|Printz, Jeff - USDA-NRCS|
|Patton, Robert - NORTH DAKOTA STATE UNIVER|
|Nyren, Anne - NORTH DAKOTA STATE UNIV|
Submitted to: Society for Range Management Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: October 2, 2008
Publication Date: February 9, 2009
Citation: Dunn, G.H., Wiles, L., Printz, J., Patton, R., Nyren, A. 2009. Effect of annual, growing season, and spring precipitation on peak standing crop at three locations. Society for Range Management Meeting Abstracts. 62:28. Technical Abstract: Ranchers and range managers in the West are at the mercy of climatic conditions that determine the amount of annual forage available on rangeland. Typically, stocking or de-stocking decisions need to be made before the final forage production level is known. Ranchers and range managers need a decision support tool that will provide a reasonably accurate prediction of forage growth potential as early in the coming growing/grazing season as possible. Numerous studies have concluded that precipitation during some critical period can explain the greatest percentage of the variation in peak standing crop (PSC). We compared three precipitation predictor variables: annual, growing season, and with-in growing season. We used grazed and un-grazed PSC and precipitation data from three locations: Streeter, ND, Miles City, MT, and Cheyenne, WY to test which precipitation variable was the best predictor variable for PSC. Of the precipitation predictor variables, within-growing season precipitation was the best predictor variable. The within-growing season period (i.e. which months and the number of months) varied slightly with location. Additionally, an individual month within the inter-growing season was identified as the most significant month from a predictor variable standpoint. Neither annual nor growing season precipitation was a good predictor variable. This information could be used to develop a decision support tool such that a rancher or range manager can estimate forage production and its variability at any given location using historical and current precipitation information.