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Studies on the Feasibility of Predicting Feedlot Performance from Certain Laboratory Grain Analyses^{1 ,2,}

Oklahoma State University ^{ref-type="fn">4}, Stillwater 74074

Abstract

Data from 14 cattle feeding trials were utilized to study the relationship between Several laboratory analyses and animal feed intake (INTAKE), gain (ADG) and feed efficiency (F/G). Laboratory analyses considered were 6, 12 and 24 hr in vitro dry matter disappearance (IV6, IV12, IV24, respectively); in vitro gas production in 1 hr and 6 hr (GP1 and GP6, respectively); and degree of gelatinization (GEL). A multiple regression equation with variables for treatment and trial classification, initial weight and the quadratic effect of initial weight was fit to the data. The effect of initial weight was significant for all three performance variables, and the quadratic effect was significant for ADG and F/G. A second model was fit excluding the treatment classification, and the maximum R² procedure was utilized to examine how well laboratory analyses accounted for variation among residuals from this second model. More variation was accounted for in the dependent variables F/G (34.96%) and INTAKE (17.81%) than ADG (5.16%) when a combination of all laboratory analyses except GEL was included in the model. Moreover, correlations between residuals of the second model and the laboratory analyses were higher for INTAKE and F/G than ADG and were all negative for INTAKE and F/G, suggesting a negative response in intake and an improved F/G ratio as starch alteration increases. Correlations between the laboratory analyses were generally quite high. This study suggests that no single laboratory analysis considered would be useful for the developriient of accurate, reliable equations for the prediction of feedlot performance, and combinations appear to have value only in the case of F/G and INTAKE.

¹ Journal Article 3282 of the Agricultural Experiment Station, Oklahoma State University, Stillwater 74074.

¹ The assistance of Drs. M. A. Brown and L. D. Young in statistical analyses is greatly appreciated.

³ Department of Animal and Range Science, New Mexico State University.

⁴ Animal Science Department.