1. Towards more efficient carbon, nitrogen and phosphorus cycling in European agricultural soils: Circular Agronomics (CA) program
- Author
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YASER OSTOVARI, Jan Willem Van Groenigen, Rachel Creamer, Julien Guigue, Eleanor Hobley, Laura Ferron, Henk Martens, Emily Overtuf, Anke Neumeier, Andreas Muskolus, Paolo Mantovi, Francesc Domingo Olivé, Thomas Guggenberger, Marek Holba, Ingrid Kögel Knabner, and Alix Vidal
- Abstract
It is estimated that only 20% of fertilizers applied annually in the European agricultural systems are converted to finished products for human consumption. These low efficiencies result in large loss of nutrients into the environment with severe negative influences on soils, water and air, and constitute unacceptable health and environmental costs. In addition, around 45% of soils in the European countries have less than 2% organic carbon (OC). Low soil OC storage is linked with negative environmental impacts including soil and water quality, climate change and biodiversity. A relevant strategy to enhance soil OC is the transformation of waste products into organic amendments for application on soils. The aim of the H2020 European project Circular Agronomics is to provide a comprehensive synthesis of practical solutions to improve the current C, N and P cycling in European agro-ecosystems. This project explores the medium and long-term effects of new and classical organic fertilizers in six countries including Germany, Spain, Italy, Netherlands, Czech Republic and Austria. The study sites will be sampled before and after applying the new organic amendments using a hydraulic corer. A full profile assessment of the C, N and P distribution, stability and bioavailability will be released up to one meter depth using a combination of classical bulk chemical analyses and state-of-the-art imaging techniques. Undisturbed soil cores will be scanned using a hyperspectral camera to reveal hotspots of C, N and P storage in the soil profile, at the micro-scale. Soil C, N and P will be modelled as a function of spectral response using a variety of machine learning approaches. These results will provide essential information to develop management strategies that reduce nutrient surplus and increase C stocks.
- Published
- 2019
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