Association between days post conception and lactation persistency in dairy cattle.

Determining the optimal insemination moment for individual cows is complex, particularly when considering the impact of pregnancy on milk production. The effect of pregnancy on the absolute milk yield has already been reported in several studies. Currently, there is limited quantitative knowledge about the association between days post conception (DPC) and lactation persistency, based on a lactation curve model, and, specifically, how persistency changes during pregnancy and relates to the days in milk at conception (DIMc). Understanding this association might provide valuable insights to determine the optimal insemination moment. This study, therefore, aimed to investigate the association between DPC and lactation persistency, with an additional focus on the influence of DIMc. Available milk production data from 2005 to 2022 were available for 23,908 cows from 87 herds located throughout the Netherlands and Belgium. Persistency was measured by a lactation curve characteristic decay, representing the time taken to halve milk production after peak yield. Decay was calculated for 8 DPC (0, 30, 60, 90, 120, 150, 180 and 210 d after DIMc) and served as the dependent variable. Independent variables included DPC, DIMc (< = 60, 61-90, 91-120, 121-150, 151-180, 181-210, > 210), parity group, DPC × parity group, DPC × DIMc and variables from 30 d before DIMc as covariates. The results showed an increase in decay, i.e., a decrease in persistency, during pregnancy for both parity groups, albeit in different ways. Specifically, from DPC 150 to DPC 210, multiparous cows showed a higher decline in persistency compared with primiparous cows. Furthermore, a later DIMc (cows conceiving later) was associated with higher persistency. Except for the early DIMc groups (DIMc < 90), DIMc does not impact the change in persistency by gestation. The findings from this study contribute to a better understanding of how DPC and DIMc during lactation influence lactation persistency, enabling more informed decision-making by farmers who wish to take persistency into account in their reproduction management.

The association between time-series milk ß-hydroxybutyrate dynamics and early reproductive performance of dairy cows.

The DeLaval Herd Navigator is an on-farm sensor system that measures on a frequent basis milk progesterone (P4) and ß-hydroxybutyrate (BHB) in individual cows to closely monitor reproductive performance and energy balance. This information provides the opportunity to investigate the dynamics of BHB measured in milk (mBHB) and study the association between mBHB and early reproductive performance. The objectives of the study were (1) to describe mBHB dynamics within the first 20 d in milk (DIM), and (2) to evaluate the association between mBHB dynamics and early reproductive performance at cow-level. Two-year time-series data from 4,133 dairy cows in 38 Dutch dairy farms were available for analysis. Data included information on mBHB, daily milk yield and the indicators of early reproductive performance, days from calving to resumption of cyclicity, days from calving to first estrus, and days from calving to first insemination. The following mBHB dynamic parameters were defined based on the first 20 DIM for each individual cow: average mBHB (AvgBHB), DIM when mBHB was for the first time ≥80 µmol/L (OnsetKeto), the total number of consecutive days a cow had mBHB concentration ≥80 µmol/L, and the number of measurements mBHB concentration was ≥80 µmol/L. Three Cox proportional hazard regression models with random herd effect were developed to evaluate the association between cow level mBHB dynamics and days from calving to resumption of cyclicity, first estrus, and first insemination. Results showed that the mean AvgBHB within 20 DIM among all cows was 73 µmol/L. The mean OnsetKeto within 20 DIM, was 8 DIM. Among all cows having hyperketolactia, 55.8% (1,350/2,419) had OnsetKeto in the first week of lactation. In total, 41.5% (1,714/4,133) of the cows did not have OnsetKeto in the first 20 DIM. An early onset of hyperketolactia was associated with delayed fertility events. Cows with higher AvgBHB have a prolonged time interval from calving to resumption of cyclicity and first estrus. Information on mBHB dynamics and the association with early reproductive performance provides insights that might be helpful to improve reproductive performance of individual dairy cows.

Stochastic model to assess bioeconomic impact of PRRS on pig farms in Costa Rica.

Despite the economic importance of PRRS and its high prevalence in Costa Rica, there are no studies on the bioeconomic impact of the disease in the country or, even, in Central America. Such studies are essential in finding cost-effective preventive measures tailored for different production circumstances. Therefore, the objective of this study was to evaluate economic and production parameters of a PRRSV-infection for a medium-sized farrow-to-finish pig farm system in Costa Rica with a farm-level stochastic Monte Carlo simulation model. The effect of PRRS was assessed by scenario analysis, in which a baseline PRRS-free situation was compared against three alternative scenarios that assumed low, medium and high PRRS effects. The PRRS effects were based on data from local farms, scientific literature and expert opinion. Sensitivity analyses were performed to assess the impact of key input parameters on output variables. Results show that at the animal level, changes between the baseline and the PRRS-high scenario were estimated as: + 25 d in age to slaughter, - 9.9 pigs to slaughter (per breeding sow/yr), + 6% annual replacement rate, - 255 d in sow productive lifetime, - 6.9 mo in age at culling of sows, and + 24 non- productive days. For a medium size local farm (n = 588 sows), a reduction of 5826 fat pigs to slaughter per farm/yr from baseline compared to PRRS-high scenario was observed. PRRS-induced loss per farm per year was estimated at -US $142,542, US $180,109 and -US $524,719 for PRRS-low, medium and high scenarios, respectively. Revenues/costs ratio changed from 1.12 in the baseline to 0.89 in the PRRS-high scenario. The production cost per kg carcass weight increased from US $2.63 for the baseline to US $3.35 in the PRRS-high scenario. PRRS-induced loss was estimated at US $77.1 per slaughtered pig/yr and US $892 per breeding sow/yr for the PRRS-high scenario. Results from the model indicate that pig farms with medium to high prevalence of PRRS will require optimal market conditions in order to have positive economic outcomes. These results can be helpful in the design of better control strategies for PRRS.

Dutch dairy farmers' perspectives on culling reasons and strategies.

Since the abolishment of the milk quota system in Europe in 2014 and the introduction of environmental policies such as the phosphate rights system in the Netherlands, the reasons for culling dairy cows might have changed. The aim of this study was to determine the culling reasons for dairy cattle and to identify farmers' culling strategies and their intentions regarding the alteration of indicated culling strategies. To this end, an online questionnaire was distributed among dairy farmers nationally that resulted in 207 responses. Results showed that the most frequent culling reasons were related to problems with reproduction, udder, and hoof health. Primiparous cows were primarily culled for miscellaneous reasons such as injury, reproduction failure, and low milk yield. Multiparous cows were culled predominantly for reproduction failure, udder health and hoof health reasons. Most respondents indicated that they consider formulating a culling strategy, based on certain rules of thumb regarding the most common reasons for culling. Most farmers also reported that culling decisions on their farms were perceived to be unavoidable, though reproductive culling decisions are primarily voluntary. Most respondents stated that they intended to reduce the culling rate for better economic gain did not intend to alter the amount of replacement stock reared. The applied rules of thumb regarding culling strategies do not seem to have changed since the policy changes in dairy farming. The question remains whether farmers' rules of thumb might have made them unaware of the actual economic consequences of their culling strategies under the altered situation.

The economic impact of drying off cows with a dry-off facilitator (cabergoline) compared with 2 methods of gradual cessation of lactation for European dairy farms.

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.

Delaying investments in sensor technology: The rationality of dairy farmers' investment decisions illustrated within the framework of real options theory.

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.

Cow characteristics and their association with udder health after different dry period lengths.

Shortening or omitting the dry period (DP) in dairy cows is of interest because of potential beneficial effects on energy balance and metabolic health. Reported effects of a short or omitted dry period on udder health are ambiguous. This study aimed to evaluate the effect of no DP (0d), a short DP (30d), or a conventional DP (60 d) on the occurrence of intramammary infections (IMI) during the precalving period and on somatic cell counts (SCC), elevations of SCC (SCC≥200,000 cells/mL), and clinical mastitis in the subsequent lactation. The study also aimed to analyze which prepartum cow characteristics are associated with udder health after different DP lengths. Holstein-Friesian dairy cows (n=167) were randomly assigned to a DP length (0, 30, or 60 d). Cows with a 0-d DP had a greater occurrence of chronic IMI and a lower occurrence of cured IMI during the precalving period than cows with a 30-d or 60-d DP. Postpartum average SCC for lactation was greater in cows with a 0-d DP than in cows with a 30-d or 60-d DP. The number of cows with at least 1 elevation of SCC, the number of elevations of SCC per affected cow, the number of cows treated for clinical mastitis, and the number of cases of mastitis per affected cow did not differ among DP lengths. Cow characteristics related to postpartum average SCC for lactation were DP length, parity, and the following interactions: DP length with prepartum elevation of SCC, DP length with fat- and protein-corrected milk (FPCM) reduction between 150 and 67d prepartum, DP length with parity and with average SCC for lactation, and last FPCM before the conventional drying-off day with average SCC for lactation. Cows with prepartum parity 1 had a lower occurrence of at least 1 elevation of SCC in subsequent lactation compared with cows with parity >2. Last SCC before the conventional drying-off day was positively associated with occurrence of clinical mastitis in the subsequent lactation. In this study, DP length was not a risk factor for either elevation of SCC or occurrence of clinical mastitis in the subsequent lactation. The identified cow characteristics could be used in a decision support model to optimize DP length for individual cows.

A prognostic model to predict the success of artificial insemination in dairy cows based on readily available data.

A prognosis of the likelihood of insemination success is valuable information for the decision to start inseminating a cow. This decision is important for the reproduction management of dairy farms. The aim of this study was to develop a prognostic model for the likelihood of successful first insemination. The parameters considered for the model are readily available on farm at the time a farmer makes breeding decisions. In the first step, variables are selected for the prognostic model that have prognostic value for the likelihood of a successful first insemination. In the second step, farm effects on the likelihood of a successful insemination are quantified and the prognostic model is cross-validated. Logistic regression with a random effect for farm was used to develop the prognostic model. Insemination and test-day milk production data from 2,000 commercial Dutch dairy farms were obtained, and 190,541 first inseminations from this data set were used for model selection. The following variables were used in the selection process: parity, days in milk, days to peak production, production level relative to herd mates, milk yield, breed of the cow, insemination season and calving season, log of the ratio of fat to protein content, and body condition score at insemination. Variables were selected in a forward selection and backward elimination, based on the Akaike information criterion. The variables that contributed most to the model were random farm effect, relative production factor, and milk yield at insemination. The parameters were estimated in a bootstrap analysis and a cross-validation was conducted within this bootstrap analysis. The parameter estimates for body condition score at insemination varied most, indicating that this effect varied most among Dutch dairy farms. The cross-validation showed that the prognosis of insemination success closely resembled the mean insemination success observed in the data set. Insemination success depends on physiological conditions of the cow, which are approximated indirectly by production and reproduction data that are routinely recorded on the farm. The model cannot be used as a detection model to distinguish cows that conceive from cows that do not. The model validation indicates, however, that routinely collected farm data and test-day milk yield records have value for the prognosis of insemination success in dairy cows.

The total cost of rearing a heifer on Dutch dairy farms: calculated versus perceived cost.

BACKGROUND: As farmers do not often keep a record of the expenditures for rearing, an economic tool that provides insight into the cost of rearing is useful. In the Netherlands, an economic tool (Jonkos) has been developed that can be used by farmers to obtain insight into the cost of rearing on their farm. The first objective of this study is to calculate the total cost of rearing young stock in Dutch dairy herds using Jonkos. The second objective is to compare the calculated total cost of rearing with the farmers' own estimation of the cost of rearing (the perceived cost). FINDINGS: Information was available for 75 herds that reared their own young stock and who had used the Jonkos tool. The perceived cost of rearing young stock was only available for 36 herds. In the 75 herds, the average herd size was 100 dairy cows. The average calculated total cost of rearing a heifer was €1,790. The average perceived total cost of rearing a heifer (including labour and housing costs) was €1,030. CONCLUSION: Most Dutch farmers in the study underestimated the total cost of rearing. The Jonkos economic tool has the advantage that herd-specific information can be entered as input values. The output of the tool can improve the awareness of farmers about the total costs of rearing. This awareness can lead to a higher priority of young stock rearing and consequently to an improved quality of young stock rearing.

Effect of sensor systems for cow management on milk production, somatic cell count, and reproduction.

To improve management on dairy herds, sensor systems have been developed that can measure physiological, behavioral, and production indicators on individual cows. It is not known whether using sensor systems also improves measures of health and production in dairy herds. The objective of this study was to investigate the effect of using sensor systems on measures of health and production in dairy herds. Data of 414 Dutch dairy farms with (n=152) and without (n=262) sensor systems were available. For these herds, information on milk production per cow, days to first service, first calving age, and somatic cell count (SCC) was provided for the years 2003 to 2013. Moreover, year of investment in sensor systems was available. For every farm year, we determined whether that year was before or after the year of investment in sensor systems on farms with an automatic milking system (AMS) or a conventional milking system (CMS), or whether it was a year on a farm that never invested in sensor systems. Separate statistical analyses were performed to determine the effect of sensor systems for mastitis detection (color, SCC, electrical conductivity, and lactate dehydrogenase sensors), estrus detection for dairy cows, estrus detection for young stock, and other sensor systems (weighing platform, rumination time sensor, fat and protein sensor, temperature sensor, milk temperature sensor, urea sensor, ß-hydroxybutyrate sensor, and other sensor systems). The AMS farms had a higher average SCC (by 12,000 cells/mL) after sensor investment, and CMS farms with a mastitis detection system had a lower average SCC (by 10,000 cells/mL) in the years after sensor investment. Having sensor systems was associated with a higher average production per cow on AMS farms, and with a lower average production per cow on CMS farms in the years after investment. The most likely reason for this lower milk production after investment was that on 96% of CMS farms, the sensor system investment occurred together with another major change at the farm, such as a new barn or a new milking system. Most likely, these other changes had led to a decrease in milk production that could not be compensated for by the use of sensor systems. Having estrus detection sensor systems did not improve reproduction performance. Labor reduction was an important reason for investing in sensor systems. Therefore, economic benefits from investments in sensor systems can be expected more from the reduction in labor costs than from improvements in measures of health and production in dairy herds.

Characterization of Dutch dairy farms using sensor systems for cow management.

To improve cow management in large dairy herds, sensors have been developed that can measure physiological, behavioral, and production indicators on individual cows. Recently, the number of dairy farms using sensor systems has increased. It is not known, however, to what extent sensor systems are used on dairy farms, and the reasons why farmers invest or not in sensor systems are unclear. The first objective of this study was to give an overview of the sensor systems currently used in the Netherlands. The second objective was to investigate the reasons for investing or not investing in sensor systems. The third objective was to characterize farms with and without sensor systems. A survey was developed to investigate first, the reasons for investing or not in sensor systems and, then, how the sensor systems are used in daily cow management. The survey was sent to 1,672 Dutch dairy farmers. The final data set consisted of 512 dairy farms (response rate of 30.6%); 202 farms indicated that they had sensor systems and 310 farms indicated that they did not have sensor systems. A wide variety of sensor systems was used on Dutch dairy farms; those for mastitis detection and estrus detection were the most-used sensor systems. The use of sensor systems was different for farms using an automatic milking system (AMS) and a conventional milking system (CMS). Reasons for investing were different for different sensor systems. For sensor systems attached to the AMS, the farmers made no conscious decision to invest: they answered that the sensors were standard in the AMS or were bought for reduced cost with the AMS. The main reasons for investing in estrus detection sensor systems were improving detection rates, gaining insights into the fertility level of the herd, improving profitability of the farm, and reducing labor. Main reasons for not investing in sensor systems were economically related. It was very difficult to characterize farms with and without sensor systems. Farms with CMS and sensor systems had more cows than CMS farms without sensor systems. Furthermore, farms with sensor systems had fewer labor hours per cow compared with farms without sensor systems. Other farm characteristics (age of the farmer, availability of a successor, growth in herd size, milk production per cow, number of cows per hectare, and milk production per hectare) did not differ for farms with and without sensor systems.

The optimal number of heifer calves to be reared as dairy replacements.

Dairy farmers often keep almost all their newborn heifer calves despite the high cost of rearing. By rearing all heifer calves, farmers have more security and retain flexibility to cope with the uncertainty in the availability of replacement heifers in time. This uncertainty is due to mortality or infertility during the rearing period and the variation in culling rate of lactating cows. The objective of this study is to provide insight in the economically optimal number of heifer calves to be reared as replacements. A herd-level stochastic simulation model was developed specific for this purpose with a herd of 100 dairy cows; the biological part of the model consisted of a dairy herd unit and rearing unit for replacement heifers. The dairy herd unit included variation in the number of culled dairy cows. The rearing unit incorporated variation in the number of heifers present in the herd by including uncertainty in mortality and variation in fertility. The dairy herd unit and rearing unit were linked by the number of replacement heifers and culled dairy cows. When not enough replacement heifers were available to replace culled dairy cows, the herd size was temporarily reduced, resulting in an additional cost for the empty slots. When the herd size reached 100 dairy cows, the available replacement heifers that were not needed were sold. It was assumed that no purchase of cows and calves occurred. The optimal percentage of 2-wk-old heifer calves to be retained was defined as the percentage of heifer calves that minimized the average net costs of rearing replacement heifers. In the default scenario, the optimal retention was 73% and the total net cost of rearing was estimated at €40,939 per herd per year. This total net cost was 6.5% lower than when all heifer calves were kept. An earlier first-calving age resulted in an optimal retention of 75%, and the net costs of rearing were €581 per herd per year lower than in the default scenario. For herds with a lower or higher culling rate of dairy cows (10 or 40% instead of 25% in the default scenario), it was optimal to retain 35 or 100% of the heifer calves per year. Herds that had a lower or higher cost of empty slots (€50 or €120 per month instead of €82 in the default scenario) had an optimal retention of 49 or 83% per year; the optimal retention percentage was dependent on farm and herd characteristics. For Dutch dairy farming conditions, it was not optimal to keep all heifer calves.

An ex ante analysis on the use of activity meters for automated estrus detection: to invest or not to invest?

The technical performance of activity meters for automated detection of estrus in dairy farming has been studied, and such meters are already used in practice. However, information on the economic consequences of using activity meters is lacking. The current study analyzes the economic benefits of a sensor system for detection of estrus and appraises the feasibility of an investment in such a system. A stochastic dynamic simulation model was used to simulate reproductive performance of a dairy herd. The number of cow places in this herd was fixed at 130. The model started with 130 randomly drawn cows (in a Monte Carlo process) and simulated calvings and replacement of these cows in subsequent years. Default herd characteristics were a conception rate of 50%, an 8-wk dry-off period, and an average milk production level of 8,310 kg per cow per 305 d. Model inputs were derived from real farm data and expertise. For the analysis, visual detection by the farmer ("without" situation) was compared with automated detection with activity meters ("with" situation). For visual estrus detection, an estrus detection rate of 50% and a specificity of 100% were assumed. For automated estrus detection, an estrus detection rate of 80% and a specificity of 95% were assumed. The results of the cow simulation model were used to estimate the difference between the annual net cash flows in the "with" and "without" situations (marginal financial effect) and the internal rate of return (IRR) as profitability indicators. The use of activity meters led to improved estrus detection and, therefore, to a decrease in the average calving interval and subsequent increase in annual milk production. For visual estrus detection, the average calving interval was 419 d and average annual milk production was 1,032,278 kg. For activity meters, the average calving interval was 403 d and the average annual milk production was 1,043,398 kg. It was estimated that the initial investment in activity meters would cost €17,728 for a herd of 130 cows, with an additional cost of €90 per year for the replacement of malfunctioning activity meters. Changes in annual net cash flows arising from using an activity meter included extra revenues from increased milk production and number of calves sold, increased costs from more inseminations, calvings, and feed consumption, and reduced costs from fewer culled cows and less labor for estrus detection. These changes in cash flows were caused mainly by changes in the technical results of the simulated dairy herds, which arose from differences in the estrus detection rate and specificity between the "with" and "without" situations. The average marginal financial effect in the "with" and "without" situations was €2,827 for the baseline scenario, with an average IRR of 11%. The IRR is a measure of the return on invested capital. Investment in activity meters was generally profitable. The most influential assumptions on the profitability of this investment were the assumed culling rules and the increase in sensitivity of estrus detection between the "without" and the "with" situation.

Cow characteristics and their association with production performance with different dry period lengths.

Shortening or omitting the dry period (DP) has been proposed as a management strategy to improve energy balance of dairy cows in early lactation. Both shortening and complete omission of the DP reduces milk production in the subsequent lactation compared with a conventional DP length of 60d. Some cows have less milk production loss than other cows after applying no DP or a short DP. The aim of this study is to evaluate which cow characteristics are associated with the amount of milk production losses following no DP or a short DP (30d). Daily production information from the lactation before and after the DP was available from 161 dairy cows (54 cows with a 0-d DP, 51 cows with a 30-d DP, and 56 cows with a 60-d DP) from a research herd. Daily production (milk, fat, and protein) until 305d in milk was estimated for all cows. Subsequently, total fat- and protein-corrected milk yield from 60d before the expected calving date until 305d in the following lactation (FPCMtotal) was estimated. A statistical analysis was performed to evaluate which cow characteristics were associated with limited or no production losses following no DP or a short DP, compared with a conventional DP length of 60d. Average FPCMtotal was 9,341, 10,499, and 10,795kg for cows with no DP, a 30-d DP, and a 60-d DP, respectively. The cow characteristics parity, daily milk production at 12wk before the expected calving date, and reduction in daily milk production between 16 and 12wk before the expected calving date were associated with production loss due to a short (30d) or no DP. Compared with 60d DP, multiparous cows had less production loss (987kg) following no DP than primiparous cows (2,132kg). The difference in FPCMtotal between the 3DP groups was largest for cows with a low milk production (e.g., 10kg/d) at 12wk before the expected calving date. The greater the reduction in milk production between 16 and 12wk before the expected calving date, the larger the difference in FPCMtotal between the 3DP groups. The difference in FPCMtotal between cows with no DP and 60d DP at a reduction in milk production between 16 and 12wk of 10% was 665kg, whereas this difference was 1,138kg at a reduction of 70%. The cow characteristics found can be used to select cows for specific DP lengths in a decision-support model to support the farmer on the economic optimal DP length for each individual cow. Output of such a decision-support model can be, for instance, to advise a 30-d DP for multiparous cows with high milk production (e.g., 25kg/d) at 12wk before the expected calving date.

Short communication: Effectiveness of tools provided by a dairy company on udder health in Dutch dairy farms.

A Dutch dairy company initiated a quality system to support dairy farmers to improve sustainability on their farm. Improvement of udder health is defined by the dairy company as one of the sustainability items. A part of that quality system is to offer farmers 3 tools to improve the udder health status of the herd. The first tool is an Udder Health Workshop at which farmers make a farm-specific action plan to improve the udder health situation in their herd. The second tool is the Udder Health Navigator, which is an internet-based program to gain insight in the actual udder health situation at the farm. The third tool is the Udder Health Checklist, which is available on the internet and it identifies farm-specific risks for udder health problems. The aim of this study was to evaluate the effectiveness of these tools in improving udder health. The bulk milk somatic cell count (BMSCC) was used as the measure of herd udder health performance. In total, 605 farms attended the Udder Health Workshop, 988 farms completed the Udder Health Navigator, and 1,855 farms completed the Udder Health Checklist in 2012. Information on BMSCC records (2 records per month) was available for 12,782 Dutch dairy farms during the years 2011 and 2012. For every farm, the average BMSCC of all months during the years 2012 and 2011 were calculated. This resulted in 306,768 average monthly observations of the BMSCC. Subsequently, all months after the completion of one of the tools were assigned a 1, and all other months were assigned a 0. A statistical analysis was carried out to compare the average monthly BMSCC of the farms that completed one or more tools with farms that did not complete one of the tools. Both completing the Udder Health Navigator and the Udder Health Checklist had a significant association with a lower average monthly BMSCC. The effect of the Udder Health Navigator and Udder Health Checklist on the BMSCC were greater in herds with a BMSCC in 2011 of 200,000 to 250,000 cells/mL and even greater for herds with a BMSCC above 250,000 cells/mL compared with herds with a BMSCC in 2011 of 150,000 to 200,000 cells/mL or less than 150,000 cells/mL. It is difficult to draw conclusions on the effect of the Udder Health Workshop due to overlap in participation between the tools. The results suggest that completing the web tools is associated with a reduction in the BMSCC of the herd.

Invited review: sensors to support health management on dairy farms.

Since the 1980s, efforts have been made to develop sensors that measure a parameter from an individual cow. The development started with individual cow recognition and was followed by sensors that measure the electrical conductivity of milk and pedometers that measure activity. The aim of this review is to provide a structured overview of the published sensor systems for dairy health management. The development of sensor systems can be described by the following 4 levels: (I) techniques that measure something about the cow (e.g., activity); (II) interpretations that summarize changes in the sensor data (e.g., increase in activity) to produce information about the cow's status (e.g., estrus); (III) integration of information where sensor information is supplemented with other information (e.g., economic information) to produce advice (e.g., whether to inseminate a cow or not); and (IV) the farmer makes a decision or the sensor system makes the decision autonomously (e.g., the inseminator is called). This review has structured a total of 126 publications describing 139 sensor systems and compared them based on the 4 levels. The publications were published in the Thomson Reuters (formerly ISI) Web of Science database from January 2002 until June 2012 or in the proceedings of 3 conferences on precision (dairy) farming in 2009, 2010, and 2011. Most studies concerned the detection of mastitis (25%), fertility (33%), and locomotion problems (30%), with fewer studies (16%) related to the detection of metabolic problems. Many studies presented sensor systems at levels I and II, but none did so at levels III and IV. Most of the work for mastitis (92%) and fertility (75%) is done at level II. For locomotion (53%) and metabolism (69%), more than half of the work is done at level I. The performance of sensor systems varies based on the choice of gold standards, algorithms, and test sizes (number of farms and cows). Studies on sensor systems for mastitis and estrus have shown that sensor systems are brought to a higher level; however, the need to improve detection performance still exists. Studies on sensor systems for locomotion problems have shown that the search continues for the most appropriate indicators, sensor techniques, and gold standards. Studies on metabolic problems show that it is still unclear which indicator reflects best the metabolic problems that should be detected. No systems with integrated decision support models have been found.

Effect of different dry period lengths on milk production and somatic cell count in subsequent lactations in commercial Dutch dairy herds.

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.

First-calving age and first-lactation milk production on Dutch dairy farms.

Farmers attempting to reduce first-calving age (FCA) need to understand which rearing management factors influence FCA and first-lactation milk production (FLP). Reduced FCA might be associated with lower FLP. This study describes the association between herd FCA, FLP, and several herd-level health and rearing management variables and describes the association between FCA and FLP at the cow level. It uses data from a 2010 survey of 100 Dutch dairy farms about general management, colostrum and milk feeding, housing, cleanliness, healthcare, disease, and breeding. It also used available data on FCA and 305-d FLP at both cow and herd level. The associations between median FCA and median FLP of the herd and herd-level health and rearing management variables were determined using multivariate regression analysis. The median FCA was associated with minimum age of first insemination, feeding of waste milk, and the amount of milk given preweaning. The median FLP was associated with median FCA and vaccination status for bovine respiratory syncytial virus. The association between FCA and FLP (based on 8,454 heifers) was analyzed with a single-effect linear mixed model, where the dependent variable was either FCA or relative FCA (defined as the difference between FCA of the heifer and median FCA of the herd to which they belonged). Heifers having an FCA of 24 mo produced, on average, 7,164 kg of milk per 305 d, and calving 1 mo earlier gave 143 kg less milk per 305 d. When FCA did not deviate from the median herd FCA, heifers produced, on average, 7,272 kg of milk per 305 d. From the median FCA of the herd, heifers calving 1 mo earlier produced 90 kg of milk per 305 d less, and heifers calving 1 mo later produced 86 kg per 305 d more. This is the first study that explained FLP using relative FCA. It assumes that heifers raised within the same farm have similar development because they are similarly managed. Similar management is reflected by the median FCA of the herd, with a deviation of the heifer's FCA from median FCA reflecting the heifer's development relative to the herd's average. The advantage of using relative FCA was that it accounts for between-farm differences in rearing management. It showed that earlier insemination without adjusting management to ensure sufficient development lowers FLP. An economic optimum exists between rearing costs, FCA, and FLP and, as a consequence, decisions with regard to young stock management should be made with care.

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.