Bayesian comparison of models for precision feeding and management in growing-finishing pigs

Precision feeding and management of growing-finishing pigs typically require mathematical models to forecast individual pig performance from past data. The current approaches, namely double exponential smoothing (DES) and dynamic linear regression are likely to have some limitations in their applicability since they: (1) assume that responses can be forecasted linearly, which only holds in the short-term, and (2) often take insufficient account of uncertainty and correlations in the estimated traits. We developed and evaluated alternative approaches to forecasting individual growth or intake responses based on nonlinear models (allometric, monomolecular, rational) and Bayesian methodology to fit models to the data and generate probabilistic forecasts. We applied these approaches to individual data from two distinct pig populations, to parameterise the models (fitting based on a training dataset) and forecast performance (forecast horizons: 1–30 d tested on a validation dataset). We found that good fitting did not guarantee accurate forecasting, which is quantitatively relevant in the medium-to-long term. Forecasts from nonlinear models were more accurate compared to those from benchmark linear models, with the allometric model being more accurate for most pigs across considered forecast horizons. While DES was the best model at fitting, it was also the least accurate at forecasting for all forecast horizons. These results enhance the understanding of how underlying biological growth responses could be approximated using straightforward mathematical relationships. The approach could be utilised to formulate optimised feeding strategies and inform management decisions, including pen allocation or end-weight prediction.