The evaluation of the relationship of lactose to production and reproduction traits in different breeding conditions of the Slovak Spotted dairy cows
Notice bibliographique
Résumé
Article Details: Received: 2020-10-23 | Accepted: 2020-11-27 | Available online: 2021-01-31 https://doi.org/10.15414/afz.2021.24.mi-prap.140-144 The aim of this study was to evaluate lactose in relation to milk production and calving interval in dairy cows of Slovak Spotted cattle. A total of 92,730 lactations from 45,800 dairy cows evaluation from 2015 to 2019 were used for investigating lactose percentage (LP), milk yield (MY), lactose yield (LY), fat percentage (FP), proteins percentage (PP) and calving interval (CI). Data were analysed using the SAS version 9.4 and linear model with fixed effects: herd-years-season (HYS), sire (S), breeding type (BT), coding of milk (Cod-MY) and coding of calving interval (Cod-CI). In the dataset the average of LP was 4.77±0.20 %, while the one of MY, LY, FP, PP and CI were 6,778.53±2,014.18 kg, 325.27±100.45 kg, 3.96±0.47 %, 3.39±0.23 % and 406.66±91.09 days. The correlation of LP with MY, LY, FP, PP and CI was equal to r = 0.1712, r = 0.3157, r = 0.0546, r = 0.1852 and r = -0.0147. These correlation coefficients were statistically highly significant P <0.0001. Among all fixed effects in the analysis of variance of LP, the most relevant effect was observed for HYS (P<.0001). Keywords: cattle, milk components, lactose, calving interval, correlation References Alessio, D. R. M. et al. (2016). Multivariate analysis of lactose content in milk of Holstein and Jersey cows. Semina: Ciências Agrárias , 37(4), 2641–2652. http://dx.doi.org/10.5433/1679-0359.2016v37n4Supl1p2641 Bolacali, M. and Öztürk, Y. (2018). Effect of non-genetic factors on milk yields traits in Simmental cows raised subtropical climate condition. Arquivo Brasileiro de Medicina Veterinária e Zootecnia , 70(1), 297–305. https://doi.org/10.1590/1678-4162-9325 Boro, P. et al. (2016). Genetic and non-genetic factors affecting milk composition in dairy cows. International Journal of Advanced Biological Research , 6(2), 170–174. BRS (2020). Average milk production of Fleckvieh in Germany. Retrieved August 12. 2020. https://www.ggi-spermex.de/en/fleckvieh/about-fleckvieh-92.html Bujko, J. (2011). Optimalization Genetic Improvement Milk Production in Population Slovak Spotted Breed . Monograph. Nitra: SAU, 78 p. in Slovak. Bujko, J. et al. (2018). Evaluation relation between traits of milk production and calving interval in breeding herds of Slovak Simmental dairy cows. Albanian Journal of Agricultural Sciences , 17(1), 31–36. http://ajas .inovacion.al/volume-17-issue-i/ Bujko, J. et al. (2019). The Analysis of Reproduction in Population of the Slovak Spotted Dairy Cows. Acta Universitatis Agriculturae Silviculturae Mendelianae Brunensis , 67(6), 1419–1426. https://doi.org/10.11118/ actaun201967061419 Bujko, J. et al. (2020). Changes in production and reproduction traits in population of the Slovak Spotted Cattle. 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(2018). Effects of body condition score and daily milk yield on reproduction traits of Czech Fleckvieh cows. Animal Reproduction(AR) , 14(Supplement 1), 1264–1269. http://dx.doi. org/10.21451/1984-3143-AR944 The Breeding Service of the Slovak Republic, S.E. (2020). Results of dairy herd milk recording in Slovak Republic at control years 2015 to 2019. BSSR. Retrieved July 20. 2020. http://test.plis.sk/volne /rocenkamagazin/rocenka.aspx?id= mlhd2019 ZAR (2020). Fleckvieh/ Simmental. [Annual report 2019]. Vienna: ZAR. Retrieved October 5. 2020. http://en.zar.at/Cattle_breeding_in_Austria.html
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».