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

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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.