Location:2012 Annual Report
1a. Objectives (from AD-416):
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
1b. Approach (from AD-416):
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.
3. Progress Report:
Field, laboratory and computer simulation studies are being conducted to develop improved predictions of stored grain packing factors. We have measured approximately 50 hard red winter (HRW) wheat bins, 30 corn bins, 12 oat bins, 8 soybean bins, and 3 grain sorghum bins across the Central, Midwest, and Southern U.S. for determining packing factor. The overall evaluation of the wheat field data showed that our current packing model based on preliminary tests was slightly under-predicting packing factor and, thus, grain weight. The percentage difference between reported (actual) grain weights and model predicted was always below 8%. However, non-circular wheat bins showed a greater tendency to over-predict. For corn bins, field data showed that the current packing model was mostly under-predicting. The percentage difference between reported (actual) grain weights and model predicted was always below 4%. For the limited soybean bins, field data showed that the current packing model was mostly under-predicting and the percentage difference between reported grain weights and model predicted weights were consistent at 5%. Twenty-seven hard red winter (HRW) wheat samples were obtained from eleven states in the HRW wheat growing areas of the U.S., composed of eight different varieties covering three production years. Samples were tested at both low (10% wet basis) and high (13% wet basis) moisture content in the laboratory uniaxial compressibility tester to determine compressibility as a function of overbearing pressure. Results showed greater compressibility for low test weight and higher moisture samples, while the differences between varieties and differences between crop years sometimes equaled the test weight and moisture differences. The individual HRW wheat samples were mixed in composite samples, most of which represent mixing that occurs in the grain transportation and handing system. Six composite samples were prepared based on growing state (Texas, Oklahoma, and Kansas) or variety (ART, Jagelene, and TAM 111). Four additional samples were prepared representing multistate regions of HRW wheat production. The composites were tested in the uniaxial compressibility tester at low and high moisture content levels. The general trends with test weight were similar to those with the individual variety samples. Overall, as with the individual variety samples, the higher moisture wheat composite samples showed greater packing than the low moisture samples, which was expected because moisture makes grain kernels more compressible. However, this moisture effect has not previously been included in stored grain auditing methods. A Windows-based software package is being developed to provide the new packing factor predictions to customers in a convenient format. The second beta version of the grain packing factor prediction software was delivered to the Risk Management Agency (RMA) in mid June, 2012. Phone discussions with RMA officials elicited several desirable revisions to the user interface that are being implemented. This version provides an easier input method then the previous one plus it includes an output form that can be readily printed by RMA users to make a hardcopy of the important results. The software includes preliminary estimates of packing for corn, hard red winter (HRW) wheat, soft red winter (SRW) wheat, soybeans, and grain sorghum. The new packing factor predictions in this software, from the results of this research, should improve grain bin inventory determinations by stored grain managers and official auditors and provide better confidence in the results than was possible with the old methods.