Empirical models to predict feed intake of growing-finishing pigs reared under high environmental temperatures

• Autor(a) principal: Perondi, Dani
• Data de Publicação: 2018
• Outros Autores: Kipper, Marcos, Andretta, Ines, Hauschild, Luciano, Lunedo, Raquel, Franceschina, Carolina Schell, Remus, Aline
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Scientia Agrícola (Online)
• Texto Completo: https://www.revistas.usp.br/sa/article/view/144632
• topic: consumption; heat stress; modelling; precision feeding; swine

ABSTRACT Several empirical models were proposed to predict feed intake (FI) of growingfinishing pigs reared under high environmental temperatures. However, these models have not been evaluated under conditions different from those in which they were developed. Twelve empirical models were evaluated using a database built after systematic literature review (observed data: 28 studies in which the FI was evaluated in pigs under high environmental temperatures). Model accuracy was assessed using the mean squared of prediction error (MSPE). Analyses were performed considering two scenarios: (1) general population, where all observed data were used in the simulation; (2) reference population, where data were filtered in order to simulate only scenarios with environment (temperature range) and animals (body weight and sex) similar to that used in the model development. Six models estimated FI values similar (p >; 0.05) to those observed in the general population, while four models produced estimates similar to the observed values in the reference populations. Most models were more accurate when they were simulated using the reference population than when the simulation considered the general database. Moving the simulation from the general database to the reference population reduced up to 98 % of the MSPE, depending on the equation. Empirical models allow to accurately predict FI of growing-finishing pigs exposed to high environmental temperatures, especially in scenarios similar to the ones used for model development. Thus, population characteristics (body weight and sex) and environment (temperature range) must be considered in the model assessment.