Application of Machine Learning Algorithms to Predict Body Condition Score from Liveweight Records of Mature Romney Ewes

Jimmy Semakula, Rene A. Corner-Thomas, Stephen T. Morris, Hugh T. Blair and Paul R. Kenyon
Additional contact information
Jimmy Semakula: School of Agriculture and Environment, Massey University, Private Bag 11222, Palmerston North 4410, New Zealand
Rene A. Corner-Thomas: School of Agriculture and Environment, Massey University, Private Bag 11222, Palmerston North 4410, New Zealand
Stephen T. Morris: School of Agriculture and Environment, Massey University, Private Bag 11222, Palmerston North 4410, New Zealand
Hugh T. Blair: School of Agriculture and Environment, Massey University, Private Bag 11222, Palmerston North 4410, New Zealand
Paul R. Kenyon: School of Agriculture and Environment, Massey University, Private Bag 11222, Palmerston North 4410, New Zealand

Agriculture, 2021, vol. 11, issue 2, 1-20

Abstract: Body condition score (BCS) in sheep ( Ovis aries ) is a widely used subjective measure of the degree of soft tissue coverage. Body condition score and liveweight are statistically related in ewes; therefore, it was hypothesized that BCS could be accurately predicted from liveweight using machine learning models. Individual ewe liveweight and body condition score data at each stage of the annual cycle (pre-breeding, pregnancy diagnosis, pre-lambing and weaning) at 43 to 54 months of age were used. Nine machine learning (ML) algorithms (ordinal logistic regression, multinomial regression, linear discriminant analysis, classification and regression tree, random forest, k-nearest neighbors, support vector machine, neural networks and gradient boosting decision trees) were applied to predict BCS from a ewe’s current and previous liveweight record. A three class BCS (1.0–2.0, 2.5–3.5, >3.5) scale was used due to high-class imbalance in the five-scale BCS data. The results showed that using ML to predict ewe BCS at 43 to 54 months of age from current and previous liveweight could be achieved with high accuracy (>85%) across all stages of the annual cycle. The gradient boosting decision tree algorithm (XGB) was the most efficient for BCS prediction regardless of season. All models had balanced specificity and sensitivity. The findings suggest that there is potential for predicting ewe BCS from liveweight using classification machine learning algorithms.

Keywords: accuracy; predictor; models; classification (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/11/2/162/pdf (application/pdf)
https://www.mdpi.com/2077-0472/11/2/162/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:11:y:2021:i:2:p:162-:d:500771

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().