Abstract

The environmental impacts of agricultural drainage have become a critical issue. There is a need to design and manage drainage and related water table control systems to satisfy both crop production and water quality objectives. The model DRAINMOD-N was used to study long-term effects of drainage system design and management on crop production, profitability, and nitrogen losses in two poorly drained soils typical of eastern North Carolina (NC), USA. Simulations were conducted for a 20-yr period (1971–1990) of continuous corn production at Plymouth, NC. The design scenarios evaluated consisted of three drain depths (0.75, 1.0, and 1.25 m), ten drain spacings (10, 15, 20, 25, 30, 40, 50, 60, 80, and 100 m), and two surface conditions (0.5 and 2.5 cm depressional storage). The management treatments included conventional drainage, controlled drainage during the summer season and controlled drainage during both the summer and winter seasons. Maximum profits for both soils were predicted for a 1.25 m drain depth and poor surface drainage (2.5 cm depressional storage). The optimum spacings were 40 and 20 m for the Portsmouth and Tomotley soils, respectively. These systems however would not be optimum from the water quality perspective. If the water quality objective is of equal importance to the productivity objective, the drainage systems need to be designed and managed to reduce NO 3–N losses while still providing an acceptable profit from the crop. Simulated results showed NO 3–N losses can be substantially reduced by decreasing drain depth, improving surface drainage, and using controlled drainage. Within this context, NO 3–N losses can be reduced by providing only the minimum subsurface drainage intensity required for production, by designing drainage systems to fit soil properties, and by using controlled drainage during periods when maximum drainage is not needed for production. The simulation results have demonstrated the applicability of DRAINMOD-N for quantifying effects of drainage design and management combinations on profits from agricultural crops and on losses of NO 3–N to the environment for specific crop, soil and climatic conditions. Thus, the model can be used to guide design and management decisions for satisfying both productivity and environmental objectives and assessing the costs and benefits of alternative choices to each set of objectives.

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