While agriculture is a critical sector to many states and the nation as a whole, information about agriculture commodity flows has historically been challenging. Agriculture is an out-of-scope sector in the Commodity Flow Survey, and agriculture export data often rely on a shipper’s export declaration, which frequently credits states from which the agriculture shipment leaves the county as opposed to states where the ag-commodity was produced. The purpose of this session is to explore contemporary research in U.S. agriculture and explore uses of agriculture commodity data in research applications. The session will also examine efforts to improve agriculture commodity data and overcome some of the data’s inherent challenges.
Evolving Supply Chains and Local Freight Flows: A GIS Analysis of Minnesota Cereal Grain Movement
Travis Fried, University of Minnesota, Twin CitiesShow Abstract
Lee Munnich, University of Minnesota, Twin Cities
Thomas Horan, University of Redlands
Brian Hilton, Claremont Graduate University
In Minnesota, technological and economic shifts in the grain supply chain have altered the way grain producers and sellers navigate their local freight network. In particular, many producers have been increasing their personal trucking capacity and taking longer trips to intermodal and domestic market options. This logistical reshaping of local grain supply chains pressure transportation officials to reconsider the consequences for road infrastructure and congested freight corridors. Studies are discussing the potential of disaggregated Commodity Flow Survey (CFS) data as a critical tool in understanding small-scale freight movement and informing infrastructural investment decisions. Utilizing ArcGIS’s Network Analysis and Hotspot tools to analyze inter-county grain trucking, our study effectively differentiates highly-active freight corridors. The model is used to further inform an ongoing infrastructure development project in the Twin Cities Metro Area by contextualizing road usage within the economic framework of the grain supply chain. However, this study finds CFS data alone fails to account for shifting supply chain conditions, and their consequent impact on the road network. Employing USDA crop production and cropland data, this study additionally builds an original, computational model that simulates corn producer shipment reaction to market price competition within two key, grain-producing counties. Results visualize how producers, during spot months, may be incentivized to haul longer distances to more competitive markets—especially emerging biofuel industries. This lesson proves crucial for state and local transportation officials who wish to identify freight infrastructure development opportunities that invigorate and accommodate growth in Minnesota’s expanding agricultural industry cluster.
Integrating GIS with Optimization Method for a Switchgrass-Based Bioethanol Supply Chain
Seyed Ali Haji Esmaeili, North Dakota State UniversityShow Abstract
Ahmad Sobhani, Oakland University
Amin Keramati, Upper Great Plains Transportation Institute
EunSu Lee, New Jersey City University
Switchgrass is considered as one of the best second generation feedstocks for bioethanol production. Taking lignocellulosic biomass, defined here as switchgrass, this study focuses on locating bioethanol facilities and multimodal storages along with designing the multimodal transport switchgrass-based bioethanol supply chain (MTSBSC) when the total system cost is minimized. Energy use and greenhouse gas emissions impacts on the MTSBSC activities are the missing parts of previous studies that are going to be fulfilled in this study within North Dakota as the testing ground. For the purpose of commercializing the production of switchgrass-based bioethanol, an integrated approach combining Geographic Information System (GIS) based analysis with a mixed integer linear programming (MILP) model is developed. The number and locations of biorefineries and multimodal storages were determined based on the GIS method and served as input for the optimization model which defines the amount of biomass shipped, processed, and converted into bioethanol together with the system related costs. Sensitivity and comparison analyses were also conducted to provide insights for demonstrating the impact of key factors on the entire supply chain and minimizing the total cost. The results show that transportation cost has the highest contribution to the total cost.
Overcoming Agriculture Export Data Challenges: Case Studies from Iowa and Nebraska
Weiwen Xie, Quetica, LLC
USDA: Agriculture Commodity Data Resources, Methods, and Recent Improvements
Kuo-Liang Chang, U.S. Department of Agriculture (USDA)
Peter Caffarelli, U.S. Department of Agriculture (USDA)