Amino Acid Linear Regression Equations for Australian Grains

Protein is the second most expensive nutrient in animal feed formulation, after energy. Diets which have excess crude protein can overload the gastrointestinal tract with excess amino acids and undigested protein (Apajalahti and Vienola, 2016), resulting in impaired feed efficiency, health and welfare issues as well as negative environmental impacts via excess nitrogen excretion. Reducing dietary protein in poultry diets can help to mitigate some of these negative effects. In poultry diets, grain can contribute up to half of the dietary crude protein; therefore, to reduce dietary crude protein, the amino acid profile of Australian grains must be comprehensive and accurate to reduce diet costs via unnecessary inclusions of expensive raw materials such as soybean meal as well as crystalline amino acids.

Raw material samples for major Australian grains (wheat, barley, sorghum and oats) were collected from across Australia for a robust dataset. Barley (n = 285), oats (n = 163), sorghum (n = 158) and wheat (n = 381) samples were scanned using a Bruker NIRS and processed using Evonik AMINONIR© Advanced 3.0. The data were analysed using SPSS statistical package (v. 24.0.0.0) and regression equations were created. Pearson correlation coefficients and interpretations of relationship strength between the two methods were based on guidelines of a strong relationship defined as R2 > 0.5 and significance was accepted at P < 0.05.

The resulting regression equations using Australian data were compared to previously published equations from Evonik AMINODat® 5.0 developed from a global database. Discrepancies between the two equation databases establishes the necessity of using Australian specific data. When the regression equations were compared to the current Ridley database, minimal differences were observed. Exceptions to this were observed at the extremities of the protein range sampled for all grains (both high and low protein), as grains of this nature are uncommon. The resulting database of 19 proteinogenic amino acids that has been developed will better prepare the Australian poultry industry for future reductions in dietary protein and refine the use of supplemental amino acids, enabling more accurate diet formulation to improve current production and maintain performance.

Table 1 - Correlation between amino acids and crude protein in common Australian grains.

ACKNOWLEDGEMENTS: This project was a result of a collaboration between Ridley Agriproducts, APR.Intern and the University of New England with funding provided by Ridley Agriproducts.