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Relative reticulo-rumen pH indicators for subacute ruminal acidosis detection in dairy cows

Published online by Cambridge University Press:  27 July 2017

C. Villot ,
B. Meunier ,
J. Bodin ,
C. Martin  and
M. Silberberg
C. Villot
Affiliation:
Université Clermont Auvergne, Institut National de la Recherche Agronomique (INRA), VetAgro Sup, UMR Herbivores, F-63122Saint-Genès-Champanelle, France Lallemand Animal Nutrition, F-31702 Blagnac, France Valorex, La Messayais, F-35210 Combourtillé, France Terrena, La Noëlle, F-44150 Ancenis, France
B. Meunier
Affiliation:
Université Clermont Auvergne, Institut National de la Recherche Agronomique (INRA), VetAgro Sup, UMR Herbivores, F-63122Saint-Genès-Champanelle, France
J. Bodin
Affiliation:
Arkesys, F-69007 Lyon, France
C. Martin
Affiliation:
Université Clermont Auvergne, Institut National de la Recherche Agronomique (INRA), VetAgro Sup, UMR Herbivores, F-63122Saint-Genès-Champanelle, France
M. Silberberg*
Affiliation:
Université Clermont Auvergne, Institut National de la Recherche Agronomique (INRA), VetAgro Sup, UMR Herbivores, F-63122Saint-Genès-Champanelle, France
*
†E-mail: [email protected]
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Abstract

Subacute ruminal acidosis (SARA) is usually characterized by abnormal and intermittent drops in rumen pH. Nevertheless, high individual animal variability in rumen pH and the difference in measurement methods for pH data acquisition decrease the sensitivity and accuracy of pH indicators for detecting SARA in ruminants. The aim of this study was to refine rumen pH indicators in long-term SARA based on individual dairy cow reticulo-rumen pH kinetics. Animal performances and rumen parameters were studied weekly in order to validate SARA syndrome and rumen pH was continuously measured using reticulo-rumen sensors. In total, 11 primiparous dairy cows were consecutively fed two different diets for 12 successive weeks: a control diet as low-starch diet (LSD; 13% starch for 4 weeks in period 1), an acidotic diet as high-starch diet (HSD; 32% starch for 4 weeks in period 2), and again the LSD diet (3 weeks in period 3). There was a 1-week dietary transition between LSD and HSD. Commonly used absolute SARA pH indicators such as daily average, area under the curve (AUC) and time spent below pH<5.8 and pH<6 were processed from absolute (raw) daily kinetics. Then signal processing was applied to raw pH values in order to calculate relative pH indicators by filtering and normalizing data to remove inter-individual variability, sensor drift and sensor noise. Normalized AUC, times spent below NpH<−0.3 and NpH<−0.5, NpH range and NpH standard deviation were calculated. Those relative pH indicators were compared with commonly used pH indicators to assess their ability to detect SARA. This syndrome induced by HSD was confirmed by consistent expected changes in milk quality, dry matter intake and acetate : propionate ratio in the rumen, whereas the ruminal concentration of lipopolysaccharide was increased. Commonly used pH SARA indicators were not able to discriminate SARA syndrome due to high animal variability and sensor drift and noise, whereas relative pH indicators developed in this study appeared more relevant for SARA detection as assessed by receiver operating characteristic tests. This work shows that absolute pH kinetics should be corrected for drift, noise and animal variability to produce relative pH indicators that are more robust for SARA detection. These relative pH indicators could be more relevant for identifying affected animals in a herd and also for comparing SARA risk among studies.

Keywords

subacute ruminal acidosis (SARA)reticulo-rumen pH sensorrelative pH indicatorssignal processingdairy cow
Type
Research Article
Information
animal , Volume 12 , Issue 3 , March 2018 , pp. 481 - 490
Copyright
© The Animal Consortium 2017 

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References

AlZahal, O, Kebreab, E, France, J and McBride, BW 2007. A mathematical approach to predicting biological values from ruminal pH measurements. Journal of Dairy Science 90, 3777–3785.Google Scholar
Association of Official Analytical Chemists 1997. Official methods of analysis, 16th edition. AOAC International., Gaithersburg, MD, USA.Google Scholar
Association of Official Analytical Chemists 2005. Official methods of analysis, 18th edition. AOAC International, Arlington, VA, USA.Google Scholar
Castro-Costa, A, Salama, AAK, Moll, X, Aguiló, J and Caja, G 2015. Using wireless rumen sensors for evaluating the effects of diet and ambient temperature in nonlactating dairy goats. Journal of Dairy Science 98, 4646–4658.CrossRefGoogle ScholarPubMed
Denman, SE and McSweeney, CS 2006. Development of a real-time PCR assay for monitoring anaerobic fungal and cellulolytic bacterial populations within the rumen. Fems Microbiology Ecology 58, 572–582.Google Scholar
Duffield, T, Plaizier, JC, Fairfield, A, Bagg, R, Vessie, G, Dick, P, Wilson, J, Aramini, J and McBride, B 2004. Comparison of techniques for measurement of rumen pH in lactating dairy cows. Journal of Dairy Science 87, 59–66.CrossRefGoogle ScholarPubMed
Edwards, JE, Huws, SA, Kim, EJ and Kingston-Smith, AH 2007. Characterization of the dynamics of initial bacterial colonization of nonconserved forage in the bovine rumen. FEMS Microbiology Ecology 62, 323–335.Google Scholar
Enemark, JMD, Jorgensen, R and Enemark, PS 2002. Rumen acidosis with special emphasis on diagnostic aspects of subclinical rumen acidosis: a review. Veterinarija ir Zootechnika 20, 16–29.Google Scholar
Faisant, N, Planchot, V, Kozlowski, F, Pacouret, M-P, Colonna, P and Champ, M 1995. Resistant starch determination adapted to products containing high level of resistant starch. Sciences des Aliments 15, 83–89.Google Scholar
Gardner, M and Altman, D 1989. Calculating confidence intervals for proportions and their differences. Statistics with confidence. British Medical Journal Publishing Group, London. 28–33.Google Scholar
Gasteiner, J, Fallast, M, Rosenkranz, S, Häusler, J, Schneider, K and Guggenberger, T 2009. Measuring rumen pH and temperature by an indwelling and wireless data transmitting unit and application under different feeding conditions. In Proceedings Fourth European Conference Precision Livestock Farming 09, Wageningen, The Netherlands, pp. 127–133.Google Scholar
Greiner, M, Pfeiffer, D and Smith, R 2000. Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Preventive Veterinary Medicine 45, 23–41.Google Scholar
Institut National de la Recherche Agronomique (INRA) 2010. Valeur alimentaire des fourrages et des matières premières: tables et prévisions. In Alimentation des bovins, ovins et caprins. Besoins des animaux. Quae. Institut National de la Recherche Agronomique, Paris, France.Google Scholar
Josse, J and Husson, F 2012. Handling missing values in exploratory multivariate data analysis methods. Journal de la Société Française de Statistique 153, 79–99.Google Scholar
Kaur, R, Garcia, S, Horadagoda, A and Fulkerson, W 2010. Evaluation of rumen probe for continuous monitoring of rumen pH, temperature and pressure. Animal Production Science 50, 98–104.Google Scholar
Kennelly, JJ, Robinson, B and Khorasani, GR 1999. Influence of carbohydrate source and buffer on rumen fermentation characteristics, milk yield, and milk composition in early-lactation Holstein cows. Journal of Dairy Science 82, 2486–2496.CrossRefGoogle ScholarPubMed
Kleen, JL and Cannizzo, C 2012. Incidence, prevalence and impact of SARA in dairy herds. Animal Feed Science and Technology 172, 4–8.Google Scholar
Kleen, JL, Hooijer, GA, Rehage, J and Noordhuizen, JPTM 2003. Subacute ruminal acidosis (SARA): a review. Journal of Veterinary Medicine Series A 50, 406–414.Google Scholar
Klevenhusen, F, Pourazad, P, Wetzels, S, Qumar, M, Khol-Parisini, A and Zebeli, Q 2014. Technical note: evaluation of a real-time wireless pH measurement system relative to intraruminal differences of digesta in dairy cattle. Journal of Animal Science 92, 5635–5639.Google Scholar
Kolver, E and De Veth, M 2002. Prediction of ruminal pH from pasture-based diets. Journal of Dairy Science 85, 1255–1266.Google Scholar
Krause, KM, Garrett, R and Oetzel, GR 2006. Understanding and preventing subacute ruminal acidosis in dairy herds: a review. Animal Feed Science and Technology 126, 215–236.Google Scholar
Mohammed, R, Stevenson, D, Weimer, P, Penner, G and Beauchemin, K 2012. Individual animal variability in ruminal bacterial communities and ruminal acidosis in primiparous Holstein cows during the periparturient period. Journal of Dairy Science 95, 6716–6730.Google Scholar
Nagaraja, TG and Titgemeyer, EC 2007. Ruminal acidosis in beef cattle: the current microbiological and nutritional outlook. Journal of Dairy Science 90 (suppl. 1), E17–E38.Google Scholar
Penner, G, Beauchemin, K and Mutsvangwa, T 2007. Severity of ruminal acidosis in primiparous Holstein cows during the periparturient period. Journal of Dairy Science 90, 365–375.CrossRefGoogle ScholarPubMed
Plaizier, JC, Krause, DO, Gozho, GN and McBride, BW 2008. Subacute ruminal acidosis in dairy cows: the physiological causes, incidence and consequences. The Veterinary Journal 176, 21–31.CrossRefGoogle ScholarPubMed
Sato, S, Ikeda, A, Tsuchiya, Y, Ikuta, K, Murayama, I, Kanehira, M, Okada, K and Mizuguchi, H 2012. Diagnosis of subacute ruminal acidosis (SARA) by continuous reticular pH measurements in cows. Veterinary Research Communication 36, 201–205.Google Scholar
Sauvant, D, Meschy, F and Mertens, D 1999. Les composantes de l’acidose ruminale et les effets acidogènes des rations. INRA Productions Animales 12, 49–60.CrossRefGoogle Scholar
Schwartzkopf-Genswein, K, Beauchemin, K, Gibb, D, Crews, D Jr, Hickman, D, Streeter, M and McAllister, T 2003. Effect of bunk management on feeding behavior, ruminal acidosis and performance of feedlot cattle: a review. Journal of Animal Science 81, 149–158.Google Scholar
Shen, J, Chai, Z, Song, L, Liu, J and Wu, Y 2012. Insertion depth of oral stomach tubes may affect the fermentation parameters of ruminal fluid collected in dairy cows. Journal of Dairy Science 95, 5978–5984.Google Scholar
Silberberg, M, Chaucheyras-Durand, F, Commun, L, Mialon, MM, Monteils, V, Mosoni, P, Morgavi, DP and Martin, C 2013. Repeated acidosis challenges and live yeast supplementation shape rumen microbiota and fermentations and modulate inflammatory status in sheep. Animal 7, 1910–1920.Google Scholar
Stefańska, B, Nowak, W, Komisarek, J, Taciak, M, Barszcz, M and Skomiał, J 2016. Prevalence and consequence of subacute ruminal acidosis in Polish dairy herds. Journal of Animal Physiology and Animal Nutrition 101, 694–702.CrossRefGoogle Scholar
Stevenson, DM and Weimer, PJ 2007. Dominance of Prevotella and low abundance of classical ruminal bacterial species in the bovine rumen revealed by relative quantification real-time PCR. Applied Microbiology and Biotechnology 75, 165–174.Google Scholar
Van Soest, P, Robertson, J and Lewis, B 1991. Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. Journal of Dairy Science 74, 3583–3597.Google Scholar
Yu, Z and Morrison, M 2004. Improved extraction of PCR-quality community DNA from digesta and fecal samples. Biotechniques 36, 808–813.CrossRefGoogle ScholarPubMed
Zebeli, Q, Dijkstra, J, Tafaj, M, Steingass, H, Ametaj, B and Drochner, W 2008. Modeling the adequacy of dietary fiber in dairy cows based on the responses of ruminal pH and milk fat production to composition of the diet. Journal of Dairy Science 91, 2046–2066.CrossRefGoogle Scholar