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dc.contributor.authorAgaba, Choice
dc.date.accessioned2019-12-19T07:05:37Z
dc.date.available2019-12-19T07:05:37Z
dc.date.issued2019-08
dc.identifier.citationAgaba, C. (2019). An assessment of rainfall onset variability for optimized planting decision making in Nakaseke District, Uganda. Masters dissertation. Makerere Universityen_US
dc.identifier.urihttp://hdl.handle.net/10570/7839
dc.descriptionA dissertation submitted to the Directorate of Research and Graduate Training in partial fulfilment of the requirements for the award of the degree of Master of Science in Environment and Natural Resources of Makerere Universityen_US
dc.description.abstractClimate variability has enormous impact on agricultural production and the well-being of communities in Uganda and the world at large. The lack of reliable anticipative information on rainfall onsets that mark the beginning of the agricultural season has made planting decisions very difficult. Advance information on delayed onset or early cessation of the rainy seasons is extremely valuable information that is commonly requested by stakeholders. The overall objective of the study was to generate data and information, and contribute to the development of decision making support systems for adapting to rainfall onset variability in Nakaseke sub-county, Nakaseke district of Uganda. Observed daily rainfall data for Kakoge weather station covering the period 1961 to 2015 was used to establish the trends of variability in rainfall onset. Rainfall onset dates were obtained using INSTAT program and summarised using Mean, Standard Deviation and coefficient of variation. Time-series plots were used to check patterns in onset dates and Mann-Kendall’s test was used to determine significance, direction and magnitude of the trends. In order to link the observed trends and farmers experiences, a household survey was carried out to investigate farmers’ perceptions and adaptation responses of changes in planting time. Pearson chi-square test for independence (χ2) was used to examine the relationship between farmers’ perceptions / adaptation responses of changes in planting time and rainfall seasons. Statistical Package for Social Scientists (SPSS) and R programming software were used to analyze run rainfall and household data. Preliminary findings from rainfall onset and household data analysis were corroborated through focus group discussions and thereafter logically organized to derive a model flowchart which guided the development of a Planting Decision Tool (PDT) to guide decision making for optimum planting time. To come up with the tool, Sublime Text 3.0 software was used to write JavaScript logic and index files containing the logic for the tool, graphs and areas where the user can view outputs in the PDT. Results show onset of rains for March, April, May (MAM) season (CV=22%) to be about four times more variable than that of September, October, November, December (SOND) season (CV=5%). False start of rains occurred once every 10 years for MAM season while no false starts were observed for SOND season over the period 1986-2015. Onsets for both MAM and SOND were seen to be more erratic in the recent decade of 2006-2015. Effects of changes in rainfall onset differed significantly (p<0.05) by season. Results from the survey show that for both MAM and SOND, the key effects of variable planting dates are reduced crop yields, and total crop loss in extreme cases. Farmers coped with the effects by practicing soil and water conservation and changing crops. From both rainfall analysis and the survey, PDT was developed. It has a simple and basic user interface with drop down menus that require prior information on the crop type, seasonal forecast type, and season and analogue year. This tool provides profound information to guide timely planting. In conclusion, developing such a tool will help farming communities to make informed decisions on timely planting. Further since the tool has incorporated the farmer experiences it will fit in well with their seasonal operations and thus help improve yields.en_US
dc.description.sponsorshipIITA, NARO and Makerere University Centre for Climate Research and Innovations (MuCCRI))en_US
dc.language.isoenen_US
dc.publisherMakerere Universityen_US
dc.subjectRainfall variabilityen_US
dc.subjectOptimized plantingen_US
dc.subjectDecision makingen_US
dc.subjectNakaseke Districten_US
dc.titleAn assessment of rainfall onset variability for optimized planting decision making in Nakaseke District, Ugandaen_US
dc.typeThesisen_US


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