Number of Downlaods: 60
Published Date: Feb 1, 1998
Shahid Zia, SDPI
A Target MOTAD risk programming model was developed and used to analyse alternative farming systems for the central Punjab of Pakistan. The profit maximising enterprise combination was found not to be significantly different than combinations that can achieve desired target income with some given levels of risk. However, enterprise mix and levels of activities for profit maximising LP model and risk efficient Target MOTAD was found to be significantly different. A comparison of MOTAD and target MOTAD was also made. Target MOTAD solutions always generated higher expected income with negative deviations less than those in the corresponding MOTAD solutions. If risk is conceived as deviations below target income, Target MOTAD solutions resulted in lower negative deviations and, thus, less risk. Moreover, the Target MOTAD efficiency frontiers are above the MOTAD efficiency frontier everywhere.
It is generally argued that small farmers in developing countries are “poor but efficient” (Schultz, 1983). The arguement is that farmers allocate their resources efficiently in the given production environment and in the light of their life-long experiences. But with the improved availability of rapidly changing new crop production technologies, modern inputs, and ever-changing government policies, farmers are now required to adjust and readjust their farm plans frequently with less information available than before. The arguement of life long experience does not seem valid anymore. Moreover, with commercialization, the farming business is becoming more and more sophisticated and risky. Farm families who find a need to adopt new technology and/or intend to change their farm organisation need more information on risk associated with the change in order to make the decision.
Few would disagree that agricultural production is a risky process. Available literature suggests that most farmers are risk-averse and that risk aversion tends to be more common among small farmers (Dillon and Scandizzo, 1978). The inclusion of farmers’ risk behavior in farm planning models is well discussed in the literature. Given that small farmers are risk averse in general, unless risk responses are adequately considered in agricultural planning models, the results generated in empirical analysis may be of little use either in direct decision making or in policy analysis (Brink and McCarl, 1978, and Boisvert and McCarl, 1990). It is further stressed by Hazell (1982) that neglect of risk averse behavior of farmers can result into overstatement of the output levels of risky enterprises and overly specialized cropping patterns.
Conventional linear programming models are widely used as a method for determining profit maximizing resource allocation. But given the fact that these models ignore potential risk associated with the enterprises considered for resource allocation and thus, may provide misleading results where farmers tend to be risk averse and considerable risk is involved in agricultural business. These deterministic models may yield farm plans that may not correspond to the real farm decision making environment. Thus, use of risk programming models to eliminate these problems seems more appropriate.