Technical efficiency for cotton growers is examined using the one and the other stochastic (SFA) and nonstochastic (DEA) production function approaches.

Technical efficiency for cotton growers is examined using the one and the other stochastic (SFA) and nonstochastic (DEA) production function approaches. The empirical application uses farm-level data from four counties in west Texas. While efficiency scores for the individual farms differed between SFA and DEA, the mean efficiency scores are invariant of the rule of estimation under the assumption of constant replys to scale. On average, irrigated farms are 80% and nonirrigated farms are 70% efficient. Findings point out to that in Texas, the irrigated farms, forward average, could reduce their expenditures onward other inputs by 10%, and the nonirrigated farms could diminish their expenditures on machinery and labor according to 12% and 13%, respectively, while producing the same horizontal of output.

Key Words: cotton, data envelopment analysis, stochastic frontier, technical efficiency

Cotton is the greatest in number important agricultural commodity in Texas, after cattle and calves, in space of times of cash receipts. In 2000 cash receipts from the sale of cotton lint and se were $115 billion, representing 10% of the total agricultural cash receipts in the state. Texas produc 4 million bales of upland cotton in 2000 which personates 23% of total U.S. cotton production. newly come cotton price decreases, however, have considerably reduc cotton profitability. The average price received on Texas cotton producers for upland cotton lint has decreased from 7460 by pound in 1995 to 5140 through pound in 2000 (U.S. Department of Agriculture, 2000)

The chaste economic stress confronting cotton farmers today has prompted research efforts in production and marketing risk management strategies. still it is equally important to assess the production and scale efficiency of specific farming units, which can help husbandmans focus on necessary adjustments within their operations and improve productivity.

Compared with the number of studies devot to measuring productive efficiency of other agricultural clips studies on cotton farmers are limited. Although burns (2001) analyzed production and outlay estimates for cotton-producing farms in the United States, and Helmers, Weiss, and Shaik (2000) measured regional efficiency and total factor productivity for U cotton-producing regions, there has been no subject of attention measuring farm-level technical efficiency for cotton farmers.

The primary objectives of this application of mind are to estimate technical efficiency of cotton-producing farms and compare the springs obtained from two alternative orders of estimation-parametric and nonparametric. Other objectives are to investigate the relationship between the farm output and the inputs given the assumption of a specific technology, and to analyze the slack input variables in space of times of their excess use in the production proces The not absent analysis contributes to the existing literature because it is the first comparative subject of attention of farm-- level efficiency of cotton farmers

The remainder of the article make headways as follows. First, we provide a background overview in succession the development and application of parametric and nonparametric estimation techniques, highlighting this discussion with respects to relevant literature. The measurement of technical efficiency is then addressed, with specific emphasis in succession the DEA and SFA moulds employed in our analysis. The description of data and our empirical originates are detailed in the nearest section, followed by a final section presenting our summary and conclusions.

Background

Since the pioneering work by means of Farrell in 1957, which drew on the subject of the works of Debreu (1951) and Koopmans (1951) a considerable effort has been directed at refining the measurement of technical efficiency. The literature forward efficiency analysis is broadly divided into deterministic and stochastic frontier methodologies. The deterministic, nonparametric approach that perform the operations indicated ined out of mathematical programming to measure efficiency is known as data envelopment analysis (DEA), while the parametric approach which uses a stochastic production, charge or profit function to estimate efficiency is called the stochastic frontier approach (SFA).

A detailed review ofboth approaches is provided [i]or[/i] part of to the other the collective works of Lovell and Schmidt (1988); Schmidt (1986); Bauer (1990); Seiford and Thrall (1990); Lovell (1993); Greene (1993); Ali and Seiford (1993); and Coelli (1995a). The in the greatest degree commonly cited models employing DEA are those unraveled by Charnes, Cooper, and Rhode (1978) and Banker, Charnes, and Cooper (1984)

In DEA, the performance of a farm is evaluated in period of times of its ability to either shrink usage of an input or expand the output plain subject to the restrictions imposed at the best-observed practices. This measure of performance is relative, in the intellect that the efficiency of each decision-making unit (DMU) is evaluated against the principally efficient DMU, and it is measured by the agency of the ratio of actual output to maximal potential output In stochastic frontier production functions (SFA) there are sum of two units error terms. One accounts for the existence of technical inefficiency, and the other accounts for random disturbances arising abroad of measurement error, luck, bad weather, etc