A Mass Balance Based Model to Evaluate the Impact of Amino Acid Profiles on the Feeding and Processed Value of Soybeans

摘要

As the soybean industry continues to grow and become more globally competitive, the interest surrounding soybean quality has also increased. Historically, soybeans have been traded as a commodity, but over the past few decades, the idea of component based pricing as a way to assess quality has become more practical. Pricing soybeans based on components would account for the high variation in soybean composition and reward high quality and consistency, while commodity pricing does not. In order for component based pricing to become a viable option for the soybean industry, a rapid, reliable, and relatively low-cost method for evaluating soybeans of varying compositions must be available.;A soybean solvent extraction plant model was developed to evaluate raw soybeans by predicting product yields and compositions and determining an estimated monetary value for a bushel of soybeans based on both major and minor constituents. Previous models only included major constituents, such as protein and oil. All phases of the solvent extraction process (soybean preparation, oil extraction, and meal formulation) are accounted for in the model. The model runs in Microsoft Excel and utilizes inputs of raw soybean composition, including concentrations of moisture, protein, oil, fiber, amino acids, carbohydrates, and fatty acids, and processing conditions. These inputs allow the model to predict the yields and compositions for crude oil and soybean meal, as well as, the weight of net hulls and mill feed used, if applicable. This model allows producers, breeders, buyers, and nutritionists to evaluate a bushel of soybeans based on how its composition affects the end-use quality of the extraction products.;Additionally, the composition of the soybean meal predicted in the model is input into the U.S. Pork Center of Excellence, National Swine Nutrition Guide feed formulation software. The software formulates a swine diet based on common feed ingredients, including corn, synthetic amino acids, monocalcium phosphate, limestone, salt, and the predicted soybean meal from the processing model. This software allows animal nutritionists to evaluate the feeding value of the predicted soybean meal based on factors such as metabolizable energy and neutral detergent fiber content of the feed, inclusion percentage, and the feed cost. Furthermore, this would provide a comparison tool for nutritionists and plant breeders to analyze the potential feeding values of raw soybeans before they are processed.