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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

The determination of the live body weight of camels (required for their successful breeding) is a rather difficult task due to the problems with handling and restraining these animals. Therefore, the main aim of this study was to predict the ABW of eight indigenous camel (Camelus dromedarius) breeds of Pakistan (Bravhi, Kachi, Kharani, Kohi, Lassi, Makrani, Pishin, and Rodbari). Selected productive (hair production, milk yield per lactation, and lactation length) and reproductive (age of puberty, age at first breeding, gestation period, dry period, and calving interval) traits served as the predictors. Six data mining methods [classification and regression trees (CARTs), chi-square automatic interaction detector (CHAID), exhaustive CHAID (EXCHAID), multivariate adaptive regression splines (MARSs), MLP, and RBF] were applied for ABW prediction. Additionally, hierarchical cluster analysis with Euclidean distance was performed for the phenotypic characterization of the camel breeds. The highest Pearson correlation coefficient between the observed and predicted values (0.84, p < 0.05) was obtained for MLP, which was also characterized by the lowest root-mean-square error (RMSE) (20.86 kg), standard deviation ratio (SDratio) (0.54), mean absolute percentage error (MAPE) (2.44%), and mean absolute deviation (MAD) (16.45 kg). The most influential predictor for all the models was the camel breed. The applied methods allowed for the moderately accurate prediction of ABW (average R2 equal to 65.0%) and the identification of the most important productive and reproductive traits affecting its value. However, one important limitation of the present study is its relatively small dataset, especially for training the ANN (MLP and RBF). Hence, the obtained preliminary results should be validated on larger datasets in the future.

Details

Title
Prediction of Mature Body Weight of Indigenous Camel (Camelus dromedarius) Breeds of Pakistan Using Data Mining Methods
Author
Zaborski, Daniel 1   VIAFID ORCID Logo  ; Grzesiak Wilhelm 1   VIAFID ORCID Logo  ; Abdul, Fatih 2 ; Asim, Faraz 3   VIAFID ORCID Logo  ; Tariq Mohammad Masood 2 ; Sheikh, Irfan Shahzad 2   VIAFID ORCID Logo  ; Waheed, Abdul 3   VIAFID ORCID Logo  ; Ullah Asad 2 ; Marghazani Illahi Bakhsh 4   VIAFID ORCID Logo  ; Mustafa Muhammad Zahid 2 ; Tırınk Cem 5   VIAFID ORCID Logo  ; Celik Senol 6   VIAFID ORCID Logo  ; Stadnytska Olha 7   VIAFID ORCID Logo  ; Klym Oleh 7   VIAFID ORCID Logo 

 Laboratory of Biostatistics, Bioinformatics and Animal Research, West Pomeranian University of Technology, 71-270 Szczecin, Poland 
 Centre for Advanced Studies in Vaccinology and Biotechnology (CASVAB), University of Balochistan, Quetta 87300, Pakistan 
 Department of Livestock and Poultry Production, Bahauddin Zakariya University, Multan 60800, Pakistan 
 Department of Animal Nutrition, Lasbela University of Agriculture, Water and Marine Sciences, Uthal 90150, Pakistan 
 Biometry and Genetics Unit, Department of Animal Science, Agricultural Faculty, Iğdır University, 76000 Iğdır, Turkey 
 Biometry and Genetics Unit, Department of Animal Science, Agricultural Faculty, Bingol University, 12000 Bingol, Turkey 
 Institute of Agriculture in the Carpathian Region of the National Academy of Agrarian Sciences of Ukraine, 81115 Obroshyne, Ukraine 
First page
2051
Publication year
2025
Publication date
2025
Publisher
MDPI AG
e-ISSN
20762615
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3233036824
Copyright
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.