The use of multiple imputation for the accurate measurements of individual feed intake by electronic feeders

A Systematic Review on Validated Precision Livestock Farming Technologies for Pig Production and Its Potential to Assess Animal Welfare

Several precision livestock farming (PLF) technologies, conceived for optimizing farming processes, are developed to detect the physical and behavioral changes of animals continuously and in real-time. The aim of this review was to explore the capacity of existing PLF technologies to contribute to the assessment of pig welfare. In a web search for commercially available PLF for pigs, 83 technologies were identified. A literature search was conducted, following systematic review guidelines (PRISMA), to identify studies on the validation of sensor technologies for assessing animal-based welfare indicators. Two validation levels were defined: internal (evaluation during system building within the same population that were used for system building) and external (evaluation on a different population than during system building). From 2,463 articles found, 111 were selected, which validated some PLF that could be applied to the assessment of animal-based welfare indicators of pigs (7% classified as external, and 93% as internal validation). From our list of commercially available PLF technologies, only 5% had been externally validated. The more often validated technologies were vision-based solutions (n = 45), followed by load-cells (n = 28; feeders and drinkers, force plates and scales), accelerometers (n = 14) and microphones (n = 14), thermal cameras (n = 10), photoelectric sensors (n = 5), radio-frequency identification (RFID) for tracking (n = 2), infrared thermometers (n = 1), and pyrometer (n = 1). Externally validated technologies were photoelectric sensors (n = 2), thermal cameras (n = 2), microphone (n = 1), load-cells (n = 1), RFID (n = 1), and pyrometer (n = 1). Measured traits included activity and posture-related behavior, feeding and drinking, other behavior, physical condition, and health. In conclusion, existing PLF technologies are potential tools for on-farm animal welfare assessment in pig production. However, validation studies are lacking for an important percentage of market available tools, and in particular research and development need to focus on identifying the feature candidates of the measures (e.g., deviations from diurnal pattern, threshold levels) that are valid signals of either negative or positive animal welfare. An important gap identified are the lack of technologies to assess affective states (both positive and negative states).

Predicting Growth and Carcass Traits in Swine Using Microbiome Data and Machine Learning Algorithms

In this paper, we evaluated the power of microbiome measures taken at three time points over the growth test period (weaning, 15 and 22 weeks) to foretell growth and carcass traits in 1039 individuals of a line of crossbred pigs. We measured prediction accuracy as the correlation between actual and predicted phenotypes in a five-fold cross-validation setting. Phenotypic traits measured included live weight measures and carcass composition obtained during the trial as well as at slaughter. We employed a null model excluding microbiome information as a baseline to assess the increase in prediction accuracy stemming from the inclusion of operational taxonomic units (OTU) as predictors. We further contrasted performance of models from the Bayesian alphabet (Bayesian Lasso) as well machine learning approaches (Random Forest and Gradient Boosting) and semi-parametric kernel models (Reproducing Kernel Hilbert space). In most cases, prediction accuracy increased significantly with the inclusion of microbiome data. Accuracy was more substantial with the inclusion of microbiome information taken at weeks 15 and 22, with values ranging from approximately 0.30 for loin traits to more than 0.50 for back fat. Conversely, microbiome composition at weaning resulted in most cases in marginal gains of prediction accuracy, suggesting that later measures might be more useful to include in predictive models. Model choice affected predictions marginally with no clear winner for any model/trait/time point. We, therefore, suggest average prediction across models as a robust strategy in fitting microbiome information. In conclusion, microbiome composition can effectively be used as a predictor of growth and composition traits, particularly for fatness traits. The inclusion of OTU predictors could potentially be used to promote fast growth of individuals while limiting fat accumulation. Early microbiome measures might not be good predictors of growth and OTU information might be best collected at later life stages. Future research should focus on the inclusion of both microbiome as well as host genome information in predictions, as well as the interaction between the two. Furthermore, the influence of the microbiome on feed efficiency as well as carcass and meat quality should be investigated.

The relationship between different measures of feed efficiency and feeding behavior traits in Duroc pigs

Utilization of feed in livestock species consists of a wide range of biological processes, and therefore, its efficiency can be expressed in various ways, including direct measurement, such as daily feed intake, as well as indicator measures, such as feeding behavior. Measuring feed efficiency is important to the swine industry, and its accuracy can be enhanced by using automated feeding systems, which record feed intake and associated feeding behavior of individual animals. Each automated feeder space is often shared among several pigs and therefore raises concerns about social interactions among pen mates with regard to feeding behavior. The study herein used a data set of 14,901 Duroc boars with individual records on feed intake, feeding behavior, and other off-test traits. These traits were modeled with and without the random spatial effect of Pen_Room, a concatenation of room and pen, or random social interaction among pen mates. The nonheritable spatial effect of common Pen-Room was observed for traits directly measuring feed intake and accounted for up to 13% of the total phenotypic variance in the average daily feeding rate. The social interaction effect explained larger proportions of phenotypic variation in all the traits studied, with the highest being 59% for ADFI in the group of feeding behaviors, 73% for residual feed intake (RFI; RFI4 and RFI6) in the feed efficiency traits, and 69% for intramuscular fat percentage in the off-test traits. After accounting for the social interaction effect, residual BW gain and RFI and BW gain (RIG) were found to have the heritability of 0.38 and 0.18, respectively, and had strong genetic correlations with growth and off-test traits. Feeding behavior traits were found to be moderately heritable, ranging from 0.14 (ADFI) to 0.52 (average daily occupation time), and some of them were strongly correlated with feed efficiency measures; for example, there was a genetic correlation of 0.88 between ADFI and RFI6. Our work suggested that accounting for the social common pen effect was important for estimating genetic parameters of traits recorded by the automated feeding system. Residual BW gain and RIG appeared to be two robust measures of feed efficiency. Feeding behavior measures are worth further investigation as indicators of feed efficiency.