PROJECT MIMYCS: A SIMULATION MODEL SYSTEM FOR SIMULATING MYCOTOXIN CONTAMINATION IN MAIZE GRAIN IN EUROPE

Abstract Mycotoxins are toxic compounds produced by fungi infecting crops starting from the field phase. Maize is one of the crops subjected to the most critical mycotoxin problems. The FP7 Marie Curie Project MIMYCS aims at the development of a simulation model system to simulate mycotoxin contamination in grain maize. MIMYCS is being implemented using the component-oriented paradigm both for model and utility components. It will be composed of three sub-models simulating maize, insect borers and fungi development. First results of the project include i) the development of a generic insect phenological model parameterized for the maize borers Ostrinia nubilalis and Sesamia nonagrioides, and applications under climate change scenarios; ii) the development of a biophysical model for the simulation of maize grain moisture during development and maturation. The sub-model simulating fungi development is under development

CLIMA: a weather generator framework

Abstract: Weather generators (WG) can be defined as collections of models to estimate site specific weather data and derived variables. Their use spans from providing inputs to a variety of biophysical models to deriving weather indices. Also, using either global circulation models or local area models inputs, sets of parameters calculated from long term weather series specific to a site can be modified to reproduce via WG synthetic series representing climate change scenarios. Finally, models implemented in WG are used for estimating missing data and to perform quality control on data collected from sensors in weather stations. The models implemented in WG vary from purely empirical to physically based. There are several models to either estimate or to generate each weather variable, with different input requirements. New models are continuously being proposed, and, whether some models to estimate specific variables are commonly accepted as reference methods, the lack of some inputs requires at times using alternate approaches. Currently available WG are applications which implement a predefined set of modelling options, in software implementations which do not allow for independent extensions by third parties. The CLIMA weather generator is a component based application which consist of a set of reusable graphical user interface (GUI) components, and of a set of extensible model components. The latter are subdivided into six namespaces to estimate variables related to air temperature, rainfall, solar radiation, evapotranspiration, wind, and leaf wetness. The time characteristic of the variables estimated varies from a day to ten minutes. Another library allows estimating climatic indices from one year of daily data at the time. The current implementation consists of a total of more than 300 models. Components are usable either via the CLIMA GUI, or via custom developed applications in a client-server architecture. The architecture of components is based on the composite and strategy as keystone design patterns. Models are implemented as single approaches (simple strategies), and as composite models (composite strategies) which are associated to models of finer granularity. Another type of model unit is represented by context strategies, which implement logic to select within associated models. Finally, the GUI allows building composite models which can be saved as libraries, to be reused both within CLIMA for weather series generation, or independently by other applications. The components are implemented as .NET libraries. They implement the test of pre- and post-conditions, and a scalable tracing via .NET listeners. All variables and parameters are documented via a description, units, default, maximum, and minimum values. Components are extensible: new models can be added independently by third parties and detected by the CLIMA application, which can also use them for data generation via building new composite libraries. Each component is made available via a software development kit which includes the code of two sample projects, either to extend or to reuse the component. CLIMA and its model components are freely available for reuse in no-profit applications.JRC.DG.G.3-Monitoring agricultural resource

A dataset of future daily weather data for crop modelling over Europe derived from climate change scenarios

Coupled atmosphere-ocean general circulation models (AOGCMs, or just GCMs for short) simulate different realizations of possible future climates at global scale under contrasting scenarios of greenhouse gases emissions. While these datasets provide several meteorological variables as output, but two of the most important ones are air temperature at the Earth's surface and daily precipitation. GCMs outputs are spatially downscaled using different methodologies, but it is accepted that such data require further processing to be used in impact models, and particularly for crop simulation models. Daily values of solar radiation, wind, air humidity, and, at times, rainfall may have values which are not realistic, and/or the daily record of data may contain values of meteorological variables which are totally uncorrelated. Crop models are deterministic, but they are typicallyrun in a stochastic fashion by using a sample of possible weather time series that can be generated using stochastic weather generators. With their random variability, these multiple years of weather data can represent the time horizon of interest. GCMs estimate climate dynamics, hence providing unique time series for a given emission scenario; the multiplicity of years to evaluate a given time horizon is consequently not available from such outputs. Furthermore, if the time horizons of interest are very close (e.g. 2020 and 2030), averaging only the non-overlapping years of the GCM weather variables time series may not adequately represent the time horizon; this may lead to apparent inversions of trends, creating artefacts also in the impact model simulations. This paper presents a database of consolidated and coherent future daily weather data covering Europe with a 25 km grid, which is adequate for crop modelling in the near-future. Climate data are derived from the ENSEMBLES downscaling of the HadCM3, ECHAM5, and ETHZ realizations of the IPCC A1B emission scenario, using for HadCM3 two different regional models for downscaling. Solar radiation, wind and relative air humidity weather variables where either estimated or collected from historical series, and derived variables reference evapotranspiration and vapour pressure deficit were estimated from other variables, ensuring consistency within daily records. Synthetic time series data were also generated using the weather generator ClimGen. All data are made available upon request to the European Commission Joint Research Centre's MARS unit.JRC.H.7-Climate Risk Managemen

identifying the most promising agronomic adaptation strategies for the tomato growing systems in southern italy via simulation modeling

Abstract The main cultivation area of the Italian processing tomato is the Southern Capitanata plain. Here, the hardest agronomic challenge is the optimization of the irrigation water use, which is often inefficiently performed by farmers, who tend to over-irrigate. This could become unsustainable in the next years, given the negative impacts of climatic changes on groundwater availability and heat stress intensification. The aim of the study was to identify the most promising agronomic strategies to optimize tomato yield and water use in Capitanata, through a modeling study relying on an extensive dataset for model calibration and evaluation (22 data sets in 2005–2018). The TOMGRO simulation model was adapted to open-field growing conditions and was coupled with a soil model to reproduce the impact of water stress on yield and fruit quality. The new model, TomGro_field, was applied on the tomato cultivation area in Capitanata at 5 × 5 km spatial resolution using an ensemble of future climatic scenarios, resulting from the combination of four General Circulation Models, two extreme Representative Concentration Pathways and five 10-years time frames (2030–2070). Our results showed an overall negative impact of climate change on tomato yields (average decrease = 5–10%), which could be reversed by i) the implementation of deficit irrigation strategies based on the restitution of 60–70% of the crop evapotranspiration, ii) the adoption of varieties with longer cycle and iii) the anticipation of 1–2 weeks in transplanting dates. The corresponding irrigation amounts applied are around 360 mm, thus reinforcing that a rational water management could be realized. Our study provides agronomic indications to tomato growers and lays the basis for a bio-economic analysis to support policy makers in charge of promoting the sustainability of the tomato growing systems

Assessing agriculture vulnerabilities for the design of effective measures for adaptation to climate change (AVEMAC project)

This final report of the AVEMAC study presents an assessment of the potential vulnerability of European agriculture to changing climatic conditions in the coming decades. The analysis is based on weather data generated from two contrasting realizations of the A1B emission scenario of the Intergovernmental Panel on Climate Change (IPCC) for the time horizons 2020 and 2030. These two realizations (obtained from two different general circulation models, downscaled using regional climate models and biascorrected) represent the warmest and coldest realizations of the A1B scenario over Europe as estimated by the ENSEMBLES project. The future weather data fed two types of analyses. The first analysis consisted in computing static agro-meteorological indicators as proxies of potential vulnerabilities of agricultural systems, expressed as changes in the classification of agricultural areas in Europe under climate constraints. The second analysis relied on biophysical modelling to characterize crop specific plant responses derived from crop growth simulations at different production levels (potential production, water-limited production, and production limited by diseases). Assessing the importance of vulnerability to climate change requires not only the localisation of relative yield changes, but also the analysis of the impact of the change on the acreage affected. Consequently, the simulation results of the impact assessment on crops were further processed to estimate the potential changes in production at sub-national (NUTS2) level. This was achieved by relating the simulation results to farm typologies in order to identify which types of systems are likely to be affected by reductions in production. The analyses of this study must be considered as a first step only, since they have neither included adaptation strategies that the farmer can take in response to changes in climate, nor a bio-economic evaluation of estimated vulnerabilities. Therefore, the main aspects and the requirements for a possible future integrated analysis at EU27 level to address climate change and agriculture with the target of providing policy support are also presented in this report. Eventually the results of this study shall help the formulation of appropriate policy options and the development of adequate policy instruments to support the adaptation to climate change of the EU agricultural sector.JRC.H.4-Monitoring Agricultural Resource

Agricultural Production and Externalities Simulator (APES) prototype to be used in Prototype 1 of SEAMLESS-IF

Production Economics,

Library of model components for process simulation relevant to production activities, Prototype 1 versions

Production Economics,

Methodological concepts for integrated assessment of agricultural and environmental policies in SEAMLESS-IF

Agricultural and Food Policy, Environmental Economics and Policy, Farm Management, Land Economics/Use,

Climate Impacts in Europe - The JRC PESETA II Project

The objective of the JRC PESETA II project is to gain insights into the sectoral and regional patterns of climate change impacts in Europe by the end of this century. The study uses a large set of climate model runs and impact categories (ten impacts: agriculture, energy, river floods, droughts, forest fires, transport infrastructure, coasts, tourism, habitat suitability of forest tree species and human health). The project integrates biophysical direct climate impacts into a macroeconomic economic model, which enables the comparison of the different impacts based on common metrics (household welfare and economic activity). Under the reference simulation the annual total damages would be around €190 billion/year, almost 2% of EU GDP. The geographical distribution of the climate damages is very asymmetric with a clear bias towards the southern European regions. More than half of the overall annual EU damages are estimated to be due to the additional premature mortality (€120 billion). Moving to a 2°C world would reduce annual climate damages by €60 billion, to €120 billion (1.2% of GDP)