A biophysical and economic model of agriculture and water in the Murray-Darling Basin, Australia

Abstract

Economic analysis of climate scenarios and alternative water policies is critical for development and implementation of appropriate water policies and programs. Mathematical models have been developed to assess water resources policies due to their ability to explicitly represent the biophysical dynamics of natural systems while integrating these within social and economic constraints. These models have been criticised, however, due to the problems of simplification, overspecialisation, plausibility and lack of empirical validation. This paper introduces a mathematical programming model which uses positive mathematical programming method to calibrate and model agriculture and water use in the Murray-Darling Basin of Australia. This paper reviews the theoretical and technical details of the model development including the key steps taken in collating and scaling the biophysical and economic data, and to address model parameterisation issues. The paper summarises results of an application of the model for assessing climate change impacts in the form of reduced rainfall and water allocations and increased crop water use for agricultural production. The results show the degree of variability in gross values under different climate scenarios compared to the base case scenario, especially in very dry years. The results also show how on-farm adaptation options and water markets can mitigate these losses.

Agricultural production systems modelling and software: Current status and future prospects

“…Over the past decade, cropping systems models have expanded in scope to become agricultural production systems models that are used in a range of applications: a) climate change and adaptation (Elliott et al, 2014;Fraisse et al, 2006;Kalaugher et al, 2013;Moore et al, 2014;Pearson et al, 2008Pearson et al, , 2011White et al, 2011), b) food security (Carberry et al, 2013), c) policy assessment (Bezlepkina et al, 2010;Bryan et al, 2011;Ittersum, 2009), d) aiding the development of tools for farmers and/or policy applications (Cichota et al, 2012;Hunt et al, 2013;Komarek et al, 2012;Parsons et al, 2011;Shafiullah, 2012), e) farmer advice Carberry et al, 2002;Hochman et al, 2009;Oliver et al, 2012), f) resource use and efficiency (Liu, 2009;Qureshi et al, 2013;Ranatunga et al, 2010;Salazar et al, 2012), g) plant breeding (Hammer et al, 2010;Hoogenboom et al, 2004;Messina et al, 2011), h) bioenergy (Persson et al, 2010a(Persson et al, , 2010b, i) livestock and mixed crop-livestock systems (Berntsen et al, 2003;Lilley and Moore, 2009;Rotz et al, 2005) and * Thematic Issue on Agricultural systems modelling & software. j) yield gap analysis (Hochman et al, 2012;Liu et al, 2012;van Ittersum et al, 2013;van Ittersum et al, 2003).…”

Measuring the effect of climate change on agriculture: A literature review of analytical models

“…The latest applications of the mathematicalprogramming model use positive mathematical programming (PMP) (Qureshi et al, 2010(Qureshi et al, , 2013Howitt et al, 2012). These surpass the traditional limitations of linear-programming methods, for example, the unavailability of detailed information about the relationship between inputs and yields through the function cost.…”

Water Price Prediction for Increasing Market Efficiency Using Random Forest Regression: A Case Study in the Western United States

“…Recognition of this challenge has led to increasing adoption of water markets in many of the world's arid and semiarid regions to facilitate water transfer across competing demand [1]. The existence of water markets provides water users with clear price signals and opportunity costs that potentially enhance the economic efficiency of water use [2,3]. In a perfectly competitive market, price acts to equalize the marginal benefits from water use across users while maximizing social welfare [4].…”