Submitted to: Biological Systems Simulation Group Proceedings
Publication Type: Abstract only
Publication Acceptance Date: 3/2/2006
Publication Date: 4/11/2006
Citation: Andales, A.A., Derner, J.D., Ahuja, L.R., Hart, R.H. 2006. Applications of a forage production model in semi-arid northern mixed-grass prairie. [Abstract]. 36th Biological Systems Simulation Group Conference, Fort Collins, CO; Apr. 11-13, 2006. Interpretive Summary: Predictions of forage production derived from site-specific environmental information (e.g. soil type, weather, plant community composition, etc.) could help land managers decide on appropriate stocking rates of livestock. The USDA-ARS GPFARM forage growth model was used to predict forage production at a northern mixed-grass prairie site in Cheyenne, WY. Long-term (20-years) and short-term (within-season) predictions were made using site-specific soil, plant, and weather information. The 20-year simulation of peak standing crop (PSC) using historical weather data (1982-2001) was used to estimate chances of occurrence of different amounts of PSC. The estimated chances had an error of 0% to 18% compared to observed PSC. The accuracy of short-term (within-season) predictions of forage production depended on the accuracy of the weather forecast. It was found that initial soil water content on May 1 along with April-May total precipitation might be useful in predicting PSC in the coming growing season.
Technical Abstract: Predictions of forage production derived from site-specific environmental information (e.g. soil type, weather, plant community composition, etc.) could help land managers decide on appropriate stocking rates of livestock. This study assessed the applicability of the GPFARM forage growth model (Andales et al., 2005) for both strategic (long-term) and tactical (within-season) prediction of forage production in northern mixed-grass prairie. An improved version of the model (Andales et al., 2006) was calibrated for conditions at the USDA-ARS High Plains Grasslands Research Station in Cheyenne, Wyoming. Long-term (1982-2001) simulations of peak standing crop (PSC) were compared to observations. Also, within-season (1983) forecasts of total aboveground biomass made on March 1, April 1, May 1, and June 1 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 exceedence probabilities for PSC < 1125 kg•ha-1 and underestimates otherwise. The generated CDF could be used strategically to estimate long-term forage production at various levels of probability, with errors in exceedence probability ranging from 0.0 to 0.18. Within-season forecasts (using normal weather) explained 77% to 94% of biomass variability in 1983. Monthly updating of the March 1 forecast, with input of actual weather to date, improved accuracy. For the northern mixed-grass prairie site considered in this study, we found that simulations of PSC were directly related to total precipitation from April to May (Fig. 2). This was found to be true at different levels of initial soil water content (SWCi) on May 1, 1983. Each SWCi value was input into the model as the initial condition for the May 1, 1983 driest, normal, and wettest forecasts. Thus, a family of simulated curves may be used to forecast PSC based on knowledge of initial soil water content and anticipated level of April – May precipitation at the site. Further development and testing of the forage simulation model will focus on the interactions between forage growth, environmental perturbations (especially drought), and grazing.