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An abrupt method to dry off cows has disadvantages and is considered inappropriate for current dairy cows due to welfare issues and risks for intramammary infections (IMI). A gradual cessation of lactation (by feeding or milking frequency reduction) has been the generally recommended method for drying off cows to prevent these adverse effects. However, a new alternative to the gradual approach is to abruptly stop milking at the same time as using cabergoline (CAB), a prolactin inhibitor. The aim of the study was to compare the net costs of 3 different methods of drying off cows [gradual reduction in feed (referred to as gradual feeding), gradual reduction in milking frequency (referred to as gradual milking), and abrupt cessation of milking with CAB]. A stochastic Monte Carlo simulation model, at cow level, was developed to calculate the net costs of applying these methods. All inputs for the model were based on literature information, authors' expertise, and expert knowledge. The net costs were determined by only including costs and benefits, which varied between the 3 methods. The model simulated a cow from 7 d before the day of drying off until the end of the next lactation. The likelihood of whether a cow was leaking milk early in the dry period was determined. Subsequently, it was determined whether or not the cow will get an IMI during the dry period, where the probability of getting an IMI was higher for cows leaking milk than for cows not leaking milk. If the IMI was not cured during the dry period, the cow had an IMI at calving. Also, milk production and feed requirements were modeled, and labor for applying the drying off method was included. For all methods, the net costs were calculated as the sum of costs for feed during the gradual feed reduction period, costs for applying the gradual-milking method, and the IMI costs during the dry period and lactation, minus the milk revenues during the transition from lactation to the dry period. Under default conditions, the average net cost of abrupt cessation of milking with CAB was €49.6/cow. The data showed that 90% of the net costs ranged from −€13.7 to €307.8/cow. The average net costs for gradual feeding and gradual milking were €99.1 and €71.5/cow, respectively. In conclusion, abrupt cessation of milking with CAB saved €49.5 and €21.9/cow on average compared with gradual feeding and gradual milking, respectively. This difference was mainly due to more milk returns and lower labor and IMI costs during lactation.

The adoption rate of sensors on dairy farms varies widely. Whereas some sensors are hardly adopted, others are adopted by many farmers. A potential rational explanation for the difference in adoption may be the expected future technological progress in the sensor technology and expected future improved decision support possibilities. For some sensors not much progress can be expected because the technology has already made enormous progress in recent years, whereas for sensors that have only recently been introduced on the market, much progress can be expected. The adoption of sensors may thus be partly explained by uncertainty about the investment decision, in which uncertainty lays in the future performance of the sensors and uncertainty about whether improved informed decision support will become available. The overall aim was to offer a plausible example of why a sensor may not be adopted now. To explain this, the role of uncertainty about technological progress in the investment decision was illustrated for highly adopted sensors (automated estrus detection) and hardly adopted sensors (automated body condition score). This theoretical illustration uses the real options theory, which accounts for the role of uncertainty in the timing of investment decisions. A discrete event model, simulating a farm of 100 dairy cows, was developed to estimate the net present value (NPV) of investing now and investing in 5 yr in both sensor systems. The results show that investing now in automated estrus detection resulted in a higher NPV than investing 5 yr from now, whereas for the automated body condition score postponing the investment resulted in a higher NPV compared with investing now. These results are in line with the observation that farmers postpone investments in sensors. Also, the current high adoption of automated estrus detection sensors can be explained because the NPV of investing now is higher than the NPV of investing in 5 yr. The results confirm that uncertainty about future sensor performance and uncertainty about whether improved decision support will become available play a role in investment decisions.

Shortening the dry period (DP) has been proposed as a management strategy to improve energy balance in early lactation. It is well known that both shortening and complete omission of the DP reduces milk production in the subsequent lactations. In most of these studies milk production data were obtained from planned animal experiments where cows were randomly assigned to DP length treatments, and cow management and diet composition did not differ among treatments. It may therefore be hypothesized that cows on commercial herds which apply a no-DP or short-DP-strategy, and support this by management adjustments, will have a less dramatic reduction in milk production. In this study, milk production and somatic cell count (SCC) following different DP lengths was investigated under commercial circumstances. Milk production of 342 cows (2,077 test-day records) was available from 5 Dutch commercial dairy herds which started a no DP-strategy for all cows. Test days of the year before applying the no-DP strategy are used as control (323 cows, 1,717 test-day records). Six other herds applied an individual cow approach and have different preplanned DP lengths within one herd. From these herds, information on 81 cows (482 test-day records) with a DP length between 0 and 20 d, 127 cows (925 test-day records) with a DP length between 21 and 35 d, and 143 cows (1,075 test-day records) with a DP length of more than 35 d was available. A generalized linear model incorporating an autoregressive covariance structure accounting for repeated test-day yields within cow was developed to estimate the daily yield (milk, fat and protein) and SCC of all cows. Applying no DP for all cows in the herd resulted in a reduction in postpartum milk production compared with within-herd control lactations (until 305 DIM) between 3.2 and 9.1 kg/d, which was a reduction of 12 and 32%, respectively. For the 6 herds that applied an individual cow approach with different preplanned DP lengths, the cow-specific DP strategy was based on milk production and SCC approximately 2 mo before calving. Cows with a preplanned DP length ranging between 0 and 20 d had a reduction in postpartum milk production between 5.7 and 13 kg/d compared with cows with a DP length of >35 d. Cows with a preplanned DP length ranging from 21 to 35 d had a numerically lower milk production (between 0.6 and 5.3 kg/d) than cows with a preplanned DP length of >35 d, but this difference was significant in only one herd. When corrected for milk yield, no difference in postpartum SCC for cows with different DP lengths was found.

The aim of this study was to assess farmers’ preferences for the performance characteristics of mastitis detection systems. Additionally, we looked at whether certain groups of farmers could be distinguished with specific preferences. Farmers’ opinions concerning mastitis detection systems, as well as general information about the farm and the farmer, were investigated with a standard questionnaire. The second part of the questionnaire was specifically aimed at elucidating preferences. Definitions of time windows and performance parameters, such as sensitivity and specificity, were incorporated into characteristics of a detection system (attributes) that reflect farmers’ daily experience. Based on data from 139 farmers, we concluded that, on average, they prefer a clinical mastitis detection system that produces a low number of false alerts, while alerting in good time and with emphasis on the more severe cases. These 3 attributes were evaluated as more important than the 3 other attributes, representing the costs of the detection system, the number of missed cases, and how long before clinical signs alerts need to be given. Variation in importance per attribute, however, was high, denoting that farmers’ preferences differ considerably. Although some significant relationships were found between farm characteristics and attributes, no clear groups of farmers with specific preferences could be distinguished. Based on these results, we advise making detection systems adaptable for the farmers to satisfy their preferences and to match the circumstances on the farm. Furthermore, these results support that for evaluation of detection algorithms comparisons have to be made at high levels of specificity (e.g., 99%) and time windows have to be kept small (preferably no more than 24 h). Key words: adaptive conjoint analysis, automatic milking system, farmer preference, mastitis detection