Incorporating machine learning with biophysical model can improve the evaluation of climate extremes impacts on wheat yield in south-eastern Australia
- Publication Type:
- Journal Article
- Agricultural and Forest Meteorology, 2019, 275 pp. 100 - 113
- Issue Date:
|Incorporating machine learning with biophysical model can improve the evaluation of climate extremes impacts on wheat yield in south-eastern Australia.pdf||Published Version||6.4 MB|
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© 2019 Accurately assessing the impacts of extreme climate events (ECEs)on crop yield can help develop effective agronomic practices to deal with climate change impacts. Process-based crop models are useful tools to evaluate climate change impacts on crop productivity but are usually limited in modelling the effects of ECEs due to over-simplification or vague description of certain process and uncertainties in parameterization. In this study, we firstly developed a hybrid model by incorporating the APSIM model outputs and growth stage-specific ECEs indicators (i.e. frost, drought and heat stress)into the Random Forest (RF)model, with the multiple linear regression (MLR)model as a benchmark. The results showed that the APSIM + RF hybrid model could explain 81% of the observed yield variations in the New South Wales wheat belt of south-eastern Australia, which had a 33% improvement in modelling accuracy compared to the APSIM model alone and 19% improvement compared to the APSIM + MLR hybrid model. Drought events during the grain-filling and vegetative stages and heat events immediately prior to anthesis were identified as the three most serious ECEs causing yield losses. We then compared the APSIM + RF hybrid model with the APSIM model to estimate the effects of future climate change on wheat yield. It was interesting to find that future yield projected from single APSIM model might have a 1–10% overestimation compared to the APSIM + RF hybrid model. The APSIM + RF hybrid model indicated that we were underestimating the effects of climate change and future yield might be lower than predicted using single APSIM informed modelling due to lack of adequately accounting for ECEs-induced yield losses. Increasing heat events around anthesis and grain-filling periods were identified to be major factors causing yield losses in the future. Therefore, we conclude that including the effects of ECEs on crop yield is necessary to accurately assess climate change impacts. We expect our proposed hybrid-modelling approach can be applied to other regions and crops and offer new insights of the effects of ECEs on crop yield.
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