Strategic and Tactical Prediction of Forage Production in Northern Mixed-Grass Prairie
Abstract
Predictions of forage production derived from site-specific environmental information (e.g., soil type, weather, plant communitycomposition, and so on) could help land managers decide on appropriate stocking rates of livestock. This study assessed theapplicability of the Great Plains Framework for Agricultural Resource Management (GPFARM) forage growth model for bothstrategic (long-term) and tactical (within-season) prediction of forage production in northern mixed-grass prairie. An improvedversion of the model was calibrated for conditions at the USDA–ARS High Plains Grasslands Research Station in Cheyenne,Wyoming. Long-term (1983–2001) simulations of peak standing crop (PSC) were compared to observations. Also, within-season(1983) forecasts of total aboveground biomass made for 1 March onward, 1 April onward, 1 May onward, and 1 June onwardwere compared to observations. The normal, driest, and wettest weather years on record (1915–1981) were used to simulateexpected values, lower bounds, and upper bounds of biomass production, respectively. The forage model explained 66% of thevariability in PSC from 1983 to 2001. The cumulative distribution function (CDF) derived from long-term simulated PSCoverestimates cumulative probabilities for PSC.1 500 kgha1. The generated CDF could be used strategically to estimatelong-term forage production at various levels of probability, with errors in cumulative probability ranging from 0.0 to 0.16.Within-season forecasts explained 77%–94% of biomass variability in 1983. It was shown that monthly updating of the forageforecast, with input of actual weather to date, improves accuracy. Further development and testing of the forage simulation modelwill focus on the interactions between forage growth, environmental perturbations (especially drought), and grazing.