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Therefore, TechGen4Health will develop an innovative technical-based whole farm approach to improve disease resistance in cattle, combining novel genomic information with milk-based analyses, sensor-based indicators for behaviour and health, climate data, manure management, calf health, as well as drone and satellite pictures for pasture characterisation.


Within WP6 the effect of cow’s health status on milk quality and, from a transgenerational perspective, on the calf health status will be investigated. UL uses different breeds, types of pasture (intensive, extensive, mountainous), and calf-rearing forms including mother-bonded rearing. This enables analyses of direct correlations between cow’s infection pressure on milk quality and on calf health. Cow-calf pairs will be used for monitoring of health traits across generations. Analyses of the lactoferrin-content via MIR (WP1) and of milk antibodies (WP3) lead to connections with the partners.

Milk-based analyses & diagnostics

Hence, various methods will be established to phenotype the endoparasite infestation. In addition to known methods based on sedimentation techniques (used for validations in the TechGen4Health framework), milk-based techniques will be (further) developed.

It has already been described to some extent that milk mid infrared (MIR) or antibody-based methods can be used to assess endoparasite infestation at individual animal or herd level (Grelet et al., 2021; Takeuchi-Storm et al., 2021), and that individual milk proteins, e.g. lactoferrin, can have antiparasitic effects (Jenssen, 2009).

An interplay among disease resistance predictors might be reflected through across-generation associations, e.g., monitoring calf health in dependency of milk quality and infection status of the dam (Hohmann et al., 2021).

WP1 aims to develop and apply dedicated milk mid-infrared (MIR) spectra-based prediction equations to detect burden of endoparasites as the core of alert and surveillance systems for the detection of endoparasite infections. We will provide a tool to inform farmers, but also public services on the in-farm prevalence and infection status of individual cows (MIR from milk recording) as well as at the herd level (MIR from bulk milk).

P2 will compile reference data sets for MIR analysis and establish the use of MIR for monitoring endoparasite infections for all partners. The transferability of the equations will be ensured through the standardisation of MIR. The equations will be validated and used throughout the partnership. The reference data sets will be expanded to include sensor data (WP2), milk antibody analyses (WP3) and other technologies.

Bulk tank milk-antibody (BTM-Ab) tests have been developed for liver fluke, lungworm and gastrointestinal nematodes (GIN) infection predictions based on Enzyme-linked Immunosorbent (ELISA) analyses. However, it is not fully clarified how to best interpret and act on the obtained information. WP3 aims to explore and validate BTM-Ab diagnostics as a tool for endoparasite control, and correlates the occurrence with drug use, as well as analyses seasonal dynamics and changes associated with preventive strategies in cow herds.

UCPH investigates BTM-Ab tests to evaluate presence of endoparasite infections in dairy herds focusing on liver and rumen flukes, lungworms, and GIN. UCPH carries out BTM-Ab tests and validates the results at herd level through individual recordings. Disease recordings and drug use will be analysed in relation to BTM-Ab at herd level, as well as BTM-Ab levels in relation to season and preventive strategies. BTM-Ab tests will be transferred to routine tests, even in partner countries.

Sensor technologies & digitalisation

In addition, a wide range of technical options will be used to monitor endoparasite infestation and its effects on the animal and on the system (grazing environment), such as animal-based and climate sensors, drones and satellite images.

Sensor technologies can be used to monitor welfare and health traits, like physiological parameters (e.g. body temperature, feed and water intake (Herlin et al., 2021), feeding and activity behaviour (Ranzato et al., 2023; Werner et al., 2018), or locomotion affected by various diseases like mastitis, lameness or parasite infections (Herlin et al., 2021).

WP2 aims to monitor endoparasite infections in grazing cattle using on-farm sensor technology. To this end, we will characterise normal (in-control) patterns and variation in the collected sensor time series to quantify perturbations (out-of-control) in performance, health, and welfare due to these infections. Ultimately, we target the development of digital tools for early warning alerts, and to analyse disease development and recovery, integrated with the outcomes of the other project WPs.

On-farm sensor time series of grazing cattle will be collected and processed to identify perturbations linked to endoparasite infections. This will result in digital tools to detect infections, predict their severity, and monitor recovery. These tools will aid in phenotyping resilient animals (link with WP4), assessing infection pressure (link with WPs 1, 3, 5, 7), and evaluating the economic impact (link with WP8) of cattle endoparasites.

Drones & satellite-based pasture characterisation

New developments of remotely controlled unmanned aerial vehicles (= drones) enrich the possibilities for farm animal monitoring and management, by, e.g., collecting temperature data emitted from ear tags (Webb et al., 2017), recording feeding behaviour (Nyamuryekung’e et al., 2016) or by estimating livestock weight (Lyu et al., 2022).

Additionally, data from satellites (RapidEye, Sentinel-2) can be used for the evaluation of grazing areas regarding various biophysical parameters and management characteristics (degradation, grazing intensity; Ali et al., 2016). These tools will be tested in different European production systems and established for widespread use in different countries.

WP5 aims to investigate the impact of climate, grassland parameters and production systems on endoparasite burden of dairy cows.

UBO will collect health and endoparasite parameters in a research farm in South Tyrol with two different production systems. Using satellites and drones, grassland parameters like pasture condition and grazing intensity, are determined. Climate data will be continuously obtained using sensors and weather station data. The aim is to find solutions for the control of endoparasite infections through system improvements based on dense ‘environmental data’. Including e.g. milk-based parameters according to the partner’s WP, production system specific early prediction models for the control of endoparasites will be developed. Herd infection risks will be modelled based on the environmental data.

Genomic technologies

In cattle, heritabilities up to 0.36 for diverse gastrointestinal infections indicate a genetic component for pathogen-specific susceptibility (May et al., 2019).

Genome-based analyses enable the identification of endoparasite-resistant animals (May et al., 2019). It is therefore essential to develop genomic predictions for endoparasite resistance that can be incorporated into herd management tools to improve animal selection and mating plans.

WP4 aims on the improvement of endoparasite resistance in grazing herds using genomic selection tools. UGI will develop genomic herd management instruments, enabling the early selection of parasite resistant female calves to produce parasite resistant grazing cows with a high welfare and health status.

UGI will enlarge existing datasets by continuing faecal sampling for endoparasite traits in Germany, supported by respective activities in all partner countries. Based on extreme phenotypes for endoparasites, we select the most susceptible and most resistant cows for whole genome sequencing. The SNP genotypes of the remaining cows with endoparasite phenotypes will be imputed to sequence level. For a subset of cows with most extreme phenotypes for infections, endoparasites will be genotyped, to infer host-parasite interactions. Genomic predictions will be enhanced considering milk spectra (WP1), milk antibodies (WP3), sensor traits (WP2), milk composition (WP6), and inferred grazing production system characteristics (WP5).

Smart manure management & automated faecal egg counting

Furthermore, modern manure management (Galama et al., 2020) contributes to reducing the spread of endoparasites through manure.

WP7 aims to reduce endoparasite infections through smart manure management. As manure serves as carrier for parasites and their eggs, WP7 will create a theoretical framework that models the spread of parasites through manure handling. The infection pressure of parasites will be analysed for manure with and without the use of modern handling techniques like emission-reducing floors, faeces-urine separation, acidification, additives and composting.

Automated faecal egg counting methods (AFEC), using artificial intelligence (AI) and automatic vision techniques, will be developed and validated with faecal lab analyses of UGI. AFEC will be used for a comprehensive assessment of the endoparasitic load of manure on floors, in storage, digesters, as RENURE (REcovered Nitrogen from manURE), and applied to land.

Combined with the results of the partners, production system specific predictions and farm specific prevention strategies will be developed and shared with the stakeholders. WLR generates manure handling guidelines to minimize parasite transmission risks.

Validation and economic evaluation methods

Faeces-based analyses, e.g. sedimentation methods are used for the validation of endoparasite load and form the basis for the development of the further methods: automated faecal egg counting method and milk-based analyses of mid-infrared (MIR) spectra, antibody determination and lactoferrin content.

The aim of WP8 is the validation and economic evaluation of the preventive health management tools and the practical implementation of dissemination activities. A pilot system for validation and economic evaluation of preventive health management tools from all partners will be implemented on LSMU Baisogala AHC dairy research farm. The results will be decisive for the future adoption and integration of these tools into routine dairy farming processes. An economic evaluation of the health management tools for detecting infections in cattle herds will be conducted. The evaluation compares the costs and benefits of these tools, assessing their impact on herd health, productivity, and overall farm profitability by applying a cost-benefit analysis model.

Subsequently, the predictions and technical implementations developed by LSMU will be finally evaluated for applicability in selected research herds in the partner countries.

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