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Land Use Misclassification Results in Water Use, Economic Value, and GHG Emission Discrepancies in California’s High-Intensity Agriculture Region

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  • Vicky Espinoza

    (Environmental Systems Graduate Group, University of California Merced, Merced, CA 95343, USA
    The Nature Conservancy, 555 Capitol Avenue, Ste 1290, Sacramento, CA 95814, USA)

  • Lorenzo Ade Booth

    (Environmental Systems Graduate Group, University of California Merced, Merced, CA 95343, USA
    Computer Science and Engineering, University of California Merced, Merced, CA 95343, USA)

  • Joshua H. Viers

    (Environmental Systems Graduate Group, University of California Merced, Merced, CA 95343, USA
    Department of Civil and Environmental Engineering, University of California Merced, Merced, CA 95343, USA)

Abstract

California’s San Joaquin Valley is both drought-prone and water-scarce but relies on high-intensity agriculture as its primary economy. Climate change adaptation strategies for high-intensity agriculture require reliable and highly resolved land use classification data to accurately account for changes in crop water demand, greenhouse gas (GHG) emissions, and farmgate revenue. Understanding direct and indirect economic impacts from potential changes to high-intensity agriculture to reduce groundwater overdrafts, such as reductions in the cultivated area or switching to less water-intensive crops, is unachievable if land use data are too coarse and inconsistent or misclassified. This study quantified the revenue, crop water requirement, and GHG emission discrepancies resulting from land use misclassification in the United States’ most complex agricultural region, California’s San Joaquin Valley. By comparing three commonly used land use classification datasets—CropScape, Land IQ, and Kern County Agriculture—this study found that CropScape led to considerable revenue and crop water requirement discrepancies compared to other sources. Crop misclassification across all datasets resulted in an underestimation of GHG emissions. The total revenue discrepancies of misclassified crops by area for the 2016 dataset comparisons result in underestimations by CropScape of around USD 3 billion and overestimation by LIQ and Kern Ag of USD 72 million. Reducing crop misclassification discrepancies is essential for crafting climate resilience strategies, especially for California, which generates USD 50 billion in annual agricultural revenue, faces increasing water scarcity, and aims to reach carbon neutrality by 2045. Additional investments are needed to produce spatial land use data that are highly resolved and locally validated, especially in high-intensity agriculture regions dominated by specialty crops with unique characteristics not well suited to national mapping efforts.

Suggested Citation

  • Vicky Espinoza & Lorenzo Ade Booth & Joshua H. Viers, 2023. "Land Use Misclassification Results in Water Use, Economic Value, and GHG Emission Discrepancies in California’s High-Intensity Agriculture Region," Sustainability, MDPI, vol. 15(8), pages 1-17, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6829-:d:1126607
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    References listed on IDEAS

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    1. Christina Bogner & Bumsuk Seo & Dorian Rohner & Björn Reineking, 2018. "Classification of rare land cover types: Distinguishing annual and perennial crops in an agricultural catchment in South Korea," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-22, January.
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    Cited by:

    1. Yi Cao & Xingping Wen & Yixiao Wang & Xuanting Zhao, 2024. "The Analysis of NPP Changes under Different Climatic Zones and under Different Land Use Types in Henan Province, 2001–2020," Sustainability, MDPI, vol. 16(18), pages 1-22, September.
    2. Wardle, Arthur R. & Bruno, Ellen, 2024. "Model Estimation using Categorical Satellite Data with Misclassification," 2024 Annual Meeting, July 28-30, New Orleans, LA 343766, Agricultural and Applied Economics Association.

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