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Optimal culling strategy in relation to biological and economic efficiency and annualized net revenue in the Japanese Black cow–calf production system

Published online by Cambridge University Press:  21 April 2011

K. OISHI ,
T. IBI ,
A. K. KAHI  and
H. HIROOKA
K. OISHI*
Affiliation:
Laboratory of Animal Husbandry Resources, Division of Applied Biosciences, Graduate School of Agriculture, Kyoto University, 606 8502 Kyoto, Japan
T. IBI
Affiliation:
Laboratory of Animal Breeding and Genetics, Division of Bioscience, Graduate School of Natural Science and Technology, Okayama University, 700 8530 Okayama, Japan
A. K. KAHI
Affiliation:
Animal Breeding and Genetics Group, Department of Animal Sciences, Egerton University, P.O. Box 536, 20115 Egerton, Kenya
H. HIROOKA
Affiliation:
Laboratory of Animal Husbandry Resources, Division of Applied Biosciences, Graduate School of Agriculture, Kyoto University, 606 8502 Kyoto, Japan
*
*To whom all correspondence should be addressed. Email: [email protected]
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Summary

The objective of the present study was to determine the optimal culling strategy in relation to biological and economic efficiency (BE and EE, respectively) and annualized net revenue (AN) in the Japanese Black cow–calf production system with special reference to the beef quality of culled cows. The herd model focused on two ways of mating: one-mating trial system (ONE) and continuous-mating trial system (CON). ONE assumed that heifers that fail to conceive are culled and cows that fail to conceive are culled at weaning of their calves, while CON assumed that mating continues until all females theoretically conceive. Least square means of carcass data of Japanese Black cows collected from a cooperative farm in Japan were used to estimate the carcass price of a cow by parity and Beef Marbling Standard (BMS) number. The simulation, assuming the current production situation in Japan, indicated that sales of culled cows accounted for 0·10–0·20 of total sales and was an important element in total production. Comparisons between ONE and CON showed that production efficiency in the current situation is higher in CON. The BE, EE and AN were higher in CON than in ONE. The two economic indicators were less sensitive to changes in annual discount rate but highly sensitive to changes in female calf price and BMS number of cows, indicating the importance of considering fluctuations in calf price and potential quality of culled cows’ carcasses when estimating the economically optimal parity of culling. The three indicators derived different optimal solutions even in the same mating trial systems, stressing the importance of choice of production indicators when determining the culling strategy and evaluating animal production.

Type
Modelling Animal Systems
Information
The Journal of Agricultural Science , Volume 149 , Issue 6 , December 2011 , pp. 783 - 799
Copyright
Copyright © Cambridge University Press 2011

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References

REFERENCES

Agricultural and Food Research Council (AFRC) (1993). Energy and Protein Requirements of Ruminants. An advisory manual prepared by the Agricultural and Food Research Council Technical Committee on Responses to Nutrients. Wallingford, UK: CAB International.Google Scholar
Apple, J. K. (1999). Influence of body condition score on live and carcass value of cull beef cows. Journal of Animal Science 77, 2610–2620.Google Scholar
Bailie, J. H. (1982). The influence of breeding management efficiency on dairy herd performance. Animal Production 34, 315–323.Google Scholar
Baptist, R. (1992). Derivation of steady-state herd productivity. Agricultural Systems 39, 253–272.Google Scholar
Barry, P. J., Ellinger, P. N., Hopkin, J. A. & Baker, C. B. (1995). Financial Management in Agriculture, 5th edn. Danville, IL: Interstate Publishers Inc.Google Scholar
Bekman, H. & Van Arendonk, J. A. M. (1993). Derivation of economic values for veal, beef and milk production traits using profit equations. Livestock Production Science 34, 35–56.Google Scholar
Bourdon, R. M. & Brinks, J. S. (1987). Simulated efficiency of range beef production. III. Culling strategies and nontraditional management systems. Journal of Animal Science 65, 963–969.Google Scholar
Brody, S. (1945). Bioenergetics and Growth. New York: Reinhold Publishing Corporation.Google Scholar
Cartwright, T. C. (1979). Size as a component of beef production efficiency: Cow-calf production. Journal of Animal Science 48, 974–980.CrossRefGoogle Scholar
Clarke, S. E., Gaskins, C. T., Hillers, J. K. & Hohenboken, W. D. (1984 a). Mathematical modeling of alternative culling and crossbreeding strategies in beef production. Journal of Animal Science 58, 6–14.Google Scholar
Clarke, S. E., Gaskins, C. T., Hillers, J. K. & Hohenboken, W. D. (1984 b). Mathematical modeling of alternative selection strategies for beef production. Journal of Animal Science 59, 308–316.Google Scholar
Congleton, W. R. Jr. & King, L. W. (1985). Culling cows on predicted future profitability with a variable planning horizon. Journal of Dairy Science 68, 2970–2983.Google Scholar
Dickerson, G. (1970). Efficiency of animal production – moulding the biological component. Journal of Animal Science 30, 849–859.Google Scholar
Fowler, V. R., Bichard, M. & Pease, A. (1976). Objectives in pig breeding. Animal Production 23, 365–387.Google Scholar
Garcia, F. & Agabriel, J. (2008). CompoCow: a predictive model to estimate variations in body composition and the energy requirements of cull cows during finishing. Journal of Agricultural Science, Cambridge 146, 251–265.Google Scholar
Harris, D. L. & Newman, S. (1994). Breeding for profit: synergism between genetic improvement and livestock production (a review). Journal of Animal Science 72, 2178–2200.Google Scholar
Hirooka, H., Groen, A. F. & Hillers, J. (1998 a). Developing breeding objectives for beef cattle production. 1. A bio-economic simulation model. Animal Science 66, 607–621.Google Scholar
Hirooka, H., Groen, A. F. & Hillers, J. (1998 b). Developing breeding objectives for beef cattle production. 2. Biological and economic values of growth and carcass traits in Japan. Animal Science 66, 623–633.Google Scholar
Ibi, T., Hirooka, H., Kahi, A. K., Sasae, Y. & Sasaki, Y. (2005). Genotype × environment interaction effects on carcass traits in Japanese Black cattle. Journal of Animal Science 83, 1503–1510.Google Scholar
Japan Livestock Technology Association (JLTA) (2007). Manual To Prevent Loss of Japanese Black Calves. Tokyo: Japan Livestock Technology Association. (In Japanese).Google Scholar
Japanese Meat Grading Association (JMGA) (1988). New Beef Grading Standards. Tokyo: Japanese Meat Grading Association (In Japanese).Google Scholar
Kristensen, A. R. (1987). Optimal replacement and ranking of dairy cows determined by a hierarchic Markov process. Livestock Production Science 16, 131–144.Google Scholar
Kuipers, A. (1982). Development and economic comparison of selection criteria for cows and bulls with a dairy herd simulation model. Ph.D. thesis. Agricultural Research Reports 913. Wageningen, The Netherlands: Centre for Agricultural Publishing and Documentation.Google Scholar
Liinamo, A. E. & Van Arendonk, J. A. M. (1999). Combining selection for carcass quality, body weight, and milk traits in dairy cattle. Journal of Dairy Science 82, 802–809.CrossRefGoogle Scholar
Meadows, C., Rajala-Schultz, P. J. & Frazer, G. S. (2005). A spreadsheet-based model demonstrating the nonuniform economic effects of varying reproductive performance in Ohio dairy herds. Journal of Dairy Science 88, 1244–1254.Google Scholar
Melton, B. E., Colette, W. A., Smith, K. & Willham, R. L. (1994). A time-dependent bioeconomic model of commercial beef breed choices. Agricultural Systems 45, 331–347.CrossRefGoogle Scholar
Miller, S. P., Wilton, J. W. & Pfeiffer, W. C. (1999). Effects of milk yield on biological efficiency and profit of beef production from birth to slaughter. Journal of Animal Science 77, 344–352.Google Scholar
Minchin, W., O'Donovan, M., Buckley, F., Kenny, D. A. & Shalloo, L. (2010). Development of a decision support tool to evaluate the financial implications of cull cow finishing under different feeding strategies. Journal of Agricultural Science, Cambridge 148, 433–443.Google Scholar
Ministry of Agriculture, Forestry and Fisheries of Japan (MAFF) (2008). The Change in Average Price of Beef Calves. Tokyo: Livestock Industry Department, Agricultural Production Bureau, Ministry of Agriculture, Forestry and Fisheries of Japan (In Japanese).Google Scholar
Ministry of Agriculture, Forestry and Fisheries of Japan (MAFF) (2009). The 84th Statistical Yearbook of Agriculture, Forestry and Fisheries. Tokyo: Statistics division of the Ministry of Agriculture, Forestry and Fisheries of Japan (In Japanese).Google Scholar
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.Google Scholar
Naazie, A., Makarechian, M. & Hudson, R. J. (1999). Evaluation of life-cycle herd efficiency in cow-calf systems of beef production. Journal of Animal Science 77, 1–11.CrossRefGoogle ScholarPubMed
National Agriculture and Food Research Organization (NARO). (2009). Japanese Feeding Standard for Beef Cattle, 5th edn. Tokyo: National Agriculture and Food Research Organization, Central Association of Livestock Industry (In Japanese).Google Scholar
Perrin, R. K. (1972). Asset replacement principles. American Journal of Agricultural Economics 54, 60–67.Google Scholar
Renquist, B. J., Oltjen, J. W., Sainz, R. D. & Calvert, C. C. (2006). Effects of age on body condition and production parameters of multiparous beef cows. Journal of Animal Science 84, 1890–1895.Google Scholar
Rogers, G. W., Van Arendonk, J. A. M. & McDaniel, B. T. (1988). Influence of production and prices on optimum culling rates and annualized net revenue. Journal of Dairy Science 71, 3453–3462.CrossRefGoogle Scholar
Rogers, L. R. F. (1972). Economics of replacement rates in commercial beef herds. Journal of Animal Science 34, 921–925.CrossRefGoogle Scholar
SAS (1999). SAS/STAT User's Guide. Version 8. Cary, NC: SAS Institute Inc.Google Scholar
Sawyer, J. E., Mathis, C. P. & Davis, B. (2004). Effects of feeding strategy and age on live animal performance, carcass characteristics, and economics of short-term feeding programs for culled beef cows. Journal of Animal Science 82, 3646–3653.Google Scholar
Seegers, H., Bareille, N. & Beaudeau, F. (1998). Effects of parity, stage of lactation and culling reason on the commercial carcass weight of French Holstein cows. Livestock Production Science 56, 79–88.Google Scholar
Stewart, H. M., Burnside, E. B., Wilton, J. W. & Pfeiffer, W. C. (1977). A dynamic programming approach to culling decisions in commercial dairy herds. Journal of Dairy Science 60, 602–617.CrossRefGoogle Scholar
Taylor, S. T. 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
Tess, M. W., Bennett, G. L. & Dickerson, G. E. (1983). Simulation of genetic changes in life cycle efficiency of pork production. I. A bioeconomic model. Journal of Animal Science 56, 336–353.Google 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.Google Scholar
Veysset, P., Bebin, D. & Lherm, M. (2005). Adaptation to Agenda 2000 (CAP reform) and optimisation of the farming system of French suckler cattle farms in the Charolais area: a model-based study. Agricultural Systems 83, 179–202.Google Scholar
Williams, C. B., Bennett, G. L. & Keele, J. W. (1995). Simulated influence of postweaning production system on performance of different biological types of cattle. III. Biological efficiency. Journal of Animal Science 73, 686–698.Google Scholar
Wolfová, M., Wolf, J., Krupová, Z. & Kica, J. (2009). Estimation of economic values for traits of dairy sheep: I. Model development. Journal of Dairy Science 92, 2183–2194.Google Scholar
Wood, P. D. P. (1967). Algebraic model of the lactation curve in cattle. Nature 216, 164–165.Google Scholar
Yager, W. A., Greer, R. C. & Burt, O. R. (1980). Optimal policies for marketing cull beef cows. American Journal of Agricultural Economics 62, 456–467.Google Scholar