Academic journal article Journal of Agricultural and Resource Economics

Factors Affecting Cow-Calf Herd Performance and Greenhouse Gas Emissions

Academic journal article Journal of Agricultural and Resource Economics

Factors Affecting Cow-Calf Herd Performance and Greenhouse Gas Emissions

Article excerpt

A Cobb-Douglas stochastic frontier function is estimated for the cow-calf enterprises in the Texas Rolling Plains using Standardized Performance Analysis (SPA) data. We find that factors promoting higher herd productivity include machinery investment, pasture-quality improvement, and protein supplement. In contrast, herd productivity is compromised by a longer breeding season, percentage of hired labor, and deviation from mean annual rainfall. Interestingly, more technically efficient farms tend to emit fewer greenhouse gas units per unit of output. For example, net greenhouse gas emissions are 6.12 and -8.70 pounds of carbon equivalent, respectively, for farms with technical efficiency below 0.8 and above 0.96.

Key words: greenhouse gas emission, standardized performance analysis, stochastic frontier analysis, technical efficiency

(ProQuest: ... denotes formulae omitted.)

Introduction

The beef cattle industry in the Rolling Plains region of Texas is inherently risky due to frequent drought conditions, volatile cattle prices, and rising input costs. Moreover, national beef consumption has declined steadily in the past three decades, dropping from 94.4 pounds per capita in 1976 to 59.7 pounds in 2010. In the face of these challenges, the Beef Cow-Calf Standardized Performance Analysis (SPA) provides an analytical tool to help farmers and ranchers identify their strengths and weaknesses in production and financial performance. In 1992, the National Cattlemen's Beef Association adopted the SPA program that had been developed through efforts of their member producers, the National Integrated Resource Management Coordinating Committee, and Cooperative Extension Specialists from multiple universities.

The goal of the Beef Cow-Calf SPA analysis is to integrate production and financial records into a single analytical tool for cow-calf operations. Typically, an SPA is completed by a rancher and an extension specialist working together. The results of each complete analysis are sent to a regional coordinator, who checks the results for accuracy and enters them into a regional database. Texas leads the country in the number of analyses completed since the SPA program began. Two decades after its inception, the SPA data provides a key tool for analyzing herd performance over multiple production regions and years.

Most previous literature analyzing SPA data has attempted to identify factors that affect the cost, production, and profit of cow-calf enterprises (Falconer, Parker, and McGrann, 1999; Dunn, 2000; Miller et al., 2001; Ramsey et al., 2005). Scant attention had been paid to determining efficiency measurements among beef cow-calf enterprises until Cho, Park, and Bevers (2011) evaluated technical efficiency and its determinants among cow herd operations.

The study of efficiency measurements began with seminal work by Farrell (1957), who suggested constructing the production function either as a parametric function or as a nonparametric piecewise-linear convex isoquant. There are two approaches to estimate a parametric model. One is the deterministic estimation, which uses a linear programming method introduced by Aigner and Chu (1968). This method ensures nonviolation of the monotonicity conditions and parametric restrictions (Färe et al., 2005). The other approach is Stochastic Frontier Analysis (SFA), which was simultaneously introduced by Aigner, Lovell, and Schmidt (1977) and Meeusen and van den Broeck (1977). Their model specification added stochastic elements to the deterministic frontiers, thus overcoming a major shortcoming of the deterministic estimation method, in which all variation from the production frontier is interpreted as inefficiency. The most popular method for constructing a nonparametric model is the Data Envelopment Analysis (DEA) method, which became widely used after Charnes, Cooper, and Rhodes (1978) reformulated Farrel's (1957) approach. DEA employs a linear programming technique to construct a nonparametric piecewise-linear frontier. …

Search by... Author
Show... All Results Primary Sources Peer-reviewed

Oops!

An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.