Discriminant Analysis for Predicting Dystocia in Beef Cattle. II. Derivation and Validation of a Prebreeding Prediction Model

Abstract

Discriminant analysis was utilized to derive and validate a model for predicting dystocia using only data available at the beginning of the breeding season. Data were collected from 211 Chianina crossbred cows (2 to 6 yr old) bred to Chianina bulls. A proportionally stratified sampling procedure divided females into an analysis sample (n = 134) on which the model was derived and a hold-out sample (n = 77) on which the prediction model was validated (tested). Variables available during the derivation stage were cow age, cow weight, pelvic height, pelvic width, pelvic area and calf sire. Dystocia was categorized as either unassisted or assisted. Occurrence of dystocia was 17.2 and 18.2% in the analysis and hold-out samples, respectively. All data were standardized to a mean of zero and a variance of one before statistical analysis. The centroid of cows experiencing dystocia differed (P less than .01) from that of cows calving unassisted in the analysis sample. Significant variables were pelvic area and cow age (standardized coefficients = .56 and .51, respectively). This model correctly classified 85.1% of the cows in the analysis sample. This was 13.5% greater than the proportional chance criterion. For model validation, prediction accuracy was 84.4% in the hold-out group, which was 14.2% greater than the proportional chance criterion. However, only 57.1% of the cows that experienced dystocia were correctly classified. Examination of the data revealed that those cows misclassified were 3 yr of age or older.(ABSTRACT TRUNCATED AT 250 WORDS)

Detection of difficult calvings in dairy cows using neural classifier

“…In the study on the oestrus detection in cows (Krieter et al 2006) sensitivity amounted to 0.78, whereas in the lameness detection in cattle (Pastell & Kujala 2007) 100 % of cows were correctly diagnosed. In the studies on the application of discriminant analysis the level of sensitivity amounted to 0.05-0.32 (Basarab et al 1993), 0.39 (Montgomery et al 1987) and 0.57 (Morrison et al 1985b), so it was relatively low and similar to the values for CF in the present study.…”

“…In the study by Krieter et al (2006) the percentage of correctly classified cows without oestrus was 100 %, whereas Pastell & Kujala (2007) obtained the percentage of correctly indicated cows without lameness amounting to 58 %. For discriminant analysis, specificity was 0.90 (Morrison et al 1985b), 0.91 (Montgomery et al 1987) and 0.73-0.99 (Basarab et al 1993). The specificity obtained in the present study was in general similar to the aforementioned values, which proves certain »carefulness« of the neural classifiers in the indication of classes comprising animals without calving problems, mastitis or lameness, whereas the strictly statistical methods did not show such »carefulness«, which is disadvantageous from the breeder's point of view.…”

Risk factors and rates of perinatal and postnatal mortality in cattle in Switzerland

“…The decrease in the mortality rate seen in calves born to dams up to the age of 5 years is possibly related to a loss of elastic strength in soft tissue structures of the birth canal because of previous stretching of the tissue. In younger cows, an immature pelvis may result in dystocia because of feto-maternal disproportion (Morrison et al, 1985). Potentially, cows with a history of dystocia were eliminated from the herd earlier than those with normal births.…”

Relationships of Pelvic Structure, Body Measurements, Pelvic Area and Calving Difficulty

“…Discriminant analysis is the appropriate multivariate technique for classifying cases in known groups. Morrison et al (1985b) reported that discriminant analysis was capable of predicting a large percentage of dystocia cases. In contrast, Rutter et al, (1983) found a discriminant model no better than a regression analysis for predicting CD.…”