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Evaluation of Maximum Entropy (Maxent) Machine Learning Model to Assess Relationships between Climate and Corn Suitability

Author

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  • Abigail Fitzgibbon

    (Esri, Redlands, CA 92373, USA)

  • Dan Pisut

    (Esri, Redlands, CA 92373, USA)

  • David Fleisher

    (Adaptive Cropping Systems Laboratory, United States Department of Agriculture, Agricultural Research Service, Beltsville, MD 20705, USA)

Abstract

Given the impact that climate change is projected to have on agriculture, it is essential to understand the mechanisms and conditions that drive agricultural land suitability. However, existing literature does not provide sufficient guidance on the best modeling methodology to study crop suitability, and there is even less research on how to evaluate the accuracy of such models. Further, studies have yet to demonstrate the use of the Maximum Entropy (Maxent) model in predicting presence and yield of large-scale field crops in the United States. In this study, we investigate the application of the Maxent model to predict crop suitability and present novel methods of evaluating its predictive ability. Maxent is a correlative machine learning model often used to predict cropland suitability. In this study, we used Maxent to model land suitability for corn production in the contiguous United States under current bioclimatic conditions. We developed methods for evaluating Maxent’s predictive ability through three comparisons: (i) classification of suitable land units and comparison of results with another similar species distribution model (Random Forest Classification), (ii) comparison of output response curves with existing literature on corn suitability thresholds, and (iii) with correlation of predicted suitability with observed extent and yield. We determined that Maxent was superior to Random Forest, especially in its modeling of areas in which land was likely suitable for corn but was not currently associated with observed corn presence. We also determined that Maxent’s predictions correlated strongly with observed yield statistics and were consistent with existing literature regarding the range of bioclimatic variable values associated with suitable production conditions for corn. We concluded that Maxent was an effective method for modeling current cropland suitability and could be applied to broader issues of agriculture–climate relationships.

Suggested Citation

  • Abigail Fitzgibbon & Dan Pisut & David Fleisher, 2022. "Evaluation of Maximum Entropy (Maxent) Machine Learning Model to Assess Relationships between Climate and Corn Suitability," Land, MDPI, vol. 11(9), pages 1-20, August.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:9:p:1382-:d:895772
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    References listed on IDEAS

    as
    1. Jacek Malczewski, 2010. "Multiple Criteria Decision Analysis and Geographic Information Systems," International Series in Operations Research & Management Science, in: Matthias Ehrgott & José Rui Figueira & Salvatore Greco (ed.), Trends in Multiple Criteria Decision Analysis, chapter 0, pages 369-395, Springer.
    2. Yuan Gao & Anyu Zhang & Yaojie Yue & Jing’ai Wang & Peng Su, 2021. "Predicting Shifts in Land Suitability for Maize Cultivation Worldwide Due to Climate Change: A Modeling Approach," Land, MDPI, vol. 10(3), pages 1-31, March.
    3. Chengyi Huang & Sjoerd Willem Duiker & Liangji Deng & Conggang Fang & Weizhong Zeng, 2015. "Influence of Precipitation on Maize Yield in the Eastern United States," Sustainability, MDPI, vol. 7(5), pages 1-15, May.
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    Cited by:

    1. Nicholas M. Short & M. Jennifer Woodward-Greene & Michael D. Buser & Daniel P. Roberts, 2023. "Scalable Knowledge Management to Meet Global 21st Century Challenges in Agriculture," Land, MDPI, vol. 12(3), pages 1-19, February.
    2. Gabriel Granco & Haoji He & Brandon Lentz & Jully Voong & Alan Reeve & Exal Vega, 2023. "Mid- and End-of-the-Century Estimation of Agricultural Suitability of California’s Specialty Crops," Land, MDPI, vol. 12(10), pages 1-18, October.

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