Development and ex post validation of prediction equations of corn energy values for growing pigs
DOI:
https://doi.org/10.5433/1679-0359.2015v36n3p1755Palavras-chave:
Digestible energy, Metabolizable energy, Mathematical model.Resumo
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 Suínos e Aves, Brazil. The equations were evaluated using regression analysis, and adjusted R² 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 (pe) 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(ADF2) – 1.6(NDF2) – 997.1(Ash2) + 151.9EECF + 23.2EENDF – 126.4CPCF + 136.4CPADF – 4.0CPNDF, with R2 = 0.81 and pe = 2.33; ME = 12574 – 1254.9CP – 1140.5EE – 1359.9ADF + 2816.3Ash + 77.6(CP2) + 92.3(CF2) + 54.1(ADF2) – 1.8(NDF2) – 1097.2(Ash2) + 240.6EECF + 26.3EENDF – 157.4CPCF + 96.5CPADF – 4.4CPNDF, with R2 = 0.89 and pe = 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.
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