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ARS Home » Midwest Area » Urbana, Illinois » Global Change and Photosynthesis Research » Research » Publications at this Location » Publication #285229
Title: Changes in field workability and drought risk from projected climate change drive spatially variable risks in Illinois cropping systems

Author
item TOMASEK, BRADLEY - University Of Illinois
item Williams, Martin
item Davis, Adam

Submitted to: Environmental Research Letters
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/2/2017
Publication Date: 2/23/2017
Citation: Tomasek, B.J., Williams, M., Davis, A.S. 2017. Changes in field workability and drought risk from projected climate change drive spatially variable risks in Illinois cropping systems. Environmental Research Letters. 12(2):e0172301.

Interpretive Summary: Increased variability in spring rainfall is predicted in coming decades for the north central corn belt of the U.S. This may complicate the scheduling and implementation of planting and other field operations in agronomic crops such as corn and soybean. Our aim was to develop improved methods for predicting field working days (FWDs). We developed two modeling approaches to estimate robust soil moisture thresholds for FWDs. Our first model used historical field work and weather records from three crop research centers in a logistic regression model. An optimal soil moisture threshold of 1.10 times the plastic limit (1.10PL) was identified. Our second model identified statewide soil moisture and temperature thresholds by optimizing the root mean square error of the predicted number of weekly statewide FWD across a 52-year dataset. This model yielded statistically smaller absolute errors in most crop reporting districts and eliminated systematic prediction bias. Previous theoretical thresholds for FWD are sub-optimal due to consistent over-prediction.

Technical Abstract: Improved prediction of field working days (FWD) is an important consideration for adapting farming systems to increased weather variability. We developed modeling approaches to estimate robust soil moisture thresholds for FWDs. Model 1 used historical field work and weather records from three crop research centers in a logistic regression model. A soil moisture threshold of 1.10 times the plastic limit (1.10PL) was identified. Model 2 identified statewide soil moisture and temperature thresholds by optimizing the root mean square error of the predicted number of weekly statewide FWD across a 52-year dataset. The resulting thresholds of either 0.89PL and a mid-range temperature of 6°C or 0.74FC and at least 5°C yielded statistically smaller absolute errors in most crop reporting districts and eliminated systematic prediction bias. Previous theoretical thresholds for FWD are sub-optimal due to consistent over-prediction.