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Development of a decision support tool to evaluate the financial implications of cull cow finishing under different feeding strategies

Published online by Cambridge University Press:  08 April 2010

W. MINCHIN ,
M. O'DONOVAN ,
F. BUCKLEY ,
D. A. KENNY  and
L. SHALLOO
W. MINCHIN
Affiliation:
Dairy Production Research Centre, Teagasc, Moorepark, Fermoy, Cork, Ireland School of Agriculture, Food Science and Veterinary Medicine, UCD, Belfield, Dublin 4, Ireland
M. O'DONOVAN
Affiliation:
Dairy Production Research Centre, Teagasc, Moorepark, Fermoy, Cork, Ireland
F. BUCKLEY
Affiliation:
Dairy Production Research Centre, Teagasc, Moorepark, Fermoy, Cork, Ireland
D. A. KENNY
Affiliation:
School of Agriculture, Food Science and Veterinary Medicine, UCD, Belfield, Dublin 4, Ireland
L. SHALLOO*
Affiliation:
Dairy Production Research Centre, Teagasc, Moorepark, Fermoy, Cork, Ireland
*
*To whom all correspondence should be addressed. Email: Laurence.Shalloo@teagasc.ie
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Summary

The objective of the current study was to develop, validate and describe a decision support system (DSS) to evaluate cull dairy cow finishing strategies. The DSS was developed within a Microsoft Excel framework. The purpose of a DSS is to assist the process of making accurate and repeatable calculations, assisting the decision on which cull cow finishing strategy is most profitable under individual farm circumstances. The model was based on data from two evaluation experiments including eight finishing strategies in total: ad libitum grass silage (GS); GS+3 kg concentrate (GS+3); GS+6 kg concentrate (GS+6); GS+9 kg concentrate (GS+9); ad libitum grass silage prior to ad libitum spring grass (GS+G); 0·75 grass silage and 0·25 straw prior to ad libitum spring grass (GS+S) and finally grass silage plus 6 kg concentrate dry matter (DM)/cow/day and milked twice daily prior to ad libitum spring grass (EXTLAC). Stochastic budgeting was included in the model to account for variability in key input and output variables on the overall profitability of various finishing strategies. The stochastic input and output variables included in the model were initial carcass value, feed strategy, concentrate cost and final carcass value. Net profit per cow was selected as the output distribution. The mean net profit per cow with the GS, GS+3, GS+6, GS+9, GS+G, GS+S and EXTLAC was €85·3, €73·7, €95·6, €58·5, €158·8 and €186·8 and €283·0, respectively. Profitability for the EXTLAC strategy was stochastically dominant to all other strategies evaluated meaning a higher level of profit and a lower level of risk is associated with the EXTLAC strategy. The optimal strategy of cull cow beef production depends greatly on the prevailing economic environment, purchase and sale price, milk price, feed costs, housing and labour.

Type
Animals
Information
The Journal of Agricultural Science , Volume 148 , Issue 4 , August 2010 , pp. 433 - 443
Copyright
Copyright © Cambridge University Press 2010

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References

REFERENCES

Binfield, J., Donnellan, T., Hanrahan, K. & Westhoff, P. (2008). FAPRI-Ireland Baseline 2007: Outlook for EU and Irish Agriculture. Carlow, Ireland: FAPRI-Ireland Partnership Teagasc.Google Scholar
Bourdon, R. M. (1998). Shortcomings of current genetic evaluation systems. Journal of Animal Science 76, 2308–2323.CrossRefGoogle ScholarPubMed
Bourdon, R. M. & Brinks, J. S. (1987). Simulated efficiency of range beef production. I. Growth and milk production. Journal of Animal Science 65, 943–955.CrossRefGoogle ScholarPubMed
Central Statistics Office (CSO). (2008). Principal Statistics in Relation to Agriculture. Cork, Ireland: Central Statistics Office.Google Scholar
Crosson, P., O'Kiely, P., O'Mara, F. P. & Wallace, M. (2006). The development of a mathematical model to investigate Irish beef production systems. Agricultural Systems 89, 349–370.CrossRefGoogle Scholar
Department of Agriculture, Food and Rural Development (DAF). (2006). Agricultural Production, Beef Carcass Classification Figures. Available online at http://www.agriculture.gov.ie/farmingsectors/beef (verified 9 February 2010).Google Scholar
Department of Agriculture, Food and Rural Development (DAF). (2008). Main Report of the Agri-Food 2010 Committee, Government Publications Office. Available online at http://www.agriculture.gov.ie/publications/2008 (verified 9 February 2010).Google Scholar
Dillon, P., Crosse, S., Stakelum, G. & Flynn, F. (1995). The effect of calving date and stocking rate on the performance of spring-calving dairy cows. Grass Forage Science 50, 286–299.CrossRefGoogle Scholar
Evans, R. D., Dillon, P., Buckley, F., Berry, D. P., Wallace, M., Ducrocq, V. & Garrick, D. J. (2006). Trends in milk production, calving rate and survival of cows in 14 Irish dairy herds as a result of the introgression of Holstein-Friesian genes. Animal Science 82, 423–434.CrossRefGoogle Scholar
Fischer Boel, M. (2007). EU milk quotas should be scrapped. The Dairy Site press release, January 2007. Available online at http://www.thedairysite.com/news/14787/eu-milk-quotas-should-be-scrapped-fischer-boel (verified 01 February 2010).Google Scholar
Frey, H. C. & Patil, S. R. (2002). Identification and review of sensitivity analysis methods. Risk Analysis 22, 553–578.CrossRefGoogle ScholarPubMed
Groves, J. & Da Rin, J. (1999). Economic and Social Impacts of Farm Internet Use. Rural Industries Research and Development Corporation. Available online at: http://rirdc.infoservices.com.au/items/99-035 (verified 9 February 2010).Google Scholar
Hennessy, T. & Thorne, F. (2006). The Impact of WTO Doha Development Round on Farming in Ireland. Galway, Ireland: Teagasc, Rural Economy Research Centre.Google Scholar
Hughes, H. (1995). IRM-FARMS Databank, 1994 Herds. North Dakota, USA: Department of Agricultural Economics, North Dakota State University.Google Scholar
Isukapalli, S. S., Roy, A. & Georgopoulos, P. G. (1998). Stochastic response surface methods (SRSMs) for uncertainty propagation: application to environmental and biological systems. Risk Analysis 18, 351–363.CrossRefGoogle ScholarPubMed
Jarrige, J. (1989). Ruminant Nutrition – Recommended Allowances and Feed Tables. London, UK: John Libby and Co. Ltd.Google Scholar
Jenkins, T. G. & Williams, C. B. (1998). DECI – decision evaluator for the cattle industry. In Proceedings of 6th World Congress on Genetics Applications for Livestock Production 27, 461–462. Armidale, Australia.Google Scholar
Kinghorn, B. P. (1999). Total resource management: tactical optimisation of vertically integrated animal production systems. In Proceedings of Inaugural Australian Workshop on the Application of Artificial Intelligence. Optimisation and Bayesian Methods in Agriculture (Eds Abbass, H. A. & Towsey, M.), pp. 95–100. Brisbane, Australia: Queensland University of Technology Press.Google Scholar
Lien, G. (2003). Assisting whole-farm decision-making through stochastic budgeting. Agricultural Systems 76, 399–413.CrossRefGoogle Scholar
Lowman, B. G., Scott, N. & Somerville, S. (1976). Condition Scoring of Cattle, Revised edn. Bulletin No. 6. Edinburgh, UK: East of Scotland College of Agriculture.Google Scholar
Lynch, T., Gregor, S. & Midmore, D. (2000). Intelligent support systems in agriculture: how can we do better? Australian Journal of Experimental Agriculture 40, 609–620.CrossRefGoogle Scholar
McCarthy, S., Horan, B., Dillon, P., O'Connor, P., Rath, M. & Shalloo, L. (2007). Economic comparison of divergent strains of Holstein-Friesian cows in various pasture-based production systems. Journal of Dairy Science 90, 1493–1505.CrossRefGoogle ScholarPubMed
Minchin, W., Buckley, F., Kenny, D. A., Monahan, F. J., Shalloo, L. & O'Donovan, M. (2009 a). Effect of grass silage and concentrate based finishing strategies on cull dairy cow performance, carcass and meat quality characteristics. Meat Science 81, 93–101.CrossRefGoogle ScholarPubMed
Minchin, W., Buckley, F., Kenny, D. A., Keane, M. G., Shalloo, L. & O'Donovan, M. (2009 b). Prediction of cull cow carcass characteristics from live weight and body condition score measured pre slaughter. Irish Journal of Agricultural and Food Research 48, 75–86.Google Scholar
Minchin, W., Buckley, F., Kenny, D. A., Monahan, F. J., Shalloo, L. & O'Donovan, M. (2010). An evaluation of over-wintering feeding strategies prior to finishing at pasture for cull dairy cows on live animal performance, carcass and meat quality characteristics. Meat Science. doi:10.1016/j.meatsci.2010.02.005.CrossRefGoogle ScholarPubMed
Naazie, A., Makarechian, M. & Hudson, R. J. (1997). Efficiency of beef production systems: description and preliminary evaluation of a model. Agricultural Systems 54, 357–380.CrossRefGoogle Scholar
Newman, S., Lynch, T. & Plummer, A. A. (1999). Aspects of success and failure of decision support systems in agriculture. In Proceedings of Inaugural Australian Workshop on the Application of Artificial Intelligence. Optimisation and Bayesian Methods in Agriculture (Eds Abbass, H. A. and Towsey, M.), pp. 17–30. Brisbane, Australia: Queensland University of Technology Press.Google Scholar
Newman, S., Lynch, T. & Plummer, A. A. (2000). Success and failure of decision support systems: learning as we go. Journal of Animal Science 77, 1–12.CrossRefGoogle Scholar
Notter, D. R., Sanders, J. O., Dickerson, G. E., Smith, G. M. & Cartwright, T. C. (1979). Simulated efficiency of beef production for a Midwestern cow-calf-feedlot management system. I. Milk production. Journal of Animal Science 49, 70–82.CrossRefGoogle Scholar
Palisade, . (2006). @Risk Advanced Risk Analysis for Spreadsheets. Newfield, NY: Palisade Corporation.Google Scholar
Petersen, B. J. (2000). Probabilistic modelling: theory and practice. Food Additives and Contaminants 17, 591–599.CrossRefGoogle ScholarPubMed
Phillips, C. V. (2000). Applying fully articulated distributions. American Journal of Epidemiology 151, Supplement 41.Google Scholar
Phillips, C. V. & Maldonado, G. M. (1999). Using Monte-Carlo methods to quantify multiple sources of error in studies. American Journal of Epidemiology 149, Supplement 17.Google Scholar
Rogers, C. A., Fitzgerald, A. C., Carr, M. A., Covey, B. R., Thomas, J. D. & Looper, M. L. (2004). On-Farm management decisions to improve beef quality of market dairy cows. Journal of Dairy Science 87, 1558–1564.CrossRefGoogle ScholarPubMed
Snders, J. O. & Cartwright, T. C. (1979). A general cattle production systems model. I. Structure of the model. Agricultural Systems 4, 217–227.CrossRefGoogle Scholar
Shalloo, L., Dillon, P., Rath, M. & Wallace, M. (2004). Description and validation of the Moorepark Dairy Systems model. Journal of Dairy Science 87, 1945–1959.CrossRefGoogle Scholar
Smith, B. (2008). Extra milk quota good for farmers, good for processors and good for the economy. Department of Agriculture, Fisheries and Food Press Release. Available online at http://www.agriculture.gov.ie/press/pressreleases/2008/november/title,16120,en.htmlGoogle Scholar
Smith, D. J. (1994). Incorporating risk into capital budgeting decisions using simulation. Management Decision 32, 20–26.CrossRefGoogle Scholar
Taylor, St. C. S., Moore, A. J., Thiessen, R. B. & Bailey, C. M. (1985). Efficiency of food utilization in traditional and sex-controlled systems of beef production. Animal Production 40, 401–440.Google Scholar
Teagasc (2008). Management Data for Farm Planning. Dublin, Ireland: Teagasc.Google Scholar
Thomas, C. V., DeLorenzo, M. A., Bray, D. R., Weldon, R. N., Bucklin, R. A. & Martin, J. G. (1997). A stochastic economic analysis of large herringbone and parallel milking parlors. Journal of Dairy Science 80, 2418–2428.CrossRefGoogle Scholar
Van Arendonk, J. A. M. (1985). Studies on the replacement policies in dairy cattle. II. Optimum policy and influence of changes in production and prices. Livestock Production Science 13, 101–121.CrossRefGoogle Scholar