New Technologies to Aid in Evaluation of Alternative Feedstuffs

High corn and soybean meal prices, and in some cases, reduced availability, are causing nutritionists to use more by-products and co-products than ever before in order to minimize diet costs. The three most common by-products being used in swine diets are DDGS, wheat midds, and bakery by-product. Managing nutrient variability among by-product sources is the primary challenge to obtaining accurate nutrient loading values for use in feed formulation. Significant research has been conducted recently regarding nutrient variability among DDGS sources and the use of various “nutritional tools” to estimate ME and digestible amino acid content among sources. Unfortunately, very little research has been conducted to develop similar “tools” for use with wheat midds and bakery by-product. Energy prediction equations for DDGS provide the capability for estimating actual energy value for specific sources but there are several concerns affecting their accuracy that must be considered when using them. Use of an in vitro digestible energy procedure resulted in accurate prediction of apparent total tract energy digestibility among single samples of 8 feedstuffs, but resulted in poor predictions within canola meal and corn DDGS sources. Use of NIR is becoming a popular method for assessing nutrient content of feed ingredients and calibrations have been developed for DDGS. There is considerable interest in using NIR to estimate ME and digestible amino acid content in by-products, but there are several challenges that must be overcome before this is a viable “tool”. Several equations have been developed to estimate amino acid content from crude protein content in corn, soybean meal, DDGS, and wheat midds, but accuracy of lysine estimation is poor. Digestible lysine in DDGS can be estimated relatively accurately from total or reactive lysine content in DDGS. Use of in vitro “tools” such as IDEA ® and AMINORED ® do not appear to provide a high degree of accuracy in digestible amino acid estimates in DDGS for swine. Color of DDGS may be a general indicator of lysine digestibility among DDGS sources, but optical density and front face fluorescence may provide more accurate prediction of digestible amino acids in DDGS if some of their challenges can be overcome. In summary, several “nutritional tools” have been developed to estimate ME and digestible amino acid content in DDGS for swine, but more refinements and validation are needed. Efforts are needed to develop similar “tools” for other common by-product ingredients used in swine diets.