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Linking drought indicators and crop yields through causality and information transfer: a phenology-based analysis

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Abstract

Drought indicators are essential for agricultural sustainability. This research employs causal inference and information theory to identify the most representative drought indicator (index or variable) for agricultural productivity. The causal connection between precipitation, maximum air temperature, drought indices and corn and soybean yield are ascertained by cross convergent mapping (CCM), while the information transfer between them is determined through transfer entropy (TE). This research is conducted on rainfed agricultural lands in Iowa, considering the phenological stages of crops. The results uncover both the causal connection between corn yield and precipitation and maximum temperature indices. Based on the analysis, the drought indices with the strongest causal relationship to crop production are SPEI-9 m and SPI-6 m during the silking period, and SPI-9 m and SPI-6 m during the doughing period. Therefore, these indices may be considered as the most effective predictors in crop yield prediction models. The study highlights the need to consider phenological periods when estimating crop production, as the causal relationship between corn yield and drought indices differs for the two phenological periods.

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Data availability

Crop phenology data were extracted from here (USDA-NASS): (https://www.nass.usda.gov/Charts_and_Maps/Crop_Progress_&_Condition/index.php). Land Cover Data: (https://lpdaac.usgs.gov/products/lgrip30v001). gridMET Drought Indices (EDDI, scPDSI, SPEI, SPI): (https://www.climatologylab.org/gridmet.html). DAYMET Meteorological (Precipitation and T max ) Data: (https://daymet.ornl.gov).

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Acknowledgements

Serhan Yeşilköy was supported in part by an appointment to the Agricultural Research Service (ARS) Research Participation Program administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the U.S. Department of Energy (DOE) and the U.S. Department of Agriculture (USDA). ORISE is managed by ORAU under DOE contract number DE-SC001466. All opinions expressed in this paper are the author’s and do not necessarily reflect the policies and views of USDA, DOE, or ORAU/ORISE. Özlem Baydaroğlu was supported in part by an appointment to the NRC Research Associateship Program at the National Oceanic and Atmospheric Administration – Global System Laboratory (NOAA-GSL), administered by the Fellowships Office of the National Academies of Sciences, Engineering, and Medicine. The statements, findings, conclusions, and recommendations are those of the author and do not necessarily reflect the views of NOAA or the U.S. Department of Commerce.

Funding

The funding for this study was provided by the University of Iowa Interdisciplinary Scalable Solutions for a Sustainable Future Project under the grant title “Watershed-Level Multicriteria Quantification of Agricultural Sustainability for Iowa”.

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Author notes

  1. Serhan Yeşilköy and Özlem Baydaroğlu equally contributed to this study.

Authors and Affiliations

  1. İstanbul Provincial Directorate of Agriculture and Forestry, Ministry of Agriculture and Forestry, İstanbul, Turkey

    Serhan Yeşilköy

  2. Oak Ridge Institute for Science and Education, Oak Ridge, TN, USA

    Serhan Yeşilköy

  3. Adaptive Cropping Systems Laboratory, USDA-ARS, Beltsville, MD, USA

    Serhan Yeşilköy

  4. Civil, Environmental and Architectural Engineering, University of Colorado-Boulder, Boulder, CO, USA

    Serhan Yeşilköy

  5. Global Systems Laboratory, NOAA, Boulder, CO, USA

    Özlem Baydaroğlu

  6. National Academies of Sciences, Engineering, and Medicine, Washington, DC, USA

    Özlem Baydaroğlu

  7. River-Coastal Science and Engineering, Tulane University, New Orleans, LA, USA

    Ibrahim Demir

  8. ByWater Institute, Tulane University, New Orleans, LA, USA

    Ibrahim Demir

Authors

  1. Serhan Yeşilköy
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  2. Özlem Baydaroğlu
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Contributions

SY: Conceptualization, Formal Analysis, Visualization and Writing-Original Draft, Editing, Data Curation; ÖB: Conceptualization, Formal Analysis, Visualization and Writing-Original Draft, Editing; ID: Writing – Review and Editing, Funding Acquisition.

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Serhan Yeşilköy.

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Yeşilköy, S., Baydaroğlu, Ö. & Demir, I. Linking drought indicators and crop yields through causality and information transfer: a phenology-based analysis.
Sci Rep (2026). https://doi.org/10.1038/s41598-025-32185-6

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  • DOI: https://doi.org/10.1038/s41598-025-32185-6

Keywords

  • Drought
  • Crop yield
  • Causality
  • Phenology
  • Cross convergent mapping
  • Transfer entropy
  • Iowa

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