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Charcoal is a key energy source for urban households in sub-Saharan Africa and charcoal production is the main cause of forest degradation across the region. We used multitemporal high-resolution remote sensing optical imagery to quantify the extent and intensity of forest degradation associated with charcoal production and its impact on forest carbon stocks for the main supplying area of an African capital. This analysis documents the advance of forest degradation and quantifies its aboveground biomass removals over a seven-year period, registering that, between 2013 and 2016, the average annual area under charcoal production was 103 km ^2 and the annual aboveground biomass removals reached 1081 000 (SD = 2461) Mg. Kiln densities in the study area rose to 2 kilns-ha, with an average of 90.7 Mg ha ^−1 of extracted aboveground biomass. Charcoal production was responsible for the degradation of 55.5% of the mopane woodlands in the study area between 2013 and 2019. We estimated post-disturbance recovery times using an ecosystem model calibrated for the study area. The simulations showed that recovery times could require up to 150 years for current aboveground biomass extraction rates. The results of the remote sensing analysis and the simulations of the ecosystem model corroborate the unsustainability of the present patterns of charcoal production. The detailed characterization of the spatial and temporal patterns of charcoal production was combined with household survey information to quantify the impact of the urban energy demand of the Maputo urban area on forest carbon stocks. The analysis shows that Maputo charcoal demand was responsible for the annual degradation of up to 175.3 km ^2 and that the contribution of the study area to this demand fluctuated between 75% and 33% over the study period. The extent, advance pace and distance from urban centers documented in this study support the idea that forest degradation from charcoal production cannot merely be considered a peri-urban process. The intensity of the aboveground biomass (AGB) removals and its contribution to forest carbon stocks changes is significant at the national and regional levels.

We used historical Landsat imagery to monitor forest degradation from charcoal production in the main supplying region of the Mozambican capital, Maputo, during a ten-year period (2008–2018). We applied a change detection method that exploits temporal NDVI dynamics associated with charcoal production. This forest degradation temporal sequence exposes the magnitude and the spatial and temporal dynamics of charcoal production, which is the main forest degradation driver in sub-Saharan Africa. The annual area under charcoal production has been steadily increasing since 2008 and reached 11 673 ha in 2018. The total forest degraded extent in the study area during the 10-year study period covered 79 630 ha, which represents 68% of the available mopane woodlands in 2008. Only 5% of the available mopane woodlands area remain undisturbed in the study area. Total gross carbon emissions associated charcoal production during this 10-year period were estimated in 1.13 Mt. These results mark forest degradation from charcoal production as the main driver of forest cover change in southern Mozambique. They also denote that, while charcoal production may be relatively localized in space, its implications for forest cover change and carbon emissions in a sub-Saharan African context are relevant at larger geographical scales. This study represents a proof of concept of the feasibility of medium resolution Earth observation data to monitor forest degradation from charcoal production in the context of the growing urban energy demand. It also highlights the potential opportunities to improve REDD+ monitoring, reporting and verification efforts in sub-Saharan Africa as a first step toward designing effective management and policy interventions.

Forests are important ecosystems that are under increasing pressure from human use and environmental change, and have a significant ability to remove carbon dioxide from the atmosphere, and are therefore the focus of policy efforts aimed at reducing deforestation and degradation as well as increasing afforestation and reforestation for climate mitigation. Critical to these efforts is the accurate monitoring, reporting and verification of current forest cover and carbon stocks. For planning, the additional step of modeling is required to quantitatively estimate forest carbon sequestration potential in response to alternative land-use and management decisions. To be most useful and of decision-relevant quality, these model estimates must be at very high spatial resolution and with very high accuracy to capture important heterogeneity on the land surface and connect to monitoring efforts. Here, we present results from a new forest carbon monitoring and modeling system that combines high-resolution remote sensing, field data, and ecological modeling to estimate contemporary above-ground forest carbon stocks, and project future forest carbon sequestration potential for the state of Maryland at 90 m resolution. Statewide, the contemporary above-ground carbon stock was estimated to be 110.8 Tg C (100.3–125.8 Tg C), with a corresponding mean above-ground biomass density of 103.7 Mg ha ^−1 which was within 2% of independent empirically-based estimates. The forest above-ground carbon sequestration potential for the state was estimated to be much larger at 314.8 Tg C, and the forest above-ground carbon sequestration potential gap (i.e. potential-current) was estimated to be 204.1 Tg C, nearly double the current stock. These results imply a large statewide potential for future carbon sequestration from afforestation and reforestation activities. The high spatial resolution of the model estimates underpinning these totals demonstrate important heterogeneity across the state and can inform prioritization of actual afforestation/reforestation opportunities. With this approach, it is now possible to quantify both the forest carbon stock and future carbon sequestration potential over large policy relevant areas with sufficient accuracy and spatial resolution to significantly advance planning.

Crop yield forecast is important, but determining productivity is an on-going challenge for agricultural communities including policy makers. An increase in the number of satellites, improved temporal and spatial resolutions and more open data policies, are leading to wider interest by the agricultural sector in exploiting space-based data at moderate spatial resolutions (10–30 m) and varying wavelengths (optical and microwave) for crop yield monitoring. This study evaluated Sentinel-1 dual-polarimetric data to forecast soybean yields one month before the harvest at field scales over a site in central Argentina. Specifically, polarimetric features were extracted from the Sentinel-1 data using the M-Chi decomposition. An Artificial Neural Network (ANN) model was trained using a time series of the single-bounce and volume scattering parameters derived from M-Chi from stacks of data acquired during the growing season. To estimate soybean yield from the ANN model, an innovative iterative retrieval method was developed. This retrieval approach improved the accuracies of the soybean yield forecast delivering a final coefficient of determination (R2) of 0.81 with a root mean square error (RMSE) of 755.81 kg/ha and mean absolute error (MAE) of 581.65 kg/ha. These accuracies demonstrate a high potential of Synthetic Aperture Radar (SAR) data from Sentinel-1 for soybean yield forecast at field scales.

Terrestrial ecosystems play a critical role in the global carbon cycle but have highly uncertain future dynamics. Ecosystem modeling that includes the scaling up of underlying mechanistic ecological processes has the potential to improve the accuracy of future projections while retaining key process-level detail. Over the past two decades, multiple modeling advances have been made to meet this challenge, such as the Ecosystem Demography (ED) model and its derivatives, including ED2 and FATES. Here, we present the global evaluation of the Ecosystem Demography model (ED v3.0), which, like its predecessors, features the formal scaling of physiological processes for individual-based vegetation dynamics to ecosystem scales, together with integrated submodules of soil biogeochemistry and soil hydrology, while retaining explicit tracking of vegetation 3-D structure. This new model version builds on previous versions and provides the first global calibration and evaluation, global tracking of the effects of climate and land-use change on vegetation 3-D structure, spin-up process and input datasets, as well as numerous other advances. Model evaluation was performed with respect to a set of important benchmarking datasets, and model estimates were within observational constraints for multiple key variables, including (i) global patterns of dominant plant functional types (broadleaf vs. evergreen), (ii) the spatial distribution, seasonal cycle, and interannual trends for global gross primary production (GPP), (iii) the global interannual variability of net biome production (NBP) and (iv) global patterns of vertical structure, including leaf area and canopy height. With this global model version, it is now possible to simulate vegetation dynamics from local to global scales and from seconds to centuries with a consistent mechanistic modeling framework amendable to data from multiple traditional and new remote sensing sources, including lidar.

Human land use activities have resulted in large changes to the biogeochemical and biophysical properties of the Earth's surface, with consequences for climate and other ecosystem services. In the future, land use activities are likely to expand and/or intensify further to meet growing demands for food, fiber, and energy. As part of the World Climate Research Program Coupled Model Intercomparison Project (CMIP6), the international community has developed the next generation of advanced Earth system models (ESMs) to estimate the combined effects of human activities (e.g., land use and fossil fuel emissions) on the carbon–climate system. A new set of historical data based on the History of the Global Environment database (HYDE), and multiple alternative scenarios of the future (2015–2100) from Integrated Assessment Model (IAM) teams, is required as input for these models. With most ESM simulations for CMIP6 now completed, it is important to document the land use patterns used by those simulations. Here we present results from the Land-Use Harmonization 2 (LUH2) project, which smoothly connects updated historical reconstructions of land use with eight new future projections in the format required for ESMs. The harmonization strategy estimates the fractional land use patterns, underlying land use transitions, key agricultural management information, and resulting secondary lands annually, while minimizing the differences between the end of the historical reconstruction and IAM initial conditions and preserving changes depicted by the IAMs in the future. The new approach builds on a similar effort from CMIP5 and is now provided at higher resolution (0.25∘×0.25∘) over a longer time domain (850–2100, with extensions to 2300) with more detail (including multiple crop and pasture types and associated management practices) using more input datasets (including Landsat remote sensing data) and updated algorithms (wood harvest and shifting cultivation); it is assessed via a new diagnostic package. The new LUH2 products contain > 50 times the information content of the datasets used in CMIP5 and are designed to enable new and improved estimates of the combined effects of land use on the global carbon–climate system.

Anthropogenic land-use and land-cover change activities play a critical role in Earth system dynamics through significant alterations to biogeophysical and biogeochemical properties at local to global scales. To quantify the magnitude of these impacts, climate models need consistent land-cover change time series at a global scale, based on land-use information from observations or dedicated land-use change models. However, a specific land-use change cannot be unambiguously mapped to a specific land-cover change. Here, nine translation rules are evaluated based on assumptions about the way land-use change could potentially impact land cover. Utilizing the Global Land-use Model 2 (GLM2), the model underlying the latest Land-Use Harmonization dataset (LUH2), the land-cover dynamics resulting from land-use change were simulated based on multiple alternative translation rules from 850 to 2015 globally. For each rule, the resulting forest cover, carbon density and carbon emissions were compared with independent estimates from remote sensing observations, U.N. Food and Agricultural Organization reports, and other studies. The translation rule previously suggested by the authors of the HYDE 3.2 dataset, that underlies LUH2, is consistent with the results of our examinations at global, country and grid scales. This rule recommends that for CMIP6 simulations, models should (1) completely clear vegetation in land-use changes from primary and secondary land (including both forested and non-forested) to cropland, urban land and managed pasture; (2) completely clear vegetation in land-use changes from primary forest and/or secondary forest to rangeland; (3) keep vegetation in land-use changes from primary non-forest and/or secondary non-forest to rangeland. Our analysis shows that this rule is one of three (out of nine) rules that produce comparable estimates of forest cover, vegetation carbon and emissions to independent estimates and also mitigate the anomalously high carbon emissions from land-use change observed in previous studies in the 1950s. According to the three translation rules, contemporary global forest area is estimated to be 37.42×106 km2, within the range derived from remote sensing products. Likewise, the estimated carbon stock is in close agreement with reference biomass datasets, particularly over regions with more than 50 % forest cover.

In Paris, France, December 2015, the Conference of the Parties (COP) to the United Nations Framework Convention on Climate Change (UNFCCC) invited the Intergovernmental Panel on Climate Change (IPCC) to provide a special report in 2018 on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways. In Nairobi, Kenya, April 2016, the IPCC panel accepted the invitation. Here we describe the response devised within the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) to provide tailored, cross-sectorally consistent impact projections to broaden the scientific basis for the report. The simulation protocol is designed to allow for (1) separation of the impacts of historical warming starting from pre-industrial conditions from impacts of other drivers such as historical land-use changes (based on pre-industrial and historical impact model simulations); (2) quantification of the impacts of additional warming up to 1.5 °C, including a potential overshoot and long-term impacts up to 2299, and comparison to higher levels of global mean temperature change (based on the low-emissions Representative Concentration Pathway RCP2.6 and a no-mitigation pathway RCP6.0) with socio-economic conditions fixed at 2005 levels; and (3) assessment of the climate effects based on the same climate scenarios while accounting for simultaneous changes in socio-economic conditions following the middle-of-the-road Shared Socioeconomic Pathway (SSP2, Fricko et al., 2016) and in particular differential bioenergy requirements associated with the transformation of the energy system to comply with RCP2.6 compared to RCP6.0. With the aim of providing the scientific basis for an aggregation of impacts across sectors and analysis of cross-sectoral interactions that may dampen or amplify sectoral impacts, the protocol is designed to facilitate consistent impact projections from a range of impact models across different sectors (global and regional hydrology, lakes, global crops, global vegetation, regional forests, global and regional marine ecosystems and fisheries, global and regional coastal infrastructure, energy supply and demand, temperature-related mortality, and global terrestrial biodiversity).

Land-use change has been the dominant source of anthropogenic carbon emissions for most of the historical period and is currently one of the largest and most uncertain components of the global carbon cycle. Advancing the scientific understanding on this topic requires that the best data be used as input to state-of-the-art models in well-organized scientific assessments. The Land-Use Harmonization 2 dataset (LUH2), previously developed and used as input for simulations of the 6th Coupled Model Intercomparison Project (CMIP6), has been updated annually to provide required input to land models in the annual Global Carbon Budget (GCB) assessments. Here we discuss the methodology for producing these annual LUH2-GCB updates and extensions which incorporate annual wood harvest data updates from the Food and Agriculture Organization (FAO) of the United Nations for dataset years after 2015 and the History Database of the Global Environment (HYDE) gridded cropland and grazing area data updates (based on annual FAO cropland and grazing area data updates) for dataset years after 2012, along with extrapolations to the current year due to a lag of 1 or more years in the FAO data releases. The resulting updated LUH2-GCB datasets have provided global, annual gridded land-use and land-use-change data relating to agricultural expansion, deforestation, wood harvesting, shifting cultivation, regrowth and afforestation, crop rotations, and pasture management and are used by both bookkeeping models and dynamic global vegetation models (DGVMs) for the GCB. For GCB 2019, a more significant update to LUH2 was produced, LUH2-GCB2019 (https://doi.org/10.3334/ORNLDAAC/1851, Chini et al., 2020b), to take advantage of new data inputs that corrected cropland and grazing areas in the globally important region of Brazil as far back as 1950. From 1951 to 2012 the LUH2-GCB2019 dataset begins to diverge from the version of LUH2 used for the World Climate Research Programme's CMIP6, with peak differences in Brazil in the year 2000 for grazing land (difference of 100 000 km2) and in the year 2009 for cropland (difference of 77 000 km2), along with significant sub-national reorganization of agricultural land-use patterns within Brazil. The LUH2-GCB2019 dataset provides the base for future LUH2-GCB updates, including the recent LUH2-GCB2020 dataset, and presents a starting point for operationalizing the creation of these datasets to reduce time lags due to the multiple input dataset and model latencies.

Crop type maps identify the spatial distribution of crop types and underpin a large range of agricultural monitoring applications ranging from early warning of crop shortfalls, crop condition assessments, production forecasts, and damage assessment from extreme weather, to agricultural statistics, agricultural insurance, and climate mitigation and adaptation decisions. Despite their importance, harmonized, up-to-date global crop type maps of the main food commodities do not exist to date. To address this critical data gap of global-scale consistent, up-to-date crop type maps, we harmonized 24 national and regional datasets from 21 sources covering 66 countries to develop a set of Best Available Crop Specific masks (BACS) over the major production and export countries for wheat, maize, rice, and soybeans, in the context of the G20 Global Agriculture Monitoring Program, GEOGLAM., (© 2023. The Author(s).)

Space-based remote sensing can make an important contribution toward monitoring greenhouse gas emissions and removals from the agriculture, forestry, and other land use (AFOLU) sector, and to understanding and addressing human-caused climate change through the UNFCCC Paris Agreement. Space agencies have begun to coordinate their efforts to identify needs, collect and harmonize available data and efforts, and plan and maintain a long-term roadmap for observations. International cooperation is crucial in developing and realizing the roadmap, and the Committee on Earth Observation Satellites (CEOS) is a key coordinating driver of this effort. Here, we first identify the data and information that will be useful to support the global stocktake (GST) of the Paris Agreement. Then, the paper explains how existing and planned space-based capabilities and products can be used and combined, particularly in the land use sector, and provides a workflow for their harmonization and contribution to greenhouse gas inventories and assessments at the national and global level., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Author(s).)

Background: In populations with a history of conflict, early identification of pregnant women who are at risk of adverse pregnancy outcomes is challenging, especially if sonography is not available. We evaluated the performance of symphysis-fundal height (SFH) for identification of high-risk pregnancies and investigated if food security and diet quality, clinical biomarkers, and stress were associated with SFH and two known indicators of maternal-fetal well-being, sonography-estimated fetal weight and amniotic fluid index (AFI). Methods: For this cross-sectional study, 61 women with high-risk pregnancies were recruited after referral to the obstetrics and gynecology unit at San José Hospital in Popayán, Colombia. Multiple stepwise linear and ordered logistic regressions were used to identify associations of SFH, sonography-estimated fetal weight and AFI classification with history of displacement, food insecurity, post-traumatic stress symptoms as well as biopsychosocial risk evaluated through the Colombian risk scale. Results: History of displacement was associated with lower SFH Z-scores, but higher hemoglobin, taking iron supplements and a higher diastolic blood pressure were associated with higher SFH Z-scores. SFH was also associated with AFI but not with sonography-estimated fetal weight. Stress indicators were associated with a higher AFI. In contrast family support, an element of the Colombian biopsychosocial risk assessment, was associated with a higher sonography-estimated fetal weight, whereas more hours of sleep/day were associated with lower sonography-estimated fetal weight. Conclusion: SFH was not only associated with biological factors known to affect maternal/fetal health but also with history of displacement, thus validating its use in conflict areas for pregnancy assessment. Associations of biopsychosocial stressors with maternal-fetal outcomes highlight the need for a systematic assessment of stress in pregnant women from conflict zones., (Copyright © 2020 González-Fernández, Sahajpal, Chagüendo, Ortiz Martínez, Herrera, Scott and Koski.)

Mosaic landscapes under shifting cultivation, with their dynamic mix of managed and natural land covers, often fall through the cracks in remote sensing-based land cover and land use classifications, as these are unable to adequately capture such landscapes' dynamic nature and complex spectral and spatial signatures. But information about such landscapes is urgently needed to improve the outcomes of global earth system modelling and large-scale carbon and greenhouse gas accounting. This study combines existing global Landsat-based deforestation data covering the years 2000 to 2014 with very high-resolution satellite imagery to visually detect the specific spatio-temporal pattern of shifting cultivation at a one-degree cell resolution worldwide. The accuracy levels of our classification were high with an overall accuracy above 87%. We estimate the current global extent of shifting cultivation and compare it to other current global mapping endeavors as well as results of literature searches. Based on an expert survey, we make a first attempt at estimating past trends as well as possible future trends in the global distribution of shifting cultivation until the end of the 21st century. With 62% of the investigated one-degree cells in the humid and sub-humid tropics currently showing signs of shifting cultivation-the majority in the Americas (41%) and Africa (37%)-this form of cultivation remains widespread, and it would be wrong to speak of its general global demise in the last decades. We estimate that shifting cultivation landscapes currently cover roughly 280 million hectares worldwide, including both cultivated fields and fallows. While only an approximation, this estimate is clearly smaller than the areas mentioned in the literature which range up to 1,000 million hectares. Based on our expert survey and historical trends we estimate a possible strong decrease in shifting cultivation over the next decades, raising issues of livelihood security and resilience among people currently depending on shifting cultivation.

Purpose: A case of probable interaction of levothyroxine and ritonavir is presented along with a review of the relevant literature and recommendations on clinical management., Summary: A 37-year-old woman with human immunodeficiency virus infection who had recently undergone thyroidectomy for a benign multinodular goiter presented to a clinic with hypothyroidism (she was also found to be pregnant), and treatment with levothyroxine 75 μg daily was initiated. While receiving antiretroviral therapy (abacavir-lamivudine and lopinavir-ritonavir) during pregnancy, the patient had persistently elevated serum thyroid-stimulating hormone (TSH) concentrations (up to 125.89 μIU/mL) despite gradual escalation of the levothyroxine dosage to 175 μg daily. An interaction between ritonavir and levothyroxine was suspected, and dolutegravir was substituted for lopinavir-ritonavir. Within 4 months, the TSH concentration had normalized. The daily levothyroxine dose was tapered over a 5-month period and stabilized at 125 μg, and TSH concentrations remained within the normal range over an 18-month follow-up period. Scoring of the case using the Drug Interaction Probability Scale yielded a score of 6 out of 11, indicating a probable interaction between levothyroxine and ritonavir. A literature search identified 4 reported cases of interactions involving levothyroxine and ritonavir., Conclusion: A potentially serious and underrecognized drug interaction between ritonavir and levothyroxine was observed in a pregnant woman with postthyroidectomy-related hypothyroidism. This case and a review of other cases reported in the literature indicate that higher-than-usual doses of levothyroxine may be required in patients who are taking ritonavir concurrently., (Copyright © 2017 by the American Society of Health-System Pharmacists, Inc. All rights reserved.)

Perennial cellulosic feedstocks may have potential to reduce life-cycle greenhouse gas (GHG) emissions by offsetting fossil fuels. However, this potential depends on meeting a number of important criteria involving land cover change, including avoiding displacement of agricultural production, not reducing uncultivated natural lands that provide biodiversity habitat and other valued ecosystem services, and avoiding the carbon debt (the amount of time needed to repay the initial carbon loss) that accompanies displacing natural lands. It is unclear whether recent agricultural expansion in the United States competes with lands potentially suited for bioenergy feedstocks. Here, we evaluate how recent land cover change (2008-2013) has affected the availability of lands potentially suited for bioenergy feedstock production in the U.S. Lake States (Minnesota, Wisconsin, Michigan) and its impact on other natural ecosystems. The region is potentially well suited for a diversity of bioenergy production systems, both grasses and woody biomass, due to the widespread forest economy in the north and agricultural economy in the south. Based on remotely-sensed data, our results show that between 2008 and 2013, 836,000 ha of non-agricultural open lands were already converted to agricultural uses in the Lake States, a loss of nearly 37%. The greatest relative changes occurred in the southern half that includes some of the most diverse cultivable lands in the country. We use transition diagrams to reveal gross changes that can be obscured if only net change is considered. Our results indicate that expansion of row crops (corn, soybean) was responsible for the majority of open land loss. Even if recently lost open lands were brought into perennial feedstock production, there would a substantial carbon debt. This reduction in open land availability for biomass production is closing the window of opportunity to establish a sustainable cellulosic feedstock economy in the Lake States as mandated by current Federal policy, incurring a substantial GHG debt, and displacing a range of other natural ecosystems and their services.

Tuberculosis (TB) is an infectious disease caused by fastidious pathogen Mycobacterium tuberculosis. TB has emerged as one of the major causes of mortality in the developing world. Role of host genetic factors that modulate disease susceptibility have not been studied widely. Recent studies have reported few genetic loci that provide impetus to this area of research. The availability of tools has enabled genome-wide scans for disease susceptibility loci associated with infectious diseases. Till now, information on human genetic variations and their associated genes that modulate TB susceptibility have not been systematically compiled. In this work, we have created a resource: HGV&TB, which hosts genetic variations reported to be associated with TB susceptibility in humans. It currently houses information on 307 variations in 98 genes. In total, 101 of these variations are exonic, whereas 78 fall in intronic regions. We also analysed the pathogenicity of the genetic variations, their phenotypic consequences and ethnic origin. Using various computational analyses, 30 variations of the 101 exonic variations were predicted to be pathogenic. The resource is freely available at http://genome.igib.res.in/hgvtb/index.html. Using integrative analysis, we have shown that the disease associated variants are selectively enriched in the immune signalling pathways which are crucial in the pathophysiology of TB. Database URL: http://genome.igib.res.in/hgvtb/index.html, (© The Author(s) 2014. Published by Oxford University Press.)

The development of effective measures to stabilize atmospheric CO2 concentration and mitigate negative impacts of climate change requires accurate quantification of the spatial variation and magnitude of the terrestrial carbon (C) flux. However, the spatial pattern and strength of terrestrial C sinks and sources remain uncertain. In this study, we designed a spatially-explicit agroecosystem modeling system by integrating the Environmental Policy Integrated Climate (EPIC) model with multiple sources of geospatial and surveyed datasets (including crop type map, elevation, climate forcing, fertilizer application, tillage type and distribution, and crop planting and harvesting date), and applied it to examine the sensitivity of cropland C flux simulations to two widely used soil databases (i.e. State Soil Geographic-STATSGO of a scale of 1:250,000 and Soil Survey Geographic-SSURGO of a scale of 1:24,000) in Iowa, USA. To efficiently execute numerous EPIC runs resulting from the use of high resolution spatial data (56m), we developed a parallelized version of EPIC. Both STATSGO and SSURGO led to similar simulations of crop yields and Net Ecosystem Production (NEP) estimates at the State level. However, substantial differences were observed at the county and sub-county (grid) levels. In general, the fine resolution SSURGO data outperformed the coarse resolution STATSGO data for county-scale crop-yield simulation, and within STATSGO, the area-weighted approach provided more accurate results. Further analysis showed that spatial distribution and magnitude of simulated NEP were more sensitive to the resolution difference between SSURGO and STATSGO at the county or grid scale. For over 60% of the cropland areas in Iowa, the deviations between STATSGO- and SSURGO-derived NEP were larger than 1MgCha(-1)yr(-1), or about half of the average cropland NEP, highlighting the significant uncertainty in spatial distribution and magnitude of simulated C fluxes resulting from differences in soil data resolution., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Legislation on biofuels production in the USA and Europe is directing food crops towards the production of grain-based ethanol, which can have detrimental consequences for soil carbon sequestration, nitrous oxide emissions, nitrate pollution, biodiversity and human health. An alternative is to grow lignocellulosic (cellulosic) crops on 'marginal' lands. Cellulosic feedstocks can have positive environmental outcomes and could make up a substantial proportion of future energy portfolios. However, the availability of marginal lands for cellulosic feedstock production, and the resulting greenhouse gas (GHG) emissions, remains uncertain. Here we evaluate the potential for marginal lands in ten Midwestern US states to produce sizeable amounts of biomass and concurrently mitigate GHG emissions. In a comparative assessment of six alternative cropping systems over 20 years, we found that successional herbaceous vegetation, once well established, has a direct GHG emissions mitigation capacity that rivals that of purpose-grown crops (-851 ± 46 grams of CO(2) equivalent emissions per square metre per year (gCO(2)e m(-2) yr(-1))). If fertilized, these communities have the capacity to produce about 63 ± 5 gigajoules of ethanol energy per hectare per year. By contrast, an adjacent, no-till corn-soybean-wheat rotation produces on average 41 ± 1 gigajoules of biofuel energy per hectare per year and has a net direct mitigation capacity of -397 ± 32 gCO(2)e m(-2) yr(-1); a continuous corn rotation would probably produce about 62 ± 7 gigajoules of biofuel energy per hectare per year, with 13% less mitigation. We also perform quantitative modelling of successional vegetation on marginal lands in the region at a resolution of 0.4 hectares, constrained by the requirement that each modelled location be within 80 kilometres of a potential biorefinery. Our results suggest that such vegetation could produce about 21 gigalitres of ethanol per year from around 11 million hectares, or approximately 25 per cent of the 2022 target for cellulosic biofuel mandated by the US Energy Independence and Security Act of 2007, with no initial carbon debt nor the indirect land-use costs associated with food-based biofuels. Other regional-scale aspects of biofuel sustainability, such as water quality and biodiversity, await future study.