DEVELOPING NEW STORED GRAIN PACK FACTORS WITH KNOWN ACCURACY FOR THE COMMON GRAINS IN TRADE UNDER A COMPLETE RANGE OF FIELD CONDITIONS
Project Number: 3020-43440-007-08
Start Date: Aug 01, 2009
End Date: Apr 30, 2014
Existing packing factor data are of unknown reliability and are widely mistrusted in the industry. Accurate data are required for government-mandated inventory control and are a crucial component of new quality management systems being developed to enable source verification in the grain handling industry. The current Farm Bill requires the Risk Management Agency (RMA) to determine the efficacy and accuracy of current pack factors and, as a result, they desire ARS to evaluate their existing packing factor data.
The new data and model developed in this research will improve the scientific basis for predicting pack factor in stored grain. We will define, for the first time, uncertainty in predicted pack factors from the old method as well as from the new model. We will produce a user-friendly, windows-based software that can be used by farmers, elevator managers, and government officials. The software will allow the user to enter needed measurements and materials for the bin and quality factors for the stored grain. This tool will calculate the average pack factor for the bin and will provide accurate estimates of the confidence intervals for those pack factors.
The objective of the project is to refine and validate a procedure with known accuracy, based on measurable physical parameters, for determining the packing of grains within upright storage structures. Factors identified for the study are:
1. structural shape and size
2. bin wall type
3. type of grain
4. time in storage
5. the impact of facility aeration systems
6. bulk density (test weight) of the incoming grain
7. moisture content of the grain
8. additional factors such as broken material and fines in the grain
The major variables affecting stored grain packing are grain type, moisture content, test weight, and bin geometry and dimensions. Variation across different regions of the U.S. must also be investigated as well as other minor factors. In order to avoid the excessive cost from experimentally determining pack factors for all grains under all conditions, we plan to use science-based modeling to reduce the total amount of data required to achieve valid results. Physical properties will be measured in the laboratory to use as inputs for modeling. A preliminary model for determining pack factor for a wide range of grains and bins has been developed and is currently being calibrated in limited experiments. We will calibrate and validate this model by measuring pack for selected grains in bins spread over the major grain producing regions of the U.S. Calibrating the model instead of developing pack factors from field measurements alone will allow us to reduce the number of bins measured from tens of thousands to several hundred. Confidence intervals will be established from the field measurements and used to characterize the predictions of the new model and will be compared to confidence intervals determined for the old method.