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

Misiura, M.; Filipe, J. A. N.; Brossard, Ludovic; Kyriazakis, Ι.

doi:10.1016/j.biosystemseng.2021.08.027

Cited by 5 publications

(2 citation statements)

References 72 publications

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“…The algorithms of linear regression (LR, LASSO, and RIDGE) obtained the worst prediction performances ( R 2 = 0.83 for SID Lys and 0.52 for ME, for scenarios with sow and housing characteristics), which means that the relationship between predictors and nutrient requirements was not linear. Misiura et al (2021) also obtained accurate results with a non-linear model compared to a linear model for precision feeding for growing-finishing pigs, by predicting feed intake and growth. The prediction of the body weight of piglets at 30 d had a MAPE value of 11.0% with the linear model, and 2.1% with the allometric model.…”

Section: Discussionmentioning

confidence: 87%

Prediction of the daily nutrient requirements of gestating sows based on sensor data and machine-learning algorithms

Durand,

Largouët,

de Beaufort

et al. 2023

Journal of Animal Science

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Precision feeding is a strategy for supplying an amount and composition of feed as close that are as possible to each animal’s nutrient requirements, with the aim of reducing feed costs and environmental losses. Usually, the nutrient requirements of gestating sows are provided by a nutrition model that requires input data such as sow and herd characteristics, but also an estimation of future farrowing performances. New sensors and automatons, such as automatic feeders and drinkers, have been developed on pig farms over the last decade, and have produced large amounts of data. This study evaluated machine-learning methods for predicting the daily nutrient requirements of gestating sows, based only on sensor data, according to various configurations of digital farms. The data of 73 gestating sows was recorded using sensors such as electronic feeders and drinker stations, connected weight scales, accelerometers, and cameras. Nine machine-learning algorithms were trained on various dataset scenarios according to different digital farm configurations (one or two sensors), in order to predict the daily metabolizable energy and standardized ileal digestible lysine requirements for each sow. The prediction results were compared to those predicted by the InraPorc model, a mechanistic model for the precision feeding of gestating sows. The scenario predictions were also evaluated with or without the housing conditions and sow characteristics at artificial insemination usually integrated into the InraPorc model. Adding housing and sow characteristics to sensor data improved the mean average percentage error by 5.58% for lysine and by 2.22% for energy. The higher correlation coefficient values for lysine (0.99) and for energy (0.95) were obtained for scenarios involving an automatic feeder system (daily duration and number of visits with or without consumption) only. The scenarios including an automatic feeder combined with another sensor gave good performance results. For the scenarios using sow and housing characteristics and automatic feeder only, the root mean square error was lower with Gradient Tree Boosting (0.91 MJ/d for energy and 0.08 g/d for lysine) compared with those obtained using linear regression (2.75 MJ/d and 1.07 g/d). The results of this study show that the daily nutrient requirements of gestating sows can be predicted accurately using data provided by sensors and machine-learning methods. It paves the way to simpler solutions for precision feeding.

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Section: Discussionmentioning

confidence: 87%

Prediction of the daily nutrient requirements of gestating sows based on sensor data and machine-learning algorithms

Durand,

Largouët,

de Beaufort

et al. 2023

Journal of Animal Science

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“…On the other hand, the so-called "statistical" strand uses a different methodology in growth analyses, usually polynomials, which allows for an independent structure without interest in inherent biology [51]. Indeed, high-order polynomials have been found to have a reasonably good fit for pig data [52][53][54][55][56], but these stochastic models have no asymptote, while the parameters of the model have no biological significance. Such approaches are used to fit polynomial growth curves to sets of internal, arbitrarily correlated data, to test linear hypotheses about the regression coefficients, and to derive confidence intervals for the growth curves.…”

Section: Modeling the Growth Of Pigsmentioning

confidence: 99%

Perspective Chapter: Tracing the Growth of the Domestic Pig

Kušec,

Djurkin Kušec,

Gvozdanović

2024

Tracing the Domestic Pig

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The phenomenon of growth can be viewed as the key basis of pig production and has therefore long been the subject of intensive research. Growth is the result of a series of biological processes in the animal and it is not surprising that an immense amount of research has been carried out by scientists from various disciplines. Since growth is influenced by genetic and environmental factors and their interaction, most of the studies mentioned here deal with these aspects. In addition, this review deals with the different methods for the description of growth and the application of some growth models in various studies in pigs. It is concluded that understanding the relationships between the factors that influence growth and developing accurate models are essential for implementing strategies to better utilize growth potential of pigs.

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The Potential Contribution of Smart Animal Nutrition in Reducing the Environmental Impacts of Livestock Systems

Mackenzie

2023

Smart Animal Production

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

Cited by 5 publications

References 72 publications

Prediction of the daily nutrient requirements of gestating sows based on sensor data and machine-learning algorithms

Prediction of the daily nutrient requirements of gestating sows based on sensor data and machine-learning algorithms

Perspective Chapter: Tracing the Growth of the Domestic Pig

The Potential Contribution of Smart Animal Nutrition in Reducing the Environmental Impacts of Livestock Systems

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