Net ecosystem exchange of CO2 and H2O fluxes from irrigated grain sorghum and maize in the Texas High Plains

Dynamics of carbon and water vapor fluxes in three typical ecosystems of Heihe River Basin, Northwestern China

Understanding the dynamics of carbon and water vapor fluxes in arid inland river basin ecosystems is essential for predicting and assessing the regional carbon-water budget amid climate change. However, studies aiming to unravel the mechanisms driving the variations and coupling process of regional carbon-water budget in a changing environment in arid regions are limited. Here, we used the eddy covariance technique to analyze the relationship between CO2 and H2O fluxes in three typical ecosystems across the upper, middle, and lower reaches of an arid inland river basin in Northwestern China. Our results showed that all ecosystems acted as carbon sinks, with the alpine swamp meadow, cropland, and desert shrubland sequestrating -300.2 ± 0.01, -644.8 ± 2.9, and - 203.7 ± 22.5 g C m-2 yr-1, respectively. Air temperature (Ta) primarily controlled daily gross primary productivity (GPP) and net ecosystem CO2 exchange (NEE) in the irrigated cropland during the growing season, while soil temperature (Ts) and vapor pressure deficit (VPD) regulated these parameters in the alpine swamp meadow and desert shrubland. Additionally, Ta and net radiation (Rn) controlled daily evapotranspiration (ET) in cropland, while Ts and Rn regulated ET at other sites. Consequently, carbon and water vapor fluxes of all three ecosystems tended to be energy-limited during the growing season. The differential responses of carbon and water vapor fluxes in the upper, middle, and lower reaches of these ecosystems to biophysical factors determined their distinct coupling and variations in water use efficiency. Notably, the desert shrub ecosystem in the lower reach of the basin maintained a stable balance between carbon gain and water loss, indicating adaptation to aridity. This study provides valuable insights into the underlying mechanisms behind the changes in carbon and water vapor fluxes and water-use efficiency in arid river basin ecosystems.

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 - 5 µmol m^-2 s^-1 (in germination stage) to - 23 µmol m^-2 s^-1 (in fibre development stage). The ecosystem was net CO₂ source during nighttime with an average NEE value of 5-8 μmol m^-2 s^-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 9 days 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 - 268.5 gC 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 (P_max, 63.3 ± 1.15 µmol CO₂ m^-2 s^-1) and apparent quantum yield (α, 0.072 ± 0.0045 µmol 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.

Variations in seasonal and inter-annual carbon fluxes in a semi-arid sandy maize cropland ecosystem in China's Horqin Sandy Land

Sandy cropland ecosystems are major terrestrial ecosystems in semi-arid regions of northern China's Horqin Sandy Land, where they play an important role in the regional carbon balance. Continuous observation of the CO₂ flux was conducted from 2014 to 2018 using the eddy covariance technique in a sandy maize cropland ecosystem in the Horqin Sandy Land. We analyzed carbon fluxes (the net ecosystem exchange (NEE) of CO₂, ecosystem respiration (R_eco), and the gross primary productivity (GPP) and their responses to environmental factors at different temporal scales using Random Forest models and correlation analysis. We found that the sandy cropland was a carbon sink, with an annual mean NEE of -124.4 g C m^-2 yr^-1. However, after accounting for carbon exports and imports, the cropland became a net carbon source, with net biome production ranging from -501.1 to -266.7 g C m^-2 yr^-1. At a daily scale, the Random Forest algorithm revealed that photosynthetic photon flux density, soil temperature, and soil moisture were the main drivers for variation of GPP, R_eco, and NEE at different integration periods. At a monthly scale, GPP and R_eco increased with increasing leaf area index (LAI), so the maize ecosystem's carbon sequestration capacity increased with increasing LAI. At an annual scale, water availability (precipitation and irrigation) played a dominant role in explaining inter-annual variability of GPP and R_eco. Affected by climate (e.g., precipitation) and field management (e.g., cultivation, irrigation), carbon fluxes differed greatly between years in the maize system.

Effect of Climate Change on CO2 Flux in Temperate Grassland, Subtropical Artificial Coniferous Forest and Tropical Rain Forest Ecosystems

The interactions between CO₂ flux, an important component of ecosystem carbon flux, and climate change vary significantly among different ecosystems. In this research, the inter-annual variation characteristics of ecosystem respiration (RE), gross ecosystem exchange (GEE), and net ecosystem exchange (NEE) were explored in the temperate grassland (TG) of Xilinhot (2004–2010), the subtropical artificial coniferous forest (SACF) of Qianyanzhou (2003–2010), and the tropical rain forest (TRF) of Xishuangbanna (2003–2010). The main factors of climate change affecting ecosystem CO₂ flux were identified by redundancy analysis, and exponential models and temperature indicators were constructed to consider the relationship between climate change and CO₂ flux. Every year from 2003 to 2010, RE and GEE first increased and then decreased, and NEE showed no significant change pattern. TG was a carbon source, whereas SACF and TRF were carbon sinks. The influence of air temperature on RE and GEE was greater than that of soil temperature, but the influence of soil moisture on RE and GEE was greater than that of air moisture. Compared with moisture and photosynthetically active radiation, temperature had the greatest impact on CO₂ flux and the exponential model had the best fitting effect. In TG and SACF, the average temperature was the most influential factor, and in TRF, the accumulated temperature was the most influential factor. These results provide theoretical support for mitigating and managing climate change and provide references for achieving carbon neutrality.

Abiotic and biotic factors contribute to CO2 exchange variation at the hourly scale in a semiarid maize cropland

Understanding the variables influencing the carbon budget in agricultural ecosystems is crucial for the prediction of future carbon dynamics. The purpose of this study was to identify the biotic and abiotic determinants of the net ecosystem CO₂ exchange (NEE) and net assimilation rate (NPP) in a semiarid maize cropland. The CO₂ exchange (NEE and NPP) was measured at different growth stages of maize plants using an improved chamber methodology. Heat map clustering of the correlation coefficients between CO₂ exchange and its driving factors demonstrated that soil temperature and air humidity were positively correlated with CO₂ emissions regardless of daytime or nighttime, while other factors affecting CO₂ exchange were negatively correlated with emissions during daytime yet positively correlated during nighttime. The machine learning algorithm random forest (RF) and structural equation modeling (SEM) were used to analyze the effects of different factors on CO₂ exchange. The RF analysis results indicated that for CO₂ exchange in the daytime, photosynthetically active radiation (PAR) was the most important variable and presented an importance score of 0.574 for NEE and 0.558 for NPP. The SEM results indicated that in the daytime PAR exerted significant direct and indirect effects on both NEE and NPP, and the standardized direct and indirect effects were -0.668 and 0.022, respectively, for NEE, and the effects were 0.655 and -0.011, respectively for NPP. Like PAR, soil water content also exerted significant direct and indirect effects on both NEE and NPP, but the remaining factors affecting CO₂ exchange only have one of the direct or indirect effects, sometimes neither. For CO₂ exchange at night, the leaf area was the most important variable and presented an importance score of 0.72 for NEE and 0.45 for NPP. At night, both the direct and indirect effects of most abiotic factors on NEE and NPP were significant.

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.

Dynamics of CO2 and H2O fluxes in Johnson grass in the U.S. Southern Great Plains

Johnson grass (Sorghum halepense (L.) Pers.) is rapidly spreading throughout the continental United States (U.S.). Thus, determining magnitudes and seasonal dynamics of carbon dioxide (CO₂) and water vapor (H₂O) fluxes in Johnson grass is crucial to understand regional changes in hydrology and carbon balance. Using eddy covariance (EC), CO₂ and H₂O fluxes were measured from June 2017 to October 2019 over a rainfed Johnson grass field in central Oklahoma. Hay was harvested from late May to early July each year, with biomass yield ~7.5 t ha^-1. Weekly averaged daily integrated net ecosystem CO₂ exchange (NEE), gross primary production (GPP), and evapotranspiration (ET) reached -8.28 ± 0.76 g C m^-2, 20.02 ± 1.62 g C m^-2, and 5.42 ± 0.26 mm, respectively. Ecosystem water use efficiency (EWUE) and ecosystem light use efficiency (ELUE) ranged from 3.22 to 3.93 g C mm^-1 ET and 0.34 to 0.41 g C mol^-1 PAR (photosynthetically active radiation), respectively, during peak growths. Based on aggregated fluxes for each month over the three years (2017-2019), cumulative annual NEE was -434 ± 112 g C m^-2, indicating a carbon gain by the Johnson grass field. Cumulative annual ET (858 ± 72 mm) was ~86% of the average annual rainfall (996 ± 100 mm). Results showed Johnson grass could be a carbon sink from May to September in the U.S. Southern Great Plains. Both NEE and ET did not decline up to air temperature (T_a) of ~33 °C and vapor pressure deficit (VPD) of ~2 kPa, suggesting optimum T_a of ≥33 °C and VPD of ≥2 kPa for the fluxes. Results indicated that Johnson grass might be well suited for dryland production in the region. Additionally, these findings provide initial baseline information on CO₂ fluxes and ET for Johnson grass relative to other forage species in the region.

Carbon dioxide fluxes in a farmland ecosystem of the southern Chinese Loess Plateau measured using a chamber-based method

Background

Farmland accounts for a relatively large fraction of the world’s vegetation cover, and the quantification of carbon fluxes over farmland is critical for understanding regional carbon budgets. The carbon cycle of farmland ecosystems has become a focus of global research in the field of carbon dynamics and cycling. The objectives of this study are to monitor the temporal variation in the net ecosystem exchange (NEE) and soil respiration in a spring maize (Zea mays L.) farmland ecosystem of the southern Loess Plateau of China.

Methods

A fully automated temperature-controlled flux chamber system was adopted in this study. The system contained nine chambers for CO₂ flux measurements, and three treatments were conducted: with and without maize plants in the chamber, as well as a bare field. Observations were conducted from June to September 2011. This time period covers the seedling, jointing, heading, grain filling, and ripening stages of spring maize. Other factors, such as air temperature (Ta), soil temperature (Ts), soil water content (SWC), photosynthetically active radiation (PAR), and precipitation (P), were simultaneously monitored.

Results

There was observed diurnal variation in the NEE of the maize ecosystem (NEE-maize). A short “noon break” occurred when the PAR intensity was at its maximum, while soil respiration rates had curves with a single peak. During the overall maize growth season, the total NEE-maize was –68.61 g C m⁻², and the soil respiration from the maize field (SR-maize) and bare field (SR-bare field) were 245.69 g C m⁻² and 114.08 g C m⁻², respectively. The temperature sensitivity of soil respiration in the maize field exceeded that in the bare field. Significant negative correlations were found between the NEE, PAR, and temperature (all p-values < 0.01), with both Ta and PAR being the primary factors that affected the CO₂ fluxes, collectively contributing 61.7%, 37.2%, and 56.8% to the NEE-maize, SR-maize, and SR-bare field, respectively. It was therefore concluded that both meteorological factors and farming practices have an important impact on the carbon balance process in corn farmland ecosystems. However, it is necessary to conduct long-term observational studies, in order to get a better understanding of the driving mechanism.