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ARS Home » Plains Area » Miles City, Montana » Livestock and Range Research Laboratory » Research » Publications at this Location » Publication #136548
Title: EVALUATION OF DROUGHT MANAGEMENT STRATEGIES FOR COW-CALF ENTERPRISES: A PRACTICAL PREDICTOR OF GROWING SEASON FORAGE PRODUCTION

Author
item KRUSE, R - MSU
item TESS, M - MSU
item Heitschmidt, Rodney
item PATERSON, J - MSU
item Klement, Keith

Submitted to: Montana State University Beef Newsletter
Publication Type: Abstract Only
Publication Acceptance Date: 4/1/2003
Publication Date: 4/1/2003
Citation: KRUSE, R.E., TESS, M.W., HEITSCHMIDT, R.K., PATERSON, J.A., KLEMENT, K.D. EVALUATION OF DROUGHT MANAGEMENT STRATEGIES FOR COW-CALF ENTERPRISES: A PRACTICAL PREDICTOR OF GROWING SEASON FORAGE PRODUCTION. MONTANA STATE UNIVERSITY BEEF NEWSLETTER. p. 9. 2003.

Interpretive Summary: Our research addresses the hypothesis that spring precipitation data can be used to predict forage production early in the growing season. Forage production on mixed-grass prairie in eastern Montana and southeastern Alberta was related to seasonal precipiation. The Rangetek range model was used to simulate yearly forage data based on historical precipitation and temperature records from the Fort Keogh Livestock Animal and Range Research Laboratory near Miles City, Montana and the Manyberries Substation near Lethbridge, Alberta. Thirty yr of climate data from fort Keogh and 50 yr of climate and forage data collected from the Manyberries Substation were used to develop regression equations predicting growing season forage production. Independent variables included monthly or seasonal precipitation as well as maximum and minimum temperature. At Fort Keogh, fall precipitation (October and November) and spring precipitation (April and May) were highly significant predictors of simulated forage production (P<0.01) where the model explained 84% of the variation seen in forage production. At the Manyberries Substation, April and May precipitation were highly significant predictors of simulated forage production (P < 0.01) where the model explained 46% of the variation using the simulated results from Rangetek. Using the actual forage data from Manyberries yeielded similar results where April and June were highly significant predictors of forage production (P < 0.01, R2=0.46). Adding July precipitation did not increase the ability of the equation to detect emerging drought. Each regression model can reasonably forecase forage production by July 1st. Our results show emerging drought can be detected based on spring and early summer precipitation (April - June).

Technical Abstract: Our research addresses the hypothesis that spring precipitation data can be used to predict forage production early in the growing season. Forage production on mixed-grass prairie in eastern Montana and southeastern Alberta was related to seasonal precipiation. The Rangetek range model was used to simulate yearly forage data based on historical precipitation and temperature records from the Fort Keogh Livestock Animal and Range Research Laboratory near Miles City, Montana and the Manyberries Substation near Lethbridge, Alberta. Thirty yr of climate data from fort Keogh and 50 yr of climate and forage data collected from the Manyberries Substation were used to develop regression equations predicting growing season forage production. Independent variables included monthly or seasonal precipitation as well as maximum and minimum temperature. At Fort Keogh, fall precipitation (October and November) and spring precipitation (April and May) were highly significant predictors of simulated forage production (P<0.01) where the model explained 84% of the variation seen in forage production. At the Manyberries Substation, April and May precipitation were highly significant predictors of simulated forage production (P < 0.01) where the model explained 46% of the variation using the simulated results from Rangetek. Using the actual forage data from Manyberries yeielded similar results where April and June were highly significant predictors of forage production (P < 0.01, R2=0.46). Adding July precipitation did not increase the ability of the equation to detect emerging drought. Each regression model can reasonably forecase forage production by July 1st. Our results show emerging drought can be detected based on spring and early summer precipitation (April - June).