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Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates as well as consistency within and among components, alongside methodology and data limitations. CO2 emissions from fossil fuels and industry (EFF) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on combined evidence from land-cover-change data, fire activity associated with deforestation, and models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the annual changes in concentration. The mean ocean CO2 sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in SOCEAN is evaluated with data products based on surveys of ocean CO2 measurements. The global residual terrestrial CO2 sink (SLAND) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models forced by observed climate, CO2, and land-cover change (some including nitrogen-carbon interactions). We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands. All uncertainties are reported as ±1σ, reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2005-2014), EFF was 9.0 ± 0.5 GtC yrg'1, ELUC was 0.9 ± 0.5 GtC yrg'1, GATM was 4.4 ± 0.1 GtC yrg'1, SOCEAN was 2.6 ± 0.5 GtC yrg'1, and SLAND was 3.0 ± 0.8 GtC yrg'1. For the year 2014 alone, EFF grew to 9.8 ± 0.5 GtC yrg'1, 0.6 % above 2013, continuing the growth trend in these emissions, albeit at a slower rate compared to the average growth of 2.2 % yrg'1 that took place during 2005-2014. Also, for 2014, ELUC was 1.1 ± 0.5 GtC yrg'1, GATM was 3.9 ± 0.2 GtC yrg'1, SOCEAN was 2.9 ± 0.5 GtC yrg'1, and SLAND was 4.1 ± 0.9 GtC yrg'1. GATM was lower in 2014 compared to the past decade (2005-2014), reflecting a larger SLAND for that year. The global atmospheric CO2 concentration reached 397.15 ± 0.10 ppm averaged over 2014. For 2015, preliminary data indicate that the growth in EFF will be near or slightly below zero, with a projection of g'0.6 [range of g'1.6 to +0.5] %, based on national emissions projections for China and the USA, and projections of gross domestic product corrected for recent changes in the carbon intensity of the global economy for the rest of the world. From this projection of EFF and assumed constant ELUC for 2015, cumulative emissions of CO2 will reach about 555 ± 55 GtC (2035 ± 205 GtCO2) for 1870-2015, about 75 % from EFF and 25 % from ELUC. This living data update documents changes in the methods and data sets used in this new carbon budget compared with previous publications of this data set (Le Quéré et al., 2015, 2014, 2013). All observations presented here can be downloaded from the Carbon Dioxide Information Analysis Center (doi:10.3334/CDIAC/GCP-2015).

A-genome Arachis species (AA; 2n = 2x = 20) are commonly used as secondary germplasm sources in cultivated peanut breeding, Arachis hypogaea L. (AABB; 2n = 4x = 40), for the introgression of various biotic and abiotic stress resistance genes. Genome doubling is critical to overcoming the hybridization barrier of infertility that arises from ploidy-level differences between wild germplasm and cultivated peanuts. To develop improved genome doubling methods, four trials of various concentrations of the mitotic inhibitor treatments colchicine, oryzalin, and trifluralin were tested on the seedlings and seeds of three A-genome species, A. cardenasii , A. correntina , and A. diogoi . A total of 494 seeds/seedlings were treated in the present four trials, with trials 1 to 3 including different concentrations of the three chemical treatments on seedlings, and trial 4 focusing on the treatment period of 5 mM colchicine solution treatment of seeds. A small number of tetraploids were produced from the colchicine and oryzalin gel treatments of seedlings, but all these tetraploid seedlings reverted to diploid or mixoploid states within six months of treatment. In contrast, the 6-h colchicine solution treatment of seeds showed the highest tetraploid conversion rate (6-13% of total treated seeds or 25-40% of surviving seedlings), and the tetraploid plants were repeatedly tested as stable tetraploids. In addition, visibly and statistically larger leaves and flowers were produced by the tetraploid versions of these three species compared to their diploid versions. As a result, stable tetraploid plants of each A-genome species were produced, and a 5 mM colchicine seed treatment is recommended for A-genome and related wild Arachis species genome doubling.

Introduction: Virginia-type peanut, Arachis hypogaea subsp. hypogaea , is the second largest market class of peanut cultivated in the United States. It is mainly used for large-seeded, in-shell products. Historically, Virginia-type peanut cultivars were developed through long-term recurrent phenotypic selection and wild species introgression projects. Contemporary genomic technologies represent a unique opportunity to revolutionize the traditional breeding pipeline. While there are genomic tools available for wild and cultivated peanuts, none are tailored specifically to applied Virginia-type cultivar development programs., Methods and Respective Results: Here, the first Virginia-type peanut reference genome, "Bailey II", was assembled. It has improved contiguity and reduced instances of manual curation in chromosome arms. Whole-genome sequencing and marker discovery was conducted on 66 peanut lines which resulted in 1.15 million markers. The high marker resolution achieved allowed 34 unique wild species introgression blocks to be cataloged in the A. hypogaea genome, some of which are known to confer resistance to one or more pathogens. To enable marker-assisted selection of the blocks, 111 PCR Allele Competitive Extension assays were designed. Forty thousand high quality markers were selected from the full set that are suitable for mid-density genotyping for genomic selection. Genomic data from representative advanced Virginia-type peanut lines suggests this is an appropriate base population for genomic selection., Discussion: The findings and tools produced in this research will allow for rapid genetic gain in the Virginia-type peanut population. Genomics-assisted breeding will allow swift response to changing biotic and abiotic threats, and ultimately the development of superior cultivars for public use and consumption., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest, (Copyright © 2023 Newman, Andres, Youngblood, Campbell, Simpson, Cannon, Scheffler, Oakley, Hulse-Kemp and Dunne.)

Key Message: Contamination at the FAD2B locus due to inadequate screening protocols is the primary cause of sporadic, insufficient oleic acid content in Virginia-type peanut. The high oleic trait in peanut is conditioned by loss-of-function mutations in a pair of homeologous enzymes and is well known to improve the shelf life of peanut products. As such, the trait is given high priority in current and future cultivars by the North Carolina State University peanut breeding program. For unknown reasons, high oleic cultivars and breeding lines intermittently failed to meet self-imposed thresholds for oleic acid content in internal testing. To determine why, a manual seed chipper, crude DNA isolation protocol, genotyping assays for both mutations, and a web-based SNP calling application were developed. The primary cause was determined to be contamination with normal oleic seeds resulting from inadequate screening protocols. In order to correct the problem, a faster screening method was acquired to accommodate a higher oleic acid threshold. Additionally, results showed the mutation in one homeolog is fixed in the program, dig date had no significant effect on oleic acid content, and minor modifiers segregating within the program explained 6% of the variation in oleic acid content., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Resolving regional carbon budgets is critical for informing land-based mitigation policy. For nine regions covering nearly the whole globe, we collected inventory estimates of carbon-stock changes complemented by satellite estimates of biomass changes where inventory data are missing. The net land-atmospheric carbon exchange (NEE) was calculated by taking the sum of the carbon-stock change and lateral carbon fluxes from crop and wood trade, and riverine-carbon export to the ocean. Summing up NEE from all regions, we obtained a global 'bottom-up' NEE for net land anthropogenic CO 2 uptake of -2.2 ± 0.6 PgC yr -1 consistent with the independent top-down NEE from the global atmospheric carbon budget during 2000-2009. This estimate is so far the most comprehensive global bottom-up carbon budget accounting, which set up an important milestone for global carbon-cycle studies. By decomposing NEE into component fluxes, we found that global soil heterotrophic respiration amounts to a source of CO 2 of 39 PgC yr -1 with an interquartile of 33-46 PgC yr -1 -a much smaller portion of net primary productivity than previously reported., (© The Author(s) 2020. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd.)

The Open-source Data Inventory for Anthropogenic CO 2 (ODIAC) is a global high-spatial resolution gridded emission data product that distributes carbon dioxide (CO 2 ) emissions from fossil fuel combustion. The emission spatial distributions are estimated at a 1×1 km spatial resolution over land using power plant profiles (emission intensity and geographical location) and satellite-observed nighttime lights. This paper describes the year 2016 version of the ODIAC emission data product (ODIAC2016) and presents analyses that help guiding data users, especially for atmospheric CO 2 tracer transport simulations and flux inversion analysis. Since the original publication in 2011, we have made modifications to our emission modeling framework in order to deliver a comprehensive global gridded emission data product. Major changes from the 2011 publication are 1) the use of emissions estimates made by the Carbon Dioxide Information Analysis Center (CDIAC) at the Oak Ridge National Laboratory (ORNL) by fuel type (solid, liquid, gas, cement manufacturing, gas flaring and international aviation and marine bunkers), 2) the use of multiple spatial emission proxies by fuel type such as nightlight data specific to gas flaring and ship/aircraft fleet tracks and 3) the inclusion of emission temporal variations. Using global fuel consumption data, we extrapolated the CDIAC emissions estimates for the recent years and produced the ODIAC2016 emission data product that covers 2000-2015. Our emission data can be viewed as an extended version of CDIAC gridded emission data product, which should allow data users to impose global fossil fuel emissions in more comprehensive manner than original CDIAC product. Our new emission modeling framework allows us to produce future versions of ODIAC emission data product with a timely update. Such capability has become more significant given the CDIAC/ORNL's shutdown. ODIAC data product could play an important role to support carbon cycle science, especially modeling studies with space-based CO 2 data collected near real time by ongoing carbon observing missions such as Japanese Greenhouse Observing SATellite (GOSAT), NASA's Orbiting Carbon Observatory 2 (OCO-2) and upcoming future missions. The ODIAC emission data product including the latest version of the ODIAC emission data (ODIAC2017, 2000-2016), is distributed from http://db.cger.nies.go.jp/dataset/ODIAC/ with a DOI.

Leaf shape varies spectacularly among plants. Leaves are the primary source of photoassimilate in crop plants, and understanding the genetic basis of variation in leaf morphology is critical to improving agricultural productivity. Leaf shape played a unique role in cotton improvement, as breeders have selected for entire and lobed leaf morphs resulting from a single locus, okra (l-D 1 ), which is responsible for the major leaf shapes in cotton. The l-D 1 locus is not only of agricultural importance in cotton, but through pioneering chimeric and morphometric studies, it has contributed to fundamental knowledge about leaf development. Here we show that an HD-Zip transcription factor homologous to the LATE MERISTEM IDENTITY1 (LMI1) gene of Arabidopsis is the causal gene underlying the l-D 1 locus. The classical okra leaf shape allele has a 133-bp tandem duplication in the promoter, correlated with elevated expression, whereas an 8-bp deletion in the third exon of the presumed wild-type normal allele causes a frame-shifted and truncated coding sequence. Our results indicate that subokra is the ancestral leaf shape of tetraploid cotton that gave rise to the okra allele and that normal is a derived mutant allele that came to predominate and define the leaf shape of cultivated cotton. Virus-induced gene silencing (VIGS) of the LMI1-like gene in an okra variety was sufficient to induce normal leaf formation. The developmental changes in leaves conferred by this gene are associated with a photosynthetic transcriptomic signature, substantiating its use by breeders to produce a superior cotton ideotype., Competing Interests: The authors declare no conflict of interest.

Nearly three-quarters of the growth in global carbon emissions from the burning of fossil fuels and cement production between 2010 and 2012 occurred in China. Yet estimates of Chinese emissions remain subject to large uncertainty; inventories of China's total fossil fuel carbon emissions in 2008 differ by 0.3 gigatonnes of carbon, or 15 per cent. The primary sources of this uncertainty are conflicting estimates of energy consumption and emission factors, the latter being uncertain because of very few actual measurements representative of the mix of Chinese fuels. Here we re-evaluate China's carbon emissions using updated and harmonized energy consumption and clinker production data and two new and comprehensive sets of measured emission factors for Chinese coal. We find that total energy consumption in China was 10 per cent higher in 2000-2012 than the value reported by China's national statistics, that emission factors for Chinese coal are on average 40 per cent lower than the default values recommended by the Intergovernmental Panel on Climate Change, and that emissions from China's cement production are 45 per cent less than recent estimates. Altogether, our revised estimate of China's CO2 emissions from fossil fuel combustion and cement production is 2.49 gigatonnes of carbon (2 standard deviations = ±7.3 per cent) in 2013, which is 14 per cent lower than the emissions reported by other prominent inventories. Over the full period 2000 to 2013, our revised estimates are 2.9 gigatonnes of carbon less than previous estimates of China's cumulative carbon emissions. Our findings suggest that overestimation of China's emissions in 2000-2013 may be larger than China's estimated total forest sink in 1990-2007 (2.66 gigatonnes of carbon) or China's land carbon sink in 2000-2009 (2.6 gigatonnes of carbon).

Key Message: A major leaf shape locus (L) was mapped with molecular markers and genomically targeted to a small region in the D-genome of cotton. By using expression analysis and candidate gene mapping, two LMI1 -like genes are identified as possible candidates for leaf shape trait in cotton. Leaf shape in cotton is an important trait that influences yield, flowering rates, disease resistance, lint trash, and the efficacy of foliar chemical application. The leaves of okra leaf cotton display a significantly enhanced lobing pattern, as well as ectopic outgrowths along the lobe margins when compared with normal leaf cotton. These phenotypes are the hallmark characteristics of mutations in various known modifiers of leaf shape that culminate in the mis/over-expression of Class I KNOX genes. To better understand the molecular and genetic processes underlying leaf shape in cotton, a normal leaf accession (PI607650) was crossed to an okra leaf breeding line (NC05AZ21). An F2 population of 236 individuals confirmed the incompletely dominant single gene nature of the okra leaf shape trait in Gossypium hirsutum L. Molecular mapping with simple sequence repeat markers localized the leaf shape gene to 5.4 cM interval in the distal region of the short arm of chromosome 15. Orthologous mapping of the closely linked markers with the sequenced diploid D-genome (Gossypium raimondii) tentatively resolved the leaf shape locus to a small genomic region. RT-PCR-based expression analysis and candidate gene mapping indicated that the okra leaf shape gene (L (o) ) in cotton might be an upstream regulator of Class I KNOX genes. The linked molecular markers and delineated genomic region in the sequenced diploid D-genome will assist in the future high-resolution mapping and map-based cloning of the leaf shape gene in cotton.