Franklin Opijah

Adapting Agriculture to Climate Change: Suitability of Banana Crop Production to Future Climate Change Over Uganda

The aim of this study was to determine suitability zones of future banana growth under a changing climate to guide the design of future adaptation options in the banana sub-sector of Uganda. The study used high resolution (~1 km) data on combined bioclimatic variables (rainfall and temperature) to map suitability zones of the banana crop while the Providing Regional Climate for Impacts Studies (PRECIS) regional climate model temperature simulations were used to estimate the effect of rising temperature on banana growth assuming other factors constant. The downscaled future climate projections were based on the Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathways (RCPs, 2.6, 4.5, 6.0 and 8.5) and Special Report on Emission Scenarios (SRES, A1B and A2) across the period 2011–2090. The methodology involved identification of banana-climate growth thresholds and developing suitability indices for banana production under the high mitigation (RCP 2.6, less adaptation), medium mitigation (RCP 4.5 and RCP 6.0, medium adaptation), no mitigation (RCP 8.5, very high adaptation) scenarios, SRES A1B and A2 scenarios. The FAO ECO-Crop tool was used to determine and map future suitability of banana growth. Banana production indices were determined using a suitability model in the Geographical Information System (GIS) spatial analyst tool. The non-linear banana-temperature regression model was used to assess the impact of future changes in temperature on banana growth. The results revealed unique and distinct banana production suitability and growth patterns for each climate scenario in the sub-periods. RCPs 2.6 and 6.5 are likely to be associated with higher levels of banana production than RCPs 4.5 and 8.5. The results further showed that projected temperature increase under SRES A1B will promote banana growth. In contrast, expected increases in temperatures under SRES A2 are likely to retard banana growth due to high moisture deficits. There is need to develop adaptation option for farming communities to maximize their agricultural production and incomes. The effectiveness of adaptation options needed to combat the impacts will be influenced by the magnitude of the expected climatic changes associated with each scenario, the timing of expected climate change extremes and sensitivity of the crop to climate. This study has provided critical information that will be useful for planning integrated adaptation practices in the banana farming subsector to promote productivity.

Seasonal Climate Predictability over Kenya Using the Regional Spectral Model

Seasonal climate prediction over Kenya poses a considerable challenge to the modeling community due to the intricate interactions among the atmospheric, oceanic and land surface processes. This paper assesses the performance of the Regional Spectral Model (RSM) in downscaling the European Centre-Hamburg (ECHam) global model outputs from 1970 to 1999 over Kenya with respect to rainfall and temperature prediction using standard verification techniques. The results show that the accuracy of simulating the annual cycle and spatial distribution of convection and precipitation over the country is still poor. The seasonal rainfall predictability over Kenya by the RSM is better during the October-December season (correlation coefficient [r] of 26%; proportion correct [PC] of 60%; Frequency Bias Index [FBI] of 111%) than in the March-May season (r of 8%; PC of 54%; FBI of 83%), but the prediction for temperature is better in the March-May season (r of 25%; PC of 53%; FBI of 124%) than the OND season (r of -11%; PC of 46%; FBI of 100%). The predictability for rainfall during the cool-dry June-August period is still low (r of -4%; PC of 49%; FBI of 52%) but that for temperature has better skill as compared to the March-May and October-December seasons (r of 49%; PC of 70%; FBI of 90%). There is need to improve the development of convective processes that govern tropical precipitating systems in the region through sensitivity analysis of cloud simulation modules in the RSM applied as well as address rare systems that episodically influence the weather over the country and the region.