Swine production system analyses: Approaches to growth model, genetics, growth agent and carcass evaluation
Abstract
A mathematical model of swine growth from weaning to finishing was developed. The model has input variables associated with feed composition, environment, type of pigs, addition of growth agent, and economics. The simulation is divided into periods to allow changing the conditions of the simulation during the growth period. Simulation is calculated on a daily basis and stopped at the point with optimal profitable slaughter weight. Efficient pork production relies on both genetic improvement and effective management practices to maximize expression of the genetic potential of swine. A replicated factorial experiment using 183 individually fed crossbred barrows was conducted. The effects of five genotypes (GT), two levels of ractopamine (RAC) treatment (0 and 20 ppm), and three treatment weight periods (WT) (59-100, 73-114 and 86-127 kg BW) on swine growth were evaluated. RAC improved (P $<$.0001) both lean growth rate and lean feed efficiency. Differences (P $<$.001) were observed among genotypes for all traits. For lean growth rate, RAC x GT interaction (P $<$.05) was observed, demonstrating greater responses to RAC in leaner genotypes. Pigs achieved greater lean growth rate in the 73-114 kg period than in the other two phases and probably plateaued in lean growth rate at 70-90 kg. Lean feed efficiency declined as body weight increased. The data were also used to examine genotype and treatment (RAC) biases in estimation of fat-standardized lean weight and to evaluate accuracies and precisions realized by use of equations based on variables derived from different technologies. Independent variables used to establish regression equations represented technologies of direct carcass measurements, optical probe data, TOBEC (Total Body Electrical Conductivity) readings and dissected (DHMLN) and fat-standardized (FSHMLN) ham lean. Genotype bias existed when using any equation from a single technology and was minimized by combining FSHMLN with one TOBEC reading, carcass length and the probe measurement of 10th rib fat depth. Large RAC biases appeared when using equations from direct carcass measurements or optical probe data and were minimized by an equation using either DHMLN or FSHMLN. A practical equation with relatively high R$\sp2$ value and small genotype and RAC biases was developed by combining TOBEC readings with direct carcass measurements of 10th rib fat depth and warm carcass weight.
Degree
Ph.D.
Advisors
Schinckel, Purdue University.
Subject Area
Livestock
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