A Real-Time Gridded Crop Model for Assessing Spatial Drought Stress on Crops in the Southeastern United States

Abstract

The severity of drought has many implications for society. Its impacts on rain-fed agriculture are especially direct, however. The southeastern United States, with substantial rain-fed agriculture and large variability in growing-season precipitation, is especially vulnerable to drought. As commodity markets, drought assistance programs, and crop insurance have matured, more advanced information is needed on the evolution and impacts of drought. So far many new drought products and indices have been developed. These products generally do not include spatial details needed in the Southeast or do not include the physiological state of the crop, however. Here, a new type of drought measure is described that incorporates high-resolution physical inputs into a crop model (corn) that evolves based on the physical–biophysical conditions. The inputs include relatively high resolution (as compared with standard surface or NOAA Cooperative Observer Program data) (5 km) radar-derived precipitation, satellite-derived insolation, and temperature analyses. The system (referred to as CropRT for gridded crop real time) is run in real time under script control to provide daily maps of crop evolution and stress. Examples of the results from the system are provided for the 2008–10 growing seasons. Plots of daily crop water stress show small subcounty-scale variations in stress and the rapid change in stress over time. Depictions of final crop yield in comparison with seasonal average stress are provided.

BayGEN: A Bayesian Space‐Time Stochastic Weather Generator

“…DSSAT simulation models have been calibrated and tested in the Pampas for soybean, maize, and wheat (Apipattanavis et al, 2010;Bert et al, 2006Bert et al, , 2007Ferreyra et al, 2001;Mercau et al, 2007;Meira et al, 1999;Podestá et al, 2009), which are commonly grown crops. Currently, DSSAT simulates crop development and growth for one point (i.e., plot); however, there are at present some tools that facilitate running a gridded version of DSSAT (Elliott et al, 2014;McNider et al, 2011).…”

Correction of table-2

“…1). The region is home to a significant amount of current hydrologic and agricultural research activity where accurate SM modeling is of significant importance (McNider et al, , 2015Mishra et al, 2013). The southeastern US represents a subtropical humid climate that typically has relatively hot and humid summers and precipitation that is generally evenly distributed throughout the year.…”

“…The majority of the soils (nearly 80 %) at the surface are classified as sand with loamy sand and sandy loam, as determined from the Soil Information for Environmental Modeling and Ecosystem Management (Miller and White, 1998). These soils are known to have relatively low water holding capacity that can lead to great temporal variation in upper layer (1-10 cm) SM conditions and relatively frequent short-term droughts (1)(2)(3)(4) week periods) during growing seasons in various parts of the region (McNider et al, 2015). Although the study region is overwhelmingly represented by a forest and shrub landscape, vegetation types that are known to adversely affect the accu- racy of MW signals, the study does present an opportunity to evaluate the performance of the merged MW-TIR profiles in a challenging environment and may provide greater confidence in the robustness of the system.…”

A Remote-Sensing Driven Tool for Estimating Crop Stress and Yields

“…This can include climate variability as well as climate change using future climate change scenarios. The DSSAT system has been employed to help with development of water management plans for the agricultural sector in Georgia [48] and is currently the basis for a real-time crop stress evaluation to monitor drought conditions in the Southeastern United States [49,50].…”

“…For example, McNider, et al [49] describe the GridDSAT model which operates at an approximately 5 km grid scale to simulate plant stress in the Southeastern US and contributes to the United States National Drought Monitor. Earlier, Heinemann, et al [51] employed DSSAT with a GIS to forecast regional irrigation water demands in Brazil.…”