Correction of INRAPORC® prediction errors for a commercial pig system

• Autor(a) principal: Magagnin,Sebastião Ferreira
• Data de Publicação: 2021
• Outros Autores: Siqueira,Simona Miléo, Moraes,Priscila de Oliveira, Dahlke,Fabiano, Hauptli,Lucélia, Warpechowski,Marson Bruck
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Ciência Rural
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782021001000652
• topic: calibration validation; systematic error; predicted value correction procedure

ABSTRACT: INRAPORC® is a mechanistic, dynamic, and deterministic model system that is used in commercial pig production. However, its use is limited as it requires performance information for animals under ad libitum (AL) feed management, which is not provided at all stages of production. Verification of the INRAPORC® calibrations were conducted in this investigation using data from a small group of animals fed with AL in a laboratory situation, to simulate the mean kinetics of a larger commercial population and generate the correction equations for the predicted body weight (BW), and backfat thickness (BT). Analyses were performed by comparing the predicted and observed data, and by submitting them to prediction calibration curve tests (b0 = 0, and b1 = 1). The obtained curves presented a systematic, fixed effect error (+2.37 mm) for BT. The predicted BW and BT values were corrected using the values of the systematic errors obtained. As a result, 100% of the BW averages observed were contained in the confidence intervals (CI) of the INRAPORC® predicted averages, without the need for corrections, and 78.5% of the actual BT averages were contained in the CI of the averages predicted by the system, after corrections. The INRAPORC® calibrations, based on a small population of animals in laboratory conditions could thus be utilized to make predictions for commercial pig production systems and for value correction procedures for the BW and BT of pig populations that have systematic errors in their prediction validations.