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: