Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-28T17:48:52.509Z Has data issue: false hasContentIssue false

Stakeholder involvement in establishing a milk quality sub-index in dairy cow breeding goals: a Delphi approach

Published online by Cambridge University Press:  17 November 2015

M. Henchion*
Affiliation:
Department of Agrifood Business and Spatial Analysis, Rural Economy and Development Programme, Teagasc Food Research Centre Ashtown, Dublin 15, Ireland
M. McCarthy
Affiliation:
Department of Food Business and Development, University College Cork, Cork, Ireland
V. C. Resconi
Affiliation:
Department of Agrifood Business and Spatial Analysis, Rural Economy and Development Programme, Teagasc Food Research Centre Ashtown, Dublin 15, Ireland
D. P. Berry
Affiliation:
Animal and Grassland Research and Innovation Centre, Teagasc, Moorepark, Fermoy, Co. Cork, Ireland
S. McParland
Affiliation:
Animal and Grassland Research and Innovation Centre, Teagasc, Moorepark, Fermoy, Co. Cork, Ireland
Get access

Abstract

The relative weighting on traits within breeding goals are generally determined by bio-economic models or profit functions. While such methods have generally delivered profitability gains to producers, and are being expanded to consider non-market values, current approaches generally do not consider the numerous and diverse stakeholders that affect, or are affected, by such tools. Based on principles of respondent anonymity, iteration, controlled feedback and statistical aggregation of feedback, a Delphi study was undertaken to gauge stakeholder opinion of the importance of detailed milk quality traits within an overall dairy breeding goal for profit, with the aim of assessing its suitability as a complementary, participatory approach to defining breeding goals. The questionnaires used over two survey rounds asked stakeholders: (a) their opinion on incorporating an explicit sub-index for milk quality into a national breeding goal; (b) the importance they would assign to a pre-determined list of milk quality traits and (c) the (relative) weighting they would give such a milk quality sub-index. Results from the survey highlighted a good degree of consensus among stakeholders on the issues raised. Similarly, revelation of the underlying assumptions and knowledge used by stakeholders to make their judgements illustrated their ability to consider a range of perspectives when evaluating traits, and to reconsider their answers based on the responses and rationales given by others, which demonstrated social learning. Finally, while the relative importance assigned by stakeholders in the Delphi survey (4% to 10%) and the results of calculations based on selection index theory of the relative emphasis that should be placed on milk quality to halt any deterioration (16%) are broadly in line, the difference indicates the benefit of considering more than one approach to determining breeding goals. This study thus illustrates the role of the Delphi technique, as a complementary approach to traditional approaches, to defining breeding goals. This has implications for how breeding goals will be defined and in determining who should be involved in the decision-making process.

Type
Research Article
Copyright
© The Animal Consortium 2015 

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

Bastin, C, Berry, DP, Coffey, MP, Strandberg, E, Urioste, JI, Veerkamp, RF and Gengler, N 2011. Consequences of selection for milk quality and robustness traits. Interbull Bulletin 44, 15.Google Scholar
Bastin, C, Berry, DP, Soyeurt, H and Gengler, N 2012. Genetic correlations of days open with production traits and contents in milk of major fatty acids predicted by mid-infrared spectrometry. Journal of Dairy Science 95, 61136121.CrossRefGoogle ScholarPubMed
Bechtold, KB and Abdulai, A 2014. Combining attitudinal statements with choice experiments to analyze preference heterogeneity for functional dairy products. Food Policy 47, 97106.CrossRefGoogle Scholar
Berry, DP 2008. Genetics – a tool to improve productivity and profitability. International Journal of Dairy Technology 61, 3035.CrossRefGoogle Scholar
Berry, DP, Kearney, JF, Twomey, K and Evans, RD 2013. Genetics of reproductive performance in seasonal calving dairy cattle production systems. Irish Journal of Agricultural and Food Research 52, 116.Google Scholar
Berry, DP, Wall, E and Pryce, JE 2014. Genetics and genomic of reproductive performances in dairy and beef cattle. Animal 8, 105121.CrossRefGoogle ScholarPubMed
Bittante, G, Cipolat-Gotet, C and Cecchinato, A 2013. Genetic parameters of different measures of cheese yield and milk nutrient recovery from an individual model cheese-manufacturing process. Journal of Dairy Science 96, 79667979.CrossRefGoogle ScholarPubMed
Bittante, G, Penasa, M and Cecchinato, A 2012. Genetics and modeling of milk coagulation properties. Journal of Dairy Science 95, 68436870.CrossRefGoogle ScholarPubMed
Bittante, GN, Cologna, N, Cecchinato, A, Marchi, MD, Penasa, M, Tiezzi, F, Endrizzi, I and Gasperi, F 2011. Monitoring of sensory attributes used in the quality payment system of Trentingrana cheese. Journal of Dairy Science 94, 56995709.CrossRefGoogle ScholarPubMed
Bjugn, R and Casati, B 2012. Stakeholder analysis: a useful tool for biobank planning. Biopreservation and Biobanking 10, 239244.CrossRefGoogle ScholarPubMed
Bourne, L and Walker, DHT 2005. Visualising and mapping stakeholder influence. Management Decision 43, 649660.CrossRefGoogle Scholar
Bovenhuis, H, MHPW, Visker and Lundén, A 2013. Selection for milk fat and milk protein composition. Advances in Animal Biosciences 4, 612617.CrossRefGoogle Scholar
Bryson, JM 2003. What to do when stakeholders matter: a guide to stakeholder identification and analysis techniques. Paper presented at the London School of Economics and Political Science, 10 February. Retrieved August 20, 2014, from http://cep.lse.ac.uk/seminarpapers/10-02-03-bry.pdf Google Scholar
Byrne, TJ, Amer, PR, Fennessy, PF, Hansen, P and Wickham, BW 2012. A preference-based approach to deriving breeding objectives: applied to sheep breeding. Animal 6, 778788.CrossRefGoogle ScholarPubMed
Coulon, JB, Delacroix-Buchet, A, Martin, B and Pirisi, A 2004. Relationships between ruminant management and sensory characteristics of cheeses: a review. Lait 84, 221241.CrossRefGoogle Scholar
DAFM 2010. FoodHarvest 2020: a vision for Irish agri-food and fisheries. Retrieved June 1, 2015, from http://www.agriculture.gov.ie/media/migration/agri-foodindustry/foodharvest2020/2020FoodHarvestExeSummary240810.pdf Google Scholar
Dalkey, N and Helmer, O 1963. An experimental application of the Delphi method to the use of experts. Management Science 9, 458467.CrossRefGoogle Scholar
Dearden, J and Hunter, E 2012. Developing opportunities for engagement through food waste recycling. In Proceedings of the 12th Annual Australasian Campuses towards Sustainability Conference, 26–28 September, Brisbane, Australia.Google Scholar
de Wit, JN 1998. Nutritional and functional characteristics of whey proteins in food products. Journal of Dairy Science 81, 597608.CrossRefGoogle ScholarPubMed
Dimitrow, MS, Mykkänen, SI, Leikola, SNS, Kivelä, S-L, Lyles, A and Airaksinen, MSA 2014. Content validations of a tool for assessing risks for drug-related problems to be used by practical nurses caring for home-dwelling clients aged ≥65 years: a Delphi survey. European Journal of Clinical Pharmacology 70, 9911002.CrossRefGoogle Scholar
Donaldson, T and Preston, LE 1995. The stakeholder theory of the corporation: concepts, evidence and implications. The Academy of Management Review 20, 6591.CrossRefGoogle Scholar
Donnellan, T, Hennessy, T, Keane, M and Thorne, F 2011. Study of the international competitiveness of Irish dairy sector at farm level. Retrieved April 20, 2015, from http://www.teagasc.ie/publications/2011/1004/CompetitivenessofMilkProductionweb230611.pdf Google Scholar
Donnellan, T, Hennessy, T and Thorne, F 2015. The end of the quota era: a history of the Irish dairy sector and future prospects. Retrieved April 20, 2015, from http://www.teagasc.ie/publications/2015/3541/End_of_the_Quota_Era_final.pdf Google Scholar
Dooley, AE, Parker, WJ, Blair, HT and Lopez-Villalobos, N 2006. Selection and segregation of herds for a valuable milk trait. Livestock Science 102, 6071.CrossRefGoogle Scholar
EFFAB 2014. Code of good practice for farm animal breeding and reproduction organisations. Retrieved August 20, 2014, from http://www.responsiblebreeding.eu/uploads/2/3/1/3/23133976/code_of_good_practice_for_animal_breeding_and_reproduction_organisations_2014_-_2016.pdf Google Scholar
Farrell, HM, Jimenez-Flores, R, Bleck, GT, Brown, EM, Butler, JE, Creamer, LK, Hicks, CL, Hollar, CM, Ng-Kwai-Hang, KF and Swaisgood, HE 2004. Nomenclature of the proteins of cows’ milk – sixth revision. Journal of Dairy Science 87, 16411674.CrossRefGoogle ScholarPubMed
Foegeding, EA, Davis, JP, Doucet, D and McGuffey, MK 2002. Advances in modifying and understanding whey protein functionality. Trends in Food Science and Technology 13, 151159.CrossRefGoogle Scholar
Gibson, JP and Kennedy, BW 1990. The use of constrained selection indexes in breeding for economic merit. Theoretical and Applied Genetics 80, 801805.CrossRefGoogle ScholarPubMed
Grunert, KG 2006. Future trends and consumer lifestyles with regard to meat consumption. Meat Science 74, 149160.CrossRefGoogle ScholarPubMed
Gustavsson, F, Buitenhuis, AJ, Johansson, M, Bertelsen, HP, Glantz, M and Poulsen, NA 2014. Effects of breed and casein genetic variants on protein profile in milk from Swedish Red, Danish Holstein, and Danish Jersey cows. Journal of Dairy Science 97, 38663877.CrossRefGoogle ScholarPubMed
Hardy, DJ, O’Brien, AP, Gaskin, CJ, O’Brien, AJ, Morrison-Ngatai, E, Skews, G, Ryan, T and McNulty, N 2004. Practical application of the Delphi technique in a bicultural mental health nursing study in New Zealand. Journal of Advanced Nursing 46, 95109.CrossRefGoogle Scholar
Harris, DL 1970. Breeding for efficiency in livestock production: defining the economic objectives. Journal of Animal Science 30, 860865.CrossRefGoogle Scholar
Hasson, F, Keeney, S and McKenna, H 2000. Research guidelines for the Delphi survey technique. Journal of Advanced Nursing 32, 10081015.CrossRefGoogle ScholarPubMed
Heck, JM, van Valenberg, HJ, Bovenhuis, H, Dijkstra, J and van Hooijdonk, TC 2012. Characterization of milk fatty acids based on genetic and herd parameters. Journal of Dairy Research 79, 3946.CrossRefGoogle ScholarPubMed
Heck, JML 2009. Milk genomics, opportunities to improve the protein and fatty acid composition in raw milk. Thesis PhD, Wageningen University, Wageningen, The Netherlands.Google Scholar
Heck, JML, van Valenberg, HJF, Dijkstra, J and van Hooijdonk, ACM 2009. Seasonal variation in the Dutch bovine raw milk composition. Journal of Dairy Science 92, 47454755.CrossRefGoogle ScholarPubMed
Henchion, M and McIntyre, B 2005. Market access and competitiveness issues for food SMEs in Europe’s lagging rural regions (LRRs). British Food Journal 107, 404422.CrossRefGoogle Scholar
Henchion, M, McIntyre, B and Commins, P 2002. Forecasting the supply chain environment for food SMEs in Ireland: a Delphi approach. In Proceedings of the Fifth International Conference on Chain and Network Management in Agribusiness and the Food Industry, Paradoxes in Food Chains and Networks, Noodwijk, 6–8 June (ed. JH Trienekens and SWF Omta), pp. 780–791. Wageningen Academic Publishers, The Netherlands.CrossRefGoogle Scholar
Hsu, C 2007. The Delphi technique: making sense of consensus, Practical assessment, research and evaluation 12. Retrieved August 19, 2014, from http://pareonline.net/pdf/v12n10.pdf Google Scholar
Lincoln, YS and Guba, EG 1985. Naturalistic inquiry. Sage, London.CrossRefGoogle Scholar
Mathur, VN, Price, ADF and Austin, SA 2008. Conceptualizing stakeholder engagement in the context of sustainability and its assessment. Construction Management and Economics 26, 601609.CrossRefGoogle Scholar
McParland, S, Giblin, L, Veerkamp, RF and Berry, DP 2010. The impact of selection on milk production on the lactoferrin content of milk in Irish Holstein-Friesians. In Proceedings of the British Society of Animal Science and the Agricultural Research Forum. Advances in Animal Biosciences, 305pp.CrossRefGoogle Scholar
Mele, M, Zotto, RD, Cassandro, M, Conte, G, Serra, A, Buccioni, A, Bittante, G and Secchiari, P 2009. Genetic parameters for conjugated linoleic acid, selected milk fatty acids, and milk fatty acid unsaturation of Italian Holstein-Friesian cows. Journal of Dairy Science 92, 392400.CrossRefGoogle ScholarPubMed
Miglior, F, Muir, BL and Van Doormaal, BJ 2005. Selection indices in Holstein cattle of various countries. Journal of Dairy Science 88, 12551263.CrossRefGoogle ScholarPubMed
Miglior, F, Sewalem, A, Jamrozik, J, Lefebvre, DM and Moore, RK 2006. Analysis of milk urea nitrogen and lactose and their effect on longevity in Canadian dairy cattle. Journal of Dairy Science 89, 48864894.CrossRefGoogle ScholarPubMed
Mills, S, Ross, RP, Hill, C, Fitzgerald, GF and Stanton, C 2011. Milk intelligence: mining milk for bioactive substances associated with human health. International Dairy Journal 21, 377401.CrossRefGoogle Scholar
More, SJ, Clegg, TA, Lynch, PJ and O’Grady, L 2013. The effect of somatic cell count data adjustment and interpretation, as outlined in European Union legislation, on herd eligibility to supply raw milk for processing of dairy products. Journal of Dairy Science 96, 36713681.CrossRefGoogle ScholarPubMed
Mrode, RA 2005. Linear models for the prediction of animal breeding values, 2nd edition. CAB International, Wallingford, Oxfordshire, UK.CrossRefGoogle Scholar
Murphy, MK, Black, NA, Lamping, DL, McKee, CM, Sanderson, CDB, Askham, J and Marteau, T 1998. Consensus development methods, and their use in clinical guideline development: a review. Retrieved May 29, 2015, from http://www.journalslibrary.nihr.ac.uk/__data/assets/pdf_file/0003/64839/FullReport-hta2030.pdf Google Scholar
Nielsen, HM, Christensen, LG and Groen, AF 2005. Derivation of sustainable breeding goals for dairy cattle using selection index theory. Journal of Dairy Science 88, 18821890.CrossRefGoogle ScholarPubMed
Nielsen, HM, Olesen, I, Navrud, S, Kolstad, K and Amer, P 2011. How to consider the value of farm animals in breeding goals. A review of current status and future challenges. Journal of Agricultural and Environmental Ethics 24, 309330.CrossRefGoogle Scholar
O’Halloran, F, Berry, DP, Bahar, B, Howard, D, Sweeney, T and Giblin, L 2010. Polymorphisms in the bovine lactoferrin promoter are associated with reproductive performance and somatic cell count. Journal of Dairy Science 93, 12531259.CrossRefGoogle ScholarPubMed
Oltenacu, P and Broom, D 2010. The impact of genetic selection for increased milk yield on the welfare of dairy cows. Animal Welfare 19, 3949.CrossRefGoogle Scholar
Parmar, BL, Freeman, RE, Harrison, JS, Wicks, AC, de Colle, S and Purnell, L 2010. Stakeholder theory: the state of the art. The Academy of Management Annals 4, 403–445.CrossRefGoogle Scholar
Quinn Patton, M 2002. Qualitative research & evaluation methods, 3rd edition. Sage Publications, London, England.Google Scholar
Rattray, W and Jelen, P 1996. Protein standardization of milk and dairy products. Trends in Food Science and Technology 7, 227234.CrossRefGoogle Scholar
Ribeiro, BE and Quintanilla, MA 2015. Transitions in biofuel technologies: an appraisal of the social impacts of cellulosic ethanol using the Delphi method. Technological Forecasting and Social Change 92, 5368.CrossRefGoogle Scholar
Seale, C 1999. Quality in qualitative research. Qualitative Inquiry 5, 465478.CrossRefGoogle Scholar
Smith, B, Torrance, N, Ferguson, J, Bennett, M, Serpell, M and Dunn, K 2012. Towards a definition of refractory neuropathic pain for epidemiological research. An international Delphi survey of experts. BMC Neurology, May 28, 1229.Google ScholarPubMed
Soyeurt, H, Bastin, C, Colinet, FG, Arnould, VMR, Berry, DP, Wall, E, Dehareng, F, Nguyen, HA, Dardenne, P, Schefers, J, Vandenplas, J, Weigel, K, Coffey, M, Theron, L, Detilleux, J, Reding, E, Gengler, N and McParland, S 2012. Mid-infrared prediction of lactoferrin content in bovine milk: potential indicator of mastits. Animal 6, 18301838.CrossRefGoogle Scholar
Soyeurt, H, Dardenne, P, Dehareng, F, Bastin, C and Gengler, N 2008. Genetic parameters of saturated and monounsaturated fatty acid content and the ratio of saturated to unsaturated fatty acids in bovine milk. Journal of Dairy Science 91, 36113626.CrossRefGoogle ScholarPubMed
Soyeurt, H, Dardenne, P, Gillon, A, Croquet, C, Vanderick, S, Mayeres, P, Bertozzi, C and Gengler, N 2006. Variation in fatty acid contents of milk and milk fat within and across breeds. Journal of Dairy Science 89, 48584865.CrossRefGoogle ScholarPubMed
Soyeurt, H, Dehareng, F, Gengler, N, McParland, S, Wall, E, Berry, DP and Coffey, M 2011. Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems, and countries. Journal of Dairy Science 94, 16571667.CrossRefGoogle ScholarPubMed
Soyeurt, H, Misztal, I and Gengler, N 2010. Genetic variability of milk components based on mid-infrared spectral data. Journal of Dairy Science 93, 17221728.CrossRefGoogle ScholarPubMed
Veerkamp, RF, Dillon, P, Kelly, E, Cromie, AR and Groen, AF 2002. Dairy cattle breeding objectives combining yield, survival and calving interval for pasture-based systems in Ireland under different milk quota scenarios. Livestock Production Science 76, 137151.CrossRefGoogle Scholar
Vigani, M, Parisi, C, Rodriguez-Cerezo, E, Barbosa, MJ, Sijtsma, L, Ploeg, M and Enzing, C 2015. Food and feed products from micro-algae: market opportunities and challenges for the EU. Trends in Food Science and Technology 42, 8192.CrossRefGoogle Scholar
Ward, W, Stebbings, S, Sherman, KJ, Cherkin, C and Baxter, GD 2014. Establishing key components of yoga interventions for musculoskeletal conditions: a Delphi survey. BMC Complementary and Alternative Medicine 14, 196.CrossRefGoogle ScholarPubMed
Winkelman, AM, Johnson, DL and MacGibbon, AKH 1999. Estimation of heritabilities and correlations associated with milk color traits. Journal of Dairy Science 82, 215224.CrossRefGoogle ScholarPubMed
Woodford, KB 2007. A2 milk, farmer decisions, and risk management. In Proceeding of the 16th International Farm Management Congress, Agriculture and Life Sciences Divisions, Lincoln University, Christchurch, New Zealand.Google Scholar
Supplementary material: File

Henchion supplementary material

Table S1

Download Henchion supplementary material(File)
File 20.2 KB