Context: Cultivation and crop rotation, influenced by federal policy, prices, and precipitation, are significant sources of land-cover heterogeneity. Characterization of heterogeneity is required to identify areas and trends of stability or change. Objectives: We analyzed a land-cover time series within a prominent agroecosystem in the US, the Yazoo-Mississippi Delta (the Delta), as a case study of which metrics capture dynamics of landscape composition, configuration, connectivity, and context. Methods: An assessment of land cover- from 2008 to 2021- was conducted and analyzed for potential differences among three Farm Bill eras. Twelve out of 23 metrics (including three new ones presented herein) examined were useful in characterizing land-cover heterogeneity. Results: Although there was no increase in cultivated land, > 72% of the Delta experienced changes in land-cover type, and ~ 3% of the Delta was stable monoculture. Configurational metrics varied across years for soybeans, cotton, and rice, indicating prevalence of field-level changes in composition; connectivity metrics revealed isolation of upland forest and rice. The amount of corn was positively associated with the previous year’s commodity prices and negatively with precipitation whereas soybean acreage was lower in high-precipitation years and more dependent on commodity prices. Farm Bill effects were mixed among categories, whereas CRP generally declined. Conclusions: The Delta experienced land-cover change with no net loss or gain of cultivated lands. Using 12 metrics that captured temporal shifts in spatial patterns, we characterized this agroecosystem as a shifting mosaic. Our approach may be useful for identifying areas of spatio-temporal heterogeneity or stability, with implications on resource management.

In situ groundwater monitoring is critical for irrigated agroecosystems and informs land cover changes. Yet, such data can pose management challenges and confound agroecological relationships. Correspondingly, satellite-based approaches, including the GRACE-constellation, are increasing. Although in situ and GRACE-derived comparisons occur, limited research considers agroecological dependencies. Herein, we examined differences in groundwater monitoring approaches (observed [in situ, O] vs. predicted [GRACE-derived, P]) within the Yazoo–Mississippi Delta (YMD), an agroecosystem in the southeastern USA. We compared variations in modeled groundwater hydrology, land cover, and irrigation dynamics of the YMD within the upper-quartile (UQ) area of interest (AOI) (highest groundwater levels) and lower-quartile (LQ) AOI (lowest groundwater levels) every year from 2008 to 2020. Spatially, OUQ and PUQ were in northern portions of the YMD, with the OLQ and PLQ in southern portions. Groundwater levels between OUQ:PUQ and OLQ:PLQ each had correlations > 0.85. Regarding land cover, most categories varied within ±2.50% between model estimates over time. Relatedly, we documented 14 instances where correlations between land use category and groundwater level were inverted across models (OLQ:PLQ (5), OUQ:OLQ (6), PUQ:PLQ (3)). Irrigation results were not statistically different among all models. Overall, our results highlight the importance of quantifying model incongruences for groundwater and land cover management.

This study measured 17 surface water quality (WQ) parameters in a 731 ha Conservation Effects Assessment Project watershed, Beasley Lake, in Mississippi, under varying flooding conditions from 2008 to 2022. To assess flooding impacts on lake WQ, data from before and after flood events were compared among three flood‐stage categories: minor flooding at 30.95 m mean sea level (MSL), 99.1 ha of the watershed inundated affecting 27.9 ha under conservation practices (CPs); moderate flooding at 32.17 m MSL, 235.1 ha of the watershed inundated affecting 69.2 ha under CPs; and major flooding at 32.78 m MSL, 331.8 ha of the watershed inundated affecting 87.0 ha under CPs. During minor flood events, only nitrite significantly increased (82%; p < 0.05) from pre‐ to post‐flood conditions. For moderate flood events, dissolved solids decreased 21%, whereas suspended solids increased 259%, and most nutrients increased from 33% to 199% (ammonium) except nitrate and N:P ratios. With major flooding, dissolved solids also decreased 22% and suspended solids increased 237%, with increased phosphorus from 25% to 164% (orthophosphate) and decreased N:P (36%). Correlation analysis showed greater flood duration (in days) and magnitude (as river stage) increased suspended solids and nutrient concentrations. Weaker relationships were observed with watershed and CP area inundation and WQ. Results will provide landowners and action agencies with predictive changes in WQ parameters based on flooding stage and potential impositions of effects through watershed areas that coincide with CPs.

Groundwater depletion poses a critical challenge to global water security, threatening ecosystems, agriculture, and sustainable development. The Mississippi Delta, a region heavily reliant on groundwater for agriculture, has experienced significant groundwater level declines due to intensive irrigation. Traditional in situ monitoring methods, while valuable, lack the spatial coverage necessary to capture regional groundwater dynamics comprehensively. This study addresses these limitations by leveraging downscaled Gravity Recovery and Climate Experiment (GRACE) data to estimate groundwater levels using random forest modeling (RFM). We applied a machine-learning approach, utilizing the “Forest-based and Boosted Classification and Regression” tool in ArcGIS Pro, (ESRI, Redlands, CA) to predict groundwater levels for April and October over a 10-year period. The model was trained and validated with well-water level records from over 400 monitoring wells, incorporating input variables such as NDVI, temperature, precipitation, and NLDAS data. Cross-validation results demonstrate the model’s high accuracy, with R² values confirming its robustness and reliability. The outputs reveal significant groundwater depletion in the central Mississippi Delta, with the lowest water level observed in the eastern Sunflower and western Leflore Counties. Notably, April 2014 recorded a minimum water level of 18.6 m, while October 2018 showed the lowest post-irrigation water level at 54.9 m. By integrating satellite data with machine learning, this research provides a framework for addressing regional water management challenges and advancing sustainable practices in water-stressed agricultural regions.