Among the Italian Piedmontese Beef Breedings, the yearly production of calves weaned per cow, that is the calves that survive during the period of 60 days following birth, is identified as the main target expressing the performance of a farm. Modelling farm dynamics in order to predict the value of this parameter is a possible solution to investigate and highlight breeding strengths, and to find alternatives to penalizing factors. The identification of such variables is a complex but solvable task, since the amount of recorded data among livestock is nowadays huge and manageable through Machine Learning techniques. Besides, the evaluation of the effectiveness of the type of management allows the breeder to consolidate the ongoing processes or, on the contrary, to adopt new management strategies. To solve this problem, we propose a Genetic Programming approach, a white-box technique suitable for big data management, and with an intrinsic ability to select important variables, providing simple models. The most frequent variables encapsulated in the models built by Genetic Programming are highlighted, and their zoological significance is investigated a posteriori, evaluating the performance of the prediction models. Moreover, two of the final expressions selected only three variables among the 48 given in input, one of which is the best performing among 2 GP models. The expressions were then analyzed in order to propose a zootechnical interpretation of the equations.Comparisons with other common techniques, including also black-box methods, are performed, in order to evaluate the performance of different type of methods in terms of accuracy and generalization ability. The different approaches of these algorithms provide more accurate models. However, the techniques do not allow feature selection and the final expressions result complex and unintelligible, often not even accessible. The structure of the Genetic Programming algorithm ensured instead a selection of the possibly most informative variables, as well as readable final expressions. The approach entailed constructive and helpful considerations to the addressed task, confirming its key-role in zootechnical field, especially in the beef breeding management.

In this paper, we propose an eco‐epidemiological predator–prey model, modeling the spread of infectious keratoconjunctivitis among domestic and wild ungulates, during the summer season, when they intermigle in high mountain pastures. The disease can be treated in the domestic animals, but for the wild herbivores, it leads to blindness, with consequent death. The model shows that the disease can lead infected herbivores or their predators to extinction, even if it does not affect the latter. Boundedness of solutions and equilibria feasibility are obtained. Stability around the different equilibrium points is analyzed through eigenvalues and the Routh–Hurwitz criterion. Simulations are carried out to support the theoretical results. Sensitivity with respect of some parameters is investigated. The prey vaccination as control measure is introduced and simulated, although at present, the vaccine is not yet available, but just being developed. It would then possibly eradicate the infection in the domestic animals, which are considered a disease reservoir. Copyright © 2016 John Wiley & Sons, Ltd.

Livestock is increasingly treated not just as food containers, but as animals that can be susceptible to stress and diseases, affecting, therefore, the production of offspring and the performance of the farm. The breeder needs a simple and useful tool to make the best decisions for his farm, as well as being able to objectively check whether the choices and investments made have improved or worsened its performance. The amount of data is huge but often dispersive: it is therefore essential to provide the farmer with a clear and comprehensible solution, that represents an additional investment. This research proposes a genetic programming approach to predict the yearly number of weaned calves per cow of a farm, namely the measure of its performance. To investigate the efficiency of genetic programming in such a problem, a dataset composed by observations on representative Piedmontese breedings was used. The results show that the algorithm is appropriate, and can perform an implicit feature selection, highlighting important variables and leading to simple and interpretable models.

Accurate livestock management can be achieved by means of predictive models. Critical factors affecting the welfare of intensive beef cattle husbandry systems can be difficult to be detected, and Machine Learning appears as a promising approach to investigate the hundreds of variables and temporal patterns lying in the data. In this article, we explore the use of Genetic Programming (GP) to build a predictive model for the performance of Piemontese beef cattle farms. In particular, we investigate the use of vectorial GP, a recently developed variant of GP, that is particularly suitable to manage data in a vectorial form. The experiments conducted on the data from 2014 to 2018 confirm that vectorial GP can outperform not only the standard version of GP but also a number of state-of-the-art Machine Learning methods, such as k-Nearest Neighbors, Generalized Linear Models, feed-forward Neural Networks, and long- and short-term memory Recurrent Neural Networks, both in terms of accuracy and generalizability. Moreover, the intrinsic ability of GP in performing an automatic feature selection, while generating interpretable predictive models, allows highlighting the main elements influencing the breeding performance.