Regional variability of drought-crop sensitivities across Iowa using unsupervised learning

S. M. Samiul Islam; Most Fatematozzohora; Ibrahim Demir

doi:10.1007/s00271-025-01074-1

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Regional variability of drought-crop sensitivities across Iowa using unsupervised learning

Journal article

Open access

Peer reviewed

Regional variability of drought-crop sensitivities across Iowa using unsupervised learning

S. M. Samiul Islam, Most Fatematozzohora and Ibrahim Demir

Irrigation science, Vol.44(2), 32

03/2026

DOI: 10.1007/s00271-025-01074-1

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Abstract

Understanding the spatial variability of crop drought sensitivity is critical for improving agricultural resilience in the face of climate change. This study presents a station-level analysis of meteorological and yield data across Iowa from 1998 to 2022 to investigate the relationship between multiple drought indices and detrended yields of corn and soybean. Eleven drought indicators were selected to capture both short-term and cumulative drought stress, including SPI at multiple timescales, SPEI, PDSI, EDDI, and CMI. Spearman correlation analysis revealed crop-specific sensitivities: corn yields were more affected by short-term atmospheric dryness (e.g., SPI-1, EDDI), while soybean yields responded more to long-term soil moisture deficits (e.g., SPI-6, PDSI, CMI). To reduce complexity and extract dominant sensitivity patterns, Principal Component Analysis (PCA) was applied, and the first principal component (PC1) was used as a summary indicator of drought sensitivity. Local Indicators of Spatial Association (LISA) revealed statistically significant clusters of vulnerability, with high-high drought sensitivity zones for both crops concentrated in South Central Iowa. In contrast, low-low clusters indicated spatial drought resilience, particularly in North Central and East Central Iowa for corn and North Central and Northwestern Iowa for soybeans. Moran’s I scatterplots confirmed moderate spatial autocorrelation in PC1 values. This study demonstrates the value of combining multivariate statistics, spatial analysis, and unsupervised learning to map crop-specific responses to drought. Identifying regionally coherent sensitivity patterns provides a robust foundation for improving early warning systems, informing localized drought adaptation strategies, and guiding climate-resilient agricultural planning across the U.S. Corn-Belt.

Agriculture

Climate Change

Vegetables

Agricultural production

Autocorrelation

Cereal crops

Climate-smart agriculture

Clusters

Corn

Correlation analysis

Crop moisture index

Crop yield

Crops

Drought

Drought index

Early warning systems

Moisture content

Multivariate analysis

Principal components analysis

Resilience

Sensitivity analysis

Soil moisture

Soybeans

Spatial analysis

Spatial discrimination learning

Spatial variations

Statistical analysis

Unsupervised learning

Variability

Warning systems

Details

Title: Subtitle: Regional variability of drought-crop sensitivities across Iowa using unsupervised learning
Creators: S. M. Samiul Islam
Most Fatematozzohora
Ibrahim Demir
Resource Type: Journal article
Publication Details: Irrigation science, Vol.44(2), 32
DOI: 10.1007/s00271-025-01074-1
ISSN: 0342-7188
eISSN: 1432-1319
Publisher: Springer Nature B.V
Language: English
Electronic publication date: 01/07/2026
Date published: 03/2026
Academic Unit: Electrical and Computer Engineering; Civil and Environmental Engineering; Injury Prevention Research Center
Record Identifier: 9985116909902771

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