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Article

Towards a Vectorial Approach to Predict Beef Farm Performance

1
Department of Veterinary Sciences, University of Torino, Largo Paolo Braccini 2, 10095 Grugliasco, Italy
2
Associazione Nazionale Allevatori Bovini Razza Piemontese, 12061 Carru, Italy
3
NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal
*
Author to whom correspondence should be addressed.
Academic Editor: Valentino Santucci
Appl. Sci. 2022, 12(3), 1137; https://doi.org/10.3390/app12031137
Received: 9 December 2021 / Revised: 9 January 2022 / Accepted: 16 January 2022 / Published: 21 January 2022
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. View Full-Text
Keywords: Evolutionary Algorithms; Genetic Programming; Machine Learning; vector-based representation; cattle breeding; Piemontese bovines; Precision Livestock Farming Evolutionary Algorithms; Genetic Programming; Machine Learning; vector-based representation; cattle breeding; Piemontese bovines; Precision Livestock Farming
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MDPI and ACS Style

Abbona, F.; Vanneschi, L.; Giacobini, M. Towards a Vectorial Approach to Predict Beef Farm Performance. Appl. Sci. 2022, 12, 1137. https://doi.org/10.3390/app12031137

AMA Style

Abbona F, Vanneschi L, Giacobini M. Towards a Vectorial Approach to Predict Beef Farm Performance. Applied Sciences. 2022; 12(3):1137. https://doi.org/10.3390/app12031137

Chicago/Turabian Style

Abbona, Francesca, Leonardo Vanneschi, and Mario Giacobini. 2022. "Towards a Vectorial Approach to Predict Beef Farm Performance" Applied Sciences 12, no. 3: 1137. https://doi.org/10.3390/app12031137

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