Application of classification trees in determining the impact of phenotypic factors on conception to first service in Holstein cattle
Heydar Ghiasi A D , Dariusz Piwczyński B , Majid Khaldari C and Magdalena Kolenda B
+ Author Affiliations
- Author Affiliations
A Department of Animal Science, Faculty of Agricultural Science, Payame Noor University, Tehran, Iran.
B Department of Genetics and General Animal Breeding, Faculty of Biology and Animal Breeding, UTP University of Science and Technology in Bydgoszcz, Poland, Mazowiecka Street 28, 85-084 Bydgoszcz, Poland.
C Department of Animal Science, Faculty of Agricultural Science, Lorestan University, Khorramabad – 68159, Lorestan, Iran.
D Corresponding author. Email: ghiasei@gmail.com
Animal Production Science 56(7) 1061-1069 https://doi.org/10.1071/AN13541
Submitted: 18 December 2013 Accepted: 20 January 2015 Published: 1 April 2015
Abstract
The aim of the study was to evaluate the impact of phenotypic factors on the probability of conception to first service. The study analysed 49 317 lactation records gathered between 1995 and 2007 in Iran, detailing six consecutive lactations in 13 herds of Holstein cows. The impact of phenotypic factors on the conception to first service was evaluated using the classification tree technique. The CHAID algorithm with the Pearson Chi-squared test was used as the division criterion. The resulting tree was six levels deep and consisted of 22 leaves. The greatest divisions in the tree occurred based on the year of calving (five divisions), herd and parity (four divisions each), level of milk production and age at calving (three divisions each). Fewer divisions occurred based on the month when the first insemination was performed (two divisions). The tree structure obtained in the present study allowed identification of combinations of levels of factors associated with highest probabilities of conception. The highest rate of conception to first service was recorded for cows that: while calving were younger than 5 years old; were in herds 2, 4, 5, 7, 8, 12 and 14; and were inseminated in January, February, March, April, May and November. Those cows were at least in their third lactation.
Additional keywords: classification tree, fertility, phenotypic factor.
References
Abdollahi-Arpanahi R, Peñagaricano F, Aliloo H, Ghiasi H, Urioste J (2013) Comparison of poisson, probit and linear models for genetic analysis of number of inseminations to conception and success at first insemination in Iranian Holstein cows. Livestock Science 153, 20–26.| Comparison of poisson, probit and linear models for genetic analysis of number of inseminations to conception and success at first insemination in Iranian Holstein cows.Crossref | GoogleScholarGoogle Scholar |
Ansari-Lari M, Kafi M, Sokhtanlo M, Ahmadi HN (2010) Reproductive performance of Holstein dairy cows in Iran. Tropical Animal Health and Production 42, 1277–1283.
| Reproductive performance of Holstein dairy cows in Iran.Crossref | GoogleScholarGoogle Scholar | 20373021PubMed |
Austin PC (2007) A comparison of regression trees, logistic regression, generalized additive models, and multivariate adaptive regression splines for predicting AMI mortality. Statistics in Medicine 26, 2937–2957.
| A comparison of regression trees, logistic regression, generalized additive models, and multivariate adaptive regression splines for predicting AMI mortality.Crossref | GoogleScholarGoogle Scholar | 17186501PubMed |
Bagnato A, Oltenacu P (1993) Genetic study of fertility traits and production in different parities in Italian Friesian cattle. Journal of Animal Breeding and Genetics 110, 126–134.
| Genetic study of fertility traits and production in different parities in Italian Friesian cattle.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC3M3mtF2iuw%3D%3D&md5=8f4c8c91ceea2e3b9c16fd3654b1de27CAS | 21395710PubMed |
Bagnato A, Oltenacu P (1994) Phenotypic evaluation of fertility traits and their association with milk production of Italian Friesian cattle. Journal of Dairy Science 77, 874–882.
| Phenotypic evaluation of fertility traits and their association with milk production of Italian Friesian cattle.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DyaK2c3it12huw%3D%3D&md5=8a0bd213a894d1d0e0093822731b2a02CAS | 8169295PubMed |
Camdeviren HA, Yazici AC, Akkus Z, Bugdayci R, Sungur MA (2007) Comparison of logistic regression model and classification tree: an application to postpartum depression data. Expert Systems with Applications 32, 987–994.
| Comparison of logistic regression model and classification tree: an application to postpartum depression data.Crossref | GoogleScholarGoogle Scholar |
Camp NJ, Slattery ML (2002) Classification tree analysis: a statistical tool to investigate risk factor interactions with an example for colon cancer (United States). Cancer Causes & Control 13, 813–823.
| Classification tree analysis: a statistical tool to investigate risk factor interactions with an example for colon cancer (United States).Crossref | GoogleScholarGoogle Scholar |
De Martonne E (1926) Quoted by Thornthwaite CW and Holzman B. In Measurement of evaporation from land and water surface. USDA Technical Bulletin 817, 1–143.
Dohoo IR (1983) The effects of calving to first service interval on reproductive performance in normal cows and cows with postpartal disease. The Canadian Veterinary Journal. La Revue Veterinaire Canadienne 24, 343–346.
Eicker S, Gröhn Y, Hertl J (1996) The association between cumulative milk yield, days open, and days to first breeding in New York Holstein cows. Journal of Dairy Science 79, 235–241.
| The association between cumulative milk yield, days open, and days to first breeding in New York Holstein cows.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK28Xit1Kltbw%3D&md5=9552182c9f6dd15e714eba1f774d0d59CAS | 8708085PubMed |
Erb H, Martin S, Ison N, Swaminathan S (1981) Interrelationships between production and reproductive diseases in Holstein cows. Conditional relationships between production and disease. Journal of Dairy Science 64, 272–281.
| Interrelationships between production and reproductive diseases in Holstein cows. Conditional relationships between production and disease.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DyaL38%2FgvVCjsQ%3D%3D&md5=92998f4c860ae811843d5bf7c5c0fc4dCAS | 7196921PubMed |
Eyduran E, Yilmaz I, Tariq M, Kaygisiz A (2013) Estimation of 305-d milk yield using regression tree method in Brown Swiss Cattle. JAPS Journal of Animal and Plant Sciences 23, 731–735.
Fourichon C, Seegers H, Malher X (2000) Effect of disease on reproduction in the dairy cow: a meta-analysis. Theriogenology 53, 1729–1759.
| Effect of disease on reproduction in the dairy cow: a meta-analysis.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BD3cvpsFensQ%3D%3D&md5=31f6e42644dde45b8fcaa2a9cf6cef7aCAS | 10968418PubMed |
Fouz R, Gandoy F, Sanjuán ML, Yus E, Diéguez FJ (2011) Factors associated with 56-day non-return rate in dairy cattle. Pesquisa Agropecuaria Brasileira 46, 648–654.
| Factors associated with 56-day non-return rate in dairy cattle.Crossref | GoogleScholarGoogle Scholar |
Ghiasi H, Pakdel A, Nejati-Javaremi A, Mehrabani-Yeganeh H, Honarvar M, González-Recio O, Carabaño MJ, Alenda R (2011) Genetic variance components for female fertility in Iranian Holstein cows. Livestock Science 139, 277–280.
| Genetic variance components for female fertility in Iranian Holstein cows.Crossref | GoogleScholarGoogle Scholar |
González-Recio O, Alenda R (2005) Genetic parameters for female fertility traits and a fertility index in Spanish dairy cattle. Journal of Dairy Science 88, 3282–3289.
| Genetic parameters for female fertility traits and a fertility index in Spanish dairy cattle.Crossref | GoogleScholarGoogle Scholar | 16107418PubMed |
González-Recio O, Pérez-Cabal M, Alenda R (2004) Economic value of female fertility and its relationship with profit in Spanish dairy cattle. Journal of Dairy Science 87, 3053–3061.
| Economic value of female fertility and its relationship with profit in Spanish dairy cattle.Crossref | GoogleScholarGoogle Scholar | 15375068PubMed |
Grzesiak W, Zaborski D, Sablik P, Pilarczyk R (2011) Detection of difficult conceptions in dairy cows using selected data mining methods. Animal Science Papers and Reports 29, 293–302.
Hammoud M, El-Zarkouny S, Oudah E (2010) Effect of sire, age at first calving, season and year of calving and parity on reproductive performance of Friesian cows under semiarid conditions in Egypt. Archiva Zootechnica 13, 60–82.
Hillers J, Senger P, Darlington R, Fleming W (1984) Effects of production, season, age of cow, days dry, and days in milk on conception to first service in large commercial dairy herds. Journal of Dairy Science 67, 861–867.
| Effects of production, season, age of cow, days dry, and days in milk on conception to first service in large commercial dairy herds.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DyaL2c3htlCqsg%3D%3D&md5=210c291d10d8a11e0953d074e81ff7e2CAS | 6539346PubMed |
Inchaisri C, Jorritsma R, Vernooij J, Vos P, Van Der Weijden G, Hogeveen H (2011) Cow effects and estimation of success of first and following inseminations in Dutch dairy cows. Reproduction in Domestic Animals 46, 1043–1049.
| Cow effects and estimation of success of first and following inseminations in Dutch dairy cows.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC3MbntV2ksw%3D%3D&md5=118f4bbf8dd31dda4986623709a5b52eCAS | 21470315PubMed |
Kass GV (1980) An exploratory technique for investigating large quantities of categorical data. Applied Statistics 29, 119–127.
| An exploratory technique for investigating large quantities of categorical data.Crossref | GoogleScholarGoogle Scholar |
Lasek M (2007) ‘Metody Data Mining w analizowaniu i prognozowaniu kondycji ekonomicznej przedsiębiorstw: Zastosowania SAS Enterprise Miner.’ (Difin SA Publishing: Warsaw)
Lemon SC, Roy J, Clark MA, Friedmann PD, Rakowski W (2003) Classification and regression tree analysis in public health: methodological review and comparison with logistic regression. Annals of Behavioral Medicine 26, 172–181.
| Classification and regression tree analysis in public health: methodological review and comparison with logistic regression.Crossref | GoogleScholarGoogle Scholar | 14644693PubMed |
Meyer C, Berger P, Koehler K (2000) Interactions among factors affecting stillbirths in Holstein cattle in the United States. Journal of Dairy Science 83, 2657–2663.
| Interactions among factors affecting stillbirths in Holstein cattle in the United States.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3cXosVClsbw%3D&md5=e4008c8cf77670ac2d1c86f8c0a6b077CAS | 11104286PubMed |
Miller R, Clay J, Norman H (2001) Relationship of somatic cell score with fertility measures. Journal of Dairy Science 84, 2543–2548.
| Relationship of somatic cell score with fertility measures.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3MXptVGltb0%3D&md5=f0b9e5773334e06eff1f877f9b0339afCAS | 11768097PubMed |
Piwczyński D, Sitkowska B (2012) Statistical modelling of somatic cell counts using the classification tree technique. Archiv fur Tierzucht 55, 332–345.
Piwczyński D, Sitkowska B, Wiśniewska E (2012) Application of classification trees and logistic regression to determine factors responsible for lamb mortality. Small Ruminant Research 103, 225–231.
| Application of classification trees and logistic regression to determine factors responsible for lamb mortality.Crossref | GoogleScholarGoogle Scholar |
Piwczyński D, Nogalski Z, Sitkowska B (2013) Statistical modeling of calving ease and stillbirths in dairy cattle using the classification tree technique. Livestock Science 154, 19–27.
| Statistical modeling of calving ease and stillbirths in dairy cattle using the classification tree technique.Crossref | GoogleScholarGoogle Scholar |
Rauw W, Kanis E, Noordhuizen-Stassen E, Grommers F (1998) Undesirable side effects of selection for high production efficiency in farm animals: a review. Livestock Production Science 56, 15–33.
| Undesirable side effects of selection for high production efficiency in farm animals: a review.Crossref | GoogleScholarGoogle Scholar |
Razmkabir M, Nejati-Javaremi A, Moradi-Shahrbabak M, Rashidi A, Sayadnejad M (2006) Estimation of genetic trends for production traits in Holstein cattle of Iran. In ‘Proceedings of the 8th world congress on genetics applied to livestock production’. Communication. pp. 01-3. (WCGALP Publishing: Belo Horizonte, Brazil)
Rodriguez-Martinez H, Hultgren J, Båge R, Bergqvist A, Svensson C, Bergsten C, Lidfors L, Gunnarsson S, Algers B, Emanuelson U (2008) Reproductive performance in high-producing dairy cows: can we sustain it under current practice. In ‘IVIS reviews in veterinary medicine’. (Ithaca, NY) Available at http://www.ivis.org [Verified 17 March 2015]
SAS Institute Inc. (2013) ‘Getting started with SAS Enterprise Miner 12.3.’ (SAS Institute Inc.: Cary, NC)
Sasser H, Nussbaum M, Beuhler M, Ford M (2008) Classification tree methods for development of decision rules for botulism and cyanide poisoning. Journal of Medical Toxicology; Official Journal of the American College of Medical Toxicology 4, 77–83.
| Classification tree methods for development of decision rules for botulism and cyanide poisoning.Crossref | GoogleScholarGoogle Scholar | 18570166PubMed |
Sönmez M, Demirci E, Türk G, Gür S (2005) Effect of season on some fertility parameters of dairy and beef cows in Elazığ province. Turkish Journal of Veterinary and Animal Sciences 29, 821–828.
Spangler M, Sapp R, Rekaya R, Bertrand J (2006) Success at first insemination in Australian Angus cattle: analysis of uncertain binary responses. Journal of Animal Science 84, 20–24.
Sun C, Su G (2010) Comparison on models for genetic evaluation of non-return rate and success in first insemination of the Danish Holstein cows. Livestock Science 127, 205–210.
| Comparison on models for genetic evaluation of non-return rate and success in first insemination of the Danish Holstein cows.Crossref | GoogleScholarGoogle Scholar |
Yilmaz I, Eyduran E, Kaygisiz A (2013) Determination of non-genetic factors influencing birth weight using regression tree method in brown-swiss cattle. Canadian Journal of Applied Sciences 1, 382–387.
