Modeling the Impact of Climate Changes on Crop Yield: Irrigated vs. Non-Irrigated Zones in Mississippi

Climate change and its impact on agriculture are challenging issues regarding food production security. Many researchers have been trying to show the direct indirect impacts of climate using different methods. In this study, we used linear regression models assess crop yield spatially temporally by managing irrigated non-irrigated fields. The data in study Tmax (maximum temperature), Tmean (mean Tmin (minimum precipitation, soybean annual yields, at county scale for Mississippi, USA, from 1980 2019. We fit a series that were evaluated based statistical measurements adjusted R-square, Akaike Information Criterion (AIC), Bayesian (BIC). According model evaluation, 1980–1992 Y[Tmax,Tmin,Precipitation]92i (BIC = 120.2) zones 1993–2002 Y[Tmax,Tmean,Precipitation]02ni 1128.9) showed best 10-year period climatic yields. These about 2 7% significant negative increase regions. Besides, agricultural districts also explained changes Tmax, Tmean, Tmin, precipitation (adjusted R-square: 13–28%) 8–73%). About 2–10% was estimated across districts, whereas −2 +17% observed districts. modeling 40-year periods whole state Mississippi (about 2.7 8.34%) but positive (+8.9%) during growing season, both Overall, assessed yields negatively affected 2–8%) zones. Both increases respectively, This pattern extent their local regional scales. methods proposed could be helpful quantify considering irrigation conditions regions periods.