Use of bootstrapped, regularised regression to identify factors associated with lamb-derived revenue on commercial sheep farms

Lima, Eliana; Green, Martin; Lovatt, Fiona; Davies, Peers; King, Lis; Kaler, Jasmeet

Abstract

The profitability of UK sheep farms is variable with many farms making a net loss. For economic sustainability, farms have to be profitable, therefore it is important to maximise income whilst controlling costs. The most important source of income in sheep flocks is from lamb production but there is little information on factors that explain variability between farms in revenue from lamb sales. The aim of this research was to identify farm, farmer and management factors likely to have the largest, most reliable associations with lamb-derived revenue. From a population of 830 sheep farms, 408 farmers completed an online questionnaire comprising over 300 variables. Total lamb-derived revenue was calculated for each farm using abattoir information including carcass classification. The median flock size was 560 ewes, median land size 265 acres, median revenue per acre from lambs sold was £197 (IQR = 120–296) and median revenue per ewe £95 (IQR = 72–123). A robust analytic approach using regularised (elastic net) regression with bootstrapping was implemented to account for multicollinearity in the data and to reduce the likelihood of model over-fitting. To provide model inference and allow ranking of variables in terms of relevance, covariate stability and coefficient distributions were evaluated. Factors with high stability and relatively large positive associations with revenue per acre were (median effect size (£); 95 % bootstrap probability interval); an increased stocking rate of 0.2 ewe/acre (13; 6–17), fertilizer being used on most of the grazing land (18; 0.1–37), the use of rotational grazing (13; 0.3–34), decreased proportion of ewes with prolapses (4; 0.3–9), separation of lame sheep from the rest of the flock (16; 0.9–37), selecting ewes for culling based on prolapses (20; 0.2–55) and infertility (20; 2–46), conducting body condition scoring of ewes at lambing (28; 3–58), early lactation (21; 1–54) or weaning (25; 2–70), increased farmer education (20; 2–54) and farmers with a positive business attitude (15; 0.2–38). Additional factors with a high stability and relatively large associations with increased revenue per ewe were; never trimming diseased feet of lame ewes (9; 0.8–22) and making use of farm records (5; 0.3–12). This is the first study in animal health epidemiology to use bootstrapped regularised regression to evaluate a wide dataset to provide a ranking of the importance of explanatory covariates. We conclude that the relatively small set of variables identified, with a potentially large influence on lamb-derived revenue, should be considered prime candidates for future intervention studies.

Citation

Lima, E., Green, M., Lovatt, F., Davies, P., King, L., & Kaler, J. (2020). Use of bootstrapped, regularised regression to identify factors associated with lamb-derived revenue on commercial sheep farms. Preventive Veterinary Medicine, 174, Article 104851. https://doi.org/10.1016/j.prevetmed.2019.104851

• Journal Article Type: Article
• Acceptance Date: Nov 18, 2019
• Online Publication Date: Nov 19, 2019
• Publication Date: 2020-01
• Deposit Date: Dec 11, 2019
• Publicly Available Date: Nov 20, 2020
• Journal: Preventive Veterinary Medicine
• Print ISSN: 0167-5877
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 174
• Article Number: 104851
• DOI: https://doi.org/10.1016/j.prevetmed.2019.104851
• Keywords: Food Animals; Animal Science and Zoology
• Public URL: https://nottingham-repository.worktribe.com/output/3535971
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0167587719305495
• Additional Information: This article is maintained by: Elsevier; Article Title: Use of bootstrapped, regularised regression to identify factors associated with lamb-derived revenue on commercial sheep farms; Journal Title: Preventive Veterinary Medicine; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.prevetmed.2019.104851; Content Type: article; Copyright: © 2019 Elsevier B.V. All rights reserved.