Literature DB >> 8617660

Application of a computer model to predict optimum slaughter end points for different biological types of feeder cattle.

C B Williams1, G L Bennett.   

Abstract

A bioeconomic model was developed to predict slaughter end points of different genotypes of feeder cattle, where profit/rotation and profit/day were maximized. Growth, feed intake, and carcass weight and composition were simulated for 17 biological types of steers. Distribution of carcass weight and proportion in four USDA quality and five USDA yield grades were obtained from predicted carcass weights and composition. Average carcass value for each genotype was calculated from these distributions under four carcass pricing systems that varied from value determined on quality grade alone to value determined on yield grade alone. Under profitable market conditions, rotation length was shorter and carcass weights lighter when the producer's goal was maximum profit/day, compared with maximum profit/rotation. A carcass value system based on yield grade alone resulted in greater profit/rotation and in lighter and leaner carcasses than a system based on quality grade alone. High correlations ( > .97) were obtained between breed profits obtained with different sets of input/output prices and carcass price discount weight ranges. This suggests that breed rankings on the basis of breed profits may not be sensitive to changes in input/output market prices. Steers that were on a grower-stocker system had leaner carcasses, heavier optimum carcass weight, greater profits, and less variation in optimum carcass weights between genotypes than steers that were started on a high-energy finishing diet at weaning. Overall results suggest that breed choices may change with different carcass grading and value systems and postweaning production systems. This model has potential to provide decision support in marketing fed cattle.

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Year:  1995        PMID: 8617660     DOI: 10.2527/1995.73102903x

Source DB:  PubMed          Journal:  J Anim Sci        ISSN: 0021-8812            Impact factor:   3.159


  2 in total

1.  ASN-ASAS SYMPOSIUM: FUTURE OF DATA ANALYTICS IN NUTRITION: Mathematical modeling in ruminant nutrition: approaches and paradigms, extant models, and thoughts for upcoming predictive analytics1,2.

Authors:  Luis O Tedeschi
Journal:  J Anim Sci       Date:  2019-04-29       Impact factor: 3.159

2.  Integrating Genomics with Nutrition Models to Improve the Prediction of Cattle Performance and Carcass Composition under Feedlot Conditions.

Authors:  Luis O Tedeschi
Journal:  PLoS One       Date:  2015-11-24       Impact factor: 3.240

  2 in total

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