A Literature Review of Modeling Approaches Applied to Data Collected in Automatic Milking Systems

Automatic milking systems (AMS) have played a pioneering role in the advancement of Precision Livestock Farming, revolutionizing the dairy farming industry on a global scale. This review specifically targets papers that focus on the use of modeling approaches within the context of AMS. We conducted a thorough review of 60 articles that specifically address the topics of cows' health, production, and behavior/management Machine Learning (ML) emerged as the most widely used method, being present in 63% of the studies, followed by statistical analysis (14%), fuzzy algorithms (9%), deterministic models (7%), and detection algorithms (7%). A significant majority of the reviewed studies (82%) primarily focused on the detection of cows' health, with a specific emphasis on mastitis, while only 11% evaluated milk production. Accurate forecasting of dairy cow milk yield and understanding the deviation between expected and observed milk yields of individual cows can offer significant benefits in dairy cow management. Likewise, the study of cows' behavior and herd management in AMSs is under-explored (7%). Despite the growing utilization of machine learning (ML) techniques in the field of dairy cow management, there remains a lack of a robust methodology for their application. Specifically, we found a substantial disparity in adequately balancing the positive and negative classes within health prediction models.

Forecasting Milking Efficiency of Dairy Cows Milked in an Automatic Milking System Using the Decision Tree Technique

In barns equipped with an automatic milking system, the profitability of production depends primarily on the milking efficiency of a cow (ME; kg/min) defined as cow milk yield per minute of box time. This study was carried out on 1823 Polish Holstein−Friesian cows milked by the automatic milking system (AMS) in 20 herds. Selected milking parameters recorded by the AMS were analyzed in the research. The aim of the study was to forecast ME using two statistical techniques (analysis of variance and decision trees). The results of the analysis of variance showed that the average ME was 1.67 kg/min. ME was associated with: year of AMS operation (being the highest in the first year), number of cows per robot (the highest in robots with 61−75 cows), lactation number (highest for multiparas), season of calving (the highest in spring), age at first calving (>36 months), days in milk (151−250 days) and finally, rear quarter to total milk yield ratio (the highest between 51% and 55%). The decision tree predicted that the highest ME (2.01 kg/min) corresponded with cows that produced more than 45 kg of milk per day, were milked less than four times/day, had a short teatcup attachment time (<7.65 s) and were milked in robots that had an occupancy lower than 56 cows.

Prediction of Lactational Milk Yield of Cows Based on Data Recorded by AMS during the Periparturient Period

Simple Summary

Barns 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.

Abstract

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.

Forecasting the milk yield of cows on farms equipped with automatic milking system with the use of decision trees

The purpose of this paper was to utilize the decision trees technique to determine the factors responsible for high monthly milk yield in Polish Holstein-Friesian cows from 27 herds equipped with milking robots. The applied statistical method-the decision tree technique-showed that the most important factors responsible for monthly milk yield of dairy cows using robots were, in descending order of importance: milking frequency, lactation number, month of milking, and type of lying stall. At the same time, it has been ascertained that the highest monthly milk yield (47.24 kg) can be expected from multiparous cows kept in barns with a deep bedding that were milked more frequently than three times per day. On the other hand, the lowest milk production (13.56 kg) was observed among dairy cows milked less frequently than two times a day, with an average number of milked quarters lower than 3.97. The application of the decision trees technique allows a breeder to select appropriate levels of environmental factors and parameters that will help to ensure maximized milk production.

Scrotal circumference, bodyweight and semen characteristics in growing dairy-breed natural-service bulls in Tasmania, Australia

AIMS

To provide herd managers with a set of decision rules allowing them to predict the likelihood that a juvenile bull is ready for Bull Breeding Soundness Evaluation (BBSE), or breeding, if bodyweight and scrotal circumference are known.

METHODS

This was a longitudinal study following two groups of young pasture-fed Holstein and Jersey bulls from northwest Tasmania, Australia. Individual scrotal circumference, bodyweight and semen characteristics were recorded at 6-8 weekly intervals, from 6-18 months of age. Classification and regression tree analyses were used to predict the probability that a bull had ≥70% normal sperm morphology based on scrotal circumference and bodyweight measurements.

RESULTS

Overall 1,661 scrotal circumference and bodyweight measurements were obtained, and 518 semen samples from 356 bulls were assessed for sperm morphology, from 16 examination sessions that took place between 29 May 2015 and 17 August 2016. Classification and regression tree analyses generated a decision tree for Holstein bulls with four node endpoints, and for Jersey bulls with three node endpoints. Diagnostic test performance showed that for Holstein bulls, using the node endpoints of scrotal circumference ≥27 cm and bodyweight ≥349 kg, 98% had ≥70% normal sperm (positive likelihood ratio 10.4; 95% CI = 2.7-41), and using the node endpoints of scrotal circumference ≥27 cm and bodyweight between 282-349 kg, 89% had ≥70% normal sperm (positive likelihood ratio 1.6; 95% CI = 0.9-2.6). For Jersey bulls, using the node endpoints of bodyweight ≥259 kg and scrotal circumference ≥29 cm, 88% had ≥70% normal sperm (positive likelihood ratio 3.4; 95% CI = 1.6-7.0).

CONCLUSIONS

This study provides a set of relatively simple decision rules based on bodyweight and scrotal circumference measurements that allows herd managers to assess the likelihood that juvenile bulls are ready for BBSE or breeding.

ABBREVIATIONS

BBSE: Bull breeding soundness evaluation; BRT: Boosted regression tree.