Introduction
Food production is a cornerstone of food security, directly influencing
the availability of essential resources necessary for sustaining life.
Food security encompasses multifaceted dimensions, including production,
availability, access, utilization, and stability over time (Capone et
al., 2014). Agricultural productivity is influenced by a combination of
factors, such as temperature and precipitation, which affect crop
development, health, annual yields, and the long-term productivity of
cropping systems (Howden et al., 2007; Liang et al., 2017; Ray et al.,
2018). In Iowa, corn and soybeans are vital for food and biofuel
production, with corn serving as a key feedstock for ethanol and
soybeans for biodiesel. Climate change exacerbates these challenges,
increasing the frequency of climatic extremes and their adverse impacts
on agricultural production (Gornall et al., 2010; Vogel et al., 2019;
Alabbad et al., 2023).
Numerous studies have investigated the impact of climate change on
agriculture across various geographical levels (Kang et al., 2009;
Olesen et al., 2011; Parry et al., 2004). However, most have not
explicitly focused on the interaction between hydrological
extremes—such as droughts and floods—and crop production.
Understanding these opposite but equally disruptive events is critical
for designing adaptive strategies to mitigate adverse effects and
improve cropping systems. This omission leaves a gap in understanding
the specific links between drought conditions and agricultural yields.
Drought, a complex natural phenomenon, profoundly impacts global
environmental, societal, and economic domains, posing significant
challenges to sustainable agriculture. Regions like Iowa, heavily
reliant on rain-fed systems, are particularly vulnerable (Haile et al.,
2020; Islam et al., 2022, 2024; Savelli et al., 2022; Sen, 2015;
Yesilkoy et al., 2023). Forecasted climate changes predict an increase
in extreme weather events, including droughts, which impact water
resources, population health, economic stability, and crop production
(Cikmaz et al., 2023; Field, 2012; Raymond et al., 2020; Sivakumar &
Stefanski, 2011; Yildirim et al., 2024). Anthropogenic activities and
climate change have intensified drought unpredictability and severity,
permanently damaging sensitive agroecosystems, increasing crop losses,
and exacerbating pest and disease outbreaks (Mahdi et al., 2015; Subedi
et al., 2023; Tadele, 2017; Yildirim et al., 2022).
For example, the flash drought in the U.S. Central Great Plains in
2012—the most severe since 1930—caused agricultural losses exceeding
$20 billion (Fuchs et al., 2012; Hoell et al., 2020; Hoerling et al.,
2014). Such extreme events underscore the urgent need for effective
drought management strategies and the development of resilient
agricultural systems. As a leading producer of corn and soybeans, Iowa
is particularly susceptible to climate variability due to its dependence
on favorable climatic conditions, high soil quality, and sufficient
water availability (Grassini et al., 2015; Kukal & Irmak, 2018).
Between 1989 and 2022, drought-related crop insurance claims in Iowa
alone amounted to over $5.3 billion, illustrating the substantial
economic toll of drought (Beach et al., 2010; Maisashvili et al., 2023).
Understanding extreme weather events such as flooding and droughts is
crucial, given their profound impacts on human life, infrastructure, and
properties (Mount et al., 2019). These events can cause extensive
damage, disrupting transportation networks (Alabbad et al., 2024),
overwhelming drainage systems, and compromising buildings’ structural
integrity, necessitating costly repairs, and posing significant risks to
human safety. Adequate comprehension and communication of these risks
are paramount, enabling communities and policymakers to adopt
initiative-taking measures (Sermet and Demir, 2022). Utilizing novel
data-driven models (Li and Demir, 2022) and decision support systems
enhances our ability to predict, monitor, and assess the extent of these
events. These systems integrate real-time data, advanced analytics (Sit
et al., 2021a; Ramirez et al., 2022), and machine learning (Sit et al.,
2021b) to provide accurate, timely information, aiding in preparedness,
response, and recovery efforts. By leveraging these technologies, we can
develop more resilient infrastructure, foster informed decision-making,
and ensure swift, coordinated actions to mitigate the adverse effects of
extreme weather, safeguarding both lives and properties.
Despite its critical importance, limited research has explicitly
investigated drought impacts on agricultural production in Iowa within
the context of climate change. Addressing this gap, this study aims to
evaluate the extent of drought’s impact on agricultural yields using
indices that incorporate both temperature and precipitation, which are
critical for computing potential evapotranspiration. This approach
offers a pathway to mitigate drought’s effects and establish a
sustainable farming system for optimal agricultural output. The urgency
of this research is underscored by the increasing frequency of extreme
weather events, including droughts, and their adverse impacts on
agricultural production. Immediate action is needed to address this
issue and ensure the future of agricultural management.
Several indices have been developed to assess drought impacts, each
leveraging distinct environmental data. Commonly used indices include
the Palmer Drought Severity Index (PDSI) (Palmer, 1965), the
Standardized Precipitation Index (SPI) (McKee et al., 1993), and the
Standardized Precipitation Evapotranspiration Index (SPEI)
(Vicente-Serrano et al., 2010). While PDSI evaluates water balance over
specific periods, its limited ability to capture short-term droughts
reduces its applicability for meteorological and agricultural
assessments. On the other hand, SPI focuses solely on precipitation but
provides flexibility across varying timescales for meteorological,
agricultural, and hydrological purposes (Laimighofer & Laaha, 2022;
McKee et al., 1993). SPEI combines precipitation and temperature data,
making it more suitable for assessing the impacts of climatic changes on
drought (Ma et al., 2014; Vicente-Serrano et al., 2010). Recent studies
have demonstrated the utility of SPEI for evaluating drought impacts on
crops globally (Potop et al., 2012; Ribeiro et al., 2019; Tian et al.,
2019).
In addition to these indices, this study considers the Evaporative
Demand Drought Index (EDDI), developed by NOAA, which serves as an early
warning indicator by assessing atmospheric dryness (Hobbins et al.,
2016; McEvoy et al., 2016). Other indices, such as the Normalized
Difference Vegetation Index (NDVI) (Krieger, 1969) and the Crop Moisture
Index (CMI) (Juhasz & Kornfield, 1978), were also analyzed to determine
their suitability in assessing drought impacts on crop yields in Iowa.
This study examines the correlation between drought indices and yields
of Iowa’s primary crops, corn, and soybeans, during 2000–2022. By
leveraging multiple datasets, including temperature, soil moisture,
evapotranspiration, and rainfall, the research provides comprehensive
insights into the interplay between drought and crop yields. The
findings aim to guide future agricultural practices and policies,
enhancing resilience and sustainability in Iowa’s agricultural sector.