EU wide analysis of the Common Agricultural Policy using spatially disaggregated data

Recent reforms of the Common Agricultural Policy shifted the emphasis towards competitiveness of the agricultural sector, rural development and environmentally sound farming approaches, acknowledging the considerable role agriculture plays in protecting nature and landscape. Significant progress in the evaluation of policy reform scenarios can be made if it is be possible to link existing economic and environmental models. An important methodological problem in this context is “bridging” the scales: whereas most bio-physical models work on field scale, comprehensive EU wide economic models generally work with large administrative regions. The research aims at improving integrated assessment of European policy options by developing methodologies that deliver spatially explicit agricultural data regarding crop shares and farming systems. First a procedure for estimating agricultural land use choices is developed bringing together high resolution information on crops and land cover as well as aggregate information from administrative regions. Combining a binary choice model with a Bayesian highest posterior density estimator, a statistical approach to break down land use choices from European administrative regions to about 100.000, so called Homogeneous Spatial Mapping Units is developed. The applied Bayesian method fully and transparently accounts for the prior information – mean and variance of land use shares obtained from binary choice models – when searching for consistency between the different scales. Next, an approach for the spatial allocation of farm information is developed. European wide farm information is so far only available at a rather aggregated administrative level. The suggested allocation approach adds a spatial dimension to all sample farms making it possible to aggregate farm types both to natural and to lower scale administrative regions. The allocation approach is implemented as a constrained optimization model searching for an optimal match between farm attributes and spatial characteristics subject to consistency constraints. The objective functions are derived from a Bayesian highest posterior density framework. Finally an approach to integrate spatially explicit farm information in an agricultural sector model in the context of a study on the abolition of the EU milk quota is presented. It presents an economic and environmental impact analysis using the CAPRI model, which has been updated with econometric estimates of milk quota rents from sample farms. Aggregated at EU level for the year 2020, production may increase by 5% while the price drop for raw milk is about 10%. Regions are identified where economic or environmental changes substantially exceed those at the Member State level. While regional nitrate leaching problems could be exacerbated, there is only weak evidence of an increased risk of land abandonment in marginal areas.EU weite Analyse der Gemeinsamen Agrarpolitik mittels räumlich disaggregierter Daten Die jüngsten Reformen der Gemeinsamen Agrarpolitik zielten auf eine verstärkte Förderung der Wettbewerbsfähigkeit des Agrarsektors, des ländlichen Raumes und der umweltverträglichen Landwirtschaft ab. Diese Reformen trugen damit auch der besonderen Rolle der Landwirtschaft beim Schutz von Natur und Landschaft Rechnung. Deutliche Fortschritte bei der Evaluierung von Politikreformen können erreicht werden, wenn die bestehenden ökonomischen und bio-physikalischen Modelle verknüpft würden. Ein wichtiges methodisches Problem liegt in diesem Zusammenhang in der Überbrückung von verschiedenen „Modellskalen“: Während die meisten bio-physikalischen Modelle auf der Ebene des Feldschlages arbeiten, modellieren EU-weite agrarökonomische Modelle in der Regel vergleichsweise große administrative Regionen. Der Forschungsbeitrag dieser Dissertation zielt auf eine Verbesserung der integrierten Bewertung der europäischen Agrarpolitikreformen ab. Hierfür werden Methoden entwickelt, die räumlich explizite landwirtschaftliche Informationen zu Bodennutzung und Anbausystemen liefern. Dabei wird zunächst ein Verfahren zur Abschätzung der landwirtschaftlichen Bodennutzung entwickelt. Dies geschieht durch die Verbindung hochaufgelöster Informationen zur pflanzlichen Bodennutzung mit aggregierten Daten aus administrativen Regionen. Ein statistischer Ansatz, der eine Kombination aus einem binären choice Modell mit einem Bayesian highest posterior density estimator darstellt, erlaubt die Disaggregation von regionalen Landnutzungsanteilen auf 100,000, so genannte homogene räumliche mapping units. Die angewandte Bayes'sche Methode erlaubt eine vollständige und transparente Darstellung der prior information - Mittelwert und Varianz der Landnutzungsanteile aus den binären choice Modellen - bei der Suche nach Konsistenz zwischen den verschiedenen Skalen. Nachfolgend wird ein Ansatz zur räumlichen Verteilung von landwirtschaftlichen Betrieben entwickelt, da EU-weite Betriebsinformationen nur auf einer hoch aggregierten Ebene erhältlich sind. Der entwickelte Allokationsalgorithmus ordnet jedem Testbetrieb eine räumliche Dimension zu, die es erlaubt, die Betriebe sowohl natürlichen als auch niedrigeren administrativen Skalen zu zuordnen. Dieser Allokationsalgorithmus ist als Optimierungsmodell mit Nebenbedingungen definiert, die bei der Suche nach einer optimalen Konsistenz zwischen betrieblichen Attributen und räumlichen Eigenschaften helfen. Die Zielfunktion wird von einem Bayesian highest posterior density estimator Ansatz abgeleitet. Zuletzt wird eine Methode zur Integration von räumlich expliziten Betriebsinformationen in das landwirtschaftliche Sektormodell CAPRI vorgestellt. Dieser Ansatz wurde im Rahmen einer Studie zu den wirtschaftlichen und ökologischen Auswirkungen der Abschaffung der EU-Milchquote entwickelt. Dabei wurden ökonometrische Schätzungen aus Testbetriebsdaten genutzt, um die regionalen Milchquotenrenten im CAPRI-Modell zu aktualisieren. Die Ergebnisse zeigen, aggregiert für die EU für das Jahr 2020, dass die Produktion sich um circa 5% erhöhen wird während der Preisrückgang für Rohmilch bei etwa 10% liegt. Weiterhin wurden Regionen identifiziert, in denen die wirtschaftlichen und ökologischen Veränderungen wesentlich die Änderungen auf Ebene der Mitgliedstaaten überschreiten. Regionale Nitratauswaschungsprobleme können sich in Folge der Quotenabschaffung verschärfen, wohingegen es nur schwache Hinweise auf eine Zunahme des Brachlandes in marginalen Gebieten gibt

Do Price Uncertainties Affect the Use of Policy Flexibilities? The Selection of Sensitive Products in WTO Agricultural Negotiations

In a context in which price uncertainty is likely to increase, expected market trends need to be taken carefully into account while negotiating international trade policy rules. This paper aims at analyzing what is their influence on the use of policy flexibilities in the context of WTO agricultural negotiations. In particular, within the market access pillar, we focus on the selection of sensitive products. Our model, TRIMAG (Tariff Reduction Impact Model for Agriculture), defined at the 8-digit level, optimizes the domestic agricultural value added subject to a maximum number of sensitive tariff lines, accounting for various future international price scenarios. Furthermore, we test the use of alternative options for the implementation of “tariff simplification”. Findings confirm that the future expected development of world and domestic prices plays an important role in the selection of sensitive products, and that tariff simplification doesn’t affect the results, if provisions to ensure the neutrality of the exercise are put in place. Furthermore, TRIMAG can be considered as a tariff aggregation tool that can be linked to agricultural simulation models that operate at a higher level of aggregation.WTO agricultural negotiations, market access, sensitive products, International Relations/Trade, F13, Q17,

Protocols for spatial analysis to be implemented in the domain editor by WP5 - Allocation of farm types spatially including the new Member states

Agricultural and Food Policy, Environmental Economics and Policy, Land Economics/Use, Production Economics,

Protocols for spatial allocation of farm types

Farm Management,

Linking an Economic Model for European Agriculture with a Mechanistic Model to Estimate Nitrogen and Carbon Losses from Arable Soils in Europe

A comprehensive assessment of policy impact on greenhouse gas (GHG) emissions from agricultural soils requires careful consideration of both socio-economic aspects and the environmental heterogeneity of the landscape. We developed a modelling framework that links the large-scale economic model for agriculture CAPRI (Common Agricultural Policy Regional Impact assessment) with the biogeochemistry model DNDC (DeNitrification DeComposition) to simulate GHG fluxes, carbon stock changes and the nitrogen budget of agricultural soils in Europe. The framework allows the ex-ante simulation of agricultural or agri-environmental policy impacts on a wide range of environmental problems such as climate change (GHG emissions), air pollution and groundwater pollution. Those environmental impacts can be analyzed in the context of economic and social indicators as calculated by the economic model. The methodology consists of four steps: (i) definition of appropriate calculation units that can be considered as homogeneous in terms of economic behaviour and environmental response; (ii) downscaling of regional agricultural statistics and farm management information from a CAPRI simulation run into the spatial calculation units; (iii) designing environmental model scenarios and model runs; and finally (iv) aggregating results for interpretation. We show the first results of the nitrogen budget in croplands in fourteen countries of the European Union and discuss possibilities to improve the detailed assessment of nitrogen and carbon fluxes from European arable soils.JRC.H.2-Air and Climat

A Major Asymmetric Dust Trap in a Transition Disk

The statistics of discovered exoplanets suggest that planets form efficiently. However, there are fundamental unsolved problems, such as excessive inward drift of particles in protoplanetary disks during planet formation. Recent theories invoke dust traps to overcome this problem. We report the detection of a dust trap in the disk around the star Oph IRS 48 using observations from the Atacama Large Millimeter/submillimeter Array (ALMA). The 0.44-millimeter-wavelength continuum map shows high-contrast crescent-shaped emission on one side of the star originating from millimeter-sized grains, whereas both the mid-infrared image (micrometer-sized dust) and the gas traced by the carbon monoxide 6-5 rotational line suggest rings centered on the star. The difference in distribution of big grains versus small grains/gas can be modeled with a vortex-shaped dust trap triggered by a companion.Comment: 25 pages, 7 figures (accepted version prior to language editing

Modelling of Energy-Crops in Agricultural Sector Models - A Review of Existing Methodologies

The present report provides an overview of the different methodologies applied in partial and general equilibrium models used to analyse biofuel policies in Europe, as well as their methodological pros and cons. While the LEITAP model is included as a general equilibrium model covering biofuel demand, partial equilibrium models are represented by ESIM, FAPRI, AGLINK/COSIMO, RAUMIS, AGMEMOD (agricultural models); POLES and PRIMES (energy sector); and EUFASOM/ENFA (forestry sector). The study is highly relevant for the current modelling work at IPTS, where models such as ESIM and AGLINK play an important role in the Integrated Modelling Platform for Agro-economic Commodity and Policy Analysis (iMAP) of the AGRILIFE Unit. Additionally, the POLES model is currently part of the model portfolio used by the Competitiveness & Sustainability Unit in several studies analysing possible technological pathways of energy production and demand for bioenergy in Europe, a result of implementing the biofuel directive. This compilation of information is also important since the implicit and explicit treatment of bioenergy, either as a demand shock to the processing of oilseeds or feedstock for bioethanol and biodiesel, or as the introduction of a biofuel-sector into a computational general equilibrium (CGE) is foreseen in the short-term by other economic models used at IPTS.JRC.J.5-Agriculture and Life Sciences in the Econom

Regional Economic Analysis of Milk Quota Reform in the EU

This report is based on the outcome of a study carried out by the European Commission's Joint Research Centre - Institute for Prospective Technological Studies (JRC-IPTS, Spain) in cooperation with EuroCARE (Bonn, Germany) and the collaboration of the Agricultural Economics Research Institute (LEI, the Netherlands) and the Catholic University of the Sacred Heart (Unicatt, Italy). The report provides an economic impact assessment of possible implications of the Health Check of the Common Agricultural Policy (CAP), with an explicit focus on regional effects of a milk quota abolition in the EU-27 in the year 2015. For the analysis the CAPRI model was updated with econometric estimates of milk quota rents at regional level and simulation results are presented for the year 2020. The detailed spatial resolution allows identifying regions where economic changes are larger than visible from aggregated impacts at Member State or European level.JRC.J.5-Agriculture and Life Sciences in the Econom

Agricultural biomass as provisioning ecosystem service: quantification of energy flows

Agro-ecosystems supply provisioning, regulating and cultural services to human society. This study focuses on the agro-ecosystem provisioning services regarding the production of agricultural biomass. These services strongly respond to the socio-economic demands of human beings, and are characterised by an injection of energy in the ecosystems production cycle which is often exceeding the ecological capacity of the ecosystem, i.e. the overall ability of the ecosystem to produce goods and services linked to its bio-physical structure and processes that take place during the agricultural production. Agricultural production is identified as ecosystem service in widely recognised ecosystem service frameworks, but currently there is no clear agreement within the scientific and policy communities on how the ecological-socio-economic flow linked to this provisioning service should be assessed, beyond a mere accounting of yields. This study attempts to provide a new insight to this issue by proposing an approach based on the energy budget, which takes into consideration the energy needed by the ecosystem to supply the service. The approach is based on the concepts of Energy Return on Investment (EROI) and Net Energy Balance (NEB), and considers different bio-physical structures and processes of agro-ecosystems. The work is structured in three parts: the first aims at estimating inputs (machinery, seeds, fertilizers, irrigation, labour) in energy terms; the second at estimating biomass output in energy terms; the third to compare actual agricultural production with three reference scenarios encompassing a range of human input (no input – low input – high input scenarios). Results show that in general terms cereal and grassland systems have the largest energy gains (both in terms of EROI and NEB). Such systems are characterised by a lower economic value of their output compared to other producing systems such as fruits, which have lower energy gains but a higher embodied energy, which is recognized in the market as valuable. Comparison of actual production systems with the high input scenario confirms that current production in Europe is already highly intensive, and that increasing the energy input would not improve the efficiency of the conversion of such additional energy into biomass. Overall, the proposed approach seems a useful tool to identify which are the factors in the agricultural production process that could be modified to improve the energy efficiency in agricultural systems and the sustainability of their production. This study can be considered as a first step in the assessment of the total energy balance of the agro-ecosystem. In fact it deals with the quantification of energy regarding human inputs and the corresponding output and further analysis should address crucial issues such as the quality of the energy and the embodied energy in the plant production, which will help to understand the full complexity of the agro-ecosystemJRC.H.4-Monitoring Agricultural Resource

SoilGrids1km — Global Soil Information Based on Automated Mapping

Background: Soils are widely recognized as a non-renewable natural resource and as biophysical carbon sinks. As such, there is a growing requirement for global soil information. Although several global soil information systems already exist, these tend to suffer from inconsistencies and limited spatial detail. Methodology/Principal Findings: We present SoilGrids1km — a global 3D soil information system at 1 km resolution — containing spatial predictions for a selection of soil properties (at six standard depths): soil organic carbon (g kg−1), soil pH, sand, silt and clay fractions (%), bulk density (kg m−3), cation-exchange capacity (cmol+/kg), coarse fragments (%), soil organic carbon stock (t ha−1), depth to bedrock (cm), World Reference Base soil groups, and USDA Soil Taxonomy suborders. Our predictions are based on global spatial prediction models which we fitted, per soil variable, using a compilation of major international soil profile databases (ca. 110,000 soil profiles), and a selection of ca. 75 global environmental covariates representing soil forming factors. Results of regression modeling indicate that the most useful covariates for modeling soils at the global scale are climatic and biomass indices (based on MODIS images), lithology, and taxonomic mapping units derived from conventional soil survey (Harmonized World Soil Database). Prediction accuracies assessed using 5–fold cross-validation were between 23–51%. Conclusions/Significance: SoilGrids1km provide an initial set of examples of soil spatial data for input into global models at a resolution and consistency not previously available. Some of the main limitations of the current version of SoilGrids1km are: (1) weak relationships between soil properties/classes and explanatory variables due to scale mismatches, (2) difficulty to obtain covariates that capture soil forming factors, (3) low sampling density and spatial clustering of soil profile locations. However, as the SoilGrids system is highly automated and flexible, increasingly accurate predictions can be generated as new input data become available. SoilGrids1km are available for download via http://soilgrids.org under a Creative Commons Non Commercial license