Publications

Kassa, AK; Zeng, HW; Wu, BF; Zhang, M; Tsehai, KK; Qin, XL; Gebremicael, TG (2025). Integrating Climate Data and Remote Sensing for Maize and Wheat Yield Modelling in Ethiopia's Key Agricultural Region. REMOTE SENSING, 17(3), 491.

Abstract
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.

DOI:
10.3390/rs17030491

ISSN:
2072-4292