| dc.description.abstract | The study examined the effect of climate variability and change on millet production in Gweri Sub County Soroti district. The specific objective were; to determine the trend of climate change and variability, assess farmers perceptions on the effects of climate change and climate variability on millet grain yields and to determine farmers adaptation practices to climate change and climate variability in Gweri Sub County, Soroti district. A cross sectional survey was adopted and data collection involved both use of primary data and secondary data. Primary data was collected from 80 households including farmers and extension workers in Gweri sub county Soroti district. Data was analysed using statistical package SPSS version 16. Result show predicted increase in rainfall and mean temperature of gweri sub county by the year 2039 respectively. Respondents acknowledged that rainfall amounts have changed, onset and cessation are dynamic, and a decline in millet yields attributed to climate variability and change was reported. The most prominent coping mechanisms include growing improved crops varieties, mixed cropping planting of 2nd season crops in 1st season and planting of 1st season crops in 2nd season due to change in climate, shifting from water stressed and flood prone areas and adjusting on planting dates and crop diversification.
In conclusion, both climate models, namely RCP 8.5 and RCP 4.5, predict an increase in annual rainfall, with values of 1830.6 and 1651.9 mm, respectively, for the year 2031. This trend is followed by similar peaks in the years 2036 and 2033. Additionally, the same models forecast a sustained and consistent rise in annual mean temperatures for Gweri Sub County, reaching 27.56°C in the year 2039, with similar high levels expected in 2032 and 2035. It's noteworthy that throughout this period, the annual mean temperatures remain within the optimal range of 26°C, favoring the growth of millet. This information was generated with the assistance of the Agricultural Model Intercomparison and Improvement Project (AGMIP), which contributed to the prediction of increased annual rainfall, and data analysis was executed to provide these valuable insights. | en_US |
