FACTORS OF INFLUENCE ON THE MORBIDITY BY MASTITIS OF COWS
Notice bibliographique
Résumé
Diseases of mastitis of dairy livestock cause significant economic losses, exceeding losses from all non-communicable diseases combined. It leads to a sharp decline in milk production or interruption of lactation, premature culling of milking premature culling of dairy herd, large material costs. In addition, the problem of mastitis is of social importance, because when eating milk containing pathogenic microorganisms or their toxins, people, especially children, also have serious illnesses. The aim of the research is to determine the factors affecting the level of cow damage with mastitis. The research was conducted in 2014–2016 on dairy farms in Donetsk region. Average milk yield per cow per year in the farms was at the level of 4000–5055 kg of milk. Before the beginning of the experiment, a structural analysis of the dairy herds was carried out on the farms, taking into account the age of the cows, the physiological state, the stage of lactation, productivity and pedigree composition. Diagnosis of subclinical mastitis in milk cows was carried out by mastidinovoy breakdown on control plates. The dependence of the number of hidden mastitis on such indicators as milk yield, lactation stage, elements of technology of keeping, age of animals and season of the year was taken into account. Research methods: experimental, supplemented by analytical studies, measurements, calculations and observations. It is showed that the structure of the herd of enterpriceses AgroPromservice, Bogoyavlinske, VostokAgro and Rossiya is approximately. It was found that with two-time milking their number was 20.5%, for two-time milking with milking it was 23.9%, and with three-time milking a day it was 17.6%. Influence of disinfection of the nipples of the mammary gland after milking with the drug "De Laval" "Dipal-concentrate" showed that with a double treatment the incidence of mastitis was 26.4%; When disinfectiont of the nipples of the breast after milking once a day, this indicator increases by 5.3%. Analysis of the disinfection of the teat with iodine and glycerin after each milking showed that the incidence of mammary gland hidden mastitis is reduced from 39.3% to 30.8%, that is, 8.5%. It was revealed that the incidence in the summer and winter periods ranged from 14.6 to 23.9%, in the spring and autumn periods from 19.5 to 36.9%. Studies have shown that the number of mammary gland diseases depends on the age of the animals, in particular, in primiparous animals it was less than in older cows. The most resistant to the disease are low-productive animals with a productivity of 3000 kg of milk and less for lactation (the number of diseases was 2.6–9.0%). With the increase in milk productivity from 4000–5500 kg of milk and more the number of diseases increases to 13.9–50.8%. Analysis of the presence of subclinical mastitis in farms, depending on the lactation stage of cows, indicates that the greatest number of hidden mastitis occurs in the second or fourth months of lactation, at 10–11 months of lactation and significantly decreases in the middle of lactation. Thus, the factors influencing the level of cow damage with mastitis were determined: milk productivity, lactation stage, milking and maintenance technology, animal age and season of year. The most influential factors are the productivity of animals and the technology of milking and keeping animals.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».