Type

Journal Article

Authors

John Dunnion
Michael L Doherty
Stephen Butler
Luke O'Grady
Caroline Fenlon

Subjects

Environment

Topics
pasture based sensitivity and specificity random effects seasonal calving logistic regression high sensitivity sensitivity analysis model building

The creation and evaluation of a model to simulate the probability of conception in seasonal-calving pasture-based dairy heifers. (2017)

Abstract Herd fertility in pasture-based dairy farms is a key driver of farm economics. Models for predicting nulliparous reproductive outcomes are rare, but age, genetics, weight, and BCS have been identified as factors influencing heifer conception. The aim of this study was to create a simulation model of heifer conception to service with thorough evaluation. Artificial Insemination service records from two research herds and ten commercial herds were provided to build and evaluate the models. All were managed as spring-calving pasture-based systems. The factors studied were related to age, genetics, and time of service. The data were split into training and testing sets and bootstrapping was used to train the models. Logistic regression (with and without random effects) and generalised additive modelling were selected as the model-building techniques. Two types of evaluation were used to test the predictive ability of the models: discrimination and calibration. Discrimination, which includes sensitivity, specificity, accuracy and ROC analysis, measures a model's ability to distinguish between positive and negative outcomes. Calibration measures the accuracy of the predicted probabilities with the Hosmer-Lemeshow goodness-of-fit, calibration plot and calibration error. After data cleaning and the removal of services with missing values, 1396 services remained to train the models and 597 were left for testing. Age, breed, genetic predicted transmitting ability for calving interval, month and year were significant in the multivariate models. The regression models also included an interaction between age and month. Year within herd was a random effect in the mixed regression model. Overall prediction accuracy was between 77.1% and 78.9%. All three models had very high sensitivity, but low specificity. The two regression models were very well-calibrated. The mean absolute calibration errors were all below 4%. Because the models were not adept at identifying unsuccessful services, they are not suggested for use in predicting the outcome of individual heifer services. Instead, they are useful for the comparison of services with different covariate values or as sub-models in whole-farm simulations. The mixed regression model was identified as the best model for prediction, as the random effects can be ignored and the other variables can be easily obtained or simulated.
Collections Ireland -> Teagasc -> PubMed

Full list of authors on original publication

John Dunnion, Michael L Doherty, Stephen Butler, Luke O'Grady, Caroline Fenlon

Experts in our system

 
2
Michael L. Doherty
Teagasc
Total Publications: 119
 
3
S T Butler
Teagasc
Total Publications: 89
 
4
Luke O'Grady
University College Dublin
Total Publications: 54
 
5
Caroline Fenlon
Teagasc
Total Publications: 7