Construction of Prediction Models of Feedstuffs Effective Energy Values for Swine

PAN Xiaohua, YANG Liang, PANG Zhihong, WANG Jianfen, XIONG Benhai

doi:10.3969/j.issn.1006-267x.2015.05.015

Chinese Journal of Animal Nutrition >

2015 , Vol. 27 >Issue 5: 1450 - 1460

DOI: https://doi.org/10.3969/j.issn.1006-267x.2015.05.015

Experimental Methods

Construction of Prediction Models of Feedstuffs Effective Energy Values for Swine

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1. Institute of Animal Science, Chinese Academy of Agricultural Sciences, State Key Laboratory of Animal Nutrition, Beijing 100193, China;
2. Yanqing Animal Health Supervision and Administration, Beijing 102100, China

Received date: 2014-11-12

Online published: 2015-05-13

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Abstract

This study was conducted to establish the relation equations between feedstuffs' chemical compositions, carbohydrate fractions and effective energy value. Based on intensively analysis of the indexes changed in the NRC 11th ed. swine feedstuff composition table, and the table was selected as data source to predict DE, ME and NE indirectly by basic chemical compositions and their different combinations, which were 6 kinds proximate nutrients [dry metter (DM), crude protein (CP), crude fiber (CF), ether extract (EE), acid hydrolysis ether extract (AEE), ash] and 5 kinds of carbohydrate (CHO) composition [starch (ST), neutral detergent fiber (NDF), acid detergent fiber (ADF), hemicellulose (HC), acid detergent lignin (ADL)] were considered as independent variables, while digestible energy (DE), metabolizable energy (ME) and net energy (NE) were treated as dependent variables, using REG process of SAS to set up these relationship equations for different feedstuff groups and different independent variable combinations. The correlation coefficient (R²) and coefficient of variation (CV) were used to evaluate the fitness of models. The results showed as follows: when considering all feedstuff as a group, universally applicable prediction models couldn't be established between DE, ME, NE and feedstuffs' chemical compositions. Further research found that when considering corn and it's by-products as a subset, 7, 6 and 7 models were built for DE, ME and NE, respectively, and their R² were 0.632 8 to 0.772 3 (CV was 6.61% to 8.40%), 0.646 9 to 0.684 9 (CV was 6.91% to 7.34%) and 0.670 5 to 0.822 1 (CV was 6.22% to 8.28%). Three and four models were built for DE and ME respectively when considering soybean and it's by-products as a subset, and their R² were 0.907 1 to 0.926 9 (CV was 5.40% to 6.09%), 0.890 7 to 0.922 3 (CV was 5.79% to 6.78%), no linear regression existed between NE and basic chemical composition. The R² and CV values above indicated that soybean group's DE and ME prediction models have higher goodness of fit than corn subsets, and for the same kind feedstuffs, the difference between DE and ME models with the same independent variables was mainly the coefficient of CP, and CP shows a greater impact on DE than ME, which guarantee ME predicted value is lower than DE. NE models showed that the main factor influenced NE values were starch. Also we complement the DE and ME values of No. 97 (soybean meal, low oligosaccharide, dehulled, solvent extracted), No. 101 (soybeans, high protein, full fat) and No. 102 (soybeans, low oligosaccharide, full fat) feedstuffs in 11th version of NRC by applying the appropriate models in this study, and their DE and ME value were 15.99, 17.35, 17.27 MJ/kg and 14.53, 16.15, 16.14 MJ/kg, respectively. In conclusion, universally applicable prediction models for effective energy values cann't obtain based on feedstuffs' ingredients composition in NRC (2012), but DE, ME and NE prediction models for corn and soybeans are established by appropriate classification of feedstuffs and different independent variables combinations. The most important factors in predicting effective energy are CP, EE, ST, ash, NDF and ADF. For the same feedstuff, the differences between DE and ME model are the coefficients of CP, and CP played an important role in the conversion of DE to ME.

Key words： NRC feedstuff composition table; digestible energy; metabolizable energy; net energy; prediction model

Cite this article

PAN Xiaohua, YANG Liang, PANG Zhihong, WANG Jianfen, XIONG Benhai . Construction of Prediction Models of Feedstuffs Effective Energy Values for Swine[J]. Chinese Journal of Animal Nutrition, 2015 , 27(5) : 1450 -1460 . DOI: 10.3969/j.issn.1006-267x.2015.05.015

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