Edward B. Rayburn

Stochastic Simulation of Pasture-Raised Beef Production Systems and Implications for the Appalachian Cow-Calf Sector

ABSTRACT Cow-calf enterprises provide significant opportunity for supplemental income to small-scale farmers in Appalachia, despite considerable production and economic uncertainty. To assess the viability of pasture-raised beef systems as alternatives to conventional production and marketing paradigms, stochastic budgets representative of several hypothetical producers of each type were constructed and evaluated via Monte Carlo techniques in terms of relative profitability and risk. Statistical distributions were utilized to capture seasonal variability in output prices, pasture availability and animal performance. Results suggest that the intensive pasture and animal management required for pasture-raised production yield greater profit and less economic risk than conventional strategies.

Measuring Legume Content in Pastures Using Digital Photographs

Quantifying botanical composition is important for evaluating management effect on legume content and legume content on pasture yield and quality. The standard for measuring botanical composition is hand separation of clipped samples. An alternative is taking point counts of botanical components on photographs of the pasture. This was tested on a rotationally stocked pasture, taking photos at 24 random sample areas, clipping areas at ground level and hand separating samples into grass, legume and forb fractions. Photos were evaluated using a grid in Microsoft PowerPoint. Point counts were calibrated to hand separated values using linear regression. Grass and legume point count components were not significantly different from hand separated values (P=0.05) but underestimated the forb fraction. Calibration regressions had R 2 values ranging from 0.45 to 0.98. Precision of this technique is dependent on the number of photos/pasture, number of points counted/photo and the number of paired samples taken for calibration. In cool-season grass-clover pastures 12 or more photos/pasture and 100 or more points/photo are a good balance between photo number and points per photo. For calibration 12 or more paired samples should be used. Photo point counts appear to be a practical method of measuring grass, legume and forb components in rotationally grazed pastures.

Alternative Methods of Estimating Forage Height and Sward Capacitance in Pastures Can Be Cross Calibrated

A variety of tools are used for measuring pasture height or capacitance. Cross calibrations between these tools would be helpful for extension staff and producers comparing measurements taken with one tool to those taken with an alternative tool. Rotationally and continuously stocked pastures in West Virginia, Pennsylvania, Maryland, and New York were sampled for forage height using a ruler, for compressed height using a falling plate meter and a rising plate meter, and for sward capacitance with a capacitance meter. Thirty to sixty measurements were made across each pasture with each device, with paddock means taken as the measurement for the device. Regressions were run using paired paddock means, testing each device as both the dependent and independent variable, with r2 ranging from 0.49 to 0.99. Residual analysis was conducted to evaluate biases due to location and stocking management using the falling plate meter means as the independent variable versus means of the other techniques. No bias in pasture measurements was found due to grazing management. There was a bias due to operator for ruler height and capacitance meter reading. These cross calibrations provide a mechanism for pasture managers to translate pasture heights or capacitance taken with one tool to those taken with another tool. Indirect Methods for Measuring Forage Mass in Pastures The measurement of sward height, compressed height, or sward capacitance is an indirect estimate of forage mass, used for determining research treatment effects and for on-farm pasture budgeting. Clipped pasture sampling is the standard method used for calibrating such tools to estimate forage mass per unit area. However, calibrations are labor-intensive and very site-specific. In research trials, the labor cost of directly measuring forage mass often limits the number of clipped samples taken. As an on-farm method, clipped sampling is not practical due to the time and labor required. However, indirect methods of measuring forage mass appear to be cost effective for improving management efficiency compared to management when forage mass is not known (4,10). Plate meters, sward sticks, and capacitance meters of various designs are used for measuring pasture height, compressed height or capacitance, and can be calibrated to provide an estimate of forage mass. The electronic capacitance meter relies on differences in dielectric constants between air and herbage. It 14 June 2007 Forage and Grazinglands measures the capacitance of the air-herbage mixture (2) and responds mainly to the surface area of the foliage (11). These tools are all quick and easy to use, but the accuracy of these estimates of forage mass is dependent on adequate and proper calibration (7). Where on-farm management or research trials are conducted using different indirect measures of forage availability, cross calibration regressions between methods would be useful and allow conversion of measurements taken with one tool to measurements taken with a second tool for comparison purposes. The purpose of this study was to develop a set of cross calibrations between compressed forage height measured with a standardized falling plate meter (8) and a commercial rising plate meter (3), forage height measured with a ruler, and sward capacitance measured with a commercial electronic capacitance meter (6) so that these methods of measuring pasture height, compressed height, or capacitance can be compared across sites. Cross Calibration Study Rotationally and continuously stocked cool-season grass and grass-legume pastures in West Virginia, Pennsylvania, Maryland, and New York were maintained in the vegetative growth stage and sampled across the growing season for forage height using a ruler (inches), compressed forage height using a standardized falling plate meter (inches) (8) and a commercial rising plate meter (centimeters) (3) (Farm Tracker electronic rising plate meter, FarmWorks, P.O. Box 433, Feilding, NZ; dimensions, 14.25-inch diameter and 0.7-lb mass), and for sward capacitance using a commercial electronic capacitance meter (Alistair George Pasture Gauge, Alistair George Manufacturing, Waihi Beach, NZ). A total of 90 paddock measurements were taken, 80 under rotational stocking and 10 under continuous stocking. Each was a mean of 30 to 60 observations. All grazing was conducted using yearling or mature beef or dairy cattle. Pastures were evaluated along established sampling transects and 30 to 60 measurements were taken randomly at regular intervals along the same sampling path. Ruler and falling plate meter heights were taken at the same sampling point, while rising plate meter heights and capacitance meter readings were taken at nearby but independent points. Measurements were averaged by method within each paddock to obtain the paddock mean ruler height, falling plate meter height, rising plate meter height, or capacitance meter reading for that paddock. Pasture ruler height was taken using a yard stick to measure the noncompressed sward height. For ruler height measurements taken in West Virginia, the end of the yard stick was placed on the soil surface and the falling plate was lowered to the surface of the pasture so that three out of the four quadrants of the plate touched a grass or legume leaf. The height of the top of the plate was the measure of ruler height. The plate was then lowered to the pasture surface, allowing the forage to completely support the plate meter. This height was the falling plate meter compressed forage height. On pastures at other locations, ruler height was evaluated subjectively by eye using the yard stick, then the falling plate was lowered to the pasture surface to measure falling plate meter compressed forage height. Regression analysis was used to develop cross-calibration equations between methods used for measuring forage height and capacitance (5). When a measurement method was used as the independent variable, it was used as a standard reference technique assumed to be measured without error. Only regression coefficients significant at P ≤ 0.05 were retained in equations (Table 1). When a regression’s intercept value was not significantly different from zero the regression was run without an intercept. To compare the accuracy of the cross calibrations, regressions using falling plate meter data as the independent variable were used to predict ruler height, rising plate meter height, and capacitance meter readings for each of the respective paddock means. Residuals were calculated by subtracting the predicted value from the observed value for each paddock. Residual values were analyzed by analysis of variance using state (West Virginia, Maryland, Pennsylvania, New York) and grazing method (rotational versus continuous stocking) as factors to determine if there was any 14 June 2007 Forage and Grazinglands bias in cross calibrations due to different measurement methods used in the states, or to pasture conditions due to grazing management (Table 2). Standard deviations about the regression (SDreg) were converted to coefficients of variation (CV) by dividing the SDreg by the mean of the dependent variable. Table 1. Cross calibration regressions between paddocks assessed using a falling plate meter (FPM, inches), a rising plate meter (RPM, cm), a ruler (RHT, inches), and sward capacitance meter (CMR, expressed as pounds of dry matter per acre calculated using a proprietary, non validated calibration equation). Table 2. Residual analysis of paddock mean ruler height (inches), rising plate meter compressed forage height (cm), and capacitance meter readings predicted using the falling plate meter compressed forage height (inches) cross calibration regressions; values followed by different letters are significantly different at the 0.05 level. * Mean of residuals. Cross Calibration of Forage Measurement Methods Forage height measured with the ruler as the dependent variable compared to the falling plate meter as the independent variable (Fig. 1) had more scatter than when the rising plate meter was compared to the falling plate meter (Fig. 2). Cross calibration regressions of ruler and rising plate meter with falling plate meter resulted in r2 values of 0.96 and 0.99, respectively (Table 1). Ruler heights taken in Pennsylvania tended to be higher than those taken in the other states (Fig 1). When comparing capacitance meter reading as the dependent variable to forage height measured with the falling plate meter as the independent variable (Fig. 3) there was more scatter, with a regression r2 of 0.72 (Table 1), than when the ruler (Fig. 1) or rising plate meter (Fig. 2) were used as the dependent variables. Independent variable Cross calibration regression R2 SDreg CV Total

Visual Reference Guide for Estimating Legume Content in Pastures

As the prices of nitrogen fertilizers rise, there is increased incentive to grow legumes for fixing nitrogen and improving forage quality in pastures and hay meadows. From a management perspective, it is important for managers to be able to estimate legume content in the stand. In research, clipping and hand separation is the standard method for measuring legume content. However, this method is impractical for farm managers. Another option is visual appraisal of the percentage surface covered by legumes. The objective of this photo reference guide is to provide a tool that pasture managers can use to assess legume content as it is related to legume cover. For each photo, the area within the quadrat was clipped and hand separated to determine the actual legume content. These photos represent a range of legume content across two ranges of forage mass. By using these photos to help estimate legume content, forage managers should be able to increase the accuracy of their visual estimate of legume content in pastures and aftermath meadows. IntroductIon There are advantages to growing legumes with the grasses in pastures and hay fields. These include providing nitrogen for plant growth and increasing forage quality, thereby reducing fertilizer cost and enhancing animal performance (Blaser et al., 1969; Blaser and Colleagues, 1986; Rayburn et al., 2006). Legume content in pastures is a dynamic characteristic that is dependent on weather, management, nitrogen accumulation in the soil, pests, and the legume and grass species present. During dry weather, pastures may be grazed closely, allowing white clover to increase and red clover seed to germinate and establish. Proper lime and fertilizer management is essential for legume production. Most clovers grow best when the soil pH is above 6.0 and soil-test phosphorous and potassium are high. When the soil-nitrogen supply is low, as for a newly planted forage stand in a crop Published in Forage and Grazinglands DOI 10.2134/FG-2011-0176-DG

Estimating Economic Risk Using Monte Carlo Enterprise Budgets

Farmers often admit that they are gamblers. By using Monte Carlo enterprise budgets they can learn the odds of obtaining a positive economic return from their farming enterprises. Deterministic spread sheet budgets can be converted to Monte Carlo or stochastic budgets using commercial add-ins such as @Risk. However, relatively simple budgets can be converted into Monte Carlo budgets by using the statistical functions found in Excel or Open Office. This management guide shows how to convert a simple deterministic hay budget into a Monte Carlo budget using the statistical functions in Excel. Farmers, Gambling, and Risk Estimation Most farmers admit that they are gamblers since they recognize that farming is a risky business. Farmers often use electronic spread sheets to develop budgets to evaluate the profitability of an enterprise based on expected prices and productivity. These budgets are most often used with a few values based on current expectations. The purpose of a Monte Carlo budget is to help the farmer calculate the odds of winning. Based on the calculated odds, the farmer will know the probability of the enterprise being profitable or exceeding a defined minimum net income. Monte Carlo is a major gambling city in Europe, and its name is used in reference to computer models that enable the user to calculate odds or probabilities of an outcome. Another name for these computer programs is stochastic models. Risk is the exposure to unfavorable consequences due to uncertainty or imperfect knowledge (5). There are several types of risk associated with agricultural businesses. These include production, market, personal, and institutional risk. Production risk occurs due to variability in weather resulting in variability in crop yield and quality. Market risk occurs due to changes in price of inputs or value of outputs, especially during times of volatile markets. Personal risk occurs due to the chance of injury or illness. Institutional risk occurs due to changes in government regulations that affect agricultural production or markets. These risks also interact. When drought over a large area reduces hay yield the price of hay within the region will go up. When the government subsidizes the production of ethanol made from corn the price of corn can go up. If the price of corn goes up it can affect the price of high quality hay. Uncertainty in all of these categories and the resulting risk are fundamental to agriculture. Complex whole-farm systems are best evaluated using sophisticated Excel add-ins such as @Risk (4). However, less complex enterprise budgets can be converted to stochastic budgets using basic statistical functions in Excel and knowledge of the distribution of input values for selected variables in the budget to give a probability distribution of the economic outcome for the enterprise.

Initial Nutritive Value and Utilization Affect Apparent Diet Quality of Grazed Forage

An on-farm study was conducted to measure the effect of forage nutrive value and utilization on diet quality selected by cattle grazing rotationally stocked pastures. In New York 20 paddocks on three farms were grazed by lactating dairy cows or heifers, and in West Virginia 47 paddocks on four farms were grazed by lactating beef cows and calves or yearlings. Most pastures were grazed for one to three days. For each nutritive value component - crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), non structural carbohydrates (NSC), and total digestible nutrients (TDN) - apparent diet quality was calculated as the components calculated mass in the pregrazing forage mass minus the calculated mass in the postgrazing forage mass divided by the forage mass disappearing during grazing. Forage utilization was calculated as pregrazing forage mass minus postgrazing forage mass divided by pregrazing forage mass. Cattle grazed selectively increasing CP, NSC, and TDN and decreasing ADF and NDF in the apparent diet compared to the pregrazing forage. Initial pasture nutritive value had the major effect on apparent diet quality. Forage utilization modified apparent intake by reducing the magnitude of selective grazing.