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Title: Strategic and Tactical Prediction of Forage Production in Northern Mixed-Grass Prairie

Author
item Andales, Allan
item Derner, Justin
item Ahuja, Lajpat
item Hart, Richard

Submitted to: Rangeland Ecology and Management
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/26/2006
Publication Date: 11/28/2006
Citation: Andales, 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 and Management. 59 (6): 576-584. doi: 10.2111/06-001R1.1.

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. This study assessed the applicability of the 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 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 underestimates non-exceedence probabilities for PSC < 1125 kg ha-1 and overestimates otherwise. The generated CDF could be used strategically to estimate long-term forage production at various levels of probability, with reasonable errors. Within-season forecasts explained 77% to 94% of biomass variability in 1983. It was shown that monthly updating of the March 1 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. Key Words: Northern Great Plains, precipitation, simulation model

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 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 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 underestimates non-exceedence probabilities for PSC < 1125 kg ha-1 and overestimates otherwise. The generated CDF could be used strategically to estimate long-term forage production at various levels of probability, with reasonable errors. Within-season forecasts explained 77% to 94% of biomass variability in 1983. It was shown that monthly updating of the March 1 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. Key Words: Northern Great Plains, precipitation, simulation model