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FFL-JA-Forskningsmidlene for jordbruk og matindustri

New approaches for management and breeding of dairy cows, in automatic milking systems (AMS)

Alternative title: Produksjon- og helsestyring i besetninger med automatiske melkingssystemer

Awarded: NOK 0.29 mill.

The AMS project comprises of three projects managed and co-financed by the Norwegian Research Council (NFR): 1) New approaches for management and breeding of dairy cows, in automatic milking systems (AMS) (NFR no. 244231) (The "main project"). 2) TINE commercial sector PhD: Udder health and somatic cell count in automatic milking systems (NFR no. 249158). 3) TINE commercial sector PhD: Efficient, hygienic and cow-friendly milking of dairy cows in automatic milking systems (NFR no. 249159). Based on the hypotheses set out in the first project, TINE established two commercial sector Phd projects that have worked with objectives within the main project. The project manager for the main project (244231) has been the principal supervisor for both commercial sector PhDs, as well as a PhD student and a postdoctoral fellow supported by the main project. In addition, the main project also funded a PhD student on dairy cattle breeding and genetics and a postdoctoral fellow in feeding. Collating the three projects has initiated a good synergy and data has been used across several projects and work packages. Results and publications from all projects are therefore reported together in this final report. Work package 1: Opportunities and challenges in feeding, health and reproduction in cows in herds with AMS. Work Package 2: Genetic analyses of new traits based on data from AMS to be used in the breeding program for Norwegian Red Work package 3: Operational management and quality of life in herds using AMS, as well as social and societal effects of converting from conventional milking systems to AMS. We have used information from infrared spectra in milk, sensor data on body weight, body condition and activity together with real time AMS data for early detection of subclinical ketosis and delayed onset of ovarian activity. Our results show that sensor data combined with novel mathematical approaches can be used to identify cows in severe negative energy balance in a timely manner to avoid impaired animal welfare in AMS herds. The current project has also evaluated a cow welfare index. Udder health has also been a major focus of the project, and we have evaluated the use of somatic cell count data from online cell counts (OCC), for the detection of cows with subclinical intramammary infections (IMI). We have also investigated the variation in OCC values from cows with and without IMI and found that a relatively low proportion of the IMI status was explained by changes in OCC. Still, our findings showed that our novel mathematical transmission model was useful for detection of cows with IMI at drying off, and for the prediction of future prevalence of IMI at herd level. We expect our model to be an important tool in future programs for preventing udder health problems, and thus increase animal welfare in the herd. Optimizing the milking process will inevitably increase udder health and thereby animal welfare in the herd. In this project, we have used AMS data to develop methods for surveillance of the milking process at cow level. This has been achieved by evaluation of parameters related to udder health, such as somatic cell count and teat-end condition. These parameters have been compared with milking time tests such as; milk flow, length of overmilking period, vacuum levels and milking time. Traditionally these parameters have been obtained through specialized equipment and procedures. With our approach we can obtain sufficiently accurate parameters for surveillance of the milking process through data accumulated by the AMS system. AMS provides objective, frequent and accurate measurement of several important traits. Combining data generated from AMS, associated sensors and traditional data from the Norwegian Dairy Herd Recording System (NDHRS) opens for improved genetic evaluations. In this project, we have identified the most important measurements related to cow milkability, behavior and temperament, and udder health based on AMS data. We have shown how these novel traits can be used to improve genetic evaluations for Norwegian Red. The project has also focused on the importance of farm management and technology adaption by AMS-farmers. The investments in AMS are made to increase the quality of life of the farmers in everyday life and to make the farm a future-oriented dairy farm. Farmers have emphasized a more flexible working day, easier physical work and that AMS increases the likelihood of future milk production on this farm. Many consider AMS as part of the future standard for modern dairy farming. The sum of all the investments on individual farms, and changes in political regulations to make the investments financially sustainable, has changed the Norwegian dairy farming landscape. Therefore, farmers with small and medium-sized farms have shown greater interest in investing in milk robots, which may slow the development towards larger and fewer farms.

Prosjektet viser at fôropptak, energibalanse, reproduksjonsevne og enkelte sjukdommer kan predikeres ved bruk av spektroskopi av melk. Vår forskning gjør at bøndene bedre kan bruke celletallsmåleren til å følge med på utviklingen av subklinisk mastitt i besetningen, og våre framskrivingsmodeller kan brukes til å forbedre rutiner og sikre best mulig fremtidig jurhelse i besetningen. Vi har funnet nye måter å måle funksjonen av et automatisk mjølkeanlegg. Dette er viktig for å å forebygge mastitt og redusere bruk av antibiotika. Vi har vist at data fra AMS kan brukes for å beregne avlsverdier, og vi har demonstrert potensialet og mulighetene som ligger i å utnytte denne typen data. Vi har dokumentert sammenhenger mellom enkeltbondens beslutning om å investere i AMS og strukturelle og samfunnsmessige konsekvenser. Prosjektet har også gitt oss et godt kunnskapsgrunnlag om hvordan mjølkeroboten påvirker økonomien og arbeidshverdagen til bonden.

Increased yield per cow and less time spent on individual supervision represents new challenges related to feeding, animal health, breeding, hygienic quality of milk and technology adaption in Norwegian herds with automated milking systems (AMS). We aim to expand on a long tradition of herd recording (NDHRS) for breeding and research purposes in Norway by combining information from NDHRS with data from AMS and sensors to improve genetic evaluations of Norwegian Red, enhance herd management, develop better feeding strategies and systems for surveillance of energy balance (EB), health and reproduction. Successful technology adaption among farmers is important for future dairy farming in Norway, and we will establish research based approaches to improve the use of surveillance systems. Finally, we will address the increasing problems related to hygienic quality of raw milk in AMS. We will improve feeding strategies in AMS by better predictions of feed intake and EB. New approaches for early detection of subclinical ketosis and impaired reproduction will also contribute with information for adjustment of individual feeding before health, production and welfare becomes compromised. New mathematical approaches will be developed in order to predict EB and health status in individual cows based on milk components, sensors, AMS- and NDHRS-data. The cows meet new challenges in AMS and the breeding program need to be adjusted accordingly. Based on data routinely recorded in AMS, we will work on new traits and improved genomic evaluation to breed cows that fit better in AMS. The current project will determine factors related to contamination of silage with spore forming bacteria. In case of funding for a commercial sector PhD, also barn and AMS factors will be investigated. Finally, the proposed project aim to address challenges related to farmers health, environment and safety, labor division, economy and general management as AMS becomes the dominant milking system in Norway

Publications from Cristin

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FFL-JA-Forskningsmidlene for jordbruk og matindustri