Impact of extreme meteorological events on crop yield: a common framework approach

The hypothesis is that yield variations due to an extreme event (cold temperature, high temperature or water deficit) is mediated by a change in Harvest Index (HI), while the main effect of weather on crop performance is already captured by existing crop models

The history of rainfall data time-resolution in a wide variety of geographical areas

Collected rainfall records by gauges lead to key forcings in most hydrological studies. Depending on sensor type and recording systems, such data are characterized by different time-resolutions (or temporal aggregations), ta. We present an historical analysis of the time-evolution of ta based on a large database of rain gauge networks operative in many study areas. Globally, ta data were collected for 25,423 rain gauge stations across 32 geo graphic areas, with larger contributions from Australia, USA, Italy and Spain. For very old networks early re cordings were manual with coarse time-resolution, typically daily or sometimes monthly. With a few exceptions, mechanical recordings on paper rolls began in the first half of the 20th century, typically with ta of 1 h or 30 min. Digital registrations started only during the last three decades of the 20th century. This short period limits investigations that require long time-series of sub-daily rainfall data, e.g, analyses of the effects of climate change on short-duration (sub-hourly) heavy rainfall. In addition, in the areas with rainfall data characterized for many years by coarse time-resolutions, annual maximum rainfall depths of short duration can be potentially underestimated and their use would produce errors in the results of successive applications. Currently, only 50% of the stations provide useful data at any time-resolution, that practically means ta = 1 min. However, a sig nificant reduction of these issues can be obtained through the information content of the present database. Finally, we suggest an integration of the database by including additional rain gauge networks to enhance its usefulness particularly in a comparative analysis of the effects of climate change on extreme rainfalls of short duration available in different locations

Modelling microbial and plant diversity in multi-species agroecosystems: the DIMIVEA project

International audienceBiogeochemical modelling is used to assess the impact of agricultural activities and climate on ecosystem carbon (C) and nutrient cycles and associated services or disservices, such as biomass production, C emissions/storage and nutrient retention. From an agroecosystem perspective, DIMIVEA is interested in the characterization and dynamic simulation of: i) the physico-chemical andbiochemical properties of soils, ii) the functional diversity of plant and soil microbial communities, and iii) their role in the provision of ecosystem services. The simulation of multi-species systems (grasslands, forests, multi-species cropping systems) ensures a detailed representation of the coupledC-N cycles, but the models used remain simplified insofar as biological diversity is reduced to simplistic patterns of interactions with the environment. The creation of simulators based on the information provided by diversity attempts to overcome the paradigm of condensing biological diversity into constant parameters. This opens up new avenues of research to be explored to explain thesynchronisation of nutrient demand and supply in multi-species systems by modelling some plant and microbial diversity. By focusing on the characteristics of plant and microbial communities in mixed vegetation canopies, the consortium aims to provide a conceptual framework for extending the potential of models towards a reliable estimation of the ecological processes that support theecosystem services provided by these vegetal communities. Moving towards the creation of explicit, dynamic and integrated simulators of microbial and plant diversity, DIMIVEA represents a new paradigm that implies that related aspects of biological diversity cannot be ignored in agroecosystem modelling studies.DIMIVEA integrates different experiences and knowledge in an attempt to model the ecological organisations that enable natural ecosystems and certain agro-systems to be productive, multifunctional (ensuring C storage, purification of drainage water, improvement of soil quality) and158 low-input. The aim is also to identify the ecological organisations to be favoured according to local soiland climate contexts, and to propose agricultural practices likely to favour them in agro-systems. For its reflections and conceptualisations, the consortium relies on the aggregate microbial modelling (i.e. stocking/de-stocking microbes) and the experimental devices of the AGROECOseqC project of the European Joint Programme Cofund on Agricultural Soil Management (EJP SOIL). The partnership is committed to organising and leading dedicated workshops within the framework of scientific events such as international conferences and study days, and to progressing towards the writing of a synthesis and positioning article, envisaged as the horizon of a collective reflection in addition to theproduction of model prototypes

A component-based framework for simulating agricultural production and externalities: Environmental and agricultural modelling: integrated approaches for policy impact assessment

International audienceAlthough existing simulation tools can be used to study the impact of agricultural management on production activities in specific environments, they suffer from several limitations. They are largely specialized for specific production activities: arable crops/cropping systems, grassland, orchards, agro-forestry, livestock etc. Also, they often have a restricted ability to simulate system externalities which may have a negative environmental impact. Furthermore, the structure of such systems neither allows an easy plug-in of modules for other agricultural production activities, nor the use of alternative components for simulating processes. Finally, such systems are proprietary systems of either research groups or projects which inhibits further development by third parties. The EU Sixth Framework Integrated Project SEAMLESS aims to provide a tool to integrate analyses of impacts on the key aspects of sustainability and multi-functionality, particularly in Europe. This requires evaluating agricultural production and system externalities for the most important agricultural production systems. It also requires a simulation framework which can be extended and updated by research teams, which allows a manageable transfer of research results to operational tools, and which is transparent with respect to its contents and its functionality. The Agricultural Production and Externalities Simulator (APES) is a modular simulation system aimed at meeting these requirements, and targeted at estimating the biophysical behavior of agricultural production systems in response to the interaction of weather and agro-technical management. APES is a framework which uses components that offer simulation options for different processes of relevance to agricultural production systems. Models are described in the associated help files of components, and a shared ontology is built on the web. Components like these, which are designed to be inherently re-usable, that is not targeted specifically to a given modelling framework, also represent a way to share modelling knowledge with other projects and the scientific community in general. This chapter describes the current state of APES development and presents modelling options in the system, and its software architecture