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Evaluating the Impact of Future Seasonal Climate Extremes on Crop Evapotranspiration of Maize in Western Kansas Using a Machine Learning Approach

Author

Listed:
  • Kelechi Igwe

    (Carl and Melinda Helwig Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS 66502, USA)

  • Vaishali Sharda

    (Carl and Melinda Helwig Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS 66502, USA)

  • Trevor Hefley

    (Department of Statistics, Kansas State University, Manhattan, KS 66502, USA)

Abstract

Data-driven technologies are employed in agriculture to optimize the use of limited resources. Crop evapotranspiration (ET) estimates the actual amount of water that crops require at different growth stages, thereby proving to be the essential information needed for precision irrigation. Crop ET is essential in areas like the US High Plains, where farmers rely on groundwater for irrigation. The sustainability of irrigated agriculture in the region is threatened by diminishing groundwater levels, and the increasing frequency of extreme events caused by climate change further exacerbates the situation. These conditions can significantly affect crop ET rates, leading to water stress, which adversely affects crop yields. In this study, we analyze historical climate data using a machine learning model to determine which of the climate extreme indices most influences crop ET. Crop ET is estimated using reference ET derived from the FAO Penman–Monteith equation, which is multiplied with the crop coefficient data estimated from the remotely sensed normalized difference vegetation index (NDVI). We found that the climate extreme indices of consecutive dry days and the mean weekly maximum temperatures most influenced crop ET. It was found that temperature-derived indices influenced crop ET more than precipitation-derived indices. Under the future climate scenarios, we predict that crop ET will increase by 0.4% and 1.7% in the near term, by 3.1% and 5.9% in the middle term, and by 3.8% and 9.6% at the end of the century under low greenhouse gas emission and high greenhouse gas emission scenarios, respectively. These predicted changes in seasonal crop ET can help agricultural producers to make well-informed decisions to optimize groundwater resources.

Suggested Citation

  • Kelechi Igwe & Vaishali Sharda & Trevor Hefley, 2023. "Evaluating the Impact of Future Seasonal Climate Extremes on Crop Evapotranspiration of Maize in Western Kansas Using a Machine Learning Approach," Land, MDPI, vol. 12(8), pages 1-26, July.
  • Handle: RePEc:gam:jlands:v:12:y:2023:i:8:p:1500-:d:1204845
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    References listed on IDEAS

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    1. van der Laan Mark J., 2006. "Statistical Inference for Variable Importance," The International Journal of Biostatistics, De Gruyter, vol. 2(1), pages 1-33, February.
    2. Yuei-An Liou & Sanjib Kumar Kar, 2014. "Evapotranspiration Estimation with Remote Sensing and Various Surface Energy Balance Algorithms—A Review," Energies, MDPI, vol. 7(5), pages 1-29, April.
    3. Torres, Alfonso F. & Walker, Wynn R. & McKee, Mac, 2011. "Forecasting daily potential evapotranspiration using machine learning and limited climatic data," Agricultural Water Management, Elsevier, vol. 98(4), pages 553-562, February.
    4. Xiang, Zaichen & Bailey, Ryan T. & Nozari, Soheil & Husain, Zainab & Kisekka, Isaya & Sharda, Vaishali & Gowda, Prasanna, 2020. "DSSAT-MODFLOW: A new modeling framework for exploring groundwater conservation strategies in irrigated areas," Agricultural Water Management, Elsevier, vol. 232(C).
    5. Kayla A. Cotterman & Anthony D. Kendall & Bruno Basso & David W. Hyndman, 2018. "Groundwater depletion and climate change: future prospects of crop production in the Central High Plains Aquifer," Climatic Change, Springer, vol. 146(1), pages 187-200, January.
    6. Federico García Suárez & Lilyan E Fulginiti & Richard K Perrin, 2019. "What Is the Use Value of Irrigation Water from the High Plains Aquifer?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 101(2), pages 455-466.
    7. Granata, Francesco, 2019. "Evapotranspiration evaluation models based on machine learning algorithms—A comparative study," Agricultural Water Management, Elsevier, vol. 217(C), pages 303-315.
    8. Comas, Louise H. & Trout, Thomas J. & DeJonge, Kendall C. & Zhang, Huihui & Gleason, Sean M., 2019. "Water productivity under strategic growth stage-based deficit irrigation in maize," Agricultural Water Management, Elsevier, vol. 212(C), pages 433-440.
    9. Ali Ajaz & Sumon Datta & Scott Stoodley, 2020. "High Plains Aquifer–State of Affairs of Irrigated Agriculture and Role of Irrigation in the Sustainability Paradigm," Sustainability, MDPI, vol. 12(9), pages 1-17, May.
    10. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    11. Campos, Isidro & Neale, Christopher M.U. & Suyker, Andrew E. & Arkebauer, Timothy J. & Gonçalves, Ivo Z., 2017. "Reflectance-based crop coefficients REDUX: For operational evapotranspiration estimates in the age of high producing hybrid varieties," Agricultural Water Management, Elsevier, vol. 187(C), pages 140-153.
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