Skip to main content
ARS Home » Research » Publications at this Location » Publication #207542
Title: A comparison of methods to estimate nutritional requirements from experimental data

Author
item PESTI, G - UGA
item VEDENOV, D - UGA
item Cason Jr, John
item BILLARD, L - UGA

Submitted to: British Poultry Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/15/2007
Publication Date: 1/1/2009
Citation: Pesti, G.M., Vedenov, D., Cason Jr, J.A., Billard, L. 2009. A comparison of methods to estimate nutritional requirements from experimental data. British Poultry Science. 50:16-32.

Interpretive Summary: Dietary requirements of research animals are determined based on statistical analysis of experimental data, but there are many ways to analyze that data. This paper compares seven major published methods of determining a requirement, along with several variations, with discussion of advantages and disadvantages of each method. The advantages of using economic analysis are also described. Examples that are discussed are based on nutritional data, but the discussion of statistical analysis also applies to experimental data in general.

Technical Abstract: 1. Research papers use a variety of methods for evaluating experiments designed to determine nutritional requirements of poultry. Growth trials result in a set of ordered pairs of data. Often, point-by-point comparisons are made between treatments using analysis of variance. This approach ignores that response variables (body weight, feed efficiency, bone ash, etc.) are continuous rather than discrete. Point-by-point analyses harvest much less than the total amount of information from the data. Regression models are more effective at gleaning information from data, but the concept of "requirements" is poorly defined by many regression models. 2. Response data from a study of the lysine requirements of young broilers was used to compare methods of determining requirements. In this study multiple range tests were compared to quadratic polynomials (QP), broken line models with linear (BLL) or quadratic (BLQ) ascending portions, the saturation kinetics model (SK) a logistic model (LM) and a compartmental (CM) model. 3. The sum of total residuals squared was used to compare the models. The SK and LM were the best fit models, followed by the CM, BLL, BLQ, and QP models. A plot of the residuals versus the nutrient intake level showed clearly that the BLQ and SK models fitted the data best in the important region where the ascending portion meets the plateau. 4. The BLQ model clearly defines the technical concept of nutritional requirements as typically defined by nutritionists. However, the SK, LM and CM models better depict the relationship typically defined by economists as the “law of diminishing marginal productivity”. The SK model was used to demonstrate how the law of diminishing marginal productivity can be applied to poultry nutrition, and how the “most economical feeding level” may replace the concept of “requirements”.