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Nutritional requirements are typically estimated based on feeding trials with animals or birds offered several amounts of the critical nutrient(s). A nutrient response function is then fitted to data from the feeding trials. Modern computer techniques allow for a variety of functional forms to be used as nutrient response functions. However, the performance of these models is almost undistinguishable from a purely statistical perspective. This paper approaches the issue of determining nutrient requirements from an economic prospective. Crude protein amounts that would maximize profits were calculated for combinations of corn, soybean meal, and live broilers prices using several nutrient response models fitted to technical data from a trial with several balanced CP amounts fed to broiler chickens. Under certain combinations of input prices, differences between the models were between 1.5 and 3.0% CP. No model consistently predicted the greatest or least CP amounts or net profits, emphasizing that the (tangential) slopes of the models change at different rates over the range of nutrient (CP) amounts studied. Models providing adequate statistical fits to research data do not necessarily provide functions that are clearly most appropriate for maximizing producer profits. [ABSTRACT FROM AUTHOR]

A variety of models have been proposed to fit nutritional input-output response data. The models are typically nonlinear; therefore, fitting the models usually requires sophisticated statistical software and training to use it. An alternative tool for fitting nutritional response models was developed by using widely available and easier-to-use Microsoft Excel software. The tool, implemented as an Excel workbook (NRM.xls), allows simultaneous fitting and side-by-side comparisons of several popular models. This study compared the results produced by the tool we developed and PROC NLIN of SAS. The models compared were the broken line (ascending linear and quadratic segments), saturation kinetics, 4-parameter logistics, sigmoidal, and exponential models. The NRM.xls workbook provided results nearly identical to those of PROC NUN. Furthermore, the workbook successfully fit several models that failed to converge in PROC NUN. Two data sets were used as examples to compare fits by the different models. The results suggest that no particular nonlinear model is necessarily best for all nutritional response data. [ABSTRACT FROM AUTHOR]