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Assessing irrigation water management utilizing the CROPGRO model in implementing deficit irrigation for peanut cultivation in Arba Minch, Ethiopia
This study evaluates irrigation strategies using the CROPGRO model to improve peanut farming in Arba Minch, Ethiopia. Given water scarcity, adopting deficit irrigation methods is vital for sustainable agriculture. Various deficit irrigation scenarios are analyzed to understand their impacts on peanut yield, water efficiency, and crop growth. The model was calibrated using 2022 season data and validated with 2023 season data. It was tested in a furrow irrigation system with four irrigation water levels: 100% (T1), 80% (T2), 60% (T3), and 40% (T4). Results showed average grain yields of 4.16 t ha−1 (T1), 3.83 t ha−1 (T2), 3.02 t ha−1 (T3), and 2.23 t ha−1 (T4), with water productivity ranging from 0.65 to 0.74. Evaluation performance of the DSSAT-CROPGRO model included soil moisture R2 (0.86–0.89) & RMSE (0.024–0.052), Grain yield R2 (0.95–0.97) & RMSE (0.03–0.05), dry biomass R2 (0.88–0.93) & RMSE (0.013–0.023), and Leaf area index R2 (0.76–0.91) & RMSE (0.028–0.03). The model’s reliability in predicting deficit irrigation impacts on land productivity offers valuable insights for tailored irrigation management in peanut cultivation in water-scarce areas like Arba Minch. These findings aid the development of sustainable agricultural approaches. Researchers and policymakers can leverage this model to improve water management and integrate it with agricultural practices for informed decision-making.
Assessing irrigation water management utilizing the CROPGRO model in implementing deficit irrigation for peanut cultivation in Arba Minch, Ethiopia
This study evaluates irrigation strategies using the CROPGRO model to improve peanut farming in Arba Minch, Ethiopia. Given water scarcity, adopting deficit irrigation methods is vital for sustainable agriculture. Various deficit irrigation scenarios are analyzed to understand their impacts on peanut yield, water efficiency, and crop growth. The model was calibrated using 2022 season data and validated with 2023 season data. It was tested in a furrow irrigation system with four irrigation water levels: 100% (T1), 80% (T2), 60% (T3), and 40% (T4). Results showed average grain yields of 4.16 t ha−1 (T1), 3.83 t ha−1 (T2), 3.02 t ha−1 (T3), and 2.23 t ha−1 (T4), with water productivity ranging from 0.65 to 0.74. Evaluation performance of the DSSAT-CROPGRO model included soil moisture R2 (0.86–0.89) & RMSE (0.024–0.052), Grain yield R2 (0.95–0.97) & RMSE (0.03–0.05), dry biomass R2 (0.88–0.93) & RMSE (0.013–0.023), and Leaf area index R2 (0.76–0.91) & RMSE (0.028–0.03). The model’s reliability in predicting deficit irrigation impacts on land productivity offers valuable insights for tailored irrigation management in peanut cultivation in water-scarce areas like Arba Minch. These findings aid the development of sustainable agricultural approaches. Researchers and policymakers can leverage this model to improve water management and integrate it with agricultural practices for informed decision-making.
Assessing irrigation water management utilizing the CROPGRO model in implementing deficit irrigation for peanut cultivation in Arba Minch, Ethiopia
Asres Getnet Workie (author) / Yohannes Smeneh Ketsela (author)
2025
Article (Journal)
Electronic Resource
Unknown
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