The purpose of the study was to use decision trees to predict increased levels of somatic cells in cow's milk. The material for the study comprised data collected in 2012-2014 from five farms in Poland equipped with an automatic milking system. Data on 803 Polish Holstein-Friesian cows were collected. In order to predict somatic cell count data mining techniques were used to build a graphical model of a decision tree. This study found that the most important factors to anticipate an elevated somatic cell count in cow's milk are milk conductivity, lactation stage, and lactation (primiparous and multiparous cow groups), as well as milking speed and rumination time. An increase in these parameters was also associated with a higher percentage of samples with an elevated somatic cell count. It has been shown that in order to keep somatic cell count low in automatic milking system herds a farmer should pay particular attention to the milking speed.
Objective: The aim of the paper was to compare the fit of data derived from daily automatic milking systems (AMS) and monthly test-day records with the use of lactation curves; data was analysed separately for primiparas and multiparas.Methods: The study was carried out on three Polish Holstein-Friesians (PHF) dairy herds. The farms were equipped with an automatic milking system which provided information on milking performance throughout lactation. Once a month cows were also subjected to test-day milkings (method A4). Most studies described in the literature are based on test-day data; therefore, we aimed to compare models based on both test-day and AMS data to determine which mathematical model (Wood or Wilmink) would be the better fit.Results: Results show that lactation curves constructed from data derived from the AMS were better adjusted to the actual milk yield (MY) data regardless of the lactation number and model. Also, we found that the Wilmink model may be a better fit for modelling the lactation curve of PHF cows milked by an AMS as it had the lowest values of Akaike information criterion, Bayesian information criterion, mean square error, the highest coefficient of determination values, and was more accurate in estimating MY than the Wood model. Although both models underestimated peak MY, mean, and total MY, the Wilmink model was closer to the real values.Conclusion: Models of lactation curves may have an economic impact and may be helpful in terms of herd management and decision-making as they assist in forecasting MY at any moment of lactation. Also, data obtained from modelling can help with monitoring milk performance of each cow, diet planning, as well as monitoring the health of the cow.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.