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Grazing systems have a favourable effect on dairy cow health and welfare. However, endoparasite infections represent a major challenge for grazing cattle worldwide, especially due to increasing anthelmintic resistance. Consequently, it is imperative to reduce the parasite pressure on grassland and respective cattle infections using various technical tools at different levels including the system, the herd and the individual.

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. TechGen4Health incorporates experts in the research field of endoparasite infections and the evaluation of grazing systems from 7 European countries. In the work packages, the project partners establish their respective core technologies in their home countries before extending and adapting them to the partner countries and their specific conditions.

WP1 addresses the detection of the endoparasite burden using milk infrared spectra, WP2 focusses on digitalisation and sensor technologies to predict infections, and WP3 will establish novel diagnostics based on bulk tank milk. WP4 develops equations for genomic predictions of endoparasite resistance. WP5 infers effects of production system characteristics on infections using technical data. In WP6, effects of the milk composition on calf health will be studied. WP7 addresses techniques of smart manure management to milden infection pressure. WP8 develops the methodological framework for comprehensive tool validations and overall economic evaluations using a cost-benefit analysis. The validations and economic evaluations will be continued in research herds from all partner countries. WP9 handles overall project management and communication.

Scientific idea and research objectives:

In the context of intensive discussions addressing animal welfare in livestock husbandry, consumers favour dairy cattle farming in grazing systems, due to anticipated favourable effects on animal health and welfare (Weinrich et al., 2014). Despite the positive effects of grazing on certain welfare indicators, Armbrecht et al. (2019) mention that pasture management needs to be optimized to enhance its benefits. Here, endoparasite infections are a major challenge in grazing dairy herds worldwide, implying tremendous economic losses due to reductions in animal welfare, milk production and fertility (Rinaldi et al., 2022). Additionally, climate change increases the parasite burden due to rising temperatures and humidity (Gauly et al., 2013), which in turn implies recursive detrimental effects on animal welfare, immunity and health (König and May, 2019). In addition, resistance to anthelmintics and the contamination of water, soil and soil-life with anthelmintics are severe problems (Vercruysse et al., 2018). Manure handling can spread respectively limit parasite infections of the grassland.

In consequence, the scientific idea of TechGen4Health is to develop various techniques and methods aiming on the reduction of the endoparasite pressure on grassland and of infections in cattle, as well as to develop early warning systems and to limit the genetic susceptibility of cattle to endoparasite infestation. In cattle, heritabilities up to 0.36 for diverse gastrointestinal infections indicate a genetic component for pathogen-specific susceptibility (May et al., 2019). However, current strategies for phenotyping endoparasite infections (e.g., FEC) are time-consuming and/or expensive (Rinaldi et al., 2022) and too complex to be used on a large scale as alert and monitoring systems (Vercruysse et al., 2018). 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).

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). 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.

Furthermore, modern manure management (Galama et al., 2020) contributes to reducing the spread of endoparasites through manure. 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. Consequently, TechGen4Health develops an innovative technical-based whole on-farm approach across national borders to reduce endoparasite burden and to improve endoparasite resistance in grazing cattle. To achieve this comprehensive goal, TechGen4Health is pursuing the following research objectives:

  1. to establish MIR-based equations for early detection of endoparasite infections via milk recording within differing production systems,
  2. to establish sensor-based endoparasite monitoring, to detect endoparasitic infections, to estimate the severity and monitor the recovery based on on-farm sensor data,
  3. to validate bulk tank milk-antibody (BTM-Ab) tests for evaluation of the presence of endoparasite infections in dairy herds,
  4. to develop genomic herd management instruments, enabling the early selection of parasite resistant animals,
  5. to investigate the impact of climate, grassland parameters and production systems on endoparasite burden of dairy cows, using drones, satellites and sensors,
  6. to investigate the effect of cow’s health status on milk quality and on calf health status,
  7. to reduce endoparasite infections through smart manure management,
  8. to validate and economically evaluate the preventive health management tools developed by the project members, and to implement the herd management tools in practical farming.

All these objectives ultimately contribute to reduce the use of anthelmintics and thus counteract the problem of anthelmintic resistance and the pollution of soil, soil life and groundwater by anthelmintic residues.

Project description:

TechGen4Health aims on the improvement of animal health and welfare (AH&W), e.g. through genomic health monitoring and/or the development of new technologies to monitor and improve AH&W. Specifically, TechGen4Health focusses on a comprehensive approach to reduce the endoparasite burden in grazing cows. The project integrates various technologies, including sensor-based assessments of performance, health and welfare parameters, the application of drone and satellite-based imaging of pasture conditions, the development of health and welfare indicators in cow milk based on milk spectra and bulk milk parameters, genomic technologies embedded in herd management programs as well as technical strategies regarding manure management. 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 latter aspect points to another innovative aspect of TechGen4Health, i.e., the analysis of the causal relationship between endoparasite infections in cows and cow’s milk parameters and, from a cross-generational perspective, on calf health. In combination with the developed genomic tools, famers can select female calves at very early ages for an ultimate improvement of the cow health status. The aim of TechGen4Health is to develop tools and methods that can be used in practical cattle farming systems to achieve an immediate impact in a broad European farming context. The combination of all these methods and technologies will lead to a sustainable reduction in the impact of endoparasite infestations on health and welfare of grazing cows, and thus also influence the performance of dairy and dual-purpose cattle in terms of milk and meat production. The use of drugs like anthelmintics can thus be reduced, contributing to a reduction in drug resistances.

Expected impact:

TechGen4Health has strong impact on enhancing animal health and welfare of cattle kept in grazing systems. In this regard, the impact on science, practical agriculture, the environment and the public/society are addressed in various ways. In all these areas, TechGen4Health aims on the prevention, detection, assessment, and management of animal health and welfare regarding the detection of endoparasitic burden in grazing cows, also helping authorities to define their policies. Due to the results, innovations and (practical) technical tools generated and developed in the framework of TechGen4Health, “new doors will be opened” stimulating the expansion of pasture-based dairy farming in a broad European context, with all associated favourable effects of grazing on certain welfare indicators.

Impact on science: Thanks to the strong networking among all project partners across country borders, comprehensive databases regarding health and infection traits and respective indicators, genetic parameters for infections and resilience in different breeds, environmental characteristics and economic parameters, will be generated. These all-encompassing databases will contribute to many international publications and scientific conference presentations, attracting the endoparasite topic in the context of cattle management in grazing systems. Developed methods to predict endoparasite resistance (e.g., statistical models for genomic predictions, MIR milk equations) and respective software packages can be used by researchers from other institutions, broadening the research of “endoparasite topics”. The structured PhD/master student education (Annex III) will stimulate young researchers to enhance their scientific ideas.

Impact on practical agriculture: TechGen4Health develops technology-based early warning systems for the early detection of endoparasite infestations, ultimately improving the cow health and welfare status (and productivity). These are sensors and technologies that are easy to integrate into the daily and routine farm management. Respective tool applications imply only limited extra labour for the dairy cattle farmer. Further benefits can be derived from milk monitoring applications in commercial milk recording laboratories, enabling the analyses of MIR, antibodies and lactoferrin content. Prompt indications in milk regarding endoparasite infections will stimulate farmers to participate at official milk recording schemes. Breeding impact, i.e., improved endoparasite resistance through the application of genomic tools, and the identification of cows with high lactoferrin content, (antiparasitic effect especially in mother-bonded calf rearing), imply sustainable impact from the transgenerational perspective. Ultimately, due to the overwhelming effect of endoparasite infections on economy, TechGen4Health contributes to increased competitiveness of dairy and dual-purpose cattle farming in grazing systems.

Impact on the environment: TechGen4Healh contributes to the limitation of anthelmintics in cattle husbandry. Hence, resistances as well as contaminations of water, soil and soil-life with anthelmintics, will be reduced. Additionally, the developed manure treatment methods have strong impact, i.e., lower emission of nutrients into the environment. Technology-based analyses of grassland parameters will improve grassland utilisation and productivity, reduce endoparasite infestation of pastures and contribute to fewer bare patches and more biodiversity in the grassland vegetation.

Impact on the public/society: Improved knowledge on endoparasite resistance will stimulate cow milk production in grazing systems. Keeping more cows on pasture perfectly reflects the demand of the society. An increasing variety of products (milk, meat) can be offered that meet the consumer demand for improved animal welfare and health in the context of animal-based food production in a “natural grazing environment”.

References:

  • Ali, I., Cawkwell, F., Dwyer, E., Barrett, B., Green, S. (2016): Satellite remote sensing of grasslands: from observation to management. J. Plant Ecol. 9: 649–671. https://doi.org/10.1093/jpe/rtw005
  • Armbrecht, L., Lambertz, C., Albers, D., Gauly, M. (2019): Assessment of welfare indicators in dairy farms offering pasture at differing levels. Animal 13: 2336–2347. https://doi.org/10.1017/S1751731119000570
  • Galama,P. J., Ouweltjes, W., Endres, M. I., Sprecher, J. R., Leso, L., Kuipers, A., Klopčič, M. (2020): Symposium review: Future of housing for dairy cattle. J. Dairy Sci. 103: 5759–5772. https://doi.org/10.3168/jds.2019-17214
  • Gauly, M., Bollwein, H., Breves, G., Brügemann, K., Dänicke, S., Das, G., Demeler, J., Hansen, H., Isselstein, J., König, S., Lohölte, M., Martinsohn, M., Meyer, U., Potthoff, M., Sanker, C., Schröder, B., Wrage, N., Meibaum, B., von Samson-Himmelstjerna, G., Stinshoff, H., Wrenzycki, C. (2013): Future consequences and challenges for dairy cow production systems arising from climate change in Central Europe – a review. Animal 7: 843–859. https://doi.org/10.1017/S1751731112002352
  • Grelet, C., Dardenne, P., Soyeurt, H., Fernandez, J. A., Vanlierde, A., Stevens, F., Gengler, N., Dehareng, F. (2021): Large-scale phenotyping in dairy sector using milk MIR spectra: Key factors affecting the quality of predictions. Methods 186: 97–111. https://doi.org/10.1016/j.ymeth.2020.07.012
  • Herlin, A., Brunberg, E., Hultgren, J., Högberg, N., Rydberg, A., Skarin, A. (2021): Animal welfare implications of digital tools for monitoring and management of cattle and sheep on pasture. Animals 11: 829. https://doi.org/10.3390/ani11030829
  • Hohmann, L. G., Yin, T., Schweizer, H. Giambra, I. J., König, S., Scholz, A. M. (2021): Comparative effects of milk containing A1 versus A2 – casein on health, growth and -casomorphin-7 level in plasma of neonatal dairy calves. Animals 11: 55. https://doi.org/10.3390/ani11010055
  • Jenssen, H. (2009): Antimicrobial activity of lactoferrin and lactoferrin derived peptides. Nova Science Publishers, Inc. New York. ISBN-10: 1606925180.
  • König, S., May, K. (2019): Invited review: Phenotyping strategies and quantitative-genetic background of resistance, tolerance and resilience associated traits in dairy cattle. Animal 13: 897–908. https://doi.org/10.1017/S1751731118003208
  • Luy, X., Li, X., Dang D., Dou, H., Wang, K., Lou, A. (2002): Unmanned Aerial Vehicle (UAV) remote sensing in grassland ecosystem monitoring: A systematic review. Remote Sens, 14: 1096. https://doi.org/10.3390/rs14051096
  • May, K., Scheper, C., Brügemann, K., Yin, T., Strube, C., Korkuć, P., Brockmann, G. A., König, S. (2019): Genome-wide associations and functional gene analyses for endoparasite resistance in an endangered population of native German Black Pied cattle. BMC Genom. 20: 277. https://doi.org/10.1186/s12864-019-5659-4
  • Nyamuryekung’e, S., Cibils, A. F., Estell, R. E., Gonzalez, A. L. (2016): Use of an unmanned aerial vehicle—Mounted video camera to assess feeding behavior of Raramuri Criollo cows. Rangel. Ecol. Manag. 69: 386–389. https://doi.org/10.1016/j.rama.2016.04.005
  • Ranzato, G, Lora, I., Aernouts, B., Adriaens, I., Gottardo, F., Cozzi, G. (2023): Sensor-based behavioral patterns can identify heat-sensitive lactating dairy cows. Int. J. Biomet. 67: 2047–2054. https://doi.org/10.1007/s00484-023-02561-w
  • Rinaldi, L., Krücken, J., Martinez-Valladares, M., Pepe, P., Maurelli, M. P., de Queiroz, C., Castilla Gómez de Agüero, V., Wang, T., Cringoli, G., Charlier, J., Gilleard, J. S., von Samson-Himmelstjerna, G. (2022): Advances in diagnosis of gastrointestinal nematodes in livestock and companion animals. Adv. Parasitol.: 118: 85–176. https://doi.org/10.1016/bs.apar.2022.07.002
  • Takeuchi-Storm, N., Thamsborg, S. M., Enemark, H. L., Boes, J., Williams, D., Denwood, M. J. (2021): Association between milk yield and milk anti-Fasciola hepatica antibody levels, and the utility of bulk tank milk samples for assessing within-herd prevalence on organic dairy farms. Vet. Parasitol. 291: 109374. https://doi.org/10.1016/j.vetpar.2021.109374
  • Vercruysse, J., Charlier, J., Van Dijk, J., Morgan, E. R., Geary, T., von Samson Himmelstjerna, G., Claerebout, E. (2018): Control of helminth ruminant infections by 2030. Parasitol. 145: 1655–1664. https://doi.org/10.1017/S003118201700227X.
  • Webb, P., Mehlhorn, S. A., Smartt, P. (2017): Developing protocols for using a UAV to monitor herd health. In Proceedings of the 2017 ASABE Annual International Meeting, Spokane, WA, USA, 16–19.
  • Weinrich, R., Kühl, S., Zühlsdorf, A., Spiller, A. (2014): Consumer attitudes in Germany towards different dairy housing systems and their implications for the marketing of pasture raised milk. IFAMA 17: 205–222.
  • Werner, J., Leso, L., Umstatter, C., Niederhauser, J., Kennedy, E., Geoghegan, A., Shalloo, L., Schick, M., O’Brien, B. (2018): Evaluation of the RumiWatchSystem for measuring grazing behaviour of cows. J. Neurosci. Methods 300: 138–146. https://doi.org/10.1016/j.jneumeth.2017.08.022