Simulation of Crop Yields in the Amhara Region using a Large area Crop Model as Driven by output from Regcm-4

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Addis Ababa University


The livelihood of people in many regions of Ethiopia depends on rainfed agriculture. Accurate prediction of crop yield could greatly improve potential famine and allow advanced planning of intervention operations. This thesis explores the feasibility of a combined Regional Climate Model (RegCM) and crop model for crop yield forecasting in Ethiopia, using wheat yield for the Amhara region as a case study. An important focus in the investigation is to validate and asses the ability of RegCM-4 regional climate model to represent the Ethiopian summer rainfall. The ability of the RegCM-4 model in capturing temporal and spatial variability of precipitation over the region of interest is evaluated using metrics spanning a wide range of temporal and spatial (Ethiopian domain average to local) scales against Global Precipitation Climatology Project (GPCP) observational datasets. The simulated period is 1995-2008. RegCM-4 shows a general overestimation of precipitation except the highlands part of the country. The precipitation bias over the Ethiopian highland, our main area of interest, is mostly less than 20%. The model captures well the observed interannual and inter-seasonal variability. On short time scales, simulated daily temperature and precipitation show a high correlation with observations, with a correlation coefficient of 0.79 for kiremit season. It is therefore that RegCM-4 has sufficiently good quality to perform climate change experiments over Ethiopia, for application to impact and adaptation studies. RegCM-4 outputs are used to drive a process-based crop model, General Large Area Model for Annaula Crop (GLAM) for hindcasting zonal wheat yields in the Amhara region. Simulated crop Radiation Use Efficiency (RUE) has been founded to be 1.81 which is expected for C3 crops. The yield in these simulations showed a negative bias (159-200kg/ha) with observed yield over Souther(North Shew Zone) and South Western(Awi Zone) of Amhara regional state. This is probably because at the field level the yield variability was mainly affected by field managements and diseases, pests and so on. GLAM does not predict the effect of the detailed field management, diseases and pests on yield variability; and also in this region there is overestimation of RegCM-4 precipitation, which might have lead to water stress in GLAM model. At regional level(for all grid cells), there were higher correlations (0.74) between observed and simulated yield. We therefore conclude that the GLAM model is suitable to simulate crop yield at regional scale (approximately 50 km) using RegCM-4 outputs



Amhara Region using a Large area Crop