The “C and N Models Inter-comparison and Improvement to assess management options for GHG mitigation in agrosystems worldwide” (CN-MIP) addresses theme 1, topic 1 of the FACCE-JPI 2013 call. Our project will coordinate international development, evaluation and inter-comparison of agricultural process-based models to reduce uncertainty in estimating greenhouse gas emissions from crops, grassland and livestock systems. The project will focus on improving the simulation of management options to enable evaluation of credible mitigation strategies adapted to diverse agrosystems under different climatic conditions. CN-MIP responds to the priority of the core theme 5 "Mitigation of Climate Change" of the FACCE-JPI strategic research agenda, to improve the greenhouse gas (GHG) inventory methods, particularly the "certified" modellingTIER3 modelling approach for quantifying emissions and the effects of mitigation options. The project also supports initiatives outlined in the Global Research Alliance (GRA) on Agricultural Greenhouse Gases, which aim to improve measurement methodology and modelling, as well as inventory of GHG emissions and C sequestration in soils. The consortium comprises eleven partners: INRA (France), University of Aberdeen (UK), Helmholt-Zentrum Postam (GER), University of Florence (IT), CRA-Consiglio per la Ricerca in Agricoltura (IT), University of Milan (It), University of Sassari (IT), New Zealand Institute for Plant and Food Research (NZ), Colorado State University (USA), Woods Hole Research Center (USA), Queensland University of Technology (AU). The proposing partners are experienced modelers and experimentalists, already involved in internationally funded projects on measuring and modelling of greenhouse gas emissions,soil carbon sequestration, and reactive nitrogen, for a variety of agricultural conditions (annual crops, grasslands, tree crops) under temperate, Mediterranean and tropical conditions (GRA CN, Livestock and Cropland groups, AgMIP, MACSUR, Reactive N RCN, NANORP, etc.). This network will provide connections and sharing of models, modelling protocols and datasets, but also the necessary interactions with stakeholders. The project will be undertaken from January 2014 to December 2016, in 4 work packages (i) Definition of model data requirements, selection of process-based CN models (i.e. DNDC, DNDC mobile, DSSAT, Roth C, DayCent, PaSim, STICS, APSIM, EPIC, CN-SIM), selection of appropriate databases; (ii) development of common protocols for modelling and model inter-comparison; (iii) identification and testing of mitigation options, improvement of models for coverage, predictive capability and reliability; (iv) dissemination and training. Deliverables will be guidelines for the selection of database and the simulation of mitigation options, evaluation of uncalibrated and calibrated model performances for an array of GHG emission outputs, improved model tools, peer-reviewed research papers, communication and reports to policy makers and stakeholders, and training sessions for students and scientists.

Access to reliable and readily available estimates of the consequence of different land use and management practices on greenhouse gas (GHG) emissions is a prerequisite for successful implementation of land use-based GHG mitigation strategies. Moreover, this information is needed at the level at which management decisions are actually made – at the field scale – and thus information systems must be: 1) easily and universally available, 2) usable by non-experts, 3) employ state-of-the-art technology and 4) be easily aggregated to larger scales. Our overall project aim is to develop and deploy a state-of-the-art system for full greenhouse gas (GHG) accounting, operational at the scale of an individual entity (e.g., farm, livestock operation). The system will be web-based, free and accessible by anyone having an internet connection. Key attributes of the system will include: 1) use of advance methods, including well-validated process-based models that are run in real-time at high spatial resolution, using site-specific data on soil properties, climate and land use and management practices; 2) flexibility, so that users can select, were appropriate, country-specific methods and emission parameters; 3) user-friendly design, making it possible for land managers and others, without specialized knowledge of GHG emission processes to use the system, in their native language; and 4) information on uncertainty, based on robust statistical methods. An important goal of the consortium will be to disseminate and promote the uptake of the COMET-Global system, including engagement and outreach to farmer organizations, environmental groups, governmental agencies and other stakeholders in each of the partner countries, as well as other researchers working on GHG mitigation in the land use sector. The proposed project directly addresses Themes and Topics described in the FACCEJPI Call Announcement, specifically Themes 1 (Improved GHG methodologies) and 2 (Study of mitigation options), with the focus being at the individual farm-scale. It also address Topic 1 (GHG emission from agricultural sources) and Topic 2 (GHG removals), by virtue of providing a full GHG analysis at the farm-scale. Further, the consortium objectives align well with objectives in the Global Research Alliance towards harmonized methods for GHG emission estimation and to activities elsewhere within FACCEJPI (e.g. MACSUR), as well as the national priorities relating to GHG mitigation in each of the partner countries. The system development will leverage an existing comprehensive web-based tool, COMET-Farm, operational in the US. In addition to implementing spatial data (climate, soil, land management) and country-specific emission factors and methods for non-soil GHG emissions, two widely used process-based models, RothC and ECOSSE, will be incorporated along with the DayCent model for estimating soil GHG emissions. The user interface will be provided with multi-lingual capabilities (English, French, Spanish, German and Italian) to provide maximum convenience on the part of a multinational user community.

The aim of this project is to learn and adapt novel methods that can solve two main challenges in my research field on fish microbiomes. These methods tackle existing challenges for extracting high quality microbial DNA as well as the ability to study anaerobic gut bacteria. At CMR (Australia) I will learn these methods enabling me to pioneer my research field of understanding fish microbiomes.What? Why? How?

What? Communication is ubiquitous in nature, and on the cellular level, protein-based signalling circuits play an essential role. The bottom-up design of such protein circuits holds great potential for developing ultra-sensitive biosensors, and I aim to develop an effective, generalizable platform for making protein-based biosensors using evolutionary methods. Why? The complexity of natural protein circuits makes them challenging to study, understand and fabricate, and our current understanding of how information processing systems evolve in biological organisms is limited. A generalizable platform for the development of protein biosensors that can detect a wide array of analytes does currently not exist, which limits their wide-spread use. How? An autonomous approach to biosensor design will be developed by making artificial allosteric protein switch circuits, and the detection of a variety of environmental analytes will be demonstrated. The biosensors will be evolved inside and outside living organisms to achieve ultra-sensitivity, and to better understand the basic principles of how signalling systems evolve inside living cells.