The aim of this study was to determine and validate prediction equations for digestible (DE) and metabolizable energy (ME) of corn for growing pigs. The prediction equations were developed based on data on the chemical composition, digestible and metabolizable energy of corn grain (30 samples) evaluated in experiments in Embrapa Suinos e Aves, Brazil. The equations were evaluated using regression analysis, and adjusted R-2 was the criterion for selection of the best models. Two equations were tested for DE and ME, each. To validate the equations, 1 experiment with 2 assays was performed to determine the values of DE and ME of 5 corn cultivars. In each assay, we used 24 growing pigs with initial average weight of 54.21 +/- 1.68 kg in complete randomized block design with 6 treatments and 4 replicates. Treatments consisted of a reference diet and 5 ration tests composed of 60% of the reference diet and 40% of corn (1 of the 5 cultivars). Based on the results of the metabolic experiment and predicted values obtained in the equations, the validation of the equations was conducted using the lowest prediction error (p(e)) as a criterion for selection. The equations that produced the most accurate estimates of DE and ME of corn were as follows: DE = 11812 - 1015.9CP - 837.9EE - 1641ADF + 2616.3Ash + 47.5(CP2) + 114.7(CF2) + 46(ADF(2)) - 1.6(NDF2) - 997.1(Ash(2)) + 151.9EECF + 23.2EENDF - 126.4CPCF + 136.4CPADF - 4.0CPNDF, with R-2 = 0.81 and p(e) = 2.33; ME = 12574 - 1254.9CP - 1140.5EE - 1359.9ADF + 2816.3Ash + 77.6(CP2) + 92.3(CF2) + 54.1(ADF(2)) - 1.8(NDF2) - 1097.2(Ash(2)) + 240.6EECF + 26.3EENDF - 157.4CPCF + 96.5CPADF - 4.4CPNDF, with R-2 = 0.89 and p(e) = 2.24. Thus, using the data on chemical composition, it is possible to derive prediction equations for DE and ME of corn for pigs; these equations seem to be valid because of the small prediction errors suggestive of high accuracy of these models.
机构:
China Agr Univ, State Key Lab Anim Nutr, Minist Agr, Feed Ind Ctr, Beijing 100193, Peoples R China
Chinese Acad Agr Sci, Feed Res Inst, Minist Agr, Key Lab Feed Biotechnol, Beijing 100081, Peoples R ChinaChina Agr Univ, State Key Lab Anim Nutr, Minist Agr, Feed Ind Ctr, Beijing 100193, Peoples R China
She, Yue
Wang, Qiuyun
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China Agr Univ, State Key Lab Anim Nutr, Minist Agr, Feed Ind Ctr, Beijing 100193, Peoples R ChinaChina Agr Univ, State Key Lab Anim Nutr, Minist Agr, Feed Ind Ctr, Beijing 100193, Peoples R China
Wang, Qiuyun
Stein, Hans H.
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Univ Illinois, Dept Anim Sci, 328 Mumford Hall, Urbana, IL 61801 USAChina Agr Univ, State Key Lab Anim Nutr, Minist Agr, Feed Ind Ctr, Beijing 100193, Peoples R China
Stein, Hans H.
Liu, Ling
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China Agr Univ, State Key Lab Anim Nutr, Minist Agr, Feed Ind Ctr, Beijing 100193, Peoples R ChinaChina Agr Univ, State Key Lab Anim Nutr, Minist Agr, Feed Ind Ctr, Beijing 100193, Peoples R China
Liu, Ling
Li, Defa
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China Agr Univ, State Key Lab Anim Nutr, Minist Agr, Feed Ind Ctr, Beijing 100193, Peoples R ChinaChina Agr Univ, State Key Lab Anim Nutr, Minist Agr, Feed Ind Ctr, Beijing 100193, Peoples R China
Li, Defa
Zhang, Shuai
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China Agr Univ, State Key Lab Anim Nutr, Minist Agr, Feed Ind Ctr, Beijing 100193, Peoples R ChinaChina Agr Univ, State Key Lab Anim Nutr, Minist Agr, Feed Ind Ctr, Beijing 100193, Peoples R China
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China Agr Univ, Minist Agr, Feed Ind Ctr, State Key Lab Anim Nutr, Beijing 100193, Peoples R ChinaChina Agr Univ, Minist Agr, Feed Ind Ctr, State Key Lab Anim Nutr, Beijing 100193, Peoples R China
Xu, Xiao
Wang, Hongliang
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China Agr Univ, Minist Agr, Feed Ind Ctr, State Key Lab Anim Nutr, Beijing 100193, Peoples R ChinaChina Agr Univ, Minist Agr, Feed Ind Ctr, State Key Lab Anim Nutr, Beijing 100193, Peoples R China
Wang, Hongliang
Piao, Xiangshu
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China Agr Univ, Minist Agr, Feed Ind Ctr, State Key Lab Anim Nutr, Beijing 100193, Peoples R ChinaChina Agr Univ, Minist Agr, Feed Ind Ctr, State Key Lab Anim Nutr, Beijing 100193, Peoples R China