The interdependencies between food and biofuel production in European agriculture - an application of EUFASOM

In the continuous quest to reduce anthropogenic emissions of carbon dioxide, the production and use of organically grown fuels in Europe has increased in importance in the recent past. However, the production of so-called biofuels is a direct competitor of agricultural food production for land, labor, water resources etc. with both land use options influencing each other depending on the respective boundary conditions defined by political regulations and economic considerations. In this study we will explore the economic and technical potentials of biofuels in Europe as well as the interdependencies between these two land use options for different economic incentives for biofuels using the European Forest and Agriculture Sector Optimization Model (EUFASOM). Key data on biodiesel and ethanol production have been gathered and are used for calibration of the model. The simulations extend until the year 2030, for which results are presented. Results indicate that moderate production targets of biofuels lead to an expansion of mainly the biodiesel production while more ambitious targets call for a focus on bioethanol. This has to do with the different levels of production efficiency depending on the production output. Growth of bioethanol feedstock is spread over entire Europe while the production of biodiesel feedstock occurs mainly in Central Europe.biodiesel, bioethanol, Europe, EUFASOM, modeling

Protest event data for Croatia, Portugal, Serbia and Spain: Focus on strike data

‘Disobedient Democracy: A Comparative Analysis of Contentious Politics in the European Semi-periphery’ is a research project implemented by the Faculty of Political Science of the University of Zagreb, in the period 2016-2021, led by Principal Investigator Danijela Dolenec and funded by the Swiss National Science Foundation (IZ11Z0_166540 – PROMYS). The overall objective of the project is to explore how protest politics advances democracy by collecting and analyzing data on protest mobilizations in four countries: Portugal, Spain, Croatia and Serbia.‎info:eu-repo/semantics/publishedVersio

Increasing crop production in Russia and Ukraine—regional and global impacts from intensification and recultivation

Russia and Ukraine are countries with relatively large untapped agricultural potentials, both in terms of abandoned agricultural land and substantial yield gaps. Here we present a comprehensive assessment of Russian and Ukrainian crop production potentials and we analyze possible impacts of their future utilization, on a regional as well as global scale. To this end, the total amount of available abandoned land and potential yields in Russia and Ukraine are estimated and explicitly implemented in an economic agricultural sector model. We find that cereal (barley, corn, and wheat) production in Russia and Ukraine could increase by up to 64% in 2030 to 267 million tons, compared to a baseline scenario. Oilseeds (rapeseed, soybean, and sunflower) production could increase by 84% to 50 million tons, respectively. In comparison to the baseline, common net exports of Ukraine and Russia could increase by up to 86.3 million tons of cereals and 18.9 million tons of oilseeds in 2030, representing 4% and 3.6% of the global production of these crops, respectively. Furthermore, we find that production potentials due to intensification are ten times larger than potentials due to recultivation of abandoned land. Consequently, we also find stronger impacts from intensification at the global scale. A utilization of crop production potentials in Russia and Ukraine could globally save up to 21 million hectares of cropland and reduce average global crop prices by more than 3%

Large scale extreme risk assessment using copulas: an application to drought events under climate change for Austria

Droughts pose a significant challenge to farmers, insurers as well as governments around the world and the situation is expected to worsen in the future due to climate change. We present a large scale drought risk assessment approach that can be used for current and future risk management purposes. Our suggested methodology is a combination of a large scale agricultural computational modelling -, extreme value-, as well as copula approach to upscale local crop yield risks to the national scale. We show that combining regional probabilistic estimates will significantly underestimate losses if the dependencies between regions during drought events are not taken explicitly into account. Among the many ways to use these results it is shown how it enables the assessment of current and future costs of subsidized drought insurance in Austria

The European Forest and Agriculture Optimisation Model -- EUFASOM

Land use is a key factor to social wellbeing and has become a major component in political negotiations. This paper describes the mathematical structure of the European Forest and Agricultural Sector Optimization Model. The model represents simultaneously observed resource and technological heterogeneity, global commodity markets, and multiple environmental qualities. Land scarcity and land competition between traditional agriculture, forests, nature reserves, pastures, and bioenergy plantations is explicitly captured. Environmental change, technological progress, and policies can be investigated in parallel. The model is well-suited to estimate competitive economic potentials of land based mitigation, leakage, and synergies and trade-offs between multiple environmental objectives.Land Use Change Optimization, Resource Scarcity, Market Competition, Welfare Maximization, Bottom-up Partial Equilibrium Analysis, Agricultural Externality Mitigation, Forest Dynamics, Global Change Adaptation, Environmental Policy Simulation, Integrated Assessment, Mathematical Programming, GAMS

Global wheat production potentials and management flexibility under the representative concentration pathways

Wheat is the third largest crop globally and an essential source of calories in human diets. Maintaining and increasing global wheat production is therefore strongly linked to food security. A large geographic variation in wheat yields across similar climates points to sizeable yield gaps in many nations, and indicates regionally variable flexibility to increase wheat production. Wheat is particularly sensitive to a changing climate thus limiting management opportunities to enable (sustainable) intensification with potentially significant implications for future wheat production. We present a comprehensive global evaluation of future wheat yields and production under distinct Representative Concentration Pathways (RCP) using the Environmental Policy Integrated Climate (EPIC) agro-ecosystem model. We project, in a geographically explicit manner, future wheat production pathways for rainfed and irrigated wheat systems. We explore agricultural management flexibility by quantifying the development of wheat yield potentials under current, rainfed, exploitable (given current irrigation infrastructure), and irrigated intensification levels. Globally, because of climate change, wheat production under conventional management (around the year 2000) would decrease across all RCPs by 37 to 52 and 54 to 103 Mt in the 2050s and 2090s, respectively. However, the exploitable and potential production gap will stay above 350 and 580 Mt, respectively, for all RCPs and time horizons, indicating that negative impacts of climate change can globally be offset by adequate intensification using currently existing irrigation infrastructure and nutrient additions. Future world wheat production on cropland already under cultivation can be increased by ~35% through intensified fertilization and ~50% through increased fertilization and extended irrigation, if sufficient water would be available. Significant potential can still be exploited, especially in rainfed wheat systems in Russia, Eastern Europe and North America

Spatio-temporal downscaling of gridded crop model yield estimates based on machine learning

Global gridded crop models (GGCMs) are essential tools for estimating agricultural crop yields and externalities at large scales, typically at coarse spatial resolutions. Higher resolution estimates are required for robust agricultural assessments at regional and local scales, where the applicability of GGCMs is often limited by low data availability and high computational demand. An approach to bridge this gap is the application of meta-models trained on GGCM output data to covariates of high spatial resolution. In this study, we explore two machine learning approaches – extreme gradient boosting and random forests - to develop meta-models for the prediction of crop model outputs at fine spatial resolutions. Machine learning algorithms are trained on global scale maize simulations of a GGCM and exemplary applied to the extent of Mexico at a finer spatial resolution. Results show very high accuracy with R2>0.96 for predictions of maize yields as well as the hydrologic externalities evapotranspiration and crop available water with also low mean bias in all cases. While limited sets of covariates such as annual climate data alone provide satisfactory results already, a comprehensive set of predictors covering annual, growing season, and monthly climate data is required to obtain high performance in reproducing climate-driven inter-annual crop yield variability. The findings presented herein provide a first proof of concept that machine learning methods are highly suitable for building crop meta-models for spatio-temporal downscaling and indicate potential for further developments towards scalable crop model emulators

The land use change impact of biofuels consumed in the EU: Quantification of area and greenhouse gas impacts

Biofuels are promoted as an option to reduce climate emissions from the transport sector. As most biofuels are currently produced from land based crops, there is a concern that the increased consumption of biofuels requires agricultural expansion at a global scale, leading to additional carbon emissions. This effect is called Indirect Land Use Change, or ILUC. The EU Renewable Energy Directive (2009/28/EC) directed the European Commission to develop a methodology to account for the ILUC effect. The current study serves to provide new insights to the European Commission and other stakeholders about these indirect carbon and land impacts from biofuels consumed in the EU, with more details on production processes and representation of individual feedstocks than was done before. ILUC cannot be observed or measured in reality, because it is entangled with a large number of other changes in agricultural markets at both global and local levels. The effect can only be estimated through the use of models. The current study is part of a continuous effort to improve the understanding and representation of ILUC

Uncertainties in global crop model frameworks: effects of cultivar distribution, crop management and soil handling on crop yield estimates

Global gridded crop models (GGCMs) combine field-scale agronomic models or sets of plant growth algorithms with gridded spatial input data to estimate spatially explicit crop yields 40 and agricultural externalities at the global scale. Differences in GGCM outputs arise from the use of different bio-physical models, setups, and input data. While algorithms have been in the focus of recent GGCM comparisons, this study investigates differences in maize and wheat yield estimates from five GGCMs based on the public domain field-scale model Environmental Policy Integrated Climate (EPIC) that participate in the AgMIP Global Gridded Crop Model 45 Intercomparison (GGCMI) project. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, geographic distribution of cultivars, and selection of subroutines e.g. for the estimation of potential evapotranspiration or soil erosion. The analyses reveal long-term trends and inter-annual yield variability in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. Absolute yield levels as well depend not only on nutrient supply but 50 also on the parameterization and distribution of crop cultivars. All GGCMs show an intermediate performance in reproducing reported absolute yield levels or inter-annual dynamics. Our findings suggest that studies focusing on the evaluation of differences in bio-physical routines may require further harmonization of input data and management assumptions in order to eliminate background noise resulting from differences in model setups. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions 55 in setups appears the best solution for bracketing such uncertainties as long as comprehensive global datasets taking into account regional differences in crop management, cultivar distributions and coefficients for parameterizing agro-environmental processes are lacking. Finally, we recommend improvements in the documentation of setups and input data of GGCMs in order to allow for sound interpretability, comparability and reproducibility of published results