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Enregistrement W2618995749 · doi:10.15414/afz.2017.20.01.01-05

Drinking regime evaluation with continuous ruminal monitoring boluses

2017· article· en· W2618995749 sur OpenAlexaboutno aff
Ondrej Hanušovský, Dániel Bíró, Milan Šimko, Branislav Gálik, Miroslav Juráček, Michal Rolinec, Róbert Herkeľ

Notice bibliographique

RevueActa fytotechnica et zootechnica/Acta fytotechnica et zootechnica · 2017
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueEffects of Environmental Stressors on Livestock
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMilkingAnimal scienceBolus (digestion)RumenBreedChemistryMedicineBiologySurgeryFood science

Résumé

récupéré en direct d'OpenAlex

Received: 2016-07-04 | Accepted: 2016-10-27 | Available online: 2017-03-31 http://dx.doi.org/10.15414/afz.2017.20.01.01-05 The aim of this study was to continuously monitored drinking regime of 7 dairy cows of Holstein breed using boluses during 24 weeks of lactation in relation to the outside temperature and observed daily drinking regime with the impact of drinking on rumen temperature at University Experimental Farm in Oponice. Animals were fed once daily and milked 3 times per day. The bolus pH and temperature values implemented via esophagus were measured every 15 minutes (96 data points per day) with accuracy ±0.1 ph and °C. Outside temperature by FREEMETEO meteorological server (48 times per day) was measured. Outside temperature can affect the drinking regime of dairy cows. During lactation weeks with higher outside temperature higher average number of drinking events (ANDE) was determined. The biggest difference between weeks in ANDE 18.33 % (p = 0.000) was found. Daily ANDE 9.25 ±1.85 and average daily temperature (ADT) 19.03 ±5.19 °C were observed. The most of the drinking events (NDE) concentrated to 4 main peaks (25.17  %) during working hours (74.98  %) was found. After the feed intake and milking the highest frequencies of NDE were observed. The highest average ruminal temperature after drinking (ARTAD) during night before first feeding due to lower NDE in this time were found. Overall ARTAD 36.86 °C was observed. The most measured ruminal temperatures after drinking (RTAD) (51.53 %) in the interval 35–37 °C were found. This research proved that continuous ruminal monitoring with boluses is an appropriate tool for drinking regime evaluation and heat stress determination in herd of dairy cows. Keywords: dairy cows, Hostein cattle, bolus, rumen, temperature, water intake, outside temperature References Arias, R., Mader, T. and Escobar, P. (2008)  Climatic factors affecting cattle performance in dairy and beef farms. Archivos de Medicina Veterinaria , vol. 40, no. 1, pp. 7-22. doi: http://dx.doi.org/10.4067/s0301-732x2008000100002 Bewley, J. M. et al. (2008)  Impact of Intake Water Temperatures on Reticular Temperatures of Lactating Dairy Cows. Journal of Dairy Science , vol. 91, no. 10, pp. 3880–3887. doi: http://dx.doi.org/10.3168/jds.2008-1159 Bodas, R. et al. (2014) Ruminal pH and temperature, papilla characteristics, and animal performance of fattening calves fed concentrate or maize silage-based diets. Chilean Journal of Agricultural Research , vol. 74, no. 3, pp. 280–285. doi: http://dx.doi.org/10.4067/s0718-58392014000300005 Brown-Brandl, T. M. et al. (2006) Comparison of heat tolerance of feedlot heifers of different breeds. Livestock Science , vol. 105, no. 1, pp. 19-26. doi: http://dx.doi.org/10.1016/j.livsci.2006.04.012 Burgos, M. S. et al. (2001) Effect of water restriction on feeding and metabolism in dairy cows. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology , vol. 280, no. 2, pp. 418-427. CARDOT, V., LE ROUX, Y. and JURJANZ, S. (2008) Drinking Behavior of Lactating Dairy Cows and Prediction of Their Water Intake. Journal of Dairy Science , vol. 91, no. 6, pp. 2257–2264. doi: http://dx.doi.org/10.3168/jds.2007-0204 Gasteiner, J. et al. (2009) Measuring rumen pH and temperature by an indwelling and wireless data transmitting unit and application under different feeding conditions. In Papers Presented at the 4th European Conference on Precision Livestock Farming , Wageningen 6-8 July 2009.  Wageningen Academic Pub, pp. 127-133. González Pereyra, A. V. et al. (2010) Influence of Water Temperature and Heat Stress on Drinking Water Intake in Dairy Cows. Chilean Journal of Agricultural Research , vol. 70, no. 2., pp 328-336. doi: http://dx.doi.org/10.4067/s0718-58392010000200017 Huzzey, J. M., von Keyserlingk, M. A. G. and  WEARY, D. M. (2005) Changes in Feeding, Drinking, and Standing Behavior of Dairy Cows During the Transition Period. Journal of Dairy Science , vol. 88, no. 7, pp. 2454–2461. doi: http://dx.doi.org/10.3168/jds.s0022-0302(05)72923-4 JAGO, J. G. et al. (2005) The drinking behaviour of dairy cows in late lactation. In 65th Conference of the New Zealand Society of Animal Production, Christchurch 21-24 June 2005. New Zealand: NZ Society Animal Production, pp. 153-156. Matarazzo, S. V. et al. (2003) Water Intake and Behavior of Dairy Cows in Response to Environmental Conditions. In Fifth International Dairy Housing Proceedings , Forth Worth, 29-31 January 2003. American Society of Agricultural and Biological Engineers, pp 213-217. doi: http://dx.doi.org/10.13031/2013.11624 Meyer, U. et al. (2004) Investigations on the water intake of lactating dairy cows. Livestock production science , vol. 90, no. 2, pp. 117-121. doi: http://dx.doi.org/10.1016/j.livprodsci.2004.03.005 Murphy, M. R. (1992) Water metabolism of dairy cattle. Journal of dairy science , vol. 75, no. 1, pp. 326-333. doi: http://dx.doi.org/10.3168/jds.S0022-0302(92)77768-6 National Research Council (2001). Nutrient Requirements of Dairy Cattle , 7th. rev. ed. Proceedings of National Academy Sciences, Washington,D.C. Nocek, J. E. and Braund, D. G. (1985) Effect of feeding frequency on diurnal dry matter and water consumption, liquid dilution rate, and milk yield in first lactation. Journal of Dairy Science , vol. 68. no. 9, pp. 2238-2247. doi: http://dx.doi.org/10.3168/jds.S0022-0302(85)81096-1 Osborne, V. R., Hacker, R. R., and McBride, B. W. (2002) Effects of heated drinking water on the production responses of lactating Holstein and Jersey cows. Canadian journal of animal science , vol. 82, no. 3,  pp. 267-273. doi: http://dx.doi.org/10.4141/A01-055 Perera, K. S. et al. (1986) Effect of season and stage of lactation on performance of Holsteins. Journal of Dairy Science , vol. 69, no. 1, pp.  228-236. doi: http://dx.doi.org/10.3168/jds.S0022-0302(86)80390-3

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Communication savante, Science ouverte, Intégrité de la recherche
Catégories consensuellesMéta-épidémiologie (sens strict), Intégrité de la recherche
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,686
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0060,002
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0000,001
Études des sciences et des technologies0,0030,002
Communication savante0,0020,003
Science ouverte0,0080,004
Intégrité de la recherche0,0020,004
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,028
Tête enseignante GPT0,283
Écart entre enseignants0,255 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations11
Publié2017
Routes d'admission1
Résumé présentoui

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