Variability In Net Ecosystem Exchange Research Articles

Sustainability in Natural Grassland in the Brazilian Pampa Biome: Livestock Production with CO2 Absorption

The Brazilian Pampa biome has natural pastures that have been used for centuries for cattle grazing. This is considered a sustainable system because it combines the conservation of natural vegetation and high-quality meat production, protecting the biome from commercial agriculture’s advances. However, whether it is a source or a sink of carbon dioxide (CO2) has yet to be evaluated. Hence, this study aimed to quantify the net ecosystem exchange (NEE) of the CO2 of a natural pasture of the Pampa biome used for livestock production. The experimental area is located in a subtropical region of southern Brazil, where eddy covariance (EC) measurements were conducted from 2015 to 2021 in a rotational cattle grazing system. The seven months of the warm season (September to March) were characterized as CO2 absorbers, while the five months of the cold season (April to August) were CO2 emitters. Throughout the six years and with complete data, the ecosystem was an absorber of atmospheric CO2, with an average value of −207.6 g C m−2 year−1. However, the significant interannual variability in NEE was observed, with cumulative values ranging from −82.0 to −385.3 g C m−2 year−1. The results suggest the coupling of climatic conditions to pasture management can be the factor that modulated the NEE interannual variability. The cattle raising system on the natural pastures of the Pampa absorbs CO2, which is further evidence of its sustainability and need for conservation.

Canopy Heterogeneity and Environmental Variability Drive Annual Budgets of Net Ecosystem Carbon Exchange in a Tidal Marsh

AbstractTidal salt marshes are important ecosystems in the global carbon cycle. Understanding their net carbon exchange with the atmosphere is required to accurately estimate their net ecosystem carbon budget (NECB). In this study, we present the interannual net ecosystem exchange (NEE) of CO2 derived from eddy covariance (EC) for a Spartina alterniflora salt marsh. We found interannual NEE could vary up to 3‐fold and range from −58.5 ± 11.3 to −222.9 ± 12.4 g C m−2 year−1 in 2016 and 2020, respectively. Further, we found that atmospheric CO2 fluxes were spatially dependent and varied across short distances. High biomass regions along tidal creek and estuary edges had up to 2‐fold higher annual NEE than lower biomass marsh interiors. In addition to the spatial variation of NEE, regions of the marsh represented by distinct canopy zonation responded to environmental drivers differently. Low elevation edges (with taller canopies) had a higher correlation with river discharge (R2 = 0.61), the main freshwater input into the system, while marsh interiors (with short canopies) were better correlated with in situ precipitation (R2 = 0.53). Lastly, we extrapolated interannual NEE to the wider marsh system, demonstrating the potential underestimation of annual NEE when not considering spatially explicit rates of NEE. Our work provides a basis for further research to understand the temporal and spatial dynamics of productivity in coastal wetlands, ecosystems which are at the forefront of experiencing climate change induced variability in precipitation, temperature, and sea level rise that have the potential to alter ecosystem productivity.

The Productivity and Carbon Exchange of an Intensively Managed Pasture in Central Kentucky

Intensive pasture management that aims at providing season-long forage while minimizing soil degradation is increasingly becoming an important grazing strategy in Kentucky. Typically, it involves the use of high-yielding warm and cool season forage species that are well suited to local soil and climate conditions, meeting the dual-purpose provision of high nutritional value while remaining resilient to grazing pressure and changing climate. Monitoring carbon exchange is a crucial component for effective pasture management to promote sustainable pastureland management practices. We hypothesized that pasturelands, when intensively managed, would exhibit a small but important CO2 cumulative uptake year-round. We used the Eddy covariance method to measure the net ecosystem exchange of CO2 (NEE) and productivity of an intensively managed pastureland at Kentucky State University Research and Demonstration station from 2015 to 2020. The study has two objectives: to quantify interannual variability in net ecosystem exchange, and examine the controlling environmental factors, in particular temperature, sunlight, and precipitation of NEE. Diurnal and seasonal fluctuations followed typical patterns of carbon uptake and release. Overall, the pasture site consistently was carbon sink except for 2016, in part due to a warmer winter season than usual, sequestering 1394 gCm−2 over the study period. Precipitation and temperature were critical environmental factors underpinning seasonal CO2 uptake and release. Of critical importance was the net carbon uptake during the non-growing season.

Persistent net release of carbon dioxide and methane from an Alaskan lowland boreal peatland complex.

Permafrost degradation in peatlands is altering vegetation and soil properties and impacting net carbon storage. We studied four adjacent sites in Alaska with varied permafrost regimes, including a black spruce forest on a peat plateau with permafrost, two collapse scar bogs of different ages formed following thermokarst, and a rich fen without permafrost. Measurements included year-round eddy covariance estimates of net carbon dioxide (CO2 ), mid-April to October methane (CH4 ) emissions, and environmental variables. From 2011 to 2022, annual rainfall was above the historical average, snow water equivalent increased, and snow-season duration shortened due to later snow return. Seasonally thawed active layer depths also increased. During this period, all ecosystems acted as slight annual sources of CO2 (13-59 g C m-2 year-1 ) and stronger sources of CH4 (11-14 g CH4 m-2 from ~April to October). The interannual variability of net ecosystem exchange was high, approximately ±100 g C m-2 year-1 , or twice what has been previously reported across other boreal sites. Net CO2 release was positively related to increased summer rainfall and winter snow water equivalent and later snow return. Controls over CH4 emissions were related to increased soil moisture and inundation status. The dominant emitter of carbon was the rich fen, which, in addition to being a source of CO2 , was also the largest CH4 emitter. These results suggest that the future carbon-source strength of boreal lowlands in Interior Alaska may be determined by the area occupied by minerotrophic fens, which are expected to become more abundant as permafrost thaw increases hydrologic connectivity. Since our measurements occur within close proximity of each other (≤1 km2 ), this study also has implications for the spatial scale and data used in benchmarking carbon cycle models and emphasizes the necessity of long-term measurements to identify carbon cycle process changes in a warming climate.

Hydrometeorological Factors Affecting the Carbon Exchange of the Himalayan Pine-dominated Ecosystem

The net carbon uptake of the natural and dense Himalayan ecosystems is not well explored at a sub-daily scale due to limited data availability. Here, we present the first-ever analysis of the hydrometeorological controls of the sub-daily scale Net Ecosystem Exchange (NEE) in a Western Himalayan pine-dominated ecosystem (Pinus roxburghii). We used observed half-hourly flux tower data for 104 weeks from December 2014 to November 2016. We found that the sub-daily (6 h) scale variability of NEE contributes 20–40% to its within-week variability. We generated transfer entropy (TE)-based weekly process networks with a maximum memory of 6 h to understand sub-daily scale processes. Using weekly networks, we present consistent causal links on a seasonal scale. The hydrometeorological variables affecting the NEE and Ecosystem Respiration (RE) at a sub-daily scale are Net Solar Radiation (NSR), Relative Humidity (RH), Surface Air Temperature (TA), and Sensible Heat Flux (SH). These variables are consistently active in the networks with seasonally varying magnitudes of TE. We also found that for both monsoon and post-monsoon, NEE receives more incoming links from meteorological variables during dry weeks than wet weeks. Precipitation (P) did not send a direct causal link to NEE or RE within a sub-daily scale but strongly influenced the causal associations between hydrometeorological and carbon exchange variables. The network does not show any immediate (within 6 h) impact of P on NEE, which probably implies a delayed response of vegetation to P through soil moisture. At higher memory, P may act as an influential background variable.

Growing-season carbon budget of alpine meadow ecosystem in the Qinghai Lake Basin: a continued carbon sink through this century according to the Biome-BGC model

BackgroundThe alpine meadow is one of the most important ecosystems in the Qinghai-Tibet Plateau (QTP), and critically sensitive to climate change and human activities. Thus, it is crucial to precisely reveal the current state and predict future trends in the carbon budget of the alpine meadow ecosystem. The objective of this study was to explore the applicability of the Biome-BGC model (BBGC) in the Qinghai Lake Basin (QLB), identify the key parameters affecting the variation of net ecosystem exchange (NEE), and further predict the future trends in carbon budget in the QLB.ResultsThe alpine meadow mainly acted as carbon sink during the growing season. For the eco-physiological factors, the YEL (Yearday to end litterfall), YSNG (Yearday to start new growth), CLEC (Canopy light extinction coefficient), FRC:LC (New fine root C: new leaf C), SLA (Canopy average specific leaf area), C:Nleaf (C:N of leaves), and FLNR (Fraction of leaf N in Rubisco) were confirmed to be the top seven parameters affecting carbon budget of the alpine meadow. For the meteorological factors, the sensitivity of NEE to precipitation was greater than that to vapor pressure deficit (VPD), and it was greater to radiation than to air temperature. Moreover, the combined effect of two different meteorological factors on NEE was higher than the individual effect of each one. In the future, warming and wetting would enhance the carbon sink capacity of the alpine meadow during the growing season, but extreme warming (over 3.84 ℃) would reduce NEE (about 2.9%) in the SSP5-8.5 scenario.ConclusionOverall, the alpine meadow ecosystem in the QLB generally performs as a carbon sink at present and in the future. It is of great significance for the achievement of the goal of carbon neutrality and the management of alpine ecosystems.

Carbon Dioxide Fluxes and Influencing Factors in the Momoge Salt Marsh Ecosystem, Jilin Province, China

This study observed the characteristics and influencing factors of the carbon fluxes of the Momoge salt marsh ecosystem over four years, which behaves as a CO2 sink. The daily, seasonal, and interannual variations in CO2 fluxes in the Momoge salt marshes were observed using the eddy covariance method and were compared with various environmental factors. An overall daily “U”-shaped distribution was observed, with uptake during the day (negative values) and release at night (positive values). Annually, the carbon fluxes in the study area roughly exhibited a “V” shape. The carbon fluxes during the non-growing season predominantly showed positive values, indicating the release of CO2 into the atmosphere. Photosynthetically active radiation was the primary influencing factor affecting the hourly and daytime variations in net ecosystem exchange (NEE) during the growing season, while temperature was the main factor influencing nighttime NEE dynamics. The air temperature, soil temperature, photosynthetically active radiation, precipitation, and water level all had significant impacts on the daily net CO2 exchange. At the monthly scale, larger values of soil temperature, air temperature, photosynthetically active radiation, and aboveground biomass corresponded to a stronger carbon absorption capacity of the ecosystem. Overall, temperature remains the primary factor for carbon fluxes in the Momoge wetlands.

Attributing interannual variability of net ecosystem exchange to modeled ecological processes in forested wetlands of contrasting stand age

Attributing interannual variability of net ecosystem exchange to modeled ecological processes in forested wetlands of contrasting stand age

Importance of the memory effect for assessing interannual variation in net ecosystem exchange

Terrestrial carbon flux shows greater interannual variation (IAV) than the ocean and drives most temporal changes in the atmospheric CO2 growth rate. Accurate estimation of the interannual variation in the terrestrial carbon budget is crucial for predicting changes in the carbon balance of ecosystems and atmospheric CO2 concentrations in the context of climate change. However, the interannual variation simulation of the terrestrial carbon budget has considerable uncertainty. One of the important reasons is the neglect of the memory effect of the ecosystem on climate change, namely the potential lagged impact of climate change. In the present work, we integrated meteorological and remote sensing vegetation data and introduced the memory effect into a deep learning model to simulate the global terrestrial net ecosystem exchange (NEE). The conclusions were as follows: (a) Considering the memory effect not only improved the simulation accuracy of the global terrestrial NEE, R2 increased by 0.37–0.55, RMSE decreased by 38.97%–52.42%, and MAE decreased by 39.77%–52.38%, but also improved the simulation accuracy of the interannual variation of NEE, R2 increased by 0.50–0.84, RMSE decreased by 37.13%–72.07%, and MAE decreased by 40.72%–66.49%. (b) The memory effect of NEE varied by ecosystem type, environmental factors, and plant development phase. The response of NEE to precipitation in most ecosystems was slower than that to temperature and radiation. The length of the comprehensive memory effect of NEE on antecedent environmental variables in the non-growth season was longer than that in the growth season. (c) NEE in the tropics has shown greater interannual variation than that in the high latitudes and dominated interannual variation in global terrestrial NEE. The tropical savanna exceeded tropical forest and contributed the largest fraction of the interannual variation of NEE in the tropics.

Factors controlling and variations of CO2 fluxes during the growing season in Gurbantunggut Desert

Although previous studies have illustrated the variation of CO2 fluxes in different terrestrial ecosystems, more needs to be reported on desert ecosystems. This study, based on the eddy covariance (EC) measurements data over the Gurbantunggut Desert, analyzed the controlling factors and variations of net ecosystem exchange (NEE), gross primary production (GPP), and ecosystem respiration (Reco) during the growing season (April-September2021) in Gurbantunggut Desert. The results were as follows: 1) Carbon fluxes in the Gurbantunggut Desert had prominent daily and seasonal variations during the growing season. On the daily scale, it showed a net uptake during the day and a net release at night, and the monthly average daily change curve of NEE was “U” shaped; on the seasonal scale, carbon uptake was observed in all months, with a peak of −37.76 gC m−2 month−1 in May. 2)The cumulative amounts of NEE, GPP, and Reco in the Gurbantunggut Desert were −193.74, 235.43, and 41.69 g C m−2, respectively, throughout the growing season, showing carbon sinks. 3) Correlation analysis with environmental factors revealed that NEE, GPP, and Reco of desert ecosystems were highly significantly correlated with photosynthetically active radiation (PAR) at different time scales (p < 0.01). Second, at the half-hourly scale, all environmental factors showed vital significance, with a high correlation with air temperature (Tair) and soil temperature (Tsoil); at the monthly scale, they were significantly correlated with Precipitation (p < 0.05). 4) Photosynthetically active radiation is the dominant factor affecting the daily net ecosystem carbon exchange rate. The two are consistent with the rectangle hyperbola model where the daily net ecosystem carbon exchange rate decreases with increasing photosynthetically active radiation. The results indicate that carbon fluxes in the growing season of the Gurbantunggut Desert ecosystem are more sensitive to the response of photosynthetically active radiation. The results have significant reference value for further understanding and research in predicting the source-sink function and influence mechanism of desert ecosystems.

Seasonal variation and controlling factors of carbon balance over dry semi-humid cropland in Guanzhong Plain

A theoretical basis for optimizing mitigation and adaptation to future climate change can be obtained by determining the carbon balance and their control mechanism in agroecosystems. To better understand summer maize (Zea mays L.)/winter wheat (Triticum aestivum L.) agroecosystems in dry semi-humid carbon balance mechanism, we conducted an in-depth analysis of three-year (2019.6–2022.6) seasonal dynamics of carbon fluxes and their driving factors in summer maize/winter wheat cropland in the Guanzhong Plain combined with path analysis. The results showed that net radiation (Rn) was the main direct controlling factor of the net ecosystem exchange (NEE) in the summer maize growing season, followed by leaf area index (LAI). However, LAI was the main factor controlling the seasonal variation of NEE in the winter wheat growing season, followed by Rn. Furthermore, LAI was the dominant direct driving factor for the gross primary productivity (GPP) in the summer maize and winter wheat growing seasons. In addition, LAI was the dominant driving factor for the total ecosystem respiration (TER) in the summer maize season, while Rn (indirect effect) and LAI (direct effect) were the main controlling factors of TER in the winter wheat season. The NEE values of the summer maize system, winter wheat system, and summer maize/winter wheat system in the study area were from 119 to 221, − 601 to − 595, and − 433 to − 291 g C m−2, respectively (the net biological production (NBP) were − 709 to − 437, 220–303, and − 496 and − 260 g C m−2, respectively). Notably, the average carbon emission rate of the summer maize/winter wheat agroecosystem in the Guanzhong Plain, considering the carbon output at harvest is 386.2 g C m−2year−1. Therefore, this study may guide assessment of carbon balance of agroecosystems in dry sub-humid regions, and provide a strong reference for achieving carbon neutrality in local agroecosystems.

Responses of carbon exchange characteristics to meteorological factors, phenology, and extreme events in a rubber plantation of Danzhou, Hainan: evidence based on multi-year data

IntroductionOn Hainan Island, a rubber plantation that occupies a large swath of land plays an important role in the regional carbon budget. However, the carbon exchange of the rubber plantation is poorly understood.MethodsIn this study, using the eddy covariance methods we measured carbon metrics in the rubber plantation for 13 years from 2010 to 2022.ResultsWe clarified that the rubber plantation is a carbon sink and the annual net ecosystem exchange (NEE), ecosystem respiration, and gross primary production were −911.89 ± 135.37, 1,528.04 ± 253.50, and 2,439.93 ± 259.63 gC·m−2·a−1, respectively. Carbon fluxes differed between interannual years; specifically, rainy season fluxes were nearly double dry season fluxes. Radiation explained 46% of the variation for NEE in rainy season, and temperature explained 36% of the variation for NEE in the dry season. LAI explained the highest proportion of the monthly variation in NEE (R2 = 0.72, p &amp;lt; 0.001), indicating that when hydrothermal conditions are sufficient phenology may be the primary factor controlling carbon sequestration of rubber plantation. Due to climate change, there is an increasing probability of extreme climate events, such as typhoons, heat waves, and drought. Thus, we compared NEE before and after such events and results show extreme climate events reduce carbon uptake in the rubber plantation. We found that typhoons reduced NEE to varying degrees on different timescales. Heat waves generally decreased NEE during the day but recovered quickly and increased carbon uptake if there was sufficient precipitation. Drought reduced carbon uptake and continued to decrease even after precipitation.DiscussionEstimating the carbon sink capacity of the rubber plantation and studying the response to regional environmental changes are important for both applied research (carbon sink research and market trading, sink enhancement, and emission reduction, etc.) and basic research (land use change, phenology change, etc.).

Intraspecific trait variability is a key feature underlying high Arctic plant community resistance to climate warming

AbstractIn the high Arctic, plant community species composition generally responds slowly to climate warming, whereas less is known about the community functional trait responses and consequences for ecosystem functioning. The slow species turnover and large distribution ranges of many Arctic plant species suggest a significant role of intraspecific trait variability in functional responses to climate change. Here we compare taxonomic and functional community compositional responses to a long‐term (17‐year) warming experiment in Svalbard, Norway, replicated across three major high Arctic habitats shaped by topography and contrasting snow regimes. We observed taxonomic compositional changes in all plant communities over time. Still, responses to experimental warming were minor and most pronounced in the drier habitats with relatively early snowmelt timing and long growing seasons (Cassiope and Dryas heaths). The habitats were clearly separated in functional trait space, defined by 12 size‐ and leaf economics‐related traits, primarily due to interspecific trait variation. Functional traits also responded to experimental warming, most prominently in the Dryas heath and mostly due to intraspecific trait variation. Leaf area and mass increased and leaf δ15N decreased in response to the warming treatment. Intraspecific trait variability ranged between 30% and 71% of the total trait variation, reflecting the functional resilience of those communities, dominated by long‐lived plants, due to either phenotypic plasticity or genotypic variation, which most likely underlies the observed resistance of high Arctic vegetation to climate warming. We further explored the consequences of trait variability for ecosystem functioning by measuring peak season CO2 fluxes. Together, environmental, taxonomic, and functional trait variables explained a large proportion of the variation in net ecosystem exchange (NEE), which increased when intraspecific trait variation was accounted for. In contrast, even though ecosystem respiration and gross ecosystem production both increased in response to warming across habitats, they were mainly driven by the direct kinetic impacts of temperature on plant physiology and biochemical processes. Our study shows that long‐term experimental warming has a modest but significant effect on plant community functional trait composition and suggests that intraspecific trait variability is a key feature underlying high Arctic ecosystem resistance to climate warming.

Drivers of Decadal Carbon Fluxes Across Temperate Ecosystems.

Long-running eddy covariance flux towers provide insights into how the terrestrial carbon cycle operates over multiple timescales. Here, we evaluated variation in net ecosystem exchange (NEE) of carbon dioxide (CO2) across the Chequamegon Ecosystem-Atmosphere Study AmeriFlux core site cluster in the upper Great Lakes region of the USA from 1997 to 2020. The tower network included two mature hardwood forests with differing management regimes (US-WCr and US-Syv), two fen wetlands with varying levels of canopy sheltering and vegetation (US-Los and US-ALQ), and a very tall (400m) landscape-level tower (US-PFa). Together, they provided over 70 site-years of observations. The 19-tower Chequamegon Heterogenous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 campaign centered around US-PFa provided additional information on the spatial variation of NEE. Decadal variability was present in all long-term sites, but cross-site coherence in interannual NEE in the earlier part of the record became weaker with time as non-climatic factors such as local disturbances likely dominated flux time series. Average decadal NEE at the tall tower transitioned from carbon source to sink to near neutral over 24years. Respiration had a greater effect than photosynthesis on driving variations in NEE at all sites. Declining snowfall offset potential increases in assimilation from warmer springs, as less-insulated soils delayed start of spring green-up. Higher CO2 increased maximum net assimilation parameters but not total gross primary productivity. Stand-scale sites were larger net sinks than the landscape tower. Clustered, long-term carbon flux observations provide value for understanding the diverse links between carbon and climate and the challenges of upscaling these responses across space.

Globally Scalable Approach to Estimate Net Ecosystem Exchange Based on Remote Sensing, Meteorological Data, and Direct Measurements of Eddy Covariance Sites

Despite a rapid development of Nature-Based Solutions (NBS) for carbon removal in recent years, the methods for evaluating NBS still have certain gaps. We propose an approach based on a combination of remote sensing data and meteorological variables to reconstruct the spatiotemporal variation of net ecosystem exchange from eddy-covariance stations. A Lagrangian particle dispersion model was used for upscaling satellite images and flux towers. We trained data-driven models based on kernel methods separately for each selected land-cover class. The results suggest that the proposed approach to quantifying carbon exchange on a medium-to-large scale by blending eddy covariance flux data with moderate resolution satellite and weather data provides a set of key advantages over previously deployed methods: (1) scalability, achieved via the validation design based on a separate set of eddy covariance stations; (2) high spatial and temporal resolution thanks to the use of Landsat imagery; and (3) robust and accurate predictions due to improved data quality control, advanced machine learning techniques, and rigorous validation. The machine learning models yielded high cross-validation results. Stratification that uses separate Fluxnet stations for each fold of validation ensures that the models are accurate across the area covered by the Fluxnet sites. Overall, we present here a globally scaled technology for the land sector based on high resolution remote sensing imagery, meteorological variables, and direct carbon flux measurements of eddy covariance flux stations.

Matching land surface phenology with the phenology of net ecosystem exchange (NEE) in the Kruger National Park, South Africa

Vegetation phenology plays an important role in the inter-annual variability of Net Ecosystem Exchange (NEE). However, the relationship between vegetation phenology and NEE is still largely unknown in the southern African region. The relatively recent availability of Eddy-Covariance (EC) flux towers in the region, which allow for measurements of the interactions between the terrestrial biosphere and the atmosphere, have expanded our understanding of Carbon Flux Phenology (CFP), but the towers' spatial coverage and representation is still limited. By contrast, satellite-based measurements of terrestrial environments provide repeat global spatial coverage that offers a complimentary approach to flux tower estimates of phenology and NEE. This study therefore aimed to investigate the relationship between Land Surface Phenology (LSP), calculated from FAPAR (Fraction of Absorbed Photosynthetically Active Radiation) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) and from MISR (Multi-angle Imaging Spectroradiometer), and CFP using 18 years of EC flux data at the Skukuza flux tower site, Kruger National Park, South Africa. Cross-correlation results showed that there is a moderate association between NEE and FAPAR from both sensors (MISR-HR, R2 = 0.54; MODIS, R2 = 0.53) where FAPAR leads NEE by 8-days. Specifically, only the start of carbon uptake (SOSNEE) has a strong correlation with FAPAR derived start of season (SOSFAPAR). In this instance, MISR-HR FAPAR outperformed MODIS at estimating the SOSNEE dates (i.e., R2 = 0.61 and R2 = 0.28, respectively) and overall showed the least difference to CFP (Mean signed difference ∼ 7–12 days). The findings show that MISR-HR FAPAR can be successfully used to approximate the SOSNEE as well as NEE. In summary, this study presents insight into the relationship between LSP and CFP that contributes to our understanding of how LSP can be used to assist monitoring of carbon dynamics in African savannas. Further investigation into the complicated nature of the interactions between environmental variables (e.g. rainfall, soil moisture, temperature, etc.) that influence both vegetation and carbon dynamics will provide important insight into the potential of upscaling NEE estimates.

Daily, seasonal and inter-annual variations in CO2 fluxes and carbon budget in a winter-wheat and summer-maize rotation system in the North China Plain

Investigating CO2 fluxes and carbon budget in agro-ecosystems is essential to develop climate smart agriculture. Here, we conducted a thorough analysis on the daily, seasonal and inter-annual variations in CO2 fluxes and carbon budget in a winter-wheat and summer-maize rotation system in the North China Plain (NCP) to better understand the CO2 flux exchange and the underlying mechanisms of carbon budget dynamics. During 2003-2010, the inter-annual variability of monthly gross primary productivity was significantly correlated with leaf area index (LAI), temperature and net radiation. The inter-annual variability of monthly ecosystem respiration was significantly correlated with LAI, soil temperature and moisture. Daily and monthly variability in net ecosystem exchange (NEE) was significantly correlated with LAI. At a seasonal scale, soil moisture was one of the primary factors controlling carbon sequestration of wheat system. Nitrogen application rate and water conditions were the primary factors controlling carbon sequestration of maize system. The NEE for winter wheat system, maize system, and winter wheat-maize rotation system in the NCP ranged from -418 to -29, -448 to -119, and -857 to -274 gCm−2, respectively. The net biome productivity (NBP) for winter wheat system, maize system, and winter wheat-maize rotation system in the NCP ranged from -223 to 151, -236 to 94, and -239 to 237 gCm−2, respectively. Taking greenhouse gas (GHG) emissions from irrigation, fertilization, herbicides, fungicide, insecticide, and field operations, the associated net GHG emissions ranged from -39 to 325 gCm−2 for wheat system and -22 to 287 gCm−2 for maize system. Wheat and maize systems in the region were a medium source of GHG emissions in most of years, mainly due to the large application rates of fertilization and irrigation. Our findings gain new insights into the mechanisms underlying the inter-annual variations in CO2 fluxes and carbon budget, highlighting the optimization of genotype, environment and management interactions to realize climate smart agriculture.

Machine Learning and Regression Analysis Reveal Different Patterns of Influence on Net Ecosystem Exchange at Two Conifer Woodland Sites

Variations in net ecosystem exchange (NEE) of carbon dioxide, and the variables influencing it, at woodland sites over multiple years determine the long term performance of those sites as carbon sinks. In this study, weekly-averaged data from two AmeriFlux sites in North America of evergreen woodland, in different climatic zones and with distinct tree and understory species, are evaluated using four multi-linear regression (MLR) and seven machine learning (ML) models. The site data extend over multiple years and conform to the FLUXNET2015 pre-processing pipeline. Twenty influencing variables are considered for site CA-LP1 and sixteen for site US-Mpj. Rigorous k-fold cross validation analysis verifies that all eleven models assessed generate reproducible NEE predictions to varying degrees of accuracy. At both sites, the best performing ML models (support vector regression (SVR), extreme gradient boosting (XGB) and multi-layer perceptron (MLP)) substantially outperform the MLR models in terms of their NEE prediction performance. The ML models also generate predicted versus measured NEE distributions that approximate cross-plot trends passing through the origin, confirming that they more realistically capture the actual NEE trend. MLR and ML models assign some level of importance to all influential variables measured but their degree of influence varies between the two sites. For the best performing SVR models, at site CA-LP1, variables air temperature, shortwave radiation outgoing, net radiation, longwave radiation outgoing, shortwave radiation incoming and vapor pressure deficit have the most influence on NEE predictions. At site US-Mpj, variables vapor pressure deficit, shortwave radiation incoming, longwave radiation incoming, air temperature, photosynthetic photon flux density incoming, shortwave radiation outgoing and precipitation exert the most influence on the model solutions. Sensible heat exerts very low influence at both sites. The methodology applied successfully determines the relative importance of influential variables in determining weekly NEE trends at both conifer woodland sites studied.

Net ecosystem CO2 exchange from jute crop (Corchorus olitorius L.) and its environmental drivers in tropical Indo-Gangetic plain using open-path eddy covariance technique.

Present study is a maiden attempt to assess net ecosystem exchange (NEE) of carbon dioxide (CO2) flux from jute crop (Corchorus olitorius L.) in the Indo-Gangetic plain by using open-path eddy covariance (EC) technique. Diurnal variations of NEE were strongly influenced by growth stages of jute crop. Daytime peak NEE varied from - 5µmolm-2s-1 (in germination stage) to - 23µmolm-2s-1 (in fibre development stage). The ecosystem was net CO2 source during nighttime with an average NEE value of 5-8μmolm-2s-1. Combining both daytime and nighttime CO2 fluxes, jute ecosystem was found to be a net CO2 sink on a daily basis except the initial 9days from date of sowing. Seasonal and growth stage-wise NEEs were computed, and the seasonal total NEE over the jute season was found to be - 268.5gC m-2(i.e. 10.3 t CO2 ha-1). In different jute growth stages, diurnal variations of NEE were strongly correlated (R2 > 0.9) with photosynthetic photon flux density (PPFD). Ecosystem level photosynthetic efficiency parameters were estimated at each growth stage of jute crop using the Michaelis-Menten equation. The maximum values of photosynthetic capacity (Pmax, 63.3 ± 1.15µmol CO2 m-2s-1) and apparent quantum yield (α, 0.072 ± 0.0045µmol CO2 µmol photon-1) were observed during the active vegetative stage, and the fibre development stage, respectively. Results of the present study would significantly contribute to understanding of the carbon flux from the Indian agro-ecosystems, which otherwise are very sparse.

Water and carbon dioxide fluxes over a “floating blanket” wetland in southwest of China with eddy covariance method

Large uncertainties exist in carbon sequestration and water exchange process in wetland ecosystems under climate change. Beihai wetland is a very rare special type of alpine marsh with plants floating on the water surface located in the southeast margin area of the Tibetan Plateau. Based on the eddy covariance measurements from July 2015 to December 2016 over Baihai wetland, we have investigated the patterns of energy and carbon dioxide fluxes between the air and the alpine marsh and their main drivers. The latent heat flux (LE) was the main consumer of available radiation which accounted for 63% of net radiation (Rn) in 2016. Rn was the main factor determining H at both wet and dry season. Air temperature (Ta) and vapor pressure deficit (VPD)were most influenced with half-hourly LE during both wet and dry season, and VPD showed a larger influence on NEE in dry season than that in wet season. The total evapotranspiration (ET) in wet season occupied 60% of annual ET with an amount of 762.3 mm yr−1 in 2016, which was much lower than the annual total precipitation (1780.2 mm yr−1). Photosynthetic active radiation (PAR) was the main controller in the half-hourly net ecosystem exchange (NEE), while Ta mainly controlled the seasonal variations in NEE. The annual total NEE, gross primary production (GPP) and ecosystem respiration (RE) were -233.8, 796.6 and 562.8 g C m−2 yr−1 in 2016. The fraction of vegetation to water surface changed from 66% to 74% in the study period. A positive linearly relationship was both found between the fraction of vegetation to water surface and CO2 uptake and evapotranspiration. The fraction of vegetation to water surface could explain 40% variation of ET and 62% variation of NEE, respectively.