The University of Arizona

Spring Precipitation as a Predictor for Peak Standing Crop of Mixed-Grass Prairie

L. J. Wiles, Gale Dunn, Jeff Printz, Bob Patton, Anne Nyren

Abstract


Ranchers and range managers need a decision support tool that provides a reasonably accurate prediction of forage growth potential early in the season to help users make destocking decisions. Erroneous stocking rate decisions can have dire economic and environmental consequences, particularly when forage production is low. Predictions must be based on information that is easily obtained and relevant to the particular range. Our goal was to evaluate monthly precipitation in spring months as a potential predictor of forage production compared to annual and growing-season precipitation. We analyzed the relationships between grazed and ungrazed peak standing crop (PSC) and precipitation using nonlinear regression and a plateau model, Akaike’s information criterion for model selection, and data from three locations: Streeter, North Dakota; Miles City, Montana; and Cheyenne, Wyoming. The plateau model included a linear segment, representing precipitation limiting production, and a plateau, an estimate of average production when precipitation is no longer the limiting factor. Both the response and predictor variables were rescaled so variability in production from average production was related to variability in precipitation from the long-term average. We found that grazing did not affect the relationship between PSC and precipitation, nor were annual or growing-season precipitation good predictor variables. The best predictor variable was total precipitation in April and May for Montana, May and June for North Dakota, and April, May, and June for Wyoming, with r2 ranging from 0.74 to 0.79 for precipitation less than long-term average. These results indicate that spring precipitation provides useful information for destocking decisions and can potentially be used to develop a decision support tool, and the results will guide our choice of possible predictor models for the tool.


Keywords


Akaike’s information criteria, decision support tools, rangeland drought, stocking decisions

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