What is microbial dormancy?

Life can be stressful. One way to deal with stress is to simply wait it out. Microbes do this by entering a state of reduced activity and increased resistance commonly called 'dormancy'. But what is dormancy? Different scientific disciplines emphasize distinct traits and phenotypic ranges in defining dormancy for their microbial species and system-specific questions of interest. Here, we propose a unified definition of microbial dormancy, using a broad framework to place earlier discipline-specific definitions in a new context. We then discuss how this new definition and framework may improve our ability to investigate dormancy using multi-omics tools. Finally, we leverage our framework to discuss the diversity of genomic mechanisms for dormancy in an extreme environment that challenges easy definitions - the permafrost.

US climate policy yields water quality cobenefits in the Mississippi Basin and Gulf of Mexico.

We utilize a coupled economy-agroecology-hydrology modeling framework to capture the cascading impacts of climate change mitigation policy on agriculture and the resulting water quality cobenefits. We analyze a policy that assigns a range of United States government's social cost of carbon estimates ($51, $76, and $152/ton of CO2-equivalents) to fossil fuel-based CO2 emissions. This policy raises energy costs and, importantly for agriculture, boosts the price of nitrogen fertilizer production. At the highest carbon price, US carbon emissions are reduced by about 50%, and nitrogen fertilizer prices rise by about 90%, leading to an approximate 15% reduction in fertilizer applications for corn production across the Mississippi River Basin. Corn and soybean production declines by about 7%, increasing crop prices by 6%, while nitrate leaching declines by about 10%. Simulated nitrate export to the Gulf of Mexico decreases by 8%, ultimately shrinking the average midsummer area of the Gulf of Mexico hypoxic area by 3% and hypoxic volume by 4%. We also consider the additional benefits of restored wetlands to mitigate nitrogen loading to reduce hypoxia in the Gulf of Mexico and find a targeted wetland restoration scenario approximately doubles the effect of a low to moderate social cost of carbon. Wetland restoration alone exhibited spillover effects that increased nitrate leaching in other parts of the basin which were mitigated with the inclusion of the carbon policy. We conclude that a national climate policy aimed at reducing greenhouse gas emissions in the United States would have important water quality cobenefits.

A Decreasing Trend of Nitrous Oxide Emissions From California Cropland From 2000 to 2015.

Mitigation of greenhouse gas emissions from agriculture requires an understanding of spatial-temporal dynamics of nitrous oxide (N2O) emissions. Process-based models can quantify N2O emissions from agricultural soils but have rarely been applied to regions with highly diverse agriculture. In this study, a process-based biogeochemical model, DeNitrification-DeComposition (DNDC), was applied to quantify spatial-temporal dynamics of direct N2O emissions from California cropland employing a wide range of cropping systems. DNDC simulated direct N2O emissions from nitrogen (N) inputs through applications of synthetic fertilizers and crop residues during 2000-2015 by linking the model with a spatial-temporal differentiated database containing data on weather, crop areas, soil properties, and management. Simulated direct N2O emissions ranged from 3,830 to 7,875 tonnes N2O-N yr-1, representing 0.73%-1.21% of the N inputs. N2O emission rates were higher for hay and field crops and lower for orchard and vineyard. State cropland total N2O emissions showed a decreasing trend primarily driven by reductions of cropland area and N inputs, the trend toward growing more orchard, and changes in irrigation. Annual direct N2O emissions declined by 47% from 2000 to 2015. Simulations showed N2O emission variations could be explained not only by cropland area and N fertilizer inputs but also climate, soil properties, and management besides N fertilization. The detailed spatial-temporal emission dynamics and driving factors provide knowledge toward effective N2O mitigation and highlight the importance of coupling process-based models with high-resolution data for characterizing the spatial-temporal variability of N2O emissions in regions with diverse croplands.

How can process-based modeling improve peat CO2 and N2O emission factors for oil palm plantations?

Oil palm plantations on peat and associated drainage generate sizeable GHG emissions. Current IPCC default emission factors (EF) for oil palm on organic soil are based on a very limited number of observations from young plantations, thereby resulting in large uncertainties in emissions estimates. To explore the potential of process-based modeling to refine oil palm peat CO2 and N2O EFs, we simulated peat GHG emissions and biogeophysical variables over 30 years in plantations of Central Kalimantan, Indonesia. The DNDC model simulated well the magnitude of C inputs (litterfall and root mortality) and dynamics of annual heterotrophic respiration and peat decomposition N2O fluxes. The modeled peat onsite CO2-C EF was lower than the IPCC default (11 Mg C ha-1 yr-1) and decreased from 7.7 ± 0.4 Mg C ha-1 yr-1 in the first decade to 3.0 ± 0.2 and 1.8 ± 0.3 Mg C ha-1 yr-1 in the second and third decades of the rotation. The modeled N2O-N EF from peat decomposition was higher than the IPCC default (1.2 kg N ha-1 yr-1) and increased from 3.5 ± 0.3 kg N ha-1 yr-1 in the first decade to 4.7-4.6 ± 0.5 kg N ha-1 yr-1 in the following ones. Modeled fertilizer-induced N2O emissions were minimal and much less than 1.6% of N inputs recommended by the IPCC in wet climates regardless of soil type. Temporal variations in EFs were strongly linked to soil C:N ratio and soil mineral N content for CO2 and fertilizer-induced N2O emissions, and to precipitation, water table level and soil NH4+ content for peat decomposition N2O emissions. These results suggest that current IPCC EFs for oil palm on organic soil could over-estimate peat onsite CO2 emissions and underestimate peat decomposition N2O emissions and that temporal variation in emissions should be considered for further improvement of EFs.

A global urban microwave backscatter time series data set for 1993-2020 using ERS, QuikSCAT, and ASCAT data.

Urban settlements are rapidly growing outward and upward, with consequences for resource use, greenhouse gas emissions, and ecosystem and public health, but rates of change are uneven around the world. Understanding trajectories and predicting consequences of global urban expansion requires quantifying rates of change with consistent, well-calibrated data. Microwave backscatter data provides important information on upward urban growth - essentially the vertical built-up area. We developed a multi-sensor, multi-decadal, gridded (0.05° lat/lon) data set of global urban microwave backscatter, 1993-2020. Comparison of backscatter from two C-band sensors (ERS and ASCAT) and one Ku-band sensor (QuikSCAT) are made at four invariant non-urban sites (~3500 km2) to evaluate instrument stability and multi-decadal pattern. For urban areas, there was a strong linear correlation (overall R2 = 0.69) between 2015 ASCAT urban backscatter and a continental-scale gridded product of building volume, across 8450 urban grid cells (0.05° × 0.05°) in Europe, China, and the USA. This urban backscatter data set provides a time series characterizing global urban change over the past three decades.

Global gridded crop harvested area, production, yield, and monthly physical area data circa 2015.

Here we provide an update to global gridded annual and monthly crop datasets. This new dataset uses the crop categories established by the Global Agro-Ecological Zones (GAEZ) Version 3 model, which is based on the Food and Agricultural Organization of the United Nations (FAO) crop production data. We used publicly available data from the FAOSTAT database as well as GAEZ Version 4 global gridded dataset to generate circa 2015 annual crop harvested area, production, and yields by crop production system (irrigated and rainfed) for 26 crops and crop categories globally at 5-minute resolution. We additionally used available data on crop rotations, cropping intensity, and planting and harvest dates to generate monthly gridded cropland data for physical areas for the 26 crops by production system. These data are in standard georeferenced gridded format, and can be used by any global hydrology, land surface, or other earth system model that requires gridded annual or monthly crop data inputs.

Permafrost thaw driven changes in hydrology and vegetation cover increase trace gas emissions and climate forcing in Stordalen Mire from 1970 to 2014.

Permafrost thaw increases active layer thickness, changes landscape hydrology and influences vegetation species composition. These changes alter belowground microbial and geochemical processes, affecting production, consumption and net emission rates of climate forcing trace gases. Net carbon dioxide (CO2) and methane (CH4) fluxes determine the radiative forcing contribution from these climate-sensitive ecosystems. Permafrost peatlands may be a mosaic of dry frozen hummocks, semi-thawed or perched sphagnum dominated areas, wet permafrost-free sedge dominated sites and open water ponds. We revisited estimates of climate forcing made for 1970 and 2000 for Stordalen Mire in northern Sweden and found the trend of increasing forcing continued into 2014. The Mire continued to transition from dry permafrost to sedge and open water areas, increasing by 100% and 35%, respectively, over the 45-year period, causing the net radiative forcing of Stordalen Mire to shift from negative to positive. This trend is driven by transitioning vegetation community composition, improved estimates of annual CO2 and CH4 exchange and a 22% increase in the IPCC's 100-year global warming potential (GWP_100) value for CH4. These results indicate that discontinuous permafrost ecosystems, while still remaining a net overall sink of C, can become a positive feedback to climate change on decadal timescales. This article is part of a discussion meeting issue 'Rising methane: is warming feeding warming? (part 2)'.

Improving a Biogeochemical Model to Simulate Microbial-Mediated Carbon Dynamics in Agricultural Ecosystems.

Soil microbes drive decomposition of soil organic matter (SOM) and regulate soil carbon (C) dynamics. Process-based models have been developed to quantify changes in soil organic carbon (SOC) and carbon dioxide (CO2) fluxes in agricultural ecosystems. However, microbial processes related to SOM decomposition have not been, or are inadequately, represented in these models, limiting predictions of SOC responses to changes in microbial activities. In this study, we developed a microbial-mediated decomposition model based on a widely used biogeochemical model, DeNitrification-DeComposition (DNDC), to simulate C dynamics in agricultural ecosystems. The model simulates organic matter decomposition, soil respiration, and SOC formation by simulating microbial and enzyme dynamics and their controls on decomposition, and considering impacts of climate, soil, crop, and farming management practices (FMPs) on C dynamics. When evaluated against field observations of net ecosystem CO2 exchange (NEE) and SOC change in two winter wheat systems, the model successfully captured both NEE and SOC changes under different FMPs. Inclusion of microbial processes improved the model's performance in simulating peak CO2 fluxes induced by residue return, primarily by capturing priming effects of residue inputs. We also investigated impacts of microbial physiology, SOM, and FMPs on soil C dynamics. Our results demonstrated that residue or manure input drove microbial activity and predominantly regulated the CO2 fluxes, and manure amendment largely regulated long-term SOC change. The microbial physiology had considerable impacts on the microbial activities and soil C dynamics, emphasizing the necessity of considering microbial physiology and activities when assessing soil C dynamics in agricultural ecosystems.

Exploring diurnal cycles of surface urban heat island intensity in Boston with land surface temperature data derived from GOES-R geostationary satellites.

The surface urban heat island (SUHI) is one of the most significant human-induced alterations to the Earth's surface climate and can aggravate health risks for city dwellers during heat waves. Although the SUHI effect has received growing attention, its diurnal cycles (i.e., the variations over the full 24 h within the diel cycle) are poorly understood because polar-orbiting satellites (e.g., Landsat Series, Sentinel, Terra, Aqua) only provide one or two observations over each repeat cycle (e.g., 16 days) with constant overpass time for the same area. Geostationary satellites provide high-frequency land surface temperature (LST) observations throughout the day and the night, and thereby offer unprecedented opportunities for exploring the diurnal cycles of SUHI. Here we examined how the SUHI intensity varied over the course of the diurnal cycle in the Boston Metropolitan Area using LST observations from the NOAA's latest generation of Geostationary Operational Environmental Satellites (GOES-R). GOES-R LST was strongly correlated with MODIS LST (R2 = 0.98, p < 0.0001) across urban core, suburban, and rural areas. We calculated the SUHI intensity at an hourly time step for both the urban core and suburban areas using GOES-R LST data. The maximum SUHI intensity for the urban core occurred near noon, and was +3.0 °C (12:00), +5.4 °C (12:00), +4.9 °C (11:00), and +3.7 °C (12:00) in winter, spring, summer, and autumn, respectively. The maximum intensity for the suburban area was about 3.0 °C lower in spring and summer and 2.0 °C lower in autumn and winter than that of the urban-core area. The minimum SUHI intensity occurred at nighttime, and ranged from -1.0 °C to +1.0 °C. The difference in the nighttime SUHI intensity between urban core and suburban area was insignificant for all seasons except the summer. The SUHI intensity showed similar diurnal variations across the seasons. Throughout the year, the maximum SUHI intensity (+2.7-+5.8 °C) at the urban core occurred at 11:00-14:00 (local time), while the minimum SUHI intensity (-0.6-+0.9 °C) was commonly observed at 00:00-07:00 and 17:00-23:00. We also found different relationships between SUHI intensity and potential drivers within a diurnal cycle, characterized by the strongest correlation with impervious surface area and population size during the middle of the day, and with tree canopy cover at night. Our research highlights the great potential of the new-generation geostationary satellites in revealing the detailed diurnal variations of SUHI. Our findings have implications for informing urban planning and public health risk management.

Publisher Correction: Tracking vegetation phenology across diverse biomes using Version 2.0 of the PhenoCam Dataset.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Tracking vegetation phenology across diverse biomes using Version 2.0 of the PhenoCam Dataset.

Monitoring vegetation phenology is critical for quantifying climate change impacts on ecosystems. We present an extensive dataset of 1783 site-years of phenological data derived from PhenoCam network imagery from 393 digital cameras, situated from tropics to tundra across a wide range of plant functional types, biomes, and climates. Most cameras are located in North America. Every half hour, cameras upload images to the PhenoCam server. Images are displayed in near-real time and provisional data products, including timeseries of the Green Chromatic Coordinate (Gcc), are made publicly available through the project web page ( https://phenocam.sr.unh.edu/webcam/gallery/ ). Processing is conducted separately for each plant functional type in the camera field of view. The PhenoCam Dataset v2.0, described here, has been fully processed and curated, including outlier detection and expert inspection, to ensure high quality data. This dataset can be used to validate satellite data products, to evaluate predictions of land surface models, to interpret the seasonality of ecosystem-scale CO2 and H2O flux data, and to study climate change impacts on the terrestrial biosphere.

21st-century modeled permafrost carbon emissions accelerated by abrupt thaw beneath lakes.

Permafrost carbon feedback (PCF) modeling has focused on gradual thaw of near-surface permafrost leading to enhanced carbon dioxide and methane emissions that accelerate global climate warming. These state-of-the-art land models have yet to incorporate deeper, abrupt thaw in the PCF. Here we use model data, supported by field observations, radiocarbon dating, and remote sensing, to show that methane and carbon dioxide emissions from abrupt thaw beneath thermokarst lakes will more than double radiative forcing from circumpolar permafrost-soil carbon fluxes this century. Abrupt thaw lake emissions are similar under moderate and high representative concentration pathways (RCP4.5 and RCP8.5), but their relative contribution to the PCF is much larger under the moderate warming scenario. Abrupt thaw accelerates mobilization of deeply frozen, ancient carbon, increasing 14C-depleted permafrost soil carbon emissions by ~125-190% compared to gradual thaw alone. These findings demonstrate the need to incorporate abrupt thaw processes in earth system models for more comprehensive projection of the PCF this century.

Genome-centric view of carbon processing in thawing permafrost.

As global temperatures rise, large amounts of carbon sequestered in permafrost are becoming available for microbial degradation. Accurate prediction of carbon gas emissions from thawing permafrost is limited by our understanding of these microbial communities. Here we use metagenomic sequencing of 214 samples from a permafrost thaw gradient to recover 1,529 metagenome-assembled genomes, including many from phyla with poor genomic representation. These genomes reflect the diversity of this complex ecosystem, with genus-level representatives for more than sixty per cent of the community. Meta-omic analysis revealed key populations involved in the degradation of organic matter, including bacteria whose genomes encode a previously undescribed fungal pathway for xylose degradation. Microbial and geochemical data highlight lineages that correlate with the production of greenhouse gases and indicate novel syntrophic relationships. Our findings link changing biogeochemistry to specific microbial lineages involved in carbon processing, and provide key information for predicting the effects of climate change on permafrost systems.

Host-linked soil viral ecology along a permafrost thaw gradient.

Climate change threatens to release abundant carbon that is sequestered at high latitudes, but the constraints on microbial metabolisms that mediate the release of methane and carbon dioxide are poorly understood1-7. The role of viruses, which are known to affect microbial dynamics, metabolism and biogeochemistry in the oceans8-10, remains largely unexplored in soil. Here, we aimed to investigate how viruses influence microbial ecology and carbon metabolism in peatland soils along a permafrost thaw gradient in Sweden. We recovered 1,907 viral populations (genomes and large genome fragments) from 197 bulk soil and size-fractionated metagenomes, 58% of which were detected in metatranscriptomes and presumed to be active. In silico predictions linked 35% of the viruses to microbial host populations, highlighting likely viral predators of key carbon-cycling microorganisms, including methanogens and methanotrophs. Lineage-specific virus/host ratios varied, suggesting that viral infection dynamics may differentially impact microbial responses to a changing climate. Virus-encoded glycoside hydrolases, including an endomannanase with confirmed functional activity, indicated that viruses influence complex carbon degradation and that viral abundances were significant predictors of methane dynamics. These findings suggest that viruses may impact ecosystem function in climate-critical, terrestrial habitats and identify multiple potential viral contributions to soil carbon cycling.

A radiative forcing analysis of tropical peatlands before and after their conversion to agricultural plantations.

The tropical peat swamp forests of South-East Asia are being rapidly converted to agricultural plantations of oil palm and Acacia creating a significant global "hot-spot" for CO2 emissions. However, the effect of this major perturbation has yet to be quantified in terms of global warming potential (GWP) and the Earth's radiative budget. We used a GWP analysis and an impulse-response model of radiative forcing to quantify the climate forcing of this shift from a long-term carbon sink to a net source of greenhouse gases (CO2 and CH4 ). In the GWP analysis, five tropical peatlands were sinks in terms of their CO2 equivalent fluxes while they remained undisturbed. However, their drainage and conversion to oil palm and Acacia plantations produced a dramatic shift to very strong net CO2 -equivalent sources. The induced losses of peat carbon are ~20× greater than the natural CO2 sequestration rates. In contrast, a radiative forcing model indicates that the magnitude of this shift from a net cooling to warming effect is ultimately related to the size of an individual peatland's carbon pool. The continuous accumulation of carbon in pristine tropical peatlands produced a progressively negative radiative forcing (i.e., cooling) that ranged from -2.1 to -6.7 nW/m2 per hectare peatland by 2010 CE, referenced to zero at the time of peat initiation. Peatland conversion to plantations leads to an immediate shift from negative to positive trend in radiative forcing (i.e., warming). If drainage persists, peak warming ranges from +3.3 to +8.7 nW/m2 per hectare of drained peatland. More importantly, this net warming impact on the Earth's radiation budget will persist for centuries to millennia after all the peat has been oxidized to CO2 . This previously unreported and undesirable impact on the Earth's radiative balance provides a scientific rationale for conserving tropical peatlands in their pristine state.

Methanotrophy across a natural permafrost thaw environment.

The fate of carbon sequestered in permafrost is a key concern for future global warming as this large carbon stock is rapidly becoming a net methane source due to widespread thaw. Methane release from permafrost is moderated by methanotrophs, which oxidise 20-60% of this methane before emission to the atmosphere. Despite the importance of methanotrophs to carbon cycling, these microorganisms are under-characterised and have not been studied across a natural permafrost thaw gradient. Here, we examine methanotroph communities from the active layer of a permafrost thaw gradient in Stordalen Mire (Abisko, Sweden) spanning three years, analysing 188 metagenomes and 24 metatranscriptomes paired with in situ biogeochemical data. Methanotroph community composition and activity varied significantly as thaw progressed from intact permafrost palsa, to partially thawed bog and fully thawed fen. Thirteen methanotroph population genomes were recovered, including two novel genomes belonging to the uncultivated upland soil cluster alpha (USCα) group and a novel potentially methanotrophic Hyphomicrobiaceae. Combined analysis of porewater δ13C-CH4 isotopes and methanotroph abundances showed methane oxidation was greatest below the oxic-anoxic interface in the bog. These results detail the direct effect of thaw on autochthonous methanotroph communities, and their consequent changes in population structure, activity and methane moderation potential.

Intercomparison of phenological transition dates derived from the PhenoCam Dataset V1.0 and MODIS satellite remote sensing.

Phenology is a valuable diagnostic of ecosystem health, and has applications to environmental monitoring and management. Here, we conduct an intercomparison analysis using phenological transition dates derived from near-surface PhenoCam imagery and MODIS satellite remote sensing. We used approximately 600 site-years of data, from 128 camera sites covering a wide range of vegetation types and climate zones. During both "greenness rising" and "greenness falling" transition phases, we found generally good agreement between PhenoCam and MODIS transition dates for agricultural, deciduous forest, and grassland sites, provided that the vegetation in the camera field of view was representative of the broader landscape. The correlation between PhenoCam and MODIS transition dates was poor for evergreen forest sites. We discuss potential reasons (including sub-pixel spatial heterogeneity, flexibility of the transition date extraction method, vegetation index sensitivity in evergreen systems, and PhenoCam geolocation uncertainty) for varying agreement between time series of vegetation indices derived from PhenoCam and MODIS imagery. This analysis increases our confidence in the ability of satellite remote sensing to accurately characterize seasonal dynamics in a range of ecosystems, and provides a basis for interpreting those dynamics in the context of tangible phenological changes occurring on the ground.

Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery.

Vegetation phenology controls the seasonality of many ecosystem processes, as well as numerous biosphere-atmosphere feedbacks. Phenology is also highly sensitive to climate change and variability. Here we present a series of datasets, together consisting of almost 750 years of observations, characterizing vegetation phenology in diverse ecosystems across North America. Our data are derived from conventional, visible-wavelength, automated digital camera imagery collected through the PhenoCam network. For each archived image, we extracted RGB (red, green, blue) colour channel information, with means and other statistics calculated across a region-of-interest (ROI) delineating a specific vegetation type. From the high-frequency (typically, 30 min) imagery, we derived time series characterizing vegetation colour, including "canopy greenness", processed to 1- and 3-day intervals. For ecosystems with one or more annual cycles of vegetation activity, we provide estimates, with uncertainties, for the start of the "greenness rising" and end of the "greenness falling" stages. The database can be used for phenological model validation and development, evaluation of satellite remote sensing data products, benchmarking earth system models, and studies of climate change impacts on terrestrial ecosystems.

Evaluating multiple causes of persistent low microwave backscatter from Amazon forests after the 2005 drought.

Amazonia has experienced large-scale regional droughts that affect forest productivity and biomass stocks. Space-borne remote sensing provides basin-wide data on impacts of meteorological anomalies, an important complement to relatively limited ground observations across the Amazon's vast and remote humid tropical forests. Morning overpass QuikScat Ku-band microwave backscatter from the forest canopy was anomalously low during the 2005 drought, relative to the full instrument record of 1999-2009, and low morning backscatter persisted for 2006-2009, after which the instrument failed. The persistent low backscatter has been suggested to be indicative of increased forest vulnerability to future drought. To better ascribe the cause of the low post-drought backscatter, we analyzed multiyear, gridded remote sensing data sets of precipitation, land surface temperature, forest cover and forest cover loss, and microwave backscatter over the 2005 drought region in the southwestern Amazon Basin (4°-12°S, 66°-76°W) and in adjacent 8°x10° regions to the north and east. We found moderate to weak correlations with the spatial distribution of persistent low backscatter for variables related to three groups of forest impacts: the 2005 drought itself, loss of forest cover, and warmer and drier dry seasons in the post-drought vs. the pre-drought years. However, these variables explained only about one quarter of the variability in depressed backscatter across the southwestern drought region. Our findings indicate that drought impact is a complex phenomenon and that better understanding can only come from more extensive ground data and/or analysis of frequent, spatially-comprehensive, high-resolution data or imagery before and after droughts.

A global view of shifting cultivation: Recent, current, and future extent.

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.