Through the analysis of multiple remote sensing satellite data sources (visible and radar) and meteorological data, in combination with the University of Reading’s detailed modelling and data-assimilation techniques, the tool will enable dairy farmers to monitor and predict pasture productivity and quality.
Trials are currently being conducted by the University of Reading over a two-year period, initially at controlled research plots, and are now being extended to operational dairy farms. This approach is driving iterative development of models and data products, which will be delivered to users through Rezatec’s geospatial web-portal platform. The platform will be upgraded and extended to support the required data streams and implement the bespoke grass growth models to create an innovative decision support tool for dairy farmers.
Alternative technologies and R&D strategies in this area are scarce, but those that are emerging generally focus on empirical vegetation indices, which can lead to unreliable and inaccurate estimates of PPQ; our proposed approach aims to offer a much more reliable product. Furthermore, most competitors using vegetation indices are focusing on crops, rather than grasslands, despite the fact that dairy production world-wide is 700 million tonnes.
“The proposed work and selected approach will ensure an accelerated route to market of the research currently being conducted by those at the forefront of remote sensing and pasture research, including colleagues at the University’s Centre for Dairy Research (CEDAR), in strong collaboration with practitioners in the dairy farm industry,” commented Professor Anne Verhoef, Principal Investigator of PASQUAL, Department of Geography and Environmental Science, the University of Reading.
This project is innovative both commercially and technically on many fronts. It pushes boundaries beyond current leading-edge world science and technology in the area of obtaining near real time estimates of grass crop productivity and quality. The University of Reading will develop a new model for predicting the productivity and quality of grasslands. This model will be informed by a broad range of data for both model development, calibration and verification. In addition to established local data (farm management/field data, weather data), the model will have direct access to multiple remote sensing satellite data sources including radar data which is well suited to monitoring tasks in cloud covered regions, such as the UK.
Dr Andrew Carrel, Chief Technology Officer, Rezatec added, “The outputs from the data modelling process will be combined with other data on Rezatec’s platform, where further processing will take place using machine learning and data mining techniques to add the predictive analysis that will make the tool highly valuable for the farming end users.”
Rezatec’s platform is designed to deliver high performance data visualization, analytics and decision support to multiple end users, and is highly scalable. It will facilitate the broad use of the farming intelligence tools and services within the UK, Europe and across the globe.