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United States Department of Agriculture

Agricultural Research Service


item Brown Brandl, Tami
item Turner, L
item Bridges, T
item Nienaber, John - Jack

Submitted to: American Society of Agri Engineers Special Meetings and Conferences Papers
Publication Type: Other
Publication Acceptance Date: 8/4/1998
Publication Date: N/A
Citation: N/A

Interpretive Summary: NCPIG is a mathematic model used to predict growth of swine. The model uses specific feed types, feed cost, environment, pig weight, pig cost, and management costs to predict animal growth and economic returns. A study was conducted on the effects of feed restriction imposed either manually or by heat stress, and the results were compared to the model predictions. These comparisons between model predictions and observed values are important because they test model accuracy. Several model modifications were required including accommodations for a lean genetic line of swine. After modifications the model was able to predict growth within an acceptable range.

Technical Abstract: The NCPIG swine growth model (Bridges et al., 1992 a, b, Usry et al., 1992) was further calibrated, by comparison with data collected at USDA-MARC (Brown-Brandl, 1998) . Growth data were measured using individually housed pigs on one of five feeding regimes: thermal neutral, ad libitum feed intake, two manual restrictions in thermal neutral conditions, and two thermally induced feed restrictions. The heat stress and restricted feed intake algorithms of NCPIG were modified slightly in order to allow NCPIG to simulate these feeding treatments. Additional genotype parameters were added to NCPIG to simulate the genotype used in the experiments. Comparisons were made between the predictions of the calibrated genotype and an existing genotype option from the model. The new genotype parameters did not aid in the prediction of average daily gain, feed intake, and days on trial. However, the calibration improved agreement between the predicted and observed values for protein and fat accretion across all treatments.

Last Modified: 10/16/2017
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