Date of Award:


Document Type:


Degree Name:

Doctor of Philosophy (PhD)


Animal, Dairy, and Veterinary Sciences

Committee Chair(s)

Lorin E. Harris


Lorin E. Harris


John C. Malecheck


Rex L. Hurst


Wyne C. Foote


Leonard C. Kearl


Data on the proximate nutrient content of feedstuffs, digestibility and energy utilization available from the International Feedstuffs Institute (Utah State University) were used to develop mathematical models for estimating energy and protein utilization of five classes of feedstuffs for various kinds of animals.

Classes of feedstuffs were subdivided into more related subclasses. Furthermore, data from all feeds were pooled together then subgrouped into more related subgroups in an attempt to gain high precision in prediction of digestible proximate nutrients and TDN from a single chemical entity by the use of simple regression models (Y = bo + b1x1).

Digestible percentages (Y) of crude protein, ether extract, crude fiber and nitrogen free extract were highly correlated with their proximate contents (Xs) of most classes, subclasses and subgroups of feedstuffs for various kinds of animals. However, the use of linear multiple regression equation resulted in more precision in estimating each digestible nutrient (Y) from proximate analysis (X1; CP%, X2; EE% , X3 ; CF% and X4; NFE%) of the different classes of feedstuffs for various kinds of animals.

Prediction of digestible proximate nutrients made it possible to calculate Tn~ by the conventional equation: TDN; DCP% + DCF% + DNFE% + 2.25 x DEE%. And to calculate digestible energy (DE) from the following equation: DE(Mcal/kg); 5.72 (DCP%) + 9.5 (DEE%)+ 4.79 (DCF%) + 4. 03 (NFE%)/100

TDN, DE and ME (Ys) were highly correlated with the digestible proximate nutrients (X1; DCP%, X2; DEE%, X3; DCF% and X4= DNFE%) and with proximate analysis (upon the use of multiple regression models).

However, TDN, DE and ME (Ys) were not predictable with high precision from any one single chemical entity (Xs) in most cases of the different classes of feedstuffs for various kinds of animals.

DE (Y) was highly correlated with TDN values (X), and ME (Y) was highly correlated with DE and TDN (Xs) values of the different classes of feedstuffs for various kinds of animals.

The inclusion of physical descriptions (qualitative factors) of feedstuffs along with chemical analysis (quantitative factors) gave promising results predicting TDN content of feedstuffs.

MEn and NEp for poultry were highly correlated with proximate analysis of the different classes of feedstuffs. NEp was also estimated with high precision from MEn. However, both MEn and NEp were not highly associated with single chemical entities.

The dissertation contains an extensive literature review on systems of evaluating nutritive value, and factors affecting digestibility of feedstuffs.

This dissertation also contains numerous equations which predict each digestible nutrient from its proximate content and from proximate analysis; TDN, DE and ME from each proximate nutrient, digestible proximate nutrients and proximate analysis; DE and from TDN; and ME from DE contents of different classes of feedstuffs for various kinds of animals. Moreover, there are complex equations to predict TDN from proximate analysis and their interactions and from proximate analysis plus physical descriptions of feedstuffs for various kinds of animals.