Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-25T07:50:28.027Z Has data issue: false hasContentIssue false
  • English
  • Français

Understanding the reproductive performance of a dairy cattle herd by using both analytical and systemic approaches: a case study based on a system experiment

Published online by Cambridge University Press:  05 March 2010

L. Gouttenoire ,
J. L. Fiorelli ,
J. M. Trommenschlager ,
X. Coquil  and
S. Cournut
L. Gouttenoire*
Affiliation:
Institut National de la Recherche Agronomique (INRA), UR 055 SAD-Aster, 662 Avenue Louis Buffet, F-88500 Mirecourt, France
J. L. Fiorelli
Affiliation:
Institut National de la Recherche Agronomique (INRA), UR 055 SAD-Aster, 662 Avenue Louis Buffet, F-88500 Mirecourt, France
J. M. Trommenschlager
Affiliation:
Institut National de la Recherche Agronomique (INRA), UR 055 SAD-Aster, 662 Avenue Louis Buffet, F-88500 Mirecourt, France
X. Coquil
Affiliation:
Institut National de la Recherche Agronomique (INRA), UR 055 SAD-Aster, 662 Avenue Louis Buffet, F-88500 Mirecourt, France
S. Cournut
Affiliation:
Ecole Nationale d’Ingénieurs des Travaux Agricoles de Clermont-Ferrand (ENITAC), Site de Marmilhat, F-63370 Lempdes, France
*
†E-mail: [email protected]
Get access

Abstract

Reproductive performance has recently been a growing concern in cattle dairy systems, but few research methodologies are available to address it as a complex problem in a livestock farming system. The aim of this paper is to propose a methodology that combines both systemic and analytical approaches in order to better understand and improve reproductive performance in a cattle dairy system. The first phase of our methodology consists in a systemic approach to build the terms of the problem. It results in formalising a set of potential risk factors relevant for the particular system under consideration. The second phase is based on an analytical approach that involves both analysing the shapes of the individual lactation curves and carrying out logistic regression procedures to study the links between reproductive performance and the previously identified potential risk factors. It makes it possible to formulate hypotheses about the biotechnical phenomena underpinning reproductive performance. The last phase is another systemic approach that aims at suggesting new practices to improve the situation. It pays particular attention to the consistency of those suggestions with the farmer’s general objectives. This methodology was applied to a French system experiment based on an organic low-input grazing system. It finally suggested to slightly modify the dates of the breeding period so as to improve reproductive performance. The formulated hypotheses leading to this suggestion involved both the breed (Holstein or Montbéliarde cows), the parity, the year and the calving date with regard to the turnout date as the identified risk factors of impaired performance. Possible use of such a methodology in any commercial farm encountering a biotechnical problem is discussed.

Keywords

dairy farmingreproductionnutritionlivestock farming systemssystem experiment
Type
Full Paper
Information
animal , Volume 4 , Issue 6 , June 2010 , pp. 827 - 841
Copyright
Copyright © The Animal Consortium 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abt, V, Pierreval, H, Lardon, S, Steffe, J 2006. Modéliser le fonctionnement et l’organisation des exploitations agricoles: quelles méthodes pour le secteur agricole? In MOSIM’06: 6e conférence Francophone de MOdélisation et SIMulation, p. 805–814. Rabat, Morocco.Google Scholar
Bonneviale, JR, Jussiau, R, Marshall, E, Bonneau, P, Capillon, A 1989. Approche globale de l’exploitation agricole: comprendre le fonctionnement de l’exploitation agricole: une méthode pour la formation et le développement. Institut National de Recherches Pédagogiques, 329 p.Google Scholar
Brunschwig, P, Veron, J, Perrot, C, Faverdin, P, Delaby, L, Seegers, H 2001. Etude technique et économique de systèmes laitiers herbagers en Pays de la Loire. Rencontres autour des Recherches sur les Ruminants 8, 237–244.Google Scholar
Buckley, F, O’Sullivan, K, Mee, JF, Evans, RD, Dillon, P 2003. Relationships among milk yield, body condition, cow weight, and reproduction in spring-calved holstein-friesians. Journal of Dairy Science 86, 2308–2319.CrossRefGoogle ScholarPubMed
Butler, WR 2003. Energy balance relationships with follicular development, ovulation and fertility in postpartum dairy cows. Livestock Production Science 83, 211–218.CrossRefGoogle Scholar
Capillon, A, Manichon, H 1988. Guide d’étude de l’exploitation agricole à l’usage des agronomes. INA P-G. Relance agronomique ADEPRINA-APECITA, 41 pp, Paris, France.Google Scholar
Chabosseau, JM, Dedieu, B 1994. Decision making process study: an example from a sheep system experiment in France. In: Livestock farming systems: research, development socio-economics and land management. Proceedings of the 3rd International Symposium, Aberdeen, Scotland, 1–2 September, 1994 (ed. JB Dent, MJ McGregor and AR Sibbald), vol. 79, pp. 308–312. Wageningen pers, Wageningen, NL.Google Scholar
Chardon, X, Rigolot, C, Baratte, C, Le Gall, A, Espagnol, S, Martin-Clouaire, R, Rellier, JP, Raison, C, Poupa, JC, Faverdin, P 2007. MELODIE: a whole-farm model to study the dynamics of nutrients in integrated dairy and pig farms. In MODSIM 2007 International Congress on Modelling and Simulation (ed. L Oxley and D Kulasiri), pp. 1638–1645. Modelling and Simulation Society of Australia and New Zealand.Google Scholar
Collard, BL, Boettcher, PJ, Dekkers, JCM, Petitclerc, D, Schaeffer, LR 2000. Relationships between energy balance and health traits of dairy cattle in early lactation. Journal of Dairy Science 83, 2683–2690.CrossRefGoogle ScholarPubMed
Coquil, X, Fiorelli, JL, Mignolet, C, Blouet, A, Foissy, D, Trommenschlager, JM, Bazard, C, Gaujour, E, Gouttenoire, L, Schrack, D 2009. Evaluation multicritère de la durabilité agro-environnementale de systèmes de polyculture élevage laitiers biologiques. Innovations Agronomiques 4, 239–247.Google Scholar
Coulon, JB, Pérochon, L 2000. Evolution de la production laitière au cours de la lactation: modèle de prédiction chez la vache laitière. INRA Productions Animales 13, 349–360.CrossRefGoogle Scholar
Cutullic, E, Delaby, L, Causeur, D, Michel, G, Disenhaus, C 2009. Hierarchy of factors affecting behavioural signs used for oestrus detection of Holstein and Normande dairy cows in a seasonal calving system. Animal Reproduction Science 113, 22–37.CrossRefGoogle Scholar
Delaby, L, Faverdin, P, Michel, G, Disenhaus, C, Peyraud, JL 2009. Effect of different feeding strategies on lactation performance of Holstein and Normande dairy cows. Animal 3, 891–905.CrossRefGoogle ScholarPubMed
Dohoo, IR, Tillard, E, Stryhn, H, Faye, B 2001. The use of multilevel models to evaluate sources of variation in reproductive performance in dairy cattle in Reunion Island. Preventive Veterinary Medicine 50, 127–144.CrossRefGoogle ScholarPubMed
Eilers, CHAM 2008. How to educate animal production systems specialists? In 59th Annual European Association for Animal Production, Vilnius, Lithuania, 24–27 August 2007, p. 237. Wageningen Academic Publisher, Wageningen, NL.Google Scholar
Garcia, SC, Holmes, CW 2001. Lactation curves of autumn-and spring-calved cows in pasture-based dairy systems. Livestock Production Science 68, 189–203.CrossRefGoogle Scholar
Gibon, A, Sibbald, AR, Flamant, JC, Lhoste, P, Revilla, R, Rubino, R, Sorensen, JT 1999. Livestock farming systems research in Europe and its potential contribution for managing towards sustainability in livestock farming. Livestock Production Science 61, 121–137.CrossRefGoogle Scholar
Girard, N, Hubert, B 1999. Modelling expert knowledge with knowledge-based systems to design decision aids: the example of a knowledge-based model on grazing management. Agricultural Systems 59, 123–144.CrossRefGoogle Scholar
Grimard, B, Freret, S, Chevallier, A, Pinto, A, Ponsart, C, Humblot, P 2006. Genetic and environmental factors influencing first service conception rate and late embryonic/foetal mortality in low fertility dairy herds. Animal Reproduction Science 91, 31–44.CrossRefGoogle ScholarPubMed
Jegou, A, Bareille, N, Brocard, V 2004. Effets de la conduite alimentaire sur la santé et la fertilité des vaches laitières: Synthèse des essais menés dans les fermes expérimentales bretonnes de Crécom et Trévarez. Rencontres autour des Recherches sur les Ruminants 11, 337.Google Scholar
Kleinbaum, DG 1994. Logistic regression: a self-learning text. Springer-Verlag, New York, USA.CrossRefGoogle Scholar
Knight, CH, Beever, DE, Sorensen, A 1999. Metabolic loads to be expected from different genotypes under different systems. Metabolic stress in dairy cows. British Society of Animal Science, Occasional Publication 24, 27–36.CrossRefGoogle Scholar
Landais, E 1992. Principes de modélisation des systèmes d’élevage. Approches graphiques. Les Cahiers de la Recherche-Développement 32, 82–95.Google Scholar
Lev, L, Campbell, DJ 1987. The temporal dimension in farming systems research: the importance of maintaining flexibility under conditions of uncertainty. Journal of Rural Studies 3, 123–132.CrossRefGoogle Scholar
Mackey, DR, Gordon, AW, McCoy, MA, Verner, M, Mayne, CS 2007. Associations between genetic merit for milk production and animal parameters and the fertility performance of dairy cows. Animal 1, 29–43.CrossRefGoogle ScholarPubMed
McCown, RL 2002. Changing systems for supporting farmers’ decisions: problems, paradigms and prospects. Agricultural Systems 74, 179–220.CrossRefGoogle Scholar
McDougall, S 2006. Reproduction performance and management of dairy cattle. Journal of Reproduction and Development 52, 185–194.CrossRefGoogle ScholarPubMed
Mintzberg, H 1987. The strategy concept I: five Ps for strategy. California Management Reveiw 30, 11–24.CrossRefGoogle Scholar
Moulin, C, Girard, N, Dedieu, B 2001. The functional analysis of feeding systems. Fourrages 167, 337–363.Google Scholar
Overton, TR, Waldron, MR 2004. Nutritional management of transition dairy cows: strategies to optimize metabolic health. Journal of Dairy Science 87, 105–119.CrossRefGoogle Scholar
Robinson, JJ, Ashworth, CJ, Rooke, JA, Mitchell, LM, McEvoy, TG 2006. Nutrition and fertility in ruminant livestock. Animal Feed Science and Technology 126, 259–276.CrossRefGoogle Scholar
Roguet, C, Faverdin, P 1999. Modèle dynamique de la lactation des vaches laitières en fonction des apports énergétiques. Rencontres autour des Recherches sur les Ruminants 6, 156.Google Scholar
Sebillotte, M, Soler, LG 1990. Les processus de décision des agriculteurs. In Modélisation systémique et systèmes agraires: décisions et organisation, (ed. J Brossier, B Vissac and JL Le Moigne), pp. 93–117. INRA Versailles, France.Google Scholar
Statistical Analysis Systems Institute 1999. SAS/STAT user's guide, version 8. SAS Institute Inc., Cary, NC, USA.Google Scholar
Tillard, E, Humblot, P, Faye, B, Lecomte, P, Dohoo, I, Bocquier, F 2008. Postcalving factors affecting conception risk in Holstein dairy cows in tropical and sub-tropical conditions. Theriogenology 69, 443–457.CrossRefGoogle ScholarPubMed
Van Der Zijpp, AJ 2008. Convergence of scientific disciplines: a necessity for system based research and development. In 59th Annual EAAP Meeting (ed. EAAP), p. 237. Wageningen Academic Publisher, Wageningen, NL.Google Scholar
Watters, RD, Guenther, JN, Brickner, AE, Rastani, RR, Crump, PM, Clark, PW, Grummer, RR 2008. Effects of dry period length on milk production and health of dairy cattle. Journal of Dairy Science 91, 2595–2603.CrossRefGoogle ScholarPubMed
Zaaijer, D, Noordhuizen, J 2003. A novel scoring system for monitoring the relationship between nutritional efficiency and fertility in dairy cows. Irish Veterinary Journal 56, 145–151.Google Scholar