State-contingent analysis of farmers’ response to weather variability: Irrigated dairy farming in the Murray Valley, Australia

Abstract

The agricultural sector is commonly regarded as one of the most vulnerable to climate change. Current understanding of the impact of climate change on this sector relies on the underlying assumptions about farmers’ possible responses to weather variability, including changes in crop choice, input combinations and land management practices. Many previous analyses rely on the implicit (and restrictive) assumption that farmers operate under a fixed technology set across different states of nature. This assumption, represented through stochastic production or profit functions, is commonly made but seldom tested, and may understate farmers’ responses to climate change if state-contingent production technologies are, in reality, more flexible. The potential for farmers to adapt production technologies in response to unforeseen events is at the core of the state-contingent approach. Advanced in Chambers and Quiggin (2000), the theory contends that producers can manage uncertainty through the allocation of productive inputs to different states of nature. In this article we test the assumption that farmers’ observed behaviour is consistent with the state-contingent production theory using farm-level data from Australia. More precisely, we estimate the milk production technology for a sample of irrigated dairy farms from the southern Murray–Darling Basin over the period from 2006-07 to 2009-10.

Keywords

Replaces

Thilak Mallawaarachchi, Céline Nauges, John Quiggin, and Orion Sanders, “State-contingent analysis of farmers’ response to weather variability: Irrigated dairy farming in the Murray Valley, Australia”, TSE Working Paper, n. 16-668, July 2016.

Reference

Thilak Mallawaarachchi, Céline Nauges, John Quiggin, and Orion Sanders, “State-contingent analysis of farmers’ response to weather variability: Irrigated dairy farming in the Murray Valley, Australia”, Australian Journal of Agricultural and Resource Economics, vol. 61, n. 1, January 2017, pp. 36–55.