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  • Author or Editor: K.J. Kamp x
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Automatic milking systems (AMS) are being implemented in a variety of conditions. There is a need to characterize individual farming practices and regional challenges in order to streamline management advice and goals for producers. Benchmarking is often used in the dairy industry to compare farms by computing percentile ranks of the production values of farms in the same geographic region or utilizing the same breed of cattle. We hypothesize that a clustering approach using herds’ production data and management information would yield improved peer groups of farms compared to conventional benchmarking methods. A mixed latent-class model-based cluster analysis of 529 North American AMS dairy farms with respect to 18 significant risk factors identified 6 clusters. Each cluster (i.e. peer group) represents unique management styles, challenges and production patterns. When compared to peer groups based on criteria similar to the conventional benchmarking standards, the 6 clusters better predict milk production per robot per day. Each cluster represents a unique management and production pattern that requires specialized advice. For example, cluster 1 farms are farms that recently installed AMS robots. Cluster 3 farms, the most northern farms, feed high amounts of concentrates through the robot to compensate for low energy feed in the bunk. In addition to general recommendations for the farms within a cluster, farms can generate their own specific goals by comparing themselves to farms within their cluster. The improvement that cluster analysis allows for is characterized by the multivariable approach and the fact that comparisons between farms can be accomplished within a cluster and between clusters as a choice.

In: Precision Dairy Farming 2016

Abstract

Irritable bowel syndrome (IBS), a disorder of gut-brain interaction, is associated with abdominal pain and stool frequency/character alterations that are linked to changes in microbiome composition. We tested whether taxa differentially abundant between females with IBS vs healthy control females (HC) are associated with daily gastrointestinal and psychological symptom severity. Participants (age 18-50 year) completed a 3-day food record and collected a stool sample during the follicular phase. They also completed a 28-day diary rating symptom intensity; analysis focused on the three days after the stool sample collection. 16S rRNA gene sequencing was used for bacterial identification. Taxon abundance was compared between IBS and HC using zero-inflated quantile analysis (ZINQ). We found that females with IBS (n = 67) had greater Bacteroides abundance (q = 0.003) and lower odds of Bifidobacterium presence (q = 0.036) compared to HC (n = 46) after adjusting for age, race, body mass index, fibre intake, and hormonal contraception use. Intestimonas, Oscillibacter, and Phascolarctobacterium were more often present and Christensenellaceae R-7 group, Collinsella, Coprococcus 2, Moryella, Prevotella 9, Ruminococcaceae UCG-002, Ruminococcaceae UCG-005, and Ruminococcaceae UCG-014 were less commonly present in IBS compared to HC. Despite multiple taxon differences in IBS vs HC, we found no significant associations between taxon presence or abundance and average daily symptom severity within the IBS group. This may indicate the need to account for interactions between microbiome, dietary intake, metabolites, and host factors.

In: Beneficial Microbes