Long‐Term Application of the Crop Water Stress Index in Midwest Agro‐Ecosystems

Abstract

Core Ideas The crop water stress index was calculated for corn, soybean, and prairie using eddy covariance and canopy temperature. Crop water stress index increased with decreasing volumetric soil water content in tallgrass prairie with net ecosystem production sensitive to water deficits. Crop water stress index in corn and soybean increased at low and high volumetric soil water content demonstrating that carbon assimilation is affected by deficit and excess soil water contents. Corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] are predominantly produced in Iowa under rainfed conditions in which the amount and distribution of rainfall, and ambient air temperature, vary substantially among years. Prairie ecosystems also represent a small but significant portion of the landscape in the Midwest, but are subjected to the same variation in rainfall. Quantifying the effect of rainfall variation on ecosystem productivity is required to understand how the absence and the oversupply of water induces plant water stress and affects plant growth. We used the crop water stress index (CWSI) using canopy temperatures obtained from infrared temperature sensors coupled with eddy flux measurements to quantify the impact of water stress on corn, soybean, and prairie net ecosystem production (NEP) in central Iowa from June to August, 2006 through 2015. The relationships between CWSI and NEP, evapotranspiration (ET), and volumetric water content (VWC) were analyzed for these three canopies. Average seasonal CWSI values varied substantially among years and sites, indicating no‐stress and extreme water stress periods. The CWSI significantly increased with decreasing ET and NEP, signaling that water stress adversely affected transpiration and C assimilation. Prairie CWSI was linearly and negatively related to VWC. Corn and soybean CWSI increased with very dry and wet soil moisture regimes, indicating that corn–soybean cropping systems were negatively affected by both the absence and oversupply of water. The CWSI approach quantifies water stress in different agroecosystems to compare the responsiveness of these systems to the dynamics of seasonal rainfall patterns.

Upscaling Evapotranspiration with Parsimonious Models in a North Carolina Vineyard

“…Thus, further analysis with a larger dataset including yield data would be needed to confirm this relationship for our study. The linear relationship between ET and CWSI v would indicate the effect of evaporative cooling on Tc (Equation 1), and similar relationships have previously been reported for other agro-ecosystems [45]. Yet, CWSI was a weak predictor of ET compared to EVI, Ta, LST, or VPD in this study.…”

Study on the agricultural crop drought index based on weights of growth stages

“…Drought indexes related to remote sensing technology mostly refer to regional drought situations from the perspective of the crop canopy temperature (Chu et al, 2019; Farahmand et al, 2015; Mu et al, 2013). Crop drought indexes, such as the CMI, the CWSI, the CWDI and the ACMI, consider agricultural drought from the perspectives of the crop water demand and the soil water supply (Dold et al, 2017; Li et al, 2014; Uang‐Aree et al, 2017; Yang, Liu, et al, 2017). The SPCEI compares the same growth stage in different years to identify agricultural drought from the perspective of the growth stage (Pei et al, 2019).…”

“…Crops are the main objects of agricultural production, so defining a drought index from the perspective of crops is one of the main methods to analyse agricultural drought. For example, the crop water stress index (CWSI) considers the crop canopy temperature and the atmospheric temperature (Dold et al, 2017). The crop water deficit index (CWDI) reflects the water deficit from the crop perspective (Yang, Liu, et al, 2017).…”

Modeling Flood-Induced Stress in Soybeans

“…It is crucial to enhance models' ability to estimate the impact of soil waterlogging on plant processes. Globally, 27% of cultivated land is impacted by flooding, resulting in over $371 billion of economic losses to crop production (Dilley et al, 2005;Zhou, 2010;Ward et al, 2013;Dold et al, 2017;Kaur et al, 2017a). With the onset of climate change, escalations in the frequency of intense rainfall events are expected to increase the prevalence of waterlogged soils and, thus, potential economic and environmental losses (Villarini and Strong, 2014;Mallakpour and Villarini, 2015;Pathak et al, 2016).…”