Evaluation of biocementation of slope soil for erosion control with low-cost materials

In this study, biocementation of slope soil was performed using low-cost, commercially available materials to create a nutrient solution with native Cytobacillus hornekea. The high cost of laboratory-grade materials and microbes for biocementation is one of the main obstacles to its popularity. However, the cost of biocementation has been reduced significantly without reducing the strength when low-cost materials were used instead of laboratory-grade materials in this study. Direct shear test results and SEM also proved the suitability of the low-cost biocementation. Artificial rainfall with an intensity of 50-60 mm/h resulted in soil erosion of around 10% and 2% without and with biocementation, respectively. The amount of produced calcium carbonate was around 3.9% while using the low-cost materials with native microbes which is quite comparable with the laboratory-grade materials (3.4%).

Biome-scale temperature sensitivity of ecosystem respiration revealed by atmospheric CO2 observations

The temperature sensitivity of ecosystem respiration regulates how the terrestrial carbon sink responds to a warming climate but has been difficult to constrain observationally beyond the plot scale. Here we use observations of atmospheric CO2 concentrations from a network of towers together with carbon flux estimates from state-of-the-art terrestrial biosphere models to characterize the temperature sensitivity of ecosystem respiration, as represented by the Arrhenius activation energy, over various North American biomes. We infer activation energies of 0.43 eV for North America and 0.38 eV to 0.53 eV for major biomes therein, which are substantially below those reported for plot-scale studies (approximately 0.65 eV). This discrepancy suggests that sparse plot-scale observations do not capture the spatial-scale dependence and biome specificity of the temperature sensitivity. We further show that adjusting the apparent temperature sensitivity in model estimates markedly improves their ability to represent observed atmospheric CO2 variability. This study provides observationally constrained estimates of the temperature sensitivity of ecosystem respiration directly at the biome scale and reveals that temperature sensitivities at this scale are lower than those based on earlier plot-scale studies. These findings call for additional work to assess the resilience of large-scale carbon sinks to warming.

Extreme heat events heighten soil respiration

In the wake of climate change, extreme events such as heatwaves are considered to be key players in the terrestrial biosphere. In the past decades, the frequency and severity of heatwaves have risen substantially, and they are projected to continue to intensify in the future. One key question is therefore: how do changes in extreme heatwaves affect the carbon cycle? Although soil respiration (Rs) is the second largest contributor to the carbon cycle, the impacts of heatwaves on Rs have not been fully understood. Using a unique set of continuous high frequency in-situ measurements from our field site, we characterize the relationship between Rs and heatwaves. We further compare the Rs response to heatwaves across ten additional sites spanning the contiguous United States (CONUS). Applying a probabilistic framework, we conclude that during heatwaves Rs rates increase significantly, on average, by ~ 26% relative to that of non-heatwave conditions over the CONUS. Since previous in-situ observations have not measured the Rs response to heatwaves (e.g., rate, amount) at the high frequency that we present here, the terrestrial feedback to the carbon cycle may be underestimated without capturing these high frequency extreme heatwave events.

Enzymes, Manganese, or Iron? Drivers of Oxidative Organic Matter Decomposition in Soils

Oxidative decomposition of soil organic matter determines the proportion of carbon that is either stored or emitted to the atmosphere as CO₂. Full conversion of organic matter to CO₂ requires oxidative mechanisms that depolymerize complex molecules into smaller, soluble monomers that can be respired by microbes. Current models attribute oxidative depolymerization largely to the activity of extracellular enzymes. Here we show that reactive manganese (Mn) and iron (Fe) intermediates, rather than other measured soil characteristics, best predict oxidative activity in temperate forest soils. Combining bioassays, spectroscopy, and wet-chemical analysis, we found that oxidative activity in surface litters was most significantly correlated to the abundance of reactive Mn(III) species. In contrast, oxidative activity in underlying mineral soils was most significantly correlated to the abundance of reactive Fe(II/III) species. Positive controls showed that both Mn(III) and Fe(II/III) species are equally potent in generating oxidative activity, but imply conventional bioassays have a systematic bias toward Fe. Combined, our results highlight the coupled biotic-abiotic nature of oxidative mechanisms, with Mn-mediated oxidation dominating within Mn-rich organic soils and Fe-mediated oxidation dominating Fe-rich mineral soils. These findings suggest microbes rely on different oxidative strategies depending on the relative availability of Fe and Mn in a given soil environment.

Comparison of Soil Greenhouse Gas Fluxes during the Spring Freeze–Thaw Period and the Growing Season in a Temperate Broadleaved Korean Pine Forest, Changbai Mountains, China

Soils in mid-high latitudes are under the great impact of freeze–thaw cycling. However, insufficient research on soil CO2, CH4, and N2O fluxes during the spring freeze–thaw (SFT) period has led to great uncertainties in estimating soil greenhouse gas (GHG) fluxes. The present study was conducted in a temperate broad-leaved Korean pine mixed forest in Northeastern China, where soils experience an apparent freeze–thaw effect in spring. The temporal variations and impact factors of soil GHG fluxes were measured during the SFT period and growing season (GS) using the static-chamber method. The results show that the soil acted as a source of atmospheric CO2 and N2O and a sink of atmospheric CH4 during the whole observation period. Soil CO2 emission and CH4 uptake were lower during the SFT period than those during the GS, whereas N2O emissions were more than six times higher during the SFT period than that during the GS. The responses of soil GHG fluxes to soil temperature (Ts) and soil moisture during the SFT and GS periods differed. During the SFT period, soil CO2 and CH4 fluxes were mainly affected by the volumetric water content (VWC) and Ts, respectively, whereas soil N2O flux was influenced jointly by Ts and VWC. The dominant controlling factor for CO2 was Ts during the GS, whereas CH4 and N2O were mainly regulated by VWC. Soil CO2 and N2O fluxes accounted for 97.3% and 3.1% of the total 100-year global warming potential (GWP100) respectively, with CH4 flux offsetting 0.4% of the total GWP100. The results highlight the importance of environmental variations to soil N2O pulse during the SFT period and the difference of soil GHG fluxes between the SFT and GS periods, which contribute to predicting the forest soil GHG fluxes and their global warming potential under global climate change.

Bimodal diel pattern in peatland ecosystem respiration rebuts uniform temperature response

Accurate projections of climate change impacts on the vast carbon stores of northern peatlands require detailed knowledge of ecosystem respiration (ER) and its heterotrophic (Rh) and autotrophic (Ra) components. Currently, however, standard flux measurement techniques, i.e. eddy covariance and manual chambers, generate empirical ER data during only night- or daytime, respectively, which are extrapolated to the daily scale based on the paradigm that assumes a uniform diel temperature response. Here, using continuous autochamber measurements, we demonstrate a distinct bimodal pattern in diel peatland ER which contrasts the unimodal pattern inherent to the classical assumption. This feature results from divergent temperature dependencies of day- and nighttime ER due to varying contributions from Rh and Ra. We further find that disregarding these bimodal dynamics causes significant bias in ER estimates across multiple temporal scales. This calls for improved process-based understanding of ER to advance our ability to simulate peatland carbon cycle-climate feedbacks.

Predicting the fate of carbon in peatlands relies on assumptions of behaviour in response to temperature. Here, the authors show that the temperature dependency of respiratory carbon losses shift strongly over day-night cycles, an overlooked facet causing bias in peatland carbon cycle simulations.

Manganese-Driven Carbon Oxidation at Oxic-Anoxic Interfaces

The formation of reactive manganese (Mn) species is emerging as a key regulator of carbon oxidation rates, and thus CO₂ emissions, in soils and sediments. Many subsurface environments are characterized by steep oxygen gradients, forming oxic-anoxic interfaces that enable rapid redox cycling of Mn. Here, we examined the impact of Mn(II)_aq oxidation along oxic-anoxic interfaces on carbon oxidation in soils using laboratory-based diffusion reactors. A combination of cyclic voltammetry, X-ray absorption spectroscopy, and X-ray microprobe imaging revealed a tight coupling between Mn(II)_aq oxidation and carbon oxidation at the oxic-anoxic interface. Specifically, zones of Mn(II)_aq oxidation across the oxic-anoxic transition also exhibited the greatest lignin oxidation potential, carbon solubilization, and oxidation. Microprobe imaging further revealed that the generation of Mn(III)-dominated precipitates coincided with carbon oxidation. Combined, our findings demonstrate that biotic Mn(II)_aq oxidation, specifically the formation of Mn(III) species, contributes to carbon oxidation along oxic-anoxic interfaces in soils and sediments. Our results suggest that we should regard carbon oxidation not merely as a function of molecular composition, which insufficiently predicts rates, but in relation to microenvironments favoring the formation of critically important oxidants such as Mn(III).

Spatial Variation of Soil Respiration in a Cropland under Winter Wheat and Summer Maize Rotation in the North China Plain

Spatial variation of soil respiration (R_s) in cropland ecosystems must be assessed to evaluate the global terrestrial carbon budget. This study aims to explore the spatial characteristics and controlling factors of R_s in a cropland under winter wheat and summer maize rotation in the North China Plain. We collected R_s data from 23 sample plots in the cropland. At the late jointing stage, the daily mean R_s of summer maize (4.74 μmol CO₂ m^-2 s^-1) was significantly higher than that of winter wheat (3.77μmol CO₂ m^-2 s^-1). However, the spatial variation of R_s in summer maize (coefficient of variation, CV = 12.2%) was lower than that in winter wheat (CV = 18.5%). A similar trend in CV was also observed for environmental factors but not for biotic factors, such as leaf area index, aboveground biomass, and canopy chlorophyll content. Pearson’s correlation analyses based on the sampling data revealed that the spatial variation of R_s was poorly explained by the spatial variations of biotic factors, environmental factors, or soil properties alone for winter wheat and summer maize. The similarly non-significant relationship was observed between R_s and the enhanced vegetation index (EVI), which was used as surrogate for plant photosynthesis. EVI was better correlated with field-measured leaf area index than the normalized difference vegetation index and red edge chlorophyll index. All the data from the 23 sample plots were categorized into three clusters based on the cluster analysis of soil carbon/nitrogen and soil organic carbon content. An apparent improvement was observed in the relationship between R_s and EVI in each cluster for both winter wheat and summer maize. The spatial variation of R_s in the cropland under winter wheat and summer maize rotation could be attributed to the differences in spatial variations of soil properties and biotic factors. The results indicate that applying cluster analysis to minimize differences in soil properties among different clusters can improve the role of remote sensing data as a proxy of plant photosynthesis in semi-empirical R_s models and benefit the acquisition of R_s in cropland ecosystems at large scales.

Contrasting diel hysteresis between soil autotrophic and heterotrophic respiration in a desert ecosystem under different rainfall scenarios

Diel hysteresis occurs often between soil CO2 efflux (R(S)) and temperature, yet, little is known if diel hysteresis occurs in the two components of R(S), i.e., autotrophic respiration (R(A)) and heterotrophic respiration (R(H)), and how diel hysteresis will respond to future rainfall change. We conducted a field experiment in a desert ecosystem in northern China simulating five different scenarios of future rain regimes. Diel variations of soil CO2 efflux and soil temperature were measured on Day 6 and Day 16 following the rain addition treatments each month during the growing season. We found contrasting responses in the diel hysteresis of R(A) and R(H) to soil temperature, with a clockwise hysteresis loop for R(H) but a counter-clockwise hysteresis loop for R(A). Rain addition significantly increased the magnitude of diel hysteresis for both R(H) and R(A) on Day 6, but had no influence on either on Day 16 when soil moisture was much lower. These findings underline the different roles of biological (i.e. plant and microbial activities) and physical-chemical (e.g. heat transport and inorganic CO2 exchange) processes in regulating the diel hysteresis of R(A) and R(H), which should be considered when estimating soil CO2 efflux in desert regions under future rainfall regime.

Global convergence in leaf respiration from estimates of thermal acclimation across time and space

Recent compilations of experimental and observational data have documented global temperature-dependent patterns of variation in leaf dark respiration (R), but it remains unclear whether local adjustments in respiration over time (through thermal acclimation) are consistent with the patterns in R found across geographical temperature gradients. We integrated results from two global empirical syntheses into a simple temperature-dependent respiration framework to compare the measured effects of respiration acclimation-over-time and variation-across-space to one another, and to a null model in which acclimation is ignored. Using these models, we projected the influence of thermal acclimation on: seasonal variation in R; spatial variation in mean annual R across a global temperature gradient; and future increases in R under climate change. The measured strength of acclimation-over-time produces differences in annual R across spatial temperature gradients that agree well with global variation-across-space. Our models further project that acclimation effects could potentially halve increases in R (compared with the null model) as the climate warms over the 21st Century. Convergence in global temperature-dependent patterns of R indicates that physiological adjustments arising from thermal acclimation are capable of explaining observed variation in leaf respiration at ambient growth temperatures across the globe.

Soil respiration as affected by long-term broiler litter application to a udult in the ozark highlands

The United States produced 8.4 billion broiler chickens () and an estimated 10.1 to 14.3 million Mg of broiler litter (BL) in 2012. Arkansas' production of 1 billion broilers in 2012 produced an estimated 1.2 to 1.7 million Mg of BL, most of which was concentrated in the Ozark Highlands region of northwest Arkansas. Increased CO release from soils associated with agricultural practices has generated concerns regarding the contribution of certain agricultural management practices to global warming. The objectives of this study were to evaluate the effects of long-term (>6 yr) BL application to a Udult on soil respiration and annual C emissions and to determine the predictability of soil respiration based on soil temperature and moisture in the Ozark Highlands region of northwest Arkansas. Soil respiration was measured routinely between May 2009 and May 2012 in response to annual BL application rates of 0, 5.6, and 11.2 Mg dry litter ha that began in 2003. Soil respiration varied ( < 0.01) with BL rate, measurement date, and year. Additions of BL stimulated respiration after application, and rainfall events after dry-soil conditions stimulated respiration in all years. Soil temperature at the 10-cm depth, 0- to 6-cm soil volumetric water content (VWC), and annual CO-C emissions were unaffected ( > 0.05) by BL application rate but differed ( < 0.01) among study years. Multiple regression indicated that soil respiration could be reasonably predicted using 2-cm-depth soil temperature (T) and the product of T and VWC as predictors ( = 0.52; < 0.01). Results indicate that organic amendments, such as BL, can stimulate release of CO from the soil to the atmosphere, potentially negatively affecting atmospheric greenhouse gas concentrations; thus, there may be application rates above which the benefits of organic amendments may be diminished by adverse environmental effects. Improved BL management strategies are needed to lessen the loss of CO from BL-amended soils.

Modeling spatial patterns of soil respiration in maize fields from vegetation and soil property factors with the use of remote sensing and geographical information system

To examine the method for estimating the spatial patterns of soil respiration (R_s) in agricultural ecosystems using remote sensing and geographical information system (GIS), R_s rates were measured at 53 sites during the peak growing season of maize in three counties in North China. Through Pearson's correlation analysis, leaf area index (LAI), canopy chlorophyll content, aboveground biomass, soil organic carbon (SOC) content, and soil total nitrogen content were selected as the factors that affected spatial variability in R_s during the peak growing season of maize. The use of a structural equation modeling approach revealed that only LAI and SOC content directly affected R_s. Meanwhile, other factors indirectly affected R_s through LAI and SOC content. When three greenness vegetation indices were extracted from an optical image of an environmental and disaster mitigation satellite in China, enhanced vegetation index (EVI) showed the best correlation with LAI and was thus used as a proxy for LAI to estimate R_s at the regional scale. The spatial distribution of SOC content was obtained by extrapolating the SOC content at the plot scale based on the kriging interpolation method in GIS. When data were pooled for 38 plots, a first-order exponential analysis indicated that approximately 73% of the spatial variability in R_s during the peak growing season of maize can be explained by EVI and SOC content. Further test analysis based on independent data from 15 plots showed that the simple exponential model had acceptable accuracy in estimating the spatial patterns of R_s in maize fields on the basis of remotely sensed EVI and GIS-interpolated SOC content, with R² of 0.69 and root-mean-square error of 0.51 µmol CO₂ m⁻² s⁻¹. The conclusions from this study provide valuable information for estimates of R_s during the peak growing season of maize in three counties in North China.