Soil Respiration Data Research Articles

How do trade-offs between ecological construction and urbanization affect regional carbon balance? A case study from China’s Yellow River Basin

Anthropogenic activities, including trade-offs between ecological construction and urbanization, alter land use by either adding or subtracting from the carbon balance. Therefore, it is unknown how the trade-offs between ecological construction and urbanization impact the regional carbon balance. We selected the Yellow River Basin for this study to shed light on how human activity affects the carbon balance and promote the advancement of the objective of becoming carbon–neutral. Using panel data, soil respiration data, and GEM-CO2 models, carbon emissions and sinks in multiple fields were quantified on a raster scale based on multi-source remote sensing data. The trade-offs between urbanization and ecological construction were then spatially illustrated through changes in ground cover. Finally, a raster-scale study was conducted to investigate the ways in which trade-offs between urbanization and ecological construction impact the regional carbon balance. The basin was able to maintain a carbon balance in 2001; however, by 2019, it experienced a severe carbon imbalance. The primary causes of this were rapid growth in energy consumption and direct household waste incineration. By 2019, 84.79% of rasters had a trade-off connection, indicating an increasing trend in the degree of trade-off between ecological construction and urbanization. This affected the pattern of land use in the basin, which in turn affected the carbon balance. Rapid urbanization has exacerbated the carbon imbalance, but ecological construction can reverse this trend. The carbon balance was negatively correlated with the trade-off between ecological construction and urbanization, and the conversion of natural resources by human activity hastened the spread of regional carbon imbalances.

A cost-effective method for quantifying soil respiration

Soil respiration is a critical indicator of microbial activity, reflecting soil health and its ability to deliver essential ecosystem services and functions. Chamber-based measurements provide direct observations and are relatively simple to implement, making them a popular choice for soil respiration studies. However, current methods to quantify soil respiration are limited in their applicability by high costs, substrate requirements, accuracy concerns and substantial setup time. With an increasing emphasis on routine analysis of soil respiration, existing methods are insufficient to provide the temporal and spatial scale required to proliferate soil respiration data. This study assesses the potential of an affordable and commercially available Non-Dispersive Infrared (NDIR) CO2 detector, used in conjunction with an easy-to assemble closed chamber, to provide reliable and accessible soil respiration measurements. The CO2 detectors utilised in this study are cost-effective yet precise digital detectors, successfully validated against a laboratory reference sensor and the standard alkali trap method. Beyond that, the detector has effectively yielded consistent results across replicates and demonstrated sensitivity similar to standard methods. The proposed detector offers an alternative to traditional laboratory methods by removing the necessity to perform soil respiration analysis in a laboratory setting. The proliferation of soil respiration data would facilitate the increasing interest in accessible information on soil respiration from stakeholders, citizen scientists and decision makers.

Asymptotic optimality of the nonnegative garrote estimator in threshold models

In this paper, it is aimed to broad Xiong's scope of analysis to the threshold models and develop an adjusted Mallows criterion to select the garrote parameter vector. Compared with other penalized least-squares methods, the nonnegative garrote (NG) method has a natural penalty and tuning parameter. Furthermore, we show the asymptotic optimality of the NG estimator by referring to the idea of the asymptotic optimality of the model averaging estimator under some regular conditions. Our investigation of finite-sample performance demonstrates that the proposed method exhibits very favorable properties with respect to the ratio of the correct number of zero estimated components (RCNZ) and the root mean square error (RMSE) compared to the least absolute shrinkage and selection operator (LASSO), the smoothly clipped absolute deviation (SCAD) and the minimax concave penalty (MCP) penalized regression methods. The proposed method is applied to the analysis of body fat data, soil respiration data and the US unemployment rate data and performs well.

2010–2014年哀牢山亚热带常绿阔叶林土壤呼吸数据集

Subtropical forests, as the largest forest type in China, play an important role in regulating global climate change and maintaining atmospheric carbon balance. The carbon emitted by soil through respiration is the main contributor of atmospheric CO2 levels, so even slight changes in soil carbon pool can significantly affect the concentration of atmospheric CO2. Therefore, at present, the magnitude and dynamic change characteristics of soil carbon storage in subtropical forests are attached great importance to. Ailao Mountain in Yunan Province stands as a crucial region for the distribution of subtropical forests in China. This study used a multichannel automated chamber system to estimate soil respiration in a subtropical evergreen broad-leaved forest ecosystem of Ailao Mountain. Ailao Mountain Station for Subtropical Forest Ecosystem Studies is a national field station and a basic observation station of the Chinese Ecosystem Research Network. We gathered soil respiration data from the subtropical evergreen broad-leaved forest in Ailao Mountain from 2011 to 2014, including soil temperature 5cm (TS), soil moisture content 10cm (SWC) and soil respiration data (RS), organized at daily, monthly and annual scales. This dataset is of great significance in revealing the effects of climate change on soil ecological processes in subtropical evergreen broad-leaved forests. It can facilitate precise evaluation of soil organic carbon emissions and forest ecosystem management, and provide both empirical and theoretical basis for further research on the effects of global changes (e.g. climate warming) on soil respiration components, especially with a focus on soil organic carbon emissions.

Carbon Dioxide Emission from Soils of the Ecotone Zone in the North of Western Siberia

The study area in the north of Western Siberia is located in the southern tundra–taiga ecotone near the southern boundary of discontinuous permafrost zone. Three contrasting ecosystems—pine forests with Albic Podzols; palsa with Histic Oxyaquic Turbic Cryosols, and bogs with Fibric Histosols—predominate in this area. The objectives of the study included evaluation of the CO2 emission from soils in the growing seasons of 2019–2022 and analysis of the factors controlling spatial and interannual variability of the emission. The study included analysis of the soil respiration (static closed chamber method) data and soil hydrothermal parameters in August for four years. In the absence of definite trends in climatic parameters over the past 10 years, a gradual increase in the soil temperature in all ecosystems and an increase in the depth of summer thawing in palsa were observed. These changes were not accompanied by significant changes in the CO2 emission. Its averaged values varied from 485 to 540 mg CO2/(m2 h) in forest ecosystems and from 150 to 255 mg CO2/(m2 h) in the palsa–bog complex with high coefficients of spatial variability. High CO2 emission in forest ecosystems is determined by a favorable hydrothermal regime, high root biomass, and good water-physical properties. Part of the CO2 produced by palsa soils is transported with suprapermafrost water flows toward the adjacent bog and is released from the surface of bog soils. Soil temperature interrelated with seasonal thawing depth proved to be a significant predictor of the spatial variability of CO2 emission from the soils of the palsa–bog complex.

Artificial neural networks and adaptive neuro-fuzzy inference systems for prediction of soil respiration in forested areas southern Brazil.

The purpose of this study was to estimate the temporal variability of CO2 emission (FCO2) from O2 influx into the soil (FO2) in a reforested area with native vegetation in the Brazilian Cerrado, as well as to understand the dynamics of soil respiration in this ecosystem. The database is composed of soil respiration data, agroclimatic variables, improved vegetation index (EVI), and soil attributes used to train machine learning algorithms: artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS). The predictive performance was evaluated based on the mean absolute error (MEA), root mean square error (RMSE), mean absolute percentage error (MAPE), agreement index (d), confidence coefficient (c), and coefficient of determination (R2). The best estimation results for validation were FCO2 with multilayer perceptron neural network (MLP) (R2 = 0.53, RMSE = 0.967µmolm-2s-1) and radial basis function neural network (RBF) (R2 = 0.54, RMSE = 0.884µmolm-2s-1) and FO2 with MLP (R2 = 0.45, RMSE = 0.093mgm-2s-1) and RBF (R2 = 0.74, 0.079mgm-2s-1). Soil temperature and macroporosity are important predictors of FCO2 and FO2. The best combination of variables for training the ANFIS was selected based on trial and error. The results were as follows: FCO2 (R2 = 16) and FO2 (R2 = 29). In all models, FCO2 outperformed FO2. A primary factor analysis was performed, and FCO2 and FO2 correlated best with the weather and soil attributes, respectively.

Aggregational differentiation of soil-respired CO2 and its δ13C variation across land-use types

Land-use type can affect CO2 emission fluxes, but whether it has effects on the δ13C of respired CO2 (δ13CO2), and whether these effects vary with the heterogeneity of the soil microenvironment remain unknown. In this study, long-term soil incubation experiments and soil fractionation were combined to determine the effects of land-use type (upland, paddy and woodland) on soil-respired CO2 and δ13CO2 with different aggregate fractions under three temperature conditions. This study extended the thermal adaptation of soil respiration to the soil microenvironmental scale, i.e., the scale of smaller aggregates than bulk soil. An exponential decay function (R = a × e−bt) was fitted to the temporal soil respiration data, separately for each combination of soil aggregate, land-use type and incubation temperature. At each incubation stage, the woodland exhibited the highest average soil respiration rates. The exponential decay trend of δ13CO2 with time was influenced by the land-use type and incubation temperature; the shift from enriched to depleted δ13CO2 values first appeared in woodland. Interestingly, carbon isotopic discrimination between the substrate and respired CO2 (Δ13C, δ13CO2 – δ13CSOC) increased with aggregate size only in woodland, especially at 30 °C. Overall, aggregational differentiation of soil-respired CO2 and its δ13C was found only in the woodland, and warming intensified these differences, suggesting that it would be difficult to generalize the integrative effects of warming on soil respiration and its isotopic fractionation at the microscale from a single ecosystem study. Our findings highlight that land-use types affect the feedback of soil carbon pool to future global warming at the soil microenvironmental scale (aggregate), and are of great significance for improving carbon emission prediction in terrestrial ecosystems.

Seasonal and diurnal soil respiration dynamics under different land management practices in the sub-tropical highland agroecology of Ethiopia.

The temporal dynamics of soil respiration change in response to different land management practices are not well documented. This study investigated the effects of soil bunds on the monthly and diurnal dynamics of soil respiration rates in the highlands of the Upper Blue Nile basin in Ethiopia. Six plots (with and without soil bunds, three replicates) were used for measurement of seasonal soil respiration, and 18 plots were used for measurement of diurnal soil respiration. We collected seasonal variation data on a monthly basis from September 2020 to August 2021. Diurnal soil respiration data were collected four times daily (5 a.m., 11 a.m., 5 p.m., and 11 p.m.) for 2weeks from 16 to 29 September 2021. A Wilcoxon signed-rank test showed that seasonal soil respiration rates differed significantly (p < 0.05) between soil bund and control plots in all seasons. In plots with soil bunds, seasonal soil respiration rates were lowest in February (1.89 ± 0.3µmol CO2 m-2s-1, mean ± SE) and highest in October (14.54 ± 0.5µmol CO2 m-2s-1). The diurnal soil respiration rate was significantly (p < 0.05) higher at 11 a.m. than at other times, and was lowest at 5 a.m. Seasonal variation in soil respiration was influenced by soil temperature negatively and moisture positively. Diurnal soil respiration was significantly affected by soil temperature but not by soil moisture. Further study is required to explore how differences in soil microorganisms between different land management practices affect soil respiration rates.

Detection of Influencing Factors of Spatial Variability of Soil Respiration in Pangquangou Nature Reserve

Soil respiration is an important process in maintaining global carbon balance. Taking the Pangquangou Nature Reserve as the research area, based on the field measurement of soil respiration (Rs) data combined with altitude (ELE), soil temperature (T), soil moisture (SWC), normalized vegetation index (NDVI), slope (slope), soil total carbon (C), total nitrogen (N), and soil bulk density (BD), we analyzed the main driving forces and interactions of Rs spatial differentiation by using the geographic detector model. The results showed that:① the spatial variation of Rs and its influencing factors in the study area was moderate. The Rs was significantly positively correlated with NDVI, T, and N (P<0.01) and negatively with ELE, slope, and SWC (P<0.01). The Rs was significantly correlated with BD(P<0.05) but not with C(P>0.05). ② The multivariate linear model composed of NDVI and T explained 64.3% of Rs spatial variation. ③ ELE, T, and NDVI were the dominant driving forces of Rs spatial differentiation in the study area, which could explain 64%, 59%, and 48% of the spatial variability. ④ The interaction of the two factors enhanced the explanatory power of Rs spatial differentiation, and the maximum interaction factors were ELE∩BD (q=0.73), and T∩slope (q=0.74), respectively. Therefore, in the process of Rs estimation, combined with topographical and environmental conditions, the interaction between multiple factors should be considered.

CO2 exchange of the ecosystem–atmosphere in a mountain forest ecosystem: Combining stable carbon isotope (δ13C) and soil respiration measurements

Stable carbon isotopes (δ13C) are a useful technique to partition the net ecosystem–atmosphere exchange (NEE) of carbon dioxide (CO2) into gross primary productivity (GPP) and respiratory (R) components. The acquisition of ecosystem flux is usually performed using the eddy covariance (EC) technique, but this method is limited under complex terrain conditions and stable atmospheric conditions. Here, we try to develop the flux partitioning theory of mountain forest ecosystem by combining isotope technology and soil respiration. We used a full growing season of isotopic profile data and soil respiration data from a temperate deciduous forest on Jiufeng Mountain to demonstrate the method and quantify its uncertainties. We found that the assessable systematic uncertainty was ± 29.97% for NEE, ±24.87% for GPP, and ± 36.82% for R. The factors that most strongly affected the uncertainties of GPP and R were canopy CO2 stomatal conductance (gco2), calculated by the Köstner model, and the carbon isotope value of the total respiration in the ecosystem at night (δ13Cn-eco), calculated by the Keeling plot method. The advantage of isotopic methods is not only the relief of terrain restrictions but also the stable atmospheric conditions. The method provides an innovative vision for the partitioning of flux in forest ecosystems, especially for mountain forests.

When does temperature matter for ecosystem respiration?

The temperature response of ecosystem processes is key to understand and predict impacts of climate change. This is especially true for respiration, given its high temperature sensitivity and major role in the global carbon cycle. However, similar intrinsic temperature sensitivity for respiration does not mean comparable temperature effects across ecosystems and biomes because non-temperature factors can be more important. Here we analyzed soil and sediment respiration data and found that in temperature ranges corresponding to high latitude mean temperatures, absolute respiration rates are more sensitive to non-temperature factors than to projected direct temperature effects. However, at higher temperatures (>20 °C) the direct effect of temperature mediated by temperature sensitivity will likely be more important over changes in non-temperature factors in shaping how respiration change over time. This supports past suggestions that the relatively small projected temperature increase at low (tropical) latitudes may have a large direct impact on absolute respiration. In contrast, absolute respiration rates at high (boreal/arctic) latitudes will likely be more sensitive on the development of the non-temperature factors than on the direct effects of the large projected temperature increase there. Social media abstract. Respiration may be less dependent to changes in temperature at higher than lower latitudes.

The effect of boreal jack pine harvest residue retention on soil environment and processes

Forest residue left on the ground following harvesting (i.e., “slash”) plays an important role in moderating the physical and chemical environment of the soil for future forest growth. Leaving too little slash can leave the soil exposed to extreme fluctuations in soil conditions and fewer nutrients that may hinder future forest growth, while leaving too much slash makes it difficult for new trees to establish or increases fire hazard. This study investigated the impacts of blading and different loadings of slash (0, 15, 30 and 60 Mg ha−1 dry mass) on soil physical (temperature and moisture), chemical (soil solution) and biological (soil respiration and net ecosystem exchange) processes over 4 summers at a harvested jack pine stand within the Island Lake Biomass Harvest Experiment in northeastern Ontario. Soil temperature and moisture were highest in the bladed and lowest in the 60 Mg ha−1 slash loading. Soil solution chemistry was generally similar among the 0, 15 and 30 Mg ha−1 slash loadings. However, total organic carbon and potassium had higher concentrations at 60 Mg ha−1 treatment and lower concentrations in the bladed treatment, the opposite trend occurred for pH and nitrate. Over three years the concentrations of cations decreased and nitrogen species increased for bladed to 30 Mg ha−1 treatments. The 60 Mg ha−1 treatment had increases in some solutes over time suggesting there is a lag effect as needles and bark are incorporated into the soil. The soil respiration data showed that lowest rates of CO2 production occurred in the bladed treatment, but increased over time as the forest floor developed. CO2 production was highest in the 60 Mg ha−1 slash loading, with high rates of soil respiration in the first year, as fine debris from slash deposited onto the soil, however little photosynthesis occurred in these treatments. Thus retention of small to moderate amounts of slash seem to be sufficient for maintaining a suitable balance of soil conditions for a regenerating forest over the short term.

High leaf area index inhibits net primary production in global temperate forest ecosystems.

Within limited growth age in some regions, forest production, including gross primary production (GPP) and net primary production (NPP), was linearly correlated with leaf area index (LAI). However, over wide range of growth age in the global scale, LAI patterns of forest production are unclear. Here, we compiled a subset from the Global Soil Respiration Database (SRDB) for global temperate forest ecosystems. The subset database mainly included forest production, soil respiration, and LAI data in 493 study sites over wide range of forest growth age (0-500years). The results showed that LAI initially increased rapidly, reached a peak at juvenility, decreased slowly until maturity, and again increased possibly with further forest aging (R2 = 0.21, P < 0.001). We found that the dynamics of both GPP and NPP across global temperate forest ecosystems were driven by LAI. GPP initially increased and subsequently stabilized with increasing LAI. NPP peaked at LAI of about 5.6m2m-2, and subsequently decreased. The decrease in NPP resulted from the asymptotic increase in GPP and the continuing decrease in the NPP/GPP ratio with increasing LAI. The decline in the NPP/GPP ratio resulted from the significant increase in autotrophic respiration (Ra), and especially after canopy closure, Ra increased more quickly with increasing LAI than GPP. These results will improve our understanding of the control of LAI on ecosystem production.

Gap-filling continuously-measured soil respiration data: A highlight of time-series-based methods

Soil respiration is an important ecosystem process that releases carbon dioxide into the atmosphere. While soil respiration can be measured continuously at high temporal resolutions, gaps in the dataset are inevitable, leading to uncertainties in carbon budget estimations. Therefore, robust methods used to fill the gaps are needed. The process-based non-linear least squares (NLS) regression is the most widely used gap-filling method, which utilizes the established relationship between the soil respiration and temperature. In addition to NLS, we also implemented three other methods based on: 1) artificial neural networks (ANN), driven by temperature and moisture measurements, 2) singular spectrum analysis (SSA), relying only on the time series itself, and 3) the expectation-maximization (EM) approach, referencing to parallel flux measurements in the spatial vicinity. Six soil respiration datasets (2017–2019) from two boreal forests were used for benchmarking. Artificial gaps were randomly introduced into the datasets and then filled using the four methods. The time-series-based methods, SSA and EM, showed higher accuracies than NLS and ANN in small gaps (<1 day). In larger gaps (15 days), the performance was similar among NLS, SSA and EM; however, ANN showed large errors in gaps that coincided with precipitation events. Compared to the observations, gap-filled data by SSA showed similar degree of variances and those filled by EM were associated with similar first-order autocorrelation coefficients. In contrast, data filled by both NLS and ANN exhibited lower variance and higher autocorrelation than the observations. For estimations of the annual soil respiration budget, NLS, SSA and EM resulted in errors between −3.7% and 5.8% given the budgets ranged from 463 to 1152 g C m−2 year−1, while ANN exhibited larger errors from −11.3 to 16.0%. Our study highlights the two time-series-based methods which showed great potential in gap-filling carbon flux data, especially when environmental variables are unavailable.

Linking thermogravimetric data with soil organic carbon fractions

Different soil fractionation approaches are frequently used to separate soil organic carbon (SOC) pools of different turnover time and shed light on soil carbon dynamics. However, these methods are laborious and time consuming. Thermogravimetry might be an alternative. Previous studies revealed relationships between soil organic matter (SOM), total nitrogen, clay content, and soil respiration and thermogravimetry (TG) data represented by TG indicators (mass loss in 10 °C intervals) and TG fractions (mass loss in larger temperature intervals). In this work, both TG indicators and fractions were correlated with the contents of SOC fractions representing different turnover times. The fractions we used were particulate organic carbon, dissolved organic carbon, organic C in the sand fraction and in stable aggregates, organic C attached to silt and clay particles and a chemically resistant fraction (<63 µm and not oxidizable by NaOCl). Fractionation and thermogravimetry was applied to 144 samples from arable and grassland top- and subsoils. The results revealed that both TG indicators and fractions or their combinations correlated with SOC fractions in grassland topsoils (R2 between = 0.70–0.92, p < 0.001 except for the chemically resistant fraction), while the correlation with SOC fractions in arable soils were significantly lower (R2 between 0.36 and 0.72, p < 0.001). No correlations were observed between TG data and SOC fractions in subsoils of arable and grassland soils. Importantly, most of the correlations were observed in soils in which carbon content correlated with TG indicator between 340 and 350 °C. This TG indicator can be used as a differentiator for the application of developed equations for modeling the functional SOC fractions of different turnover times in various types of soils. Thermogravimetry is not able to replace classical fractionation schemes. However, the results showed that thermogravimetry can be an additional tool to assess SOM quality in different land use systems besides traditional chemical-physical fractionation methods.

Determinants of temperature sensitivity of soil respiration with the decline of a foundation species.

The eastern hemlock (Tsuga canadensis) is an important foundation species that is currently declining throughout eastern U.S. forests due to the exotic pests hemlock woolly adelgid (Adelges tsugae) and elongate hemlock scale (Fiorinia externa). Hemlock is often replaced by deciduous tree species, such as black birch (Betula lenta), and has been shown to have large consequences for carbon dynamics due to a substantial loss of soil organic layer carbon storage in hemlock forests when replaced by birch and higher decomposition found in black birch stands. Soil carbon is one of the most important components of the global carbon cycle and has high potential to feedback to climate change when large portions of stored carbon are lost to the atmosphere. There is a general consensus that soil respiration increases with temperature, but there has yet to be a consensus on how temperature sensitivity of soil respiration is affected by various biotic and abiotic factors, such as soil moisture and substrate quality. In this study, the effects of soil temperature and soil moisture on soil respiration (Rs), the temperature sensitivity of soil respiration (Q10), and soil basal respiration (R10) were investigated for hemlock, young birch, and mature birch forest types annually for three years. The Rs values of the three forest types were primarily driven by soil temperature rather than by soil moisture across all years. Soil respiration data collected from hemlock, young birch, and mature birch stands were used to determine annual Q10 and R10 values. The Q10 and R10 values were not significantly different between forest stands, but they were significantly different over the three years. Determinants of Q10 and R10 differed between forest type, with soil moisture primarily influencing Q10 in hemlock and mature birch stands and soil temperature primarily influencing R10 in mature birch stands. The results suggest a complex interaction of soil moisture and soil temperature, and potentially substrate quality and quantity, as determinants of temperature sensitivities in eastern U.S. forests that have transitioned from hemlock-dominated to black birch-dominated forests.

Constraining estimates of global soil respiration by quantifying sources of variability.

Quantifying global soil respiration (RSG ) and its response to temperature change are critical for predicting the turnover of terrestrial carbon stocks and their feedbacks to climate change. Currently, estimates of RSG range from 68 to 98PgCyear-1 , causing considerable uncertainty in the global carbon budget. We argue the source of this variability lies in the upscaling assumptions regarding the model format, data timescales, and precipitation component. To quantify the variability and constrain RSG , we developed RSG models using Random Forest and exponential models, and used different timescales (daily, monthly, and annual) of soil respiration (RS ) and climate data to predict RSG . From the resulting RSG estimates (range=66.62-100.72Pg), we calculated variability associated with each assumption. Among model formats, using monthly RS data rather than annual data decreased RSG by 7.43-9.46Pg; however, RSG calculated from daily RS data was only 1.83Pg lower than the RSG from monthly data. Using mean annual precipitation and temperature data instead of monthly data caused +4.84 and -4.36Pg C differences, respectively. If the timescale of RS data is constant, RSG estimated by the first-order exponential (93.2Pg) was greater than the Random Forest (78.76Pg) or second-order exponential (76.18Pg) estimates. These results highlight the importance of variation at subannual timescales for upscaling to RSG. The results indicated RSG is lower than in recent papers and the current benchmark for land models (98PgCyear-1 ), and thus may change the predicted rates of terrestrial carbon turnover and the carbon to climate feedback as global temperatures rise.

Precipitation events reduce soil respiration in a coastal wetland based on four-year continuous field measurements

Coastal wetlands are considered as a significant sink for global carbon because their organic-rich soils. Given exposed to shallow water tables, water from groundwater is transported upward to the root zone through capillary rise, thus soil moisture in coastal wetlands is relatively high even when there is no precipitation. We expected that as precipitation occurred, the soils in coastal wetlands might become quickly saturated and lead to the development of anoxic conditions. We further hypothesized that such anoxic conditions might decrease soil respiration by limiting oxygen availability and biological activities of roots and microorganisms. Based on continuous automated soil respiration data collected in a coastal wetland in the Yellow River Delta over 4 years (2012–2015), the results showed that on the annual scale, cumulative soil respiration was 317, 321, 231, and 274 g C m−2 yr-1 for 2012, 2013, 2014, and 2015, respectively, with an average of 286 g C m−2 yr-1. The rate of soil respiration increased exponentially with soil temperature during each year and its two seasons (growing season and non-growing season). In addition, soil respiration was significantly related to soil moisture during the growing season, but was not affected by soil moisture during the non-growing season. After each precipitation event, soil respiration was significantly negatively correlated with soil moisture under different initial soil water contents. There was a significant positive correlation between changes in soil respiration and changes in soil moisture following precipitation events. Moreover, the increase of soil moisture following precipitation events changed the temperature response of soil respiration. Our study indicated that precipitation events could decrease soil respiration by increasing soil moisture and inducing anoxic conditions in the coastal wetland. Therefore, we speculate that the continuation of decreasing precipitation and increasing temperature trends in the Yellow River Delta may increase soil carbon losses in the coastal wetland due to the increase in soil respiration.

Multi-site calibration and validation of a net ecosystem carbon exchange model for croplands

Croplands play an important role in the carbon budget of many regions. However, the estimation of their carbon balance remains difficult due to diversity and complexity of the processes involved. We report the coupling of a one-dimensional soil water, heat, and CO2 flux model (SOILCO2), a pool concept of soil carbon turnover (RothC), and a crop growth module (SUCROS) to predict the net ecosystem exchange (NEE) of carbon. The coupled model, further referred to as AgroC, was extended with routines for managed grassland as well as for root exudation and root decay. In a first step, the coupled model was applied to two winter wheat sites and one upland grassland site in Germany. The model was calibrated based on soil water content, soil temperature, biometric, and soil respiration measurements for each site, and validated in terms of hourly NEE measured with the eddy covariance technique. The overall model performance of AgroC was sufficient with a model efficiency above 0.78 and a correlation coefficient above 0.91 for NEE. In a second step, AgroC was optimized with eddy covariance NEE measurements to examine the effect of different objective functions, constraints, and data-transformations on estimated NEE. It was found that NEE showed a distinct sensitivity to the choice of objective function and the inclusion of soil respiration data in the optimization process. In particular, both positive and negative day- and nighttime fluxes were found to be sensitive to the selected optimization strategy. Additional consideration of soil respiration measurements improved the simulation of small positive fluxes remarkably. Even though the model performance of the selected optimization strategies did not diverge substantially, the resulting cumulative NEE over simulation time period differed substantially. Therefore, it is concluded that data-transformations, definitions of objective functions, and data sources have to be considered cautiously when a terrestrial ecosystem model is used to determine NEE by means of eddy covariance measurements.

Trees enhance soil carbon sequestration and nutrient cycling in a silvopastoral system in south-western Nicaragua

Tree occurrence in silvopastoral systems of Central America has been under pressure for various reasons including attempts to improve grassland productivity and the need for wood. However, scattered isolated trees are also recognized to provide ecosystem services like shade, fodder and fruits that are important to cattle in the dry season. In addition, trees may enhance the climate change mitigation potential of silvopastoral systems through increased carbon (C) uptake and subsequent soil carbon sequestration. Through differences in plant traits like nutrient uptake, canopy structure and litter quality, tree species may have an effect on C and nutrient cycling. Due to a prevailing north-easterly wind in the study area, three distinct areas associated with the impact of tree litter deposition were identified: (1) open pasture—no tree litter deposition; (2) tree canopy—above and belowground tree litter; and (3) leaf litter cone—aboveground tree litter deposition. Furthermore, the effect of tree species, Guazuma ulmifolia and Crescentia alata, were considered. The presence of trees, as compared to pasture, caused larger topsoil C, N and P contents. In the subsoil, C content was also larger due to tree presence. Soil fractionation showed that tree-induced larger litter input subsequently increased free and occluded OM fractions and ultimately increased stabilized SOM fractions. Therefore, trees were found to enhance soil C sequestration in these silvopastoral systems. This is also supported by the soil respiration data. Although the respiration rates in the pasture subplots were lower than in the leaf litter subplots, the difference was not significant, which suggests that part of the extra C input to the leaf litter subplots stayed in the soil. Nutrient cycling was also enhanced by tree presence, but with a clear differentiation between species. C. alata (Jícaro) enhanced available and stabilized forms of organic N, while G. ulmifolia (Guácimo) enhanced available soil P and stabilized organic P.