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Mid- and End-of-the-Century Estimation of Agricultural Suitability of California’s Specialty Crops

Author

Listed:
  • Gabriel Granco

    (Department of Geography and Anthropology, California State Polytechnic University, Pomona, CA 91768, USA)

  • Haoji He

    (Department of Geography and Anthropology, California State Polytechnic University, Pomona, CA 91768, USA)

  • Brandon Lentz

    (Department of Geography and Anthropology, California State Polytechnic University, Pomona, CA 91768, USA)

  • Jully Voong

    (Department of Geography and Anthropology, California State Polytechnic University, Pomona, CA 91768, USA)

  • Alan Reeve

    (Department of Geography and Anthropology, California State Polytechnic University, Pomona, CA 91768, USA)

  • Exal Vega

    (Department of Geography and Anthropology, California State Polytechnic University, Pomona, CA 91768, USA)

Abstract

Specialty crops with long economic life cycles have lower adaptability and flexibility to climate change, making long-term planning crucial. This study examines the impact of climate change on almond, citrus, pistachio, and walnut production in California, using a machine learning approach to estimate crop suitability under current and future environmental conditions. We used recent satellite-observed cropland data to generate an occurrence dataset for these crops. Ecological data including bioclimatic variables derived from global circulation models developed under the Coupled Model Intercomparison Project Phase 6 (CMIP6) and surface variables were used to model suitability. The bioclimatic variables relating to temperature and precipitation had the largest effect on each crop’s suitability estimation. The results indicate that suitable areas for almonds, citrus, and walnuts will change significantly within 20 years due to climatic change, and the change will be even greater by the end of the century, indicating a potential loss of 94% of the current suitable area. The results for pistachios indicate change in the spatial distribution of suitable area but the total area is predicted to remain near the current suitable area. Policymakers, researchers, and farmers must work together to develop proactive adaptation strategies to mitigate the negative effects of climate change on specialty crop production. The application of a species distribution model for agriculture suitability provides critical information for future work on adaptation to climate change, identifying areas to target for further analysis.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:10:p:1907-:d:1257250
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    References listed on IDEAS

    as
    1. Amber Kerr & Jake Dialesandro & Kerri Steenwerth & Nathan Lopez-Brody & Emile Elias, 2018. "Vulnerability of California specialty crops to projected mid-century temperature changes," Climatic Change, Springer, vol. 148(3), pages 419-436, June.
    2. Lauren E. Parker & John T. Abatzoglou, 2018. "Shifts in the thermal niche of almond under climate change," Climatic Change, Springer, vol. 147(1), pages 211-224, March.
    3. Hamzeh Ahmadi & Gholamabbas Fallah Ghalhari & Mohammad Baaghideh, 2019. "Correction to: Impacts of climate change on apple tree cultivation areas in Iran," Climatic Change, Springer, vol. 153(1), pages 105-105, March.
    4. David Lobell & Christopher Field, 2011. "California perennial crops in a changing climate," Climatic Change, Springer, vol. 109(1), pages 317-333, December.
    5. Hamzeh Ahmadi & Gholamabbas Fallah Ghalhari & Mohammad Baaghideh, 2019. "Impacts of climate change on apple tree cultivation areas in Iran," Climatic Change, Springer, vol. 153(1), pages 91-103, March.
    6. João A. Santos & Ricardo Costa & Helder Fraga, 2017. "Climate change impacts on thermal growing conditions of main fruit species in Portugal," Climatic Change, Springer, vol. 140(2), pages 273-286, January.
    7. 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.
    8. Zeng, Yiwen & Low, Bi Wei & Yeo, Darren C.J., 2016. "Novel methods to select environmental variables in MaxEnt: A case study using invasive crayfish," Ecological Modelling, Elsevier, vol. 341(C), pages 5-13.
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