The evaluation of soil erosion is often assessed using traditional soil-loss models such as the Revised Universal Soil-Loss Equation (RUSLE) and the Soil and Water Assessment Tool (SWAT). These models provide quantitative outputs for sediment yield and are often integrated with geographic information systems (GIS). The work described here is focused on transitioning towards a qualitative assessment of erosion potential using Multi-Criteria Decision Analysis (MCDA), for improved decision-support and watershed-management prioritization in a northern Gulf of Mexico coastal watershed. The foundation of this work conceptually defined watershed erosion potential based on terrain slope, geomorphology, land cover, and soil erodibility (as defined by the soil K-factor) with precipitation as a driver. These criteria were evaluated using a weighted linear combination (WLC) model to map generalized erosion potential. The sensitivity of individual criteria was accessed with the one-at-a-time (OAT) method, which simply removed one criterion and re-evaluated erosion potential. The soil erodibility and slope were found to have the most influence on erosion-potential modeling. Expert input was added through MCDA using the Analytical Hierarchy Process (AHP). The AHP allows for experts to rank criteria, providing a quantitative metric (weight) for the qualitative data. The individual AHP weights were altered in one-percent increments to help identify areas of alignment or commonality in erosion potential across the drainage basin. These areas were used to identify outliers and to develop an analysis mask for watershed management area prioritization. A comparison of the WLC, AHP, ensembled model (average of WLC and AHP models), and SWAT output data resulted in visual geographic alignment between the WLC and AHP erosion-potential output with the SWAT sediment-yield output. These observations yielded similar results between the qualitative and quantitative erosion-potential assessment approaches, with alignment in the upper and lower ranks of the mapped erosion potentials and sediment yields. The MCDA, using the AHP and ensembled modeling for mapping watershed potential, provided the advantage of more quickly mapping erosion potential in coastal watersheds for improved management of the environmental resources linked to erosion.

Climate change and its impact on agriculture are challenging issues regarding food production and food security. Many researchers have been trying to show the direct and indirect impacts of climate change on agriculture using different methods. In this study, we used linear regression models to assess the impact of climate on crop yield spatially and temporally by managing irrigated and non-irrigated crop fields. The climate data used in this study are Tmax (maximum temperature), Tmean (mean temperature), Tmin (minimum temperature), precipitation, and soybean annual yields, at county scale for Mississippi, USA, from 1980 to 2019. We fit a series of linear models that were evaluated based on statistical measurements of adjusted R-square, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). According to the statistical model evaluation, the 1980–1992 model Y[Tmax,Tmin,Precipitation]_92i (BIC = 120.2) for irrigated zones and the 1993–2002 model Y[Tmax,Tmean,Precipitation]_02ni (BIC = 1128.9) for non-irrigated zones showed the best fit for the 10-year period of climatic impacts on crop yields. These models showed about 2 to 7% significant negative impact of Tmax increase on the crop yield for irrigated and non-irrigated regions. Besides, the models for different agricultural districts also explained the changes of Tmax, Tmean, Tmin, and precipitation in the irrigated (adjusted R-square: 13–28%) and non-irrigated zones (adjusted R-square: 8–73%). About 2–10% negative impact of Tmax was estimated across different agricultural districts, whereas about −2 to +17% impacts of precipitation were observed for different districts. The modeling of 40-year periods of the whole state of Mississippi estimated a negative impact of Tmax (about 2.7 to 8.34%) but a positive impact of Tmean (+8.9%) on crop yield during the crop growing season, for both irrigated and non-irrigated regions. Overall, we assessed that crop yields were negatively affected (about 2–8%) by the increase of Tmax during the growing season, for both irrigated and non-irrigated zones. Both positive and negative impacts on crop yields were observed for the increases of Tmean, Tmin, and precipitation, respectively, for irrigated and non-irrigated zones. This study showed the pattern and extent of Tmax, Tmean, Tmin, and precipitation and their impacts on soybean yield at local and regional scales. The methods and the models proposed in this study could be helpful to quantify the climate change impacts on crop yields by considering irrigation conditions for different regions and periods.