Europe will face increasing pressure on agricultural systems due to increasing global food demands, competing claims on land resources and decreasing possibilities to displace production outside Europe. Moreover, increasing societal demands for a wide range of ecosystem services and biodiversity protection call for transitions towards intensive agricultural systems that have minimal detrimental environmental effects. As a response to these major societal challenges, sustainable intensification (SI) is gaining attention. SI cannot be implemented through a generic, single development pathway for all agricultural systems. Alternative trajectories and actions to achieve SI depend on the local and contextual agronomic, environmental and socio-economic conditions. The project VITAL explores transition processes of European agricultural systems towards sustainably intensified production. VITAL identifies how differences in agricultural systems, their spatial frameworks and the role of actors, lead to, or inhibit, alternate transition processes of SI. The feasibility of different SI pathways is upscaled across Europe, hence moving beyond the level of individual farms and regions. Suitable spatial configurations of SI across land use systems are identified, accounting for the landscape and regional context. VITAL aims to: • Identify key conditions of agricultural land systems that allow systems to shift toward sustainable intensification states; and triggers and transition pathways towards such states. • Develop and operationalize sustainability indicators that reflect a land system’s position in a space of production intensity, ecological resilience and socio-economic viability, which together determine a region’s adaptive capacity towards sustainable intensification. • Draw upon real-world, operational exemplars, to understand how conditions, triggers and pathways interact, and how they link to value chains and valorisation. • Embed regional developments in sustainable intensification trajectories in larger contexts (national, EU and global) to understand the potential of up- and out-scaling of regional best-practice examples. By working with stakeholders at farm, regional and European level VITAL will deliver 1) an analytical framework that allows determining feasible future states of sustainable intensification; 2) assessment tools and indicators to evaluate alternative SI trajectories; 3) an assessment of the suitability of SI trajectories across different European land use systems and locations; 4) novel land system architectures based on SI; and 5) insights in the role of social networks of transition in adopting SI.

WEEDELEC 2017 We propose in this project an alternative solution to global chemical weeding, which combines aerial means for weed detection coupled with a robotized ground weeding system based on high voltage electrical energy. The project will rely on commercial solutions concerning the aerial and ground vehicles (respectively UAV and robot). It will more particularly focus on major technical and scientific issues for the development of a future integrated weeding solution, i.e.: - weed detection and identification, using hyperspectral imagery and deep learning techniques - weed behavior when they are exposed to electrical stress, especially by investigating the relationship between the kind of electrical shock to be applied and the weed electrical impedance and phenology. Questions related to aerial and robot-embedded weed detection systems will also be addressed, as well as possible environmental and safety effects of electrical shock usage on weeds, in order to design a safe integrated weeding strategy. This project draws on previous results obtained in the FP4 European project Patchwork (electrical weeding 1995), in the FP7 European project RHEA (Integrated weeding solution, 2012) and on the Plant@Net project, devoted to automatic plant identification by deep learning. It also relies on the expertise of plant phenology scientists and weed scientists. The WeedElec project will be an opportunity to enrich Plant@net image databases, to produce a database of electrical signatures of main weed species, to develop and test new robust algorithms for weed detection and identification, and to validate an innovative weeding solution with no chemicals. The experimentations will be led in field crop and market gardening plots, in order to cover variate crop and weed typologies

The CHLOR2NOU project aims to develop new monitoring tools for CLD and its TPs, to provide new knowledge on the fate and risk of CLD TPs, and to explore realistic alternative approaches for pollution remediation. The postulate of the non-degradability of CLDs commonly admitted for several decades has had a strong negative impact on pollution management by ruling out the possibility of CLD degradation. The representation of CLD in the FWI society and in the scientific community is therefore of paramount importance. The CHLOR2NOU project is divided into 7 Work Packages that bring together scientists from various background: the WP1 with the synthesis of CLD TPs, CLD baits and fluorescent macromolecular cages; the WP2 that deals with innovative analytical methods: (i) routine laboratory method for the detection of CLD TPs in environmental and food matrices, (ii) immunoassay using a CLD-selective antibody, (iii) a semi-high-throughput detection protocol based on the recognition of CLD by a fluorescent macromolecular cage; the WP3 dedicated to toxicological and ecotoxicological studies in order to define the toxicity profile of CLD TPs; the WP4 with several analytical campaigns to obtain a first estimate of the possible exposure to CLD TPs; the WP5 that aims at studying the fate of CLD TPs, in particular in FWI soils, while defining degradation indicators; the WP6 that is focused on the study of realistic agronomic and environmental conditions capable to favor CLD degradation; the last WP centered on the representation of CLD in the FWI society at large. A co-construction method will be used to help the population and stakeholders to better assimilate the scientific results.

Ever-increasing waste production has prompted the need for new provisions regarding waste management to ensure sustainable development. There is now a global consensus among scientists, economists, politicians and civil society stakeholders on the necessity to recycle resources and close loops in a circular economy. Agricultural recycling makes it possible to effectively and synergistically use livestock, urban and agro-industrial organic waste (OW). From a waste management standpoint, aerobic digestion (composting) and anaerobic digestion are the most obvious and operational processes for OW treatment prior to soil application. Composting OW is seen as an effective method for diverting organic materials from landfills, while reducing the waste volume, eliminating pathogens and creating a stable product suitable for application in crop fields. Anaerobic digestion has also significantly increased in several European countries and represents an opportunity to convert OW into biogas and organic fertilizer (digestate). The choice of using either raw OW, compost or digestate as fertilizer and soil amendment should be based on a comprehensive assessment of potential benefits and negative effects. Among these negative effects, the lack of understanding regarding the impact of treatments on contaminant speciation, microbial pathogen selection and antimicrobial resistance emergence, and the scarcity of knowledge on the fate of contaminants following soil OW application are key scientific challenges that the DIGESTATE project aims to meet. The overall objective of DIGESTATE is to develop an original environmental assessment of OW treatments and agricultural recycling. Such environmental assessment involves estimation of environmental consequences (positive and negative) expected to result from OW treatment and recycling scenarios prior to decision making. This assessment will include indicators which are: (i) conventional (agronomic quality of the OW; energy recovery of treatment processes) and (ii) non-standard (fate of contaminants following OW application in water-soil-plant systems). We will focus our efforts on the ecodynamics of three main classes of contaminants in water-soil-plant systems: (i) trace elements: Cu and Zn, (ii) organic pollutants: PAHs, nonylphenols and pharmaceuticals and (iii) microbial pathogens and antimicrobial resistance genes. We will compare the impact of two major digestion treatments (composting, anaerobic digestion and their combination) on: (i) the speciation of organic and inorganic contaminants, the selection of particular microbial groups and genetic properties in OW, and (ii) the fate (phytoavailability and transport in soil) of contaminants after soil OW application. The scientific programme is based on laboratory experiments, modeling tools and multidisciplinary approaches. First, contaminant quantification and speciation will be assessed for selected raw and treated OW (WP1). Then fundamental knowledge will be produced on contaminant-bearing phases formed during OW treatment (WP2). After OW spreading on a soil, the proportion of contaminants taken up by plants or transported through the soil will be experimentally quantified (WP3). The experimental and modelling datasets from WP1, 2 and 3 will finally fuel the environmental assessment of OW treatment and recycling based on innovative assessment methodologies (WP4).

FFAST aims at describing wheat genotypes functioning through an innovative model-assisted phenotyping strategy. Currently, studies on field phenotyping are mostly focused on exploiting directly structural traits observations (e.g. leaf area, height) to establish statistical models with genetic characteristics. However, structural traits are highly determined by the environment, and such empirical models are insufficient to describe genotypes functioning. FFAST proposes an alternative approach using functional plant models (FPM, also known as crop process-based models) to describe the eco-physiological mechanisms that produce a differentiated response of the genotype to the environment (GxE). This model-assisted strategy consists in assimilating large observational datasets of multiple structural traits over different growing environments to retrieve, for each genotype, a set of varietal parameters of a FPM. These varietal parameters describe the genotype functioning and constitute functional traits, closely linked to its genetic characteristics. The model-assisted phenotyping method will be evaluated in a panel of ten bread wheat genotypes that will be monitored on phenotyping experiments and by satellite. The phenotyping experiments will be conducted in the Toulouse, Clermont-Ferrand and Montpellier sites –part of the PHENOME-EMPHASIS phenotyping infrastructure– during three years. That will permit to acquire high-throughput observations of multiple structural traits (leaf area, canopy height, heading date, ears density…) in different environments. Nevertheless, as a large environmental variability is essential to retrieve accurately functional traits, FFAST will investigate the use of high-resolution satellite platforms to provide additional cost-efficient observations of structural traits for specific genotypes over contrasted environments. Three genotypes of the panel will be monitored by satellite on 40 distant commercial fields over a climatic gradient in eastern France. Images from Sentinel 2 and PlanetScope satellite constellations will be used to retrieve frequent observations of some essential traits like the leaf green area index (GAI) and the fraction of absorbed photosynthetically active radiation (fAPAR). The estimation of functional traits from the observations will rely on a data assimilation framework based on the Sirius Quality FPM, specifically developed for wheat, which will be linked to the architectural model Adel Wheat. This will permit to improve the description of structure-driven processes such as light interception/absorption or evapotranspiration. Bayesian Monte Carlo methods will be used to retrieve varietal parameters of Sirius Quality from the structural traits observations for each genotype. The resulting posterior distribution of varietal parameters for all the genotypes will be analysed to identify those parameters –or groups of parameters characterizing the same mechanism– presenting statistically different posterior distributions among genotypes. Those parameters will constitute functional traits. The approach proposed by FFAST will be validated evaluating the reliability of the functional traits identified to predict the genotype performance in different environments from those used during the assimilation. This will permit to evaluate as well the role of remote sensing observations over different environments in the FFAST approach, compared to expensive multi-site phenotyping experiments. The project results will be disseminated through scientific papers in different domains: phenomics, eco- physiology, crop modelling and remote sensing. The observational datasets collected for the 10 genotypes will be also made public through a data paper. Moreover, the development of a methodology to produce multi-constellation GAI and fAPAR observations suitable for plant phenotyping will permit HIPHEN –enterprise partner in FFAST– to open new commercial services.