Use of MARS algorithm for predicting mature weight of different camel (Camelus dromedarius) breeds reared in Pakistan and morphological characterization via cluster analysis.
Camel
Cluster analysis
MARS
Morphological characterization
Journal
Tropical animal health and production
ISSN: 1573-7438
Titre abrégé: Trop Anim Health Prod
Pays: United States
ID NLM: 1277355
Informations de publication
Date de publication:
03 Mar 2021
03 Mar 2021
Historique:
received:
31
10
2020
accepted:
21
02
2021
entrez:
4
3
2021
pubmed:
5
3
2021
medline:
25
6
2021
Statut:
epublish
Résumé
Mature weight is a significant trait that can be influenced by age, sex, breed, production system, and climate conditions in camels. In camel breeding, it is essential to describe breed standards of the studied camel breeds as part of morphological characterization and to determine morphological traits positively influencing mature weight within the scope of indirect selection criteria. This study was to find the best one among candidate models in prediction of mature weight from several morphological traits measured for eight camel breeds (Bravhi, Kachi, Kharani, Kohi, Lassi, Makrani, Pishin, and Rodbari) raised under Pakistan conditions. The morphological measurements taken from the camels in the study were birth weight (BW), weaning weight (WW), mature weight (MW), age of ridding (ARD), face length (FL), face width (FW), head length (HL), head width (HW), ear length (EL), ear width (EW), neck length (NL), neck width (NW), hump length (HL), hump width (HuW), heart girth (HG), withers height (WH), body length (BL), fore leg length (FLL), and hind leg length (HLL), respectively. In the prediction of mature body weight as a response variable, the optimal MARS predictive model with 15 terms selected by train function of the caret package produced very high predictive performance without encountering overfitting problem. Goodness of fit criteria were estimated to measure predictive quality of the MARS model using ehaGoF package available in R environment. Morphological characterization of the camel breeds was performed with hierarchical cluster analysis (HCA) on the basis of Euclidean distance-Single linkage. At the first step of hierarchical cluster analysis, the similarity level of Bravhi and Kachi camel breeds was the highest with 85.3569 (%). At the second step, Makrani joined to new cluster of Bravhi and Kachi camels found at the first step, and the similarity level of the new cluster comprising Bravhi, Kachi, and Makrani breeds was found as 84.5562 (%). MW was significantly correlated with BW (0.677), WW (0.536), HL (0.524), HuW (0.529), and ARD (0.375) at P < 0.01, and there was the highest correlation of 0.994 between HHL and FLL (P < 0.01). As a result, it could be suggested that results of MARS modeling may help camel breeders to reproduce the elite camel populations and to describe characteristics associated positively with MW within the scope of indirect selection criteria.
Identifiants
pubmed: 33660132
doi: 10.1007/s11250-021-02633-2
pii: 10.1007/s11250-021-02633-2
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
191Références
Abebe, W., Getinet, A.M., Mekonnen, H.M., 2002. Study on live weight, carcass weight and dressing percentage of Issa camels in Ethiopia, Revue de Médecine Vétérinaire, 153(11), 713-716.
Abouheif, M. A., Basmaeil, S. M., Bakkar, M. N., 1986. Estimation of body and carcass weights in Saudi Arabian Najdi male camels, Arab Gulf Journal of Science Research, 4, 733–743.
Akin, M., Eyduran, E., Reed, B.M., 2016. Use of RSM and CHAID data mining algorithm for predicting mineral nutrition of hazelnut, Plant Cell, Tissue and Organ Culture 128(2), 303-316
doi: 10.1007/s11240-016-1110-6
Akin, M., Eyduran, E., Niedz, R.P., Reed, B.M., 2017. Developing hazelnut tissue culture free of ion confounding, Plant Cell, Tissue and Organ Culture 13(3), 483-494
doi: 10.1007/s11240-017-1238-z
Akin, M., Hand, C., Eyduran, E., Reed, B.M., 2018. Predicting minor nutrient requirements of hazelnut shoot cultures using regression trees, Plant Cell, Tissue and Organ Culture, 132(3), 545-559
doi: 10.1007/s11240-017-1353-x
Akin, M., Eyduran, S.P., Eyduran, E., Reed, B.M., 2020. Analysis of macro nutrient related growth responses using multivariate adaptive regression splines, Plant Cell, Tissue and Organ Culture, 140, 661-670
doi: 10.1007/s11240-019-01763-8
Akpa, Nwachi, G., Abbaya, Yohanna, H., Saley, Edouard, M., 2017. Comparative Evaluation of The Influence of Species, Age and Sex on Carcass Characteristics of Camels, Cattle, Sheep and Goats in Sahel Environment, Animal Research International, 14(1), 2588 – 2597.
Ali, M., Eyduran, E., Tariq, M. M., Tirink, C., Abbas, F., Bajwa, M. A., Baloch, M. H., Nizamani, A. H., Waheed, A., Awan, M. A., Shah, S. H., Ahmad, Z., Jan, S., 2015. Comparison of artificial neural network and decision tree algorithms used for predicting live weight at post weaning period from some biometrical characteristics in Harnai sheep, Pakistan Journal of Zoology, 47, 1579-1585.
Almutairi, S.E., Boujenane, I., Musaad, A., Awad-Acharari, F., 2010. Genetic and nongenetic effects for milk yield and growth traits in Saudi camels, Tropical Animal Health and Production, 42, 1845-1853.
doi: 10.1007/s11250-010-9647-6
Arthur, C.K., Temeng, V.A., Ziggah, Y.Y., 2020. Multivariate Adaptive Regression Splines (MARS) approach to blast-induced ground vibration prediction, International Journal of Mining, Reclamation and Environment, 34(3), 198-222.
doi: 10.1080/17480930.2019.1577940
Aytekin, I., Eyduran, E., Karadas, K., Akşahan, R., Keskin, I., 2018. Prediction of fattening final live weight from some body measurements and fattening period in young bulls of crossbred and exotic breeds using MARS data mining algorithm, Pakistan Journal of Zoology, 50: 189-195.
doi: 10.17582/journal.pjz/2018.50.1.189.195
Boué, A., 1949. Essai de barymétrie chez le dromadaire Nord-africain, Revue d’Élevage et de Médecine vétérinaire des Pays tropicaux, 3(1), 13–16.
doi: 10.19182/remvt.6857
Boujenane, I., 2019. Comparison of body weight estimation equations for camels (Camelus dromedarius). Tropical Animal Health and Production, 51, 1003–1007.
doi: 10.1007/s11250-018-1771-8
Bucci, T.J., Soliman, A.M., Botros, B.A.M., Kerkor, M.E., 1984. Abdominal circumference at the hump as an index of body weight in dromedary camels, Indian Veterinary Journal, 61, 26–30.
Celik, S., Yilmaz, O., 2018. Prediction of body weight of Turkish tazi dogs using data mining Techniques: Classification and Regression Tree (CART) and multivariate adaptive regression splines (MARS), Pakistan Journal of Zoology, 50(2), 575-583.
doi: 10.17582/journal.pjz/2018.50.2.575.583
Eyduran, E., (2020). ehaGoF: Calculates Goodness of Fit Statistics. R package version 0.1.1. https://CRAN.R-project.org/package=ehaGoF .
Eyduran E, Akin M., Eyduran S.P., 2019. Application of Multivariate Adaptive Regression Splines through R Software, Nobel Academic Publishing, Ankara.
FAO, 2019. http://www.fao.org/faostat/en/#data/QA Access date: 30.10.2020.
Faraz, A., 2020. Portrayal of Camelid production in desert ecosystem of Pakistan, Journal of Zoological Research, 2(3), 15-20.
doi: 10.30564/jzr.v2i3.2104
Faraz A., Younas, M, Lateef, M, Yaqoob, M., Muhammad, G., 2017. Comparative growth performance of Marecha Calves (Camelus dromedarius) reared under semi-intensive and extensive management systems, The Journal of Animal and Plant Sciences, 27, 1067-1074.
Faraz A, Waheed, A, Mirza, R.H., Ishaq, H.M., 2019. Socio economic status and associated constraints of camel production in desert Thal Punjab, Pakistan. Journal of Fisheries and Livestock Production, 7(01), 288.
Field, C. R., 1979. Camel growth and milk production in Marasabit district, Northern Kenya, 215–240. IFS Prov. Rep. No. 6: Camels Int. Sci., Stockholm, Sweden.
Friedman, J., 1991. Multivariate adaptive regression splines, Annals of Statistics 19(1), 1–67.
Genc, S., Soysal, M.I., 2018. Parametric and Nonparametric Post Hoc Tests. Black Sea Journal of Engineering and Science 1(1): 18-27.
Gozuacik, C., Eyduran, E., Cam, H., Kara, M.K. 2018. Detection of infection preferences of the alfalfa seed chalcid, Bruchophagus roddi Gussakovskiy, 1933 (Hymenoptera: Eurytomidae) in alfalfa (Medicago sativa L.) fields of Igdir, Turkey. Legume Research 41(1): 150-154.
Graber, M., 1966. Etudes dans certaines conditions africaines de l’action antiparasitaire de Thiabendazole sur divers helminthes des animaux domestiques, II. Dromadaire, Revue d’Élevage et de Médecine vétérinaire des Pays tropicaux, 19, 527–543.
Grzesiak, W., Zaborski, D., 2012. Examples of the use of data mining methods in animal breeding. In: Data mining applications in engineering and medicine (ed. A Karahoca). InTech, Rijeka, Croatia, in IntechOpen 303–324.
Hussain, A., Aujla, K.M., Hassan, S., 2013. Production and Marketing of Camel Products in Semidesert and Desert Areas of Pakistan, Pakistan Journal of Agricultural Research. 26(2), 130-142.
Kadim, I.T., Mhgoub, O., Purchas, R.W., 2008. A review of the growth, and of the carcass and meat quality characteristics of the one-humped camel (Camelus dromedaries), Meat Science 80, 555–569.
doi: 10.1016/j.meatsci.2008.02.010
Keskin, N., Yagci, A., Keskin, S., 2013. A Study on Determination of Some Quality Characteristics of Sivas-Gemerek Region Grapes, Yuzuncu Yıl University Journal of Agricultural Sciences. 23(3), 271-278.
Khan, B. B., Arshad, I., Riaz, M. (2003). Production and management of camels. University of Agriculture, Faisalabad, Department of Livestock Management.
Kovalchuk, I.Y., Mukhitdinova, Z., Turdiyev, T., Madiyeva, G., Akin, M., Eyduran, E., Reed, B.M., 2017. Modeling some mineral nutrient requirements for micropropagated wild apricot shoot cultures, Plant Cell, Tissue and Organ Culture, 129(2), 325-335
doi: 10.1007/s11240-017-1180-0
Kovalchuk, I.Y., Mukhitdinova, Z., Turdiyev, T., Madiyeva, G., Akin, M., Eyduran, E., Reed, B.M., 2018. Nitrogen ions and nitrogen ion proportions impact the growth of apricot (Prunus armeniaca) shoot cultures, Plant Cell, Tissue and Organ Culture, 133(2), 263-273.
doi: 10.1007/s11240-018-1379-8
Kuhn, M. 2020. caret: classification and regression training. R package version 6.0-86. https://CRAN.R-project.org/package=caret .
Kuhn, M., and Contributions from Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, T., Zachary Mayer, Z., Kenkel, B., the R Core Team, Benesty, M., Lescarbeau, R., Ziem, A., Scrucca, L., Tang, Y., Candan, C., Hunt, T, 2019. caret: Classification and Regression Training, R package version 6.0-84. https://CRAN.R-project.org/package=caret .
Kuhn, M., Johnson, K., 2013. Applied Predictive Modeling. New York: Springer.
doi: 10.1007/978-1-4614-6849-3
Meghelli, I., Kaouadji, Z., Yilmaz, O., Cemal, I., Karaca, O., Gaouar, S.B.S., 2020. Morphometric characterization and estimating body weight of two Algerian camel breeds using morphometric measurements, Tropical Animal Health and Production, 52, 2505–2512.
doi: 10.1007/s11250-020-02204-x
Mendes, M., Akkartal, E., 2009. Regression tree analysis for predicting slaughter weight in broilers. Italian Journal of Animal Science, 8, 615-624.
doi: 10.4081/ijas.2009.615
Mibody, M.A.E., Iranmanesh, M., Motamedi-Mojdehi, R., Meymandi, M.G., 2016. Quantitative genetic analysis of Dromedary camel (Camelus dromedaris) under pastoral management in central desert of Iran, International Journal of Advanced Biological and Biomedical Research 4(1), 10–14.
doi: 10.18869/IJABBR.2016.10
Milborrow, 2019 Derived from mda:mars by Trevor Hastie and Rob Tibshirani. Uses Alan Miller’s Fortran utilities with Thomas Lumley’s leaps wrapper. 2019. earth: multivariate adaptive regression splines. R package version 5.1.2. https://CRAN.R-project.org/package=earth .
Minitab 19 Statistical Software. 2020. [Computer software]. State 761 College, PA: Minitab, Inc. ( https://www.minitab.com ) Trial version.
Mungai, I.P., Githaiga, W.P., Margaret, W.M.M., 2010. Correlation of actual live weight and estimates of live weights of Camel calves (Camelus dromedarius) in Samburu District of Northern Kenya, Journal of Camelid Science 3: 26-32.
Olfaz, M., Tirink, C. and Onder, H., 2018. Use of CART and CHAID algorithms in Karayaka sheep breeding, Journal of Faculty of Veterinary Medicine, Kafkas University, 25, 105-110.
R Core Team. 2019. R: A language and environment for statistical computing. R Foundation for Statistical Computing, R version: 3.6.2. Vienna, Austria. URL: https://www.R-project.org/ .
Revelle, W. 2020. psych: procedures for personality and psychological research. Northwestern University, Evanston, Illinois, USA. Version = 2.0.12. https://CRAN.R-project.org/package=psych .
Sahraei, M. A., Duman, H., Çodur, M. Y., Eyduran, E. 2021. Prediction of transportation energy demand: multivariate adaptive regression splines. Energy, 120090. https://doi.org/10.1016/j.energy.2021.1200902021.120090 .
Tyasi, T.L., Eyduran, E. & Celik, S., 2021. Comparison of tree-based regression tree methods for predicting live body weight from morphological traits in Hy-line silver brown commercial layer and indigenous Potchefstroom Koekoek breeds raised in South Africa. Tropical Animal Health and Production 53, 7. https://doi.org/10.1007/s11250-020-02443-y .
doi: 10.1007/s11250-020-02443-y
Wilson, R. T., 1978. Studies on the livestock of Southern Darfur, Sudan. V. Notes on camels. Tropical Animal Health and Production, 10, 19–25.
doi: 10.1007/BF02235296
Yagil, R., 1994. The Camel in Today’s World. A Handbook for Camel Breeding, Deutsche Welthungerhilfe, Bonn, Germany.
Zaborski, D., Ali, M., Eyduran, E., Grzesiak, W., Tariq, M.M., Abbas, F., Waheed, A., Tirink, C., 2019. Prediction of selected reproductive traits of indigenous Harnai sheep under the farm management system via various data mining algorithms. Pakistan Journal of Zoology, 51, 421-431.
doi: 10.17582/journal.pjz/2019.51.2.421.431