Direct human influence on atmospheric CO2 seasonality from increased cropland productivity

Multiple spatial scales of bacterial and fungal structural and functional traits affect carbon mineralization

Studying the functional heterogeneity of soil microorganisms at different spatial scales and linking it to soil carbon mineralization is crucial for predicting the response of soil carbon stability to environmental changes and human disturbance. Here, a total of 429 soil samples were collected from typical paddy fields in China, and the bacterial and fungal communities as well as functional genes related to carbon mineralization in the soil were analysed using MiSeq sequencing and GeoChip gene microarray technology. We postulate that CO2 emissions resulting from bacterial and fungal carbon mineralization are contingent upon their respective carbon consumption strategies, which rely on the regulation of interactions between biodiversity and functional genes. Our results showed that the spatial turnover of the fungal community was 2-4 times that of the bacterial community from hundreds of meters to thousands of kilometres. The effect of spatial scale exerted a greater impact on the composition rather than the functional characteristics of the microbial community. Furthermore, based on the establishment of functional networks at different spatial scales, we observed that both bacteria and fungi within the top 10 taxa associated with carbon mineralization exhibited a prevalence of generalist species at the regional scale. This study emphasizes the significance of spatial scaling patterns in soil bacterial and fungal carbon degradation functions, deepening our understanding of how the relationship between microbial decomposers and soil heterogeneity impacts carbon mineralization and subsequent greenhouse gas emissions.

Wildfire-induced increases in photosynthesis in boreal forest ecosystems of North America

Observations of the annual cycle of atmospheric CO2 in high northern latitudes provide evidence for an increase in terrestrial metabolism in Arctic tundra and boreal forest ecosystems. However, the mechanisms driving these changes are not yet fully understood. One proposed hypothesis is that ecological change from disturbance, such as wildfire, could increase the magnitude and change the phase of net ecosystem exchange through shifts in plant community composition. Yet, little quantitative work has evaluated this potential mechanism at a regional scale. Here we investigate how fire disturbance influences landscape-level patterns of photosynthesis across western boreal North America. We use Alaska and Canadian large fire databases to identify the perimeters of wildfires, a Landsat-derived land cover time series to characterize plant functional types (PFTs), and solar-induced fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2) as a proxy for photosynthesis. We analyze these datasets to characterize post-fire changes in plant succession and photosynthetic activity using a space-for-time approach. We find that increases in herbaceous and sparse vegetation, shrub, and deciduous broadleaf forest PFTs during mid-succession yield enhancements in SIF by 8-40% during June and July for 2- to 59-year stands relative to pre-fire controls. From the analysis of post-fire land cover changes within individual ecoregions and modeling, we identify two mechanisms by which fires contribute to long-term trends in SIF. First, increases in annual burning are shifting the stand age distribution, leading to increases in the abundance of shrubs and deciduous broadleaf forests that have considerably higher SIF during early- and mid-summer. Second, fire appears to facilitate a long-term shift from evergreen conifer to broadleaf deciduous forest in the Boreal Plain ecoregion. These findings suggest that increasing fire can contribute substantially to positive trends in seasonal CO2 exchange without a close coupling to long-term increases in carbon storage.

Greening of human-dominated ecosystems in India

Satellite data show the Earth has been greening and identify croplands in India as one of the most prominent greening hotspots. Though India's agriculture has been dependent on irrigation enhancement to reduce crop water stress and increase production, the spatiotemporal dynamics of how irrigation influenced the satellite observed greenness remains unclear. Here, we use satellite-derived leaf area data and survey-based agricultural statistics together with results from state-of-the-art Land Surface Models (LSM) to investigate the role of irrigation in the greening of India's croplands. We find that satellite observations provide multiple lines of evidence showing strong contributions of irrigation to significant greening during dry season and in drier environments. The national statistics support irrigation-driven yield enhancement and increased dry season cropping intensity. These suggest a continuous shift in India's agriculture toward an irrigation-driven dry season cropping system and confirm the importance of land management in the greening phenomenon. However, the LSMs identify CO2 fertilization as a primary driver of greening whereas land use and management have marginal impacts on the simulated leaf area changes. This finding urges a closer collaboration of the modeling, Earth observation, and land system science communities to improve representation of land management in the Earth system modeling.

Monitoring nature's calendar from space: Emerging topics in land surface phenology and associated opportunities for science applications

Vegetation phenology has been viewed as the nature's calendar and an integrative indicator of plant-climate interactions. The correct representation of vegetation phenology is important for models to accurately simulate the exchange of carbon, water, and energy between the vegetated land surface and the atmosphere. Remote sensing has advanced the monitoring of vegetation phenology by providing spatially and temporally continuous data that together with conventional ground observations offers a unique contribution to our knowledge about the environmental impact on ecosystems as well as the ecological adaptations and feedback to global climate change. Land surface phenology (LSP) is defined as the use of satellites to monitor seasonal dynamics in vegetated land surfaces and to estimate phenological transition dates. LSP, as an interdisciplinary subject among remote sensing, ecology, and biometeorology, has undergone rapid development over the past few decades. Recent advances in sensor technologies, as well as data fusion techniques, have enabled novel phenology retrieval algorithms that refine phenology details at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. As such, here we summarize the recent advances in LSP and the associated opportunities for science applications. We focus on the remaining challenges, promising techniques, and emerging topics that together we believe will truly form the very frontier of the global LSP research field.

Enhance seasonal amplitude of atmospheric CO2 by the changing Southern Ocean carbon sink

The enhanced seasonal amplitude of atmospheric CO₂ has been viewed so far primarily as a Northern Hemisphere phenomenon. Yet, analyses of atmospheric CO₂ records from 49 stations between 1980 and 2018 reveal substantial trends and variations in this amplitude globally. While no significant trends can be discerned before 2000 in most places, strong positive trends emerge after 2000 in the southern high latitudes. Using factorial simulations with an atmospheric transport model and analyses of surface ocean Pco₂ observations, we show that the increase is best explained by the onset of increasing seasonality of air-sea CO₂ exchange over the Southern Ocean around 2000. Underlying these changes is the long-term ocean acidification trend that tends to enhance the seasonality of the air-sea fluxes, but this trend is modified by the decadal variability of the Southern Ocean carbon sink. The seasonal variations of atmospheric CO₂ thus emerge as a sensitive recorder of the variations of the Southern Ocean carbon sink.

Impact of Changing Winds on the Mauna Loa CO2 Seasonal Cycle in Relation to the Pacific Decadal Oscillation

Long-term measurements at the Mauna Loa Observatory (MLO) show that the CO₂ seasonal cycle amplitude (SCA) increased from 1959 to 2019 at an overall rate of 0.22  ±  0.034 ppm decade^-1 while also varying on interannual to decadal time scales. These SCA changes are a signature of changes in land ecological CO₂ fluxes as well as shifting winds. Simulations with the TM3 tracer transport model and CO₂ fluxes from the Jena CarboScope CO₂ Inversion suggest that shifting winds alone have contributed to a decrease in SCA of -0.10  ±  0.022 ppm decade^-1 from 1959 to 2019, partly offsetting the observed long-term SCA increase associated with enhanced ecosystem net primary production. According to these simulations and MIROC-ACTM simulations, the shorter-term variability of MLO SCA is nearly equally driven by varying ecological CO₂ fluxes (49%) and varying winds (51%). We also show that the MLO SCA is strongly correlated with the Pacific Decadal Oscillation (PDO) due to varying winds, as well as with a closely related wind index (U-PDO). Since 1980, 44% of the wind-driven SCA decrease has been tied to a secular trend in the U-PDO, which is associated with a progressive weakening of westerly winds at 700 mbar over the central Pacific from 20°N to 40°N. Similar impacts of varying winds on the SCA are seen in simulations at other low-latitude Pacific stations, illustrating the difficulty of constraining trend and variability of land CO₂ fluxes using observations from low latitudes due to the complexity of circulation changes.

Elevational Gradient of Climate-Driving Effects on Cropland Ecosystem Net Primary Productivity in Alpine Region of the Southwest China

Investigating elevational gradient of climate driving effects on cropland ecosystem net primary productivity (NPP) plays an important role in food security in alpine region. We simulated cropland NPP by coupling a remote sensing model with an ecosystem process model and explored elevational gradient of climate driving effects on it in an alpine region of the southwest China during 1981–2014. The results showed that cropland NPP increased significantly with a rate of 3.85 gC m−2 year−1 year−1 under significant increasing solar radiation and climate warming and drying, among which the increasing solar radiation was the main driving factor of the increasing NPP. The driving effect of climate warming on cropland NPP shifted from negative at low elevations to positive at high elevations, which was caused by the fragile ecosystem characteristics and frequent drought at low elevations and a higher temperature sensitivity of cropland ecosystem at high elevations. Different effects of climate warming on NPP change at different elevations caused different results when we analyzed the climate-driving effects on cropland NPP at different spatial scales. These results reminded us that we should take the elevational gradient of climate driving effects into account when we manage food security in the alpine region.

Fusing Geostationary Satellite Observations with Harmonized Landsat-8 and Sentinel-2 Time Series for Monitoring Field-Scale Land Surface Phenology

Accurate and timely land surface phenology (LSP) provides essential information for investigating the responses of terrestrial ecosystems to climate changes and quantifying carbon and surface energy cycles on the Earth. LSP has been widely investigated using daily Visible Infrared Imaging Radiometer Suite (VIIRS) or Moderate Resolution Imaging Spectroradiometer (MODIS) observations, but the resultant phenometrics are frequently influenced by surface heterogeneity and persistent cloud contamination in the time series observations. Recently, LSP has been derived from Landsat-8 and Sentinel-2 time series providing detailed spatial pattern, but the results are of high uncertainties because of poor temporal resolution. With the availability of data from Advanced Baseline Imager (ABI) onboard a new generation of geostationary satellites that observe the earth every 10–15 min, daily cloud-free time series could be obtained with high opportunities. Therefore, this study investigates the generation of synthetic high spatiotemporal resolution time series by fusing the harmonized Landsat-8 and Sentinel-2 (HLS) time series with the temporal shape of ABI data for monitoring field-scale (30 m) LSP. The algorithm is verified by detecting the timings of greenup and senescence onsets around north Wisconsin/Michigan states, United States, where cloud cover is frequent during spring rainy season. The LSP detections from HLS-ABI are compared with those from HLS or ABI alone and are further evaluated using PhenoCam observations. The result indicates that (1) ABI could provide ~3 times more high-quality observations than HLS around spring greenup onset; (2) the greenup and senescence onsets derived from ABI and HLS-ABI are spatially consistent and statistically comparable with a median difference less than 1 and 10-days, respectively; (3) greenup and senescence onsets derived from HLS data show sharp boundaries around the orbit-overlapped areas and shifts of ~13 days delay and ~15 days ahead, respectively, relative to HLS-ABI detections; and (4) HLS-ABI greenup and senescence onsets align closely to PhenoCam observations with an absolute average difference of less than 2 days and 5 days, respectively, which are much better than phenology detections from ABI or HLS alone. The result suggests that the proposed approach could be implemented the monitor of 30 m LSP over regions with persistent cloud cover.

COS-derived GPP relationships with temperature and light help explain high-latitude atmospheric CO2 seasonal cycle amplification

In the Arctic and Boreal region (ABR) where warming is especially pronounced, the increase of gross primary production (GPP) has been suggested as an important driver for the increase of the atmospheric CO₂ seasonal cycle amplitude (SCA). However, the role of GPP relative to changes in ecosystem respiration (ER) remains unclear, largely due to our inability to quantify these gross fluxes on regional scales. Here, we use atmospheric carbonyl sulfide (COS) measurements to provide observation-based estimates of GPP over the North American ABR. Our annual GPP estimate is 3.6 (2.4 to 5.5) PgC · y^-1 between 2009 and 2013, the uncertainty of which is smaller than the range of GPP estimated from terrestrial ecosystem models (1.5 to 9.8 PgC · y^-1). Our COS-derived monthly GPP shows significant correlations in space and time with satellite-based GPP proxies, solar-induced chlorophyll fluorescence, and near-infrared reflectance of vegetation. Furthermore, the derived monthly GPP displays two different linear relationships with soil temperature in spring versus autumn, whereas the relationship between monthly ER and soil temperature is best described by a single quadratic relationship throughout the year. In spring to midsummer, when GPP is most strongly correlated with soil temperature, our results suggest the warming-induced increases of GPP likely exceeded the increases of ER over the past four decades. In autumn, however, increases of ER were likely greater than GPP due to light limitations on GPP, thereby enhancing autumn net carbon emissions. Both effects have likely contributed to the atmospheric CO₂ SCA amplification observed in the ABR.

Predicting carbon and water vapor fluxes using machine learning and novel feature ranking algorithms

Gap-filling eddy covariance flux data using quantitative approaches has increased over the past decade. Numerous methods have been proposed previously, including look-up table approaches, parametric methods, process-based models, and machine learning. Particularly, the REddyProc package from the Max Planck Institute for Biogeochemistry and ONEFlux package from AmeriFlux have been widely used in many studies. However, there is no consensus regarding the optimal model and feature selection method that could be used for predicting different flux targets (Net Ecosystem Exchange, NEE; or Evapotranspiration -ET), due to the limited systematic comparative research based on the identical site-data. Here, we compared NEE and ET gap-filling/prediction performance of the least-square-based linear model, artificial neural network, random forest (RF), and support vector machine (SVM) using data obtained from four major row-crop and forage agroecosystems located in the subtropical or the climate-transition zones in the US. Additionally, we tested the impacts of different training-testing data partitioning settings, including a 10-fold time-series sequential (10FTS), a 10-fold cross validation (CV) routine with single data point (10FCV), daily (10FCVD), weekly (10FCVW) and monthly (10FCVM) gap length, and a 7/14-day flanking window (FW) approach; and implemented a novel Sliced Inverse Regression-based Recursive Feature Elimination algorithm (SIRRFE). We benchmarked the model performance against REddyProc and ONEFlux-produced results. Our results indicated that accurate NEE and ET prediction models could be systematically constructed using SVM/RF and only a few top informative features. The gap-filling performance of ONEFlux is generally satisfactory (R² = 0.39-0.71), but results from REddyProc could be very limited or even unreliable in many cases (R² = 0.01-0.67). Overall, SIRRFE-refined SVM models yielded excellent results for predicting NEE (R² = 0.46-0.92) and ET (R² = 0.74-0.91). Finally, the performance of various models was greatly affected by the types of ecosystem, predicting targets, and training algorithms; but was insensitive towards training-testing partitioning. Our research provided more insights into constructing novel gap-filling models and understanding the underlying drivers affecting boundary layer carbon/water fluxes on an ecosystem level.

Effects of elevated CO2 on resistant and susceptible rice cultivar and its primary host, brown planthopper (BPH), Nilaparvata lugens (Stål)

The elevated CO2 (eCO2) has positive response on plant growth and negative response on insect pests. As a contemplation, the feeding pattern of the brown plant hopper, Nilaparvata lugens Stål on susceptible and resistant rice cultivars and their growth rates exposed to eCO2 conditions were analyzed. The eCO2 treatment showed significant differences in percentage of emergence and rice biomass that were consistent across the rice cultivars, when compared to the ambient conditions. Similarly, increase in carbon and decrese in nitrogen ratio of leaves and alterations in defensive peroxidase enzyme levels were observed, but was non-linear among the cultivars tested. Lower survivorship and nutritional indices of N. lugens were observed in conditions of eCO2 levels over ambient conditions. Results were nonlinear in manner. We conclude that the plant carbon accumulation increased due to eCO2, causing physiological changes that decreased nitrogen content. Similarly, eCO2 increased insect feeding, and did alter other variables such as their biology or reproduction.

Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO2

Atmospheric carbon dioxide concentration ([CO₂ ]) is increasing, which increases leaf-scale photosynthesis and intrinsic water-use efficiency. These direct responses have the potential to increase plant growth, vegetation biomass, and soil organic matter; transferring carbon from the atmosphere into terrestrial ecosystems (a carbon sink). A substantial global terrestrial carbon sink would slow the rate of [CO₂ ] increase and thus climate change. However, ecosystem CO₂ responses are complex or confounded by concurrent changes in multiple agents of global change and evidence for a [CO₂ ]-driven terrestrial carbon sink can appear contradictory. Here we synthesize theory and broad, multidisciplinary evidence for the effects of increasing [CO₂ ] (iCO₂ ) on the global terrestrial carbon sink. Evidence suggests a substantial increase in global photosynthesis since pre-industrial times. Established theory, supported by experiments, indicates that iCO₂ is likely responsible for about half of the increase. Global carbon budgeting, atmospheric data, and forest inventories indicate a historical carbon sink, and these apparent iCO₂ responses are high in comparison to experiments and predictions from theory. Plant mortality and soil carbon iCO₂ responses are highly uncertain. In conclusion, a range of evidence supports a positive terrestrial carbon sink in response to iCO₂ , albeit with uncertain magnitude and strong suggestion of a role for additional agents of global change.

Siberian and temperate ecosystems shape Northern Hemisphere atmospheric CO2 seasonal amplification

The amplitude of the atmospheric CO2 seasonal cycle has increased by 30 to 50% in the Northern Hemisphere (NH) since the 1960s, suggesting widespread ecological changes in the northern extratropics. However, substantial uncertainty remains in the continental and regional drivers of this prominent amplitude increase. Here we present a quantitative regional attribution of CO2 seasonal amplification over the past 4 decades, using a tagged atmospheric transport model prescribed with observationally constrained fluxes. We find that seasonal flux changes in Siberian and temperate ecosystems together shape the observed amplitude increases in the NH. At the surface of northern high latitudes, enhanced seasonal carbon exchange in Siberia is the dominant contributor (followed by temperate ecosystems). Arctic-boreal North America shows much smaller changes in flux seasonality and has only localized impacts. These continental contrasts, based on an atmospheric approach, corroborate heterogeneous vegetation greening and browning trends from field and remote-sensing observations, providing independent evidence for regionally divergent ecological responses and carbon dynamics to global change drivers. Over surface midlatitudes and throughout the midtroposphere, increased seasonal carbon exchange in temperate ecosystems is the dominant contributor to CO2 amplification, albeit with considerable contributions from Siberia. Representing the mechanisms that control the high-latitude asymmetry in flux amplification found in this study should be an important goal for mechanistic land surface models moving forward.

Causes of slowing-down seasonal CO2 amplitude at Mauna Loa

Changing amplitude of the seasonal cycle of atmospheric CO₂ (SCA) in the northern hemisphere is an emerging carbon cycle property. Mauna Loa (MLO) station (20°N, 156°W), which has the longest continuous northern hemisphere CO₂ record, shows an increasing SCA before the 1980s (p < .01), followed by no significant change thereafter. We analyzed the potential driving factors of SCA slowing-down, with an ensemble of dynamic global vegetation models (DGVMs) coupled with an atmospheric transport model. We found that slowing-down of SCA at MLO is primarily explained by response of net biome productivity (NBP) to climate change, and by changes in atmospheric circulations. Through NBP, climate change increases SCA at MLO before the 1980s and decreases it afterwards. The effect of climate change on the slowing-down of SCA at MLO is mainly exerted by intensified drought stress acting to offset the acceleration driven by CO₂ fertilization. This challenges the view that CO₂ fertilization is the dominant cause of emergent SCA trends at northern sites south of 40°N. The contribution of agricultural intensification on the deceleration of SCA at MLO was elusive according to land-atmosphere CO₂ flux estimated by DGVMs and atmospheric inversions. Our results also show the necessity to adequately account for changing circulation patterns in understanding carbon cycle dynamics observed from atmospheric observations and in using these observations to benchmark DGVMs.

Harmonized global maps of above and belowground biomass carbon density in the year 2010

Remotely sensed biomass carbon density maps are widely used for myriad scientific and policy applications, but all remain limited in scope. They often only represent a single vegetation type and rarely account for carbon stocks in belowground biomass. To date, no global product integrates these disparate estimates into an all-encompassing map at a scale appropriate for many modelling or decision-making applications. We developed an approach for harmonizing vegetation-specific maps of both above and belowground biomass into a single, comprehensive representation of each. We overlaid input maps and allocated their estimates in proportion to the relative spatial extent of each vegetation type using ancillary maps of percent tree cover and landcover, and a rule-based decision schema. The resulting maps consistently and seamlessly report biomass carbon density estimates across a wide range of vegetation types in 2010 with quantified uncertainty. They do so for the globe at an unprecedented 300-meter spatial resolution and can be used to more holistically account for diverse vegetation carbon stocks in global analyses and greenhouse gas inventories.

Higher than expected CO2 fertilization inferred from leaf to global observations

Several lines of evidence point to an increase in the activity of the terrestrial biosphere over recent decades, impacting the global net land carbon sink (NLS) and its control on the growth of atmospheric carbon dioxide (ca ). Global terrestrial gross primary production (GPP)-the rate of carbon fixation by photosynthesis-is estimated to have risen by (31 ± 5)% since 1900, but the relative contributions of different putative drivers to this increase are not well known. Here we identify the rising atmospheric CO2 concentration as the dominant driver. We reconcile leaf-level and global atmospheric constraints on trends in modeled biospheric activity to reveal a global CO2 fertilization effect on photosynthesis of 30% since 1900, or 47% for a doubling of ca above the pre-industrial level. Our historic value is nearly twice as high as current estimates (17 ± 4)% that do not use the full range of available constraints. Consequently, under a future low-emission scenario, we project a land carbon sink (174 PgC, 2006-2099) that is 57 PgC larger than if a lower CO2 fertilization effect comparable with current estimates is assumed. These findings suggest a larger beneficial role of the land carbon sink in modulating future excess anthropogenic CO2 consistent with the target of the Paris Agreement to stay below 2°C warming, and underscore the importance of preserving terrestrial carbon sinks.

Evidence of Carbon Uptake Associated with Vegetation Greening Trends in Eastern China

Persistent and widespread increase of vegetation cover, identified as greening, has been observed in areas of the planet over late 20th century and early 21st century by satellite-derived vegetation indices. It is difficult to verify whether these regions are net carbon sinks or sources by studying vegetation indices alone. In this study, we investigate greening trends in Eastern China (EC) and corresponding trends in atmospheric CO2 concentrations. We used multiple vegetation indices including NDVI and EVI to characterize changes in vegetation activity over EC from 2003 to 2016. Gap-filled time series of column-averaged CO2 dry air mole fraction (XCO2) from January 2003 to May 2016, based on observations from SCIAMACHY, GOSAT, and OCO-2 satellites, were used to calculate XCO2 changes during growing season for 13 years. We derived a relationship between XCO2 and surface net CO2 fluxes from two inversion model simulations, CarbonTracker and Monitoring Atmospheric Composition and Climate (MACC), and used those relationships to estimate the biospheric CO2 flux enhancement based on satellite observed XCO2 changes. We observed significant growing period (GP) greening trends in NDVI and EVI related to cropland intensification and forest growth in the region. After removing the influence of large urban center CO2 emissions, we estimated an enhanced XCO2 drawdown during the GP of −0.070 to −0.084 ppm yr−1. Increased carbon uptake during the GP was estimated to be 28.41 to 46.04 Tg C, mainly from land management, which could offset about 2–3% of EC’s annual fossil fuel emissions. These results show the potential of using multi-satellite observed XCO2 to estimate carbon fluxes from the regional biosphere, which could be used to verify natural sinks included as national contributions of greenhouse gas emissions reduction in international climate change agreements like the UNFCC Paris Accord.

The influence of rising tropospheric carbon dioxide and ozone on plant productivity

Human activities result in a wide array of pollutants being released to the atmosphere. A number of these pollutants have direct effects on plants, including carbon dioxide (CO2 ), which is the substrate for photosynthesis, and ozone (O3 ), a damaging oxidant. How plants respond to changes in these atmospheric air pollutants, both directly and indirectly, feeds back on atmospheric composition and climate, global net primary productivity and ecosystem service provisioning. Here we discuss the past, current and future trends in emissions of CO2 and O3 and synthesise the current atmospheric CO2 and O3 budgets, describing the important role of vegetation in determining the atmospheric burden of those pollutants. While increased atmospheric CO2 concentration over the past 150 years has been accompanied by greater CO2 assimilation and storage in terrestrial ecosystems, there is evidence that rising temperatures and increased drought stress may limit the ability of future terrestrial ecosystems to buffer against atmospheric emissions. Long-term Free Air CO2 or O3 Enrichment (FACE) experiments provide critical experimentation about the effects of future CO2 and O3 on ecosystems, and highlight the important interactive effects of temperature, nutrients and water supply in determining ecosystem responses to air pollution. Long-term experimentation in both natural and cropping systems is needed to provide critical empirical data for modelling the effects of air pollutants on plant productivity in the decades to come.

Impacts of Rapid Socioeconomic Development on Cropping Intensity Dynamics in China during 2001–2016

Changes in cropping intensity reflect not only changes in land use but also the transformation of land functions. Although both natural conditions and socioeconomic factors can influence the spatial distribution of the cropping intensity and its changes, socioeconomic developments related to human activities can exert great impacts on short term cropping intensity changes. The driving force of this change has a high level of uncertainty; and few researchers have implemented comprehensive studies on the underlying driving forces and mechanisms of these changes. This study produced cropping intensity maps in China from 2001 to 2016 using remote sensing data and analyzed the impacts of socioeconomic drivers on cropping intensity and its changes in nine major agricultural zones in China. We found that the average annual cropping intensity in all nine agricultural zones increased from 2001 to 2016 under rapid socioeconomic development, and the trends in the seven major agricultural zones were significantly increased (p < 0.05), based on a Mann–Kendall test, except for the Northeast China Plain (NE Plain) and Qinghai Tibet Plateau (QT Plateau). Based on the results from the Geo-Detector, a widely used geospatial analysis tool, the dominant factors that affected cropping intensity distribution were related to the arable land output in the plain regions and topography in the mountainous regions. The factors that affected cropping intensity changes were mainly related to the arable land area and crop yields in northern China, and regional economic developments, such as machinery power input and farmers’ income in southern China. These findings provide useful cropping intensity data and profound insights for policymaking on how to use cultivated land resources efficiently and sustainably.

High ecosystem stability of evergreen broadleaf forests under severe droughts

Global increase in drought occurrences threatens the stability of terrestrial ecosystem functioning. Evergreen broadleaf forests (EBFs) keep leaves throughout the year, and therefore could experience higher drought risks than other biomes. However, the recent temporal variability of global vegetation productivity or land carbon sink is mainly driven by non-evergreen ecosystems, such as semiarid grasslands, croplands, and boreal forests. Thus, we hypothesize that EBFs have higher stability than other biomes under the increasingly extreme droughts. Here we use long-term Standardized Precipitation and Evaporation Index (SPEI) data and satellite-derived Enhanced Vegetation Index (EVI) products to quantify the temporal stability (ratio of mean annual EVI to its SD), resistance (ability to maintain its original levels during droughts), and resilience (rate of EVI recovering to pre-drought levels) at biome and global scales. We identified significantly increasing trends of annual drought severity (SPEI range: -0.08 to -1.80), area (areal fraction range: 2%-19%), and duration (month range: 7.9-9.1) in the EBF biome over 2000-2014. However, EBFs showed the highest resistance of EVI to droughts, but no significant differences in resilience of EVI to droughts were found among biomes (forests, grasslands, savannas, and shrublands). Global resistance and resilience of EVI to droughts were largely affected by temperature and solar radiation. These findings suggest that EBFs have higher stability than other biomes despite the greater drought exposure. Thus, the conservation of EBFs is critical for stabilizing global vegetation productivity and land carbon sink under more-intense climate extremes in the future.

The Sensitivity of Land-Atmosphere Coupling to Modern Agriculture in the Northern Midlatitudes

Modern agricultural land cover and management are important as regional climate forcings. Previous work has shown that land cover change can significantly impact key climate variables, including turbulent fluxes, precipitation, and surface temperature. However, fewer studies have investigated how intensive crop management can impact background climate conditions, such as the strength of land-atmosphere coupling and evaporative regime. We conduct sensitivity experiments using a state-of-the-art climate model with modified vegetation characteristics to represent modern crop cover and management, using observed crop-specific leaf area indexes and calendars. We quantify changes in land-atmosphere interactions and climate over intensively cultivated regions situated at transitions between moisture- and energy-limited conditions. Results show that modern intensive agriculture has significant and geographically varying impacts on regional evaporative regimes and background climate conditions. Over the northern Great Plains, modern crop intensity increases the model simulated precipitation and soil moisture, weakening hydrologic coupling by increasing surface water availability and reducing moisture limits on evapotranspiration. In the U.S. Midwest, higher growing season evapotranspiration, coupled with winter and spring rainfall declines, reduces regional soil moisture, while crop albedo changes also reduce net surface radiation. This results overall in reduced dependency of regional surface temperature on latent heat fluxes. In central Asia, a combination of reduced net surface energy and enhanced pre-growing season precipitation amplify the energy-limited evaporative regime. These results highlight the need for improved representations of agriculture in global climate models to better account for regional climate impacts and interactions with other anthropogenic forcings.

Enhanced peak growth of global vegetation and its key mechanisms

The annual peak growth of vegetation is critical in characterizing the capacity of terrestrial ecosystem productivity and shaping the seasonality of atmospheric CO₂ concentrations. The recent greening of global lands suggests an increasing trend of terrestrial vegetation growth, but whether or not the peak growth has been globally enhanced still remains unclear. Here, we use two global datasets of gross primary productivity (GPP) and a satellite-derived Normalized Difference Vegetation Index (NDVI) to characterize recent changes in annual peak vegetation growth (that is, GPP_max and NDVI_max). We demonstrate that the peak in the growth of global vegetation has been linearly increasing during the past three decades. About 65% of the NDVI_max variation is evenly explained by expanding croplands (21%), rising CO₂ (22%) and intensifying nitrogen deposition (22%). The contribution of expanding croplands to the peak growth trend is substantiated by measurements from eddy-flux towers, sun-induced chlorophyll fluorescence and a global database of plant traits, all of which demonstrate that croplands have a higher photosynthetic capacity than other vegetation types. The large contribution of CO₂ is also supported by a meta-analysis of 466 manipulative experiments and 15 terrestrial biosphere models. Furthermore, we show that the contribution of GPP_max to the change in annual GPP is less in the tropics than in other regions. These multiple lines of evidence reveal an increasing trend in the peak growth of global vegetation. The findings highlight the important roles of agricultural intensification and atmospheric changes in reshaping the seasonality of global vegetation growth.

Implications of Observed and Simulated Soil Carbon Sequestration for Management Options in Corn-based Rotations

Managing cropping systems to sequester soil organic C (SOC) improves soil health and resilience to changing climate. Perennial crops, no-till planting, manure, and cover crops can add SOC; however, their impacts have not been well documented in the northeastern United States. Our objectives were (i) to monitor SOC from a bioenergy cropping study in Pennsylvania that included a corn ( L.)-soybean [ (L.) Merr.]-alfalfa ( L.) rotation, switchgrass ( L.), and reed canarygrass ( L.); (ii) to use the CQESTR model to predict SOC sequestration in the bioenergy crops (with and without projected climate change); and (iii) to use CQESTR to simulate influence of tillage, manure, cover cropping, and corn stover removal in typical dairy forage (silage corn-alfalfa) or grain corn-soybean rotations. Over 8 yr, measured SOC increased 0.4, 1.1, and 0.8 Mg C ha yr in the bioenergy rotation, reed canarygrass, and switchgrass, respectively. Simulated and measured data were significantly correlated ( < 0.001) at all depths. Predicted sequestration (8-14 Mg C ha over 40 yr) in dairy forage rotations was much larger than with corn-soybean rotations (-4.0-0.6 Mg C ha over 40 yr), due to multiple years of perennial alfalfa. No-till increased sequestration in the simulated dairy forage rotation and prevented a net loss of C in corn-soybean rotations. Simulations indicated limited impact of cover crops and manure on long-term SOC sequestration. The low solids content of liquid dairy manure is the likely reason for the less-than-expected impact of manure. Overall, simulations suggest that inclusion of alfalfa provides the greatest potential for SOC sequestration with a typical Pennsylvania crop rotation.

Simulated Soil Organic Carbon Changes in Maryland Are Affected by Tillage, Climate Change, and Crop Yield

The impact of climate change on soil organic C (SOC) stocks in no-till (NT) and conventionally tilled (CT) agricultural systems is poorly understood. The objective of this study was to simulate the impact of projected climate change on SOC to 50-cm soil depth for grain cropping systems in the southern Mid-Atlantic region of the United States. We used SOC and other data from the long-term Farming Systems Project in Beltsville, MD, and CQESTR, a process-based soil C model, to predict the impact of cropping systems and climate (air temperature and precipitation) on SOC for a 40-yr period (2012-2052). Since future crop yields are uncertain, we simulated five scenarios with differing yield levels (crop yields from 1996-2014, and at 10 or 30% greater or lesser than these yields). Without change in climate or crop yields (baseline conditions) CQESTR predicted an increase in SOC of 0.014 and 0.021 Mg ha yr in CT and NT, respectively. Predicted climate change alone resulted in an SOC increase of only 0.002 Mg ha yr in NT and a decrease of 0.017 Mg ha yr in CT. Crop yield declines of 10 and 30% led to SOC decreases between 2 and 8% compared with 2012 levels. Increasing crop yield by 10 and 30% was sufficient to raise SOC 2 and 7%, respectively, above the climate-only scenario under both CT and NT between 2012 and 2052. Results indicate that under these simulated conditions, the negative impact of climate change on SOC levels could be mitigated by crop yield increases.

Simulated Soil Organic Carbon Response to Tillage, Yield, and Climate Change in the Southeastern Coastal Plains

Intensive tillage, low-residue crops, and a warm, humid climate have contributed to soil organic carbon (SOC) loss in the southeastern Coastal Plains region. Conservation (CnT) tillage and winter cover cropping are current management practices to rebuild SOC; however, there is sparse long-term field data showing how these management practices perform under variable climate conditions. The objectives of this study were to use CQESTR, a process-based C model, to simulate SOC in the top 15 cm of a loamy sand soil (fine-loamy, kaolinitic, thermic Typic Kandiudult) under conventional (CvT) or CnT tillage to elucidate the impact of projected climate change and crop yields on SOC relative to management and recommend the best agriculture management to increase SOC. Conservation tillage was predicted to increase SOC by 0.10 to 0.64 Mg C ha for six of eight crop rotations compared with CvT by 2033. The addition of a winter crop [rye ( L.) or winter wheat ( L.)] to a corn ( L.)-cotton ( L.) or corn-soybean [ (L.) Merr.] rotation increased SOC by 1.47 to 2.55 Mg C ha. A continued increase in crop yields following historical trends could increase SOC by 0.28 Mg C ha, whereas climate change is unlikely to have a significant impact on SOC except in the corn-cotton or corn-soybean rotations where SOC decreased up to 0.15 Mg C ha by 2033. The adoption of CnT and cover crop management with high-residue-producing corn will likely increase SOC accretion in loamy sand soils. Simulation results indicate that soil C saturation may be reached in high-residue rotations, and increasing SOC deeper in the soil profile will be required for long-term SOC accretion beyond 2030.

Simulating Soil Organic Carbon Responses to Cropping Intensity, Tillage, and Climate Change in Pacific Northwest Dryland

Managing dryland cropping systems to increase soil organic C (SOC) under changing climate is challenging after decades of winter wheat ( L.)-fallow and moldboard plow tillage (W-F/MP). The objective was to use CQESTR, a process-based C model, and SOC data collected in 2004, 2008, and 2012 to predict the best management to increase SOC under changing climate in four cropping systems, which included continuous wheat under no tillage (W-W/NT), wheat and sorghum × sudangrass [ (L.) Moench. × L.] under no tillage, wheat-fallow under sweep tillage, and W-F/MP. Since future yields and climate are uncertain, 20 scenarios for each cropping system were simulated with four climate projections and five crop yield scenarios (current crop yields, and 10 or 30% greater or lesser yields). Measured and simulated SOC were significantly ( < 0.0001) correlated ( = 0.98) at all soil depths. Predicted SOC changes ranged from -12.03 to 2.56 Mg C ha in the 1-m soil depth for W-F/MP and W-W/NT, respectively, during the 2012 to 2052 predictive period. Only W-W/NT sequestered SOC at a rate of 0.06 Mg C ha yr under current crop yields and climate. Under climate change and yield scenarios, W-W/NT lost SOC except with a 30% wheat yield increase for 40 yr. Predicted SOC increases in W-W/NT were 0.71, 1.16, and 0.88 Mg C ha under the Oregon Climate Assessment Reports for low emissions and high emissions and the Regional Climate Model version 3 with boundary conditions from the Third Generation Coupled Global Climate Model, respectively, with 30% yield increases. Continuous no-till cropping would increase SOC and improve soil health and resiliency to lessen the impact of extreme weather.

Simulated Soil Organic Carbon Responses to Crop Rotation, Tillage, and Climate Change in North Dakota

Understanding how agricultural management and climate change affect soil organic carbon (SOC) stocks is particularly important for dryland agriculture regions that have been losing SOC over time due to fallow and tillage practices, and it can lead to development of agricultural practice(s) that reduce the impact of climate change on crop production. The objectives of this study were: (i) to simulate SOC dynamics in the top 30 cm of soil during a 20-yr (1993-2012) field study using CQESTR, a process-based C model; (ii) to predict the impact of changes in management, crop production, and climate change from 2013 to 2032; and (iii) to identify the best dryland cropping systems to maintain or increase SOC stocks under projected climate change in central North Dakota. Intensifying crop rotations was predicted to have a greater impact on SOC stocks than tillage (minimum tillage [MT], no-till [NT]) during 2013 to 2032, as SOC was highly correlated to biomass input ( = 0.91, = 0.00053). Converting from a MT spring wheat (SW, L.)-fallow rotation to a NT continuous SW rotation increased annualized biomass additions by 2.77 Mg ha (82%) and SOC by 0.22 Mg C ha yr. Under the assumption that crop production will stay at the 1993 to 2012 average, climate change is predicted to have a minor impact on SOC (approximately -6.5%) relative to crop rotation management. The CQESTR model predicted that the addition of another SW or rye ( L.) crop would have a greater effect on SOC stocks (0- to 30-cm depth) than conversion from MT to NT or climate change from 2013 to 2032.

On the causes of trends in the seasonal amplitude of atmospheric CO2

No consensus has yet been reached on the major factors driving the observed increase in the seasonal amplitude of atmospheric CO₂ in the northern latitudes. In this study, we used atmospheric CO₂ records from 26 northern hemisphere stations with a temporal coverage longer than 15 years, and an atmospheric transport model prescribed with net biome productivity (NBP) from an ensemble of nine terrestrial ecosystem models, to attribute change in the seasonal amplitude of atmospheric CO₂ . We found significant (p < .05) increases in seasonal peak-to-trough CO₂ amplitude (AMP_P-T ) at nine stations, and in trough-to-peak amplitude (AMP_T-P ) at eight stations over the last three decades. Most of the stations that recorded increasing amplitudes are in Arctic and boreal regions (>50°N), consistent with previous observations that the amplitude increased faster at Barrow (Arctic) than at Mauna Loa (subtropics). The multi-model ensemble mean (MMEM) shows that the response of ecosystem carbon cycling to rising CO₂ concentration (eCO₂ ) and climate change are dominant drivers of the increase in AMP_P-T and AMP_T-P in the high latitudes. At the Barrow station, the observed increase of AMP_P-T and AMP_T-P over the last 33 years is explained by eCO₂ (39% and 42%) almost equally than by climate change (32% and 35%). The increased carbon losses during the months with a net carbon release in response to eCO₂ are associated with higher ecosystem respiration due to the increase in carbon storage caused by eCO₂ during carbon uptake period. Air-sea CO₂ fluxes (10% for AMP_P-T and 11% for AMP_T-P ) and the impacts of land-use change (marginally significant 3% for AMP_P-T and 4% for AMP_T-P ) also contributed to the CO₂ measured at Barrow, highlighting the role of these factors in regulating seasonal changes in the global carbon cycle.

Representing agriculture in Earth System Models: approaches and priorities for development

Earth System Model (ESM) advances now enable improved representations of spatially and temporally varying anthropogenic climate forcings. One critical forcing is global agriculture, which is now extensive in land-use and intensive in management, owing to 20th century development trends. Agriculture and food systems now contribute nearly 30% of global greenhouse gas emissions and require copious inputs and resources, such as fertilizer, water, and land. Much uncertainty remains in quantifying important agriculture-climate interactions, including surface moisture and energy balances and biogeochemical cycling. Despite these externalities and uncertainties, agriculture is increasingly being leveraged to function as a net sink of anthropogenic carbon, and there is much emphasis on future sustainable intensification. Given its significance as a major environmental and climate forcing, there now exist a variety of approaches to represent agriculture in ESMs. These approaches are reviewed herein, and range from idealized representations of agricultural extent to the development of coupled climate-crop models that capture dynamic feedbacks. We highlight the robust agriculture-climate interactions and responses identified by these modeling efforts, as well as existing uncertainties and model limitations. To this end, coordinated and benchmarking assessments of land-use-climate feedbacks can be leveraged for further improvements in ESM's agricultural representations. We suggest key areas for continued model development, including incorporating irrigation and biogeochemical cycling in particular. Lastly, we pose several critical research questions to guide future work. Our review focuses on ESM representations of climate-surface interactions over managed agricultural lands, rather than on ESMs as an estimation tool for crop yields and productivity.

Terrestrial Carbon Cycle Variability

A growing literature is reporting on how the terrestrial carbon cycle is experiencing year-to-year variability because of climate anomalies and trends caused by global change. As CO ₂ concentration records in the atmosphere exceed 50 years and as satellite records reach over 30 years in length, we are becoming better able to address carbon cycle variability and trends. Here we review how variable the carbon cycle is, how large the trends in its gross and net fluxes are, and how well the signal can be separated from noise. We explore mechanisms that explain year-to-year variability and trends by deconstructing the global carbon budget.

The CO ₂ concentration record is detecting a significant increase in the seasonal amplitude between 1958 and now. Inferential methods provide a variety of explanations for this result, but a conclusive attribution remains elusive. Scientists have reported that this trend is a consequence of the greening of the biosphere, stronger northern latitude photosynthesis, more photosynthesis by semi-arid ecosystems, agriculture and the green revolution, tropical temperature anomalies, or increased winter respiration.

At the global scale, variability in the terrestrial carbon cycle can be due to changes in constituent fluxes, gross primary productivity, plant respiration and heterotrophic (microbial) respiration, and losses due to fire, land use change, soil erosion, or harvesting. It remains controversial whether or not there is a significant trend in global primary productivity (due to rising CO ₂, temperature, nitrogen deposition, changing land use, and preponderance of wet and dry regions). The degree to which year-to-year variability in temperature and precipitation anomalies affect global primary productivity also remains uncertain. For perspective, interannual variability in global gross primary productivity is relatively small (on the order of 2 Pg-C y ^-1) with respect to a large and uncertain background (123 +/- 4 Pg-C y ^-1), and detected trends in global primary productivity are even smaller (33 Tg-C y ^-2). Yet residual carbon balance methods infer that the terrestrial biosphere is experiencing a significant and growing carbon sink. Possible explanations for this large and growing net land sink include roles of land use change and greening of the land, regional enhancement of photosynthesis, and down regulation of plant and soil respiration with warming temperatures. Longer time series of variables needed to provide top-down and bottom-up assessments of the carbon cycle are needed to resolve these pressing and unresolved issues regarding how, why, and at what rates gross and net carbon fluxes are changing.

Changes in soil organic carbon in croplands subjected to fertilizer management: a global meta-analysis

Cropland soil organic carbon (SOC) is undergoing substantial alterations due to both environmental and anthropogenic changes. Although numerous case studies have been conducted, there remains a lack of quantification of the consequences of such environmental and anthropogenic changes on the SOC sequestration across global agricultural systems. Here, we conducted a global meta-analysis of SOC changes under different fertilizer managements, namely unbalanced application of chemical fertilizers (UCF), balanced application of chemical fertilizers (CF), chemical fertilizers with straw application (CFS), and chemical fertilizers with manure application (CFM). We show that topsoil organic carbon (C) increased by 0.9 (0.7-1.0, 95% confidence interval (CI)) g kg(-1) (10.0%, relative change, hereafter the same), 1.7 (1.2-2.3) g kg(-1) (15.4%), 2.0 (1.9-2.2) g kg(-1) (19.5%) and 3.5 (3.2-3.8) g kg(-1) (36.2%) under UCF, CF, CFS and CFM, respectively. The C sequestration durations were estimated as 28-73 years under CFS and 26-117 years under CFM but with high variability across climatic regions. At least 2.0 Mg ha(-1) yr(-1) C input is needed to maintain the SOC in ~85% cases. We highlight a great C sequestration potential of applying CF, and adopting CFS and CFM is highly important for either improving or maintaining current SOC stocks across all agro-ecosystems.

Comparison of solar-induced chlorophyll fluorescence, light-use efficiency, and process-based GPP models in maize

Accurately quantifying cropland gross primary production (GPP) is of great importance to monitor cropland status and carbon budgets. Satellite-based light-use efficiency (LUE) models and process-based terrestrial biosphere models (TBMs) have been widely used to quantify cropland GPP at different scales in past decades. However, model estimates of GPP are still subject to large uncertainties, especially for croplands. More recently, space-borne solar-induced chlorophyll fluorescence (SIF) has shown the ability to monitor photosynthesis from space, providing new insights into actual photosynthesis monitoring. In this study, we examined the potential of SIF data to describe maize phenology and evaluated three GPP modeling approaches (space-borne SIF retrievals, a LUE-based vegetation photosynthesis model [VPM], and a process-based soil canopy observation of photochemistry and energy flux [SCOPE] model constrained by SIF) at a maize (Zea mays L.) site in Mead, Nebraska, USA. The result shows that SIF captured the seasonal variations (particularly during the early and late growing season) of tower-derived GPP (GPP_EC) much better than did satellite-based vegetation indices (enhanced vegetation index [EVI] and land surface water index [LSWI]). Consequently, SIF was strongly correlated with GPP_EC than were EVI and LSWI. Evaluation of GPP estimates against GPP_EC during the growing season demonstrated that all three modeling approaches provided reasonable estimates of maize GPP, with Pearson's correlation coefficients (r) of 0.97, 0.94, and 0.93 for the SCOPE, VPM, and SIF models, respectively. The SCOPE model provided the best simulation of maize GPP when SIF observations were incorporated through optimizing the key parameter of maximum carboxylation capacity (Vcmax). Our results illustrate the potential of SIF data to offer an additional way to investigate the seasonality of photosynthetic activity, to constrain process-based models for improving GPP estimates, and to reasonably estimate GPP by integrating SIF and GPP_EC data without dependency on climate inputs and satellite-based vegetation indices.

Tropical nighttime warming as a dominant driver of variability in the terrestrial carbon sink

The terrestrial biosphere is currently a strong carbon (C) sink but may switch to a source in the 21st century as climate-driven losses exceed CO2-driven C gains, thereby accelerating global warming. Although it has long been recognized that tropical climate plays a critical role in regulating interannual climate variability, the causal link between changes in temperature and precipitation and terrestrial processes remains uncertain. Here, we combine atmospheric mass balance, remote sensing-modeled datasets of vegetation C uptake, and climate datasets to characterize the temporal variability of the terrestrial C sink and determine the dominant climate drivers of this variability. We show that the interannual variability of global land C sink has grown by 50-100% over the past 50 y. We further find that interannual land C sink variability is most strongly linked to tropical nighttime warming, likely through respiration. This apparent sensitivity of respiration to nighttime temperatures, which are projected to increase faster than global average temperatures, suggests that C stored in tropical forests may be vulnerable to future warming.