Submitted to: Professional Animal Scientist
Publication Type: Peer reviewed journal
Publication Acceptance Date: 3/1/2007
Publication Date: 6/1/2007
Citation: Kruse, R.E., Tess, M.W., and Heitschmidt, R.K. 2007. Drought management for the Northern Great Plains. I. A practical predictor of annual forage production. Professional Animal Scientist 23:224-233. Interpretive Summary: Drought is an inherent trait of rangeland ecosystems including Northern Great Plains rangelands. Because ranchers are eternal optimists relative to future precipitation events, most drought management strategies can be characterized as reactive rather than proactive. The objective of this research was to examine the relationships between varying early season climatic variables and annual rangeland forage production and to use these relationships to predict present and on-coming drought conditions. The Rangetek simulation model was used to simulate annual forage production at two Northern Great Plains locations; Miles City, Montana and Manyberries, Alberta, Canada. Stepwise regression procedures were then used to examine fundamental relationships between the simulated forage yield index, a measure of annual forage production, and monthly precipitation and monthly maximum and minimum temperatures. At Miles City, April and May precipitation together explained 68% of the variation among years in forage yield index with 84% of the variation explained by combining current year’s April and May precipitation and previous year’s October and November precipitation. At Manyberries, April and May precipitation explained 44% of annual variation in simulated forage yield index. However, when these same relationships were examined among a 50-year forage production data set at Manyberries, 50% of the variation was explained by April, May, and June precipitation. Study results imply that total annual forage production can be estimated with considerable confidence in the Northern Great Plains by July 1st. This in turn implies that drought conditions and their associated impacts on annual forage production can be predicted with considerable confidence by July 1 thereby allowing rangeland managers the opportunity to implement proactive drought management strategies early in the growing year thereby reducing both ecological and economic risks.
Technical Abstract: This research addressed the hypotheses 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, Montana, and the Agriculture and Agri-Food Canada Manyberries Substation, Lethbridge, Alberta. Monthly precipitation and monthly maximum and minimum temperatures were used to develop regression equations predicting growing season forage production at Fort Keogh and Manyberries. At Fort Keogh, 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, April and May precipitation were predictors of simulated forage yield index (P < 0.01, R2 = 0.44). Using the actual forage data from Manyberries 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 did find July precipitation as a significant predictor, adding July precipitation did not increase the ability of the equation to detect emerging drought. These results imply that annual forage production can be estimated with considerable confidence by July 1st and that forage produced by early July is a good indicator of growing season forage production. Early season drought detection provides much-needed flexibility in devising management alternatives to minimize the negative impacts of drought on rangelands and beef enterprises.