Comparing technical efficiency of farms with an automatic milking system and a conventional milking system

Changing from a conventional milking system (CMS) to an automatic milking system (AMS) necessitates a new management approach and a corresponding change in labor tasks. Together with labor savings, AMS farms have been found to have higher capital costs, primarily because of higher maintenance costs and depreciation. Therefore, it is hypothesized that AMS farms differ from CMS farms in capital:labor ratio and possibly their technical efficiency, at least during a transition learning period. The current study used actual farm accounting data from dairy farms in the Netherlands with an AMS and a CMS to investigate the empirical substitution of capital for labor in the AMS farms and to determine if the technical efficiency of the AMS farms differed from the CMS farms. The technical efficiency estimates were obtained with data envelopment analysis. The 63 AMS farms and the 337 CMS farms in the data set did not differ in general farm characteristics such as the number of cows, number of hectares, and the amount of milk quota. Farms with AMS have significantly higher capital costs (€12.71 per 100 kg of milk) than CMS farms (€10.10 per 100 kg of milk). Total labor costs and net outputs were not significantly different between AMS and CMS farms. A clear substitution of capital for labor with the adoption of an AMS could not be observed. Although the AMS farms have a slightly lower technical efficiency (0.76) than the CMS farms (0.78), a significant difference in these estimates was not observed. This indicates that the farms were not different in their ability to use inputs (capital, labor, cows, and land) to produce outputs (total farm revenues). The technical efficiency of farms invested in an AMS in 2008 or earlier was not different from the farms invested in 2009 or 2010, indicating that a learning effect during the transition period was not observed. The results indicate that the economic performance of AMS and CMS farms are similar. What these results show is that other than higher capital costs, the use of AMS rather than a CMS does not affect farm efficiency and that the learning costs to use an AMS are not present as measured by any fall in technical efficiency. Key words: automatic milking , dairy , data envelopment analysis , technical efficienc

Does clinical mastitis in the first 100 days of lactation 1 predict increased mastitis occurrence and shorter herd life in dairy cows?

The objectives of this study were to estimate the direct effects of clinical mastitis (CM) occurring in early productive life (defined as the first 100 d of the first lactation) of Holstein dairy cows on the future rate of CM occurrence and on the length of total productive lifetime. Information on CM cases and other data occurring in 55,144 lactations in 24,831 cows in 5 New York State Holstein herds was collected from January 2004 until February 2014. For the first objective, a generalized linear mixed model with a Poisson distribution was used to study the effects of CM cases occurring in the first 100 d of a cow's first lactation, as well as farm indicator and number of days in the cow's lifetime, on the future lifetime rate of CM. Only cows that had completed their productive life [i.e., all had been culled (or sold) or had died; n = 14,440 cows] were included in this analysis. For the second objective, a Cox proportional hazards model was used to study the effects of CM cases occurring in the first 100 d of a cow's first lactation on the length of total productive lifetime. The model was stratified by farm. All 24,831 cows were included in this analysis with right censoring. Cows experienced between 0 and 4 CM cases in the first 100 d of lactation 1. Over their lifetime, cows experienced between 0 and 25 CM cases. During the study period, 10% of all cows died and nearly half of all cows were culled. The average length of productive life, including censored observations, was 2.0 yr after first calving. Compared with cows having no CM cases in the first 100 d of lactation 1, cows with 1 CM case in that time period had a 1.5 times higher rate of total number of CM cases over their lifetime. Cows with 2 (or 3 or more) CM cases in the first 100 d of lactation 1 had a 1.7 times (or 2.6 times) higher rate of total number of CM cases over their lifetime. For each additional CM case occurring in the first 100 d of lactation 1, the hazard rate of culling increased by 34%. Given economic conditions for preferentially culling mastitic cows, the study findings may help farmers make optimal decisions with regard to culling of such cows.</p

Efficiency of dairy farms participating and not participating in veterinary herd health management programs

This paper compares farm efficiencies between dairies who were participating in a veterinary herd health management (VHHM) program with dairies not participating in such a program, to determine whether participation has an association with farm efficiency. In 2011, 572 dairy farmers received a questionnaire concerning the participation and execution of a VHHM program on their farms. Data from the questionnaire were combined with farm accountancy data from 2008 through 2012 from farms that used calendar year accounting periods, and were analyzed using Stochastic Frontier Analysis (SFA). Two separate models were specified: model 1 was the basic stochastic frontier model (output: total revenue; input: feed costs, land costs, cattle costs, non-operational costs), without explanatory variables embedded into the efficiency component of the error term. Model 2 was an expansion of model 1 which included explanatory variables (number of FTE; total kg milk delivered; price of concentrate; milk per hectare; cows per FTE; nutritional yield per hectare) inserted into the efficiency component of the joint error term. Both models were estimated with the financial parameters expressed per 100 kg fat and protein corrected milk and per cow. Land costs, cattle costs, feed costs and non-operational costs were statistically significant and positive in all models (P <0.01). Frequency distributions of the efficiency scores for the VHHM dairies and the non-VHHM dairies were plotted in a kernel density plot, and differences were tested using the Kolmogorov–Smirnov two-sample test. VHHM dairies had higher total revenue per cow, but not per 100 kg milk. For all SFA models, the difference in distribution was not statistically different between VHHM dairies and non-VHHM dairies (P values 0.94, 0.35, 0.95 and 0.89 for the basic and complete model per 100 kg fat and protein corrected milk and per cow respectively). Therefore we conclude that with our data farm participation in VHHM is not related to overall farm efficiency

Use of a dynamic programming model to estimate the value of clinical mastitis treatment and prevention options utilized by dairy producers

This study discusses and demonstrates the construction and application of a specially structured dynamic programming replacement and insemination optimization and simulation model of the dairy cow that includes detailed representation of repeated episodes of clinical mastitis (CM). The application determined optimal individual cow decisions, which were then compared with mastitis culling rules of thumb to determine the deviation of these rules from optimality. Calculation of break-even values of applying a preventive measure to reduce CM incidence or severity in the form of two fictitious mastitis vaccines were also carried out. Model input parameters were based on data from five large dairy herds in New York State (about 16,000 lactations), but these parameters can be easily changed for other types of dairy operations. To demonstrate the usefulness of this model to assist individual cow CM treatment options, the cost of the decision to cull a third lactation cow, six months after calving, with the first case of CM, was negative $3 USD for a low milk producing open or pregnant cow (i.e. cull), and positive$850 USD for a high milk producing and pregnant cow (i.e. treat even with extremely high treatment cost). Compared with the model recommended optimal policy, a rule of thumb decision to routinely treat all cows with a first case of CM resulted in an additional cost of $2 per cow per year, a very low deviation from optimality and suggests all cows should be given a second chance. A policy of replacing all CM cows after their second CM case after treatment of the first CM incident resulted in an additional cost of$27 per cow per year. If the decision was to replace all cows only after their third CM case this cost estimate was $8. The cost-benefit of applying two fictitious vaccinations showed that a vaccination reducing the milk loss caused by Gram-negative CM by 50$14 per cow per year. A vaccination that would result in additional reduction of the risk of CM by 50% had a break-even cost of $37 per cow per year.Dairy cows Dynamic programming Mastitis

New York Economic Handbook 1982 Agricultural Situation and Outlook

A.E. Ext. 81-28This publication contains information pertaining to the general economic situation and New York agriculture. It is prepared primarily for the use of professional agricultural workers in New York State, U.S.D.A. Agricultural Handbook No. 592, "1981 Handbook of Agricultural Charts," provides current reference material pertaining to the nation's agricultural situation

New York Economic Handbook 1983: Agricultural Situation and Outlook

A.E. Ext. 82-37This publication contains information pertaining to the general economic situation and New York agriculture. It is prepared primarily for the use of professional agricultural workers in New York State. U.S.D.A. Agricultural Handbook No. 609, "1982 Handbook of Agricultural Charts," provides current reference material pertaining to the nation's agricultural situation. "Current Economic Situation" is a two-page monthly release that carries the latest figures for selected economic indicators and highlights current developments. This release is a supplement to the Economic Handbook and is available to anyone requesting to be on the mailing list by writing to Department of Agricultural Economics, Cornell University, 442 Warren Hall, Ithaca, New York 14853-0398