The "Corn Belt" is a commonly used term, but often referenced as a vaguely defined region in the Midwest USA. A few key studies have delineated synoptic maps of the Corn Belt boundaries going back to the early 20th century, but a modern flexible and accessible framework for mapping the Corn Belt in space and time is needed. New tools provide reference maps for the Corn Belt in the 21st century and the ability to quantify space-time changes in corn cropping patterns. The Landuse and Agricultural Management Practices web-Service (LAMPS) was used to estimate the average corn (maize, Zea mays L.) area in each county of the contiguous 48 USA states for the years 2010-2016. LAMPS provides a modified areal Fraction of corn (F) used to map the Corn Belt at three intensity levels, for example. The resulting patterns illustrate a mostly contiguous Midwest Corn Belt surrounded by more scattered regions, including southern and eastern regions. We also mapped irrigated areas and temporal changes in F. Mapped patterns have the potential to help researchers study issues related to food, feed, biofuel, and water security.

Simulation of vertical soil hydrology is a critical component of predicting more complex multidimensional soil water dynamics in space and time. The AgroEcoSystem (AgES) model is identified here as a single land‐unit application of the three‐dimensional AgES‐W (Watershed) model. AgES simulates vertical soil water dynamics using global and layered soil response functions with conceptual storages as state variables. A detailed description of the response functions that control infiltration, evaporation, and soil‐water processes facilitates sensitivity analysis, model calibration, and evaluation against volumetric soil‐water content (SWC) at measured layers. The Object Modeling System links AgES to a Shuffled Complex Evolution calibration tool called Luca. We used Luca and fractional factorial experimental designs to analyze parameter sensitivities, then applied different strategies of implementing Luca to layered SWC data. The profile dynamics of the simulated SWC resulted in depth‐averaged Nash–Sutcliffe Efficiency (NSE) values of 0.60 to 0.95 for calibration in 2003 and 2005, and up to 0.80 for 4 yr used for (cross‐)evaluation. Using the 2005 calibration parameters, NSE became negative in 2009 and 2011 due to large negative values at some depths with low variance in SWC. Optimal parameter sets for each calibration year were not unique, and model results did not fully capture the measured dynamics. Even so, AgES simulations compared favorably with previous simulations of SWC at this site using a Richards' equation model. These results provide new understanding of the model responses and interactions between functions controlling the vertical flow and storage of water to aid watershed modeling.

Phenology is critical in simulating crop production and hydrology and must be sufficiently robust to respond to varying environments, soils, and management practices. Phenological algorithms typically focus on the air temperature response function and rarely quantify the phenological responses to varying water deficits, particularly for versions of the Environmental Policy Integrated Climate model (EPIC)‐based plant growth component used in many agroecosystem models. Three EPIC‐based plant growth components (Soil Water Assessment Tool [SWAT], Wind Erosion Prediction System [WEPS], and the Unified Plant Growth Model [UPGM]) have been incorporated into the spatially distributed Agricultural Ecosystems Services model [AgES], and only the UPGM includes a phenological response to varying water deficits. These three plant components were used to evaluate the phenological responses of winter wheat (Triticum aestivum L.) to varying water deficits and whether having a water stress factor in UPGM improves the simulation of phenology. A 3‐yr irrigation study and a 4‐yr study across a rainfed landscape were used in the evaluation. Only the UPGM simulated all five of the developmental stagesmeasured. The UPGM was the only component that simulated a phenological response to variable water deficits, resulting in better prediction of phenology. For example, the RMSE (days) and relative error (RE, days) decreased and index of agreement (d) increased in predicting maturity from SWAT (RMSE = 18.4; RE = 9.2; d = 0.34) to WEPS (RMSE = 6.2; RE = 1.0, d = 0.63) to the UPGM (RMSE = 6.1; RE = 0.1; d = 0.70). Incorporating phenological responses to varying water deficits improves the accuracy and robustness of predicting phenology, which is particularly important in spatially distributed agroecosystem models. Core Ideas Phenology is critical in accurately simulating crop production and hydrology. The AgES watershed model evaluated three EPIC‐based plant growth components. Only UPGM was able to simulate phenological responses to varying water deficits. The results promote more robust simulation of phenology in varying environments.

Contour bank farming is a well-known agricultural management technique in areas which are characterised by intensive and erosive rainfalls. Contour banks are designed to reduce the flow velocity of overland flow and to intercept water before it concentrates in rills, thereby reducing the risk of soil erosion and land degradation. By their structure, contour banks noticeably impact surface runoff pattern both temporally and spatially. Also subsurface flow may be affected by contour banks.For example, if contour banks intersect the A-and B-horizon of the soil, it can cause significant infiltration of water into the C-horizon, which if saline, can generate saline interflow to downslope areas. Although these aspects have been highlighted in previous research efforts, the quantitative and qualitative impacts of contours on runoff generation and associated erosion dynamics or salinisation are rarely considered in process-based hydrological modelling approaches. In this study an approach was developed to improve distributed hydrological and erosion modelling by integrating contour banks in the delineation and routing of Hydrological Response Units. Applying the distributed and process-based hydrological model J2000 which was modified with a contour bank and erosion module it could be shown that the implementation of contour banks improved the model performance significantly.