Elsevier

Ecological Economics

Volume 79, July 2012, Pages 105-112
Ecological Economics

Analysis
Mitigating economic risk from climate variability in rain-fed agriculture through enterprise mix diversification

https://doi.org/10.1016/j.ecolecon.2012.04.025Get rights and content

Abstract

Climate variability, and its increase with climate change, pose substantial economic risks to agriculturalists and hence, limit their ability to respond to global challenges such as food security. Enterprise mix diversification is the most common, and is widely regarded as the most effective, strategy for mitigating multiple sources of short-term economic risk to agricultural enterprises. However, assessments of enterprise mix diversification as a strategy for mitigating climate risks to ensure long term viability of agricultural enterprises are sparse. Using the Lower Murray region in southern Australia as a case study, we combined APSIM modelling with Monte Carlo simulation, probability theory, and finance techniques, to assess the extent to which enterprise mix diversification can mitigate climate-induced variability in long term net returns from rain-fed agriculture. We found that diversification can reduce the standard deviation by up to A$200 ha 1, or 52% of mean net returns; increase the probability of breaking even by up to 20%, and increase the mean of 10% of worst probable annual net returns (Conditional Value at Risk) by up to A$100 ha 1. We conclude that enterprise mix diversification can also be an effective strategy for hedging against climate-induced economic risk for agriculturalists in marginal areas.

Highlights

► We assess whether diversification can mitigate climate-driven variability in agricultural returns. ► We assess the economic impact of switching to a diversified farming system. ► Yields between any two farm enterprises were not perfectly correlated. ► Diversification can abate climate-driven economic risk in moderate rainfall areas. ► Diversification is less effective in low- and high-rainfall agricultural areas.

Introduction

Many of the world's major agricultural regions are characterised by uncertain and variable climatic conditions including temperature and rainfall (Azam-Ali, 2007, Furuya and Kobayashi, 2009, Naylor et al., 2007, Power et al., 1999, Wang et al., 2009). Notwithstanding variance in market input costs and commodity prices (Hazell et al., 1990, Ramaswami et al., 2003), climate variability is the principal source of risk affecting long term economic viability of rain-fed agricultural systems both in industrial (Iglesias and Quiroga, 2007, Lotze-Campen and Schellnhuber, 2009, Marton et al., 2007) and smallholder farming systems (Kurukulasuriya and Ajwad, 2007, Magombeyi and Taigbenu, 2008). Climate models predict an increase in future climate variability and a significant increase in the frequency of below-average rainfalls and above-average temperatures in many agricultural regions (IPCC, 2007, Naylor et al., 2007, Suppiah et al., 2006). All else being equal, this is likely to increase the uncertainty and variability in agricultural yields and net returns, and increase the frequency with which these are below average (John et al., 2005, Wang et al., 2009). Consequently, the viability of agricultural enterprises will become increasingly threatened in the long run.

To manage the severity of the impact of climate variability on net returns, agriculturalists routinely adopt mitigation strategies involving various adjustments in enterprise mix, and production technologies and techniques (Bryant et al., 2000, Kelkar et al., 2008, van Ittersum et al., 2003). The diversification of agricultural enterprise mixes consisting of several different crops and livestock (hereafter, diversification), is widely regarded as the most common and effective strategy for mitigating climate-induced variability in net returns from rain-fed agriculture (Amita, 2006, Azam-Ali, 2007, Correal et al., 2006). Diversification can also reduce the magnitude and frequency of below-average net returns under climate uncertainty (Berhanu et al., 2007).

The benefits of diversification are premised on the utilization of imperfectly correlated net returns from multiple agricultural enterprises. Most of the benefit of diversification comes from hedging against market input and commodity price fluctuations (Bhende and Venkataram, 1994, Ramaswami et al., 2003, World-Bank, 2004). However, here we propose that diversification may also be beneficial for hedging against climatic variability. When the impacts of climatic variability differ between multiple agricultural enterprises, losses from investments in some enterprises are offset by gains, or moderated by less severe losses, in other enterprises thereby reducing the impact on overall net returns (Fraser, 2007, Fraser et al., 2005). Conversely, the benefits of diversification typically come at a cost of reduced expected short-term net returns (Chan et al., 1998, Markowitz, 1952a, Markowitz, 1952b, Markowitz, 1994). This is because diversification involves investing in multiple enterprises to mitigate long term uncertainty and variability even when investments in alternative non-diversified enterprises may offer higher expected net returns in the short term (Cooper et al., 2008). As such, it is necessary to quantify the tradeoffs between the benefits and costs of diversification when assessing the benefits of agricultural diversification. Further, the nature and strength of correlated yields across alternative agricultural enterprises need to be fully understood and quantified when assessing the benefits of agricultural diversification. There is a general consensus from the finance literature that not considering the nature and strength of correlated yields may under- or over-estimate the benefit of diversification (Bangun et al., 2006, Chan et al., 1998, Chan et al., 1999, Markowitz, 1952a, Markowitz, 1952b, Markowitz, 1994).

Several studies have assessed various factors affecting the potential for enterprise mix diversification to mitigate multiple sources of risk to agricultural enterprises with the objective of aiding agriculturalists' short term decision making (Barkley et al., 2008, Cooper et al., 2008, Hardaker et al., 2004, Kingwell, 1994, Pannell et al., 2000). Major sources of short term risk and uncertainty assessed in these studies broadly consist of price and production risks including changes in commodity and input prices, and variability in yields due to weather, pests and diseases. To achieve this, these studies applied various methods including crop modelling (Cooper et al., 2008), stochastic risk modelling (Kingwell, 1994), utility and probability theory (Kingwell, 1994, Ladanyi, 2008, Pannell et al., 2000), and portfolio theory (Barkley et al., 2008). Luo et al. (2005) assessed the impact of climate change in increasing yield risk. However, few other studies have considered long term climatic sources of uncertainty and risk (Lien et al., 2009). Lien et al. (2009) speculate that this is because relevant historical data necessary for long term analyses are usually sparse and that most studies rely on a few observations of net returns. However, long-term risk and uncertainty analyses are important for informing risk management strategies that ensure long-run economic viability of agricultural enterprises (Lien et al., 2009, Meza and Silva, 2009). In the context of increasingly frequent droughts in many of the worlds agricultural regions (Furuya and Kobayashi, 2009, Howden et al., 2007, IPCC, 2007, Lotze-Campen and Schellnhuber, 2009) and growing threats to global food security (Fraser, 2007, Fraser et al., 2005, Yang, 2009), the effectiveness of diversification at mitigating the risk of crop failure bears significant relevance. Further, emerging markets for ecosystem services (Yang et al., 2010) provide alternative enterprises with returns that may be even less correlated with agricultural returns, thus broadening the scope for diversification as an effective strategy for mitigating the risk of low incomes.

In this study, we assessed the ability of enterprise mix diversification to mitigate climate-induced variability in long-term economic net returns from rain-fed agriculture. We leave the application and operationalization of diversification to future work. Variability was assessed based on historical data. Using a case study in the 11.8 million hectare Lower Murray region in southern Australia, we fitted probability density functions to modelled long term crop and livestock yield data. We considered four alternative agricultural enterprise types consisting of three non-diversified enterprises and one diversified enterprise comprised of a mix of rain-fed agricultural enterprises. We used Monte Carlo simulation to quantify the variability in yields and, via a profit function, net returns. We quantified the benefits and costs of enterprise mix diversification using techniques from finance theory including the probability of break-even and Conditional Value at Risk (CVaR). We quantified the trade-off between the reduced variability in returns, measured using the value of standard deviation, and reduced expected net returns, and assessed the spatial heterogeneity in these effects across the region. We discuss the implications of diversification as an adaptation strategy for agriculturalists to cope with increasing climatic variability.

Section snippets

Study Area

The Lower Murray region (Fig. 1) in southern Australia covers a total area of 11,871,363 ha. Mean annual rainfall ranges from 200 mm yr 1 in the drier northern areas of the SAMDB to 1400 mm yr 1 in the southern Wimmera. Rain-fed agriculture is the dominant land use covering over 50% of the region and is an important component of the regional economy (Bryan et al., 2011). The average size of agricultural land used for rain-fed agriculture in the study area is around 1000 ha. Agricultural systems vary

Results

We present quantitative results on climate-induced yield variability, Q1I, yield correlations, ρi,i, variability in net economic returns, NRi,d, and assess impacts of switching to diversification. First, we summarise results on yield variability, and correlations broadly across the region, but we refer the reader to the online supporting material for more detailed results for the nine illustrative zones. Second, we summarise results on variability in net economic returns, and impacts of

Economic Impacts of Diversification

We have demonstrated the ability of diversification to mitigate the impacts of climate-driven variability in net returns from rain-fed agriculture in the Lower Murray region in southern Australia. In dryer areas with more variable rainfall, diversification was not beneficial because it introduced more water-intensive crops — wheat and lupins, which did not do well. In wetter areas, diversification introduced low-price crops — sheep and lupins, into areas that were highly suitable for an

Conclusion

Agriculturalists already practice diversification for various traditional reasons including but not limited to management of short-term risk due to variance in market input costs and commodity prices, and disease and weed control, but diversification is not widely used as a strategy for mitigating long-term climate risks. Current levels of diversification to meet other objectives would inevitably yield incidental benefits including mitigating long-term climate risks however, we propose

References (60)

  • M.S. Magombeyi et al.

    Crop yield risk analysis and mitigation of smallholder farmers at quaternary catchment level: case study of B72A in Olifants river basin, South Africa

    Physics and Chemistry of the Earth

    (2008)
  • D.J. Pannell et al.

    Are we risking too much? Perspectives on risk in farm modelling

    Agricultural Economics

    (2000)
  • R.T. Rockafellar et al.

    Conditional value-at-risk for general loss distributions

    Journal of Banking & Finance

    (2002)
  • R.J. Thomas

    Opportunities to reduce the vulnerability of dryland farmers in Central and West Asia and North Africa to climate change

    Agriculture, Ecosystems & Environment

    (2008)
  • M.K. van Ittersum et al.

    Sensitivity of productivity and deep drainage of wheat cropping systems in a Mediterranean environment to changes in CO2, temperature and precipitation

    Agriculture, Ecosystems & Environment

    (2003)
  • E. Wang et al.

    Modelling farming systems performance at catchment and regional scales to support natural resource management

    NJAS - Wageningen Journal of Life Sciences

    (2009)
  • W.H. Yang et al.

    A conservation industry for sustaining natural capital and ecosystem services in agricultural landscapes

    Ecological Economics

    (2010)
  • S. Amita

    Changing interface between agriculture and livestocks: a study of livelihood options under dry land systems in Gujarat

  • S. Azam-Ali

    Agricultural diversification: the potential for underutilised crops in Africa's changing climates

    Rivista Di Biologia-Biology Forum

    (2007)
  • P. Bangun et al.

    Evaluating covariance and selecting the risk model for asset allocation

  • A. Barkley et al.

    Wheat variety selection to maximize returns and minimize risk: an application of portfolio theory

    Journal of Agricultural and Applied Economics

    (2008)
  • W. Berhanu et al.

    Diversification and livelihood sustainability in a semi-arid environment: a case study from Southern Ethiopia

    Journal of Development Studies

    (2007)
  • B.A. Bryan et al.

    Lower Murray Landscape Futures Dryland Component: Volume 3 — Preliminary Analysis and Modelling. CSIRO Water for a Healthy Country

    (2007)
  • B.A. Bryan et al.

    Mapping economic returns to agriculture for informing environmental policy in the Murray–Darling Basin, Australia

    Environmental Modeling and Assessment

    (2009)
  • B.A. Bryan et al.

    Biofuels agriculture: landscape-scale trade-offs between fuel, economics, carbon, energy, food, and fiber

    Global Change Biology Bioenergy

    (2010)
  • C.R. Bryant et al.

    Adaptation in Canadian agriculture to climatic variability and change

    Climate Change

    (2000)
  • L.K.C. Chan et al.

    The risk and return from factors

    Journal of Financial and Quantitative Analysis

    (1998)
  • L.K.C. Chan et al.

    On portfolio optimization: forecasting covariances and choosing the risk model

    Review of Financial Studies

    (1999)
  • E. Correal et al.
  • T.T. Deressa et al.

    Economic impact of climate change on crop production in Ethiopia: evidence from cross-section measures

    Journal of African Economies

    (2009)
  • Cited by (51)

    View all citing articles on Scopus
    View full text