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<p>Climate change is influencing the livelihoods of rainfed dependent economy. This study aimed to project future changes in climate, and its impacts on teff yield in northwestern Ethiopia. Both observed (1981–2020) and projected (2040–2100) climate data were used mainly to understand the impact of climate dynamics on teff yield, while observed teff yield (2006–2020) was used for model training and validation. The distribution mapping (DM) and power transformation (PT) bias correction methods were utilized for temperature and rainfall data bias correction. Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest (RF) algorithms were used for impact analysis. Annual precipitation is projected to increase by 4.19% (5.67%) under the Shared Socioeconomic Pathways (SSP1-2.6) by the middle (end) of the century and expected to increase by 12.91% (17.51%) under SSP5-8.5 by the middle (end) of the century. The mean annual maximum temperature is projected to increase by 0.36&#xa0;°C (0.34&#xa0;°C) under SSP1-2.6 to 1.27&#xa0;°C (2.97&#xa0;°C) under SSP5-8.5 by the middle (end) of the century. Random Forest (RF) showed better performance with a MAE of 71.87&#xa0;kg/ha, RMSE of 97.93&#xa0;kg/ha, and coefficient of determination (R<sup>2</sup>) of 0.84. Results show that teff yield is projected to decline by 2.4% (4.25%) under SSP1-2.6 to 10.2% (12.16%) under SSP5-8.5 by the middle (end) of the century. Proactive adaptation strategies such as using high rainfall and heat tolerant varieties are necessary to mitigate the projected decrease in teff yield.</p>

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Modeling future climate change effects on teff (Eragrostis tef) yield under CMIP6 scenarios in Horro Guduru Wallaga zone, northwestern Ethiopia

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  • 发表日期：

    2026年5月31日

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  • Tolamariam Chimdessa Deressa,
  • Fedhasa Benti Chalchissa,
  • Dessalegn Obsi Gemeda

摘要

Climate change is influencing the livelihoods of rainfed dependent economy. This study aimed to project future changes in climate, and its impacts on teff yield in northwestern Ethiopia. Both observed (1981–2020) and projected (2040–2100) climate data were used mainly to understand the impact of climate dynamics on teff yield, while observed teff yield (2006–2020) was used for model training and validation. The distribution mapping (DM) and power transformation (PT) bias correction methods were utilized for temperature and rainfall data bias correction. Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest (RF) algorithms were used for impact analysis. Annual precipitation is projected to increase by 4.19% (5.67%) under the Shared Socioeconomic Pathways (SSP1-2.6) by the middle (end) of the century and expected to increase by 12.91% (17.51%) under SSP5-8.5 by the middle (end) of the century. The mean annual maximum temperature is projected to increase by 0.36 °C (0.34 °C) under SSP1-2.6 to 1.27 °C (2.97 °C) under SSP5-8.5 by the middle (end) of the century. Random Forest (RF) showed better performance with a MAE of 71.87 kg/ha, RMSE of 97.93 kg/ha, and coefficient of determination (R2) of 0.84. Results show that teff yield is projected to decline by 2.4% (4.25%) under SSP1-2.6 to 10.2% (12.16%) under SSP5-8.5 by the middle (end) of the century. Proactive adaptation strategies such as using high rainfall and heat tolerant varieties are necessary to mitigate the projected decrease in teff yield.