Abstract:
Climate change poses a serious threat to agricultural production, especially Ethiopia’s
agricultural system, dominantly rain-fed farming, is highly vulnerable to these changes and
modeling its impact can help to mitigate the risks and improve yield productivity. The maize,
dominant crop, productivity under changing climate is not well understood in South Omo, Southern
Ethiopia. This study aims to model the impact of future climate on maize productivity and identify
adaptation strategies. The observed climate data was obtained from the Ethiopian Meteorological
Institute, crop yield data from respective agricultural districts, and soil data from the Soil Grids
website. Mann–Kendall’s trend test and Sen’s slope estimator were used to assess the significance
of changes and magnitude in rainfall and temperature. Climate projections for the near-term
(2015-2040), mid-term (2041-2070), and long-term (2071-2100) utilized five GCMs (CNRM-CM6
1, GFDL-ESM4, MIROC-6, MPI-ESM1-2-HR and UKESM1-0-LL) under different scenarios
(SSP126, SSP370, and SSP585). The DSSAT-CERES maize crop model was calibrated and
validated to assess the climate change impact on maize productivity and identify optimal
adaptation measures. The performance of the model was evaluated by statistical metrics like
RMSE, MAE and D-index. Analysis of past climate trends revealed that the annual Tmin and Tmax
have increased by 0.027°C and 0.047°C per year, respectively, while rainfall has exhibited low to
moderate variability from 1985-2014. The projected Tmin and Tmax are expected to increase by
0.8°C to 7.7°C and 0.3°C to 6.5°C respectively across all models, under SSP scenarios at all time
periods. Rainfall is projected to change by -18.0 % to 24.6% across models, with most model’s
project reductions in the area. The climate change impact analysis shows a strong impact on maize
productivity across the districts and yield will be declined by 2.6% to 23.4% in Maale, change by -28.1% to 1.7% in Benatsemay, -41.9% to 13.9% in Hammer district due to projected increasing
temperature and erratic variations of rainfall. From the model simulations it is recommended to
shift the sowing dates to early sowing (March 14), increasing planting density (8.8 plants/m2) and
applying above the recommended nitrate level (69kg/ha) to minimize the risk and increase the yield
in the study region.
