CHAID AND LOGISTIC REGRESSION APPROACHES FOR ASSESSING THE EFFECTS OF NON-GENETIC FACTORS ON LAMB MORTALITY
M. Topal1, E. Emsen2 and A. M. Yağanoğlu3
1 Department of Biostatistics, Kastamonu Faculty of Medicine, Kastamonu University, Kastamonu, Turkey.
2 Animal Raising Unit, Department of Animal Science, Faculty of Agriculture, Ataturk University, Erzurum, Turkey.
3 Biometry and Genetics Unit, Department of Animal Science, Faculty of Agriculture, Ataturk University, Erzurum, Turkey.
Corresponding Author e-mail: email@example.com
Lamb output from the ewe flock is a key determinant of the profitability of sheep farming. Here, we assessed the association between various factors (ewe breed, month of birth, year of birth, birth type, lamb sex and lamb birth weight) on lamb mortality (within the first 60 days of life) using data collected in northern Turkey between 2006 and 2014. The study included a total of 1958 lambs, including the Romanov (R), Awassi (I), Kivircik (K), Tuj (T), Anarom (AN), R×I (Romanov×Awassi), R×K (Romanov×Kivircik), R×A (Romanov×Akkaraman), R×M (Romanov×Morkaraman) and F1 Romanov (Romanov× Turkish native) breeds. CHAID (Chi-Square Automatic Interaction Detector) analysis correctly classified 99.2% of surviving lambs and 12.4% of dying lambs, while 100% of surviving lambs and no dying lambs were correctly classified by logistic regression analysis. CHAID and logistic regression analyses correctly determined 91.5% and 91.1% of lamb mortality, respectively. The most important variables for the estimation of lamb mortality in the CHAID and logistic regression models were month of birth and lamb breed. Based on our findings, we propose that the CHAID algorithm (AUC of 0.843) is better to classify lamb mortality than a logistic regression analysis approach (AUC of 0.613).
Key words: Lamb mortality, Ewe breed, CHAID, logistic regression.