NATURE CLIMATE CHANGE | ADVANCE ONLINE PUBLICATION | www.nature.com/natureclimatechange 1 D espite two decades of effort to curb emissions of CO 2 and other greenhouse gases (GHGs), emissions grew faster during the 2000s than in the 1990s 1 , and by 2010 had reached ~50 Gt CO 2 equivalent (CO 2 eq) yr −1 (refs 2,3). The continuing rise in emissions is a growing challenge for meeting the international goal of limiting warming to less than 2 °C relative to the pre-industrial era, particularly without stringent climate policies to decrease emissions in the near future 2-4 . As negative emissions technologies (NETs) seem ever more necessary 3,[5][6][7][8][9][10] To have a >50% chance of limiting warming below 2 °C, most recent scenarios from integrated assessment models (IAMs) require large-scale deployment of negative emissions technologies (NETs). These are technologies that result in the net removal of greenhouse gases from the atmosphere. We quantify potential global impacts of the different NETs on various factors (such as land, greenhouse gas emissions, water, albedo, nutrients and energy) to determine the biophysical limits to, and economic costs of, their widespread application. Resource implications vary between technologies and need to be satisfactorily addressed if NETs are to have a significant role in achieving climate goals.options, to be able to decide which pathways are most desirable for dealing with climate change.There are distinct classes of NETs, such as: (1) bioenergy with carbon capture and storage (BECCS) 11,12 ; (2) direct air capture of CO 2 from ambient air by engineered chemical reactions (DAC) 13,14 ; (3) enhanced weathering of minerals (EW) 15 , where natural weathering to remove CO 2 from the atmosphere is accelerated and the products stored in soils, or buried in land or deep ocean [16][17][18][19] ; (4) afforestation and reforestation (AR) to fix atmospheric carbon in biomass and soils [20][21][22] ; (5) manipulation of carbon uptake by the ocean, either

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This paper presents a new algorithm, Nitrous Oxide Emission (NOE) for simulating the emission of the greenhouse gas N 2 O from agricultural soils. N 2 O fluxes are calculated as the result of production through denitrification and nitrification and reduction through the last step of denitrification. Actual denitrification and nitrification rates are calculated from biological parameters and soil water-filled pore space, temperature and mineral nitrogen contents. New suggestions in NOE consisted in introducing (1) biological sitespecific parameters of soil N 2 O reduction and (2) reduction of the N 2 O produced through nitrification to N 2 through denitrification. This paper includes a database of 64 N 2 O fluxes measured on the field scale with corresponding environmental parameters collected from five agricultural situations in France. This database was used to test the validity of this algorithm. Site per site comparison of simulated N 2 O fluxes against observed data leads to mixed results. For 80% of the tested points, measured and simulated fluxes are in accordance whereas the others resulted in an important discrepancy. The origin of this discrepancy is discussed. On the other hand, mean annual fluxes measured on each site were strongly correlated to mean simulated annual fluxes. The biological site-specific parameter of soil N 2 O reduction introduced into NOE appeared particularly useful to discriminate the general level of N 2 O emissions from site to site. Furthermore, the relevance of NOE was confirmed by comparing measured and simulated N 2 O fluxes using some data from the US TRAGNET database. We suggest the use of NOE on a regional scale in order to predict mean annual N 2 O emissions.

To respect the Paris agreement targeting a limitation of global warming below 2°C by 2100, and possibly below 1.5°C, drastic reductions of greenhouse gas emissions are mandatory but not sufficient. Large‐scale deployment of other climate mitigation strategies is also necessary. Among these, increasing soil organic carbon (SOC) stocks is an important lever because carbon in soils can be stored for long periods and land management options to achieve this already exist and have been widely tested. However, agricultural soils are also an important source of nitrous oxide (N2O), a powerful greenhouse gas, and increasing SOC may influence N2O emissions, likely causing an increase in many cases, thus tending to offset the climate change benefit from increased SOC storage. Here we review the main agricultural management options for increasing SOC stocks. We evaluate the amount of SOC that can be stored as well as resulting changes in N2O emissions to better estimate the climate benefits of these management options. Based on quantitative data obtained from published meta‐analyses and from our current level of understanding, we conclude that the climate mitigation induced by increased SOC storage is generally overestimated if associated N2O emissions are not considered but, with the exception of reduced tillage, is never fully offset. Some options (e.g. biochar or non‐pyrogenic C amendment application) may even decrease N2O emissions.

The introduction of crops resistant to the broad spectrum herbicide glyphosate, N-(phosphonomethyl)glycine, may constitute an answer to increased contamination of the environment by herbicides, since it should reduce the total amount of herbicide needed and the number of active ingredients. However, there are few published data comparing the fate of glyphosate in the environment, particularly in soil, with that of substitute herbicides. The objective of this study is to compare the fate of glyphosate in three soils with that of four herbicides frequently used on crops that might be glyphosate resistant: trifluralin, alpha,alpha,alpha-trifluoro-2,6-dinitro-N,N-dipropyl-p-toluidine, and metazachlor, 2-chloro-N-(pyrazol-1-ylmethyl)acet-2',6'-xylidide for oilseed rape, metamitron, 4-amino-4,5-dihydro-3-methyl-6-phenyl-1,2,4-triazin-5-one for sugarbeet and sulcotrione, 2-(2-chloro-4-mesylbenzoyl)cyclohexane-1,3-dione for maize. The distribution of herbicides between the volatilized, mineralized, extractable and non-extractable fractions was studied, along with the formation of their metabolites in laboratory experiments using 14C-labelled herbicides, over a period of 140 days. The main dissipation pathways were mineralization for glyphosate and sulcotrione, volatilization for trifluralin and non-extractable residues formation for metazachlor and metamitron. The five herbicides had low persistence. Glyphosate had the shortest half-life, which varied with soil type, whereas trifluralin had the longest. The half-lives of metazachlor and sulcotrione were comparable, whereas that of metamitron was highly variable. Glyphosate, metazachlor and sulcotrione were degraded into persistent metabolites. Low amounts of trifluralin and metamitron metabolites were observed. At 140 days after herbicide applications, the amounts of glyphosate and its metabolite residues in soils were the lowest in two soils, but not in the third soil, a loamy sand with low pH. The environmental advantage in using glyphosate due to its rapid degradation is counterbalanced by accumulation of aminomethylphosphonic acid specifically in the context of extensive use of glyphosate.

In recent years, liquid biofuels for transport have benefited from significant political support due to their potential role in curbing climate change and reducing our dependence on fossil fuels. They may also participate to rural development by providing new markets for agricultural production. However, the growth of energy crops has raised concerns due to their high consumption of conventional fuels, fertilizers and pesticides, their impacts on ecosystems and their competition for arable land with food crops. Lowinput species such as Jatropha curcas, a perennial, inedible crop well adapted to semiarid regions, has received much interest as a new alternative for biofuel production, minimizing adverse effects on the environment and food supply. Here, we used life-cycle assessment to quantify the benefits of J. curcas biofuel production in West Africa in terms of greenhouse gas emissions and fossil energy use, compared with fossil diesel fuel and other biofuels. Biodiesel from J. curcas has a much higher performance than current biofuels, relative to oil-derived diesel fuels. Under West Africa conditions, J. curcas biodiesel allows a 72% saving in greenhouse gas emissions compared with conventional diesel fuel, and its energy yield (the ratio of biodiesel energy output to fossil energy input) is 4.7. J. curcas production studied is eco-compatible for the impacts under consideration and fits into the context of sustainable development.

Dynamic crop models are frequently used in ecology, agronomy and environmental sciences for simulating crop and environmental variables at a discrete time step. They often include a large number of parameters whose values are uncertain, and it is often impossible to estimate all these parameters accurately. A common practice consists in selecting a subset of parameters by global sensitivity analysis, estimating the selected parameters from data, and setting the others to some nominal values. For a discrete-time model, global sensitivity analyses can be applied sequentially at each simulation date. In the case of dynamic crop models, simulations are usually computed at a daily time step and the sequential implementation of global sensitivity analysis at each simulation date can result in several hundreds of sensitivity indices, with one index per parameter per simulation date. It is not easy to identify the most important parameters based on such a large number of values. In this paper, an alternative method called multivariate global sensitivity analysis was investigated. More precisely, the purposes of this paper are (i) to compare the sensitivity indices and associated parameter rankings computed by the sequential and the multivariate global sensitivity analyses, (ii) to assess the value of multivariate sensitivity analysis for selecting the model parameters to estimate from data. Sequential and multivariate sensitivity analyses were compared by using two dynamic models: a model simulating wheat biomass named WWDM and a model simulating N2O gaseous emission in crop fields named CERES-EGC. N2O measurements collected in several experimental plots were used to evaluate how parameter selection based on multivariate sensitivity analysis can improve the CERES-EGC predictions. The results showed that sequential and multivariate sensitivity analyses provide modellers with different types of information for models which exhibit a high variability of sensitivity index values over time. Conversely, when the parameter influence is quite constant over time, the two methods give more similar results. The results also showed that the estimation of the parameters with the highest sensitivity indices led to a strong reduction of the prediction errors of the model CERES-EGC