Time series modeling of retained placenta, metritis, and ketosis in Holstein cows and heifers and its association with climate variables in a hot-arid zone
Abstract
Aim. To forecast the monthly percentage of Holstein cows and heifers at a high-input dairy farm experiencing retained placenta (RP), puerperal metritis (PM), and clinical ketosis (CK). Methods. An autoregressive integrated moving average (ARIMA) model was employed to predict future monthly cases of these diseases using time series data. These puerperal diseases were observed on a single dairy farm with 2560 to 3300 milking cows over seven years, from 2014 to 2020. Results. The highest predicted RP incidence in cows was in May (11.3%; 95% CI=6.3-16.4), while the lowest was in November (5.4%; 95% CI=0.5-10.4). For heifers, the peak predicted RP occurrence was in August (20.6%; 95% Cl=11.0-23.1), and the lowest was in December (10.5%; 95% CI=7.9-13.0). The highest projected CK occurrence in cows was in June (3.0%; 95% CI=1.8-4.3) and the lowest was in November (1.1%; 95% CI=-0.1-2.4). For heifers, CK was most likely in May (2.7%; 95% CI=0.9-4.5) and least in December (0.7%; 95% CI=-1.1-2.5). Conclusions. Both cows and heifers showed an increasing trend in RP, PM, and CK during summer months; ARIMA models effectively tracked disease trends throughout the year and can aid in health management decisions for dairy cows.
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