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Predicting spatial and temporal variability in crop yields: an inter-comparison of machine learning, regression and

Guoyong Leng1,2, Jim W Hall2

  • 1Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.

Environmental Research Letters : ERL [Web Site]
|May 13, 2020
PubMed
Summary

Machine learning models excel at predicting US maize yield averages, variability, and extremes under climate change. This approach offers a more efficient and accurate method for assessing future crop production risks.

Keywords:
climate changecrop modelcrop yieldmachine learningstatistical model

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Area of Science:

  • Agricultural Science
  • Climate Change Impact Assessment
  • Computational Modeling

Background:

  • Previous crop yield assessments primarily used process-based or statistical models, focusing on average yields.
  • There is increasing interest in understanding crop yield variability and extremes due to climate change.
  • Evaluating different modeling approaches is crucial for accurate climate impact predictions.

Purpose of the Study:

  • To compare the strengths and weaknesses of process-based, regression, and machine learning models in simulating US maize yield.
  • To identify the most effective method for predicting yield averages, variability, and extremes under climate change.
  • To project future US maize yields and the frequency of extreme low-yield events under global warming scenarios.

Main Methods:

  • Simulated US maize yield using process-based crop models, a traditional regression model, and a machine-learning algorithm.
  • Assessed model performance in reproducing observed yield averages, variability, and probability distributions.
  • Estimated yield changes and the frequency of extreme low-yield events under 1.5°C and 2°C global warming scenarios.

Main Results:

  • Machine learning and regression models accurately reproduced observed yield averages, outperforming process-based models with significant bias.
  • Machine learning demonstrated the highest skill in simulating yield probability distributions, followed by regression and then process-based models.
  • Machine learning explained 93% of observed yield variability, compared to 51% for regression and 42% for process-based models.
  • Projected US maize yield decrease of 13.5% under a 2°C warming scenario.
  • Increased frequency of yields falling below the 10th percentile under future warming scenarios (19% for 1.5°C, 25% for 2°C).

Conclusions:

  • Machine learning and regression models are more computationally efficient and accurate for simulating crop yield variability and extremes than traditional process-based models.
  • Machine learning provides a robust framework for probabilistic risk analysis of climate impacts on crop production.
  • Future climate change poses significant risks to US maize production, impacting food supply and trade.