19.5 1.5 96.3 74.6 2.5 11.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 11 23 0.0 26.6 17.7 22.0 12.2 -21.5 2.2 94.5 58.0 5.4 26.8 30090 GILLEMBERG -23.8333 28.9667 1100 1999 11 24 3.8 26.7 18.5 21.7 12.6 -24.0 2.5 95.0 61.8 5.0 23.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 11 25 8.6 22.8 18.6 20.0 10.7 -24.0 2.3 95.4 75.7 2.7 12.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 11 26 20.6 25.0 18.0 20.7 11.5 -24.0 1.7 96.2 72.0 3.2 14.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 11 27 49.0 26.6 18.0 20.3 12.3 -24.0 2.3 96.8 66.8 3.1 11.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 11 28 10.2 26.2 16.9 20.8 11.6 -21.5 2.0 97.7 64.4 4.4 21.5 30090 GILLEMBERG -23.8333 28.9667 1100 1999 11 29 1.6 23.7 17.3 20.1 10.5 -23.0 3.0 92.3 76.5 3.1 14.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 11 30 4.0 20.5 16.3 18.0 8.4 -17.5 2.5 94.1 74.7 2.4 10.5 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 1 0.0 23.1 16.0 18.9 9.5 -18.5 2.1 91.2 66.5 3.8 18.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 2 0.0 24.7 15.9 20.3 10.3 -20.5 2.6 90.8 59.6 5.6 30.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 3 1.8 26.0 17.1 20.7 11.6 -21.0 2.3 94.3 61.2 5.1 25.5 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 4 0.4 24.5 18.6 21.2 11.5 -24.0 1.8 94.9 70.0 3.4 15.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 5 0.0 28.1 18.4 22.6 13.2 -24.0 2.2 94.5 59.0 5.8 28.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 6 5.0 29.6 17.0 22.7 13.3 -21.5 2.0 95.4 57.2 5.9 26.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 7 0.2 25.7 16.9 21.2 11.3 -21.0 2.7 95.4 59.7 5.2 26.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 8 0.0 27.3 18.3 22.3 12.8 -24.0 2.7 94.4 52.2 6.1 31.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 9 0.0 26.3 18.1 21.8 12.2 -24.0 2.7 95.4 57.2 4.9 23.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 10 0.0 25.8 18.3 21.6 12.1 -24.0 3.1 86.6 54.7 5.1 26.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 11 0.2 27.3 17.9 22.1 12.6 -23.5 2.2 94.4 64.6 5.6 27.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 12 12.4 24.0 16.9 20.5 10.4 -23.0 2.2 97.3 58.4 3.4 15.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 13 3.2 23.0 16.6 19.6 9.8 -20.0 2.9 97.3 70.9 3.7 18.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 14 0.0 26.7 13.1 20.5 9.9 -17.0 2.1 96.4 70.9 6.7 35.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 15 0.0 28.0 16.5 21.9 12.2 -21.0 1.8 95.4 38.5 5.9 28.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 16 0.0 29.3 17.2 22.9 13.2 -23.0 2.3 94.4 39.8 6.9 34.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 17 0.0 26.7 16.4 21.6 11.6 -22.5 2.3 89.5 39.8 6.6 35.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 18 0.0 29.3 16.9 22.9 13.1 -21.5 2.3 90.5 47.2 7.1 35.4 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 19 0.0 31.4 17.7 24.5 14.5 -23.0 2.0 94.4 52.2 6.6 29.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 20 0.2 33.5 20.3 26.0 16.9 -24.0 2.2 81.8 47.2 6.7 27.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 21 2.4 29.4 17.8 22.4 13.6 -23.0 2.2 95.4 46.0 4.9 19.5 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 22 2.4 27.8 18.0 22.3 12.9 -22.5 2.7 89.3 72.3 4.8 23.2 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 23 0.0 30.3 19.3 24.7 14.8 -24.0 2.7 90.6 54.4 6.4 28.1 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 24 0.0 31.9 18.6 25.9 15.3 -24.0 1.9 92.7 44.9 7.6 35.3 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 25 0.0 32.9 20.2 27.2 16.5 -24.0 2.6 84.8 40.9 8.0 32.8 162 CompNo Name Lat Long Alt Year M Day Rain Tmax Tmin Tave HU UCU U2 RHx RHn ETo Rs 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 26 0.0 31.2 19.7 25.4 15.4 -24.0 2.7 83.6 47.9 7.2 30.0 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 27 0.0 31.6 20.8 26.3 16.2 -24.0 3.2 89.0 50.4 7.4 30.6 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 28 35.6 30.6 16.9 24.7 13.7 -23.5 3.4 98.0 51.1 6.6 28.9 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 29 12.2 28.1 16.8 22.3 12.4 -21.0 1.5 98.3 64.2 5.1 25.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 30 0.0 30.1 19.8 24.8 15.0 -24.0 1.4 94.6 56.4 6.8 33.7 30090 GILLEMBERG -23.8333 28.9667 1100 1999 12 31 3.2 30.1 20.3 24.3 15.2 -24.0 1.6 93.5 58.4 6.0 28.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 1 2.6 28.5 19.5 23.0 14.0 -24.0 2.3 95.7 66.6 5.4 26.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 2 0.2 29.0 20.2 23.4 14.6 -24.0 2.2 94.4 64.0 6.1 30.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 3 20.2 25.2 19.2 21.5 12.2 -24.0 3.5 98.4 74.8 4.2 22.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 4 0.0 26.9 19.4 22.1 13.2 -24.0 2.8 93.5 70.7 5.3 27.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 5 0.0 27.3 18.7 22.4 13.0 -24.0 1.8 92.5 65.1 4.9 23.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 6 0.0 29.7 19.6 24.4 14.7 -24.0 1.6 94.3 62.2 6.3 31.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 7 3.4 25.7 19.9 22.8 12.8 -24.0 1.5 93.5 71.7 3.6 16.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 8 17.6 30.3 18.7 23.0 14.5 -24.0 2.3 96.6 57.2 6.6 31.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 9 0.0 25.3 18.2 21.0 11.7 -24.0 2.2 93.4 71.6 4.0 19.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 10 0.2 27.6 18.7 22.6 13.1 -24.0 1.6 94.8 65.3 4.8 23.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 11 0.0 27.7 18.1 22.9 12.9 -24.0 2.1 97.8 50.4 6.5 33.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 12 0.0 29.3 18.2 23.5 13.7 -24.0 2.0 93.7 51.0 6.4 30.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 13 0.0 24.8 17.9 21.1 11.3 -23.5 3.9 84.0 61.3 5.2 23.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 14 0.0 23.7 15.7 19.6 9.7 -18.0 2.7 85.1 63.8 4.2 19.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 15 0.0 21.7 15.8 18.7 8.7 -18.5 3.4 88.8 69.3 3.3 14.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 16 34.2 20.8 17.4 19.0 9.1 -21.0 2.5 97.2 87.3 1.9 9.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 17 6.8 19.8 17.2 18.1 8.5 -17.0 4.4 95.5 77.4 2.5 12.0 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 18 1.0 19.6 15.5 17.4 7.5 -14.5 2.0 91.1 69.6 2.9 13.6 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 19 0.0 21.7 12.9 17.9 7.3 -14.5 1.8 95.1 59.5 4.0 21.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 20 0.0 24.1 14.9 19.0 9.5 -15.5 2.1 90.2 58.9 4.6 23.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 21 0.0 25.9 14.1 20.0 10.0 -17.0 1.7 93.8 55.2 6.2 34.6 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 22 0.0 27.7 15.2 21.6 11.5 -19.0 1.5 91.1 53.5 6.4 34.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 23 0.0 27.0 15.4 21.6 11.2 -21.0 2.1 91.9 50.8 6.4 33.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 24 0.0 26.5 17.2 21.4 11.9 -22.0 2.6 89.1 53.3 6.2 31.0 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 25 0.0 18.5 16.7 17.7 7.6 -3.0 1.7 83.7 75.9 0.7 0.0 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 26 7.8 28.6 20.1 24.3 14.3 -23.4 2.3 95.0 49.2 3.9 12.8 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 27 8.6 25.3 19.7 23.2 12.5 -13.0 2.3 96.9 71.8 3.8 18.8 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 28 0.0 28.7 18.9 22.7 13.8 -24.0 1.4 97.8 54.8 5.5 26.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 29 0.0 31.3 17.4 23.9 14.4 -22.5 1.6 92.0 37.0 7.4 35.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 30 0.0 29.5 18.6 23.9 14.1 -24.0 1.8 94.7 54.7 6.4 31.5 163 CompNo Name Lat Long Alt Year M Day Rain Tmax Tmin Tave HU UCU U2 RHx RHn ETo Rs 30090 GILLEMBERG -23.8333 28.9667 1100 2000 1 31 0.0 31.6 17.9 24.9 14.7 -23.5 1.8 97.4 48.8 7.2 35.0 30090 GILLEMBERG -23.8333 28.9667 1100 2000 2 1 0.0 30.4 20.5 24.8 15.4 -24.0 2.0 90.9 55.7 6.7 31.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 2 2 6.0 29.8 19.5 23.7 14.6 -24.0 2.2 95.9 61.4 5.4 24.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 2 3 0.0 29.1 17.0 22.8 13.1 -22.0 2.2 95.6 50.8 6.0 28.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 2 4 0.0 27.2 14.9 21.8 11.0 -20.5 2.4 93.8 58.4 5.6 28.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 2 5 0.2 26.9 17.9 22.0 12.4 -23.5 3.3 90.0 63.5 4.2 16.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 2 6 20.8 20.9 19.0 20.1 10.0 -24.0 3.0 96.3 88.7 5.7 41.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 2 7 22.8 25.7 19.2 22.3 12.5 -24.0 3.4 97.7 76.3 4.1 22.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 2 8 17.6 24.4 18.9 21.5 11.7 -24.0 2.7 97.0 80.9 2.4 10.0 30090 GILLEMBERG -23.8333 28.9667 1100 2000 2 9 5.2 23.6 19.9 21.2 11.8 -24.0 1.8 96.4 81.9 1.9 7.6 30090 GILLEMBERG -23.8333 28.9667 1100 2000 2 10 33.2 25.3 19.8 21.9 12.5 -24.0 2.6 98.1 74.4 3.7 18.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 2 11 26.4 26.6 19.9 22.4 13.3 -24.0 2.0 97.4 71.1 4.2 20.6 30090 GILLEMBERG -23.8333 28.9667 1100 2000 2 12 9.8 27.4 19.0 22.3 13.2 -24.0 1.6 98.2 66.0 5.0 25.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 2 13 0.0 26.2 20.3 22.1 13.2 -24.0 1.9 94.7 75.9 3.9 19.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 2 14 0.0 26.7 19.9 22.6 13.3 -24.0 2.4 93.8 65.9 4.9 23.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 2 15 17.4 28.0 19.5 22.6 13.7 -24.0 1.9 97.0 65.3 4.9 23.8 30090 GILLEMBERG -23.8333 28.9667 1100 2000 2 16 0.2 25.2 18.7 21.5 11.9 -24.0 3.7 97.5 71.5 4.3 23.0 30090 GILLEMBERG -23.8333 28.9667 1100 2000 2 17 0.0 24.1 16.0 20.0 10.0 -22.0 3.3 93.3 68.4 4.8 27.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 2 18 0.0 25.7 14.4 20.1 10.1 -16.5 2.6 95.7 63.7 4.8 25.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 2 19 0.0 26.0 16.0 21.1 11.0 -20.5 1.7 96.6 67.9 4.3 22.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 2 20 0.0 29.2 17.4 22.7 13.3 -23.0 1.4 96.5 56.5 4.8 22.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 2 21 0.4 28.9 19.8 23.9 14.3 -24.0 1.6 95.5 51.8 5.6 26.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 2 22 0.0 27.2 16.2 22.2 11.7 -22.0 2.1 95.2 58.7 4.7 22.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 2 23 0.0 25.6 19.8 22.4 12.7 -24.0 4.0 83.7 63.9 4.4 16.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 2 24 36.2 22.5 16.7 18.9 9.6 -20.0 3.9 97.1 73.9 2.3 7.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 2 25 33.0 23.4 17.3 19.8 10.3 -21.0 4.1 97.7 71.0 3.0 12.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 2 26 0.0 24.1 17.5 20.3 10.8 -21.0 2.8 91.5 74.2 2.9 12.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 2 27 0.0 25.0 17.2 20.9 11.1 -22.0 2.1 96.7 71.4 3.4 16.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 2 28 0.0 24.6 15.6 20.1 10.1 -20.0 3.0 94.5 59.1 4.8 24.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 2 29 0.0 25.7 17.3 21.2 11.5 -20.5 2.3 94.8 64.8 4.8 25.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 1 0.0 27.4 17.8 22.5 12.6 -23.5 1.5 95.1 59.6 5.1 26.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 2 0.0 27.9 19.7 23.0 13.8 -24.0 2.5 93.1 59.2 5.1 23.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 3 0.0 26.6 20.0 22.5 13.3 -24.0 1.9 90.9 64.7 4.5 21.6 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 4 0.0 27.3 19.2 22.6 13.3 -24.0 1.4 89.5 63.6 4.1 19.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 5 5.0 27.3 19.1 21.6 13.2 -24.0 1.4 94.6 61.8 4.2 19.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 6 0.2 27.5 18.4 21.8 12.9 -24.0 1.5 93.7 59.9 4.9 24.7 164 CompNo Name Lat Long Alt Year M Day Rain Tmax Tmin Tave HU UCU U2 RHx RHn ETo Rs 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 7 10.2 25.2 17.0 20.7 11.1 -22.0 1.6 97.3 69.5 3.6 18.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 8 0.0 25.6 15.4 20.3 10.5 -19.5 1.8 95.6 60.2 4.2 21.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 9 0.2 26.3 15.0 20.7 10.6 -18.0 1.4 99.3 56.9 4.3 22.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 10 0.0 29.4 16.0 22.3 12.7 -21.0 1.9 97.0 43.0 6.0 29.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 11 0.0 28.3 18.1 22.9 13.2 -24.0 2.7 96.4 55.0 5.5 26.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 12 0.6 26.4 17.5 21.1 11.9 -21.5 2.8 94.6 60.6 4.7 22.6 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 13 1.2 25.5 18.0 20.9 11.8 -24.0 1.9 96.1 61.9 3.6 16.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 14 0.0 27.0 16.9 21.6 12.0 -22.0 1.6 95.3 59.9 4.7 24.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 15 0.0 28.1 16.8 22.6 12.4 -22.0 1.3 97.7 59.5 4.4 22.6 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 16 1.6 27.3 19.0 22.6 13.1 -24.0 1.4 96.0 63.8 3.8 17.8 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 17 1.6 25.9 19.8 22.4 12.8 -24.0 1.4 96.1 73.7 3.5 17.8 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 18 4.4 26.5 19.7 21.9 13.1 -24.0 1.5 98.0 68.9 2.8 12.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 19 29.2 22.5 18.1 20.0 10.3 -24.0 1.7 98.5 76.6 2.1 9.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 20 15.6 23.6 18.8 19.9 11.2 -24.0 1.7 96.6 75.8 2.1 8.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 21 1.2 23.6 18.2 20.4 10.9 -24.0 2.4 94.7 70.9 3.5 18.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 22 5.8 26.3 17.5 21.3 11.9 -21.5 1.4 93.9 64.1 4.0 20.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 23 0.4 26.8 16.9 21.6 11.9 -21.5 1.7 96.9 58.4 4.6 24.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 24 0.0 26.9 18.0 21.8 12.4 -23.5 1.3 96.0 56.9 3.9 18.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 25 0.0 27.1 16.4 21.1 11.7 -22.0 1.2 97.2 57.5 4.5 24.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 26 0.2 28.1 16.8 21.2 12.4 -22.0 1.8 95.4 59.2 4.4 21.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 27 0.0 21.7 16.7 19.0 9.2 -20.5 2.1 95.8 73.3 2.1 8.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 28 0.0 22.8 14.3 18.1 8.6 -14.0 1.1 97.9 71.7 2.3 11.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 29 0.0 25.4 15.7 20.2 10.5 -20.5 2.1 94.0 59.4 3.6 16.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 30 0.0 24.3 17.2 20.0 10.7 -20.5 1.3 94.4 69.9 2.6 12.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 3 31 5.4 25.9 17.7 20.0 11.8 -21.5 1.9 95.7 63.6 3.0 12.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 4 1 0.0 23.6 16.2 19.3 9.9 -18.5 2.5 93.5 61.9 3.5 17.0 30090 GILLEMBERG -23.8333 28.9667 1100 2000 4 2 0.0 24.6 15.0 18.8 9.8 -15.0 1.5 94.4 57.0 3.9 21.0 30090 GILLEMBERG -23.8333 28.9667 1100 2000 4 3 0.0 26.4 13.9 19.8 10.1 -17.0 1.1 98.2 55.7 4.0 22.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 4 4 0.0 26.6 15.2 20.8 10.9 -18.0 1.2 96.6 54.6 4.0 21.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 4 5 58.4 18.2 13.5 15.4 5.8 -5.0 2.4 98.9 79.3 1.6 6.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 4 6 5.6 18.8 13.1 15.3 5.9 -7.0 1.4 98.1 73.2 2.2 12.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 4 7 0.2 22.1 12.5 16.5 7.3 -9.5 1.2 98.7 60.7 2.8 15.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 4 8 0.2 23.3 12.5 17.4 7.9 -12.0 1.2 98.3 55.8 3.3 18.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 4 9 0.0 25.9 12.5 18.8 9.2 -14.0 1.6 95.9 47.9 4.3 23.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 4 10 0.0 23.2 12.5 18.0 7.9 -14.0 1.7 96.7 57.2 3.8 22.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 4 11 0.0 23.8 14.0 18.7 8.9 -15.5 1.3 97.8 62.4 3.3 18.8 165 CompNo Name Lat Long Alt Year M Day Rain Tmax Tmin Tave HU UCU U2 RHx RHn ETo Rs 30090 GILLEMBERG -23.8333 28.9667 1100 2000 4 12 0.0 26.5 13.6 19.5 10.0 -15.0 1.3 97.8 47.2 4.0 21.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 4 13 0.0 26.6 14.1 19.9 10.3 -17.5 1.7 92.7 49.2 4.1 20.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 4 14 31.4 22.7 14.8 18.1 8.7 -14.0 1.9 98.7 66.1 2.7 14.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 4 15 15.8 18.1 13.7 15.5 5.9 -5.5 1.3 97.8 81.7 1.3 5.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 4 16 5.0 21.2 13.5 16.1 7.3 -7.5 1.6 96.7 67.1 2.7 15.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 4 17 0.2 24.4 11.5 17.0 8.0 -8.0 1.0 99.1 56.5 3.6 22.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 4 18 0.2 24.1 13.2 18.2 8.7 -12.5 2.1 95.0 47.8 4.0 21.6 30090 GILLEMBERG -23.8333 28.9667 1100 2000 4 19 0.0 21.8 13.2 16.8 7.5 -9.5 1.9 95.7 60.5 2.8 14.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 4 20 0.0 20.3 12.9 16.2 6.6 -9.5 1.7 94.3 64.5 2.5 13.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 4 21 7.2 20.0 13.1 16.1 6.6 -9.0 1.1 98.3 73.4 1.8 9.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 4 22 0.4 25.1 12.1 17.7 8.6 -11.0 1.0 99.1 47.8 3.7 22.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 4 23 0.0 26.1 11.9 18.7 9.0 -12.0 1.2 95.7 46.1 3.7 21.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 4 24 0.0 26.8 13.4 19.6 10.1 -15.0 1.7 86.6 45.1 4.3 22.8 30090 GILLEMBERG -23.8333 28.9667 1100 2000 4 25 0.0 25.0 13.3 19.1 9.1 -15.5 1.8 97.0 56.6 3.7 21.8 30090 GILLEMBERG -23.8333 28.9667 1100 2000 4 26 0.0 28.0 13.1 20.0 10.5 -14.5 1.5 95.5 44.0 4.2 22.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 4 27 0.0 28.3 13.7 21.1 11.0 -19.0 1.9 89.7 39.4 4.6 22.6 30090 GILLEMBERG -23.8333 28.9667 1100 2000 4 28 0.0 18.2 13.5 15.2 5.8 -5.5 3.4 88.4 67.3 2.8 16.0 30090 GILLEMBERG -23.8333 28.9667 1100 2000 4 29 0.0 18.8 12.8 15.3 5.8 -5.5 2.9 89.4 66.9 2.4 11.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 4 30 0.0 19.5 11.5 15.2 5.5 -5.5 1.8 95.9 61.1 2.7 17.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 1 0.0 22.9 8.7 15.4 5.8 -4.0 1.1 98.4 53.8 3.2 21.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 2 0.0 24.5 10.2 16.7 7.4 -7.0 1.3 95.1 38.8 3.6 21.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 3 0.0 22.1 10.3 16.8 6.2 -10.5 2.3 91.3 53.9 3.4 20.0 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 4 7.2 21.6 11.9 16.5 6.8 -9.0 2.1 97.2 66.9 2.4 12.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 5 38.8 20.4 10.9 14.2 5.6 -0.5 2.1 97.9 69.0 2.5 16.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 6 0.2 20.9 8.4 14.0 4.6 0.5 1.3 99.6 54.2 2.9 20.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 7 0.0 21.1 9.0 14.5 5.0 -2.0 1.9 91.1 56.7 3.2 21.0 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 8 0.0 20.8 8.6 14.4 4.7 -1.5 1.6 95.0 59.9 2.6 16.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 9 0.0 22.7 10.0 16.1 6.3 -6.5 1.6 96.0 53.8 3.0 17.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 10 0.0 20.7 9.5 14.6 5.1 -2.5 1.3 97.9 55.7 2.6 16.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 11 0.0 22.0 8.1 15.2 5.0 -4.0 1.3 96.8 56.5 2.9 20.6 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 12 0.0 20.2 11.3 14.9 5.7 -2.0 1.9 94.9 58.1 2.5 13.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 13 0.0 18.2 3.6 10.9 0.9 9.5 1.4 95.8 49.6 2.7 20.6 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 14 0.0 19.7 4.2 11.5 2.0 6.5 1.4 96.6 42.7 2.9 20.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 15 0.0 20.9 6.8 13.3 3.8 3.0 1.6 87.6 42.3 3.1 18.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 16 0.0 19.9 7.5 13.7 3.7 0.5 1.4 93.2 54.2 2.7 19.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 17 0.0 21.3 7.1 12.9 4.2 5.0 1.3 99.2 45.9 2.7 17.8 166 CompNo Name Lat Long Alt Year M Day Rain Tmax Tmin Tave HU UCU U2 RHx RHn ETo Rs 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 18 0.0 22.9 7.6 14.9 5.2 -1.0 1.4 96.9 46.0 2.9 17.0 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 19 0.0 22.3 8.7 15.2 5.5 -3.5 1.4 92.2 48.6 3.0 19.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 20 0.0 21.8 10.7 15.3 6.3 -3.5 1.3 90.9 51.6 2.8 19.0 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 21 0.0 23.2 7.7 14.7 5.4 -1.0 1.2 93.0 48.0 2.8 18.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 22 0.0 22.6 8.0 14.7 5.3 -1.5 1.2 89.0 49.5 2.8 19.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 23 0.0 22.9 7.2 15.0 5.1 -2.0 1.5 91.4 48.6 3.0 18.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 24 0.0 20.7 11.9 14.9 6.3 -4.5 2.3 91.7 55.8 3.0 18.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 25 0.0 21.2 10.6 15.6 5.9 -5.5 2.4 91.2 57.8 3.0 18.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 26 5.4 20.7 9.3 14.3 5.0 -3.0 1.7 95.3 63.8 2.2 12.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 27 0.0 21.6 5.8 13.0 3.7 3.0 1.4 96.0 41.1 2.9 19.6 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 28 0.0 18.7 7.9 12.3 3.3 5.5 1.4 96.3 61.9 1.9 11.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 29 0.0 20.5 5.2 12.3 2.9 5.0 1.0 94.3 47.5 2.5 19.0 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 30 0.0 22.9 6.4 13.2 4.6 3.0 1.1 89.0 42.2 2.8 19.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 5 31 0.0 22.9 5.7 14.3 4.3 0.5 1.6 82.5 31.9 3.4 19.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 6 1 0.0 20.1 5.9 13.2 3.0 2.5 0.6 95.6 51.8 2.1 18.6 30090 GILLEMBERG -23.8333 28.9667 1100 2000 6 2 0.0 20.3 6.7 12.6 3.5 4.5 2.1 97.6 52.8 2.7 18.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 6 3 0.0 20.4 5.4 12.6 2.9 4.0 0.8 98.1 48.0 2.0 17.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 6 4 0.2 18.8 7.7 12.1 3.2 6.0 1.5 94.2 53.0 2.3 11.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 6 5 0.2 16.4 9.4 13.1 2.9 2.5 1.8 93.1 71.3 1.6 8.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 6 6 0.0 17.0 6.3 11.2 1.7 11.0 1.9 99.2 70.4 1.4 6.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 6 7 0.0 18.9 6.7 12.5 2.8 4.5 2.0 98.5 61.9 2.3 16.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 6 8 0.2 20.0 8.3 12.8 4.1 3.0 1.7 100.0 57.1 2.5 16.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 6 9 0.0 22.2 7.2 14.2 4.7 0.0 1.9 96.4 50.7 2.9 18.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 6 10 0.0 19.6 8.9 13.8 4.3 -0.5 1.7 97.2 65.3 2.0 11.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 6 11 0.0 22.7 7.9 14.7 5.3 -2.0 1.7 94.6 43.8 3.1 17.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 6 12 0.0 24.1 9.5 15.8 6.8 -5.5 1.8 88.5 40.4 3.3 15.0 30090 GILLEMBERG -23.8333 28.9667 1100 2000 6 13 0.0 20.2 10.2 14.0 5.2 0.5 1.6 95.9 55.7 2.5 16.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 6 14 0.0 23.0 7.8 13.9 5.4 1.5 1.5 98.1 45.9 3.0 17.8 30090 GILLEMBERG -23.8333 28.9667 1100 2000 6 15 0.0 22.6 7.1 14.5 4.8 -1.0 1.7 96.1 41.2 3.1 18.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 6 16 0.0 20.9 9.8 15.7 5.3 -6.0 2.1 88.2 55.5 2.3 9.6 30090 GILLEMBERG -23.8333 28.9667 1100 2000 6 17 0.0 22.4 13.4 16.9 7.9 -9.0 2.0 90.9 49.3 2.8 13.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 6 18 0.0 19.7 13.3 15.6 6.5 -7.0 1.7 89.9 60.4 2.1 8.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 6 19 0.0 18.5 12.0 14.7 5.2 -2.5 2.2 90.4 58.8 2.3 14.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 6 20 9.4 13.2 11.0 11.9 2.1 9.0 1.8 96.8 87.6 1.7 19.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 6 21 0.2 17.1 9.5 12.9 3.3 4.5 1.6 100.0 78.8 1.3 7.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 6 22 0.2 19.8 8.1 13.1 4.0 3.0 1.7 99.7 62.8 2.3 17.0 167 CompNo Name Lat Long Alt Year M Day Rain Tmax Tmin Tave HU UCU U2 RHx RHn ETo Rs 30090 GILLEMBERG -23.8333 28.9667 1100 2000 6 23 8.8 23.9 8.0 15.6 5.9 -5.0 2.0 96.3 50.4 3.0 17.0 30090 GILLEMBERG -23.8333 28.9667 1100 2000 6 24 0.6 15.4 9.6 13.0 2.5 4.0 1.7 98.7 83.7 1.0 4.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 6 25 0.0 19.8 7.5 12.6 3.6 4.0 1.7 99.7 55.9 2.4 15.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 6 26 0.2 22.3 5.8 13.5 4.1 2.5 1.7 95.6 44.3 3.0 18.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 6 27 0.0 22.1 8.2 14.4 5.2 -2.0 1.5 96.8 51.0 2.9 17.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 6 28 0.0 20.4 6.9 13.3 3.6 3.0 1.9 98.2 50.6 2.7 17.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 6 29 0.0 20.9 5.5 12.8 3.2 2.5 1.8 97.2 52.9 2.7 17.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 6 30 0.0 17.2 5.8 11.2 1.5 9.5 1.7 95.8 56.4 2.2 14.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 1 0.0 17.9 6.2 11.1 2.0 10.0 1.2 97.6 53.8 1.8 11.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 2 0.0 19.8 7.1 12.4 3.5 5.5 1.3 96.1 43.8 2.5 18.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 3 0.0 18.6 5.6 11.2 2.1 8.5 1.3 97.1 56.7 2.1 16.0 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 4 0.0 18.8 4.7 10.9 1.8 9.0 1.2 97.2 52.5 2.2 17.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 5 0.0 20.1 5.4 11.9 2.8 6.0 1.9 94.5 46.4 2.8 18.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 6 0.0 18.2 3.9 11.2 1.0 8.0 2.0 93.7 52.1 2.5 17.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 7 0.0 18.2 5.4 11.8 1.8 7.5 1.7 96.3 54.4 2.4 18.6 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 8 0.0 18.0 6.5 11.3 2.3 9.5 1.6 95.8 51.0 2.3 15.8 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 9 0.0 19.4 6.2 11.7 2.8 7.0 1.3 97.6 53.2 2.2 16.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 10 0.0 21.0 5.8 13.0 3.4 3.5 1.4 97.1 49.2 2.6 18.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 11 0.0 20.9 8.9 14.7 4.9 -3.5 1.3 100.0 59.7 2.2 14.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 12 0.0 18.4 9.8 13.3 4.1 2.0 1.2 100.0 99.1 1.2 10.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 13 0.8 19.6 9.0 13.5 4.3 2.0 1.2 100.0 99.6 1.3 11.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 14 0.0 22.0 8.0 14.7 5.0 -3.0 1.3 N/A N/A 2.7 16.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 15 0.0 23.8 7.9 16.0 5.8 -6.0 2.0 N/A N/A 3.5 19.0 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 16 0.0 15.1 4.2 8.9 0.0 17.5 2.0 100.0 100.0 1.5 20.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 17 0.0 18.5 2.3 9.8 0.4 11.0 2.3 100.0 98.8 1.5 19.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 18 0.0 24.0 2.1 11.5 3.0 5.5 1.6 100.0 98.8 2.0 20.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 19 0.0 25.6 4.6 14.8 5.1 0.0 1.7 N/A N/A 3.0 20.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 20 0.0 21.4 5.1 12.9 3.3 5.0 2.3 100.0 99.5 1.8 19.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 21 0.0 17.3 1.5 9.3 0.0 9.5 2.4 100.0 55.3 2.4 17.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 22 0.0 20.3 4.2 11.7 2.2 5.5 2.2 97.2 42.0 3.1 19.0 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 23 0.0 15.1 6.3 10.6 0.7 12.5 2.8 95.9 60.9 2.1 15.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 24 0.0 18.0 6.5 11.4 2.2 9.0 2.7 94.8 53.0 2.7 19.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 25 0.0 23.0 3.0 12.3 3.0 4.0 2.2 98.4 33.7 3.6 19.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 26 0.0 18.2 9.5 13.1 3.9 2.5 2.5 90.7 55.7 2.6 16.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 27 0.0 18.4 8.2 12.2 3.3 6.0 3.0 96.2 51.4 2.5 13.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 28 0.0 20.3 5.3 12.3 2.8 4.5 2.9 94.6 48.6 3.1 19.9 168 CompNo Name Lat Long Alt Year M Day Rain Tmax Tmin Tave HU UCU U2 RHx RHn ETo Rs 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 29 0.0 22.1 4.2 13.0 3.1 1.5 2.3 93.4 38.4 3.5 19.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 30 0.0 21.4 8.3 14.3 4.9 -0.5 2.4 76.1 53.3 3.3 20.6 30090 GILLEMBERG -23.8333 28.9667 1100 2000 7 31 0.0 22.3 6.8 14.5 4.5 -2.0 2.5 89.3 36.5 3.6 19.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 1 0.0 21.5 10.6 15.7 6.1 -5.5 1.7 81.1 42.0 2.8 19.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 2 0.0 19.3 9.3 13.4 4.3 0.5 1.3 93.2 48.7 2.9 21.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 3 0.0 23.0 7.2 14.6 5.1 -2.0 2.1 96.8 45.4 3.6 20.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 4 0.0 26.4 9.7 17.6 8.0 -9.0 3.8 84.5 33.1 5.5 21.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 5 0.0 21.1 12.4 15.6 6.8 -6.5 2.0 93.9 56.3 3.1 19.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 6 0.0 22.8 11.3 15.4 7.0 -4.5 2.1 95.8 52.3 3.3 18.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 7 0.0 20.1 9.6 14.1 4.8 -0.5 1.8 93.6 55.8 2.6 14.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 8 0.0 23.1 6.6 14.4 4.8 -1.0 1.4 90.9 36.0 3.5 21.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 9 0.0 22.5 6.8 14.1 4.7 0.0 1.6 86.7 42.6 3.6 21.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 10 0.0 22.5 6.6 14.4 4.6 -0.5 1.9 82.1 39.2 3.9 22.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 11 0.0 23.6 7.5 14.9 5.5 -1.5 2.6 85.8 38.8 4.3 20.6 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 12 0.0 21.3 9.4 15.3 5.4 -5.5 2.9 79.8 49.1 3.9 21.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 13 0.0 21.6 7.8 14.6 4.7 -1.5 1.9 96.9 40.4 3.3 16.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 14 0.0 21.0 8.0 14.0 4.5 0.0 2.0 95.9 43.8 3.6 22.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 15 0.0 21.0 5.8 12.8 3.4 3.5 3.5 94.1 46.6 4.0 23.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 16 0.0 22.3 7.1 13.6 4.7 2.0 1.8 97.3 43.2 3.7 23.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 17 0.0 24.9 5.5 15.1 5.2 -2.0 1.4 88.0 37.5 3.9 23.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 18 0.0 24.3 10.2 17.0 7.3 -9.0 3.3 73.9 38.0 5.2 23.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 19 0.0 19.7 9.5 14.6 4.6 -4.0 1.9 88.4 52.3 3.4 22.6 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 20 0.0 19.8 7.8 13.2 3.8 3.0 2.7 91.3 48.0 3.7 22.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 21 0.0 21.8 7.4 14.5 4.6 -2.0 2.5 89.1 44.0 4.1 23.8 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 22 0.0 25.2 8.3 16.6 6.8 -6.5 1.8 82.8 37.7 4.4 23.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 23 0.0 24.9 11.1 17.6 8.0 -8.5 1.9 81.8 40.1 4.4 22.6 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 24 0.0 24.9 10.1 17.4 7.5 -9.0 1.7 95.5 45.5 3.9 21.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 25 0.0 25.1 10.2 17.6 7.7 -9.5 1.6 84.3 45.2 4.1 21.8 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 26 0.0 25.1 11.0 17.9 8.1 -11.5 1.5 90.9 42.2 3.9 20.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 27 0.0 25.4 10.2 17.8 7.8 -11.0 3.7 94.1 41.0 5.1 22.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 28 0.0 26.4 9.6 18.1 8.0 -11.0 2.5 94.3 36.3 4.9 22.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 29 0.0 26.6 9.0 18.0 7.8 -10.0 2.3 90.8 36.2 5.0 23.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 30 0.0 29.5 10.0 19.2 9.7 -9.0 2.0 77.5 30.6 5.6 24.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 8 31 0.0 30.9 11.2 20.5 11.0 -12.0 1.4 60.6 26.6 5.4 25.8 30090 GILLEMBERG -23.8333 28.9667 1100 2000 9 1 0.0 31.3 12.0 22.0 11.6 -16.5 1.8 55.8 27.3 6.0 25.6 30090 GILLEMBERG -23.8333 28.9667 1100 2000 9 2 0.0 30.1 14.1 22.0 12.1 -19.0 2.0 82.3 30.3 5.5 22.9 169 CompNo Name Lat Long Alt Year M Day Rain Tmax Tmin Tave HU UCU U2 RHx RHn ETo Rs 30090 GILLEMBERG -23.8333 28.9667 1100 2000 9 3 0.0 31.3 13.1 21.9 12.2 -17.0 1.7 70.1 26.3 5.5 22.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 9 4 0.0 28.1 14.4 20.6 11.3 -15.5 2.3 86.4 36.2 4.9 18.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 9 5 0.0 22.7 13.3 17.6 8.0 -13.0 1.8 91.6 59.7 3.0 14.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 9 6 0.0 29.1 12.9 20.0 11.0 -14.0 2.3 93.7 41.6 5.0 20.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 9 7 0.0 30.0 11.5 21.2 10.7 -15.0 2.2 81.8 23.5 6.4 27.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 9 8 0.0 21.4 12.3 16.3 6.9 -9.0 1.3 87.0 46.2 4.2 25.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 9 9 0.0 24.8 11.0 17.6 7.9 -10.5 1.8 88.1 41.7 4.8 26.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 9 10 0.0 31.5 11.0 21.6 11.2 -15.0 2.6 82.5 29.9 6.5 24.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 9 11 0.0 22.3 11.5 17.2 6.9 -12.5 2.5 81.5 40.2 4.7 24.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 9 12 0.0 20.0 11.0 14.5 5.5 -2.5 3.0 86.8 56.0 3.7 19.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 9 13 0.0 29.2 9.0 19.1 9.1 -11.0 3.6 94.6 41.8 6.1 25.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 9 14 0.0 33.1 18.1 25.6 15.6 -24.0 1.8 72.7 29.1 6.4 25.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 9 15 0.0 19.8 12.4 16.2 6.1 -10.5 3.0 90.2 68.6 3.6 23.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 9 16 0.0 19.3 12.1 15.5 5.7 -7.0 2.3 91.2 71.7 2.4 11.6 30090 GILLEMBERG -23.8333 28.9667 1100 2000 9 17 0.0 28.2 14.1 19.8 11.2 -14.5 3.6 94.3 50.5 5.3 22.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 9 18 0.0 24.9 12.9 18.1 8.9 -12.0 3.8 84.3 51.1 5.0 21.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 9 19 1.0 18.8 12.7 15.0 5.8 -5.5 3.6 92.7 69.5 2.7 13.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 9 20 0.0 18.3 13.2 15.3 5.8 -6.5 2.9 89.5 71.8 2.1 7.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 9 21 0.0 21.8 10.6 15.4 6.2 -6.5 1.7 97.2 59.2 3.3 18.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 9 22 0.0 21.7 10.8 16.2 6.3 -8.0 2.4 96.0 57.9 3.7 20.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 9 23 0.0 24.1 12.3 17.5 8.2 -11.0 2.8 95.3 52.3 4.4 22.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 9 24 0.0 23.4 11.8 17.6 7.6 -12.0 2.0 92.1 49.9 4.3 22.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 9 25 0.0 28.9 12.0 20.9 10.5 -16.0 2.2 85.7 39.1 6.0 28.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 9 26 0.0 30.7 14.4 22.8 12.5 -20.0 2.0 72.4 36.0 6.3 27.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 9 27 0.0 22.2 14.0 17.4 8.1 -6.0 2.6 86.2 62.0 2.9 11.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 9 28 0.0 29.9 12.9 21.5 11.4 -17.0 3.2 94.7 38.4 4.0 10.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 9 29 0.0 30.3 15.0 22.3 12.6 -19.5 3.4 89.9 39.5 3.9 8.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 9 30 0.0 30.7 17.2 23.5 14.0 -23.5 2.8 73.7 37.7 4.3 9.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 1 0.2 32.8 15.6 24.5 14.2 -20.5 0.7 70.5 32.7 2.7 9.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 2 0.0 24.1 15.6 20.0 9.8 -22.0 0.6 90.5 61.2 1.9 8.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 3 0.2 24.0 14.4 18.3 9.2 -13.0 0.6 94.7 58.3 2.2 8.8 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 4 0.0 27.5 12.7 20.3 10.1 -17.0 0.6 93.6 50.7 1.9 6.8 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 5 0.0 32.2 16.1 24.8 14.2 -20.5 3.4 79.7 35.4 3.2 9.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 6 0.0 28.8 19.5 23.2 14.1 -24.0 3.7 71.7 47.0 5.0 9.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 7 0.0 27.3 17.1 21.5 12.2 -21.5 4.1 88.6 49.2 4.5 9.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 8 0.0 26.8 17.8 21.2 12.3 -22.5 3.6 85.4 47.0 3.9 4.9 170 CompNo Name Lat Long Alt Year M Day Rain Tmax Tmin Tave HU UCU U2 RHx RHn ETo Rs 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 9 0.0 30.1 19.1 23.5 14.6 -24.0 2.5 71.7 42.1 4.5 8.6 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 10 0.0 34.8 19.5 26.6 17.1 -24.0 2.6 68.0 30.0 5.8 9.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 11 0.0 19.9 13.9 16.9 6.9 -12.0 3.9 87.9 73.5 2.0 3.6 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 12 0.0 21.2 13.0 16.3 7.1 -8.5 3.5 86.7 61.2 3.1 9.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 13 0.0 29.0 11.0 19.8 10.0 -14.0 2.0 96.4 40.8 4.0 11.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 14 0.0 31.5 15.2 23.6 13.3 -21.0 2.3 78.7 33.2 5.0 10.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 15 0.0 30.9 16.2 23.2 13.6 -21.5 2.6 72.1 28.3 5.6 11.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 16 0.0 31.6 15.1 23.5 13.4 -19.5 2.3 77.6 29.8 5.3 11.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 17 0.0 33.8 16.5 25.2 15.2 -22.5 2.6 76.6 25.5 6.1 11.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 18 0.0 32.8 16.7 24.7 14.7 -22.5 2.6 88.0 31.9 5.3 10.8 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 19 4.4 33.7 18.0 24.4 15.9 -24.0 2.7 79.8 31.6 5.4 8.6 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 20 0.0 23.2 16.2 19.4 9.7 -20.0 3.9 89.5 62.7 3.2 9.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 21 0.8 29.3 14.8 19.6 12.0 -15.0 3.2 91.2 43.4 4.5 8.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 22 0.0 23.4 14.6 18.1 9.0 -13.5 3.0 88.9 58.9 2.9 6.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 23 0.0 25.3 14.3 19.7 9.8 -16.5 2.3 91.0 53.7 3.1 8.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 24 0.0 29.9 15.3 22.8 12.6 -20.5 2.0 86.5 40.5 4.1 9.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 25 0.0 31.8 17.6 24.9 14.7 -23.5 2.6 74.5 36.7 5.1 9.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 26 6.2 21.8 16.4 18.8 9.1 -20.0 1.8 92.5 69.4 1.5 2.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 27 24.0 19.9 14.9 16.8 7.4 -9.5 2.3 97.7 79.0 1.3 2.6 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 28 0.2 21.8 14.5 17.0 8.1 -9.0 2.7 92.8 65.3 2.2 4.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 29 0.0 25.7 12.5 19.4 9.1 -15.5 1.9 94.6 50.6 3.5 12.0 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 30 38.0 29.0 15.3 20.7 12.2 -15.5 2.7 94.3 38.4 4.6 11.6 30090 GILLEMBERG -23.8333 28.9667 1100 2000 10 31 0.2 17.1 12.8 14.9 4.9 -4.0 3.5 95.6 78.9 1.6 5.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 11 1 3.8 22.3 14.0 17.4 8.1 -12.0 2.5 93.0 68.6 2.0 4.6 30090 GILLEMBERG -23.8333 28.9667 1100 2000 11 2 0.2 26.5 16.5 21.0 11.5 -20.5 2.7 88.8 55.8 3.3 7.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 11 3 0.0 25.7 17.6 20.2 11.6 -12.0 2.5 93.1 59.2 2.2 5.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 11 4 0.0 31.0 13.8 22.4 12.4 -19.1 3.1 100.0 74.7 1.7 0.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 11 5 0.0 30.1 15.0 22.6 12.6 -19.4 3.2 95.1 38.6 5.6 22.0 30090 GILLEMBERG -23.8333 28.9667 1100 2000 11 6 0.0 27.5 18.8 23.2 13.2 -20.7 3.2 97.3 59.3 6.9 31.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 11 7 0.0 27.5 17.8 22.7 12.7 -19.6 3.1 99.4 67.0 6.0 26.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 11 8 0.0 29.5 18.8 24.2 14.2 -22.8 2.8 94.0 64.4 5.4 21.8 30090 GILLEMBERG -23.8333 28.9667 1100 2000 11 9 0.0 30.0 18.3 24.2 14.2 -22.8 2.7 89.7 63.1 6.4 27.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 11 10 0.0 32.0 18.2 25.1 15.1 -24.0 3.0 97.3 61.8 6.6 26.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 11 11 0.0 30.5 17.3 23.9 13.9 -22.3 2.8 100.0 56.7 6.2 26.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 11 12 0.0 30.0 17.0 23.5 13.5 -21.4 2.5 100.0 64.4 6.8 30.0 30090 GILLEMBERG -23.8333 28.9667 1100 2000 11 13 0.0 33.3 17.3 25.3 15.3 -24.0 2.5 100.0 77.3 7.5 33.3 171 CompNo Name Lat Long Alt Year M Day Rain Tmax Tmin Tave HU UCU U2 RHx RHn ETo Rs 30090 GILLEMBERG -23.8333 28.9667 1100 2000 11 14 0.0 25.1 16.8 21.0 11.0 -16.1 2.6 87.1 55.3 6.6 31.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 11 15 0.0 26.0 13.8 19.9 9.9 -13.8 2.2 90.4 59.1 9.2 49.9 30090 GILLEMBERG -23.8333 28.9667 1100 2000 11 16 0.0 26.5 16.6 21.6 11.6 -17.3 2.6 88.2 43.1 5.1 20.8 30090 GILLEMBERG -23.8333 28.9667 1100 2000 11 17 0.0 25.5 14.3 19.9 9.9 -13.8 2.9 91.5 55.6 4.3 16.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 11 18 4.0 24.5 16.3 20.4 10.4 -14.9 2.9 98.2 50.1 3.8 14.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 11 19 5.0 22.5 14.8 18.7 8.7 -11.2 2.7 99.7 60.7 5.6 28.0 30090 GILLEMBERG -23.8333 28.9667 1100 2000 11 20 0.0 25.0 12.3 18.7 8.7 -11.2 2.4 99.8 59.6 3.6 11.8 30090 GILLEMBERG -23.8333 28.9667 1100 2000 11 21 0.0 26.0 16.8 21.4 11.4 -17.0 1.9 100.0 67.0 3.6 11.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 11 22 3.0 21.5 17.3 19.4 9.4 -12.8 2.1 97.3 67.0 6.4 32.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 11 23 0.0 24.9 14.3 19.6 9.6 -13.2 2.2 97.3 77.3 5.4 24.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 11 24 0.0 31.6 12.9 22.3 12.3 -18.8 2.3 100.0 63.1 6.9 30.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 11 25 15.5 27.0 17.5 22.3 12.3 -18.8 2.3 91.9 38.6 5.8 23.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 11 26 14.0 29.7 14.8 22.3 12.3 -18.8 2.8 100.0 70.9 6.6 29.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 11 27 0.0 27.2 19.8 23.5 13.5 -21.4 2.7 95.1 56.7 4.1 14.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 11 28 0.0 27.0 19.4 23.2 13.2 -20.8 2.5 90.8 63.1 3.4 9.8 30090 GILLEMBERG -23.8333 28.9667 1100 2000 11 29 0.0 30.3 19.4 24.9 14.9 -24.0 2.4 97.3 70.9 4.5 15.8 30090 GILLEMBERG -23.8333 28.9667 1100 2000 11 30 0.0 25.5 19.3 22.4 12.4 -19.1 2.2 100.0 58.0 4.1 14.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 1 13.0 25.3 14.7 20.0 10.0 -14.0 2.5 91.4 77.3 5.7 29.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 2 23.0 30.4 12.8 21.6 11.6 -17.4 2.4 96.6 64.4 6.1 29.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 3 21.5 31.9 13.5 22.7 12.7 -19.8 2.6 93.5 38.6 5.8 26.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 4 7.0 26.8 14.9 20.9 10.9 -15.8 2.8 84.1 38.6 4.9 21.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 5 2.0 25.9 15.6 20.8 10.8 -15.6 2.4 98.7 63.1 4.6 20.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 6 0.0 28.8 15.2 22.0 12.0 -18.3 2.0 98.7 70.8 4.7 20.6 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 7 3.5 30.3 17.1 23.7 13.7 -21.9 2.0 93.5 56.7 5.8 26.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 8 2.3 31.0 19.9 25.5 15.5 -24.0 2.7 81.0 46.4 4.3 14.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 9 0.0 24.1 19.4 21.8 11.8 -17.7 2.3 95.6 64.4 1.7 1.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 10 0.0 28.3 17.9 23.1 13.1 -20.6 2.1 96.6 88.9 3.6 15.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 11 0.0 27.6 19.3 23.5 13.5 -21.3 2.2 94.5 65.7 2.4 5.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 12 2.0 23.3 17.7 20.5 10.5 -15.1 2.7 96.6 83.7 5.7 29.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 13 6.5 29.0 15.4 22.2 12.2 -18.7 2.3 90.4 77.3 8.8 47.4 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 14 0.0 26.3 11.9 19.1 9.1 -12.1 2.1 97.6 70.8 8.5 48.6 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 15 0.0 29.3 15.6 22.5 12.5 -19.2 2.4 93.5 51.5 6.1 29.1 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 16 4.0 30.7 11.9 21.3 11.3 -16.8 2.1 98.7 65.7 6.1 29.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 17 0.0 30.8 12.1 21.5 11.5 -17.1 2.7 95.6 39.9 4.6 18.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 18 0.0 31.7 15.9 23.8 13.8 -22.1 2.6 89.3 36.1 5.8 25.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 19 0.0 31.1 14.9 23.0 13.0 -20.4 2.5 94.5 43.8 5.4 22.4 172 CompNo Name Lat Long Alt Year M Day Rain Tmax Tmin Tave HU UCU U2 RHx RHn ETo Rs 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 20 0.0 33.9 16.1 25.0 15.0 -24.0 2.2 92.4 51.5 5.0 19.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 21 58.4 27.6 19.0 24.4 13.3 -12.0 2.8 93.8 57.7 2.2 4.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 22 8.2 24.8 17.3 20.6 11.1 -22.0 1.4 96.0 69.1 2.0 5.7 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 23 0.0 27.4 17.4 22.4 12.4 -22.5 1.6 94.5 55.4 3.3 11.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 24 0.2 29.1 18.2 23.7 13.7 -24.0 1.6 84.0 58.2 3.5 11.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 25 2.4 29.8 18.6 23.5 14.2 -24.0 1.7 95.3 55.3 3.3 9.8 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 26 0.2 23.5 17.5 20.1 10.5 -22.0 3.4 91.0 68.7 2.9 9.3 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 27 0.0 23.3 16.1 19.3 9.7 -19.0 2.0 90.2 67.5 2.4 6.8 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 28 0.0 29.7 14.9 22.3 12.3 -19.0 1.4 95.9 52.5 3.6 12.5 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 29 0.0 32.3 18.8 25.4 15.5 -24.0 1.5 86.9 46.6 4.1 12.2 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 30 1.2 30.6 20.3 24.2 15.4 -24.0 1.6 82.5 56.1 3.4 9.8 30090 GILLEMBERG -23.8333 28.9667 1100 2000 12 31 0.0 31.6 19.3 24.8 15.4 -24.0 1.8 90.4 49.8 4.1 12.1 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 1 1.4 26.4 17.4 22.3 11.9 -23.5 2.6 93.2 55.9 3.2 8.0 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 2 0.0 24.4 12.8 19.1 8.6 -16.0 2.7 80.4 54.3 3.6 10.6 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 3 0.0 25.9 13.4 19.6 9.6 -16.0 2.4 88.8 52.5 3.8 12.6 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 4 0.0 27.3 13.0 20.1 10.1 -15.5 2.3 88.7 50.6 3.9 12.1 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 5 0.0 30.2 11.9 21.7 11.1 -17.0 2.0 90.8 45.8 4.3 12.5 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 6 0.0 31.9 14.9 23.6 13.4 -20.0 1.7 88.4 42.5 4.4 12.9 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 7 3.4 32.0 18.9 24.0 15.5 -24.0 2.8 75.4 44.6 5.2 11.6 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 8 0.0 29.8 17.8 23.3 13.8 -23.0 2.1 89.2 48.2 4.2 12.9 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 9 0.0 31.6 19.0 24.6 15.3 -24.0 2.0 87.7 46.4 4.3 11.7 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 10 0.0 35.2 19.0 27.1 17.0 -24.0 1.7 86.3 38.0 4.8 12.5 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 11 0.0 37.0 19.4 27.9 17.2 -24.0 2.0 83.7 28.4 5.7 12.0 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 12 0.0 31.2 21.5 25.3 16.3 -24.0 3.4 84.8 51.0 4.7 9.7 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 13 0.0 30.7 19.1 24.2 14.9 -24.0 2.4 88.3 42.6 4.6 11.3 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 14 0.0 32.6 19.8 25.6 16.2 -24.0 2.0 69.0 41.3 4.8 11.4 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 15 0.0 31.7 21.1 25.6 16.4 -24.0 2.9 83.8 48.3 4.6 9.7 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 16 0.0 28.1 18.4 22.7 13.3 -24.0 2.8 91.5 58.9 3.2 7.2 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 17 0.0 26.0 18.5 21.5 12.2 -24.0 2.5 91.0 63.0 2.5 4.6 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 18 0.0 28.4 17.6 21.7 13.0 -22.5 1.9 86.7 51.8 3.3 8.3 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 19 0.0 32.3 16.0 24.7 14.2 -22.0 1.9 84.9 43.0 4.4 11.4 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 20 0.0 32.0 20.5 25.4 16.3 -24.0 2.2 85.2 47.2 4.3 10.4 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 21 4.0 32.4 21.0 25.1 16.7 -24.0 2.7 84.6 45.6 4.7 9.9 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 22 0.0 29.0 19.0 23.6 14.0 -24.0 2.7 88.6 47.8 4.1 9.1 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 23 0.0 30.4 18.6 24.1 14.5 -24.0 1.8 88.8 47.0 3.8 10.1 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 24 6.8 34.2 19.8 25.7 17.0 -24.0 2.4 85.7 40.3 5.0 10.7 173 CompNo Name Lat Long Alt Year M Day Rain Tmax Tmin Tave HU UCU U2 RHx RHn ETo Rs 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 25 0.0 29.8 19.5 23.6 14.6 -24.0 3.5 85.4 48.5 4.9 11.8 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 26 0.0 30.5 16.3 23.4 13.4 -21.0 2.3 92.0 42.9 4.6 13.2 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 27 0.0 29.1 16.2 23.4 12.7 -22.5 2.2 91.0 49.2 3.6 8.8 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 28 0.0 31.3 17.4 24.2 14.3 -23.0 1.9 93.6 41.0 4.0 10.0 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 29 0.8 31.7 18.6 24.6 15.2 -24.0 2.2 87.5 44.0 4.5 11.8 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 30 0.2 33.0 19.3 25.9 16.2 -24.0 2.5 84.2 43.7 5.0 11.9 30090 GILLEMBERG -23.8333 28.9667 1100 2001 1 31 7.6 32.9 19.8 24.5 16.3 -24.0 2.7 94.4 42.2 4.5 8.7 30090 GILLEMBERG -23.8333 28.9667 1100 2001 2 1 0.2 27.5 19.5 22.5 13.5 -24.0 1.8 88.5 60.0 2.8 7.0 30090 GILLEMBERG -23.8333 28.9667 1100 2001 2 2 0.0 32.6 18.2 25.1 15.4 -24.0 1.8 90.3 39.0 4.5 12.1 30090 GILLEMBERG -23.8333 28.9667 1100 2001 2 3 0.0 33.9 21.0 27.1 17.5 -24.0 2.3 70.5 39.9 5.1 10.8 30090 GILLEMBERG -23.8333 28.9667 1100 2001 2 4 10.6 30.2 17.7 23.2 14.0 -23.5 3.7 90.7 51.9 4.4 9.3 30090 GILLEMBERG -23.8333 28.9667 1100 2001 2 5 0.2 25.6 15.1 20.1 10.3 -18.0 2.1 94.2 61.1 2.7 7.4 30090 GILLEMBERG -23.8333 28.9667 1100 2001 2 6 0.2 25.4 18.5 21.4 11.9 -24.0 2.1 89.6 68.8 2.4 6.6 30090 GILLEMBERG -23.8333 28.9667 1100 2001 2 7 0.0 27.2 18.4 22.0 12.8 -24.0 1.8 93.6 62.7 2.6 7.1 30090 GILLEMBERG -23.8333 28.9667 1100 2001 2 8 0.0 29.1 19.2 23.7 14.2 -24.0 2.1 90.0 49.9 3.5 8.5 30090 GILLEMBERG -23.8333 28.9667 1100 2001 2 9 0.0 26.8 17.5 22.0 12.1 -23.0 3.3 86.2 50.4 4.1 9.6 30090 GILLEMBERG -23.8333 28.9667 1100 2001 2 10 0.0 24.7 19.2 21.2 11.9 -24.0 2.6 84.8 57.6 3.0 6.3 30090 GILLEMBERG -23.8333 28.9667 1100 2001 2 11 0.0 26.0 18.4 21.6 12.2 -24.0 2.6 85.2 54.6 3.2 7.1 30090 GILLEMBERG -23.8333 28.9667 1100 2001 2 12 0.0 28.5 15.4 21.9 11.9 -18.5 2.0 92.9 48.4 3.7 10.8 30090 GILLEMBERG -23.8333 28.9667 1100 2001 2 13 0.0 31.0 17.5 24.1 14.2 -22.0 1.9 91.3 38.3 4.3 11.7 30090 GILLEMBERG -23.8333 28.9667 1100 2001 2 14 0.0 27.4 18.2 23.1 12.8 -24.0 2.0 85.7 58.5 2.7 5.7 30090 GILLEMBERG -23.8333 28.9667 1100 2001 2 15 0.0 26.6 18.2 21.4 12.4 -24.0 2.8 85.3 56.7 3.4 7.3 30090 GILLEMBERG -23.8333 28.9667 1100 2001 2 16 0.0 27.8 17.4 21.8 12.6 -21.0 2.0 89.0 53.0 3.3 9.1 30090 GILLEMBERG -23.8333 28.9667 1100 2001 2 17 0.0 32.1 18.3 24.8 15.2 -24.0 2.1 89.1 39.6 4.4 10.7 30090 GILLEMBERG -23.8333 28.9667 1100 2001 2 18 26.8 28.6 19.4 22.9 14.0 -24.0 2.9 94.1 54.1 3.5 7.7 30090 GILLEMBERG -23.8333 28.9667 1100 2001 2 19 10.6 25.5 19.6 21.1 12.5 -24.0 1.7 96.1 73.4 1.9 5.6 30090 GILLEMBERG -23.8333 28.9667 1100 2001 2 20 20.0 27.3 19.4 22.2 13.3 -24.0 2.0 95.9 64.1 2.6 7.2 30090 GILLEMBERG -23.8333 28.9667 1100 2001 2 21 9.4 22.4 19.4 20.4 10.9 -24.0 1.8 95.5 79.8 1.2 2.2 30090 GILLEMBERG -23.8333 28.9667 1100 2001 2 22 0.0 26.8 18.4 22.0 12.6 -24.0 2.0 91.6 62.7 2.7 7.6 30090 GILLEMBERG -23.8333 28.9667 1100 2001 2 23 0.0 29.3 18.6 23.6 13.9 -24.0 1.4 96.2 55.0 3.2 10.8 30090 GILLEMBERG -23.8333 28.9667 1100 2001 2 24 0.0 26.9 19.7 22.9 13.3 -24.0 3.0 93.9 63.1 3.0 8.0 30090 GILLEMBERG -23.8333 28.9667 1100 2001 2 25 0.0 26.0 18.3 21.9 12.1 -24.0 2.4 89.7 64.8 2.6 6.4 30090 GILLEMBERG -23.8333 28.9667 1100 2001 2 26 0.0 22.6 19.1 20.7 10.8 -24.0 2.1 88.8 74.8 1.7 3.1 30090 GILLEMBERG -23.8333 28.9667 1100 2001 2 27 33.8 19.0 16.6 17.8 7.8 -17.0 1.5 96.0 86.3 1.0 2.1 30090 GILLEMBERG -23.8333 28.9667 1100 2001 2 28 18.4 17.3 15.8 16.3 6.6 -10.5 1.5 96.3 91.2 0.9 2.2 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 1 0.0 21.8 14.5 17.3 8.2 -11.5 1.3 97.3 70.6 1.7 5.3 174 CompNo Name Lat Long Alt Year M Day Rain Tmax Tmin Tave HU UCU U2 RHx RHn ETo Rs 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 2 0.4 26.5 14.0 19.9 10.2 -16.5 1.4 98.8 56.2 2.4 7.2 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 3 0.0 28.6 16.5 21.7 12.5 -22.0 1.4 96.8 43.1 3.2 9.4 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 4 0.0 28.2 18.1 22.6 13.2 -24.0 2.3 89.0 51.4 3.7 10.8 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 5 0.0 27.3 17.6 21.9 12.4 -22.5 1.7 90.4 56.4 2.9 8.4 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 6 0.0 28.7 18.0 22.9 13.4 -24.0 1.3 94.4 57.7 2.7 8.3 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 7 0.0 29.8 18.5 23.8 14.1 -24.0 1.8 93.9 54.1 3.1 8.5 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 8 0.0 30.3 17.3 23.9 13.8 -22.5 1.6 95.2 47.3 3.4 10.0 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 9 10.4 29.3 19.6 22.9 14.5 -24.0 1.9 93.4 59.1 2.8 6.8 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 10 0.0 27.8 19.8 22.6 13.8 -24.0 1.4 93.2 65.7 2.4 7.8 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 11 0.0 29.3 17.6 23.5 13.5 -23.0 1.8 88.9 52.0 3.5 10.8 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 12 0.0 27.7 17.2 22.8 12.5 -22.0 1.2 94.4 65.1 2.3 7.6 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 13 0.0 24.4 17.3 21.5 10.9 -23.0 3.9 85.3 66.0 3.2 8.2 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 14 0.0 24.7 15.1 19.7 9.9 -16.5 2.6 91.9 60.1 2.9 7.9 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 15 0.0 26.7 16.4 21.0 11.5 -20.5 2.3 92.7 55.4 3.1 8.7 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 16 0.0 26.2 14.7 20.5 10.4 -19.0 2.0 95.9 60.0 2.6 7.3 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 17 0.0 26.7 15.9 20.5 11.3 -19.5 1.3 96.6 62.1 2.2 6.5 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 18 0.0 29.1 15.3 21.8 12.2 -19.0 1.5 95.8 54.0 2.9 8.7 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 19 1.2 20.7 16.4 18.3 8.6 -18.0 2.9 92.9 75.8 1.7 3.6 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 20 0.0 24.9 14.1 19.2 9.5 -16.5 1.7 91.1 61.2 2.7 9.9 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 21 0.0 25.2 14.3 19.7 9.8 -16.5 2.3 94.6 59.4 2.8 8.5 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 22 0.0 22.8 16.3 19.4 9.6 -19.5 2.8 92.1 65.8 2.4 6.7 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 23 0.0 23.1 17.1 20.0 10.1 -20.5 2.8 90.1 67.4 2.3 5.4 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 24 3.0 19.8 17.6 18.7 8.7 -22.0 2.0 93.9 84.3 1.1 1.8 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 25 2.0 24.7 17.3 19.8 11.0 -19.5 1.1 96.8 65.6 1.7 4.5 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 26 0.4 28.1 15.0 20.7 11.5 -17.0 1.0 97.8 57.2 2.4 8.5 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 27 0.0 28.4 16.1 22.2 12.3 -20.5 2.0 94.8 57.5 3.0 9.3 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 28 0.0 27.1 18.9 21.9 13.0 -24.0 1.8 92.3 63.2 2.3 5.5 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 29 0.0 27.7 17.5 21.6 12.6 -23.0 1.8 96.1 55.3 2.5 5.4 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 30 0.0 27.0 16.2 21.4 11.6 -22.5 1.8 96.1 54.8 2.9 8.9 30090 GILLEMBERG -23.8333 28.9667 1100 2001 3 31 0.0 29.4 15.8 22.1 12.6 -20.5 1.4 95.6 46.8 3.1 9.4 30090 GILLEMBERG -23.8333 28.9667 1100 2001 4 1 0.0 28.9 17.3 22.9 13.1 -23.0 2.3 95.9 52.0 3.2 7.9 30090 GILLEMBERG -23.8333 28.9667 1100 2001 4 2 0.2 28.4 20.3 23.0 14.3 -24.0 1.4 93.2 56.4 2.3 5.3 30090 GILLEMBERG -23.8333 28.9667 1100 2001 4 3 0.6 30.4 17.6 22.4 14.0 -23.0 1.8 94.9 52.8 3.0 7.2 30090 GILLEMBERG -23.8333 28.9667 1100 2001 4 4 15.0 20.8 17.4 19.1 9.1 -22.0 2.6 95.3 80.9 1.3 2.4 30090 GILLEMBERG -23.8333 28.9667 1100 2001 4 5 0.0 23.2 15.7 18.5 9.5 -15.5 2.3 95.6 61.8 2.4 7.1 30090 GILLEMBERG -23.8333 28.9667 1100 2001 4 6 0.0 21.4 13.8 17.1 7.6 -11.0 2.2 91.6 63.1 2.4 8.7 175 CompNo Name Lat Long Alt Year M Day Rain Tmax Tmin Tave HU UCU U2 RHx RHn ETo Rs 30090 GILLEMBERG -23.8333 28.9667 1100 2001 4 7 0.0 21.6 12.7 16.9 7.2 -11.5 1.7 93.2 64.7 2.1 7.5 30090 GILLEMBERG -23.8333 28.9667 1100 2001 4 8 0.0 23.8 15.3 18.7 9.6 -15.5 1.5 88.0 59.4 2.2 5.9 30090 GILLEMBERG -23.8333 28.9667 1100 2001 4 9 2.8 24.3 13.2 17.6 8.7 -11.5 1.5 96.7 62.6 2.0 5.8 30090 GILLEMBERG -23.8333 28.9667 1100 2001 4 10 0.0 24.9 16.2 19.4 10.5 -19.0 1.4 95.8 63.5 1.9 5.2 30090 GILLEMBERG -23.8333 28.9667 1100 2001 4 11 0.0 25.7 15.7 19.9 10.7 -18.0 1.9 93.8 56.9 2.6 7.5 30090 GILLEMBERG -23.8333 28.9667 1100 2001 4 12 1.4 26.3 14.4 19.2 10.3 -15.5 1.3 95.4 52.8 2.4 7.4 30090 GILLEMBERG -23.8333 28.9667 1100 2001 4 13 0.2 26.7 13.9 19.7 10.3 -15.0 1.2 96.6 57.6 2.3 7.3 30090 GILLEMBERG -23.8333 28.9667 1100 2001 4 14 0.0 24.3 16.6 19.8 10.5 -20.0 3.1 87.0 61.4 3.0 7.8 30090 GILLEMBERG -23.8333 28.9667 1100 2001 4 15 0.0 20.1 14.0 17.3 7.0 -13.5 2.2 94.5 70.7 1.5 3.0 30090 GILLEMBERG -23.8333 28.9667 1100 2001 4 16 0.0 23.4 13.0 17.2 8.2 -11.5 1.6 95.4 61.8 2.0 6.1 30090 GILLEMBERG -23.8333 28.9667 1100 2001 4 17 0.0 27.1 11.2 18.5 9.1 -12.5 1.2 97.5 52.5 2.3 7.1 30090 GILLEMBERG -23.8333 28.9667 1100 2001 4 18 8.4 26.6 14.5 20.0 10.6 -15.0 1.8 91.8 56.4 2.5 6.9 30090 GILLEMBERG -23.8333 28.9667 1100 2001 4 19 0.0 26.0 15.4 19.7 10.7 -18.0 1.3 95.6 59.9 2.1 6.4 30090 GILLEMBERG -23.8333 28.9667 1100 2001 4 20 0.0 24.2 14.6 19.3 9.4 -17.0 1.4 95.2 66.5 1.7 5.0 30090 GILLEMBERG -23.8333 28.9667 1100 2001 4 21 0.0 24.7 15.0 19.7 9.8 -18.0 1.3 95.5 57.5 2.0 6.3 30090 GILLEMBERG -23.8333 28.9667 1100 2001 4 22 0.0 26.1 12.4 19.0 9.2 -14.5 1.1 99.3 69.6 1.9 7.9 30090 GILLEMBERG -23.8333 28.9667 1100 2001 4 23 0.0 27.6 13.2 19.9 10.4 -14.5 1.2 91.4 32.4 2.7 8.0 30090 GILLEMBERG -23.8333 28.9667 1100 2001 4 24 0.0 28.6 13.3 21.1 10.9 -19.5 1.5 92.4 33.9 3.0 8.2 30090 GILLEMBERG -23.8333 28.9667 1100 2001 4 25 0.0 29.9 12.9 21.6 11.4 -18.0 1.5 92.4 27.7 3.2 8.2 30090 GILLEMBERG -23.8333 28.9667 1100 2001 4 26 0.0 29.3 14.1 21.6 11.7 -18.5 2.4 87.5 33.9 3.7 8.1 30090 GILLEMBERG -23.8333 28.9667 1100 2001 4 27 1.0 22.2 15.6 19.0 8.9 -18.0 1.5 91.4 80.1 1.5 2.9 30090 GILLEMBERG -23.8333 28.9667 1100 2001 4 28 0.0 28.2 13.3 20.1 10.8 -15.0 1.3 93.4 29.3 2.8 7.7 30090 GILLEMBERG -23.8333 28.9667 1100 2001 4 29 0.0 28.2 14.1 21.4 11.2 -18.0 1.4 90.4 33.9 2.7 7.0 30090 GILLEMBERG -23.8333 28.9667 1100 2001 4 30 0.0 25.4 17.8 21.1 11.6 -23.0 2.4 92.4 66.2 2.1 5.0 Lat = Latitude Tmax = Maximum temperature RHx = Maximum humidity HU = Heat Units Long = Longitude Tmin = Minimum temperature Rhn = Minimum humidity Alt = Altitude Tave = Average temperature Eto = Evapotranspiration M = Month CU = Chill Units Rs = Solar radiation 176 177 178 APPENDIX IV : WATERKLOOF CLIMATIC DATA Date DOY Rn T RH WS Rain Tx Tn RHx RHn CU HU 2006/07/01 182 6.72 9.99 64.92 1.63 0 15.16 4.75 84 47.41 12.5 0 2006/07/02 183 10.84 9.46 60.73 1.34 0 16.67 2.56 87 32.08 13 0 2006/07/03 184 12.59 7.91 56.54 1.32 0 16.91 1.22 86 25.49 10 0 2006/07/04 185 12.73 7.9 50.81 1.06 0 17.58 -0.4 82 21.57 8 0 2006/07/05 186 12.43 10.88 42.37 1.17 0 20.35 -0.1 76 21.76 4.5 0.15 2006/07/06 187 12.56 12.7 41.17 1.81 0 21.95 6.67 62 19.81 5 4.31 2006/07/07 188 13 6.4 56.4 1.27 0 14.84 -0.4 84 24.23 11 0 2006/07/08 189 13 6.44 53.84 1.32 0 17.3 -3.3 87 23.66 5 0 2006/07/09 190 12.88 9.64 43.37 0.91 0 18.17 2.69 61 24.26 11 0.43 2006/07/10 191 13.08 8.06 54.54 0.79 0 18.39 -0.6 85 25.55 5.5 0 2006/07/11 192 13.33 9.31 44.16 1.12 0 19.49 -0.8 82 16.45 4 0 2006/07/12 193 13.14 11.58 37.26 1.27 0 20.85 4.61 53 19.97 6 2.73 2006/07/13 194 12.97 11.83 39.66 2.25 0 19.39 6.53 58 23.2 7 2.96 2006/07/14 195 7.11 11.43 55.18 3.33 0 18.15 3.15 77 36.13 7.5 0.65 2006/07/15 196 11.91 10.39 58.4 1.83 0 19.68 -0.7 89 33.96 0 0 2006/07/16 197 8.4 14.31 48.98 3.47 0 19.53 11.2 58 33.47 0 5.37 2006/07/17 198 12.22 12.22 54.94 2.04 0 19.6 4.65 74 33.87 5 2.13 2006/07/18 199 13.27 9.25 49.25 0.7 0 20.23 -1.9 88 17.28 3 0 2006/07/19 200 12.8 10.95 47.16 1.11 0 22.75 -0.6 74 22.2 0 1.06 2006/07/20 201 12.04 15.63 36.7 2.06 0 23.34 8.07 62 18.04 0 5.71 2006/07/21 202 3.67 13.88 44.76 2.03 0 17.57 7.37 79 31.85 1 2.47 2006/07/22 203 8.72 4.67 69.94 1.92 0 9.71 -3.5 88 51.31 19 0 2006/07/23 204 14.16 2.81 48.41 1.03 0 11.57 -4.8 83 22.88 10.5 0 2006/07/24 205 14.47 5.57 34.54 1.27 0 17.82 -6.2 64 11.36 6.5 0 2006/07/25 206 13.74 10.26 33.97 0.97 0 22.2 -1.3 62 13.35 2.5 0.47 2006/07/26 207 13.53 14.59 34.01 1.43 0 24.58 5.34 63 16.01 0 4.96 2006/07/27 208 13.21 16.15 30.34 1.53 0 25.29 10.8 45 16.04 0 8.05 2006/07/28 209 13.54 13.89 32.66 1.03 0 23 6.45 55 15.35 1.5 4.73 2006/07/29 210 14.08 12.02 34.01 1.07 0 22.35 1.56 67 13.49 4.5 1.96 2006/07/30 211 14.09 11.96 29.39 1.05 0 21.76 1.76 52 13.36 2.5 1.76 179 Date DOY Rn T RH WS Rain Tx Tn RHx RHn CU HU 2006/07/31 212 14.2 12.88 29.57 1.82 0 21.05 6.29 42 15.65 3.5 3.67 2006/08/01 213 2.38 8.06 81.83 3.22 19.3 12.89 3.58 97 43.44 20 0 2006/08/02 214 3.82 3.66 96.28 5.01 6.6 6.06 1.63 98 93.6 18.5 0 2006/08/03 215 11.99 6.5 76.88 4.47 0.25 11.78 2.78 96 43.97 21 0 2006/08/04 216 11.39 5.96 73.65 2.25 0 11.54 1.2 92 45.1 18 0 2006/08/05 217 15.66 5.03 72.72 0.98 0.25 12.8 -1.4 96 38.46 11 0 2006/08/06 218 15.97 8.01 56.26 0.86 0 17.44 -1.3 93 23.13 6 0 2006/08/07 219 16.25 12.12 34.87 1.29 0 21.7 4.31 55 13.79 4 3.01 2006/08/08 220 16.39 11.61 35.42 1.6 0 20.01 4.04 71 12.53 7 2.02 2006/08/09 221 16.37 9 53.91 0.82 0 19.02 1 86 21.69 9 0.01 2006/08/10 222 15.85 13.12 51.37 3.34 0 21.53 5.98 83 21.54 3 3.75 2006/08/11 223 15.64 14.6 44.52 2.61 0 23.08 7.34 73 19.64 0 5.21 2006/08/12 224 16.87 9.34 53.64 1.51 0 15.44 3.58 76 32.54 14.5 0 2006/08/13 225 17.01 11.63 43.68 1.63 0 22.68 -0.4 89 13.82 0 1.16 2006/08/14 226 15.59 16.17 32.07 2.7 0 25.05 9.35 56 15.28 0 7.2 2006/08/15 227 10.74 9.13 60.02 2.93 0 14.17 3.8 91 36.82 15.5 0 2006/08/16 228 17.16 8.11 65.65 1.46 0 16.69 1.85 93 31.64 13 0 2006/08/17 229 17.39 10.61 51.81 0.98 0 19.98 -0.7 88 23.99 1 0 2006/08/18 230 16.98 12.87 50.61 1.3 0 20.55 6.92 72 32.26 4.5 3.73 2006/08/19 231 17.63 13.77 44.14 1.05 0 21.77 7.75 76 16.34 1.5 4.76 2006/08/20 232 17.71 14.08 36.64 1.78 0 23.94 2.15 78 13.09 0 3.04 2006/08/21 233 14.53 15.19 50.66 6.3 0 20.9 10.1 77 31.31 0 5.5 2006/08/22 234 4.07 11.36 81.08 2.96 18.54 13.88 9.69 97 49.39 9.5 1.79 2006/08/23 235 13.18 13.42 78.2 4.76 9.4 18.79 9.51 97 51.82 0 4.15 2006/08/24 236 3.69 9.42 93.38 2.42 32 12.49 6.15 97 88.2 17.5 0 2006/08/25 237 16.22 10.12 77.79 1.41 0 17.24 4.02 97 48.93 10.5 0.63 2006/08/26 238 18.55 11.26 68.98 1.34 0 19.01 3.19 95 40.08 5.5 1.1 2006/08/27 239 16.45 13.34 65.74 2.77 0 20.28 9.24 80 45.37 0.5 4.76 2006/08/28 240 17.74 15.06 56.61 1.52 0 22.53 6.55 93 29.61 0 4.54 2006/08/29 241 18.97 11.07 53.2 3.59 0 16.41 0.34 79 26.23 5.5 0 2006/08/30 242 19.95 3.52 60.87 2.3 0 11.2 -3.2 89 29.63 10.5 0 180 Date DOY Rn T RH WS Rain Tx Tn RHx RHn CU HU 2006/08/31 243 19.83 8.45 48.89 1.25 0 18.71 -1.5 86 19.8 1.5 0 2006/09/01 244 19.92 12.2 41.01 1.45 0 20.4 4.82 62 19.67 4 2.61 2006/09/02 245 19.08 14.38 37.26 1.25 0 22.5 8.09 59 18.77 0 5.29 2006/09/03 246 19.42 15.04 44.31 0.96 0 25.34 3.57 84 13.22 0 4.45 2006/09/04 247 19 15.16 50.49 3.43 0 22.26 10.3 73 24.46 0 6.26 2006/09/05 248 20.11 14.97 46.3 1.88 0 22.45 9.01 69 22.83 0 5.73 2006/09/06 249 18.13 15.66 43.77 1.32 0 23.64 8.73 73 21.82 0 6.19 2006/09/07 250 19.34 17.17 47.76 3.64 0 23.79 12.1 79 25.09 0 7.93 2006/09/08 251 19.09 16.16 49.13 2.31 0 22.22 8.48 78 30.7 0 5.35 2006/09/09 252 20.49 15.26 50.7 1.24 0 24.14 6.84 85 23.96 0 5.49 2006/09/10 253 20.89 17.81 38.63 1.2 0 25.81 10.9 67 18.64 0 8.38 2006/09/11 254 21.06 19.32 30.41 1.35 0 27.14 12.2 51 14.49 0 9.66 2006/09/12 255 20.86 19.17 29.93 1.34 0 28.52 8.82 62 13.05 0 8.67 2006/09/13 256 20.66 18.48 36.49 2.59 0 26.2 12.6 56 17.88 0 9.42 2006/09/14 257 20.57 19.02 38.87 1.44 0 27.75 9.01 74 17.51 0 8.38 2006/09/15 258 12.08 17.6 50.03 1.97 0.76 24.57 12.9 80 23.23 0 8.71 2006/09/16 259 20.38 14.99 39.05 1.97 0 23.54 6.96 78 11.73 0 5.25 2006/09/17 260 15.33 10.3 57.68 2.9 0 15.68 4.33 90 26.37 15.5 0 2006/09/18 261 22.64 8.84 54.39 1.92 0 16.71 0.81 93 21.14 9.5 0 2006/09/19 262 23.11 11.47 35.67 1.85 0 20.77 3.17 61 12.86 5.5 1.97 2006/09/20 263 23.32 10.25 43.09 1.43 0 19.42 1.28 79 15.26 3.5 0.35 2006/09/21 264 23.11 12.21 38.77 1.05 0 22.42 1.39 64 17.74 0 1.91 2006/09/22 265 21.93 15.02 35.42 1.65 0 24.94 3.27 68 14.12 0 4.11 2006/09/23 266 23.62 18.03 26.8 2.17 0 27.68 7.29 56 10.47 0 7.48 2006/09/24 267 23.73 18.96 23.36 1.67 0 28.8 6.79 61 9.17 0 7.8 2006/09/25 268 23.54 17.22 25.87 3.4 0 23.92 8.52 69 10.97 0 6.22 2006/09/26 269 22.46 10.4 65.17 3.26 0 17.55 5.66 91 36.16 12.5 1.6 2006/09/27 270 23.56 13.25 55.88 1.54 0 22.58 3.42 94 23.79 0.5 3 2006/09/28 271 24.29 16.88 36.26 1.3 0 26.53 7.3 74 11.86 0 6.91 2006/09/29 272 24.39 17.76 34.54 1.24 0 28.19 4.99 67 12.79 0 6.59 2006/09/30 273 24.54 19.43 31.65 1.06 0 29.38 7.04 67 11.33 0 8.21 181 Date DOY Rn T RH WS Rain Tx Tn RHx RHn CU HU 2006/10/01 274 19.44 20.08 25.12 2.83 0 29 11.1 43 12.79 0 10.02 2006/10/02 275 11.21 12.42 40.82 1.15 0 18.58 4.75 74 22.61 5 1.67 2006/10/03 276 24.14 12.14 48.58 2.17 0 21.11 4.57 87 16.79 4 2.84 2006/10/04 277 25.36 9.07 57.04 1.41 0 17.21 0.61 93 23.33 8.5 0 2006/10/05 278 25.87 11.61 45.21 1.14 0 21.15 0.79 94 14.65 1 0.97 2006/10/06 279 22.95 21.2 41.14 3.97 0 30.91 11.4 72 12.69 0 11.16 2006/10/07 280 10.58 18.79 62.38 2.58 2.29 23.77 15.3 87 41.74 0 9.54 2006/10/08 281 14.11 19.17 70.61 3.02 9.14 25.75 15.2 93 34.76 0 10.5 2006/10/09 282 18.19 15.36 70.37 1.57 9.4 22.05 8.51 96 34.47 0 5.28 2006/10/10 283 25.64 15.69 62.14 1.37 0 25.89 3.97 97 26.31 0 4.93 2006/10/11 284 25.01 19.07 51.22 2.43 0 26.9 12.2 82 17.27 0 9.54 2006/10/12 285 24.59 19.56 50.41 2.45 0 26.66 14 78 25.55 0 10.34 2006/10/13 286 16.54 18.57 57.63 1.2 1.27 26.13 11.5 82 30.97 0 8.82 2006/10/14 287 23.5 21.52 43.98 1.83 0 28.8 13.1 83 19.43 0 10.96 2006/10/15 288 22.76 22.04 39 1.9 0 29.4 14.7 65 19.8 0 12.04 2006/10/16 289 24.68 21.97 44.48 2.97 0 29.39 17.4 72 20.33 0 13.39 2006/10/17 290 19.64 20.11 58.44 2.1 1.02 27.13 14.1 91 30.44 0 10.62 2006/10/18 291 25.93 19.96 59.95 2.13 0.25 28.81 10.6 96 21.56 0 9.71 2006/10/19 292 18.73 18.44 71.01 4.74 9.91 26.5 14.2 90 44.19 0 10.32 2006/10/20 293 23.36 19.22 66.71 2.74 1.02 27.64 13.7 88 37.67 0 10.65 2006/10/21 294 25.67 21.45 42.99 2.09 0 29.45 14.4 85 11.1 0 11.92 2006/10/22 295 27.93 20.25 47.13 1.37 0 29.53 10.6 94 13.39 0 10.05 2006/10/23 296 28.21 22.01 36.72 1.09 0 31.03 11.7 74 14.81 0 11.38 2006/10/24 297 28.78 24.73 22.83 2.38 0 33.86 17.6 42 9.17 0 14.79 2006/10/25 298 27.87 23.15 24.06 1.66 0 31.54 12.4 42 14.32 0 11.95 2006/10/26 299 15.14 20.35 34.83 1.66 0 26.08 13.7 68 22.83 0 9.88 2006/10/27 300 23.28 20.56 53.42 2.54 2.03 30.1 11.4 78 22.65 0 10.73 2006/10/28 301 24.61 21.44 56.68 3.31 0 28.92 15.1 86 29.46 0 11.98 2006/10/29 302 16.47 20.61 61.3 2.48 0.25 27.94 15.3 92 33.74 0 11.62 2006/10/30 303 13.76 20.13 59.29 2.25 0.51 25.72 15.4 74 42.4 0 10.54 2006/10/31 304 22.61 20.72 55.89 1.83 0 29.71 12.8 93 20.2 0 11.26 182 2006/11/01 305 17.98 20.68 65.57 2.85 41.16 27.53 16 98 38.31 0 11.76 2006/11/02 306 8.06 15.12 91.08 1.88 31.24 19.74 11.8 98 80.7 0 5.75 2006/11/03 307 30.16 13.93 59.38 2.71 0 19.98 8.13 95 26.76 0 4.05 2006/11/04 308 30.51 14.8 46.15 1.4 0 22.45 5.22 83 20.97 0 3.84 2006/11/05 309 30.48 13.78 52.82 2.43 0 20.2 7.25 89 21.84 0.5 3.72 183 184 185 186 APPENDIX VI : VEGETATIVE TRAITS SEASON 1 CLADODES REMOVED WITH PRUNING (cladno) Plant Plant Plant Plant Plant Plant Plant Plant Plant Plant Variety 1 2 3 4 5 6 7 8 9 10 Average Skinners Court 19 16 20 35 19 24 24 17 28 27 22.90 Nudosa 43 60 72 53 44 32 36 46 37 47 47.00 Gymno Carpo 50 38 61 66 113 45 35 44 48 66 56.60 Morado 145 84 33 121 77 34 53 58 64 56 72.50 Zastron 100 68 75 101 90 56 49 36 32 49 65.60 Malta 64 72 51 40 129 29 30 42 46 27 53.00 Algerian 97 111 47 67 68 43 39 37 35 19 56.30 Turpin 111 124 115 104 168 69 72 99 136 78 107.60 Meyers 122 91 131 68 129 69 90 79 89 51 91.90 Roedtan 34 104 70 70 93 58 43 74 77 97 72.00 Tormentosa 40 34 36 20 24 12 17 18 16 16 23.30 X28 42 50 39 19 37 10 18 12 20 25 27.20 Ficus-indice 20 24 23 36 12 5 12 20 8 26 18.60 Nepgen 39 24 23 27 31 28 18 17 11 21 23.90 Sicilian Indian Fig 102 3 78 40 81 55 42 39 54 75 56.90 Seasonal Mean 53.02 CLADODES REMAINING AFTER PRUNING (cladleft) Plant Plant Plant Plant Plant Plant Plant Plant Plant Plant Variety 1 2 3 4 5 6 7 8 9 10 Average Skinners Court 36 28 24 39 23 33 46 40 39 23 33.10 Nudosa 72 81 75 69 71 68 58 59 84 85 72.20 Gymno Carpo 90 86 96 93 117 87 71 74 79 80 87.30 Morado 115 101 48 99 81 71 88 64 89 85 84.10 Zastron 108 72 79 82 90 67 74 89 70 72 80.30 Malta 103 94 89 89 110 77 61 87 78 57 84.50 Algerian 111 106 93 76 92 88 84 72 66 46 83.40 187 Plant Plant Plant Plant Plant Plant Plant Plant Plant Plant Variety 1 2 3 4 5 6 7 8 9 10 Average Turpin 113 49 112 97 132 81 78 66 75 80 88.30 Meyers 107 101 93 81 89 84 77 85 86 53 85.60 Roedtan 66 104 87 86 75 69 66 67 90 92 80.20 Tormentosa 28 35 28 40 28 39 33 38 41 43 35.30 X28 40 49 52 42 48 35 37 37 46 47 43.30 Ficus-indice 26 34 31 28 25 14 32 27 20 34 27.10 Nepgen 26 19 42 46 34 27 26 49 34 52 35.50 Sicilian Indian Fig 47 4 58 50 52 56 46 54 61 74 50.20 Seasonal Mean 64.69 CLADODE MASS (cmass) Plant Plant Plant Plant Plant Plant Plant Plant Plant Plant Variety 1 2 3 4 5 6 7 8 9 10 Average Skinners Court 2.74 2.58 3.03 2.37 3.34 2.48 2.89 2.88 2.53 3.13 2.80 Nudosa 1.13 1.37 1.27 1.28 1.24 1.36 1.51 1.53 1.51 1.43 1.36 Gymno Carpo 1.16 1.06 1.02 0.85 0.79 1.23 1.32 1.32 1.40 1.48 1.16 Morado 0.86 0.88 1.53 0.97 1.06 1.42 1.22 1.31 1.15 1.40 1.18 Zastron 0.87 0.93 0.85 1.04 0.84 1.33 1.43 1.45 1.32 1.62 1.17 Malta 0.76 0.88 0.81 0.96 0.70 1.29 1.45 1.39 1.24 1.50 1.10 Algerian 0.96 0.88 1.06 0.95 1.05 1.37 1.65 1.68 1.49 1.19 1.23 Turpin 0.84 0.77 0.75 0.71 0.77 1.45 1.50 1.13 1.21 1.37 1.05 Meyers 0.87 0.84 0.98 0.94 0.94 1.39 1.41 1.32 1.57 1.14 1.14 Roedtan 0.92 0.95 0.89 0.88 0.80 1.20 1.40 1.45 1.26 1.12 1.09 Tormentosa 1.19 1.30 1.02 1.01 1.25 1.83 1.93 1.54 1.69 2.10 1.49 X28 1.28 0.98 1.21 1.40 1.35 1.36 1.52 1.55 1.28 1.56 1.35 Ficus-indice 1.09 1.06 1.01 0.93 0.93 1.08 1.81 1.92 1.16 1.64 1.26 Nepgen 1.05 1.14 0.87 0.82 0.96 1.18 1.51 1.28 1.27 1.28 1.14 Sicilian Indian Fig 1.04 0.45 1.16 1.15 0.84 1.50 1.30 1.65 1.67 1.76 1.25 Seasonal Mean 1.32 188 CLADODE YIELD (cyieldp) Plant Plant Plant Plant Plant Plant Plant Plant Plant Plant Variety 1 2 3 4 5 6 7 8 9 10 Average Skinners Court 52.00 41.25 60.60 82.90 63.40 59.60 69.35 48.95 70.95 84.60 63.36 Nudosa 48.75 82.10 91.60 68.10 54.40 43.40 54.35 70.15 55.85 67.40 63.61 Gymno Carpo 58.15 40.15 62.52 55.80 89.40 55.35 46.10 57.95 67.25 97.55 63.02 Morado 124.45 73.90 50.50 117.20 81.80 48.35 64.50 76.15 73.40 78.15 78.84 Zastron 86.55 62.90 63.40 105.30 75.45 74.45 69.90 52.30 42.35 79.20 71.18 Malta 48.40 63.35 41.50 38.35 90.80 37.50 43.50 58.40 57.15 40.50 51.95 Algerian 92.80 97.60 49.60 63.60 71.10 59.05 64.20 62.15 52.00 22.65 63.48 Turpin 93.30 95.75 85.90 74.35 129.50 100.10 108.25 111.65 164.65 106.60 107.01 Meyers 106.15 76.50 127.90 63.90 121.10 95.90 127.05 104.15 139.75 58.10 102.05 Roedtan 31.40 98.60 62.30 61.85 74.25 69.75 60.15 107.00 97.15 108.45 77.09 Tormentosa 47.75 44.35 36.75 20.20 30.10 21.90 32.75 27.65 27.00 33.65 32.21 X28 53.70 48.95 47.20 26.55 49.85 13.55 27.40 18.60 25.65 39.00 35.05 Ficus-indice 21.80 25.45 23.25 33.40 11.10 5.40 21.70 38.40 9.30 42.55 23.24 Nepgen 40.85 27.40 19.90 22.15 29.90 33.05 27.25 21.75 14.00 26.80 26.31 Sicilian Indian Fig 106.15 1.35 90.80 45.95 67.90 82.65 54.40 64.20 90.20 132.15 73.58 Seasonal Mean 62.13 189 APPENDIX VI : VEGETATIVE TRAITS SEASON 2 CLADODES REMOVED WITH PRUNING (cladno) Plant Plant Plant Plant Plant Plant Plant Plant Plant Plant Variety 1 2 3 4 5 6 7 8 9 10 Average Skinners Court 19 16 20 35 19 24 24 17 28 27 22.90 Nudosa 23 40 35 24 20 30 37 42 56 35 34.20 Gymno Carpo 32 30 51 42 37 36 27 28 37 38 35.80 Morado 52 47 24 30 21 13 38 20 50 64 35.90 Zastron 43 26 40 47 35 50 66 65 57 78 50.70 Malta 43 48 43 37 45 34 35 45 36 40 40.60 Algerian 37 31 32 48 24 31 32 35 20 14 30.40 Turpin 58 15 45 28 77 38 48 51 62 33 45.50 Meyers 42 34 70 49 32 31 39 41 36 20 39.40 Roedtan 20 42 27 53 54 27 33 33 39 56 38.40 Ofer 49 30 32 37 41 34 35 33 42 37 37.00 Tormentosa 31 34 28 27 27 22 24 21 36 28 27.80 X28 31 30 32 16 33 14 32 24 25 25 26.20 Ficus-indice 24 33 36 4 23 9 53 41 11 41 27.50 Nepgen 29 27 51 42 29 32 26 40 52 39 36.70 Sicilian Indian Fig 83 2 81 55 45 59 26 52 54 96 55.30 R1259 42 60 14 30 26 31 36 29 41 31 34.00 R1251 30 65 14 17 30 30 5 43 22 14 27.00 Van As 21 29 5 5 14 16 1 4 30 38 16.30 Cross X 16 21 28 46 10 16 21 15 22 25 22.00 BergxMexican 38 25 33 21 18 30 31 40 34 54 32.40 Santa Rosa 28 35 22 32 42 39 20 40 29 36 32.30 Schagen 29 27 42 15 29 41 38 27 24 27 29.90 Seasonal Mean 33.83 190 CLADODES REMAINING AFTER PRUNING (cladleft) Plant Plant Plant Plant Plant Plant Plant Plant Plant Plant Variety 1 2 3 4 5 6 7 8 9 10 Average Skinners Court 36 42 51 50 44 45 40 23 54 47 43.20 Nudosa 91 84 71 78 76 85 69 80 116 98 84.80 Gymno Carpo 104 86 103 92 114 95 78 91 95 90 94.80 Morado 118 110 45 93 85 87 81 73 89 105 88.60 Zastron 92 73 82 71 95 95 79 100 77 101 86.50 Malta 98 87 90 74 97 84 67 84 82 65 82.80 Algerian 89 117 95 99 95 101 103 91 86 61 93.70 Turpin 123 70 114 103 132 80 71 80 99 84 95.60 Meyers 110 98 90 98 95 93 80 84 83 55 88.60 Roedtan 77 116 108 103 74 90 87 76 97 89 91.70 Ofer 89 75 55 67 77 66 72 81 58 66 70.60 Tormentosa 47 56 40 38 47 32 46 38 50 42 43.60 X28 42 45 52 49 65 40 34 39 48 50 46.40 Ficus-indice 37 34 38 26 24 20 35 39 24 40 31.70 Nepgen 42 36 49 77 57 33 27 58 35 58 47.20 Sicilian Indian Fig 64 7 78 54 55 70 53 60 74 87 60.20 R1259 48 49 23 25 32 24 25 27 33 30 31.60 R1251 27 52 16 23 31 21 22 33 20 11 25.60 Van As 22 23 16 12 21 16 13 18 24 17 18.20 Cross X 45 38 37 49 24 29 39 28 25 26 34.00 BergxMexican 39 43 31 44 22 35 34 26 19 54 34.70 Santa Rosa 42 31 40 39 43 40 33 38 29 30 36.50 Schagen 35 25 34 33 37 31 28 31 17 29 30.00 Seasonal Mean 59.16 191 CLADODE MASS (cmass) Plant Plant Plant Plant Plant Plant Plant Plant Plant Plant Variety 1 2 3 4 5 6 7 8 9 10 Average Skinners Court 2.74 2.58 3.03 2.37 2.52 2.48 2.89 2.88 2.53 3.13 2.71 Nudosa 1.93 1.72 1.70 1.71 2.17 2.09 1.85 1.85 1.62 1.92 1.86 Gymno Carpo 1.49 1.45 1.36 1.35 1.29 1.68 1.80 1.42 1.36 1.74 1.49 Morado 1.54 1.29 2.32 1.62 1.60 1.74 1.62 1.83 1.70 1.56 1.68 Zastron 1.47 1.52 1.47 1.56 1.37 1.45 1.55 1.46 1.44 1.22 1.45 Malta 1.16 1.40 1.38 1.53 1.50 1.67 1.82 1.70 1.66 1.54 1.54 Algerian 1.94 1.58 1.46 1.33 1.57 1.63 1.62 1.45 1.64 1.56 1.58 Turpin 1.44 1.08 1.22 1.29 1.34 1.70 2.12 1.77 1.56 1.84 1.54 Meyers 1.77 1.74 1.46 1.36 1.77 1.79 1.88 1.84 2.03 1.84 1.75 Roedtan 1.21 1.32 1.26 1.15 1.39 1.55 1.74 1.65 1.71 1.56 1.45 Ofer 1.57 1.66 1.54 1.40 1.69 1.42 1.36 1.98 1.22 1.90 1.57 Tormentosa 1.89 1.66 2.10 1.75 1.71 2.07 2.29 2.16 2.06 2.22 1.99 X28 1.96 1.94 1.98 1.94 1.92 2.29 2.33 2.05 1.82 2.33 2.06 Ficus-indice 1.65 1.95 1.72 1.61 1.75 1.61 2.28 2.33 2.06 2.14 1.91 Nepgen 1.42 1.44 0.85 1.15 1.15 1.92 2.72 1.73 1.67 2.03 1.61 Sicilian Indian Fig 1.73 1.58 1.53 1.73 1.81 1.92 2.14 2.13 2.10 2.21 1.89 R1259 2.01 2.13 1.73 1.65 1.96 1.64 1.99 1.90 1.90 1.89 1.88 R1251 1.94 2.14 1.13 1.52 1.62 1.89 0.24 1.90 1.55 1.38 1.53 Van As 1.65 1.52 1.13 1.06 0.75 1.37 1.40 1.29 1.93 1.62 1.37 Cross X 1.99 2.20 2.04 2.44 1.95 1.97 1.75 2.01 1.72 1.66 1.97 BergxMexican 1.87 2.16 1.69 1.82 1.83 2.09 1.78 1.60 1.38 1.49 1.77 Santa Rosa 1.98 1.78 1.96 1.88 2.14 1.55 1.88 2.18 1.56 1.82 1.87 Schagen 2.08 2.26 2.11 2.29 2.17 1.65 2.21 2.42 2.00 1.91 2.11 Seasonal Mean 1.76 192 CLADODE YIELD (cyieldp) Plant Plant Plant Plant Plant Plant Plant Plant Plant Plant Variety 1 2 3 4 5 6 7 8 9 10 Average Skinners Court 52.00 41.25 60.60 82.90 47.80 59.60 69.35 48.95 70.95 84.60 61.80 Nudosa 44.30 68.80 59.65 41.05 43.40 62.80 68.30 77.55 90.90 67.20 62.40 Gymno Carpo 47.55 43.55 69.60 56.75 47.55 60.45 48.65 39.65 50.30 66.00 53.01 Morado 80.15 60.55 55.65 48.45 33.50 22.65 61.60 36.50 84.85 100.05 58.40 Zastron 63.05 39.60 58.95 73.10 47.80 72.45 102.10 95.15 81.95 95.50 72.97 Malta 49.90 67.30 59.45 56.75 67.70 56.65 63.60 76.60 59.90 61.75 61.96 Algerian 71.65 48.90 46.80 64.05 37.60 50.40 51.70 50.60 32.80 21.85 47.64 Turpin 83.60 16.15 54.95 36.05 102.95 64.70 101.70 90.20 96.70 60.70 70.77 Meyers 74.20 59.10 102.05 66.50 56.75 55.60 73.45 75.25 73.25 36.80 67.30 Roedtan 24.20 55.45 34.00 60.90 75.15 41.80 57.30 54.55 66.60 87.60 55.76 Ofer 77.10 49.85 49.40 51.80 69.40 48.35 47.65 65.35 51.05 70.20 58.02 Tormentosa 58.55 56.45 58.70 47.30 46.25 45.55 55.00 45.45 74.20 62.15 54.96 X28 60.85 58.15 63.45 31.00 63.45 32.05 74.70 49.20 45.55 58.20 53.66 Ficus-indice 39.55 64.30 61.90 6.45 40.25 14.50 120.85 95.40 22.65 87.70 55.36 Nepgen 41.15 38.75 43.45 48.50 33.25 61.45 70.60 69.25 87.05 79.30 57.28 Sicilian Indian Fig 143.25 3.15 123.85 95.20 81.60 113.00 55.60 110.55 113.20 211.90 105.13 R1259 84.35 127.55 24.25 49.50 50.95 50.90 71.70 55.15 78.10 58.60 65.11 R1251 58.10 139.40 15.80 25.90 48.55 56.70 1.20 81.70 34.10 19.25 48.07 Van As 34.65 44.15 5.65 5.30 10.50 21.85 1.40 5.15 57.85 61.60 24.81 Cross X 31.90 46.15 57.15 112.20 19.50 31.45 36.85 30.15 37.80 41.45 44.46 BergxMexican 70.90 54.05 55.80 38.15 32.85 62.75 55.30 63.80 46.80 80.55 56.10 Santa Rosa 55.45 62.25 43.05 60.00 89.95 60.55 37.55 87.25 45.25 65.45 60.68 Schagen 60.30 60.95 88.75 34.35 62.80 67.65 84.05 65.40 47.90 51.60 62.38 Seasonal Mean 59.04 APPENDIX VII: ALIGNMENT OF D1/D2 SEQUENCE DATA OF ALL YEAST ISOLATES WITH POSSIBLE BIOCONTROL APPLICATION AGAINST CACTUS PEAR PATHOGENS RHOMUC --------------------GAAGCGGGAAGAGCTCAAATTTATAATCTGGCA-CCTTCG 39 RHOKRA --------------------GAAGCGGGAAGAGCTCAAATTTATAATCTGGCA-CCTTCG 39 CYSFER -------------------------GGGAAAAGCTCAAATTTAAAATCTGGCAGTCTACG 35 CRYALB --------------------GAAGCGGGAAGAGCTCAAATTTGAAATCTGGTAGCCTTCG 40 CRYALBLIQ --------------------GAAGCGGGAAGAGCTCAAATTTGAAATCTGGTAGCCTTCG 40 CRYSAI (72) --------------------GAAGCGGGAAGAGCTCAAATTTGAAATCTGGTAGCCTTCG 40 CRYSAI (109) TTCCCCTAGTAACGGCGAGTGAAGCGGGAAGAGCTCAAATTTGAAATCTGGTAGCCTTCG 60 CRYSAI (110) -------------------------GGGAAGAGCTCAAATTTGAAATCTGGTAGCCTTCG 35 CRYSAI (22) -------------------------GGGAAGAGCTCAAATTTGAAATCTGGTAGCCTTCG 35 HANCLE ---------------------AAGCGGTAAAAGCTCAAATTTGAAATCTGGTA-CTTTCA 38 ** ** *********** ******* * * * RHOMUC GT-GTCCGAGTTGTAATCTCTAGAAATGTTTTCCGCGTTGGACCGCACACAAGTCTGTTG 98 RHOKRA GT-GTCCGAGTTGTAATCTCTAGAAGTGTTTTCCGCGTTGGACCGCACACAAGTCTGTTG 98 CYSFER ATTGTCCGAATTGTAATCTCGAGAAGTGTTTTCCGCGTTGGCCTGTGCACAAGTCCCTTG 95 CRYALB GTTGCCCGAGTTGTAATCTAGAGAAGTGTTTTCCGTGCCGGCCCATGTACAAGTCCCTTG 100 CRYALBLIQ GTTGCCCGAGTTGTAATCTAGAGAAGTGTTTTCCGTGCCGGCCCATGTACAAGTCCCTTG 100 CRYSAI (72) GTTGCCCGAGTTGTAATCTAGAGAAGTGTTTTCCGCGTTGGCCCATGTACAAGTCCCTTG 100 CRYSAI (109) GTTGCCCGAGTTGTAATCTAGAGAAGTGTTTTCCGCGTTGGCCCATGTACAAGTCCCTTG 120 CRYSAI (110) GTTGCCCGAGTTGTAATCTAGAGAAGTGTTTTCCGCGTTGGCCCATGTACAAGTCCCTTG 95 CRYSAI (22) GTTGCCCGAGTTGTAATCTAGAGAAGTGTTTTCCGCGTTGGCCCATGTACAAGTCCCTTG 95 HANCLE GT-GCCCGAGTTGTAATTTGTAGAATTTGTCTTTGATTAGGTCCTTGTCTATGTTCCTTG 97 * * **** ******* * **** * * * * ** * * ** *** RHOMUC GAATACAGCGGCATAGTGG-TGAGACCCCCGTATATGGTGCGGACGCCCAGCGCTTTGTG 157 RHOKRA GAATACAGCGGCACAGTGG-TGATACCCCCGTACACGGTGCGGACGCCCAGCGCTTTGTG 157 CYSFER GAACAGGGCGTCATAGAGGGTGAGAATCCCGTCCTTGGCACAGACACCCAATGCTTTGTG 155 CRYALB GAACAGGGCGTCATAGAGGGTGAGAATCCCGTCCTTGACATGGACCCCCGGTGCTTTGTG 160 CRYALBLIQ GAACAGGGCGTCATAGAGGGTGAGAATCCCGTCCTTGACATGGACCCCCGGTGCTCTGTG 160 CRYSAI (72) GAACAGGGCGTCATAGAGGGTGAGAATCCCGTCCTTGACATGGACCCCCAATGCTTTGTG 160 CRYSAI (109) GAACAGGGCGTCATAGAGGGTGAGAATCCCGTCCTTGACATGGACCCCCAATGCTTTGTG 180 CRYSAI (110) GAACAGGGCGTCATAGAGGGTGAGAATCCCGTCCTTGACATGGACCCCCAATGCTTTGTG 155 CRYSAI (22) GAACAGGGCGTCATAGAGGGTGAGAATCCCGTCCTTGACATGGACCCCCAATGCTTTGTG 155 HANCLE GAACAGGACGTCATAGAGGGTGAGAATCCCGT--TTGGCGAGGATACCTTTT-CTCTGTA 154 *** * ** ** ** ** *** * ***** * ** ** ** *** RHOMUC ATACATTTTCGAAGAGTCGAGTTGTTTGGGAATGCAGCTCAAATTGGGTGGTAAATTCCA 217 RHOKRA ATACACTTTCAATGAGTCGAGTTGTTTGGGAATGCAGCTCAAATTGGGTGGTAAATTCCA 217 CYSFER ATACACTCTCAATGAGTCGAGTTGTTTGGGAATGCAGCTCAAAATGGGAGGTAAATTCCT 215 CRYALB ATACACTTTCAACGAGTCGAGTTGTTTGGGAATGCAGCTCAAAATGGGTGGTGAATTCCA 220 CRYALBLIQ ATACACTTTCAACGAGTCGAGTTGTTTGGGAATGCAGCTCAAAATGGGTGGTGAATTCCA 220 CRYSAI (72) ATACACTTTCAACGAGTCGAGTTGTTTGGGAATGCAGCTCAAAATGGGTGGTGAATTCCA 220 CRYSAI (109) ATACACTTTCAACGAGTCGAGTTGTTTGGGAATGCAGCTCAAAATGGGTGGTGAATTCCA 240 CRYSAI (110) ATACACTTTCAACGAGTCGAGTTGTTTGGGAATGCAGCTCAAAATGGGTGGTGAATTCCA 215 CRYSAI (22) ATACACTTTCAACGAGTCGAGTTGTTTGGGAATGCAGCTCAAAATGGGTGGTGAATTCCA 215 HANCLE AGACTTTTTCGAAGAGTCGAGTTGTTTGGGAATGCAGCTCAAAGTGGGTGGTAAATTCCA 214 * ** * ** * ****************************** **** *** ****** RHOMUC = Rhodotorula mucilaginosa RHOKRA = Rhodosporidium kratochvilovae CYSFER = Cystofilobasidium ferigula CRYALB = Cryptococcus albidosimilis CRYALBLIQ = Cryptococcus albidosimilis / liquefaciens CRYSAI (72) = Cryptococcus saitoi (isolate number 72) CRYSAI (109) = Cryptococcus saitoi (isolate number 109) CRYSAI (22) = Cryptococcus saitoi (isolate number 22) HANCLE = Hanseniaspora clermontiae * = all the nucleotides in that column are identical in all the sequences in the alignment - = signifies a gap in the nucleotide sequence Sequences highlighted in green have been identified as Cryptococcus saitoi, those highlighted in pink have been identified as C. albidodimilis or liquefaciens. 193 RHOMUC TCTAAAGCTAAATATTGGCGAGAGACCGATAGCGAACAAGTACCGTGAGGGAAAGATGAA 277 RHOKRA TCTAAAGCTAAATATTGGCGAGAGACCGATAGCGAACAAGTACCGTGAGGGAAAGATGAA 277 CYSFER TCTAAAGCTAAATACTGGCGAGAGACCGATAGCGAACAAGTACCGTGAGGGAAAGATGAA 275 CRYALB TCTAAAGCTAAATATTGGCGAGAGACCGATAGCGAACAAGTACCGTGAGGGAAAGATGAA 280 CRYALBLIQ TCTAAAGCTAAATATTGGCGAGAGACCGATAGCGAACAAGTACCGTGAGGGAAAGATGAA 280 CRYSAI (72) TCTAAAGCTAAATATTGGCGAGAGACCGATAGCGAACAAGTACCGTGAGGGAAAGATGAA 280 CRYSAI (109) TCTAAAGCTAAATATTGGCGAGAGACCGATAGCGAACAAGTACCGTGAGGGAAAGATGAA 300 CRYSAI (110) TCTAAAGCTAAATATTGGCGAGAGACCGATAGCGAACAAGTACCGTGAGGGAAAGATGAA 275 CRYSAI (22) TCTAAAGCTAAATATTGGCGAGAGACCGATAGCGAACAAGTACCGTGAGGGAAAGATGAA 275 HANCLE TCTAAAGCTAAATATTGGCGAGAGACCGATAGCGAACAAGTACAGTGATGGAAAGATGAA 274 ************** **************************** **** *********** RHOMUC AAGCACTTTGGAAAGAGAGTTAA-CAGTACGTGAAATTGTTGGAAGGGAAACGCTTGAAG 336 RHOKRA AAGCACTTTGGAAAGAGAGTTAA-CAGTACGTGAAATTGTTGGAAGGGAAACGCTTGAAG 336 CYSFER AAGCACTTTGGAAAGAGAGTCAAACAGTACGTGAAATTGTTGAAAGGGAAACGATTGAAG 335 CRYALB AAGCACTTTGGAAAGAGAGTTAAACAGTACGTGAAATTGTTGAAAGGGAAACGATTGAAG 340 CRYALBLIQ AAGCACTTTGGAAAGAGAGTTAAACAGTACGTGAAATTGTTGAAAGGGAAACGATTGAAG 340 CRYSAI (72) AAGCACTTTGGAAAGAGAGTTAAACAGTACGTGAAATTGTTGAAAGGGAAACGATTGAAG 340 CRYSAI (109) AAGCACTTTGGAAAGAGAGTTAAACAGTACGTGAAATTGTTGAAAGGGAAACGATTGAAG 360 CRYSAI (110) AAGCACTTTGGAAAGAGAGTTAAACAGTACGTGAAATTGTTGAAAGGGAAACGATTGAAG 335 CRYSAI (22) AAGCACTTTGGAAAGAGAGTTAAACAGTACGTGAAATTGTTGAAAGGGAAACGATTGAAG 335 HANCLE AAGAACTTTGAAAAGAGAGTGAAAAAGTACGTGAAATTGTTGAAAGGGAAGGGCATTTGA 334 *** ****** ********* ** ***************** ******* * * RHOMUC TCAGACTTG------CTTGCC-G-AGCAA---TC----------------------GGTT 363 RHOKRA TCAGACTTG------CTTGCC-GGAGCTTGCTTC----------------------GGTT 367 CYSFER TCAGTCGTG------CTAGCCTGGATCCAGCCTTATGGTGTATCTCCA--------GGTC 381 CRYALB TCAGTCATG------CTCTTTGGT---------------ATTTATATC--------ATTG 371 CRYALBLIQ TCAGTCATG------CTCTTTGGT---------------ATTTATATC--------ATTG 371 CRYSAI (72) TCAGTCATG------CTCTTTGGATTAAGCCGTTCTGCGGTGTATTTC--------ATTG 386 CRYSAI (109) TCAGTCATG------CTCTTTGGATTAAGCCGTTCTGCGGTGTATTTC--------ATTG 406 CRYSAI (110) TCAGTCATG------CTCTTTGGATTAAGCCGTTCTGCGGTGTATTTC--------ATTG 381 CRYSAI (22) TCAGTCATG------CTCTTTGGATTAAGCCGTTCTGCGGTGTATTTC--------ATTG 381 HANCLE TCAGACATGGTGTTTTTTGCATGCACTCGCCTCTCGTGGGCTTGGGCCTCTCAAAAATTT 394 **** * ** * * * RHOMUC TGCAGG-CCAGCATCAGTTTTCCGG----------------------------------- 387 RHOKRA TGCAGG-CCAGCATCAGTTTTCCGGGGTGGATAATGGTGGTTTGAAGGTAGCAGCCTCGG 426 CYSFER GGCAGG-TCAGCATCAGTTTGGGAGGGTTAACAAGGGAGTTAGGAATGTGGCAACCTCGG 440 CRYALB AGTGGGGTCAACATCAGTTTTGATCGATGGATAAAGGCACTAGGAAGGTAGCACTCTCGG 431 CRYALBLIQ AGTGGGGTCAACATCAGTTTTGATCGATGGATAAAGGCACTAGGAAGGTAGCACTCTCGG 431 CRYSAI (72) AGCGGGGTCAACATCAGTTTTGATCGCTGGAAAAGGGCAGGAGGAAGGTAGCACTCTCGG 446 CRYSAI (109) AGCGGGGTCAACATCAGTTTTGATCGCTGGAAAAGGGCAG-------------------- 446 CRYSAI (110) AGCGGGGTCAACATCAGTTTTGATCGCTGGAAAAGGGCAGGAGGAAGGTAGCACTCTCGG 441 CRYSAI (22) AGCGGGGTCAACATCAGTTTTGATCGCTGGAAAAGGGCAGGAGGAAGGTAGCACTCTCGG 441 HANCLE CACTGGGCCAACATCAATTCTGGCAGCAGGATAAAT-CATTAAGAATGTAGCTACTTCGG 453 ** ** ***** ** RHOMUC ------------------------------------------------------------ RHOKRA CTGTG-TTATAGCTTTCCACTGGATACATCCTGGGGGACTGAGGAACGCAGCGTGCTTTT 485 CYSFER TTGTG-TTATAGCCTAGCTTCGCATTGATCCTGCTGGACTGAGGAACGCAGTGCGCC--- 496 CRYALB GTGAACTTATAGCCTAGCGTCATATACATTGATTGGGACTGAGGAACGCAGCATGCCTTT 491 CRYALBLIQ GTGAACTTATAGCCTAGCGTCATATACATTGATTGGGACTGAGGAACGCAGCATGCCTTT 491 CRYSAI (72) GTGAACTTATAGCCTCTTGTCGTATACAGTGATTGGGACTGAGGAACGCAGCATGCCTTT 506 CRYSAI (109) ------------------------------------------------------------ CRYSAI (110) GTGAACTTATAGCCTCTTGTCGTATACAGTGATTGGGACTGAGGAACGCAGCATGCCTTT 501 CRYSAI (22) GTGAACTTATAGCCTCTTGTCGTATACAGTGATTGGGACTGAGGAACGCAGCATGCCTTT 501 HANCLE TAGTG-TTATAGCTTTTTGGAATACT-GTTAGCCGGGATTGAGGACTGC----------- 500 RHOMUC -------------------------------------------- RHOKRA TGCGAAGGTTTCGACCTTTTCACGCTTAGGATGCTGGTGTAATG 529 CYSFER --CGCAAGGGTTGGTCTTCGGAC--------------------- 517 CRYALB ATGGCCGG------------------------------------ 499 CRYALBLIQ ATGGCCGGGATTCGTCCACGTACA-------------------- 515 CRYSAI (72) -TGGCCGGGATTCGTCCACGT----------------------- 526 CRYSAI (109) -------------------------------------------- CRYSAI (110) -TGGCCGGGATTCGTCCACG------------------------ 520 CRYSAI (22) -TGGC--------------------------------------- 505 HANCLE -------------------------------------------- 194