A farm-level model to evaluate the impacts of current energy policy options

Abstract

This thesis attempts to show the value of farm-level studies as useful additions to aggregated, national or regional models that are used to analyse the effects of policy on an agricultural economy. A literature survey (Chapter 2) illustrates many applications of the aggregated approach to the problems that scarce or costly energy will pose for agriculture. It is argued that viewing potential responses to such energy scenarios at the level of the farm business is likely to yield greater understanding of the ways such scenarios will impact on existing farm systems. Such an approach has application both to the concerns the energy problem poses for the farming community and to the needs of policy-makers in the spheres of agricultural and energy policy. Indeed, it is a feature of policy oriented aggregated studies in agriculture that they often have to make assumptions about the response of the farm sector (for example, the price elasticity of demand for fuel) that can only be verified by detailed study of farm systems, or groups of farm systems. Thus this approach can provide real benefits to those whose concern is the future of farming in New Zealand.

A multi-period mixed integer programming model (based on linear programming) is fully specified (in Chapter 3) and used to simulate activity on a case-study mixed cropping farm in Canterbury. The model is validated and run to show the effects, first of imposed fuel rationing and second of energy price increases, on the optimal farm system. Increased prices of fuel and the indirect effects they have on the prices of other inputs are estimated and used in the second scenario.

Results of these analyses quantify changes in the optimal system in terms of input use, output and the balance of enterprises within the farm system. These results are also expressed in terms of performance criteria: profitability (measured as total revenue less variable and semi-fixed costs), efficiency of support energy use and marginal products of inputs.

Generally, results show that net revenue maximising farm systems are resistant to change under very large energy price increases, despite profitability being rapidly affected (i.e. the optimal use of resources changed little whereas the level of profit at the optimum changes markedly). Such a result shows that farm fuel demand is price inelastic. This contrasts with results under fuel rationing, where farm systems change under even small (i.e. less than five per cent) cuts in the use of fuel for productive purposes and profitability declines slowly (i.e. the optimal use of resources changes quite markedly, whilst net revenue declines rather more modestly). Shadow price results indicate a rapidly increasing marginal value of fuel as its use is restricted.

The concluding chapter discusses the implications of the scenarios tested both for the particular farm and for policy-makers. Prospects for further development and application of the model are also discussed. A bibliography and appendices layout sources of reference and data used in specifying the model.