|Printz, Jeff -|
Submitted to: Rangeland Ecology and Management
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: September 1, 2013
Publication Date: September 1, 2013
Citation: Dunn, G.H., Gutwein, M., Green, T.R., Menger, A.L., Printz, J.L. 2013. The drought calculator: decision support tool for predicting forage growth during drought. Rangeland Ecology and Management. 66(5): 570-578. Interpretive Summary: Reduced FGP as a result of drought can be predicted beginning in May at some locations with a weighted average of monthly total precipitation. Accuracy of predictions earlier in the season than May will depend on the accuracy of forecasting precipitation. The model is simple and quick to use, which are essential characteristics of any decision tool. It is incorporated in a spreadsheet designed with the guidance of rangeland managers. Predictions are easily interpreted as FGP relative to average production at a location and the monthly precipitation is readily available from multiple sources. Results indicate that the tool is useful for discriminating drought effects on FGP classification being above or below the long-term average, which is a basis for stocking decisions. Model efficiency statistics for drought years based on cross validation shows that the decision tool is more accurate than simply assuming the long-term mean of those below average years, which implies that it provides a quantitative advantage to producers for their stocking decisions in drought years. Use of this decision tool is most likely limited by the available data used to determine the weights of monthly precipitation for prediction of FGP.
Technical Abstract: The Drought Calculator (DC), a spreadsheet-based decision support system, was developed to help ranchers and range managers predict reductions in forage production due to drought. Forage growth potential (FGP) is predicted as a weighted average of monthly precipitation during the spring. Precipitation and forage production, standardized to a value of one to reflect the long-term average, can be interpreted as the proportion of average production. To evaluate use of the DC across the Great Plains, a spreadsheet was developed that uses historical production data to calibrate the model for a location. The accuracy of the model and calibration method was then tested using data from Wyoming (WY) and North Dakota (ND), USA. In ND, FGP was most sensitive to variation in precipitation in May and June, and in WY, to variation in April, May and June. Weights in these months ranged from 0.25 to 0.39. Prediction was better for WY than ND. For WY, the model identified 86% of the years with FGP reduced by drought when precipitation from January through June was used for the prediction. Users may expect that when below average FGP is predicted, the likelihood that FGP will be below average is 86% and FGP will be overestimated by 50% of average production. For ND, only 67% of the drought years were predicted and the likelihood that FGP will be mistakenly predicted as below average is 50%. Predicting FGP earlier than April in WY will require accurate forecasts of April-June precipitation. Use of the DC is most limited by insufficient forage data to determine the significance of precipitation in that month for FGP. Nonetheless, the results indicate that the decision tool is useful for discriminating drought effects on FGP classification being above or below the long-term average, and it provides a quantitative advantage to producers for their stocking decisions in drought years.