"DIFFERENT RELEVANT PROPERTIES AND FUNCTIONS OF SOIL, AS WELL AS PROCESSES THAT TAKE PLACE IN SOIL, ARE MAINLY DETERMINED BY ITS STRUCTURE. PARTICLE SIZE DISTRIBUTION (PSD) AND THE GEOMETRICAL CONFIGURATION OF THE PORE SPACE PLAY A CRUCIAL ROLE IN THE STRUCTURAL DEFINITION OF SOILS. OUR FORMER RESEARCH PROJECTS HAVE BEEN CONCERNED WITH THE MODELLING AND MATHEMATICAL ANALYSIS OF THOSE TWO ATTRIBUTES OF SOILS.

THE MAIN GOAL OF THE PRESENT PROPOSAL IS TO PROMOTE THE CEBAS-CSIC PARTICIPATION IN THE CALL H2020-SFS-2014-2. A: EXTERNAL NUTRIENTS. THE PROPOSED ACTIVITIES WITHIN THIS GRANT APPLICATION WILL SUPPORT THE COORDINATION BETWEEN THE PROJECT LEADER AND REST OF PARTNERS IN ORDER TO INCREASE THE POSSIBILITY TO OBTAIN THE GRANT.

The PredICTor project has two main deliverables, i) an online decision support tool for evaluating an intended field traffic situation for a given soil condition and with given machinery (for farmers and agricultural advisers), and ii) an online tool for creating European-wide maps of the wheel load carrying capacity, which is defined as the maximum wheel load the soil can carry at given soil moisture conditions and for a given tyre and tyre inflation pressure (for authorities / soil protection offices).

Abstract This project aims to design, develop and implement an innovative web-based decision support platform (soilquality.org.uk) for improved soil management in UK arable and lowland grassland systems. Using levy funding, farmers' demand for tools to manage soil health has been met in Australia through soilquality.org.au. The soilquality.org.au platform, which provides the ability to compare individual test results within a robust decision-support framework, is unique and highly valued by farmers' organisations.

Soil-for-Life® (SfL) drives continuous improvements in crop production/utilisation resulting in direct increases in marketable yield and operational efficiencies. SfL is underpinned by an emerging innovative interdisciplinary field ‘agri-informatics’ whereby statistics and database management techniques are used to exploit knowledge held in multiple ’big data’ sets.

Abstract In current practice, a tractor mounted sensor to calculate Normalized Difference Vegetation Index (NDVI) detects live, green vegetation from a target area and can be used to analyse crop nutritional requirements. By adding high-resolution satellite data it is possible to achieve a variable rate (VR) fertiliser recommendation. Current practice lacks two key factors in the determination of optimum N supply to growing crops: availability of high-resolution data to inform on soil fertility status; and technologies that ensure accurate and consistent placement of nutrient.

Abstract This project will address key agronomic/soil related challenges currently impeding sustainable intensification (SI). Current levels of wastage and field losses across the UK horticultural sector are not sustainable and are largely attributed to soil degradation-related constraints and inappropriate crop management approaches. This project will focus on minimising wastage and field losses as underpinned by sustainable soil management. Soils are the foundation of food production as they support crop production, and for growers, soil is their primary business asset.

This project is funded by the innovation program of the Federal Ministry of Food and Agriculture (BMEL), funding agency is the Federal Office for Agriculture and Food (BLE) . mobile soil-sensor-module and data fusion for resource efficient plant production The small-scale knowledge of soil properties combined with further information is an essential basis for crop production and largely determines the use of resources. Recent methods - with laboratory analyses of soil samples - do not offer options for online verification of the measured results and require transporting of soil.

development of a robotic solution for slugs and mice control Slugs can cause substantial damages in agricultural plant production. Existing methods to fight those slugs do not deliver satisfying results. Therefore the MSR-bot-Project aims to develop a robot that detects slugs and fights them using a manipulator. The planed robot navigates autonomously across agricultural land. It detects slugs using digital image processing. The robot is provided with a control that enables the device to anticipate the development of the slug population.