Using weather records with a forge production model to forecast range forage production.

This paper describes a method for calculating site specific forecast yields and their associated probabilities of occurrence. A physically based range forage model, which utilizes beginning soil water content and daily precipitation, mean air temperature, and solar radiation as inputs, calculates the ratio of actual transpiration (T) to potential transpiration (Tp) as a yield index. Annual yield is calculated by the relationship: yield = potential site yield (yield when water is nonlimiting) X T/T,. By using the current year’s beginning soil water content and weather data for a number of years, a population of yields is generated (one yield for each year of weather data). From the population of yields, a mean and various confidence intervals around the mean can be calculated as the forcast yield and its associated confidence intervals. The forecast procedure was tested using 55 years (1917-1971) of weather records and 12 years (1967-1978) of actual yield and soil water data for an upland range site in eastern Montana. An expected two thirds of the field measured yields were within a standard deviation of the forecasted yields for the April, May, and June forecasts. Yield forecasting is common in the agriculture industry. Forecasts help identify areas of surplus and shortage, and help establish markets. Range forage yield forecasts can be additionally beneficial in helping determine annual carrying capacities for more efficient grazing management. This is especially important during drought years when advanced warnings enable grazing adjustments to accommodate livestock needs and protect range resources from damage due to overgrazing. Probability based yield forecasts can be made in 2 general ways: (I) the stochastic analyses of long-term yield data such as described by Hanson et al. (1982); and (2) the generation of populations of yields from which means and other distribution parameters can be calculated (Arkin et al. 1980). The first method requires long-term yield records where management has been relatively constant-no fertilization, species improvement, etc. (such records are scarce). The second method requires a population of annual weather variables and the establishment of quantitative relationships between the weather variables and plant growth. The weather variables can be obtained from weather records or generated stochastically as described by Arkin et al. (1980), Richardson (1981), or Larsen and Pense (1982). The establishment of quantitative relationships between weather variables and plant growth has commonly been expressed in regression models such as described by Rogler and Haas (1947), Authors are range scientist and agricultural engineer, respectively; USDA-AR% 270 South Orchard, Boise, Ida. 83705; and range conservationist, USDI, Bureau of Land Management, P.O. Box 30157 Billings, Mont. 59107. This article is a contribution from the Northwest Watershed Research Center, USDA-ARS. and Bureau of Land Management, USDI; in cooperation with the Agricultural Experiment Station, University of Idaho, Moscow 83843. Manuscript received February 28, 1983. JOURNAL OF RANGE MANAGEMENT 37(l), January 1984 Smoliak (1956), Currie and Peterson (1966), and Johnston et al. (1969). In their simplest form, these models are linear regression equations with forage yield as the dependent variable and annual or seasonal precipitation or available water as the independent variable (Rogler and Haas 1947). To account for the effect of the precipitation distribution during the growing season, multiple regression equations have been developed which use various combinations of monthly precipitation as the independent variables (Currie and Peterson 1966). Multiple regression models often include beginning soil water content and/or climatic parameters such as pan evaporation as independent variables (Smoliak 1956, Johnston et al. 1969). Regression models, however, are usually site specific, and thus their application is limited to the site and other environmental conditions from which the data used to develop the regression relationships were obtained. To extend the application of regression models over a range of sites and environmental conditions, site factors, such as soil texture and soil depth, have been considered. A simple way to account for forage yield variations due to site characteristics is the use of the long-term site average yield as one of the independent variables. Such a factor integrates the total effect of site factors such as texture, slope, and species composition. The use of yield and precipitation indices as the dependent and independent variables, respectively, has provided an effective means of extending the geographical application of regression models (Sneva and Hyder 1962). These indices are the expression of the yield and precipitation variables as portions of the long-term median yields and precipitation, respectively. In recent years, there have been increasing efforts to quantify the physical aspects of plant growth in relationship to the environment, particularly for cultivated crops. The result of these efforts has been the development of physically based plant growth and production models. The grassland simulation model, ELM (Innis 1978) and the model described by Wight and Hanks (1981) are examples of physically based models for the growth and production of range vegetation. Such models are more transferable among range sites, ecosystems, and climates than are the regression models. This paper describes and evaluates a method of using weather records in conjunction with a physically based forage yield model to make yield forecasts with confidence intervals and stated probabilities of occurrence. The forecast method is similar to that used by Arkin et al. (1980) to forecast the growth and yield of sorghum, except that the weather variables were obtained directly from weather records rather than from stochastic generation. Methods and Procedures A forage production model known as ERHYM (Wight and Neff 1983) was used to relate soil water and climatic parameters to plant