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Accurate forecasting of dairy cow milk yield is useful to dairy farmers, both in relation to financial planning and for detection of deviating yield patterns, which can be an indicator of mastitis and other diseases. In this study we developed a dynamic linear model (DLM) designed to forecast milk yields of individual cows per milking, as they are milked in milking robots. The DLM implements a Wood's function to account for the expected total daily milk yield. It further implements a second-degree polynomial function to account for the effect of the time intervals between milkings on the proportion of the expected total daily milk yield. By combining these 2 functions in a dynamic framework, the DLM was able to continuously forecast the amount of milk to be produced in a given milking. Data from 169,774 milkings on 5 different farms in 2 different countries were used in this study. A separate farm-specific implementation of the DLM was made for each of the 5 farms. To determine which factors would influence the forecast accuracy, the standardized forecast errors of the DLM were described with a linear mixed effects model (lme). This lme included lactation stage (early, middle, or late), somatic cell count (SCC) level (nonelevated or elevated), and whether or not the proper farm-specific version of the DLM was used. The standardized forecast errors of the DLM were only affected by SCC level and interactions between SCC level and lactation stage. Therefore, we concluded that the implementation of Wood's function combined with a second-degree polynomial is useful for dynamic modeling of milk yield in milking robots, and that this model has potential to be used as part of a mastitis detection system., (Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The impact of gastrointestinal (GI) nematode infections in dairy farming has traditionally been assessed using partial productivity indicators. But such approaches ignore the impact of infection on the performance of the whole farm. In this study, efficiency analysis was used to study the association of the GI nematode Ostertagia ostertagi on the technical efficiency of dairy farms. Five years of accountancy data were linked to GI nematode infection data gained from a longitudinal parasitic monitoring campaign. The level of exposure to GI nematodes was based on bulk-tank milk ELISA tests, which measure the antibodies to O. ostertagi and was expressed as an optical density ratio (ODR). Two unbalanced data panels were created for the period 2006 to 2010. The first data panel contained 198 observations from the Belgian Farm Accountancy Data Network (Brussels, Belgium) and the second contained 622 observations from the Boerenbond Flemish farmers' union (Leuven, Belgium) accountancy system (Tiber Farm Accounting System). We used the stochastic frontier analysis approach and defined inefficiency effect models specified with the Cobb-Douglas and transcendental logarithmic (Translog) functional form. To assess the efficiency scores, milk production was considered as the main output variable. Six input variables were used: concentrates, roughage, pasture, number of dairy cows, animal health costs, and labor. The ODR of each individual farm served as an explanatory variable of inefficiency. An increase in the level of exposure to GI nematodes was associated with a decrease in technical efficiency. Exposure to GI nematodes constrains the productivity of pasture, health, and labor but does not cause inefficiency in the use of concentrates, roughage, and dairy cows. Lowering the level of infection in the interquartile range (0.271 ODR) was associated with an average milk production increase of 27, 19, and 9L/cow per year for Farm Accountancy Data Network farms and 63, 49, and 23L/cow per year for Tiber Farm Accounting System farms in the low- (0-90), medium- (90-95), and high- (95-99) efficiency score groups, respectively. The potential milk increase associated with reducing the level of infection was higher for highly efficient farms (6.7% of the total possible milk increase when becoming fully technically efficient) than for less efficient farms (3.8% of the total possible milk increase when becoming fully technically efficient)., (Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Subclinical infections with gastrointestinal nematodes and liver fluke are important causes of production losses in grazing cattle. Although there is an extensive compilation of literature describing the effect of these infections on animal performance, only a few attempts have been made to convert these production losses to an economic cost. Here, we propose a novel tool (ParaCalc(®)), available as a web-application, to provide herd-specific estimates of the costs of these infections on dairy farms. ParaCalc(®) is a deterministic spread-sheet model where results from diagnostic methods to monitor the helminth infection status on a herd and anthelmintic usage are used as input parameters. Default values are provided to describe the effects of the infections on production and the cost of these production losses, but the latter can be adapted to improve the herd-specificity of the cost estimate. After development, ParaCalc(®) was applied on input parameters that were available for 93 Belgian dairy herds. In addition, the tool was provided to 6 veterinarians and their user experiences were evaluated. The estimated median [25th-75th percentile] cost per year per cow was € 46 [29-58] and € 6 [0-19] for gastrointestinal nematode and liver fluke infection, respectively. For both infections, the major components in the total costs were those associated with milk production losses in the adult cows. The veterinarians evaluated ParaCalc(®) as a useful tool to raise the farmers' awareness on the costs of worm infections, providing added value for their services. However, the score given for user-friendliness was diverse among users. Although the model behind ParaCalc(®) is a strong simplification of the real herd processes inducing economic losses, the tool may be used in the future to support economic decisions on helminth control., (Copyright © 2011 Elsevier B.V. All rights reserved.)