Title Abstract "Helminth infections: Do they affect the productive efficiency of specialised dairy farms?" "Subclinical infections with gastrointestinal (GI) nematodes and liver fluke are an important cause of production losses in grazing dairy cattle. Attempts to evaluate the economic impact of these production losses were mainly based on partial analysis techniques, and few studies have looked more integrally at the impact on productive efficiency at farm level. The objective of this research is to analyse the effect of GI nematode and liver fluke infections on the technical efficiency in dairy farms. Farm-specific results from a parasitic monitoring campaign, expressed as an optical density ratio (ODR), are linked with individual farm data from the Belgian Farm Accountancy Data Network (FADN). As a result, a dataset of about 70 specialised dairy farms is obtained, combining economic and epidemiologic information. Their technical efficiency (TE) is calculated with non-parametric data envelopment (DEA) analysis. Multiple variants of the DEA approach are used, differing in the way they incorporate infection in the production model. Rank correlation, regression models, and cluster analysis are used to analyse the relationship between TE and the level of helminth infection. Preliminary data analysis shows an ODR mean and standard deviation of 0.82 ± 0.21 and 0.79 ± 0.34, for GI nematode and liver fluke infections, respectively. More than 80% of the farms have an ODR above 0.5, suggestive for a negative effect of helminth infections on milk production. The TE scores, which range between 0 (totally inefficient) and 1 (fully efficient), show a mean technical efficiency of 0.708. More than 90% of the farms are shown to produce at an inefficient level." "Helminth infections: do they affect the productive efficiency of speacialised dairy farms?" "Mariska van der Voort, Jef Van Meensel, Ludwig Lauwers, Johannes Charlier" "Participatory assessment of sustainability and resilience of three specialized farming systems" "Wim Paas, Isabeau Coopmans, Simone Severini, Martin K Van Ittersum, Miranda P. M. Meuwissen, Pytrik Reidsma" "There is a need for participatory methods that simultaneously assess agricultural sustainability and resilience at farming system level, as resilience is needed to deal with shocks and stresses on the pathways to more sustainable systems. We present the Framework of Participatory Impact Assessment for Sustainable and Resilient FARMing systems (FoPIA-SURE-Farm). FoPIA-SUREFarm investigates farming system functioning, dynamics of main indicators, and specifies resilience for different resilience capacities, i.e., robustness, adaptability, and transformability. Three case studies with specialized farming systems serve as an example for the used methodology: starch potato production in Veenkoloniën, The Netherlands; dairy production in Flanders, Belgium; and hazelnut production in Lazio, Italy. In all three farming systems, functions that related to food production, economic viability, and maintaining natural resources were perceived as most important. Perceived overall performance of system functions suggest moderate sustainability of the studied farming systems. In the studied systems, robustness was perceived to be stronger than adaptability and transformability. This indicates that finding pathways to higher sustainability, which requires adaptability and transformability, will be a challenging process. General characteristics of farming systems that supposedly convey general resilience, the so-called resilience attributes, were indeed perceived to contribute positively to resilience. Profitability, having production coupled with local and natural resources, heterogeneity of farm types, social self-organization, and infrastructure for innovation were assessed as being important resilience attributes. The relative importance of some resilience attributes in the studied systems differed from case to case, e.g., heterogeneity of farm types. This indicates that the local context in general, and stakeholder perspectives in particular, are important when evaluating general resilience and policy options based on resilience attributes. Overall, FoPIA-SURE-Farm results seem a good starting point for raising awareness, further assessments, and eventually for developing a shared vision and action plan for improving sustainability and resilience of farming systems" "Validating sustainability indicators: focus on ecological aspects of Flemish dairy farms" "Marijke Meul, Frank Nevens" "Higher sustainability performance of intensive grazing versus zero-grazing dairy systems" "Marijke Meul, Steven VAN PASSEL, Dirk Fremaut, Geert Haesaert" "Although grazing of dairy cows is an integral part of dairy farming in many European countries, farmers today more often choose for zero-grazing systems, where cows are housed throughout the year. Some studies already compared grazing and zero-grazing systems for specific issues such as labor efficiency, environmental impact, or animal welfare. In our study, we perform a more integrated evaluation, considering relevant ecological, economic, and social aspects. This allows for a balanced and more complete comparison of the sustainability performance of the two production methods. We evaluated ten intensive grazing and ten zero-grazing specialized Flemish dairy farms on the use of nutrients and energy, productivity and profitability, labor input, and animal welfare. In addition, we put special effort in formulating useful management advice for farmers. Therefore, we combined a detailed analysis of the sustainability indicators with an intensive interaction and discussion with farmers and farm advisors. Results show that, on average, the zero-grazing farms performed significantly worse from an ecological and economic point of view. This fact is explained mainly due to a less efficient use of concentrates and byproducts. Social sustainability performance did not differ significantly between the two groups. As a result, the integrated sustainability performance was significantly lower for the zero-grazing group. This finding shows that a further shift from intensive grazing to zero-grazing can move dairy farming in Flanders further away from sustainability. An important advice to improve the ecological and economic performance of zero-grazing farms is to optimize cows' rations to include more forages and optimize forage production and use. More detailed site- and case-specific management advice for farmers of both groups was provided during a discussion meeting. We consider this an essential additional step to any sustainability evaluation, since progress can only be made when monitoring results are translated into practical measures." "Potential of life cycle assessment to support environmental decision making at commercial dairy farms" "Marijke Meul, Corina E. Van Middelaar, Imke J. M. de Boer, Steven VAN PASSEL, Dirk Fremaut, Geert Haesaert" "In this paper, we evaluate the potential of life cycle assessment (LCA) to support environmental decision making at commercial dairy farms. To achieve this, we follow a four-step method that allows converting environmental assessment results using LCA into case-specific advice for farmers. This is illustrated in a case-study involving 20 specialized Flemish dairy farms. Calculated LCA indicators are normalized into scores between 0 and 100, whereby a score of 100 is assumed optimal, to allow for a mutual comparison of indicators for different environmental impact categories. Next, major farm and management characteristics affecting environmental performance are identified using multiple regression and correlation analyses. Finally, comparing specific farm and management characteristics with those of best performing farms identifies farm-specific optimization strategies. We conclude that this approach complies with most of the identified critical success factors for the successful implementation of LCA as a decision support system for farmers. Key aspects herein are (i) the flexibility and accessibility of the model, (ii) the use of readily available farm data, (iii) farm advisors being intended model users, (iv) the identification of key farm and management characteristics affecting environmental performance and (v) the organization of discussion sessions involving farmers and farm advisors. However, attention should be paid (i) to provide sufficient training and guidance for farm advisors on the use of the applied LCA model and the interpretation of results, (ii) to evaluate the correctness of the used data and (iii) to keep the model up-to-date according to new scientific insights and knowledge concerning LCA methodology. (C) 2014 Elsevier Ltd. All rights reserved." "Unraveling the microbiota of teat apices of clinically healthy lactating dairy cows, with special emphasis on coagulase-negative staphylococci" "G Braem, Sarne De Vliegher, Bert Verbist, Veerle Piessens, Luc De Vuyst, F Leroy" "Swab samples (n=72) obtained from the teat apex of lactating dairy cows without visual signs of inflammation (n=18) were gathered on 2 well-managed Flemish dairy herds (herds 1 and 2) during the same month to assess the bacterial diversity of teat apices before milking. A combination of both culture-dependent [plating and (GTG)(5)-PCR fingerprinting of the colonies] and culture-independent [denaturing gradient gel electrophoresis (PCR-DGGE)] techniques indicated that the teat apices contain a wide diversity of bacterial genera. Despite a low bacterial load, 20 bacterial genera of 3 phyla (Actinobacteria, Firmicutes, and Proteobacteria) were present. The most prevalent bacteria were the coagulase-negative staphylococci (CNS), encompassing a total of 15 species, which were identified to the species level using a combination of (GTG)(5)-PCR fingerprinting, gene sequencing (16S ribosomal RNA and rpoB genes), and a novel PCR-DGGE technique based on the tuf-PCR amplicon. Overall bacterial diversity did not differ significantly between the herds or between noninfected and subclinically infected quarters in herd 1. In herd 1, borderline significant lower CNS species diversity was found on teat apices of noninfected quarters compared with subclinically infected quarters. The most prevalent CNS species were Staphylococcus haemolyticus and Staphylococcus equorum in both herds and Staphylococcus carnosus in herd 2." "Unraveling the microbiota of teat apîces of clinically healthy lactating dairy cows with special emphasis on coagulase-negative staphylococci." "Gorik Braem, Sarne De Vliegher, Bert Verbist, Veerle Piessens, E. Van Coillie" "Swab samples (n=72) obtained from the teat apex of lactating dairy cows without visual signs of inflammation (n=18) were gathered on 2 well-managed Flemish dairy herds (herds 1 and 2) during the same month to assess the bacterial diversity of teat apices before milking. A combination of both culture-dependent [plating and (GTG)5-PCR fingerprinting of the colonies] and culture-independent [denaturing gradient gel electrophoresis (PCR-DGGE)] techniques indicated that the teat apices contain a wide diversity of bacterial genera. Despite a low bacterial load, 20 bacterial genera of 3 phyla (Actinobacteria, Firmicutes, and Proteobacteria) were present. The most prevalent bacteria were the coagulase-negative staphylococci (CNS), encompassing a total of 15 species, which were identified to the species level using a combination of (GTG)5-PCR fingerprinting, gene sequencing (16S ribosomal RNA and rpoB genes), and a novel PCR-DGGE technique based on the tuf-PCR amplicon. Overall bacterial diversity did not differ significantly between the herds or between noninfected and subclinically infected quarters in herd 1. In herd 1, borderline significant lower CNS species diversity was found on teat apices of noninfected quarters compared with subclinically infected quarters. The most prevalent CNS species were Staphylococcus haemolyticus and Staphylococcus equorum in both herds and Staphylococcus carnosus in herd 2." "Data-based monitoring of dairy cows - milk progesterone as a mirror of fertility" "Ines Adriaens" "With an annual production value of 639 million euros, dairy production is the fourth most important agricultural sector of Flanders. About 18% of all farms have dairy cows, summing up to a total of more than 300 000 animals. The modern dairy sector is characterized by strong specialization and scale enlargement, in which technology plays a prominent role. This technology supports the management on farm, and secures that despite the large herd sizes, individual animals can remain monitored. Although several technological developments are already commercialized and implemented on farm, still many challenges remain. Not in the least, there is a challenge of interpretation: likewise all other biological systems, dairy cows show a large individuality and variability, which renders it complex and challenging. Certainly in the case of monitoring systems for individual animals to support decision making on farm, there is a need for smart, physiology-based interpretation of the sensor data. Bad reproduction performance is the second most important cause of economic losses on Flemish dairy farms, summing up to on average 49€ per cow per year. Crucial to this aspect of dairy farming is the correct and timely identification of the cows' fertility status. Correct detection of estrus, onset of cyclicity and pregnancy allow to optimize (re-)insemination and treatments, which in their turn contribute to shorter calving intervals. Classically, estrus detection is done via visual observations of external symptoms or by using technology to continuously monitor increased restlessness in the estrous period. Although these technologies have shown their merit, they are not capable of identifying onset of cyclicity, ovarian problems or pregnancy. In the last decades, recent developments have led to technology for the automated measurement of milk progesterone on farm. Milk progesterone, in contrast to behavior-based technology, reflects the presence or absence of a corpus luteum on the ovaries. Monitoring the dynamics of the progesterone concentration therefore has the potential to provide a more complete image of a cow's fertility status. To this end, clear and consistent interpretation of the progesterone time series, while working in a cost-efficient and automated setting is essential. More concretely, the progesterone data should be converted to solid actions, while taking the variability caused by individuality of the animals, but also by measurement errors into account. The main objective of this PhD work was to develop an online monitoring algorithm based on milk progesterone, capable of unambiguously discriminating between the different fertility statuses, which could be implemented on farm and work in a cost-effective setting. More specifically, this could be translated into following sub goals: (1) mathematical characterization of the milk progesterone concentration, taking into account the physiological background of luteal dynamics; (2) integration of the mathematical model into an on-line monitoring system implementable on farm; (3) investigation of the link between the luteolytic drop in milk progesterone and ovulation time in order to improve insight in the optimal insemination window; and (4) benchmark the proposed methodology against the current state of the art for progesterone monitoring. By investigating both the sensitivity and specificity of the algorithms, but also looking into robustness for missing samples during the crucial moment of luteolysis, this work was further validated. Growth and regression of the corpus luteum are associated with a steady increase and a sudden decrease of progesterone. Each of these dynamics can be linked to a specific reproduction status and described with a mathematical function. During the postpartum anestrus phase after calving, no progesterone is produced and a constant suffices to characterize baseline height. Once cyclicity commenced, the successive increases and decreases can accurately be described with a sigmoidal Hill and Gompertz function respectively. After insemination and when successful conception resulted in the establishment of pregnancy, the development of a pregnancy corpus luteum and its associated increase in progesterone again can be characterized with the Hill function. These functions have the advantage that the lengths of the follicular and luteal phases might vary, even as the slopes of the increases and decreases, while still maintaining the general physiology-based shape of each cycle. Another important advantage of these functions is that they can easily be implemented in an on-line monitoring system. More specifically, the general principle is to detect onset of cyclicity via the deviation of the progesterone concentration from the baseline. Next, the increasing Hill function is fitted, and updated each time a new measurement becomes available. The residual of this new measurement with the fitted function is evaluated via a statistical process control chart in order to detect large negative deviations from the fitted luteal concentration. If there is enough evidence for a decrease in progesterone, the decreasing Gompertz function is added. At this time, the mathematical model can be used to calculate several model-based indicators, which allows to characterize the cycle and moment of luteolysis independently of the real sampling rate. Once luteolysis is detected, it is important to know when the succeeding ovulation will follow in order to optimize timing of insemination and maximize the chance on conception. The high workload and costs to investigate this, make this research rather difficult. Two studies were conducted to determine timing of ovulation after luteolysis. In the first, the ovarian status of the cows was synchronized and ultrasonography used to detect ovulation during the second estrus after synchronization. For the second study, the cows were not synchronized but the preovulatory LH surge was taken as a proxy for ovulation. In the latter study, also the alerts raised by the visual observation of estrus symptoms and an activity-sensor system were included. Progesterone-based systems were more sensitive and had a higher positive predictive value than visual observation of estrus symptoms and the activity based sensors. Moreover, it was shown that ovulation and the LH surge follow on average 77 ± 10 hours and 62 ± 12 hours after luteolysis detected by the developed algorithm. Using the indicators derived from the mathematical model describing the estrous cycle, a more consistent relation with the LH surge was found. However, as until now the sampling frequency was very high, the true value of these indicators was not yet exposed. To further validate the developed system and to compare this with the current state of the art for monitoring milk progesterone, a large dataset with all possible (combinations of) progesterone patterns was required. Therefore, a reproduction function model coupled to a lifetime performance model was employed to simulate scaled progesterone profiles, and a technique to convert these scaled profiles into realistic curves as they were measured on farm, was developed. Different sampling schemes, from which the reduced sampling scheme mimicked the case of missing values during luteolysis, were applied to this dataset. The sensitivity, specificity and robustness of the alerts was investigated and compared to a multiprocess Kalman filter in combination with a fixed threshold, which is the currently implemented tool to interpret the progesterone values on farm. We showed that the developed algorithm captured luteolysis almost simultaneously with the simulated luteolysis, in contrast to the Kalman filter for which the alerts came on average 1 to 2 milkings later. Moreover, the analysis provided evidence that alerts based on a model-derived indicator taking the height and baseline of the curves into account, allowed to estimate moment of luteolysis consistently, independent of missing samples. Monitoring milk progesterone allows to identify the different fertility status of lactating dairy cows on farm. However, to optimize farm management, not only identification is important, but also the concrete actions provided to the farmers. Unfortunately, whether or not it is the 'best' choice to inseminate, to treat or to cull is dependent on more than only the cows: ideally, also the physiological background of the animals, the economic environment, the farm system and so on should be taking into account. This type of overall data-integration for the optimization of decision support was identified as the main topic for future research." "Use of animal based measures for the assessment of dairy cow welfare" "Sophie de Graaf, Frank Tuyttens" "The overall aim of the project was to evaluate the use of routinely collected animal based measures (ABMs) for an evaluation of the overall animal welfare in dairy cow herds. ABMs being able to detect worst adverse effects in relation to animal welfare were identified based on the existing literature and expert opinion. The validity and robustness of these ABMs were evaluated and cow mortality, somatic cell count and lameness were selected for further study. A number of factors of variation were selected using expert opinion and used in a model to collate routinely collected data from Italy, Belgium and Denmark on selected ABMs. The routinely collected data was uploaded to the Data Collection Framework platform at EFSA and the data management in this process was evaluated. Five research datasets from Italy, Belgium, Denmark and France including information on ABMs as well as a measure of ’overall animal welfare’ at herd level were analysed to evaluate the association between the ABMs (individually or in combination) and overall welfare. The measure of ’overall animal welfare’ were not the same for all datasets. Except from the Italian data, the association between the ABMs and the different overall welfare measures were generally weak. Likewise, combining more than one ABM only improved the prediction of the overall welfare in the Italian dataset. Analyses of the other datasets could not confirm this finding. Finally, suggestions for future recordings of ABMs not routinely collected at the moment were given with a special focus on lameness. In conclusion, the relationship between selected ABMs and overall welfare at the herd level is complex and still not sufficiently studied. Therefore, a system using routinely collected ABMs to predict the overall welfare at herd level in dairy herds does not seem realistic based on the results from the present study."