Customized recommendations for production management clusters of North American automatic milking systems

Abstract

Automatic milking systems (AMS) are implemented in a variety of situations and environments. Consequently, there is a need to characterize individual farming practices and regional challenges to streamline management advice and objectives for producers. Benchmarking is often used in the dairy industry to compare farms by computing percentile ranks of the production values of groups of farms. Grouping for conventional benchmarking is commonly limited to the use of a few factors such as farms' geographic region or breed of cattle. We hypothesized that herds' production data and management information could be clustered in a meaningful way using cluster analysis and that this clustering approach would yield better peer groups of farms than benchmarking methods based on criteria such as country, region, breed, or breed and region. By applying mixed latent-class model-based cluster analysis to 529 North American AMS dairy farms with respect to 18 significant risk factors, 6 clusters were identified. Each cluster (i.e., peer group) represented unique management styles, challenges, and production patterns. When compared with peer groups based on criteria similar to the conventional benchmarking standards, the 6 clusters better predicted milk produced (kilograms) per robot per day. Each cluster represented a unique management and production pattern that requires specialized advice. For example, cluster 1 farms were those that recently installed AMS robots, whereas cluster 3 farms (the most northern farms) fed high amounts of concentrates through the robot to compensate for low-energy feed in the bunk. In addition to general recommendations for farms within a cluster, individual farms can generate their own specific goals by comparing themselves to farms within their cluster. This is very comparable to benchmarking but adds the specific characteristics of the peer group, resulting in better farm management advice. The improvement that cluster analysis allows for is characterized by the multivariable approach and the fact that comparisons between production units can be accomplished within a cluster and between clusters as a choice.

Herd clustering strategies and corresponding genetic evaluations based on social–ecological characteristics for a local endangered cattle breed

“…This, in turn, can lead to biased genetic calculations (Strabel and Szwaczkowski, 1999). As an alternative, HC or HCtest-month effects were suggested as CG effects in geneticstatistical modeling approaches (Vasconcelos et al, 2008). The general aim of CG creation is to depict environmental conditions influencing cow traits as detailed as possible (Kuehn et al, 2007;Osorio-Avalos et al, 2015).…”

“…Alternative modeling strategies to create CG were introduced by Strabel et al (2005) and Vasconcelos et al (2008). They grouped herds according to herd size or average milk yield per herd.…”

Application of Data Mining in the Field of Human Resource Management: A Review

“…[26] used information entropy with improved clustering algorithm to process high-dimensional data and consistent data in discrete manufacturing quality management systems. [27] provided a management recommendation based on a hybrid latent class model for important risk factors in the milk manufacturing industry. [28] proposed a fuzzy clustering method for manufacturing lean management.…”

Propuesta de mejora de productividad basado en un Sistema Básico de Gestión por Procesos en las Mypes productoras de granada en el distrito de Santiago en Ica

“…Se comenta que alcanzar rendimientos de cosecha más altos, con un uso más efectivo de los recursos, es una cuestión central del sector agrícola. El manejo de la producción en las empresas requiere conocer las fortalezas y debilidades para lograr hacer mejoras continuas y establecer objetivos de gestión y producción apropiados, según señala Tremblay et al (2016).…”