Application of the nitrogen-tillage-residue-management (NTRM) model for corn grown in low-input and conventional agricultural systems

Radke, J.K., Shaffer, M.J., Kroll, K.S. and Saponara, J., 1991. Application of the nitrogentillage-residue-management (NTRM) model for corn grown in low-input and conventional agricultural systems. Ecol. Modelling, 55: 241-255. Unlike many conventional agricultural systems, low-input management techniques minimize the use of nutrients and pesticides produced off the farm. This study evaluates the performance of the NTRM model in simulating low-input and conventionally managed field corn (Zea mays L.) grown in a comparative cropping systems experiment initiated in 1981 on a site in southeastern Pennsylvania, USA. NTRM is a comprehensive model that requires data on weather, soil properties, management, and crop characteristics. For our study, it predicted daily soil water, nitrogen, and temperature with depth; daily biomass and leaf area; and final grain yield. Conventionally grown corn in 1985 was used for yield calibration of the model; other data collected in 1985, 1986, and 1987 provided validation. Early model runs gave accurate soil temperature predictions but poor soil water predictions compared to field measurements. Model simulations suggested an underground water source later verified at the field site. A modification of NTRM resulted in greatly improved soil water predictions. Simulation of a hairy vetch (Vicia villosa Roth.) plowdown showed the need for more detailed submodels of residue incorporation and decomposition. NTRM generally provided accurate estimates of harvest biomass, grain yield, and soil NO3-N levels throughout the season. On-going model additions, such as crop-weed interactions and intercropping, will enhance NTRM as a model for simulating low-input systems. 1 Present address: USDA Agricultural Research Service, National Soil Tilth Lab, Ames, IA 50011, USA. 2 Present address: Cornell University, Ithaca, NY 14853, USA. 0304-3800/91/$03.50 © 1991 Elsevier Science Publishers B.V. 242 J .K. R A D K E ET AI.