Abstract

Simple SummaryBarns equipped with the automatic milking system (AMS) record huge amounts of data on milk flow rate, milk yield and composition, milk temperature, amount of concentrate intake and rumination time. Our study attempted to use this information, recorded during the periparturient period (divided into subperiods: second (14–8 days) and first (7–1 days) week before calving; 1–4, 5–7, 8–14, 15–21 and 22–28 days of lactation), to predict lactation milk yield in Polish Holstein–Friesian cows. In the first stage of statistical analysis, coefficients of simple correlation between lactation milk yield and AMS parameters were calculated. We found that prediction of lactation milk yield based on individual pieces of data may be ineffective—the calculated coefficients of correlation were low or moderate. In the next step of data analysis, we used a modern data mining technique in the form of decision trees. Based on the graphic, easy-to-interpret decision tree, we concluded that the highest lactation yield is to be expected for cows with completed lactations (survived until the next lactation), which were milked 4.07 times per day on average in the 4th week of lactation.Early prediction of lactation milk yield enables more efficient herd management. Therefore, this study attempted to predict lactation milk yield (LMY) in 524 Polish Holstein–Friesian cows, based on information recorded by the automatic milking system (AMS) in the periparturient period. The cows calved in 2016 and/or 2017 and were used in 3 herds equipped with milking robots. In the first stage of data analysis, calculations were made of the coefficients of simple correlation between rumination time (expressed as mean time per cow during the periparturient period: second (14–8 days) and first (7–1 days) week before calving, 1–4, 5–7, 8–14, 15–21 and 22–28 days of lactation), electrical conductivity and temperature of milk (expressed as means per cow on days 1–4, 5–7, 8–14, 15–21 and 22–28), amount of concentrate intake, number of milkings/day, milking time/visit, milk speed and lactation milk yield. In the next step of the statistical analysis, a decision tree technique was employed to determine factors responsible for LMY. The study showed that the correlation coefficients between LMY and AMS traits recorded during the periparturient period were low or moderate, ranging from 0.002 to 0.312. Prediction of LMY from the constructed decision tree model was found to be possible. The employed Classification and Regression Trees (CART) algorithm demonstrated that the highest lactation yield is to be expected for cows with completed lactations (survived until the next lactation), which were milked 4.07 times per day on average in the 4th week of lactation. We proved that the application of the decision tree method could allow breeders to select, already in the postparturient period, appropriate levels of AMS milking variables, which will ensure high milk yield per lactation.

Highlights

  • From the economic point of view, milk yield is the most important productive trait of cows

  • Positive correlations between rumination time and milk yield of early lactation cows were reported by Antanaitis et al [4], Soriani et al [5], Calamari et al [7], Liboreiro et al [8], but the authors did not analyze the incidence of these correlations in particular weeks after calving

  • The aim of the study was to determine the possibility of using automatic milking system (AMS) data for periparturient cows to predict their lactation milk yield

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Summary

Introduction

From the economic point of view, milk yield is the most important productive trait of cows. Unlike the CMS, the AMS records numerous data (milking parameters and milk characteristics—milk composition and cytological quality, electrical conductivity, temperature) during successive visits of the cows to the milking robot [2], and allows for easier and more thorough monitoring of daily rhythms and behaviors of the cows during the entire production cycle [3]. Rumination time provides extensive information about the quality of feed offered, but can be used to predict the cow’s milk yield [5]. Box time, milking time and milking speed are important for utilizing AMS efficiently, because short milking time and the ability to quickly leave the AMS after the last teat cup is removed are desirable traits [9]

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