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University of Nairobi (2021)

Modelling Green Gram Production in Kenya Under the Current and Future Climates

Mugo, Jane W

Titre : Modelling Green Gram Production in Kenya Under the Current and Future Climates

Auteur : Mugo, Jane W

Université de soutenance : University of Nairobi

Grade : Doctor of Philosophy in Meteorology 2021

Résumé partiel
Green gram is one of the legumes considered suitable for cultivation in the Arid and Semi-Arid Lands (ASALs) of Kenya. However, the area that is currently suitable remains small due to inadequate knowledge on the variation of climatic elements in space and time in the ASALs. The changing climate may have an effect on the areas presently suitable for green gram production. This study purposed to model the suitability of green grams in Kenya under the current and projected future climates. The CORDEX RCA4 models’ ability to simulate the observed rainfall and temperature from Climate Research Unit (CRU) datasets were assessed using statistical measures of bias and normalised root mean square error (NRMSE). The bias in rainfall was reduced by using an ensemble of the models adjusted using the scaling method. The temporal analysis of temperature and rainfall were assessed using the Mann Kendall test to determine whether there was an increasing or decreasing trend in the datasets. Mapping for different levels of green gram suitability in Kenya was done through the use of a weighted overlay of climate, soil, and topography parameters. The APSIM model was calibrated for four varieties of green gram, namely Biashara, Tosha, N26, and KS20 varieties to evaluate the impact of climate change on green gram yield, biomass and days to maturity in a highly suitable region. Although the CORDEX models and their ensemble did not replicate the spatial and temporal variability of rainfall during the MAM and OND season very well, the models and their ensemble captured the temperature pattern well. The rainfall ensemble, despite performing better than the individual CORDEX models, still showed notable biases, necessitating bias adjustment before further use in green gram crop modelling. The bias-corrected ensemble of rainfall and the ensemble of temperature were then used to study the space and time variability of rainfall under baseline (1971 to 2000) and future RCP 4.5 and RCP 8.5 scenarios (2021 to 2050) and their effect on green gram production


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