Publications

<p>This dataset contains all data (in csv format) linked to the figures from the ESSD submitted paper: <strong>"The consolidated European synthesis of CH<sub>4</sub> and N<sub>2</sub>O emissions for EU27 and UK: 1990-2018"</strong></p>\n\n<p>Petrescu,<sup> </sup>A. M. R., Qiu,<sup> </sup>C., Ciais,<sup> </sup>P., Thompson, R.L.,<sup> </sup>Peylin, P., McGrath, M. J., Solazzo,<sup> </sup>E.,<sup> </sup>Janssens-Maenhout, G,<sup> </sup>Tubiello, F. N., Bergamaschi, P., Brunner, D., Peters, G. P., Höglund-Isaksson, L., Regnier, P., Lauerwald, R., Bastviken, D.,<sup> </sup>Tsuruta, A.,<sup> </sup>Winiwarter, W.,<sup> </sup>Patra, P. K., Kuhnert, M., Orregioni, G. D., Crippa, M.,<sup> </sup>Saunois, M.,<sup> </sup>Perugini, L., Markkanen, T.,<sup> </sup>Aalto, T.,<sup> </sup>Groot Zwaaftink, C. D., Yao, Y., Wilson, C.,<sup> </sup>Conchedda, G., Günther, D., Leip,<sup> </sup>A., Smith,<sup> </sup>P., Haussaire,<sup> </sup>J.-M., Leppänen, A., Manning, A. J., McNorton, J., Brockmann, P., and Dolman, A. J.: The consolidated European synthesis of CH<sub>4</sub> and N<sub>2</sub>O emissions for EU27 and UK: 1990-2018, Earth Syst. Sci. Data Discuss., essd-2020-367, in review, 2020.</p>\n\n<p> </p>

<title>Abstract</title> Improved management of agricultural soils plays a critical role in mitigating climate change and achieving the Agricultural Sustainable Development Goals.. We studied the temporal effects of the adoption of no-tillage (NT) management, often touted as an important carbon sequestration strategy, on soil organic carbon (SOC) storage in surface and subsurface soil layers by performing a meta-analysis of 1061 pairs of published experimental data comparing NT and conventional tillage (CT). In the early years of adoption, NT increased surface (0-10 cm) SOC storage compared to CT but reduced it in deeper layers leading to a decrease of SOC in the entire soil profile. These NT-driven SOC losses diminished over time and the net change was approaching zero at 14 years. Our findings demonstrate that NT is not a “silver bullet” for drawing down atmospheric CO₂ and regenerating the lost SOC in cropping soils globally and highlight the importance of long-term NT for the recovery of initial SOC losses.

Abstract This article evaluates the suitability of the ECOSSE model to estimate soil greenhouse gas (GHG) fluxes from short rotation coppice willow ( SRC ‐Willow), short rotation forestry ( SRF ‐Scots Pine) and Miscanthus after land‐use change from conventional systems (grassland and arable). We simulate heterotrophic respiration ( R h ), nitrous oxide (N 2 O) and methane ( CH 4 ) fluxes at four paired sites in the UK and compare them to estimates of R h derived from the ecosystem respiration estimated from eddy covariance ( EC ) and R h estimated from chamber ( IRGA ) measurements, as well as direct measurements of N 2 O and CH 4 fluxes. Significant association between modelled and EC ‐derived R h was found under M iscanthus , with correlation coefficient ( r ) ranging between 0.54 and 0.70. Association between IRGA ‐derived R h and modelled outputs was statistically significant at the Aberystwyth site ( r = 0.64), but not significant at the Lincolnshire site ( r = 0.29). At all SRC ‐Willow sites, significant association was found between modelled and measurement‐derived R h (0.44 ≤ r ≤ 0.77); significant error was found only for the EC ‐derived R h at the Lincolnshire site. Significant association and no significant error were also found for SRF ‐Scots Pine and perennial grass. For the arable fields, the modelled CO 2 correlated well just with the IRGA ‐derived R h at one site ( r = 0.75). No bias in the model was found at any site, regardless of the measurement type used for the model evaluation. Across all land uses, fluxes of CH 4 and N 2 O were shown to represent a small proportion of the total GHG balance; these fluxes have been modelled adequately on a monthly time‐step. This study provides confidence in using ECOSSE for predicting the impacts of future land use on GHG balance, at site level as well as at national level.

Abstract. Agricultural intensification has contributed greatly to the sustained food supply of China's 1.3 billion population over the 30 year period during 1982–2011. Intensification has several and widely recognized negative environmental impacts including depletion of water resources, pollution of water bodies, greenhouse gas emissions and soil acidification. However, there have been few studies over this period on the impacts of intensification on soil organic carbon (SOC) at the regional level. The present study was conducted in Huantai county, a typical intensive farming region in Northern China, to analyze the temporal dynamics of SOC influenced by climate and farming practices. The results indicate that from 1982 to 2011, SOC content and stock in the 0–20 cm layer of the cropland increased from 7.8 ± 1.6 to 11.0 ± 2.3 g kg–1 (41%) and 21 ± 4.3 to 33.0 ± 7.0 Mg ha–1 (54%), respectively. The SOC stock (0–20 cm) of the farmland for the entire county increased from 0.75 to 1.2 Tg (59%). Correlation analysis revealed that incorporation of crop residues significantly increased SOC, while increase in the mean annual temperature decreased the SOC level. Therefore, agricultural intensification has increased crop productivity and contributed to SOC sequestration in Northern China. In the near future, more appropriate technologies and practices must be developed and implemented for a maintenance or enhancement of SOC in this region and elsewhere in Northern China, that also reduce non-CO2 greenhouse gas emissions, since the climate benefit from the additional SOC storage is estimated to be smaller than the negative climate impacts of N2O from N fertilizer additions.

Abstract. Quantification of land surface-atmosphere fluxes of carbon dioxide (CO2) fluxes and their trends and uncertainties is essential for monitoring progress of the EU27+UK bloc as it strives to meet ambitious targets determined by both international agreements and internal regulation. This study provides a consolidated synthesis of fossil sources (CO2 fossil) and natural sources and sinks over land (CO2 land) using bottom-up (BU) and top-down (TD) approaches for the European Union and United Kingdom (EU27+UK), updating earlier syntheses (Petrescu et al., 2020, 2021b). Given the wide scope of the work and the variety of approaches involved, this study aims to answer essential questions identified in the previous syntheses and understand the differences between datasets, particularly for poorly characterized fluxes from managed ecosystems. The work integrates updated emission inventory data, process-based model results, data-driven sectoral model results, and inverse modeling estimates, extending the previous period 1990–2018 to the year 2020 to the extent possible. BU and TD products are compared with European National Greenhouse Gas Inventories (NGHGIs) reported by Parties including the year 2019 under the United Nations Framework Convention on Climate Change (UNFCCC). The uncertainties of the EU27+UK NGHGI were evaluated using the standard deviation reported by the EU Member States following the guidelines of the Intergovernmental Panel on Climate Change (IPCC) and harmonized by gap-filling procedures. Variation in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), originate from within-model uncertainty related to parameterization as well as structural differences between models. By comparing NGHGIs with other approaches, key sources of differences between estimates arise primarily in activities. System boundaries and emission categories create differences in CO2 fossil datasets, while different land use definitions for reporting emissions from Land Use, Land Use Change and Forestry (LULUCF) activities result in differences for CO2 land. The latter has important consequences for atmospheric inversions, leading to inversions reporting stronger sinks in vegetation and soils than are reported by the NGHGI. For CO2 fossil emissions, after harmonizing estimates based on common activities and selecting the most recent year available for all datasets, the UNFCCC NGHGI for the EU27+UK accounts for 3392 ± 49 Tg CO2 yr-1 (926 ± 13 Tg C yr-1), while eight other BU sources report a mean value of 3340 [3238,3401] [25th,75th percentile] Tg CO2 yr-1 (948 [937,961] Tg C yr-1). The sole top-down inversion of fossil emissions currently available accounts for 3800 Tg CO2 yr-1 (1038 Tg C yr-1), a value close to that of the NGHGI, but for which uncertainty estimates are not yet available. For the net CO2 land fluxes, during the most recent five-year period including the NGHGI estimates, the NGHGI accounted for -91 ± 32 Tg C yr-1 while six other BU approaches reported a mean sink of -62 [-117,-49] Tg C yr-1 and a 15-member ensemble of dynamic global vegetation models (DGVMs) reported -69 [-152,-5] Tg C yr-1. The five-year mean of three TD regional ensembles combined with one non-ensemble inversion of -73 Tg C yr-1 has a slightly smaller spread (0th–100th percentile of [-135,45] Tg C yr-1), and was calculated after removing land-atmosphere CO2 fluxes caused by lateral transport of carbon (crops, wood trade and inland waters) resulting in increased agreement with the the NGHGI and bottom-up approaches. Results at the sub-sector level (Forestland, Cropland, Grassland) show generally good agreement between the NGHGI and sub-sector-specific models, but results for a DGVM are mixed. Overall, for both CO2 fossil and net CO2 land fluxes, we find current independent approaches are consistent with the NGHGI at the scale of the EU27+UK. We conclude that CO2 emissions from fossil sources have decreased over the past 30 years in the EU27+UK, while large uncertainties on net uptake of CO2 by the land surface prevent trend identification. In addition, a gap on the order of 1000 Tg C yr-1 between CO2 fossil emissions and net CO2 uptake by the land exists regardless of the type of approach (NGHGI, TD, BU), falling well outside all available estimates of uncertainties. However, uncertainties in top-down approaches to estimate CO2 fossil emissions remain uncharacterized and are likely substantial. The data used to plot the figures are available at https://doi.org/10.5281/zenodo.7365863.

The trees in pastures are recognized for the benefits they provide to livestock, farmers, and the environment; nevertheless, their study has been restricted to small areas, making it difficult to upscale this information to national levels. For tropical developing countries, it is particularly important to understand the contribution of these systems to national carbon budgets. However, the costs associated with performing field measurements might limit the acquisition of this information. The use of unoccupied aerial systems (UAS) for ecological surveys has proved useful for collecting information at larger scales and with significantly lower costs. This study proposes a methodology that integrates field and UAS surveys to study trees on pasture areas across different terrain conditions. Our overall objective was to test the suitability of UAS surveys to the estimation of aboveground biomass (AGB), relying mainly on open-source software. The tree heights and crown diameters were measured on 0.1-hectare circular plots installed on pasture areas on livestock farms in the Amazon and Coastal regions in Ecuador. An UAS survey was performed on 1-hectare plots containing the circular plots. Field measurements were compared against canopy-height model values and biomass estimates using the two sources of information. Our results demonstrate that UAS surveys can be useful for identifying tree spatial arrangements and provide good estimates of tree height (RMSE values ranged from 0.01 to 3.53 m), crown diameter (RMSE values ranged from 0.04 to 4.47 m), and tree density (density differences ranging from 21.5 to 64.3%), which have a direct impact on biomass estimates. The differences in biomass estimates between the UAS and the field-measured values ranged from 25 to 75%, depending on site characteristics, such as slope and tree coverage. The results suggest that UASs are reliable and feasible tools with which to study tree characteristics on pastures, covering larger areas than field methods only.

A rapidly growing population and unprecedented dietary changes are placing the Indian food system under increasing pressure. Agriculture accounts for 90% of freshwater use in India, and ongoing environmental changes could have substantial effects on water availability and distribution.1Immerzeel WW Van Beek LP Bierkens MF Climate change will affect the Asian water towers.Science. 2010; 328: 1382-1385Crossref PubMed Scopus (2332) Google Scholar Droughts in 2016–17 in India led to food insecurity, migration, and other major social disruptions, and scant evidence exists on which to base policy action to respond to these pressures. Our Sustainable and Healthy Diets in India (SAHDI) project aimed to generate novel methods for the academic community and new data specifically to inform evidence-based policies to support healthy and environmentally sustainable diets in India. We adapted the Cool Farm Tool to generate new estimates of the greenhouse gas emissions2Vetter SH Sapkota TB Hillier J et al.Greenhouse gas emissions from agricultural food production to supply Indian diets: implications for climate change mitigation.Agric Ecosyst Environ. 2017; 237: 234-241Crossref PubMed Scopus (114) Google Scholar and water footprints3Kayatz B Hillier J Harris F et al.Assessment of spatial and temporal variability of water use for cereal production in India using a new farmer engagement tool, the Cool Farm Tool Water.Geophys Res Abstr. 2018; 20 (abstr).: 8377Google Scholar associated with the production of foods in India. We then defined typical Indian dietary patterns4Ebrahim S Kinra S Bowen L et al.The effect of rural-to-urban migration on obesity and diabetes in India: a cross-sectional study.PLoS Med. 2010; 7: e1000268Crossref PubMed Scopus (250) Google Scholar, 5Joy EJ Green R Agrawal S et al.Dietary patterns and non-communicable disease risk in Indian adults: secondary analysis of Indian Migration Study data.Public Health Nutr. 2017; : 1-10Google Scholar and quantified their greenhouse gas and water footprints; the greenhouse gas emissions of average diets in India are roughly half and the blue (irrigation) water footprints are nearly double those of diets in the UK.6Green R Joy EJM Milner J et al.Environmental Impacts of Typical Dietary Patterns in India.FASEB J. 2017; 31 (651.2 (abstr).)PubMed Google Scholar These environmental footprints vary substantially, with diets consumed by urban, younger, and wealthier adults having the highest greenhouse gas and water footprints, highlighting the potential for increasing food-related environmental impacts as dietary patterns change. We identified opportunities for dietary changes that could help to mitigate future water scarcity due to population pressures.7Milner J Joy EJ Green R et al.Projected health effects of realistic dietary changes to address freshwater constraints in India: a modelling study.Lancet Plan Health. 2017; 1: e26-e32Summary Full Text Full Text PDF PubMed Scopus (24) Google Scholar To provide more detailed data on this crucial issue we are conducting in-depth analyses of potential scenarios (including both the production and consumption of food) to quantify the environmental impacts of India's food system. The importance of the SAHDI project is threefold. First, the generation of new interdisciplinary methods linking data from the environment, food system, and health sectors will be useful to the related academic community. Second, the provision of new data for India is engaging policy makers in the importance of the linkages between the environment and health. Our novel data on sustainable diets in India can be used for example to inform culturally relevant dietary recommendations, as have been produced in The Netherlands and Brazil.8Health Council of the NetherlandsDutch dietary guidelines 2015. Health Council of the Netherlands, The HagueNov 4, 2015Google Scholar, 9Monteiro CA Cannon G Moubarac J-C et al.Dietary guidelines to nourish humanity and the planet in the twenty-first century. A blueprint from Brazil.Public Health Nutr. 2015; 18: 2311-2322Crossref PubMed Scopus (173) Google Scholar Our data also provide evidence for behaviour change interventions to stimulate more sustainable dietary consumption patterns—eg, we have used our data to develop educational resources for local schools.5Joy EJ Green R Agrawal S et al.Dietary patterns and non-communicable disease risk in Indian adults: secondary analysis of Indian Migration Study data.Public Health Nutr. 2017; : 1-10Google Scholar, 10Joy E Food for thought: connecting food choices to environmental impact with school children in India. Opinion Views from London School of Hygiene & Tropical Medicine.http://blogs.lshtm.ac.uk/news/2016/05/10/food-for-thought-connecting-food-choices-to-environmental-impact-with-school-children-in-india/Date: 2016Date accessed: January 8, 2018Google Scholar Third, the development of new modules in the Cool Farm Tool will enable agricultural workers to assess their on-farm environmental impacts, raise awareness, and promote more sustainable agricultural practices. Through our engagement with the Cool Farm Alliance, some of the largest growers and purchasers of agricultural products in India can now analyse on-farm water use in addition to greenhouse gas emissions in their supply chains and identify opportunities to respond to declining water availability, thus improving the sustainability of the food system now and in the future. Funding Wellcome Trust Contributors RG, EJ, SA, and JMa processed the dietary data. FH, BK, and TA processed the water footprint data. RG, EJ, JH, and PS processed the greenhouse gas data. JMi did the health modelling. All authors contributed to analysis and interpretation of the data and the writing of the report. AD, RG, EJ, PS, and AH were the project leads and designed the study. Declaration of interests We declare no competing interests. Acknowledgments This abstract forms part of the Sustainable and Healthy Diets in India (SAHDI) project supported by the Wellcome Trust Our Planet, Our Health programme (grant number 103932). The Wellcome Trust had no role in the design, analysis, or writing of this abstract.