Livestock Management During Drought in the Northern Great Plains. I. a Practical Predictor of Annual Forage Production1

The Professional Animal Scientist 23 (2007):224–233 Livestock Management During Drought in the Northern Great Plains. I. A Practical Predictor of Annual Forage Production1 R. E. Kruse,* M. W. Tess,*2 and R. K. Heitschmidt† *Animal and Range Sciences Department, Montana State University, Bozeman 59717; and USDA- ARS, Fort Keogh Livestock and Range Research Laboratory, Miles City, MT 59301 minimum temperatures were used to minimize the negative impacts of ABSTRACT develop regression equations predicting drought on rangelands and beef enter- growing season forage production at prises. This research addressed the hypothe- the Fort Keogh Laboratory and Manyb- sis that spring precipitation data can erries Substation. At Fort Keogh Labo- Key words: forecasting, primary be used to detect agricultural drought ratory, a combination of fall (October production, simulation, modeling early in the growing season. The and November) and spring (April and Rangetek range model was used to May) precipitation were predictors of INTRODUCTION simulate yearly forage data based on simulated forage yield index (P < historical precipitation and tempera- 2 0.01, R = 0.84). At Manyberries Sub- Drought is an inherent trait of ture records from the USDA-ARS Fort station, April and May precipitation most rangelands including those Keogh Livestock and Range Research were predictors of simulated forage found in the Northern Great Laboratory (Miles City, MT) and the < 2 yield index (P 0.01, R = 0.44). Us- Plains. For example, Hurtt (1951) Agriculture and Agri-Food Canada Ma- ing the actual forage data from Ma- reported drought occurred once ev- nyberries Substation (Lethbridge, AB, nyberries Substation yielded similar re- ery 5 yr in southeastern Montana, Canada). Monthly total precipitation sults, in that April, May, and June and Johnson (1985) reported and monthly average maximum and were predictors of forage production (P drought occurred once every 4 yr < 2 0.01, R = 0.50). Although the re- in southeastern Alberta. gression equation for actual forage pro- The impacts of drought on range- duction data from Manyberries Substa- 1Research was conducted under a coopera- land ecosystems are numerous and tive agreement between USDA Agriculture tion did indicate that July precipita- well documented with much of the Research Service and the Montana Agricul- tion was a significant predictor, early ecological research conducted ture Experiment Station. Mention of a pro- adding July precipitation did not in- on Great Plains rangelands being prietary product does not constitute a guar- crease the ability of the equation to de- driven by the droughts of the antee or warranty of the product by USDA, tect reduced forage production. These 1930s (Weaver and Albertson, Montana Agriculture Experiment Station, results imply that annual forage pro- 1936, 1939, 1944; Ellison and or the authors and does not imply its ap- duction can be estimated with consid- Woolfolk, 1937; Whitman et al., proval to the exclusion of other products erable confidence by July 1 and that 1943; Albertson et al., 1957). In that may also be suitable. The USDA-ARS forage produced by early July is a good general, research has shown that Northern Plains Area is an equal opportu- indicator of total growing season for- nity and affirmative action employer and drought conditions reduce both all agency services are available without dis- age production. Early season detection quantity and quality of forage pro- crimination. of drought effects on forage production duced and consumed with the re- 2Corresponding author: mwtess@ provides much-needed flexibility in de- sulting effect being a general de- montana.edu vising management alternatives to cline in animal production on both Prediction of Forage Production During Drought 225 an individual animal and per unit age yield (Rangetek) to simulate season, date of peak standing crop, area of land basis (Valentine, 1990; long periods of time representing date of end of growing season, and Weltzin and McPherson, 2003; the 2 locations (Miles City and a relative growth curve. Soil inputs Heitschmidt and Vermeire, 2005, Lethbridge). An existing large data include number of soil layers, tex- 2006). set (Lethbridge, AB) was used to ture, thickness, percent OM, per- For livestock managers a funda- evaluate the simulation approach. cent sand, percent clay, percent mental problem with drought is its Rangetek (Wight and Neff, 1983), rock fragments, bulk density, and unpredictability. Hence, livestock a modified version of the Ekalaka initial soil water. Runoff and site management strategies and tactics Rangeland Hydrology and Yield variables include longitude, lati- are often reactive rather than proac- Model (ERHYM-II; Wight and tude, elevation, slope, and aspect. tive. In this study the effects of Hanks, 1981; Wight, 1987) was Water enters the system as precip- drought were confined to effects used to simulate yearly forage yield itation and may leave the system on forage production. The broad based on historical precipitation as runoff, deep drainage, evapora- objective of this study was to iden- and temperature records from the tion, or plant transpiration. As wa- tify key climatic variables associ- USDA-ARS Fort Keogh Livestock ter accumulates in the soil, progres- ated with drought in the Northern and Range Research Laboratory sively deeper soil layers are filled to Great Plains whereby impending ef- near Miles City, MT (46° 22′ N field capacity, and excess water fects on forage production could be 105° 5′ W) and from the Agricul- drains to the next layer. Water may predicted with considerable confi- ture and Agri-Food Canada Manyb- be lost from the system as runoff dence. Previous research at this lo- erries Substation near Lethbridge, or deep drainage if soil profile is sat- cation has shown that on average AB, Canada (49° 7′ N, 110° 28′ W). urated. Soil evaporation and plant about 90% of Northern Great The 30 yr of climate data from Fort transpiration, in that order, remove Plains annual graminoid produc- Keogh Laboratory and 50 yr of data water from the soil profile, begin- tion is completed by early July from the Manyberries Substation ning with the uppermost layer. Po- (Heitschmidt and Vermeire, 2005). (Smoliak, 1986) were used to de- tential evapotranspirative demand As a result, we hypothesized that velop regression equations pre- is calculated with the Jensen and high-impact variables would most dicting growing season forage pro- Haise (1963) evapotranspiration likely be from a fairly short period duction (simulated yield index). equation. The potential demand is of time (e.g., spring) rather than an The 50 yr of forage production partitioned into potential transpira- entire frost-free growing season. data were also used from the Ma- tion and potential evaporation; val- Successfully meeting this objective nyberries Substation (Smoliak, ues are used to calculate actual tran- was prerequisite to developing the 1986) to develop regression equa- spiration and evaporation (John- cow-calf drought management strat- tions predicting actual forage son, 1985). egies presented in a companion pa- yield. Output from the model is in the per (Kruse et al., 2007). form of a yield index. The yield in- Rangetek Model dex calculated by the model is an MATERIALS AND METHODS Rangetek is a climate or water- estimate of plant growth for cur- balance model, which provides rent climatic conditions and site pa- Our approach was to develop daily simulation of soil water evap- rameters and is expressed as a frac- and evaluate predictors (multiple re- oration, transpiration, runoff, and tion of site potential yield (John- gression equations) of annual for- soil water routing for individual son, 1985). This cumulative index age yield from 3 different data sets: range sites. It can utilize real-time equals the ratio of actual transpira- one simulated based on climatic climate data to simulate ongoing tion to potential transpiration. The and soil data from Miles City, MT; processes, or it can utilize long- index, or predicted total plant one simulated based on climatic term weather records to simulate yield, is calculated on the date that and soil data from Lethbridge, AB, runoff and herbage production un- peak standing crop occurs. The Canada; and one composed of ac- der a variety of climatic conditions product of site potential yield (kg/ tual forage and climatic data col- and management practices (Wight, ha) and the yield index therefore lected at Lethbridge, AB. Prelimi- 1987). Driving variables are daily provides an estimate of cumulative nary analyses of several models re- precipitation, minimum and maxi- production (Johnson, 1985). The vealed small local data sets (i.e., mum air temperatures, and solar ra- yield index is considered a good in- limited to a few years and multiple diation, which can be simulated by dicator of the growing season cli- sites in Miles City, MT) were inade- the model. Plant variables required mate as it relates to plant growth quate to satisfactorily address our by the model include percent gram- and enables comparisons of range objectives. Hence, we used a locally inoids, percent bare ground, leaf treatments or vegetation invento- validated model of rangeland for- area index, date of start of growing ries among years or range sites by 226 Kruse et al. accounting for a large portion of are loamy Aridic Haploborolls and Data were collated to correspond climate-induced variation in plant vegetation belongs to the Stipa-Bou- to the growing season year to en- response (Wight, 1987). teloua faciation of the Mixed Prairie able a measure of agricultural Association (Smoliak, 1986). Princi- drought, when moisture during the Fort Keogh Laboratory, pal herbaceous species are needle- growing season is inadequate to Miles City, MT and-thread, western wheatgrass, support healthy forage growth to blue grama, prairie junegrass (Koel- maturity, prevent extreme forage Herbage production data were eria cristata (Lam.) Beauv.), Sand- stress, and permit normal forage from an unpublished study con- berg’s bluegrass (Poa sandbergii Va- production (Kulshreshtha, 1989; ducted by the USDA Natural Re- sey), and threadleaf sedge (Smoliak, Thurow and Taylor, 1999).

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