Automated Data Provisioning for Crop Rotation Management and Tillage Operation Modeling With Ages-W.

Several models are available for spatial and temporal simulation of agro-ecosystems. Inputs to these models include land use practices such as crop sequences and dates of management practices. The USDA Natural Resources Conservation Service is developing a Land Management and Operation Database (LMOD) which contains potential model input, however LMOD data is not spatially structured. Thus the Crop Rotation and Management Builder (CRMB) was developed as a Java-based web service to provide the missing spatial information. CRMB uses the CropScape web service from the USDA National Agricultural Statistics Service (NASS) which contains a remote sensing based raster Crop Data Layer (CDL) for a specific year and a spatial Area of Interest (AOI).  Here, we demonstrate spatial data provisioning to the component-based AgroEcoSystem-Watershed (AgES-W) implemented under the Object Modeling System. AgES-W simulates hydrological responses, water quality, and agronomic processes across spatially distributed and interconnected hydrological response units (HRUs). Land use inputs are required for each HRU across a watershed. CRMB handles crop and management data provisioning for AgES-W as follows. It queries the annual main crop information from CropScape for available CDL years. CRMB selects the predominant crop after reclassifying crop information within each HRU to adjust the final crop area using provided accuracy values. The CRMB tool also identifies irrigated and non-irrigated dominant crops using additional spatial layer information. It then detects a sequence of main crops for every HRU and matches the crop sequence to available crop rotation information and all associated management tillage operation information obtained from LMOD. Finally, CRMB generates required management input files for the AgES-W model over the desired simulation period. CRMB will be evaluated using known crop rotations, tillage, planting, and harvest dates to assess its model-independent performance, followed by evaluation of AgES-W output sensitivity to deviations from the automated data provisioning.