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Modelling long-term risk profiles of wheat grain yield with limited climate data

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  • Bracho-Mujica, Gennady
  • Hayman, Peter T.
  • Ostendorf, Bertram

Abstract

Long-term, continuous, accurate, daily weather records for precipitation, temperature and solar radiation are critical inputs for modelling long-term climate risk in cropping systems. However, comprehensive weather data often exhibit short record length and missing or inaccurate records, which can lead to inconsistencies. Using risk profiles (cumulative probability curves of crop yield) as a tool for quantifying the performance of cropping systems under climate variability, this study examines how sensitive risk profiles of a worldwide staple food crop are to temporal coverage of climate data, and additionally to the presence of extreme weather events. Here, we focused on the risk profile of modelled wheat grain yield across the Australian grain-belt using high-quality weather records. To test the effect of the discontinuity and limited record length often found in weather records, long-term risk profiles (i.e. obtained for a baseline period of 100 years, from 1917 to 2016) were compared with long-term risk profiles constructed using variable temporal coverages (record lengths 10, 20, …, 90 years, and three sampling periods: random, continuous and non-continuous). Long-term risk profiles based on >40 years showed reasonable small bias and root mean square errors when compared to those built for the baseline period, implying that even relatively short climate records can produce reliable long-term performance indicators. Long-term risk profiles able to account for severe frost and heat events required longer climate records (60 years). For most locations in Australia, long-term risk profiles built using data from the last 10–40 years also revealed negative yield trends which may be partially attributed to climate change. Results were consistent across soils and different simulated sowing dates. Findings highlight rainfall as the main climate driver of wheat productivity and the importance of the record length and period considered for extreme weather event analysis in agricultural studies.

Suggested Citation

  • Bracho-Mujica, Gennady & Hayman, Peter T. & Ostendorf, Bertram, 2019. "Modelling long-term risk profiles of wheat grain yield with limited climate data," Agricultural Systems, Elsevier, vol. 173(C), pages 393-402.
    • Handle: RePEc:eee:agisys:v:173:y:2019:i:c:p:393-402
    DOI: 10.1016/j.agsy.2019.03.010
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    References listed on IDEAS

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    1. Richard H. Day, 1965. "Probability Distributions of Field Crop Yields," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 47(3), pages 713-741.
    2. Jens H. Christensen & Ole B. Christensen, 2003. "Severe summertime flooding in Europe," Nature, Nature, vol. 421(6925), pages 805-806, February.
    3. Hammer, G. L. & Hansen, J. W. & Phillips, J. G. & Mjelde, J. W. & Hill, H. & Love, A. & Potgieter, A., 2001. "Advances in application of climate prediction in agriculture," Agricultural Systems, Elsevier, vol. 70(2-3), pages 515-553.
    4. William R. Travis, 2016. "Mapping future crop geographies," Nature Climate Change, Nature, vol. 6(6), pages 544-545, June.
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