Grass-tech

Results:

This project demonstrated that it was feasible for grass cover measures to be collected using satellite images, although there were some challenges on cloud cover. It also demonstrated that grass growth models could be developed for English conditions and they could be developed to use data generated by satellite images to re-set the predictions.

Further validation work is needed, plus commercialisation of the approach via an app is required.

The Problem:

Grass is the cheapest feed available, but very few producers measure it regularly due to the perceived time commitment or lack of tools to use the measurements to make effective decisions.

Work in Australia and NZ has shown that satellite images can be used to predict grass growth. If this technology was successful in the UK then the technology will enable producers to improve yield and quality by optimising the timing of silage harvest, producing grass growth curves for bench marking and creating yield/quality maps which will enable precision management of inputs.

Aims and Objectives:

• to investigate the feasibility of measuring grass yield and quality remotely by using satellite sensing technologies
• to evaluate the different satellite sensing technologies for grass biomass and quality
• to make use of remotely sensed information to produce realistic grass growth curves from various models
• to test the feasibility of the models with historic data from beef, sheep and dairy farms

Approach:

Most of this project is funded by Innovate UK with AHDB (Beef & Lamb and Dairy) providing some funding and in-kind support.

Beef, lamb and dairy producers from across GB who regularly measure grass will be recruited and fields of interest will be identified.

ADAS will then collect data from satellites on biomass and compare it with measurements taken on the ground. Additional work will involve monitoring grass growth and quality including dry matter (DM), metabolisable energy (ME), Digestability (D value) and crude protein (CP) on 12-16 fields (4 areas in each field) on two dates (1st silage cut and 2nd silage cut). The tests will be done on a wide range of grass types (different ley ages, grass varieties, clover densities and manuring practices). This information will then be compared with optical and radar satellite information.

Part of the work will be developing tools to help producers use the information gathered from the variety of sources.