Strategic and Tactical Prediction of Forage Production in Northern Mixed-Grass Prairie
Keywordsdecision support tools
Great Plains Framework for Agricultural Resource Management (GPFARM)
Northern Great Plains
peak standing crop
MetadataShow full item record
CitationAndales, A. A., Derner, J. D., Ahuja, L. R., & Hart, R. H. (2006). Strategic and tactical prediction of forage production in northern mixed-grass prairie. Rangeland Ecology & Management, 59(6), 576-584.
PublisherSociety for Range Management
JournalRangeland Ecology & Management
AbstractPredictions 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 the applicability of the Great Plains Framework for Agricultural Resource Management (GPFARM) forage growth model for both strategic (long-term) and tactical (within-season) prediction of forage production in northern mixed-grass prairie. An improved version 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 onward were compared to observations. The normal, driest, and wettest weather years on record (1915-1981) were used to simulate expected values, lower bounds, and upper bounds of biomass production, respectively. The forage model explained 66% of the variability in PSC from 1983 to 2001. The cumulative distribution function (CDF) derived from long-term simulated PSC overestimates cumulative probabilities for PSC.1 500 kg ha-1. The generated CDF could be used strategically to estimate long-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 forage forecast, with input of actual weather to date, improves accuracy. Further development and testing of the forage simulation model will focus on the interactions between forage growth, environmental perturbations (especially drought), and grazing.