Dairy farmers use herd management systems, behavioral sensors, feeding lists, breeding schedules, and health records to document herd characteristics. Consequently, large amounts of dairy data are becoming available. However, a lack of data integration makes it difficult for farmers to analyze the data on their dairy farm, which indicates that these data are currently not being used to their full potential. Hence, multiple issues in dairy farming such as low longevity, poor performance, and health issues remain. We aimed to evaluate whether machine learning (ML) methods can solve some of these existing issues in dairy farming. This review summarizes peer-reviewed ML papers published in the dairy sector between 2015 and 2020. Ultimately, 97 papers from the subdomains of management, physiology, reproduction, behavior analysis, and feeding were considered in this review. The results confirm that ML algorithms have become common tools in most areas of dairy research, particularly to predict data. Despite the quantity of research available, most tested algorithms have not performed sufficiently for a reliable implementation in practice. This may be due to poor training data. The availability of data resources from multiple farms covering longer periods would be useful to improve prediction accuracies. In conclusion, ML is a promising tool in dairy research, which could be used to develop and improve decision support for farmers. As the cow is a multifactorial system, ML algorithms could analyze integrated data sources that describe and ultimately allow managing cows according to all relevant influencing factors. However, both the integration of multiple data sources and the obtainability of public data currently remain challenging.

Milking postures have shifted from seated milking in tethered stalls to milking in a standing position in parlors. However, the musculoskeletal workload of dairy farmers remains high. Previous studies have shown that different working heights affect ergonomics, but they could not objectively evaluate and quantify the workload. The aim of the present study was to assess the effect of working height in different milking parlor types on the milker's workload during the task of attaching milking clusters. Computer-assisted recording and long-term analysis of movements were used to record positions of joints and body regions while performing certain tasks in terms of angular degrees of joints (ADJ) according to the neutral zero method. The 5th, 50th, and 95th percentiles described the distribution of angular degree values measured for each joint. The ADJ were evaluated according to international standards and other scientific literature on the issue to assess the muscular load. The workload was compared between 5 parlor types (auto tandem, herringbone 30°, herringbone 50°, parallel, and rotary) on 15 farms with 2 subjects per parlor and 1 milking period per subject. The working height was defined as a coefficient based on the milker's body height, the floor level, and the cow's udder height. The data recorded during the attachment task were analyzed using generalized linear mixed-effects models taking into account the hierarchical experimental design. The results indicated that the interaction of the cow's udder height, the milker's body height, and the parlor type had a larger effect on ergonomics than each parameter had independently. The interaction was significant in at least 1 of the 3 percentiles in 28 out of 31 ADJ. The postural differences between parlor types, however, were minor. A milking health formula was created to calculate the ideal depth of pit by considering the parlor type, the milker's height, and the mean herd udder height. This formula can be used to develop individual recommendations for future parlor construction.

Summary Broad‐leaved dock (Rumex obtusifolius L.) is a troublesome weed that predominantly grows in pastures and grassland. We hypothesised that frequent defoliation of Rumex will, over time, result in a reduction in root weight and leaf area, to the point where the impact on grass production is negligible. In order to investigate this hypothesis, we conducted three experiments. The objective of the first experiment was to perform a preliminary test of the hypothesis, using potted plants growing in the controlled conditions of a glasshouse. This experiment showed a rapid decline in leaf growth in plants that were defoliated weekly. The objective of the second experiment was to test the hypothesis in realistic outdoor conditions while still being able to collect detailed plant growth information. This experiment confirmed the findings of the glasshouse experiment and provided evidence that leaf growth ceased as a result of a dwindling supply of carbohydrate reserves in the root. Defoliated plants did not exhibit increased mortality. Finally, the objective of the third experiment was to test the hypothesis in a commercial pasture where normal field operations, specifically grass harvesting (three times) and slurry injection (twice), were performed. The results of this experiment were consistent with the results of the other two experiments. We conclude that weekly defoliation, maintained for three or more months, is an effective method to control (reduce the impact on grass production), but not kill, R. obtusifolius in pasture.

No abstract