Location:2013 Annual Report
1a. Objectives (from AD-416):
The objective of this cooperative research is to obtain new field and laboratory data to refine and calibrate a science-based model for determining the packing of grains within upright storage structures. Laboratory data on bulk grain compression characteristics will be obtained for wheat, corn, soybeans, grain sorghum, oats, and barley. The refined model will be incorporated into a user-friendly software tool for use by producers, elevator operators, and government officials for predicting stored grain pack factors. Field measurements of grain packing will be obtained from several states in the southeastern U.S. in partnership with collaborators at ARS, Kansas State University, and the University of Kentucky who will also make field measurements. Field data will be collected primarily for wheat, corn, and soybeans and also for grain sorghum, oats, and barley when those crops are available.
1b. Approach (from AD-416):
This research is part of a larger, nationwide project to refine and validate a procedure with known accuracy, based on measurable physical parameters, for determining the packing of grains within upright storage structures. Because grain is somewhat compressible when subjected to the cumulative weight exerted from the material above, accurate packing factors are required to determine the mass of grain in storage from bin dimensions and test weights. Inventory control is critical for stored grain managers due to financial aspects (auditing by state agencies) and for the future utilization of quality management systems. The major variables affecting stored grain packing are grain type, moisture content, test weight, internal friction, and bin wall material, geometry, and dimensions. Variation across different regions of the U.S. must be investigated as well as other minor factors. A preliminary model for determining packing factors for a wide range of grains and bins is being developed that employs the differential form of Janssen’s equation to estimate the pressure and in-bin bulk density for a given depth of grain in a bin. In the larger project, this model will be calibrated and validated by measuring packing factors for selected grains in bins in all of the major grain producing regions of the U.S. As part of that nationwide effort, the Cooperator will measure packing factors in selected states in reasonable proximity to their locations. Improved estimates of the compressibility of grains as a function of overburden pressure will be obtained using a laboratory apparatus designed to simulate internal pressure from various depths of overbearing grain. Field measurements of packing factors will be obtained by measuring the height of grain in bins of known dimensions and wall materials as they are filled and/or discharged with a measured mass of grain.
3. Progress Report:
New stored grain packing factors being developed in this project need to be made available in a convenient format. A Windows-based software package is being developed using our model to provide the new packing factor predictions to customers. Two significant revisions to the grain packing factor prediction software were delivered to the Risk Management Agency (RMA), one in February and one in July, 2013. Most needs of the RMA officials have been addressed and we are now working with the 17 RMA-listed crop insurance providers to refine the program for their needs. The most recent version provides both input and output options that match the current hand-methods as much as possible. Other significant additions to the new versions are: an additional input to account for "cubic feet of deductions" (for aeration ducts or other items inside the bin), capability for treating different bin cross-sections (rectangular and hexagonal are now included along with round and square), an option for handling round outdoor piles, and an option to input hopper bottom dimensions without requiring a hopper angle measurement. This model based on extensive laboratory and field data is contributing to a greater understanding stored grain packing factors and should provide greater confidence in the packing factor predictions being developed compared to the old packing factor predictions.