Yield potential determines Australian wheat growers’ capacity to close yield gaps while mitigating economic risk

Abstract

Australia's farmers are among the most efficient in the world, despite a relatively large gap between potential and achieved waterlimited grain yield. With wheat yield gaps typically > 1.7 t/ha or 50% of the water-limited yield, it is important to investigate the degree to which this gap may be attributable to (rational) subprofit-maximising input levels in response to risk and risk aversion in many major grain-growing regions, particularly those with lower and more variable rainfall. Here, we use a set of 14 case study sites across the Australian wheatbelt to examine the risk-return profile of several agronomic management practices and show the extent to which the farmers' risk attitude determines their decision-making. Using a novel profit-risk-utility framework that incorporates crop simulation, probability theory, finance techniques and risk-aversion analysis, we were able to better demonstrate how farmers might select practices that manage economic risk across sites ranging from low to high rainfall. Results varied with risk preference and yield potential. However, there are real opportunities to close the yield gap by adopting non-limiting or near non-limiting nitrogen fertiliser practices and controlling fallow weeds. We show for the first time that yields associated with current best practice can be surpassed for most levels of risk aversion by adopting an emergent practice of optimising the sitespecific time of sowing and matching variety to time of sowing. For some sites and risk profiles, the emerging best practice package which includes additional N fertiliser is also profitable under risk. We also propose a modified integrated framework for yield gaps. Here, we distinguish allocative input constrains due to risk aversion from those due to access to resources, and we account for an innovation gap where the current agronomic frontier is shifted upwards by growers successfully, implementing new technologies that are not yet part of current best practice.

Intercropping—Evaluating the Advantages to Broadacre Systems

“…Mean-variance/mean-standard deviation analyses and stochastic dominance approaches e.g., [74,75] typically have been used, but may not be sufficiently discriminating between many high-risk alternatives [76]. Today, the Stochastic Efficiency with Respect to a Function (SERF) method developed and popularised by Hardaker et al [77,78] is being used more frequently [79][80][81]. The SERF method ranks alternatives based on the certainty equivalent (CE) for a specified range of attitudes to risk.…”

Disaggregating the Value of Conservation Agriculture to Inform Smallholder Transition to Sustainable Farming: A Mexican Case Study

“…Value-Ag is a micro-level bioeconomic modelling framework, and it is part of a rich history of bioeconomic models [55] that have attempted to capture technology choice in dynamic, complex agri-food systems by focusing on their interactions in each context. The Value-Ag framework (Figure 2) effectively combines different tools and their outputs at different scales: (i) whole-farm profit simulated with the Integrated Analysis Tool (IAT) [56]; (ii) risk and uncertainty metrics borrowed from a Profit-Risk-Utility Framework (PRUF) [33,54]; (iii) adoption predicted by the Smallholder ADOPT (Adoption and Diffusion Outcomes Prediction Tool) [57,58]; and (iv) impact assessment through an out-scaled Net Present Value (NPV) analysis [49].…”

“…Sadras et al argued that overlooking resource trade-offs, scaling/adoption drivers, and context-specificity in crop research often lead to over-optimistic projections, experimental shortcomings, and irrelevant (controlled-environment) agronomy [32]. Overlooking the socioeconomic and risk contexts further limits the closure of yield and profit gaps [33].…”

Value-Ag: An integrated model for rapid ex-ante impact evaluation of agricultural innovations in smallholder systems

“…The climate risk and level of farmer risk aversion associated with each scenario is assessed through a set of metrics borrowed from a profit-risk-utility framework described in Monjardino et al (2019) (Figure 1). In brief, each risk profile is determined through the combination of standard deviation (SD) and coefficient of variation (CV) of the 10-year average net profit, probability of a positive net profit [P(π ≥ 0)] and conditional value at risk of the lowest 10% of net profits (CVaR0.1) as a measure of downside risk.…”