Doctoral Degrees (Soil, Crop and Climate Sciences)

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Browsing Doctoral Degrees (Soil, Crop and Climate Sciences) by Author "Diga, Girma Mamo"

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    Using seasonal climate outlook to advise on sorghum production in the Central Rift Valley of Ethiopia
    (University of the Free State, 2005-12) Diga, Girma Mamo; Walker, Sue
    English: Seasonal rainfall is an important source of water for rainfed farming in the semi-arid regions of the world, where rainfall is marginal and variable. However, as rains are unpredictable in terms of onset, amount and distribution, there is a need to understand the variability and other basic rainfall features in order to use the information in agricultural decision making. More specifically, combining the seasonal rainfall prediction with crop water requirement and soil water information is the core component to successful agriculture. The ultimate objective of this study was to characterize and obtain a better understanding of the most important rainfall features that form the basis for classifying the areas into homogenous rainfall zones and then to develop a seasonal rainfall prediction model for the Central Rift Valley (CRV) of Ethiopia. The source data for the analyses was primarily obtained from the National Meteorological Services Agency (NMSA) and partly from Melkassa Agricultural Research Centre (MARC) and the web site of the International Research Institute for Climate and Society (IRI). Rainfall variability and time series analyses were done using INSTAT 2.51 and coded time method, respectively. Rainfall onset and March-April- May (MAM) rainfall totals are the two most variable features both at Miesso and Abomssa. For both stations, rainfall end date displays the least variability. Rainfall onset date at Miesso ranges from the lower quartile (25 percentile) of DOY 61 to the upper quartile (75 percentile) of DOY 179 with a 42% coefficient of variation (cv). At Miesso, the main rainy season terminates during the last days of September (DOY 272 - 274) once in four years and terminates before DOY 293 in three out of four years. At Abomssa, the c.v for the lower quartile (DOY 61) to the upper quartile (DOY 134) was found to be 40.5%. At both locations, planting earlier than 15 March (DOY 75) only proves successful once in every four years. Further, at Miesso this upper quartile statistic can extend up to the DOY 179, whereas at Abomssa planting earlier than 15 April (DOY 134) is possible in three out of four years (75 percentile). At Abomssa, rainfall terminates by DOY 286 and the end of October (DOY 305) for the 25 and 75 percentile points respectively. From the time series analyses, there was no conclusive evidence for the existence of a trend for both Miesso and Abomssa, information which is useful for long-term research and development planning, as well as seasonal rainfall prediction for the study area. The classification study for the spatial rainfall pattern resulted in four homogenous rainfall zones that form distinct development and research units, using the FORTRAN- 90 based NAVORS2 program. The south facing Alem Tena-Langano zone has a better rainfall pattern than drier zones and thus formed zone 1. The southern, southwestern and southeastern area has formed the wet zone (zone 2), the northwestern to northeastern facing part (Debre-Zeit-Nazerth-Dera) that receives a higher rainfall amount than zone 1 has formed zone 3 and finally, the drier northeastern part constituted zone 4. Twenty seven seasonal rainfall prediction models with varied performance skills that can be used for the operational farming were developed for the March-September monthly rainfall using the Climate Predictability Tool (CPT v.4.01) from IRI. It was understood that with increased observing networks and data availability, useful operational climate prediction could be achieved for a smaller spatial unit and with a short lead-time. The tempo-spatial water requirement satisfaction pattern analyses were conducted using AGROMETSHELL v.1.0 of the FAO. Fourteen concurrent sorghum-growing seasons that give a general picture of crop water requirement satisfaction were mapped. The southern, southwestern and southeastern parts (zone 2) of the CRV constitute the most favourable location for growing a range of sorghum maturity groups. The northwestern and central (zone 3) parts constitute the next most suitable zone. The wide northeastern drylands (zone 4) of the study area, except the pocket area of Miesso-Assebot plain, does not warrant economic farming of sorghum under rainfed conditions. From the growth stage-based Water Requirement Satisfaction Index (WRSI) analyses, mid-season / flowering stage of the sorghum cultivars was found to be three times more sensitive to changes in sorghum yields for both cultivars and experimental sites as compared to the WRSI from the rest of growth stages. The results from the water production function analyses (WPF) also indicated the potential of WRSI for prediction of the long-term sorghum yields. The cumulative density function (CDF) and stochastic dominance analyses for the 120-day grain sorghum cultivar grown at Miesso show the June planting to be the most efficient set by first degree stochastic dominance (FSD), while May was found efficient for Melkassa. The CDF for Arsi Negele shows April planting date to be the best set. Therefore, these planting dates are to be preferred by farmers seeking ‘more’ yield at the respective locations, regardless of their attitude towards risk. The sensitivity analyses conducted using different levels of the seasonal rainfall related input variable combinations (sorghum planting date, maturity date, number of rainy days and WRSI) for Miesso, Melkassa and Arsi Negele provide useful information. By keeping input variables other than WRSI at the most preferred level (i.e. early planting date, extended maturity date, and greater number of rainy days) and only changing WRSI from 100% to 75% resulted in a 49.7% yield reduction in case of Miesso, 40.8% in case of Melkassa and 24.3% in case of Arsi Negele. Further, when WRSI was reduced down to 50%, there was a total crop failure in the case of Miesso and Melkassa, while the reduction was 48.6% for the Arsi Negele case. Similar results were found when WRSI was varied across other input level combinations. Visual Basic v.6.0 was used to write the algorithm for the decision support tool (DST) relating sorghum planting dates in CRV, to which the name ABBABOKA 1.0 was given. By using the rainfall prediction information from three different sources (the new prediction model developed in chapter 3, NMSA and ICPAC), ABBABOKA suggests the best possible planting alternatives for a given homogenous rainfall zone and planting season. When decision making under this predictive information alone is not sufficient, soil water parameters need to be consulted for more reliable decision making. This simple and briefly constructed ABBABOKA is expected to provide a suite of guidelines to the users. Certainly, this constitutes a significant departure from the fixed ‘best bet’ recommendations I learned from research systems in the past. It is recommended that the time-space classification of agricultural areas into homogeneous zones needs to be extended to the rest of the country together with the tailored rainfall prediction information. Research needs to be geared towards crop water requirements, climate risks and simulation modelling aspects. A network of weather stations and soil database needs to be developed in order to promote the soilcrop- climate research in Ethiopian agriculture. More importantly, the use of decision support tools and the well-established models (like APSIM) need to be included in agricultural research and development efforts.