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With farm-scale knowledge of how production practices affect water quality, land managers and agricultural producers can make more informed decisions on implementing soil and water conservation practices that sustain productivity and protect water resources. The Lower Mississippi River Basin is a major agricultural production region for rice (Oryza sativa L.), cotton (Gossypium hirsutum L.), and soybean (Glycine max [L.] Merr.); however, there is limited knowledge on how agronomic practices in those southern cropping systems affect water quality. Results from environmental models indicate commercial fertilizer from agricultural fields contributes substantially to the hypoxic zone in the northern Gulf of Mexico. To expand understanding of runoff water quality at the field scale, edge-of-field water quality monitoring studies were carried out in nine commercial rice, cotton, and soybean fields in northeastern Arkansas over multiple seasons. These year-round monitoring activities generated 23 site-years of data on sediment and nutrient concentrations and loads with various soils and crop rotation regimes. Discharge from runoff, nitrite-nitrogen (NO2-N), nitrate-nitrogen (NO3-N), total N, soluble phosphorus (P), total phosphorus (TP), and suspended sediment concentration on a per-event basis were measured. The results indicate that the nongrowing season loads and concentrations of several measured components were statistically higher than those measured during the growing season, lending support to the need for off-season practices such as winter cover crops and winter shallow water storage. Lower concentrations and loads of nutrients and sediment were observed in rice fields compared to cotton and soybean systems. These differences are likely due to soil type but are also related to the water management system of flooded rice fields compared to furrow irrigated row crops. These findings provide baseline runoff water quality data and will help inform regional budgets of nutrients and sediment loss. Such information can assist land managers and conservationists in directing resources more effectively. These data also will support modelers in their efforts to calibrate, verify, validate, and estimate uncertainty for simulations of nutrients and sediment loss.

Initial findings from agricultural water quality monitoring at the edge-of-field in Arkansas