Machine Learning as an aid to management decisions on high somatic cell counts in dairy farms

Abstract

High somatic cell counts (SCC) is associated with mastitis infection, in dairy herds, worldwide. This work describes Machine Learning (ML) techniques designed to improve the information offered to farmers on animals producing high SCCs according to particular herd profiles. The analysed population included 71 dairy farms in Asturias (Northern Spain) and a total of 2,407 lactating cows. Four sources of information were available: a) a questionnaire survey describing facilities, milking routines and management practices of the farms studied; b) dairy recording information; c) classification of the cows suspected of being healthy or subclinical mastitic according to farmers' expertise; and d) positive or negative scores with respect to the California Mastitis Test (CMT). The decimal logarithm of the SCC (linear score), lactation number, herd size, lactating cows per milker, milk urea concentration, number of clusters per milker and actual SCC are shown to be the most informative attributes for mimicking both farmers' expertise or CMT performance in order to identify animals producing persistently high SCCs in dairy herds. However, to improve the identification of cows suspected of being non-healthy, the system uses other information related to management and milking routines. Decision rules to predict CMT performance can provide useful, additional information to farmers to improve the management of dairy herds included in milk recording programs.

Using machine learning procedures to ascertain the influence of beef carcass profiles on carcass conformation scores

“…In other words, the relevancy (see Goyache et al, 2005) of the different attributes (and their weight) to obtain sound SEUROP conformation grading was assessed. The method employed consisted in the gradual reduction of the number of attributes.…”

Short Communication: Early Detection of Mastitis Using Infrared Thermography in Dairy Cows

“…In the present study, we used a CMT scoring system that is frequently applied for screening dairy cows for IMI (Sargeant et al, 2001). This CMT score increases as SCC increases (Goyache et al, 2005). The sensitivities for detecting IMI with any pathogen, IMI with a major pathogen, and IMI with a minor pathogen were 56.7, 66.7, and 49.5%, respectively (Sargeant et al, 2001).…”

Einfluss differenzierter Eutervorbereitungszeiten auf Melkbarkeitsmerkmale bei Milchkühen in Kuba

“…maschinelle Vorstimulation beim Maschinenmelken ist aber eine entscheidende Voraussetzung für die rechtzeitige Auslösung des Milchejektionsreflexes und die Gesunderhaltung der Euter. Die Erkenntnis, dass der alleinige Reiz, den die pulsierenden Zitzengummis beim Maschinenmelken auf die Zitzen ausüben, selten eine vollwertige Milchejektion hervorruft, macht einen Komplex der Eutervorbereitung notwendig, der eine Reizung hauptsächlich der berührungsempfindlichen Zitzen-rezeptoren garantiert und das Vormelken, das Euterreinigen und die Massage beinhaltet (MIELKE et al 1962;KANSWOHL, 1986;HAMANN, 1992;NAUMANN et al, 1998;SCHULZ, 2003;Tröger 2003;SKRZYPEK et al, 2003;GOYACHE et al, 2005). Bei den angegebenen 60 s handelt es sich um keinen fixen Wert, da die optimale Stimulationsdauer in Abhängigkeit von der Milchmenge im Euter stark variiert (WEISS et al, 2005).…”