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Animals
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Data-Mining Methods Applied to Livestock Management
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Data-Mining Methods Applied to Livestock Management

A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section "Animal System and Management".

Deadline for manuscript submissions: closed (20 May 2023) | Viewed by 7625

Special Issue Editors

Prof. Dr. Wilhelm Grzesiak

E-Mail Website
Guest Editor
Department of Ruminants Science, West Pomeranian University of Technology, Szczecin, Poland
Interests: biostatistics; animal science; prediction; cattle; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Daniel Zaborski

E-Mail Website
Guest Editor
Department of Ruminants Science, West Pomeranian University of Technology, Szczecin, Poland
Interests: biostatistics; animal science; prediction; cattle; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

I would like to cordially invite you to share your ideas, achievements, and discoveries, in which an important role was played by the application of data mining (DM) methods to widely understood livestock farming in a special issue of the Animals journal. The use of DM methods in this particular field is associated with the appearance of fast and capacious computers and the collection of large quantities of data on modern farms by different types of sensors. Many such methods have been developed to solve complex problems in animal breeding, e.g., artificial neural networks (ANN), decision trees, naïve Bayes classifier (NBC), support vector machines (SVM), k-means clustering, k-nearest neighbor algorithm, multivariate adaptive regression splines (MARS), etc. These methods, applied to large data sets, often produce better results than classical statistical approaches. With the use of these methods, which practically do not have serious limitations, various problems can be resolved, although the lack of solutions and exciting results also provides important information for researchers, involved in similar projects.

Prof. Dr. Wilhelm Grzesiak
Dr. Daniel Zaborski
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Animals is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (4 papers)

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Research

23 pages, 3563 KiB  
Article
Investigation of the Relationships between Coat Colour, Sex, and Morphological Characteristics in Donkeys Using Data Mining Algorithms
by Şenol Çelik and Orhan Yılmaz
Animals 2023, 13(14), 2366; https://doi.org/10.3390/ani13142366 - 20 Jul 2023
Viewed by 993
Abstract
This study was carried out in order to determine the morphological characteristics, body coat colour distribution, and body dimensions of donkeys raised in Turkey, as well as to determine the relationships between these factors. For this reason, the predictive performance of various machine [...] Read more.
This study was carried out in order to determine the morphological characteristics, body coat colour distribution, and body dimensions of donkeys raised in Turkey, as well as to determine the relationships between these factors. For this reason, the predictive performance of various machine learning algorithms (i.e., CHAID, Random Forest, ALM, MARS, and Bagging MARS) were compared, utilising the biometric data of donkeys. In particular, mean measurements were taken from a total of 371 donkeys (252 male and 119 female) with descriptive statistical values as follows: height at withers, 100.7 cm; rump height, 103.1 cm; body length, 103.8 cm; chest circumference, 112.8 cm; chest depth, 45.7 cm; chest width, 29.1 cm; front shin circumference, 13.5 cm; head length, 55 cm; and ear length, 22 cm. The body colour distribution of the donkeys considered in this study was calculated as 39.35% grey, 19.95% white, 21.83% black, and 18.87% brown. Model fit statistics, including the coefficient of determination (R2), mean square error, root-mean-square error (RMSE), mean absolute percentage error (MAPE), and standard deviation ratio (SD ratio), were calculated to measure the predictive ability of the fitted models. The MARS algorithm was found to be the best model for defining the body length of donkeys, with the highest R2 value (0.916) and the lowest RMSE, MAPE, and SD ratio values (2.173, 1.615, and 0.291, respectively). The experimental results indicate that the most suitable model is the MARS algorithm, which provides a good alternative to other data mining algorithms for predicting the body length of donkeys. Full article
(This article belongs to the Special Issue Data-Mining Methods Applied to Livestock Management)
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16 pages, 1381 KiB  
Article
Classification of Daily Body Weight Gains in Beef Calves Using Decision Trees, Artificial Neural Networks, and Logistic Regression
by Wilhelm Grzesiak, Daniel Zaborski, Renata Pilarczyk, Jerzy Wójcik and Krzysztof Adamczyk
Animals 2023, 13(12), 1956; https://doi.org/10.3390/ani13121956 - 11 Jun 2023
Cited by 1 | Viewed by 2424
Abstract
The aim of the present study was to compare the predictive performance of decision trees, artificial neural networks, and logistic regression used for the classification of daily body weight gains in beef calves. A total of 680 pure-breed Simmental and 373 Limousin cows [...] Read more.
The aim of the present study was to compare the predictive performance of decision trees, artificial neural networks, and logistic regression used for the classification of daily body weight gains in beef calves. A total of 680 pure-breed Simmental and 373 Limousin cows from the largest farm in the West Pomeranian Province, whose calves were fattened between 2014 and 2016, were included in the study. Pre-weaning daily body weight gains were divided into two categories: A—equal to or lower than the weighted mean for each breed and sex and B—higher than the mean. Models were developed separately for each breed. Sensitivity, specificity, accuracy, and area under the curve on a test set for the best model (random forest) were 0.83, 0.67, 0.76, and 0.82 and 0.68, 0.86, 0.78, and 0.81 for the Limousin and Simmental breeds, respectively. The most important predictors were daily weight gains of the dam when she was a calf, daily weight gains of the first calf, sex of the third calf, milk yield at first lactation, birth weight of the third calf, dam birth weight, dam hip height, and second calving season. The selected machine learning models can be used quite effectively for the classification of calves based on their daily weight gains. Full article
(This article belongs to the Special Issue Data-Mining Methods Applied to Livestock Management)
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11 pages, 619 KiB  
Communication
Using Multivariate Adaptive Regression Splines to Estimate the Body Weight of Savanna Goats
by Lebo Trudy Rashijane, Kwena Mokoena and Thobela Louis Tyasi
Animals 2023, 13(7), 1146; https://doi.org/10.3390/ani13071146 - 24 Mar 2023
Viewed by 1162
Abstract
The Savanna goat breed is an indigenous goat breed in South Africa that is reared for meat production. Live body weight is an important tool for livestock management, selection and feeding. The use of multivariate adaptive regression splines (MARS) to predict the live [...] Read more.
The Savanna goat breed is an indigenous goat breed in South Africa that is reared for meat production. Live body weight is an important tool for livestock management, selection and feeding. The use of multivariate adaptive regression splines (MARS) to predict the live body weight of Savanna goats remains poorly understood. The study was conducted to investigate the influence of linear body measurements on the body weight of Savanna goats using MARS. In total, 173 Savanna goats between the ages of two and five years were used to collect body weight (BW), body length (BL), heart girth (HG), rump height (RH) and withers height (WH). MARS was used as a data mining algorithm for data analysis. The best predictive model was achieved from the training dataset with the highest coefficient of determination and Pearson’s correlation coefficient (0.959 and 0.961), respectively. BW was influenced positively when WH > 63 cm and HG >100 cm with a coefficient of 0.51 and 2.71, respectively. The interaction of WH > 63 cm and BL < 75 cm, WH < 68 cm and HG < 100 cm with a coefficient of 0.28 and 0.02 had a positive influence on Savanna goat BW, while male goats had a negative influence (−4.57). The findings of the study suggest that MARS can be used to estimate the BW in Savanna goats. This finding will be helpful to farmers in the selection of breeding stock and precision in the day-to-day activities such as feeding, marketing and veterinary services. Full article
(This article belongs to the Special Issue Data-Mining Methods Applied to Livestock Management)
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12 pages, 1110 KiB  
Article
Estimation of Body Weight Based on Biometric Measurements by Using Random Forest Regression, Support Vector Regression and CART Algorithms
by Cem Tırınk, Dariusz Piwczyński, Magdalena Kolenda and Hasan Önder
Animals 2023, 13(5), 798; https://doi.org/10.3390/ani13050798 - 22 Feb 2023
Cited by 3 | Viewed by 2040
Abstract
The study’s main goal was to compare several data mining and machine learning algorithms to estimate body weight based on body measurements at a different share of Polish Merino in the genotype of crossbreds (share of Suffolk and Polish Merino genotypes). The study [...] Read more.
The study’s main goal was to compare several data mining and machine learning algorithms to estimate body weight based on body measurements at a different share of Polish Merino in the genotype of crossbreds (share of Suffolk and Polish Merino genotypes). The study estimated the capabilities of CART, support vector regression and random forest regression algorithms. To compare the estimation performances of the evaluated algorithms and determine the best model for estimating body weight, various body measurements and sex and birth type characteristics were assessed. Data from 344 sheep were used to estimate the body weights. The root means square error, standard deviation ratio, Pearson’s correlation coefficient, mean absolute percentage error, coefficient of determination and Akaike’s information criterion were used to assess the algorithms. A random forest regression algorithm may help breeders obtain a unique Polish Merino Suffolk cross population that would increase meat production. Full article
(This article belongs to the Special Issue Data-Mining Methods Applied to Livestock Management)
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