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.