Classification of Spanish autochthonous bovine breeds. Morphometric study using classical and heuristic techniques

Serrano, Evangelina Rodero; González, Ana M.; Luque, Manuel Melendo; Herrera, Mariano; Gutiérrez‐Estrada, Juan Carlos

doi:10.1016/j.livsci.2011.09.022

Cited by 8 publications

(7 citation statements)

References 28 publications

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“…As we can see in Tables 3 and 4, the MLP and 1NN methodologies had the greatest efficiency in the two cases for breed and aptitude separately. These results are partiality different from those obtained by Rodero et al (2012) in the classification of Andalusian cattle breeds, where the best methodologies were SVM and MLP. This could be the consequence that for that study the sample was too homogenous than the present work, there were no different types of aptitudes amongst the breeds (all were meat aptitude) or system of breeding (all raised under a free range grazing).…”

Section: Discussioncontrasting

confidence: 97%

“…But their application for classification of domestic AnGR biodiversity has been scarcely investigated. Rodero et al (2012) found in Andalusian cattle breeds that the results obtained by heuristic classification techniques improved from those obtained using LDA. The same circumstance was observed by Bhatia et al (2010) using computer learning techniques to study the possibility to identify AnGR.…”

Section: Introductionmentioning

confidence: 96%

“…However, to compare and differentiate between known breeds, the discriminate analysis is used to validate a previous classification (FAO, 2012). Jordana et al (1993), Herrera et al (1996), Capote et al (1998) and Rodero et al (2003Rodero et al ( , 2012 have reported a comparison between autochthonous Spanish goat breeds according to morphological characteristics. Differences in morphological traits are still widely used to classify AnGR, especially in countries that have not yet clearly identified the biodiversity of local livestock populations (Crepaldi et al, 2001;Macciotta et al, 2002;Lanari et al, 2003;Nsoso et al, 2004;Zaitoun et al, 2005;Traoré et al, 2006;Dossa et al, 2007;Vargas et al, 2007;Traoré et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Classification of goat genetic resources using morphological traits. Comparison of machine learning techniques with linear discriminant analysis

et al. 2015

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Section: Discussioncontrasting

confidence: 97%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

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Classification of goat genetic resources using morphological traits. Comparison of machine learning techniques with linear discriminant analysis

et al. 2015

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“…In addition, the Student-Newman-Keuls test was used to determine the existence of significant differences between breeds. A complete description of the method can be found in Herrera et al (1996) and Rodero et al (2012) among others. The second step was the study of the concrete relationships that exist among the discriminated groups.…”

Section: Discussionmentioning

confidence: 99%

Multivariate analysis of the milk coagulation process in ovine breeds from Spain

Caballero-Villalobos

Figueroa

Xibrraku

et al. 2018

Journal of Dairy Science

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In Spain, ewe milk is mainly used for cheesemaking, and farming systems have traditionally been based on the use of autochthonous breeds. However, in recent years, the progressive introduction of highly productive foreign breeds in Spanish farms has led to an increasing interest in the characterization of dairy sheep breeds to evaluate whether genetic selection schemes should focus on productivity or milk technological aptitude. The purpose of this work was to explore milk composition and coagulation to classify 4 of the main dairy sheep breeds used in Spain. This study included 832 individual ewe milk samples from the breeds Manchega, Assaf, Merino de Grazalema, and Merino de Los Pedroches. Samples were analyzed for native pH, composition (fat, protein, lactose, and total solids), coagulation properties, and individual laboratory curd yield. An indicator of coagulation efficiency was also determined. Canonical discriminant analysis was performed to establish differences and similarities among breeds based on the measured variables. In addition, cluster analysis was performed to study and quantify the concrete relationships among the discriminated groups. Discriminant analysis proved to be a powerful tool to accurately draw distinctions between breeds. In all cases, discrimination among breeds was evident and the 4 breeds could be easily differentiated. Cluster analysis showed greater similarity between Merino de Grazalema and Assaf compared with the other breeds, and F-statistics indicated a higher discriminating ability for the variables related to milk composition. However, Merino de Grazalema and Manchega were difficult to separate according to milk composition, but the coagulation process differenced them clearly. Coagulation also evidenced similarities between Manchega and Merino de Los Pedroches, although the latter was revealed to be the most different breed of all 4, which could lay the ground for its differentiation as an independent breed in the Official Catalogue of Spanish Livestock Breeds.

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“…Many are the research studies in the cattle sector based on the application of ML techniques in farm management, to model for example the individual intake of cow feed, to optimize health and fertility, and to identify potential disease predictors for several pathologies, such as Bovine Viral Diarrhoea Virus (BVDV), Infectious Bovines Rhinotracheitis (IBR), Bovine Tuberculosis (TB), lameness, and mastitis (Bovine Diseases and Resources;Yao et al, 2016;Williams et al, 2016;Amrine et al, 2014;Guzhva et al, 2016;Machado et al, 2015;Nasirahmadi et al, 2017;Ortiz-Pelaez and Pfeiffer, 2008;Rodero et al, 2012). Beef cattle management is usually an extensive breeding system: the bovines are bred completely on pastures and they are not subject to massive checks such as dairy cattle, since they are more resistant and less exposed to stress factors (Derner et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Towards modelling beef cattle management with Genetic Programming

Abbona

Vanneschi

Bona³

et al. 2020

Livestock Science

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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.

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Classification of Spanish autochthonous bovine breeds. Morphometric study using classical and heuristic techniques

Cited by 8 publications

References 28 publications

Classification of goat genetic resources using morphological traits. Comparison of machine learning techniques with linear discriminant analysis

Classification of goat genetic resources using morphological traits. Comparison of machine learning techniques with linear discriminant analysis

Multivariate analysis of the milk coagulation process in ovine breeds from Spain

Towards modelling beef cattle management with Genetic Programming

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