10/12/2023 0 Comments Animal age terms![]() We also included the inbreeding coefficient as an additional genetic metric. , which has a size of n × 20 2) estimated breeding values: this group includes EBV of 7 traits, namely, sow longevity, piglet vitality, back fat thickness, loin depth thickness, total number of born piglets, mothering ability, and daily gain. This group of features forms an input feature matrix of 20 features that we denote by The available data were split in 3 groups: 1) phenotypic data: this group consists of phenotypic records that domain experts believe to be relevant to growth, including sex, recorded weights (at birth and at the start of the finishing phase), age at 30 kg, birth farm, litter/sow information (e.g., gestation length, parity number, number of born piglets), and performance metrics of similar animals like the average age at 120 kg of farm-mates of the same sex. Additionally, we aim to rank different groups of features based on their effectiveness in predicting slaughter age in pigs. We aim to demonstrate that a model-free, machine learning approach can be used for the prediction of slaughter age in pigs, and by extension, other related phenotypes. It allows the inclusion of heterogeneous data types without hypotheses on which underlying distributions generate them. Unlike traditional statistical analysis, machine learning emphasizes prediction accuracy of the models rather than the fit of the data to predetermined statistical models or structures ( Breiman, 2001b). In this paper, we used a machine learning approach, namely the random forest ( RF) algorithm ( Breiman, 2001a), to combine different types of predictors, phenotype, estimated breeding value ( EBV), along with pedigree and pedigree-genetic relationship data. Therefore, it is not effective to isolate one, or few of these factors, as predictors of future weight or growth ( Gonyou, 1998 and references therein).īut with the rise of modern performance recording systems in pig production, which record large volumes of phenotypic, genetic, and environmental data ( Ma et al., 2012 Kim et al., 2014), and the development of computational methods that can utilize these data, more accurate growth predictions can be attained. Therefore, a good estimate of each pig’s future growth performance can improve the efficiency at pig farms and breeding facilities, for example, by using those estimates to assign pigs to groups that will be nearly uniform in weight at a target age, or groups that will reach a target weight at a nearly uniform age.Īs with other farm animals, pig growth is a complex phenomenon that is influenced by many factors, including sex, age, weight history, feed intake, genetics, health, sow and litter characteristics, and farm conditions ( Apichottanakul et al., 2012). This would incur additional feed cost and labor hours, especially if the farm implements an all-in/all-out management system. For instance, if a group of pigs in a finishing pen contains slow growers, then those pigs must be retained in the pen until they reach market weight before the pen can be cleared to receive a new group. Variation in body growth speed has a big impact on pig farming, since it directly affects key elements of production costs like feed, logistics, and veterinary medical care ( Patience et al., 2004). Estimated breeding value, pedigree, or pedigree-genetic features interchangeably explain 2% of additional variance when added to the phenotypic features, while explaining, respectively, 38%, 39%, and 34% of the variance when used separately. Our results showed that relevant phenotypic features were the most effective in predicting the output (age at 120 kg), explaining approximately 62% of its variance (i.e., R 2 = 0.62). ![]() Moreover, we presented a 2-step data reduction procedure, based on random projections ( RPs) and principal component analysis ( PCA), to extract features from the pedigree and genetic similarity matrices for use as inputs in the prediction models. Data of 32,979 purebred Large White pigs were provided by Topigs Norsvin, consisting of phenotypic data, estimated breeding values ( EBVs), along with pedigree and pedigree-genetic relationships. Additionally, we used the variable importance score from RF to quantify the importance of different types of input data for that prediction. In this paper, we used machine learning, namely random forest ( RF) regression, for predicting the age at which the slaughter weight of 120 kg is reached. However, making these predictions is challenging, due to the natural variation in how individual pigs grow, and the different causes of this variation. In particular, predicting future growth is extremely useful, since it can help in determining feed costs, pen space requirements, and the age at which a pig reaches a desired slaughter weight. The weight of a pig and the rate of its growth are key elements in pig production.
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