Dynamic programming to investigate financial impacts of mastitis control decisions in milk production systems

Abstract

S. An adaptive stochastic dynamic programming model was used to solve the optimum replacement decision problem for the dairy cow under a range of alternative mastitis control procedures. The model predicted that reducing milk yield losses and somatic cell count penalties by using milking machine test, postmilking teat disinfection and dry cow therapy added approximately £4, £10 and £13 respectively to an original annuity equivalent net present value for the replacement heifer of £286. Assuming that these procedures also reduced involuntary culling due to mastitis by 50 % added £8n90 to the annuity. This latter figure indicated that an important part of the benefit of mastitis control procedures might come from a reduction in the culling risk of persistent clinical cases. We concluded that the strength of the dynamic programming model in this context was that it provided an integrated evaluation of the various impacts of each alternative mastitis procedure in the long term, which is essential for correct economic evaluation of mastitis.Mastitis control programmes for the dairy cow generally include several procedures. The cost and efficacy of each procedure are different ; therefore, to identify the economically optimum combination of procedures requires an assessment of the marginal contribution of each (McInerney et al. 1992). In recent years, Howard et al. (1987), Gill et al. (1990), Miller (1991 and Yalcin et al. (1999) have assessed the marginal economic contribution of several mastitis control procedures in mastitis control programmes in the USA, Canada and Scotland respectively. All of these studies employed multiple regression analysis to establish the relationships between mastitis-induced financial losses and mastitis control procedures.Regression analysis is comparative static. It represents a snapshot of events taking place in a dynamic system (the milk production system) and assumes that other components of the system stay unchanged. It is a useful technique when the focus is a comparison of the performance of herds in their use of mastitis control procedures at a point in time. However, it offers only limited guidance to farmspecific mastitis control decisions because milk production, mastitis and mastitis control takes place in a dynamic environment dictated by many stochastic events. Outcomes of a mastitis control decision taken in such a system are influenced by these events. For example, other management policies (e.g. replacement and feeding ‡ For correspondence ; e-mail a.stott!ab.sac.ac.uk.

Economic effects of bovine mastitis and mastitis management: A review

“…Despite the high economic damage of mastitis (Table 1), the most recent paper found on calculating the average cost of mastitis is almost 10 years old. (52,53). In some publications (1,20,25,29,45), results were presented in such a way that we could not transform them to data that could be compared to other studies.…”

Estimation of economic values and financial losses associated with clinical mastitis and somatic cell score in Holstein dairy cattle

“…The effect of mastitis on future milk yield of a cow, milk quality (fat and protein content, SCC), reproductive traits and length of productive life have to be added to the direct CM losses, for example, when comparing costs for mastitis control programs with the financial benefits of decreasing CM incidence (Yalcin and Stott, 2000;Seegers et al, 2003). The objective of the present study was to estimate the economic value of mastitis incidence in line with the breeding goal or the aggregate genotype.…”

Decision tree analysis to evaluate dry cow strategies under UK conditions

“…Economic analysis comparing the use of dry cow antibiotic or internal teat sealant is not available. Most economic analyses have concluded that the use of dry cow antibiotic can be financially beneficial either in terms of effect on increased milk yield, lower SCC or reduced clinical mastitis compared with untreated cows (Berry et al 1997;Allore et al 1998 ;Yalcin et al 1999;Yalcin & Stott, 2000;Oliver et al 2003). Deciding which strategy to use, at the individual cow level, can be helped by decision tree analysis.…”