PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2011 | 29 | 4 |
Tytuł artykułu

Detection of difficult conceptions in dairy cows using selected data mining methods

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In the present study, the detection of difficult conceptions in dairy cows using selected data mining methods – na ve Bayes classifier (NBC) and regression and classifications trees (CART) is presented. The set of 11 diagnostic variables was used, which included, among others, number of lactation, artificial insemination (AI) season, age of inseminated cow, proportion of HF-genes in cow genotype, sex of calf from preceding calving, length of pregnancy, milk, protein and fat yield. Two conception classes were distinguished: the GOOD class, if a cow conceived after one or two AIs and the “POOR” class, if more than two AIs per conception were required. The models were characterized by capability of predicting the membership of conceptions to either class. Correctness of predictions was 83%. CART proved to be more precise in detecting conceptions of the POOR class (sensitivity) compared with predictions by NBC (P≤0.01). Specificity was similar for both classifiers (90% and 93%). Among the variables determining conception class, calving-to-conception interval, calving interval and the difference between the mean body condition and condition at AI were the most significant variables for CART. Utilization of these classifiers, particularly of CART, may help a breeder to appropriately prepare cows for AI, thus contributing to the improved financial results of a herd.
Wydawca
-
Rocznik
Tom
29
Numer
4
Opis fizyczny
p.293-302,fig.,ref.
Twórcy
autor
  • Department of Ruminants Science, West Pomeranian University of Technology, Doktora Judyma 10, 71-460 Szczecin, Poland
autor
  • Department of Ruminants Science, West Pomeranian University of Technology, Doktora Judyma 10, 71-460 Szczecin, Poland
autor
  • Department of Ruminants Science, West Pomeranian University of Technology, Doktora Judyma 10, 71-460 Szczecin, Poland
autor
  • Department of Ruminants Science, West Pomeranian University of Technology, Doktora Judyma 10, 71-460 Szczecin, Poland
Bibliografia
  • DE’ATH G., FABRICIUS K.E., 2000 – Classifications and regression trees: a powerful yet simple technique for ecological data analysis. Ecology 81, 3178-3192.
  • DOMECQ J.J., SKIDMORE A.L., LLOYD J.W., KANEENE J.B., 1997 – Relationship between body condition scores and conception at first artificial insemination in a large dairy herd of high yielding Holstein cows. Journal of Dairy Science 80, 113-120.
  • FERGUSON J.D., GALLIGAN D.T., THOMSEN N., 1994 – Principal descriptors of body condition in Holstein dairy cattle. Journal of Dairy Science 77, 2695-2703.
  • FRAWLEY W., PIATETSKY-SHAPIRO G., MATHEUS C., 1992 – Knowledge Discovery in Databases: An Overview. AI Magazine 13, 57-70.
  • GRZESIAK W., SABLIK P., ZABORSKI D., ŻUKIEWICZ A., DYBUS A., SZATKOWSKA I., 2009 – Zastosowanie metody MARS do klasyfikowania zabiegów inseminacyjnych u bydła mlecznego (Application of MARS method in classification of inseminations of dairy cattle). In Polish, summary in English. Roczniki Naukowe Polskiego Towarzystwa Zootechnicznego 5, 43-56.
  • GRZESIAK W., ZABORSKI D., SABLIK P., ŻUKIEWICZ A., DYBUS A., SZATKOWSKA I., 2010 – Detection of cows with insemination problems using selected classification models. Computers and Electronics in Agriculture 74, 265- 273.
  • HASTIE T., TIBSHIRANI R., FRIEDMAN J., 2001 – The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics), 271.
  • JANUŚ E., BORKOWSKA E., 2006 – Wielkość podstawowych wskaźników płodności u krów o różnej wydajności mlecznej (Selected indices of fertility of cows of different milk production). In Polish, Summary in English. Annales Universitatis Mariae Curie-Skłodowska Lublin – Polonia.Sectio EE 24, 33-37.
  • JAŚKOWSKI J.M., SZENFELD J., 1999 – Wpływ ilości i jakości nasienia oraz techniki unasienniania na wyniki zacieleń krów (The influence of the quantity and quality of semen and insemination techniques on results of pregnancies in cows). In Polish, Summary in English. Medycyna Weterynaryjna 55, 160-162.
  • LAROSE D. T., 2006 – Data Mining Methods and Models. John Wiley & Sons, Inc. Hoboken, 228-252.
  • MALDONADO-CASTILLO I., ERAMIAN M.G., PIERSON R.A., SINGH J., ADAMS G.P., 2007 – Classification of bovine reproductive cycle phase using ultrasound-detected features. Fourth Canadian Conference on Computer and Robot Vision (CRV 2007) 28-30 May 2007, Montreal,Quebec, Canada, pp. 258-265.
  • MORRISON D.G., HUMES P.E., KEITH N.K., GODKE R.A., 1985a – Discriminant analysis for predicting dystocia in beef cattle. I. Comparison with regression analysis. Journal of Animal Science 60, 608-616.
  • MORRISON D.G., HUMES P.E., KEITH N.K., GODKE R.A., 1985b – Discriminant analysis for predicting dystocia in beef cattle. II. Derivation and validation of a prebreeding prediction model.Journal of Animal Science 60, 617-621.
  • NEBEL R.L., MCGILLIARD M.L., 1993 – Interactions of high milk yield and reproductive performance in dairy cows. Journal of Dairy Science 76, 3257-3268.
  • PIETERSMA D., LACROIX R., LEFEBVRE D., WADE K.M., 2002 – Decision-tree induction to interpret lactation curves. Canadian Biosystems Engineering/Le génie des biosystemes au Canada 44, 7.1-7.13.
  • PIWCZYŃSKI D., 2009 – Using classification trees in statistical analysis of discrete sheep reproduction traits. Journal of Central European Agriculture 10, 303-310.
  • SORENSEN J.T., OSTERGAARD S., 2003 – Economic consequences of postponed first insemination of cows in a dairy cattle herd. Livestock Production Science 79, 145–153.
  • STATSOFT, INC. (2007). STATISTICA (data analysis software system), version 8.0.
  • SUGIURA N., 1978 – Further analysis of the data by Akaike’s information criterion and the finite corrections. Communications in Statistics – Theory and Methods A7, 13-26.
  • THIRUNAVUKKARASU M., KATHIRAVAN G., 2006 – Predicting the probability of conception in artificially inseminated bovines – A logistic regression analysis. Journal of Animal and Veterinary Advances 5, 522-527.
  • TITTONELL P., SHEPHERD K.D., VANLAUWE B., GILLER K.E., 2008 – Unravelling the effects of soil and crop management on maize productivity in smallholder agricultural systems of western Kenya—An application of classification and regression tree analysis. Agriculture, Ecosystems and Environment 123, 137–150.
  • WHITE M.E., GLICKMAN L.T., BARNES-PALLESEN F.D., STEM E.S. 3RD, DINSMORE P.,POWERS M.S., POWERS P., SMITH M.C., MONTGOMERY M.E., JASKO D., 1986 – Accuracy of a discriminant analysis model for prediction of coliform mastitis in dairy cows and a comparison with clinical prediction. Cornell Veterinarian 76, 342-347.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.agro-7cda6280-b9ea-4eaa-8fbd-3b0d65b8e6fc
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.