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dc.contributor.advisorWalker, S.
dc.contributor.authorHaasbroek, Pieter Daniel
dc.date.accessioned2015-08-17T12:45:42Z
dc.date.available2015-08-17T12:45:42Z
dc.date.issued2006-11
dc.identifier.urihttp://hdl.handle.net/11660/892
dc.description.abstractEnglish : Downy Mildew (Plasmopara viticola) is known as one of the most important vineyard diseases in the Western Cape, because it has the capability to develop and spread very fast, and so cause large crop losses in certain years. In 1992 an Austrian researcher developed the Metos automatic weather station and associated software, to predict the occurrence of primary and secondary infection of downy mildew. This Metos weather station’s software was adapted for South African climatic conditions during 1995 and is known as the Metos-2 model. The Metos-2 model however had some shortcomings that needed to be improved. The most important of this was that the model was not sensitive enough to accurately calculate infections, and furthermore it gives only a “Yes/No” warning of possible primary and/or secondary infections. The Metos-2 model makes use of measured leaf wetness values from a leaf wetness sensor that is probably considered as one of the most inaccurate meteorological sensors. During 1995 - 2005 the Metos-2 model has been thoroughly tested and used by the disease management division of ARC Infruitec-Nietvoorbij, to warn the industry of possible downy mildew outbreaks. Results over these years have shown that more sprays were needed within the preventative spraying programs, as opposed to recommendations of the Metos-2 model, for the same or even improved control of downy mildew. On the other hand the results of the Metos-2 model compared to the Metos model, gave similar warnings for both primary and secondary infections. It is however very difficult to get clear similarities/differences between what the Metos-2 model has calculated and what had really occurred in the vineyards. This can be attributed mainly to the accumulation effect of downy mildew infections. With the development of the Downy Mildew Early Warning Model (DSVWmodel), two important changes were made, namely the leaf wetness was replaced with a mathematical, non-linear regression and the Metos-2 model’s “Yes/No” warnings for downy mildew infections were replaced with four classes of possible risks. The calculated leaf wetness of the DSVW-model, that uses measured relative humidity and air temperature as input values, had a significant coefficient of determination of 0.70, compared with measured leaf wetness. The DSVW-model’s four risk classes of possible infections (primary and secondary) are as follows: zero infection (0 %), low infection (1 - 34 %), medium (35 - 74 %) and a high risk class (75 - 100 %). To test the DSVWmodel’s accuracy and reliability, historical weather data (1998 - 2003) and measured disease outbreak data in the Stellenbosch, Robertson and Paarl areas were used to run both the Metos-2 and the DSVW-models. Primary as well as secondary infections were predicted by the models. When the DSVW-model and the Metos-2 model’s infection warnings were correlated with disease outbreaks, of the two, the DSVW-model showed consistently similar or better correlations with the measured disease outbreak data. The DSVW-model also calculated on a regular basis more primary and secondary infections, than the Metos-2 model, which at times did not warn of any downy mildew infections, although outbreaks of downy mildew did occur soon after. Producers can use the new DSVW-model with confidence, together with one or other prevention spray program, for the control of downy mildew.en_ZA
dc.description.abstractAfrikaans: Donsige skimmel (Plasmopara viticola) word allerweë as een van die heel belangrikste wingerdsiektes in die Wes-Kaap beskou en moet feitlik jaarliks bestry word, aangesien die siekte die vermoë het om baie vinnig te ontwikkel, versprei en veroorsaak in sommige jare groot oesverliese. ’n Oostenryker het gedurende 1992 ’n outomatiese Metos klimaat weerstasie en gepaardgaande sagteware ontwikkel, wat primêre en sekondêre infeksies by donsige skimmel voorspel. Gedurende 1995 is die Metos weerstasie se sagteware aangepas vir Suid-Afrikaanse weerstoestande en staan bekend as die Metos-2 model. Die Metos-2 model bevat sekere leemtes of tekortkominge wat verder verbeter moes word. Die grootste hiervan is dat die model infeksies nie sensitief aandui nie en verder gee dit tans net ’n kwalitatiewe “Ja of Nee” waarskuwing, van moontlike primêre en/of sekondêre infeksies. Verder maak die Metos-2 model ook gebruik van gemete blaarnatheidswaardes wat afkomstig is van ’n blaarnatheidsensor, wat beskou word as een van die mees onakkuraatste weerkundige sensors. Van 1995 - 2005 is die Metos-2 model al deeglik getoets en gebruik deur die siektebestuur afdeling van LNR Infruitec- Nietvoorbij, om die bedryf van moontlike donsige skimmel infeksie uitbrake te waarsku. Uit hierdie resultate het dit geblyk dat meer toedienings by voorkomende bespuitingsprogramme benodig word, teenoor die aanbevelings volgens die Metos-2 model, vir dieselfde of beter beheer van donsige skimmel. Daarenteen het resultate van die Metos-2 model teenoor die Metos model, soortgelyke waarskuwings getoon vir beide primêre en sekondêre infeksies. Dit is egter baie moeilik om duidelike oreenkomste/verskille te verkry tussen wat die Metos-2 model voorspel het en wat uiteindelik in die wingerd daarbuite gebeur het. Hierdie tendens kan grootliks toegeskryf word aan die akkumulasie effek van donsige skimmel wat plaasvind. Met die ontwikkeling van die Donsige Skimmel Vroeg-Waarskuwings Model (DSVW-model), is twee groot veranderinge gemaak, naamlik die gemete blaarnatheid is vervang met ’n wiskundige, nie-lineêre regressie formule en die Metos-2 model se “Ja/Nee” waarskuwings vir donsige skimmel infeksies, is vervang met vier klasse van moontlike risiko’s. Die voortaan berekende blaarnatheid by die DSVW-model, met relatiewe humiditeit en lugtemperatuur as insette, het ’n betekenisvolle koëffisiënt van determinasie van 0.70 getoon, teenoor gemete blaarnatheid. Die DSVW-model se vier risiko-klasse van moontlike infeksies (primêr en sekondêr) is as volg opgestel: geen infeksie klas (0 %), lae infeksie klas (1 - 34 %), medium infeksie (35 - 74 %) en ’n hoë infeksie klas (75 - 100 %). Om die DSVW-model se akkuraatheid en betroubaarheid te kon toets, is historiese weerdata en siektevoorkomsdata (1998 - 2003) van die Stellenbosch, Robertson en Paarl omgewings geneem en geloop op beide die Metos-2 en die DSVW-model. Waar die DSVW-model en die Metos-2 model se infeksie waarskuwings teen die werklike siektevoorkomsdata vergelyk is, het die DSVW-model telkens soortgelyke en selfs beter korrelasies getoon met die gemete siektevoorkomsdata. Die DSVW-model het hier ook deurgaans meer primêre en sekondêre infeksies bereken, teenoor die Metos-2 model, wat soms by sekere donsige skimmel uitbrake, vooraf geen waarskuwings aangetoon het nie. Produsente kan die nuwe DSVW-model met vertroue aanwend, saam met een of ander voorkomende spuitprogram, vir die beheer van donsige skimmel.
dc.language.isoaf
dc.publisherUniversity of the Free Stateen_ZA
dc.subjectVineyards -- Diseases and pests -- South Africa -- Western Cape -- Forecastingen_ZA
dc.subjectModelen_ZA
dc.subjectLeaf wetnessen_ZA
dc.subjectRainfallen_ZA
dc.subjectAir temperatureen_ZA
dc.subjectRelative humidityen_ZA
dc.subjectPrimary infectionsen_ZA
dc.subjectSecondary infectionsen_ZA
dc.subjectDSVW-modelen_ZA
dc.subjectDowny mildewen_ZA
dc.subjectMetosen_ZA
dc.subjectDowny mildew diseases -- South Africa -- Western Cape -- Forecastingen_ZA
dc.subjectDissertation (M.Sc. Agric. (Agrometeorology))--University of the Free State, 2006en_ZA
dc.titleVerfyning en verbetering van 'n donsige skimmel waarskuwingsmodel vir die Wes-Kaap
dc.typeDissertationen_ZA
dc.rights.holderUniversity of the Free Stateen_ZA


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