Separable Programming for Considering Risk in Farm Planning

Thomas, Wayne C.; Blakeslee, Leroy; Rogers, LeRoy; Whittlesey, Norman K.

doi:10.2307/1238711

Cited by 26 publications

(14 citation statements)

References 6 publications

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“…Hazell and Scandizzo [21] and Paris [33] are some examples of a large number of studies which developed several variants of risk programming models. Several empirical investigations have also been made using these models in selecting optimal crop mix and in testing the behaviourial assumptions of the producers towards risk [10,16,18,24,25,28,32,37]. Binswanger [8,9] using an experimental gambling approach to study the attitude towards risk of semi-arid tropical farmers of India concluded that wealth does not influence risk significantly at higher pay-off levels and that differences in investment behaviour could not be explained by the differences in their constraint sets.…”

Section: The Modelmentioning

confidence: 99%

Agroforestry, attitude towards risk and nutrient availability: a case study of south Indian farming systems

Babu

Rajasekaran

1991

Agroforest Syst

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Introduction of two systems of agroforestry to the farmers portfolio is evaluated for their changes in cropping pattern, input use, income generation, farmers attitude towards risk and nutrient availability. Two different types of farmers are studied under both irrigated and dryland farming systems. Farm survey data collected from south Indian villages have been used with a mean-variance framework to identify the risk aversion levels of farmers. The results indicate that the risk-taking preferences of farmers should be given consideration in evaluating the impact of agroforestry systems. Among the two agroforestry systems analyzed, the one with drumstick is shown to increase the risk of crop production while the one with leucaena reduces the risk and enables farmers to invest in more risky cash crops. The impact of agroforestry on crop allocation, input use and income differs due to the differences in resource availability of farmers. The influence of agroforestry on nutrient availability of the farm households also differs based on the components of agroforestry, orientation of farming and the nature of farming systems. It is argued that design of agroforestry systems should consider differences in resource constraints in farming systems and risk attitudes of farmers towards their allocation decisions and that such considerations would largely enhance the successful adoption of agroforestry in developing countries.

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Section: The Modelmentioning

confidence: 99%

Agroforestry, attitude towards risk and nutrient availability: a case study of south Indian farming systems

Babu

Rajasekaran

1991

Agroforest Syst

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“…No attempt was made in the article by Thomas, et al, [2 ] to solve the example problem by QP. However, this point is not discussed here.…”

Section: Comparing Et¡ Frontiersmentioning

confidence: 99%

“…However, this point is not discussed here. The decision maker is usually concerned with the resulting farm enterprise combinations and with the actual solution E, V relationshŸ Therefore the "efficiency frontier" shown in Figure 2 of [2] is meaningless and obviously wrong in the sense that no curve on that diagram is positioned to show its comparative efficiency. The total income variance restraint, specified asa right-hand side value for equation (2.1), is measured in the vertical axis.…”

Section: Comparing Et¡ Frontiersmentioning

confidence: 99%

Separable Programming for Considering Risk in Farm Planning: Comment

Chen

1973

American J Agri Economics

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“…Thomas et al have used separable programming to approximate the nonlinear total variance constraint when the mean return is maximized [4,10].…”

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Marginal Risk Constraint Linear Program for Activity Analysis

Chen

Baker

1974

American J Agri Economics

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A dichotomous Marginal Risk Constraint (MRC) criterion is proposed to take explicit account of activity income covariance relationships and to restrict the choice of activities within the range of rational combinations. A parametric LP solution procedure is also presented. The E,V boundary approximated is satisfactorily close to that generated from QP.A LTH OU GH the Mean-Variability (E,V) criterion, and thus quadratic programming (QP), is theoretically appealing, it is more difficult to handle computationally than is deterministic linear programming (LP). At the present stage in the art of computation, the computer code for QP models is also much more restrictive in size than are codes available for LP models. These facts place a premium on developing alternative linear models to approximate efficient E, V farm plans. Efficiency of the alternative model can be measured by departure of the approximate E, V boundary from the boundary generated by QP.Hazell has proposed a model which minimizes total absolute deviations and which, he claims, retains the good properties of QP models while allowing use of the LP code [2, 6, 11]. Thomas et al. have used separable programming to approximate the nonlinear total variance constraint when the mean return is maximized [4,10].In this article a Marginal Risk Constraint (MRC) criterion is proposed. This criterion can be fitted into a linear model with dichotomous marginal risk constraints, along with the usual resource restrictions. A multi-stage LP algorithm is proposed to approximate the E,V boundary. The Marginal Risk Constraint Criterion AssumptionsThe return from a set of farm enterprise combinations is assumed to be randomly distributed

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Separable Programming for Considering Risk in Farm Planning

Cited by 26 publications

References 6 publications

Agroforestry, attitude towards risk and nutrient availability: a case study of south Indian farming systems

Agroforestry, attitude towards risk and nutrient availability: a case study of south Indian farming systems

Separable Programming for Considering Risk in Farm Planning: Comment

Marginal Risk Constraint Linear Program for Activity Analysis

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