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This paper forms a part of a series of studies aiming to estimate the costs of abortion in Iranian dairy herds. In previous studies we have determined mastitis as a significant risk factor for abortion. In order to provide a more reliable estimation of the costs associated with abortion in Iranian dairy herds, the risk of a cow getting infected with mastitis needs to be included. Data from 6 commercial herds and 32,191 cows was assigned to 3-weeks in milk (3-WIM) records from 1 to 567 d after calving (1st–27th 3-WIM). The effect of herd, parity, calving season, past incidence of abortion, cumulative FCM yield level (CFCML), past incidence of mastitis in previous 3-WIM periods (EMAS), days in milk (DIM) and their significant 2-way interactions on mastitis in current 3-WIM period (MAS) were evaluated using a logistic regression model. Mastitis rate (MR) in studied herds was on average 28.3%. Results show that herd, parity, EMAS, CFCML, calving season, and lactation stage significantly (P < 0.01) influenced the risk of MAS. The risk of MAS increased with lactation number. Cows with EMAS had 4.98 times greater risk of MAS compared to cows with no EMAS. Additionally, cows with medium-level CFCML (i.e. 2, 3, and 4) had a higher risk of MAS compared to cows on level 1 and 5. Calving during the spring significantly (P < 0.01) increased the risk of MAS compared to other seasons. Past incidence of abortion, however, was not significantly associated with MAS, but remained in the final model because of the interaction with other factors. It can be concluded that a risk factor analysis with all significant interactions is more informative than a model without the interactions, especially when making the optimal decision for a cow with given characteristics. Moreover, knowledge on the effect of influential factors on mastitis will be useful when designing mastitis control programs at herd or national level.

Abortions, especially those occurring during late pregnancy, lead to considerable economic losses. To estimate the financial losses related to pregnancy loss, at first the influencing factors on abortion need to be identified. Thus, the objective of this study was to determine and quantify the risk factors and their interactions for abortion in Iranian dairy herds. Based on data from 6 commercial herds, logistic regression was used to identify the risk factors for abortion. The basic time unit used in the study was a 3-week period corresponding to an estrus cycle. Thus, stage of lactation is measured as number of 3-week periods in milk (3-WIM) and stage of pregnancy accordingly as number of 3-week periods in pregnancy. After removing the records with missing information, the analysis included 482,071 3-WIM records for 26,289 pregnant cows collected between 2005 and 2014. The investigated factors were herd effect, pregnancy stage, previous abortion, calving month, cumulative fat corrected milk (FCM) yield level, mastitis in current 3-weeks in milk, accumulated number of mastitis and all 2-way interactions. Pregnancy tests were performed between 35 and 50 days after insemination. Abortion was defined as fetal death or return to estrus after confirmed pregnancy between 63 and 252 days in pregnancy. The overall rate of abortion, calculated as the number of aborted cows divided by the number of pregnant cows, was 15.4% ranging from 13.6% to 17.4% at herd level. The results of the logistic regression analysis showed that the risk of abortion differs between herds. Furthermore, all other investigated factors interacted significantly with herd thus illustrating that the effects of risk factors also differ between herds. Other significant risk factors included parity (interacting with pregnancy stage, mastitis, lactation stage and previous abortion), calving month, mastitis (interacting with pregnancy stage), pregnancy stage (interacting with previous abortion and mastitis)

Modern dairy herds resemble factories. Cows, organized in production units, are manufacturingmilk from many components (e.g. concentrates, silage). However, both production units andcomponents can greatly differ between each other. Therefore, production optimization based ongeneral recommendations might be inefficient. Instead, as in manufacturing industry, decisionsupport could be based on systematic experimentation within ongoing production system. Thisconcept, known as Evolutionary Operations (EVOP), is based on small changes to the productionsystem. However, a challenge here is lack of a tool which would allow a farmer to assess how smallchanges, for example in feeding, influence productivity. The objective of this study was to constructa multivariate dynamic linear model (DLM) to assess the intervention effect on milk production.The DLM was built to account for intervention at individual and herd level. It consisted of anobservation and a system equation. The observation equation links the observations to parametersdescribing the herd (lactation curve), individual cows and an intervention effect. The systemequation expresses how the parameters may change over time. The lactation curve was modeledby two linear expressions and was parameterized using: milk yield 60 days after calving, slope overthe first 60 days in milk and slope after 60 days in milk. The variance components of the DLM wereestimated using a maximum likelihood method. The application of the model was demonstratedon a field experiment in a commercial herd with 4 automatic milking systems (AMS). The herdwas split into 2 groups based on the AMS. The experiment relied on two steps. The first step wasto reduce the feed energy given to cows in the AMS and instead supply the feed energy to the cowsat the feed bunk. The second step was to reduce the feed energy given in two of the four AMS. TheDLM presented here was successful in providing estimates of the effects on milk yield of change infeed energy gi, Modern dairy herds resemble factories. Cows, organized in production units, are manufacturing milk from many components (e.g. concentrates, silage). However, both production units and components can greatly differ between each other. Therefore, production optimization based on general recommendations might be inefficient. Instead, as in manufacturing industry, decision support could be based on systematic experimentation within ongoing production system. This concept, known as Evolutionary Operations (EVOP), is based on small changes to the production system. However, a challenge here is lack of a tool which would allow a farmer to assess how small changes, for example in feeding, influence productivity. The objective of this study was to construct a multivariate dynamic linear model (DLM) to assess the intervention effect on milk production. The DLM was built to account for intervention at individual and herd level. It consisted of an observation and a system equation. The observation equation links the observations to parameters describing the herd (lactation curve), individual cows and an intervention effect. The system equation expresses how the parameters may change over time. The lactation curve was modeled by two linear expressions and was parameterized using: milk yield 60 days after calving, slope over the first 60 days in milk and slope after 60 days in milk. The variance components of the DLM were estimated using a maximum likelihood method. The application of the model was demonstrated on a field experiment in a commercial herd with 4 automatic milking systems (AMS). The herd was split into 2 groups based on the AMS. The experiment relied on two steps. The first step was to reduce the feed energy given to cows in the AMS and instead supply the feed energy to the cows at the feed bunk. The second step was to reduce the feed energy given in two of the four AMS. The DLM presented here was successful in providing estimates of the effects on milk yield of cha