Abstract
Abstract Pervious assessments of crop yield response to climate change are mainly aided with either process-based models or statistical models, with a focus on predicting the changes in average yields, whilst there is growing interest in yield variability and extremes. In this study, we simulate US maize yield using process-based models, traditional regression model and a machine-learning algorithm, and importantly, identify the weakness and strength of each method in simulating the average, variability and extremes of maize yield across the country. We show that both regression and machine learning models can well reproduce the observed pattern of yield averages, while large bias is found for process-based crop models even fed with harmonized parameters. As for the probability distribution of yields, machine learning shows the best skill, followed by regression model and process-based models. For the country as a whole, machine learning can explain 93% of observed yield variability, followed by regression model (51%) and process-based models (42%). Based on the improved capability of the machine learning algorithm, we estimate that US maize yield is projected to decrease by 13.5% under the 2 °C global warming scenario (by ∼2050 s). Yields less than or equal to the 10th percentile in the yield distribution for the baseline period are predicted to occur in 19% and 25% of years in 1.5 °C (by ∼2040 s) and 2 °C global warming scenarios, with potentially significant implications for food supply, prices and trade. The machine learning and regression methods are computationally much more efficient than process-based models, making it feasible to do probabilistic risk analysis of climate impacts on crop production for a wide range of future scenarios.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by. Read at the source ↗
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Claims 10
Claim · model-written summary
Regression and machine learning reproduced observed yield-average patterns.
Not reportedTaxon
Verbatim
e0“both regression and machine learning models can well reproduce the observed pattern of yield averages”
Claim · model-written summary
Process-based crop models had large bias in yield averages despite harmonized parameters.
Not reportedTaxon
Verbatim
e0“large bias is found for process-based crop models even fed with harmonized parameters”
Claim · model-written summary
Machine learning had the best skill for yield distributions, followed by regression and process-based models.
Verbatim
e0“machine learning shows the best skill, followed by regression model and process-based models”
Claim · model-written summary
Machine learning explained 93% of observed yield variability.
Verbatim
e0“machine learning can explain 93% of observed yield variability”
Claim · model-written summary
Regression explained 51% of observed yield variability.
Verbatim
e0“followed by regression model (51%)”
Claim · model-written summary
Process-based models explained 42% of observed yield variability.
Verbatim
e0“process-based models (42%)”
Claim · model-written summary
US maize yield was projected to decrease 13.5% under 2 °C warming.
- Outcome
- maize yielde0
- Direction
- decreasee0
- Value
- 13.5%e0
- Qualifier
- 2 °C global warming scenario (by ∼2050 s)e0
Zea maysOrganisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“US maize yield is projected to decrease by 13.5% under the 2 °C global warming scenario (by ∼2050 s)”
Claim · model-written summary
Baseline low-yield years were projected in 19% of years at 1.5 °C warming.
Not reportedTaxon
Verbatim
e0“Yields less than or equal to the 10th percentile in the yield distribution for the baseline period are predicted to occur in 19% and 25% of years in 1.5 °C (by ∼2040 s) and 2 °C global warming scenarios”
Claim · model-written summary
Baseline low-yield years were projected in 25% of years at 2 °C warming.
- Outcome
- Yields less than or equal to the 10th percentilee0
- Value
- 25%e0
- Qualifier
- 2 °C global warming scenariose0
Not reportedTaxon
Verbatim
e0“Yields less than or equal to the 10th percentile in the yield distribution for the baseline period are predicted to occur in 19% and 25% of years in 1.5 °C (by ∼2040 s) and 2 °C global warming scenarios”
Claim · model-written summary
Machine learning and regression were computationally more efficient than process-based models.
Verbatim
e0“machine learning and regression methods are computationally much more efficient than process-based models”