Prediction of Wheat Yield by General Large area Model for Annual Crops as Driven by Regional Climate Model and Observational Data
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Date
2010-07
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Addis Ababa University
Abstract
A new process based crop model, the general large area model (GLAM) for annual crops
and a Regional Climate Model (RegCM3) have been used for this study to predict the
wheat yield in Southern nations, nationalities and people's region (SNNPR) of Ethiopia.
This study aims to demonstrate how RegCM3 and GLAM could be used to forecast
wheat yield. RegCM3 is used to predict precipitation, maximum temperature, minimum
temperature and solar radiation over SNNPR. These variables are used in GLAM as
inputs for yield forcast. All the internal consistency checks that are used to ensure the
performance of the crop model prove that GLAM performs magni cently. The observed
and the simulated yield exhibit a high correlation on the central part of the study area.
However, GLAM yield has a negative bias which is found to be related with water stress.
The water stress is con rmed from RegCM3 precipitation forcast which has a negative
bias with respect to observed precipitation from Central Research Unit (CRU). As a result
of this low correlation of observed and simulated yield has been detected in the North-
east and South-west part. The model can be easily extended to any annual crop for the
investigation of the impacts of climate variability (or change) on crop yield over large
areas
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Prediction of Wheat Yield