Livestock Management During Drought in the Northern Great Plains. I. A Practical Predictor of Annual Forage Production

Abstract

This research addressed the hypothesis that spring precipitation data can be used to detect agricultural drought early in the growing season. The Rangetek range model was used to simulate yearly forage data based on historical precipitation and temperature records from the USDA-ARS Fort Keogh Livestock and Range Research Laboratory (Miles City, MT) and the Agriculture and Agri-Food Canada Manyberries Substation (Lethbridge, AB, Canada). Monthly total precipitation and monthly average maximum and minimum temperatures were used to develop regression equations predicting growing season forage production at the Fort Keogh Laboratory and Manyberries Substation. At Fort Keogh Laboratory, a combination of fall (October and November) and spring (April and May) precipitation were predictors of simulated forage yield index (P < 0.01, R2 = 0.84). At Manyberries Substation, April and May precipitation were predictors of simulated forage yield index (P < 0.01, R2 = 0.44). Using the actual forage data from Manyberries Substation yielded similar results, in that April, May, and June were predictors of forage production (P < 0.01, R2 = 0.50). Although the regression equation for actual forage production data from Manyberries Substation did indicate that July precipitation was a significant predictor, adding July precipitation did not increase the ability of the equation to detect reduced forage production. These results imply that annual forage production can be estimated with considerable confidence by July 1 and that forage produced by early July is a good indicator of total growing season forage production. Early season detection of drought effects on forage production provides much-needed flexibility in devising management alternatives to minimize the negative impacts of drought on rangelands and beef enterprises.