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Assessing the potential for beneficial diversification in rain-fed agricultural enterprises

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  • Kandulu, John

Abstract

Climate change and climate variability induce uncertainty in yields, and thus threaten long term economic viability of rain-fed agricultural enterprises. Enterprise mix diversification is the most common, and is widely regarded as the most effective, strategy for mitigating multiple sources of farm business risk. We assess the potential for enterprise mix diversification in mitigating climate induced variability in long term net returns from rain-fed agriculture. We build on APSIM modelling and apply Monte Carlo simulation, probability theory, and finance techniques, to assess the potential for enterprise mix diversification to mitigate climate-induced variability in long term economic returns from rain-fed agriculture. We consider four alternative farm enterprise types consisting of three non-diversified farm enterprises and one diversified farm enterprise consisting of a correlated mix of rain-fed agricultural activities. We analyse a decision to switch from a non-diversified agricultural enterprise with the highest expected return to a diversified agricultural enterprise consisting of a mix of agricultural enterprises. Correlation analysis showed that yields were not perfectly correlated (i.e. are less than 1) indicating that changes in climate variables cause non-proportional impacts on yield production. We conclude that at best, diversification can reduce the standard deviation of net returns by up to about A$110 Ha-1, or 52% of mean net returns; increase the probability of below-average net returns by up to about 4% and increase the mean of 10% of worst probable annual net returns by up to A$54/ha. At worst, diversification can reduce the mean of net returns by up to about A$95 Ha-1, or 46%.

Suggested Citation

  • Kandulu, John, 2011. "Assessing the potential for beneficial diversification in rain-fed agricultural enterprises," 2011 Conference (55th), February 8-11, 2011, Melbourne, Australia 100568, Australian Agricultural and Resource Economics Society.
  • Handle: RePEc:ags:aare11:100568
    DOI: 10.22004/ag.econ.100568
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    1. Lien, Gudbrand & Hardaker, J. Brian & Asseldonk, Marcel A.P.M. van & Richardson, James W., 2009. "Risk programming and sparse data: how to get more reliable results," Agricultural Systems, Elsevier, vol. 101(1-2), pages 42-48, June.
    2. Temesgen Tadesse Deressa & Rashid M. Hassan, 2009. "Economic Impact of Climate Change on Crop Production in Ethiopia: Evidence from Cross-section Measures," Journal of African Economies, Centre for the Study of African Economies, vol. 18(4), pages 529-554, August.
    3. Kingwell, R. S., 1994. "Risk attitude and dryland farm management," Agricultural Systems, Elsevier, vol. 45(2), pages 191-202.
    4. Chan, Louis K C & Karceski, Jason & Lakonishok, Josef, 1999. "On Portfolio Optimization: Forecasting Covariances and Choosing the Risk Model," The Review of Financial Studies, Society for Financial Studies, vol. 12(5), pages 937-974.
    5. Louis K.C. Chan & Jason Karceski & Josef Lakonishok, 1999. "On Portfolio Optimization: Forecasting Covariances and Choosing the Risk Model," NBER Working Papers 7039, National Bureau of Economic Research, Inc.
    6. Merton, Robert C., 1980. "On estimating the expected return on the market : An exploratory investigation," Journal of Financial Economics, Elsevier, vol. 8(4), pages 323-361, December.
    7. Pannell, David J. & Malcolm, Bill & Kingwell, Ross S., 2000. "Are we risking too much? Perspectives on risk in farm modelling," Agricultural Economics, Blackwell, vol. 23(1), pages 69-78, June.
    8. Bhende, M. J. & Venkataram, J. V., 1994. "Impact of diversification on household income and risk: A whole-farm modelling approach," Agricultural Systems, Elsevier, vol. 44(3), pages 301-312.
    9. Fulco Ludwig & Stephen Milroy & Senthold Asseng, 2009. "Impacts of recent climate change on wheat production systems in Western Australia," Climatic Change, Springer, vol. 92(3), pages 495-517, February.
    10. Bharat Ramaswami & Shamika Ravi & S.D. Chopra, 2003. "Risk management in agriculture," Discussion Papers 03-08, Indian Statistical Institute, Delhi.
    11. Chan, Louis K. C. & Karceski, Jason & Lakonishok, Josef, 1998. "The Risk and Return from Factors," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 33(2), pages 159-188, June.
    12. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
    13. Hardaker, J. Brian & Lien, Gudbrand, 2010. "Probabilities for decision analysis in agriculture and rural resource economics: The need for a paradigm change," Agricultural Systems, Elsevier, vol. 103(6), pages 345-350, July.
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