Quantitative Quality Assessment of the Greenhouse Gas Inventory for Agriculture in Europe

The greenhouse gas inventory of the European Communities and its estimation of the uncertainty is build from 15 individual and independent greenhouse gas inventories. This presents a particular challenge and is possible only if homogeneous information is available for all Member States and if a proper evaluation of correlation between Member States is performed. To this purpose, we present a methodology that estimates a quantitative measure for the aggregated Tier-level as well as the uncertainty for the main categories in the agriculture sector. In contrast to the approach suggested in the IPCC guidelines, that uses uncertainty estimates for activity data and emissions factors for each source category, the presented method uses quantitative information from individual parameters used in the inventory calculations, in combination with a well defined procedure to aggregate the information. Not surprisingly, N2O emissions from agricultural soils are found to be dominating the uncertainty and also the importance of correlation if uncertainties are combined for the whole of Europe. The biggest challenge seems to be to conceptually harmonize the uncertainty estimates for the activity data (which tend to be underestimated) and emission factors (which tend to be overestimated).JRC.DDG.H.2-Climate chang

A comparison of three learning methods to predict N2O fluxes and N leaching

International audienceThe environmental costs of intensive farming activities are often under-estimated or not included into rural development plans, even though they play an important role in addressing future society's needs. This paper focuses on the use of statistical learning methods to predict N2O emissions and N leaching under several conservative scenarios, in order to provide an alternative approach to deterministic models on a macro-scale. To that aim, three learning methods, namely neural networks (multilayer perceptrons), SVM and random forests, are compared and provide accurate solutions

The role of nitrogen in achieving sustainable food systems for healthy diets

The ‘food system’ urgently needs a sustainable transformation. Two major challenges have to be solved: the food system has to provide food security with healthy, accessible, affordable, safe and diverse food for all, and it has to do so within the safe operating space of the planetary boundaries, where the pollution from reactive nitrogen turned out to be the largest bottleneck. Here we argue that thinking strategically about how to balance nitrogen flows throughout the food system will make current food systems more resilient and robust. Looking from a material and a governance perspective on the food system, we highlight major nitrogen losses and policy blind spots originating from a compartmentalization of food system spheres. We conclude that a participatory and integrated approach to manage nitrogen flows throughout the food system is necessary to stay within regional and global nitrogen boundaries, and will additionally provide synergies with a sustainable and healthy diet for all. © 2020 The Author

A comparison of eight metamodeling techniques for the simulation of N2O fluxes and N leaching from corn crops

International audienceThe environmental costs of intensive farming activities are often under-estimated or not traded by the market, even though they play an important role in addressing future society's needs. The estimation of nitrogen (N) dynamics is thus an important issue which demands detailed simulation based methods and their integrated use to correctly represent complex and non-linear interactions into cropping systems. To calculate the N2O flux and N leaching from European arable lands, a modeling framework has been developed by linking the CAPRI agro-economic dataset with the DNDC-EUROPE bio-geo-chemical model. But, despite the great power of modern calculators, their use at continental scale is often too computationally costly. By comparing several statistical methods this paper aims to design a metamodel able to approximate the expensive code of the detailed modeling approach, devising the best compromise between estimation performance and simulation speed. We describe the use of two parametric (linear) models and six non-parametric approaches: two methods based on splines (ACOSSO and SDR), one method based on kriging (DACE), a neural networks method (multilayer perceptron, MLP), SVM and a bagging method (random forest, RF). This analysis shows that, as long as few data are available to train the model, splines approaches lead to best results, while when the size of training dataset increases, SVM and RF provide faster and more accurate solutions

A complete rethink is needed on how greenhouse gas emissions are quantified for national reporting

The 2015 Conference of the Parties (COP21) in Paris has for the first time agreed that both developed and developing countries need to reduce greenhouse gas (GHG) emissions to maintain a global average temperature ‘well below’ 2°C and aim to limit the increase to less than 1.5°C above pre-industrial temperatures. This requires more ambitious emission reduction targets and an increased level of cooperation and transparency between countries. With the start of the second Kyoto Commitment period in 2013, and the 2015 Paris Agreement, it is, therefore, timely to reconsider how GHG emissions are determined and verified

A New European Plant-specific Emission Inventory of Biogenic Volatile Organic Compounds for Use in Atmospheric Transport Models

We present a new European plant-specific emission inventory for isoprene, monoterpenes, sesquiterpenes and oxygenated VOC (OVOC), on a spatial resolution of 0.089×0.089 degrees, for implementation in atmospheric transport models. The inventory incorporates more accurate data on foliar biomass densities from several litterfall databases that became available in the last years for the main tree species in Europe. A bioclimatic correction factor was introduced to correct the foliar biomass densities of trees and crops for the different plant growth conditions that can be found in Pan-Europe. Long-term seasonal variability of agriculture and forest emissions was taken into account by implementing a new growing season concept. The 2004¿2005 averaged annual total biogenic volatile organic compound (BVOC) emissions for the Pan-European domain are estimated to be about 12 Tg with a large contribution from the OVOC class of about 4.5 Tg and from monoterpenes of about 4 Tg. Annual isoprene emissions are found to be about 3.5 Tg, insensitive to the chosen emission algorithm. Emissions of OVOC were found to originate to a large extent from agriculture. Further experiments on crop emissions should be carried out to check the validity of the applied standard emission factors. The new inventory aims at a fully transparent and verifiable aggregation of detailed land use information and at the inclusion of plant-specific emission data. Though plant-specific land use data is available with relatively high accuracy, a lack of experimental biomass densities and emission data on terpenes, sesquiterpenes and oxygenated VOC, in particular for agricultural plants, currently limits the setup of a highly accurate plant-specific emission inventory.JRC.H.2-Climate chang

LAND AREA PREDICTION MODEL (LAPM) Accuracy Assessment

This document shows the methodology used to assess the accuracy of the new Land Area Prediction Model (LAPM), as well as the results of the assessment. LAPM aims at predicting land-use areas within each fine-scale Homogenous Spatial Units (HSU). It has been evaluated its accuracy comparing its predictions with LPIS data aggregated to HSU level for France and the NetherlandsJRC.D.5-Food Securit

Integrating the Agricultural Sector into the New EU Climate Policy Framework for 2030: A Scenario Analysis to Highlight Potential Impacts and Challenges

According to the European Council's recent agreement on domestic climate and energy goals, greenhouse gas emissions from sectors outside the EU's Emission Trading Scheme have to be cut by 30% below 2005 levels by 2030. So far no decision has been taken on agriculture's specific involvement in mitigation obligations or on how mitigation targets would be distributed between Member States. Based on hypothetical assumptions, we employ the CAPRI model to illustrate and highlight some potential impacts and challenges related to an integration of the agricultural sector into the new EU climate policy framework. Results of the hypothetical mitigation policy scenario show important impacts on EU agriculture, in particular the livestock sector, if the distribution key of the current Effort Sharing Decision would be rigidly applied as in our assumptions. The results highlight the importance of a targeted but flexible implementation of mitigation policy instruments in the EU and its Member States, as well as the need for a wider consideration and adoption of technological mitigation options