Dairy Farm Management Research Articles

Biosecurity Protocols A Dairy Farm Management Tool for Protecting Calves Health

Optimal calf health on dairy farms is an essential factor for safeguarding the animal for future health and productivity. After all, the calves are the investment for the future of the dairy farm Brand et al.

Accuracy of direct and indirect methods for assessing bovine colostrum quality using a latent class model fit within a Bayesian framework

Feeding high-quality colostrum is essential for calf health and future productivity. Therefore, accurate assessment of colostrum quality is a key component of dairy farm management plans. Direct and indirect methods are available for assessment of colostrum quality; however, the indirect methods are rapid, inexpensive, and can be performed under field settings. A hierarchical latent class model fit within a Bayesian framework was used to estimate the sensitivity (Se) and specificity (Sp) of the radial immunodiffusion (RID) assay, transmission infrared (TIR) spectroscopy, and digital Brix refractometer for the assessment of low-quality bovine colostrum in Atlantic Canada dairy herds. The secondary objective of the study was to describe the distribution of herd prevalence of low-quality colostrum. Colostrum quality of 591 samples from 42 commercial Holstein dairy herds in 4 Atlantic Canada provinces was assessed using RID, TIR spectroscopy, and digital Brix refractometer. The accuracy of all tests at different Brix value thresholds was estimated using Bayesian latent class models to obtain posterior estimates [medians and 95% Bayesian credibility intervals (95% BCI)] for each parameter. Using a threshold of <23% for digital Brix refractometer and <50 g/L for RID and TIR spectroscopy, median (95% BCI) Se estimates were 73.2 (68.4-77.7), 86.2 (80.6-91.0), and 91.9% (89.0-94.2), respectively. Median (95% BCI) Sp estimates were 85.2% (81.0-88.9) for digital Brix refractometer, 99.4% (97.0-100) for RID, and 90.7% (87.8-93.2) for TIR spectroscopy. Median (95% BCI) within-herd low-quality colostrum prevalence was estimated at 32.5% (27.9-37.4). In conclusion, using digital Brix refractometer at a Brix threshold of <23% could reduce feeding of low-quality colostrum to calves and improve colostrum and calf management practices in Atlantic Canada dairy herds. The TIR spectroscopy showed high Se in detection of low-quality colostrum. However, the RID assay, which is used as the reference test in several studies, showed limited Se for detection of low-quality colostrum.

Estimating Pasture Biomass Using Sentinel-2 Imagery and Machine Learning

Effective dairy farm management requires the regular estimation and prediction of pasture biomass. This study explored the suitability of high spatio-temporal resolution Sentinel-2 imagery and the applicability of advanced machine learning techniques for estimating aboveground biomass at the paddock level in five dairy farms across northern Tasmania, Australia. A sequential neural network model was developed by integrating Sentinel-2 time-series data, weekly field biomass observations and daily climate variables from 2017 to 2018. Linear least-squares regression was employed for evaluating the results for model calibration and validation. Optimal model performance was realised with an R2 of ≈0.6, a root-mean-square error (RMSE) of ≈356 kg dry matter (DM)/ha and a mean absolute error (MAE) of 262 kg DM/ha. These performance markers indicated the results were within the variability of the pasture biomass measured in the field, and therefore represent a relatively high prediction accuracy. Sensitivity analysis further revealed what impact each farm’s in situ measurement, pasture management and grazing practices have on the model’s predictions. The study demonstrated the potential benefits and feasibility of improving biomass estimation in a cheap and rapid manner over traditional field measurement and commonly used remote-sensing methods. The proposed approach will help farmers and policymakers to estimate the amount of pasture present for optimising grazing management and improving decision-making regarding dairy farming.

Effect of grouping on behaviour of dairy heifers and cows in the transition period.

Regrouping dairy cows is a common feature of dairy farm management. Cows are grouped based on lactation stage, age, milk yield and other factors. Regrouping cows during the dry period (from far-off area to close up area and from close up area to the main herd) brings new challenges. This is especially true for heifers who, after being confirmed gravid, may be grouped into a new pen with dried off cows. The aims of this study were to determine how grouping affects activity, nearest neighbour relationships and aggression, and how heifers' acclimatization to a new group differs from cows. Therefore, the hypotheses were that regrouping cows has less of an effect on older cows compared to heifers, and cows' individuality affects acclimatization to a new group. Aggression data were recorded using a video camera that was directed at the feed bunk, and activity was recorded with activity monitors that were attached around the right hind leg. Synchrony and distance to nearest neighbour were recorded, as was the cows' location on the first 3 d from the day they returned to the main herd. Motion index, mean number of steps and number of lying bouts were significantly higher after calving compared to the week before calving and the difference was higher amongst heifers compared to cows (P < 0.001). Both cows and heifers lay down more in the strawyard compared to cubicle housing (P < 0.01) and cows were more aggressive than heifers in both housing systems (P < 0.001 and P < 0.05, respectively). As hypothesized, heifers were more affected by regrouping and cows with more experience settled quicker to their new environment.

Assisted Reproductive Technologies for Decision Support in Reproductive Management of Dairy Cattle in Ke

The human population is expected to double in Africa by 2030 and with it a consequent rise in demand for livestock products. Reproductive technologies improve livestock productivity however, uptake of the technologies has been low in Africa, yet this is where there is a dire need for livestock products as the human population surges. This study aimed to assess knowledge extent and understanding the use of assisted reproductive technologies in decision support for reproductive management of dairy cattle in Kenya. Dairy farmers and veterinary practitioners were interviewed using a questionnaire to obtain relevant data on knowledge, attitude and practices in the use of assisted reproductive technologies in reproductive management in dairy farms. Observable heat signs was the only method used by participants to ascertain estrus in a cow. Artificial insemination technique was adopted by all farmers as the breeding method of choice. The analysis highlighted that the respondents with low levels of formal education had less knowledge on reproductive technologies and displayed risky practices. Overall, a larger proportion of respondents indicated that the assisted reproductive technologies would be important in reproductive management. The relatively low levels of awareness, higher levels of education and a willingness to use the assisted reproductive technologies in reproductive management of dairy cattle strengthen the logic of improving productivity through the application of these assisted reproductive technologies as well as improve levels of awareness of reproductive management practices. Keywords: attitude, heat detection, knowledge, pregnancy diagnosis, practices, progesterone

Environmental Impacts of Milking Cows in Latvia

Increasing pressures surrounding efficiency and sustainability are key global drivers in dairy farm management strategies. However, for numerous resource-based, social, and economic reasons sustainable intensification strategies are herd-size dependent. In this study, we investigated the environmental impacts of Latvia’s dairy farms with different management practices. The herd size-dependent management groups varied from extensively managed small herds with 1–9 cows, extending to stepwise more intensively managed herds with 10–50, 51–100, 100–200, and over 200 milking cows. The aim is to compare the environmental impacts of different size-based production strategies on Latvia’s dairy farms. The results show that the gross greenhouse gas emissions differ by 29%: from 1.09 kg CO2 equivalents (CO2e) per kg of raw milk for the farms with 51–100 cows, down to 0.84 kg CO2e/kg milk for farms with more than 200 cows. However, the land use differs even more—the largest farms use 2.25 times less land per kg of milk than the smallest farms. Global warming potential, marine eutrophication, terrestrial acidification, and ecotoxicity were highest for the mid-sized farms. If current domestic, farm-based protein feeds were to be substituted with imported soy feed (one of the most popular high-protein feeds) the environmental impacts of Latvian dairy production would significantly increase, e.g., land use would increase by 18% and the global warming potential by 43%. Environmental policy approaches for steering the farms should consider the overall effects of operation size on environmental quality, in order to support the best practices for each farm type and steer systematic change in the country. The limitations of this study are linked to national data availability (e.g., national data on feed production, heifer breeding, differences among farms regards soil type, manure management, the proximity to marine or aquatic habitats) and methodological shortcomings (e.g., excluding emissions of carbon sequestration, the use of proxy allocation, and excluding social and biodiversity impacts in life-cycle assessment). Further research is needed to improve the data quality, the allocation method, and provide farm-size-specific information on outputs, heifer breeding, manure storage, and handling.

Precision dairy farming: Opportunities and challenges for India

Effective management of a dairy farm has to focus on individual animal apart from group or herd management since 'smallest production unit in the dairy is the individual animal’. In this context, precision dairy farming (PDF) aims to manage the basic production unit in order to exploit its maximal production capacity. PDF is the use of information and technology based farm management system to measure physiological, behavioural and production indicators of individual animals to improve management strategies, profitability and farm performance. PDF applications are finding their way on dairy farms, although there seem to be differences in the uptake of PDF applications between dairy systems. The authors have attempted to identify different PDF tools utilized across the globe and have highlighted the status of adoption in Indian scenario by highlighting about few farms/organizations involved in its utilization and uptake over the years. In this direction, the authors have also focused on several benefits and challenges faced by developing countries including India since the benefits are often not immediately apparent and they require more management expertise along with an investment of time and money to realize. In addition, the adoption rate depends on various factors like farmer education, farm size, perceptions of risk, ownership of a non-farm business etc. Addressing these issues is very essential for the uptake of technologies and hence, an effort has been made to propose strategies for adoption and operationalization of PDF in India and other developing countries where the similar scenario exists. The study also highlights that PDF in many developing countries including India is in its infancy, but there are tremendous opportunities for improvements in individual animal and herd management in dairy farms. The progressive farmers or the farmers’ groups, with guidance from the public and private sectors, and professional associations, can adopt it on a limited scale as the technology shows potential for raising yields and economic returns on fields with significant variability, and for minimizing environmental degradation. Additional research needs to be undertaken to examine the adoption process for not only successful adoption of technology, but also to solve the issues associated with the technology adoption. Further, right extension approaches and advisory services for the farmers interested in PDF needs to be undertaken for its effective application under different socio-economic and ecological conditions.

Dairy Brain: A Step Towards Smarter Management in Dairy Farms

Dairy Brain: A Step Towards Smarter Management in Dairy Farms

Rapid quantitative analysis of nitrogen and phosphorus through the whole chain of manure management in dairy farms by fusion model

Field monitoring technology plays a vital role for returning the animal manure back to the cropland with high-efficiency and accuracy, particular in the complex rotation system of manure management in Chinese intensive farms. The comprehensive quantitative analysis models were proposed and built for determining the content of the nitrogen (N) and the phosphorus (P) through the whole chain of manure management in different dairy farms under multiple conditions. 249 manure samples were collected from 31 intensive dairy farms in Tianjin both in summer and autumn. The effect of seasons on the distribution characteristics of the N and P in the manure was analyzed. Near infrared spectra were collected and principal component analysis (PCA) was performed. Partial least squares (PLS) was used to establish the intra-season and inter-season models. It was found that the contents of the N and P in the manure varied with seasons. The prediction performance of intra-season models was better than that of inter-season models. Fusion model of two seasons were also established. The coefficient of determination of external validation (R2pred) for the N and P were 0.972 and 0.901, respectively. The residual predictive deviations (RPD) were 5.98 and 3.18, respectively. The results showed that the fusion model could enhance the universality and stability for predicting the N and P contents through the whole chain of manure management under the influence of various factors. The study not only supports for the development of on-spot detecting instrument but also guides for the rational recycling of manure in practice as well.

Prediction of pregnancy state from milk mid-infrared (MIR) spectroscopy in dairy cows

Prediction of pregnancy state from milk mid-infrared (MIR) spectroscopy in dairy cows

179 Open discussion of data bottlenecks in the dairy industry: The Dairy Brain Coordinated Innovation Network

Abstract New dairy farm management technologies produce vast amounts of data. Although more data support better decisions, managing data also comes with critical challenges. In an effort to create awareness and develop guidelines for data management in the dairy industry, the University of Wisconsin Dairy Brain project is nurturing a Coordinated Innovation Network (CIN). The USDA definition of a CIN is a large “community that addresses bottlenecks [...] by bringing together experts from different disciplines and domains to identify innovative and synergistic solutions.” The Dairy Brain CIN currently has 81 members from 9 different countries. It is a growing community open to anyone who is interested in being part of the process of data collection, data engineering techniques and data usage in dairy farms. One of the objectives of the CIN is to write some design documents regarding data issues. During the first official meeting in September 2019, several CIN subcommittees were created with the purpose to write and publish opinion articles about some critical topics: 1) data security and ownership; 2) best practices of data collection and communication; 3) farm adoption; and 4) creating value from data. The idea behind these opinion articles was to generate industry-wide awareness and to start a meaningful discussion. These opinion articles have been published in Hoard’s Dairyman magazine as a series in the following dates: February 10, March 25, April 10, April 25 and May 10, 2020. In addition, an online forum was created to encourage larger industry participation. These opinion articles are the foundation of more in-depth technical articles. This presentation will focus on summarizing the content of the published opinion articles and their subsequent deliberations. The presentation will promote further discussion and invite stakeholders to join the Dairy Brain CIN and/or participate and contribute in the wider discussion.

Conceptual principles of strategic management of dairy enterprises

The article examines the strategy development framework of dairy enterprises, which provide a gradual and consistent change in production activity principles in the direction of growth.It is proved that the development strategy can be effective only through the introduction of organizational and economic mechanism of interaction of the structural centers creating income, which provides a reproductive process within the business unit on the extended type. The feasibility of strategic development justification of dairy enterprises in conditions of uncertainty and strengthening of crisis phenomenon was highlighted, for the following reasons: the dairy industry is represented by a set of economic entities, the majority of which does not belong to large integrated structures; the sectoral specificity of milk production complicates the possibility of influencing on the determination of business priority parameters.It is argued that in agriculture the most acceptable is the strategy of differentiated growth at the individual economic entity level, which contributes to the fuller use of production and resource potential, reduces the risk of commercial losses, increases sustainability, etc.). It is proved that strategic management in dairy farms is a process of developing a promising model of the future organization’ condition, which allows to take into account the impact of environmental factors and includes a set of adaptation and growth strategies to neutralize and flexibly adapt to their negative impact.It is shown that the model of strategic management in dairy farming should focus on the intensive type of development on the basis of introduction of innovative technologies and includes six consistent stages. It is highlighted that the dairy farming development strategy in agricultural enterprises should be focused on maximizing the “return” of the incentive characteristics of the industry basing on the flexible use of all strategy elements.It is argued that one of the measures to mitigate the negative impact of environmental factors on the dairy farming development is to increase the internal potential of innovation in both production and marketing environments of agricultural enterprises. Key words: strategic management, development strategy, enterprise, dairy cattle breeding, innovations.

An on-farm rotational grazing trial: restricting access time to pasture did not affect the productivity of a dairy sheep flock in spring

In the Mediterranean area, semi-extensive mixed farming systems characterise the inland landscape and guarantee the maintenance of several agroecosystem services. Rationed and rotational grazing have been proposed as efficient agroecological practices for sustainable intensification. Previous studies have highlighted that, during spring, restricted access time to pasture of below four hours per day decreases herbage intake and milk production of rotationally stoked ewes. The aim of this study was to assess the effect of restricted access time on daily herbage intake and milk productivity of Sarda ewes under rotational grazing management in a real farm located in southern Tuscany. The entire flock of 145 lactating ewes was allotted two homogenous groups and rotationally stoked on a grass-legume mixed pasture and fed about 1.7 kg of DM ewe–1 d–1 of supplementation. Each group was then assigned to the following treatments: two hours per day of access time to pasture, from 10:00 to 12:00 CET, and four hours per day of access time to pasture, from 10:00 to 14:00 CET. Data on herbage yield and quality, dry matter intake and milk yield were collected from April 5 to May 10, 2018. The rotational grazing schedule was conducted allowing for abundant herbage and exploiting high-nutritive biomass of the upper horizon sward. The results of our on-farm experiment showed that restricting daily access time to pasture down two hours did not reduce the potential daily herbage intake and the milk performance of dairy ewes rotationally stocked on a mixed pasture in spring. Our study also highlighted the capacity of ewes to self-regulate the herbage intake in order to meet their energy requirements, when neutral detergent fibre is not a limiting factor. Our outcomes should encourage researchers and farmers to co-design further on-farm experiments.&#x0D; Highlights - The challenge lies in comparing data of dairy sheep productivity collected at real farm condition with previous data measured in Mediterranean research stations. - In this study, no difference in milk production was found between ewes allowed to graze two hours and four hours per day in spring. - The ewes’ herbage intake rate correlates positively with pasture quality and a mixed pasture with a high share of legumes led to a high intake rate. - We believe that the outcomes in this manuscript can contribute to increase knowledge about the sustainable management of small ruminant dairy farms in the contest of rural areas.

Review: Application and Prospective Discussion of Machine Learning for the Management of Dairy Farms.

Simple SummaryMachine learning (ML) offers new approaches for analyzing data and is particularly interesting for large datasets. Dairy farmers implement a wide range of sensors, which create large amounts of data, in farming. Therefore, they offer an interesting area for data-driven research. In this review, we show how ML methods have already been used in the scientific literature and describe the potential that these may offer for the future. We found that ML methods were applied to predict data in a variety of areas in dairy farming such as milk yield or energy consumption; however, larger integrated datasets are required to improve the reliability of the algorithms developed.Dairy farmers use herd management systems, behavioral sensors, feeding lists, breeding schedules, and health records to document herd characteristics. Consequently, large amounts of dairy data are becoming available. However, a lack of data integration makes it difficult for farmers to analyze the data on their dairy farm, which indicates that these data are currently not being used to their full potential. Hence, multiple issues in dairy farming such as low longevity, poor performance, and health issues remain. We aimed to evaluate whether machine learning (ML) methods can solve some of these existing issues in dairy farming. This review summarizes peer-reviewed ML papers published in the dairy sector between 2015 and 2020. Ultimately, 97 papers from the subdomains of management, physiology, reproduction, behavior analysis, and feeding were considered in this review. The results confirm that ML algorithms have become common tools in most areas of dairy research, particularly to predict data. Despite the quantity of research available, most tested algorithms have not performed sufficiently for a reliable implementation in practice. This may be due to poor training data. The availability of data resources from multiple farms covering longer periods would be useful to improve prediction accuracies. In conclusion, ML is a promising tool in dairy research, which could be used to develop and improve decision support for farmers. As the cow is a multifactorial system, ML algorithms could analyze integrated data sources that describe and ultimately allow managing cows according to all relevant influencing factors. However, both the integration of multiple data sources and the obtainability of public data currently remain challenging.

How to manage cows yielding 20,000 kg of milk: technical challenges and environmental implications

The world cow milk production will reach between 810 and nearly 1,000 Mt in 2050, implying changes in dairy farm management as well implications in environmental impact, especially as far as greenhouse gases (GHG) emissions, and nitrogen and phosphorus excretions are concerned. The future dairy farms will need to become smarter, profitable, and high yielding to continuously improve the sustainability of milk production. Among western countries, the Italian dairy industry has good performances both for milk yield and quality. Most of its milk is used to produce highly exported PDO cheeses with high added value. It could represent a model to study the impact of accelerated phenotypic trend on technical and environmental challenges. Assuming a constant average increase of milk yield equal to the actual phenotypic trend (+128 kg per cow and per year), the production of the current best cows (20t/head year−1) will become the average herd performance of the intensive dairy farms in 2030. Thus, maintaining the current Italian milk production (equal to 12.1 Mt), the higher milk production per head would cause a reduction of the environmental impact of 11.4% and 60.1% for GHG, 9.1% and 36.0% for N, 15.8% and 52.6% for P considering two scenarios of present phenotypic trend or 20t/head year−1, respectively. To cope with this challenge, technical suggestions for breeding and feeding the 20t dairy cow are given. Highlights Current top Italian cows represent the 2030 average of dairy herds Genomics and management will drive the productivity success Efficiency will reduce dairy environmental impact

Analysis of Dairy Farm Management Strategy Development in North Sulawesi

This research is intended to find a strategy model for developing a dairy farm in North Sulawesi. This is considered important since it will only be developed by the local government in response to the increasing demand for milk and beef, while the availability of production decreases. The results of the analysis state that the long-term management strategy for developing a dairy farm in North Sulawesi is largely determined by building the driving forces of production and markets.

Occurrence of visible losses and relationship with corn silage management in dairy farms in the State of Paraná

The aim of this study was to relate the occurrence of visible losses in silage (effluent, spoiled top-layer and during feedout) with silage-making practices, physical and chemical characteristics of silage, and milk composition in Brazilian dairy herds. One-hundred and eight silos from 95 farms, in the State of Parana, were visited for data collection. Data were analyzed by Fisher's Exact and Pearson Correlation Test. Effluent loss was higher in silages with the lowest dry matter content. Using unwalled clamp (drive-over piles) silos, neglecting a protection over the plastic film, and unloading silage with a bucket increased the occurrence of top spoilage. Feedout losses were higher in farms where: the crop was harvested with self-propelled machines; the particle size was larger, and the silage density was lower. There was no relationship between visible losses and silage composition or milk composition, except for milk fat content that, unexpectedly, there was a positive correlation with spoiled silage in the top-layer. Silage losses are reduced by adopting good practices during silage production and feedout.

Photovoltaic systems on dairy farms: Financial and renewable multi-objective optimization (FARMOO) analysis

The aim of this study was to develop a financial and renewable multi-objective optimization (FARMOO) method for dairy farms. Due to increased global milk production and European Union policies concerning renewable energy contributions, the optimization of dairy farms from financial and renewable standpoints is crucial. The FARMOO method found the optimal combination of dairy farm equipment and management practices, based on a trade-off parameter which quantified the relative importance of maximizing farm net profit and maximizing farm renewable contribution. A PV system model was developed and validated to assess the financial performance and renewable contribution of this technology in a dairy farming context. Seven PV system sizes were investigated, ranging from 2 kWp to 11 kWp. Multi-objective optimization using a Genetic Algorithm was implemented to find the optimal combination of equipment and management practices based on the aforementioned trade-off parameter. For a test case of a 195 cow spring calving dairy farm in Ireland, it was found that when the relative importance of farm net profit was high, a PV system was not included in the optimal farm configuration. When net profit and renewable contribution were of equal importance, the optimal farm configuration included an 11 kWp PV system with a scheduled water heating load at 10:00. Multi-objective optimization was carried out for the same test case with the goals of maximizing farm net profit and minimizing farm CO2 emissions. Under this scenario, the optimal farm configuration included an 11 kWp PV system when the relative importance of farm net profit was low. This study included a sensitivity analysis which investigated the use of a 40% grant aid on PV system capital costs. This sensitivity analysis did not significantly improve the financial feasibility of PV systems on dairy farms. Moreover, it was found that load shifting of a farm’s water heating enabled the majority of the PV system’s electricity output to be consumed. Hence the use of batteries with small PV systems on dairy farms may not be necessary. The method described in this study will be used to inform policy and provide decision support relating to PV systems on dairy farms.

Effects of employer management on employee recruitment, satisfaction, engagement, and retention on large US dairy farms

Lack of employee engagement and satisfaction and high turnover rate of employees are major problems on some US dairy farms. Farm adoption of human resource management (HRM) practices varies widely. Using feedback from 168 employees from 12 large dairy farms, our objectives were to understand employee perceptions of HRM strengths and weaknesses and their influence on (1) employee satisfaction, (2) retention of employees, (3) willingness of employees to recommend the farm as a place to work, and (4) level of employee engagement on the farm. Employees who rated their employers higher for almost any management-related question (MRQ) were more satisfied in their jobs and more likely to recommend their place of work to other potential employees. Employees reported a higher intention to stay on the farm when employers scored higher on MRQs connected to employer-employee relationships. Employees who rated "Relationships" (a component consisting of 5 MRQs) higher were more likely to have a higher rate of satisfaction, were more likely to intend to stay at their job, and were more likely to recommend their place of work to others. No single management question was positively related to employee engagement (as measured by employees having and sharing ideas to improve the business); however, ethnicity, gender, job role, duration of employment, and employee's self-appraisal of their desire to learn and commitment to the farm were each associated with engagement. Female employees were less likely to provide ideas to their employers (compared with male employees), as were Spanish-speaking employees (compared with English-speaking employees). Differences between Spanish- and English-speaking employees were also present in measures such as intention to stay (shorter) and willingness to recommend the farm as a place to work (higher). Employees who rated themselves higher on their desire to learn and commitment to the farm were more likely to provide ideas to their employers, as were longer-term employees. In conclusion, we inferred that dairy farm management can improve employee retention and engagement through improved use of HRM practices.

Individual dairy cow management: achievements, obstacles and prospects.

This review deals with the prospects and achievements of individual dairy cow management (IDCM) and the obstacles and difficulties encountered in attempts to successfully apply IDCM into routine dairy management. All aspects of dairy farm management, health, reproduction, nutrition and welfare are discussed in relation to IDCM. In addition, new IDCM R&D goals in these management fields are suggested, with practical steps to achieve them. The development of management technologies is spurred by the availability of off-the-shelf sensors and expanded recording capacity, data storage, and computing capabilities, as well as by demands for sustainable dairy production and improved animal wellbeing at a time of increasing herd size and milk production per cow. Management technologies are sought that would enable the full expression of genetic and physiological potential of each cow in the herd, to achieve the dairy operation's economic goals whilst optimizing the animal's wellbeing. Results and conclusions from the literature, as well as practical experience supported by published and unpublished data are analyzed and discussed. The object of these efforts is to identify knowledge and management routine gaps in the practical dairy operation, in order to point out directions and improvements for successful implementation of IDCM in the dairy cows' health, reproduction, nutrition and wellbeing.