Figure 1: Land use and land classification map of Iowa. The map, derived from the USDA’s Cropland Data Layer (CDL), highlights the distribution of key agricultural regions, with classifications for corn, soybeans, and other land uses. The CDL’s pixel-level accuracy exceeds 80% for primary crops such as corn and soybeans.

Data and Methods

The study utilized data from 126 meteorological stations across Iowa, chosen for their comprehensive and consistent data availability. These stations monitor key atmospheric variables, including rainfall, temperature, humidity, and solar radiation. Additionally, land cover data were obtained from the USDA’s Cropland Data Layer (CDL), which provides raster-based classifications of land use. Each pixel in the CDL is categorized based on land-use type, with an accuracy exceeding 80% for primary crops such as corn and soybeans (Boryan et al., 2011; Johnson et al., 2010). Data integration involved matching the spatial resolution of the meteorological data with crop-specific land cover maps using interpolation techniques.

Crop Yields

Crop yield data for corn and soybeans from 2000–2022 were obtained from USDA’s NASS Quick Stats database (USDA, 2023). These yields represent statewide averages based on field surveys, farmer reports, remote sensing, and statistical modeling. While statewide yields were used for this analysis, future studies could investigate finer-scale yield data to differentiate between irrigated and non-irrigated crops, as irrigation practices can significantly influence drought resilience.
Standardized Residual Yield Series (SRYS)
A linear regression approach was applied to detrend crop yield data to account for technological advancements and climate adaptations influencing yield trends. The residuals from this regression, termed the Standardized Residual Yield Series (SRYS), represent deviations attributed to weather variability (Liu et al., 2018). SRYS is calculated as follows: