Subsurface drain flow and crop yield predictions for different drain spacings using DRAINMOD |
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| Institution: | 1. Texas Agricultural Experiment Station, Blackland Research Center, 720 East Blackland Road, Temple, TX 76502, USA;2. Agri-Waste Technology, Inc., Raleigh, North Carolina, NC, USA;3. Department of Agricultural and Biological Engineering, 225 S University Street, Purdue University, West Lafayette, IN 47907-2093, USA;4. Department of Agronomy, Lilly Hall of Life Sciences, 915 W. State Street, Purdue University, West Lafayette, IN 47907-2054, USA;1. Department of Biological Sciences, 192 Galvin Life Sciences, University of Notre Dame, Notre Dame, IN 46556, United States;2. Soil Drainage Research Unit, USDA ARS, 590 Woody Hayes Drive, Columbus, OH 43210, United States;3. School of Public and Environmental Affairs, 1315 E. 10th Street, Indiana University, Bloomington, IN 47405, United States;1. USDA-ARS Soil Drainage Research Unit, 590 Woody Hayes Drive, Columbus, OH 43210, United States;2. The Ohio State University, Food Agricultural and Biological Engineering Department, 590 Woody Hayes Drive, Columbus, OH 43210, United States;1. Department of Irrigation and Drainage, China Institute of Water Resources and Hydropower Research, Beijing, 100048, China;2. National Center of Efficient Irrigation Engineering and Technology Research—Beijing, Beijing, 100048, China;3. State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, 100038, China;4. Water Resources Research Institute of Anhui Province and Huaihe River Commission, Ministry of Water Resources, Bengbu, 233000, China;5. Key Laboratory of Water Conservancy and Water Resources of Anhui Province, Bengbu, 233000, China;1. Department of Water Engineering, Sari Agricultural Sciences and Natural Resources University, Sari, Iran;2. Department of Irrigation and Drainage Engineering, Tarbiat Modares University, Tehran, Iran;3. Water Engineering Department, Sari Agricultural Sciences and Natural Resources University, Sari, Iran;4. College of Agriculture, Payame Noor University, Tehran, Iran;5. Agriculture Research, Education and Extension Organization, Tehran, Iran;1. BlueLeaf Inc., 310 Chapleau Street, Drummondville, Quebec, J2B 5E9, Canada;2. Queen''s University, Department of Civil Engineering, Ellis Hall, 58 University Avenue, Kingston, Ontario, K7L 3N9, Canada;3. McGill University, Department of Natural Resource Science, Macdonald Campus, 21,111 Lakeshore Road, Ste-Anne-de Bellevue, Quebec, H9X 3V9, Canada |
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| Abstract: | DRAINMOD was run for 15 years to predict and compare drain flow for three drain spacings and crop yield for four drain spacings at the Southeastern Purdue Agricultural Center (SEPAC). Data from two continuous years of daily drain flow from one spacing were used to calibrate the eight most uncertain parameters using a multi-objective calibration function and an automatic calibration method. The model was tested using the remaining field data for the 5, 10, and 20 m drain spacings for drain flow and the additional 40 m spacing for yield predictions. Nash–Sutcliffe efficiency (EF) for daily drain flow simulations for the calibration years and drain spacing ranged from 0.62 to 0.79. The daily EF for model testing ranged from ?0.66 to 0.81, with the average deviations of 0.01 to 0.07 cm/day and standard errors of 0.03–0.17 cm/day. On a monthly basis, 91% of plot years had EF values over 0.5 and 76% over 0.6 for years with on-site rainfall data. The total yearly drain flow was predicted within ±25% in 71% of plot years, and within ±50% in 93% of plot years with on-site rainfall data. Statistical tests of daily drain flow EF values for three spacings and percent errors of crop relative yield for four spacings indicated that the reliability of the model is not significantly different among different spacings, supporting the use of DRAINMOD to study the efficiencies of different drain spacings and to guide the drain spacing design for specific soils. In general, the model correctly predicted the pattern of yearly relative yield change. The relative corn (Zea mays L.) and soybean (Glycine max L.) yields were well predicted on average, with percent errors ranging from 1.3 to 9.7% for corn and from ?3.3 to 10.3% for soybean. |
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