Development of a computer system or smart application for calculation of a dairy cow's future value on basis of infomation in the Danish Cattle Database with a specific concern of paratuberculosis.

The overall objective of FUTUREMILQ is to devlop technologies and concepts that can ensure a future high food quality from existing and future milking systems. The purpose is to develop new methods for measurement of quality decreasing free acids, to develop a fast method for analysis of secific types of spores and to develop systems for monitoring and early warning.

Within the pilot project Smart Dairy Farming, companies, research institutes and farmers together to develop innovative resources in the field of animal health, fertility and nutrition. This means farmers can extend the life of their cows. The concrete result consists of sensors, indicators, decision models and advisory products that help in making the right choices when caring for cows. Proper care contributes to better health and longer life expectancy of the animals. Thus, this project contributes to the sustainability of the chain.

The objectives of this project are: 1. Generate adequate information to understand the relationship between Somatic Cell Count (SCC), Diffrential Cell Count (DCC) and mastitis (intramammary infections) in dairy herds. 2. Develop guidelines to cost effective monitoring and control of mastitis in dairy cattle herds that should be offered with the commercail DCC product 3. Develop a commercial DCC product that will be used to automate the counting of DCC

Objectives 1.To develop a novel application for smartphones to capture on-farm ketosis monitoring data. The app will be designed to work on android phones and i-phone/i-pads. The app will allow collection of data for the three most common testing modalities (urine, milk, and blood ketones).

The aim of the project is to develop a new connected rubber mattress for dairy cows, improving their welfare and monitoring of breeding parameters.

The focus of this project is to develop and enable an intelligent system that will apply precision management to whole farm grassland and grazing systems. The goal is to optimize grass quality, utilization efficiency, and ultimately profitability, with minimal labour requirement and maximum objectivity. To precisely allocate to the cow herd the absolutely correct area of grass, it is necessary to have an accurate ‘real-time’ measure of grass quality (as well as quantity).

Profitability on grass-based systems is driven by degree of grass utilization. This is influenced by increased growth and optimum management of that growth. Frequent measurement of grass parameters, e.g. herbage yield, height, density will facilitate increased herbage production and utilization. However, traditionally such measurement on farms is limited. The potential use of ICT for grass measurement is dramatic.

This multidisciplinary project seeks to integrate and extend existing state of the art technologies to ensure sustainable and responsible management of dairy units, with focus on cow health, milk quality and reduced emissions. We shall focus on milk metabolomic methods for determination of metabolic health, biomarker technologies for assessment of systemic health and accelerometer collars for measuring various activities including feeding behaviour, and hence intake. We shall also have access to NIR technology for feed quality assessment and rumen-bolus technology for measurment of rumen pH.

The main goal is to optimise the feeding strategy for grazing cattle and to improve the methods of pasture management. An in the frame of the project developed pasture robot and a modified automatic grazing system (AGS) will be integrated into existing herd management software (HMS), providing an optimal feeding strategy for cattle and pasture maintenance. A robot will be redesigned and sensors for detection of biomass, cowpats etc. and actuators, a mulcher and a seeder, will be implemented.

In this project we will develop an evaluation platform that demonstrates through research the potential for an Internet of Things (IoT) enabled FMIS with animal-centric ICT, production databases & best practice standards to assist farmers optimise sustainable livestock production. In this respect SILF will take an integrated approach to solving issues with environmental impact and animal welfare during livestock production.

The project ‘Multi channel Disposable Sensors for Animal Health Disease Diagnostics’ aims to develop microfluidic, electronic biosensors for Bovine Respiratory Disease (BRD). BRD is a leading natural cause of death in cattle and has substantial economic impact on the US and Irish food industries. BRD is typically diagnosed via ELISA, which can be expensive and slow to provide definitive results. There are at present no commercially-available field-based electronic tests for animal diseases.

This project seeks to develop innovative 3D imaging technology to enhance the simultaneous measurement of cow body condition score (BCS), liveweight and mobility (gait) as a highly advanced management decision-making tool. The aim is to improve the pace at which these key quality and production traits are identified for animal welfare, sustainability and profitability.

Development and prove of concept of a farm management system for automatisation of farm processes with usage of building referenced spacetimedata, use case: dairy production With the development of new procedures to the location of animals, tools and actors in stall systems new dimensions of the individual data processing and information extraction for animals through the consideration of building referenced space-time data open up.

a tool for surveillance of animal wellfare in dairy cattle Animal welfare is a multidimensional concept, which is based on a good animal health and the species-specific behavior. Both are largely determined by management. These include the daily animal control and surveillance routinely available process data. Therefore, the research project aims to develop a monitoring tool that allows the farmer an effective control on the herd and individual level. The monitoring of animal welfare of individual animals or groups of animals is paramount based on the observed behavior.