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

Mitigation measures in the Agriculture, Forestry, and Other Land Use (AFOLU) sector. Quantifying mitigation effects at the farm level and in national greenhouse gas inventories

This document analyses potential greenhouse gas (GHG) mitigation measures in the Agriculture, Forestry and Other Land Uses (AFOLU) sector, looking at their reportability with the IPCC methods. The main conclusions that can be drawn from this analysis are: • Mitigation strategies target either ‘observation’ or ‘parameter’ • Data changes usually have an effect on more than one emission category • All mitigation measures impact farmer’s income • Mitigation measures can be grouped in mitigation mechanism groups • Measures using the mitigation strategy ‘observation’ are relatively straight-forward to be traced in GHG inventories, some with additional data collection required • Measures using the mitigation strategy ‘parameter’ often require research programs to develop (national) differentiated emission factors • Assessing mitigation measures at the farm level is easier at the level of mitigation mechanism groups than at the level of individual mitigation measures • A modular GHG calculator tool would provide highest flexibility for farm level GHG monitoringJRC.D.5-Food Securit

A Comparison of Three Learning Methods to Predict N2O Fluxes and N Leaching

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

Evaluation of the Livestock Sector's Contribution to the EU Greenhouse Gas Emissions - Phase 1 (GGELS)

This interim report constitutes the third and final deliverable of the study "Evaluation of the livestock sector's contribution to the EU greenhouse gas emissions - Phase 1 (GGELS)", in accordance with the terms of reference of the Administrative Arrangement (AA) No. AGRI-2008-0245. It reports on Work Packages 2, 3 and 4. A report on Work package 1 constituted the project¿s first deliverable, which was accepted in October 2008. It is thus not covered in this report. This report aims to provide DG AGRI, as well as other possible users of the GGELS project results, with a clear though exhaustive insight in the work preformed during GGELS Phase 1 and the intermediate results produced. GGELS being a multi-disciplinary research project spanning across three JRC institutes and 5 actions, this GGELS Phase 1 report is largely a collection of output produced by the different partners. Each of these contributions constitutes a separate section in this report.JRC.G.3-Monitoring agricultural resource

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

Sensitivity of the process-based model DNDC on microbiological parameters

International audienceProcess-based model such as DNDC rely on a large numbers of parameters which were defined by the model developer on the basis of existing references. Subsequently, some values have been changed to improve model performance for specific applications, often without adequate documentation. Many of these parameters are thus estimates of the real values appropriate for local conditions introducing approximation errors for applications at larger scales. Spatially explicit datasets might be required for some parameters for which model output is highly sensitive. We will present a sensitivity analysis of 38 mainly micro-biological internal parameter of DNDC-EUROPE