In this project we study traits related to breeding, feeding, health and reproduction in dairy cows on AMS farms. Management of AMS farms is also an important aim of the Project.
The current project focus on opportunities and challenges on Norwegian farms with automatic milking systems (AMS), such as; feeding, health and reproduction, breeding for better adaptation of dairy cows to AMS, management of AMS farms and farmers technology adaptation. Furthermore, the applicability of milking time testing in AMS is adressed. An important part of the project deals with the development of novel mathematical approaches to model multidimensional data
Milk production, subclinical ketosis and reproductive performance of dairy cows are strongly influenced by their energy status during the first weeks after calving
Evaluation of relationships between teat-end condition and milking time tests such as; milk flow, length of overmilking period, vacuum levels in the teatcups, total milking time and vacuum in the mouth piece chamber
In the AMS herds, the cows meet new challenges in the environment and the breeding program needs to be adjusted accordingly with respect to traits, trait definitions and weights in the total merit index.
Technology adaptation and new management methods in AMS herds influence quality of life for farmers
The main aim of the feeding part of the AMS-project is to develop procedures for dynamic adjustment of feeding in agreement with feed intake and energy balance.
The project is funded by The Foundation for Research Levy on Agricultural Products, Tine, Geno and DeLaval
Large amounts of heterogeneous data has been generated in the AMS project. To establish reliable predictions of metabolic function and ovarian activity, data from different sources has been combined. This part of the project is strongly affiliated with the activities of The Biospectroscopy and Data Modeling Group at The Department of Matematical sciences and Technology at NMBU
Energy balance, health, and reproduction in AMS
Identification of cows with increased risk of subclinical ketosis or impaired ovarian function due to negative energy balance enable farmers to initiate feed corrections to avoid compromised animal health and reproduction. We have used information from infrared spectra in milk to determine energy balance in dairy cattle during the first 100 days in milk. We are also using automated body condition scoring (camera), activity and rumination, together with real time AMS data for early detection of subclinical ketosis and delayed onset of ovarian activity.
We have also developed methods for detection and monitoring of cows with subclinical intramammary infection by the use of online cell count (OCC) (http://www.delaval.com). OCC deliver information on a cow’s milk somatic cell count from every milking, but thresholds between pathology and normal processes have not been sufficiently established prior to the AMS project. We have validated the discriminatory capacity of this device and used OCC to build a deterministic forecasting model of the herd udder health status.
Milking time testing
We have found relationships between teat-end condition and milking time tests such as; milk flow, length of overmilking period, vacuum levels in the teatcups, total milking time and vacuum in the mouth piece chamber. We conclude that quarter milk flow rate provide useful information when evaluating teat-end condition in AMS herds. We have also found that vacuum level can be expressed by the milk flow variables in an AMS system. We have compared milk time testing in parlor, tie stalls and AMS to determine the impact on SCC between milking systems. We have also studied how much of the variability of SCC that can be explained by different traits monitored by the AMS system.
We have successfully used milking time as a proxy for milkability in heritability studies, and also found kick off and incomplete milkings to be useful for the assessment of temperanmet in cows milked by AMS.
Technology adaptation, quality of life and new management methods for farmers with AMS herds
We have studied farmer’s perceptions of quality of life, health environment and safety and economic revenue on AMS farms in Norway. Data from both NDHRS and the financial dairy herd recording system has been used.
We have expressed the cows energy balance by Fourier Transform Infrared Spectroscopi. We have studied the We have feed a group of cows using a standard lactation curve and compared this approach with a dynamic feeding regime. We have aslo assessed the association between chewing time and fiber content in feed.
Post doc focusing on feeding of cattle
Post doc focusing on Mathematical modelling
Ph.d student focusing on udder Health in cattle
Ph.d student focusing on milk time testing
Ph.d student focusing on breeding of cattle
Ph.d student focusing on reproduction in cattle
Egil Petter Stræte, Norsk senter for bygdeforskning:
Astrid Johansen, NIBIO
Håvard Steinshamn, NIBIO
Bjørn Gunnar Hansen, Tine
Telefon 67 23 00 00
Faks 67 23 06 91
Kart: Mazemap / (pdf)