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Controlling for the effects of climate on total factor productivity: A case study of Australian farms

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  • Chancellor, Will
  • Hughes, Neal
  • Zhao, Shiji
  • Soh, Wei Ying
  • Valle, Haydn
  • Boult, Christopher

Abstract

Estimates of agricultural Total Factor Productivity (TFP) can be highly sensitive to both short-run climate variability and long-term climate change. This is particularly true in Australia where drought impacts are responsible for most of the annual volatility in official farm TFP statistics. While climate variability can obscure short-term productivity trends, researchers have typically assumed that long run TFP trends are largely unaffected. However, in the presence of global climate change this assumption becomes problematic. For example, in Australia, shifts to higher temperatures and lower winter season rainfall over the last 20–30 years have had a significant negative impact on agricultural productivity. This study presents a framework to account for the effects of climate variability on TFP estimates. In contrast with previous work, this approach applies a reduced form machine learning based model of farm production to generate synthetic (climate-adjusted) farm-level input and output data sets. It therefore has advantages in terms of flexibility—since the synthetic datasets can be combined with any existing TFP estimation framework. In this study, the approach is applied to estimate climate-adjusted TFP indices (TFP under an assumption of constant long-run average climate conditions) for a range of Australian agricultural sectors.

Suggested Citation

  • Chancellor, Will & Hughes, Neal & Zhao, Shiji & Soh, Wei Ying & Valle, Haydn & Boult, Christopher, 2021. "Controlling for the effects of climate on total factor productivity: A case study of Australian farms," Food Policy, Elsevier, vol. 102(C).
    • Handle: RePEc:eee:jfpoli:v:102:y:2021:i:c:s0306919221000701
    DOI: 10.1016/j.foodpol.2021.102091
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    Cited by:

    1. Neal Hughes & Michael Lu & Wei Ying Soh & Kenton Lawson, 2022. "Modelling the effects of climate change on the profitability of Australian farms," Climatic Change, Springer, vol. 172(1), pages 1-22, May.
    2. Lindikaya W. Myeki & Yonas T. Bahta & Nicolette Matthews, 2022. "Exploring the Growth of Agricultural Productivity in AFRICA: A Färe-Primont Index Approach," Agriculture, MDPI, vol. 12(8), pages 1-17, August.
    3. Amanda R. Bourne & John Bruce & Meredith M. Guthrie & Li-Ann Koh & Kaylene Parker & Stanley Mastrantonis & Igor Veljanoski, 2023. "Identifying areas of high drought risk in southwest Western Australia," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(2), pages 1361-1385, September.
    4. Hughes, Neal & Soh, Wei Ying & Lawson, Kenton & Lu, Michael, 2022. "Improving the performance of micro-simulation models with machine learning: The case of Australian farms," Economic Modelling, Elsevier, vol. 115(C).

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    More about this item

    Keywords

    Total factor productivity; Machine learning; Climate variability; Climate change; Australian farms;
    All these keywords.

    JEL classification:

    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • Q10 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - General
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation

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