Magnetic states of linear defects in graphene monolayers: effects of strain and interaction

The combined effects of defect-defect interaction and of uniaxial or biaxial strains of up to 10\% on the development of magnetic states on the defect-core-localized quasi-one-dimensional electronic states generated by the so-called 558 linear extended defect in graphene monolayers are investigated by means of {\it ab initio} calculations. Results are analyzed on the basis of the heuristics of the Stoner criterion. We find that conditions for the emergence of magnetic states on the 558 defect can be tuned by uniaxial tensile parallel strains (along the defect direction) at both limits of isolated and interacting 558 defects. Parallel strains are shown to lead to two cooperative effects that favor the emergence of itinerant magnetism: enhancement of the DOS of the resonant defect states in the region of the Fermi level and tuning of the Fermi level to the maximum of the related DOS peak. A perpendicular strain is likewise shown to enhance the DOS of the defect states, but it also effects a detunig of the Fermi level that shifts away from the maximum of the DOS of the defect states, which inhibts the emergence of magnetic states. As a result, under biaxial strains the stabilization of a magnetic state depends on the relative magnitudes of the two components of strain.Comment: 9 pages 8 figure

Global Research Alliance N2O chamber methodology guidelines : Summary of modeling approaches

Acknowledgements Funding for this publication was provided by the New Zealand Government to support the objectives of the Livestock Research Group of the Global Research Alliance on Agricultural Greenhouse Gases. Individual authors work contribute to the following projects for which support has been received: Climate smart use of Norwegian organic soils (MYR, 2017-2022) project funded by the Research Council of Norway (decision no. 281109); Scottish Government's Strategic Research Programme, SuperG (under EU Horizon 2020 programme); DEVIL (NE/M021327/1), Soils-R-GRREAT (NE/P019455/1) and the EU H2020 project under Grant Agreement 774378—Coordination of International Research Cooperation on Soil Carbon Sequestration in Agriculture (CIRCASA); to project J-001793, Science and Technology Branch, Agriculture and Agri-Food Canada; and New Zealand Ministry of Business, Innovation and Employment (MBIE) core funding. Thanks to Alasdair Noble and the anonymous reviewers for helpful comments on a draft of this paper and to Anne Austin for editing services.Peer reviewedPublisher PD

Evaluating the Potential of Legumes to Mitigate N2_{2}O Emissions From Permanent Grassland Using Process-Based Models

A potential strategy for mitigating nitrous oxide (N$_{2}$O) emissions from permanent grasslands is the partial substitution of fertilizer nitrogen (N$_{fert}$) with symbiotically fixed nitrogen (N$_{symb}$) from legumes. The input of N$_{symb}$ reduces the energy costs of producing fertilizer and provides a supply of nitrogen (N) for plants that is more synchronous to plant demand than occasional fertilizer applications. Legumes have been promoted as a potential N$_{2}$O mitigation strategy for grasslands, but evidence to support their efficacy is limited, partly due to the difficulty in conducting experiments across the large range of potential combinations of legume proportions and fertilizer N inputs. These experimental constraints can be overcome by biogeochemical models that can vary legume‐fertilizer combinations and subsequently aid the design of targeted experiments. Using two variants each of two biogeochemical models (APSIM and DayCent), we tested the N$_{2}$O mitigation potential and productivity of full factorial combinations of legume proportions and fertilizer rates for five temperate grassland sites across the globe. Both models showed that replacing fertilizer with legumes reduced N$_{2}$O emissions without reducing productivity across a broad range of legume‐fertilizer combinations. Although the models were consistent with the relative changes of N$_{2}$O emissions compared to the baseline scenario (200 kg N ha$^{-1}$ yr$^{-1}$; no legumes), they predicted different levels of absolute N$_{2}$O emissions and thus also of absolute N$_{2}$O emission reductions; both were greater in DayCent than in APSIM. We recommend confirming these results with experimental studies assessing the effect of clover proportions in the range 30–50% and ≤150 kg N ha$^{-1}$ yr$^{-1}$ input as these were identified as best‐bet climate smart agricultural practices

Global Research Alliance N2 O chamber methodology guidelines:Introduction, with health and safety considerations

Non-steady-state (NSS) chamber techniques have been used for decades to measure nitrous oxide (N₂O) fluxes from agricultural soils. These techniques are widely used because they are relatively inexpensive, easy to adopt, versatile, and adaptable to varying conditions. Much of our current understanding of the drivers of N₂O emissions is based on studies using NSS chambers. These chamber techniques require decisions regarding multiple methodological aspects (e.g., chamber materials and geometry, deployment, sample analysis, and data and statistical analysis), each of which may significantly affect the results. Variation in methodological details can lead to challenges in comparing results between studies and assessment of reliability and uncertainty. Therefore, the New Zealand Government, in support of the objectives of the Livestock Research Group of the Global Research Alliance on Agricultural Greenhouse Gases (GRA), funded two international projects to, first, develop standardized guidelines on the use of NSS chamber techniques and, second, refine them based on the most up to date knowledge and methods. This introductory paper summarizes a collection of papers that represent the revised guidelines. Each article summarizes existing knowledge and provides guidance and minimum requirements on chamber design, deployment, sample collection, storage and analysis, automated chambers, flux calculations, statistical analysis, emission factor estimation and data reporting, modeling, and “gap-filling” approaches. The minimum requirements are not meant to be highly prescriptive but instead provide researchers with clear direction on best practices and factors that need to be considered. Health and safety considerations of NSS chamber techniques are also provided with this introductory paper

Integrating spot short-term measurements of carbon emissions and backward dietary energy partition calculations to estimate intake in lactating dairy cows fed ad libitum or restricted

The objective of this study was to use spot short-term measurements of CH4 (QCH4) and CO2 (QCO2) integrated with backward dietary energy partition calculations to estimate dry matter intake (DMI) in lactating dairy cows. Twelve multiparous cows averaging 173 ± 37 d in milk and 4 primiparous cows averaging 179 ± 27 d in milk were blocked by days in milk, parity, and DMI (as a percentage of body weight) and, within each block, randomly assigned to 1 of 2 treatments: ad libitum intake (AL) or restricted intake (RI = 90% DMI) according to a crossover design. Each experimental period lasted 22 d with 14 d for treatments adaptation and 8 d for data and sample collection. Diets contained (dry matter basis): 40% corn silage, 12% grass–legume haylage, and 48% concentrate. Spot short-term gas measurements were taken in 5-min sampling periods from 15 cows (1 cow refused sampling) using a portable, automated, open-circuit gas quantification system (GreenFeed, C-Lock Inc., Rapid City, SD) with intervals of 12 h between the 2 daily samples. Sampling points were advanced 2 h from a day to the next to yield 16 gas samples per cow over 8 d to account for diurnal variation in QCH4 and QCO2. The following equations were used sequentially to estimate DMI: (1) heat production (MJ/d) = (4.96 + 16.07 ÷ respiratory quotient) × QCO2; respiratory quotient = 0.95; (2) metabolizable energy intake (MJ/d) = (heat production + milk energy) ± tissue energy balance; (3) digestible energy (DE) intake (MJ/d) = metabolizable energy + CH4 energy + urinary energy; (4) gross energy (GE) intake (MJ/d) = DE + [(DE ÷ in vitro true dry matter digestibility) − DE]; and (5) DMI (kg/d) = GE intake estimated ÷ diet GE concentration. Data were analyzed using the MIXED procedure of SAS (SAS Institute Inc., Cary, NC) and Fit Model procedure in JMP (α = 0.05; SAS Institute Inc.). Cows significantly differed in DMI measured (23.8 vs. 22.4 kg/d for AL and RI, respectively). Dry matter intake estimated using QCH4 and QCO2 coupled with dietary backward energy partition calculations (Equations 1 to 5 above) was highest in cows fed for AL (22.5 vs. 20.2 kg/d). The resulting R2 were 0.28 between DMI measured and DMI estimated by gaseous measurements, and 0.36 between DMI measured and DMI predicted by the National Research Council model (2001). Results showed that spot short-term measurements of QCH4 and QCO2 coupled with dietary backward estimations of energy partition underestimated DMI by 7.8%. However, the approach proposed herein was able to significantly discriminate differences in DMI between cows fed for AL or RI

Towards a representative assessment of methane and nitrous oxide emissions and mitigation options from manure management of beef cattle feedlots in Brazil

We conducted an inventory to estimate methane (CH4) and nitrous oxide (N2O) emissions from beef cattle feedlot manure in Brazil for the year of 2010. The aim was to determine (CH4) and (N2O) emissions from beef cattle feedlot manure in Brazil using the IPCC United Nations Intergovernmental Panel on Climate Change approach and present a framework that structures priority research for decreasing uncertainties and assessing mitigation scenarios. The analysis consisted of the use of specific farm-scale activity data applied to the 2006 (IPCC) guideline equations for animal manure management updated with specific parameters for Brazil conditions. Uncertainties were assessed by error-propagation technique. The results indicated that 376.6 GgCO(2)eq were emitted from the manure management of beef cattle feedlots in Brazil in 2010. Nitrous oxide accounted for 61 % of total emissions, out of which 69 % came from direct emissions. Uncertainties were high, comprising -30 to +80 %. Solid storage-heap and field application were the largest sources of greenhouse gas (GHG) emissions (81 % of total emissions) and held most of the variance in uncertainties. Although, due to limitations in the IPCC methodology for integrating GHG emissions at farm-scale, we could not account for emissions occurring from different lengths of time in each manure management compartment prior to field application. As a consequence, this GHG inventory lacks consistence. The use of more robust methodologies such as process-based models are recommended for improvements, however they are currently unavailable because there is a lack of key data for Brazil conditions for validating those models. Our literature revision shows that the most effective research for raising those data would track emissions from manure: generated from male Nellore (Bos Indicus) cattle fed for 90 days with a high-energy diet, removed only at the end of feeding period and held in heaps over 60 days before being applied to maize (Zea mays L.) cropping fields under clay soil. The proposed research and methodology approaches described in this work is required to establish a manure management emission assessment that will become more responsive to the changing practices on Brazilian beef cattle feedlots and, consequently, permitting implication of mitigation scenarios to be ascertained

Global Research Alliance N2O chamber methodology guidelines: Guidelines for gap-filling missing measurements

Nitrous oxide (N2O) is a potent greenhouse gas that is primarily emitted from agriculture. Sampling limitations have generally resulted in discontinuous N2O observations over the course of any given year. The status quo for interpolating between sampling points has been to use a simple linear interpolation. This can be problematic with N2O emissions, since they are highly variable and sampling bias around these peak emission periods can have dramatic impacts on cumulative emissions. Here, we outline five gap-filling practices: linear interpolation, generalized additive models (GAMs), autoregressive integrated moving average (ARIMA), random forest (RF), and neural networks (NNs) that have been used for gap-filling soil N2O emissions. To facilitate the use of improved gap-filling methods, we describe the five methods and then provide strengths and challenges or weaknesses of each method so that model selection can be improved. We then outline a protocol that details data organization and selection, splitting of data into training and testing datasets, building and testing models, and reporting results. Use of advanced gap-filling methods within a standardized protocol is likely to increase transparency, improve emission estimates, reduce uncertainty, and increase capacity to quantify the impact of mitigation practices