Statistical modeling of calving ease and stillbirths in dairy cattle using the classification tree technique

Piwczyński, Dariusz; Nogalski, Zenon; Sitkowska, Beata

doi:10.1016/j.livsci.2013.02.013

Cited by 31 publications

(53 citation statements)

References 34 publications

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“…It was similar to that (61.50%) reported by Piwczyński et al (2013), who established four different categories of calving difficulty. It was also comparable to the accuracy (50 to 60.20%) recorded by Johnson et al (1988), who studied the possibility of dystocia detection (with the fi ve classes of calving diffi culty) in Hereford heifers using discriminant function analysis.…”

Section: Zaborski D Et Al Data Mining For Calving Diffi Cultysupporting

confidence: 90%

“…In the above-mentioned study, the last considered splitting variable was management system. It should be noted that although the CART and CHAID trees in our study and that by Piwczyński et al (2013) utilized a similar set of independent variables, the fi nal structure of the resulting decision trees was somewhat diff erent. Obviously, some factors described by Piwczyński et al (2013; such as lactation number) were not available in our study, which included only heifers.…”

Section: Discussionmentioning

confidence: 84%

“…In the study by Piwczyński et al (2013) on the use of CART and CHAID for the analysis of signifi cant predictors of calving diffi culty in Polish HolsteinFriesian black-and-white cows, the fi rst division of the whole data set in the root node was based on lactation number. The two subsequent divisions were based on calf birth weight and the third one on pregnancy length and this variable was used for splitting twice (at the threshold values of 282 and 284 days, respectively).…”

Section: Discussionmentioning

confidence: 99%

“…One such approach involves the use of statistical methods, especially those from the fi eld of data mining. There are numerous data mining algorithms, some of which have already been applied to animal farming (Piwczyński et al, 2013). Decision trees, belonging to this group of algorithms, are characterized by a relatively easy interpretation and implementation.…”

Section: Introductionmentioning

confidence: 99%

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Comparison between data mining methods to assess calving difficulty in cattle

Zaborski

Proskura

Grzesiak

2017

Rev Colomb Cienc Pecu

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Comparison between data mining methods to assess calving diffi culty in cattle ¤ Comparación entre métodos de minería de datos para evaluar la difi cultad al parto en ganado AbstractBackground: Dystocia in cattle results in adverse consequences (increased calf morbidity and mortality, decreased fertility, and milk production, lower cow survival and reduced welfare) leading to considerable economic losses. Objective: To classify calvings in dairy cattle according to their diffi culty using selected data mining methods [classifi cation and regression trees (CART), chi-square automatic interaction detection trees (CHAID) and quick, unbiased, effi cient, statistical trees (QUEST)], and to identify the most signifi cant factors aff ecting calving diffi culty. The results of data mining methods were compared with those of a more traditional generalized linear model (GLM). Methods: A total of 1,342 calving records of Polish HolsteinFriesian black-and-white heifers from four farms were used. Calving diffi culty was divided into three categories (easy, moderate and diffi cult). Results: The percentages of calvings correctly classifi ed by CART, CHAID, QUEST, and GLM were as follows: 35.14, 18.92, 19.82, and 43.24% (easy), 68.70, 73.91, 81.74, and 41.74% (moderate), and 77.27, 85.45, 73.64, and 81.82% (diffi cult), respectively. The most important factors aff ecting calving diffi culty were bull's rank (based on the mean calving diffi culty score of its daughters), calving age, farm category (based on its mean milk yield) and calving season. Conclusion: All classifi cation models were satisfactory and could predict the class of calving diffi culty.

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Section: Zaborski D Et Al Data Mining For Calving Diffi Cultysupporting

confidence: 90%

Section: Discussionmentioning

confidence: 84%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison between data mining methods to assess calving difficulty in cattle

Zaborski

Proskura

Grzesiak

2017

Rev Colomb Cienc Pecu

View full text Add to dashboard Cite

show abstract

“…breed and parity), calf gender (Norman, et al, 2010) calf weight and management (e.g., housing and pre-calving movement) (Mee, et al, 2014, Piwczynski, et al, 2013. Farmers can influence these risk factors through management, for instance, by changing their breeding strategy but also by human supervision during the calving process.…”

Section: Introductionmentioning

confidence: 99%