| Intro |
Traditional methods for crop data collection are labor-intensive, inefficient and,
more costly compared to remote sensing (RS) techniques. This study aims to identify
key climatic variables influencing maize and wheat yields and develop predictive models
while also evaluating the performance of the CropWatch cloud yield prediction model
(CW_YPM) in major agricultural regions of Ethiopia. Climate data from 54 meteorological
stations spanning 2000–2021 were analyzed. RS data, including NDVI from MODIS at
250 m resolution, agroecological zones, and observed crop yield data, were utilized for
model prediction and validation. Correlation analysis and a stepwise modeling approach
with multiple regression models were applied. The results revealed regional variations in
the effects of climatic parameters on yields, with vapor pressure deficits showing negative
correlations and rainfall exhibiting positive correlations. Non-linear models generally outperformed
linear models in yield prediction—using both climate-only (CO) and combined
climate-NDVI data. The best CO model for maize in the Horo Guduru area achieved an
RMSE of 0.392 tons/ha, an R2 of 0.94, and an index of agreement (d) of 0.984. Incorporating
NDVI improved accuracy, with the best maize model in the Illu Ababor area achieving an
RMSE of 0.477 tons/ha, an R2 of 0.91, and d of 0.976. CW_YPM also performed effectively
across the study area. This research highlights the value of integrating critical climatic
variables with the NDVI to enhance crop yield forecasting in Ethiopia, thereby-supporting
agricultural planning and food security initiatives. |
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