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Aim: Soil respiration (RS) is one of the largest fluxes in the global carbon cycle. It is composed of respiration by roots and heterotrophic organisms, with each component having distinctive drivers and sensitivities and, consequently, varying feedback potential to climate change. Global drivers of the total flux of RS are widely studied and generally accepted, but our understanding of the factors governing its component fluxes lags far behind. Location: Global. Time period: 1962–2015. Major taxa studied: Plant roots and soil microbes. Methods: Combining a newly updated global database of RS partitioning measurements with biotic and abiotic variables, we examined the world‐wide distribution of the proportion of the annual root respiration (Rroot) contribution to RS (Rroot:RS). We investigated how Rroot:RS varies by ecosystem type, measurement method and climate. We used a random forest model to predict the relationship between field measurements of Rroot:RS (n = 880) and 14 biotic and abiotic factors, including climate, nitrogen deposition, soil, mycorrhiza, biomass and satellite‐derived greenness. Finally, we present a global map of predicted Rroot:RS at 0.5° resolution. Results: Consistent with previous studies, we found no clear trends of mean Rroot:RS across ecosystem types and measurement methods. The area‐weighted overall mean of predicted Rroot:RS was.42 (SD ±.18; i.e., 42% of total global RS was generated by plant roots). Predicted Rroot:RS and related environmental factors did exhibit clear spatial patterns between climatic regions, with higher Rroot:RS in dry and cold regions and lower Rroot:RS in temperate and tropical regions. Main conclusions: Given that Rroot:RS is linked to plant carbon use efficiency (CUE, the ratio of net primary production to gross primary production), but generated from measurements completely independent of plant CUE, it might provide crucial insights into ecosystem‐ to global‐scale carbon cycling. This study thus provides a framework to explore global carbon allocation under global climate change. [ABSTRACT FROM AUTHOR]

Soil erosion is a major threat to soil resources, causing environmental degradation and contributing to poverty in many parts of the world. Many field experiments have been performed over the past century to study spatio-temporal patterns of soil erosion caused by surface runoff under different environmental conditions. However, these data have never been integrated together in a way that can inform efforts to understand and model soil erosion at different spatial and temporal scales. Here, we designed a database titled AWESOME: Archive for Water Erosion and Sediment Outflow Measurements (Jian et al., 2022). The AWESOME database compiles field measurements of annual soil erosion and sediment yield caused by surface runoff, with data derived from sites around the globe. It includes four soil erosion-related indicators (surface runoff, annual erosion, annual sediment yield, and soil nutrient loss) and more than sixty variables for meta-data that describe the location, climate, soil properties, experimental design (e.g., soil erosion measurement method, field scale, replication), and bibliographic information (e.g., author name and year of publication). Currently, measurements from 1985 geographic sites with unique combinations of longitude and latitude, representing 75 countries, have been compiled into AWESOME. We provide an example of linking AWESOME with an external climate dataset, and identify correlations between soil erosion and several environmental variables. Annual soil erosion rates were most influenced by vegetation type and soil texture group. Annual soil erosion rates exhibited significant negative relationships with plant coverage, soil clay content, soil pH, and soil organic carbon content, and significant positive relationships with annual precipitation and soil bulk density. AWESOME aims to be a freely available, and global framework for compiling field soil erosion and subsequent sediment yield measurements, and to provide data sources to support statistical evaluations, model validation and applications, as well as a better understanding of spatial and temporal patterns of soil erosion. [ABSTRACT FROM AUTHOR]

Soil respiration (RS), the soil‐to‐atmosphere CO2 flux that is a major component of the global carbon cycle, is strongly influenced by local soil temperature (Tsoil) and water content (SWC). Regional to global‐scale RS modelling thus requires this information at local scales, but few high‐quality, wall‐to‐wall (global) Tsoil and SWC data exist. As a result, such modelling efforts commonly use air temperature (Tair) and monthly precipitation (Pm) as surrogate predictors, but their site‐scale accuracy and potential bias are unknown. Here, we used monthly data from 880 sites across a wide variety of different environmental conditions (i.e., climate, ecosystem type, elevation, vegetation leaf habit and drainage conditions) to determine the suitability of Tair as a surrogate for Tsoil, and data from 507 sites to examine the suitability of Pm as a surrogate for SWC. Site‐specific linear and second‐order exponential non‐linear models were compared using model evaluation metrics (i.e., slope, p‐value of slope, root mean square error [RMSE], index of agreement and model efficiency). We found that Tsoil and Tair are highly correlated and explain similar RS variability. In contrast, Pm is not a good surrogate for SWC, even though Pm explains a similar amount of RS variability to SWC. The wide variability in the site‐specific relationships between RS and SWC means that no single relationship can be used for large‐scale modelling. The results from this study support the use of Tair in continental‐to‐global scale RS models, and highlight the urgent need for continental‐to‐global scale SWC datasets for the modelling and evaluation of future soil carbon dynamics under global climate change. Highlights: The accuracy of air temperature and precipitation as surrogates in global soil respiration modelling is unknown.Monthly air temperature and soil temperature are strongly correlated and explained similar amounts of variability in soil respiration.Relationships between precipitation and soil water content are extremely variable by region, thus precipitation is a poor surrogate in global modelling.There is a need for accurate multiscale soil moisture datasets to evaluate future soil carbon dynamics. [ABSTRACT FROM AUTHOR]

Microbially explicit models may improve understanding and projections of carbon dynamics in response to future climate change, but their fidelity in simulating global‐scale soil heterotrophic respiration (RH), a stringent test for soil biogeochemical models, has never been evaluated. We used statistical global RH products, as well as 7821 daily site‐scale RH measurements, to evaluate the spatiotemporal performance of one first‐order decay model (CASA‐CNP) and two microbially explicit biogeochemical models (CORPSE and MIMICS) that were forced by two different input datasets. CORPSE and MIMICS did not provide any measurable performance improvement; instead, the models were highly sensitive to the input data used to drive them. Spatial variability in RH fluxes was generally well simulated except in the northern middle latitudes (~50°N) and arid regions; models captured the seasonal variability of RH well, but showed more divergence in tropic and arctic regions. Our results demonstrate that the next generation of biogeochemical models shows promise but also needs to be improved for realistic spatiotemporal variability of RH. Finally, we emphasize the importance of net primary production, soil moisture, and soil temperature inputs, and that jointly evaluating soil models for their spatial (global scale) and temporal (site scale) performance provides crucial benchmarks for improving biogeochemical models. [ABSTRACT FROM AUTHOR]

• We examined the R a / R h ratio using the latest global soil respiration database. • The global average R a / R h was 0.66 ± 0.21, varying by climate, ecosystem, and soil. • The SHAP plots showed soil C:N and BD influenced R a and R h differently. • Vegetation traits, especially LAI, exhibited a two-phase influence on R a / R h. The ratio of soil autotrophic-to-heterotrophic respiration (or R a /R h) varies as a function of trade-offs between the production (release) of carbon dioxide (CO 2) by both (i) plant roots and their symbionts, and (ii) microbe-induced decomposition of litter and soil organic matter. Comprehensive knowledge of this ratio's global distribution and its biome-specific drivers is notably limited. Based on the newest Global Soil Respiration Database, we conducted a systematic analysis of R a / R h in a global assessment of 404 independent studies, considering both direct and indirect effects of climatic, edaphic and vegetation features on R a / R h. The study proceeded by using machine-learning and conventional techniques, leveraging extreme gradient boosting (xgboost) in the isolation of crucial biophysical variables in the calculation of R a / R h and structural equation modeling (SEM) in discerning potential causal relationships among retained variables. Our results showed that the global average R a / R h was 0.66 ± 0.21 (mean ± standard deviation), and strongly regulated by mean annual temperature, mean annual precipitation, and soil properties. Variation in soil organic-carbon-to-nitrogen ratios (i.e., C:N) and soil bulk density had distinct influence on root respiration and microbe-induced decomposition. Specifically, higher soil C:N signaled nitrogen scarcity, which suppressed plant growth and, consequently, root respiration (R a). Meanwhile, microbes continued to maintain near-unchanged heterotrophic respiration rates (R h) by accessing soil organic matter as food substrate. This differential response contributed to the global variations in R a / R h. Vegetation-related variables, such as leaf area index and net primary production, exhibited a biphasic influence on R a / R h , with R a / R h initially declining due to higher aboveground biomass allocation, and subsequently increasing as plants shifted biomass accumulation belowground. Our findings underscored the complex interplay among climatic, soil and vegetation factors in shaping root and microbial activity, providing valuable insights into belowground C dynamics. However, uncertainties remain regarding the differentiation across various regions and seasons. [ABSTRACT FROM AUTHOR]

Field-measured soil respiration (RS , the soil-to-atmosphere CO2 flux) observations were compiled into a global soil respiration database (SRDB) a decade ago, a resource that has been widely used by the biogeochemistry community to advance our understanding of RS dynamics. Novel carbon cycle science questions require updated and augmented global information with better interoperability among datasets. Here, we restructured and updated the global RS database to version SRDB-V5. The updated version has all previous fields revised for consistency and simplicity, and it has several new fields to include ancillary information (e.g., RS measurement time, collar insertion depth, collar area). The new SRDB-V5 includes published papers through 2017 (800 independent studies), where total observations increased from 6633 in SRDB-V4 to 10 366 in SRDB-V5. The SRDB-V5 features more RS data published in the Russian and Chinese scientific literature and has an improved global spatio-temporal coverage and improved global climate space representation. We also restructured the database so that it has stronger interoperability with other datasets related to carbon cycle science. For instance, linking SRDB-V5 with an hourly timescale global soil respiration database (HGRsD) and a community database for continuous soil respiration (COSORE) enables researchers to explore new questions. The updated SRDB-V5 aims to be a data framework for the scientific community to share seasonal to annual field RS measurements, and it provides opportunities for the biogeochemistry community to better understand the spatial and temporal variability in RS , its components, and the overall carbon cycle. The database can be downloaded at https://github.com/bpbond/srdb and will be made available in the Oak Ridge National Laboratory's Distributed Active Archive Center (ORNL DAAC). All data and code to reproduce the results in this study can be found at 10.5281/zenodo.3876443 (Jian and Bond-Lamberty, 2020). [ABSTRACT FROM AUTHOR]

Soil respiration (Rs) plays an important role in terrestrial–atmospheric carbon exchange but remains one of the least studied components of the carbon cycle. How environmental changes influence Rs, and in turn, how Rs influences terrestrial carbon storage in China is unclear. Here, we estimated spatial patterns and temporal trends in Rs from 1961 to 2014 to determine the influence of the recent warming hiatus on the Rs temporal variability in China. We evaluated the relationship between Rs and a set of environmental factors and developed biome‐specific Rs models that were parameterized based on 2,862 Rs records and 18 continuous variables (e.g., air temperature, precipitation and leaf area index). Based on the biome‐specific Rs models and environmental information, we estimated the annual Rs as well as the change trend of Rs in China during the study period. The mean annual Rs from 1961 to 2014 was 3.50 (confidence interval ± 0.87) Pg·C·year−1. Rs in China increased at a rate of 0.008 Pg·C·year−2 between 1982 and 1998 (warming period), but was static between 1999 and 2014 (warming hiatus). This reduction in Rs increase rate may have an important role in enhancing terrestrial carbon sequestration. These findings confirm that temporal heterogeneity in Rs could be influenced by regional environmental changes, and further help in improving the understanding of soil carbon dynamics in China. Highlights: No study in China has integrated multiple biotic and abiotic factors and analysed how soil respiration correlates with different factors across biomes.In this study, biome‐specific models, including biotic and abiotic factors, were developed to estimate soil respiration in China.Annual soil respiration in China was estimated to be 3.50 (confidence interval ± 0.87) Pg·C·year‐1.Annual soil respiration in China had greater deceleration compared to the global deceleration between 1999 and 2014. [ABSTRACT FROM AUTHOR]

Soil erosion is a major threat to soil resources, continuing to cause environmental degradation and social poverty in many parts of the world. Many field and laboratory experiments have been performed over the past century to study spatio-temporal patterns of soil erosion caused by surface runoff under different environmental conditions. However, these historical data have never been integrated together in a way that can inform current and future efforts to understand and model soil erosion at different scales. Here, we designed a database (SoilErosionDB) to compile field and laboratory measurements of soil erosion caused by surface runoff, with data coming from sites across the globe. The SoilErosionDB includes 18 columns for soil erosion related indicators and 73 columns for background information that describe factors such as latitude, longitude, climate, elevation, and soil type. Currently, measurements from 99 geographic sites and 22 countries around the world have been compiled into SoilErosionDB. We provide examples of linking SoilErosionDB with an external climate dataset and using annual precipitation to explain annual soil erosion variability under different environmental conditions. All data and code to reproduce the results in this study can be found at: Jian, J., Du, X., Stewart, R., Tan, Z. and Bond-Lamberty, B.: jinshijian/SoilErosionDB: First release of SoilErosionDB, Zenodo, https://doi.or g/10.5281/zenodo.4030875, 2020b. All data are also available through GitHub: https://github.com/ jinshijian/SoilErosionDB. [ABSTRACT FROM AUTHOR]

Field-measured soil respiration (RS, the soil-to-atmosphere CO2 flux) observations were compiled into a global soil respiration database (SRDB) a decade ago, a resource that has been widely used by the biogeochemistry community to advance our understanding of RS dynamics. Novel carbon cycle sciences questions require updated and augmented global information with better interoperability among datasets. Here, we restructured and updated the global RS database to version SRDB-V5. The updated version has all previous fields revised for consistency and simplicity, and it has several new fields to include ancillary information (e.g., RS measurement time, collar insertion depth, collar area). The new SRDB-V5 includes published papers through 2017 (800 independent studies) where total observations increased from 6633 in SRDB-V4 to 10366 in SRDB-V5. The SRDB-V5 features more RS data published in Russian and Chinese scientific literature, has an improved global spatio-temporal coverage, and improved global climate-space representation. We also restructured the database so that it has stronger interoperability with other datasets related to carbon-cycle science. For instance, linking SRDB-V5 with an hourly timescale global soil respiration database (HGRsD) and an open community database for continuous soil respiration and other chamber flux data (COSORE) enables researchers to explore new questions. The updated SRDB-V5 aims to be a data framework for the scientific community to share seasonal to annual field RS measurements, and it provides opportunities for the biogeochemistry community to better understand the spatial and temporal variability of RS, its components, and the overall carbon cycle. The database can be downloaded at https://github.com/bpbond/srdb and ORNL DAAC [Submitted]. All data and code to reproduce the results in this study can be found at: Jian, Jinshi, Bond-Lamberty, Ben. (2020). jinshijian/ESSD: SRDB-V5 first release (Version v1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3876443. [ABSTRACT FROM AUTHOR]

Soil respiration (Rs) rates fluctuate daily and seasonally; therefore, the timing of measurements is critical when estimating the daily mean and scaling up to annual Rs rates. Temporal fluctuations also vary with climate and biome, yet the current recommendation for when to measure Rs (e.g., 09:00 to 12:00) has not been evaluated for different climates, biomes, and seasons. To provide more refined recommendations for measuring Rs, we: 1) analyzed the diurnal and seasonal fluctuations of Rs and tested the accuracy of typical measurement practices under different climates and biomes, and 2) identified the measurement frequency necessary for different climates and biomes to achieve certain levels of accuracy in estimating annual Rs. Across biomes, diurnal variation in Rs is considerable in spring and summer, moderate in autumn, and minimal in winter, and closely related with soil temperature. Based on these diurnal patterns, the best measurement time for estimating the daily mean Rs was 10:00 in all climates and biomes, which is within the recommended range of 9:00 and 12:00 previously identified for temperate forests. Measurements made between 20:00 and 23:00 also accurately estimated the daily mean Rs. Regions with high plant coverage over the year have lower seasonal variation and require less measurement frequency. For global scale estimates, Rs needs to be measured once per day to attain an accuracy of ±10% of the Rs population mean with 95% confidence, and once per month to achieve ±30% with a confidence of 80%. Results from this study provide guidelines that reduce measurement frequency while retaining reasonable accuracy for better Rs estimates using manual chamber systems. [ABSTRACT FROM AUTHOR]

Abstract: 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 98 Pg C year−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 = 66.62–100.72 Pg), we calculated variability associated with each assumption. Among model formats, using monthly RS data rather than annual data decreased RSG by 7.43–9.46 Pg; however, RSG calculated from daily RS data was only 1.83 Pg lower than the RSG from monthly data. Using mean annual precipitation and temperature data instead of monthly data caused +4.84 and −4.36 Pg C differences, respectively. If the timescale of RS data is constant, RSG estimated by the first‐order exponential (93.2 Pg) was greater than the Random Forest (78.76 Pg) or second‐order exponential (76.18 Pg) 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 (98 Pg C year−1), and thus may change the predicted rates of terrestrial carbon turnover and the carbon to climate feedback as global temperatures rise. [ABSTRACT FROM AUTHOR]

[Display omitted] • We built pedotransfer functions for hydraulic conductivity with data from 11 cities. • Models predicted near-saturated hydraulic conductivity (K n) with moderate accuracy. • Predicted K n values were mostly the same order-of-magnitude as measured K n values. • Sand and clay percentages were most important input parameters for predicting K n. • The models have been made available as an open-source software package. Pedotransfer functions (PTFs) provide point predictions of soil hydraulic properties from more readily measured soil characteristics, yet uncertainties and biases in measurement methods, sampling distributions, and boundary conditions can limit accuracy when estimating near-saturated hydraulic conductivity (K n). These limitations may be particularly problematic in understudied urban landscapes that often contain altered hydraulic properties. To better treat deficiencies in PTF performance, we addressed three objectives, which were to: 1) develop PTFs to predict urban K n , 2) assess bulk density and coarse fragments as explanatory variables; and 3) evaluate the predictive capability of these PTFs by comparing their output to measured hydraulic conductivity values from three other studies of urban soil hydraulics. We used artificial neural networks (ANN) and random forest (RF) approaches to predict urban K n , with the training dataset including 307 tension infiltrometer tests and other measurements drawn from urban soil assessments in 11 U.S. cities. The PTFs utilized a hierarchy of inputs, starting with percentage sand, silt, clay, and then adding percentage coarse fragments and bulk density. The ANN models performed similar to the RF models, and all models exhibited similar or better predictive performance as models results collected from published articles. The inclusion of bulk density or coarse fragments did not improve accuracy over soil texture alone. Possible reasons for this result include low correlation between K n and bulk density and the exclusion of large voids during flow measurements with tension infiltrometers. The models have been made available as an open-source software package to encourage adoption by users working in urban systems. [ABSTRACT FROM AUTHOR]

• Soil respiration at mean temperature can predict annual soil respiration with well-quantified errors. • Aridity should be taking into consideration. • Findings could be used to improve soil respiration measurement frequency and greatly decrease cost. Accurately scaling soil respiration (SR, the soil-to-atmosphere flow of CO 2) across time and space is important to constrain and understand ecosystem to global scale SR, the largest terrestrial carbon flux to the atmosphere. Year-round SR measurements are however expensive and sometimes impossible to perform. Bahn et al. (2010) developed an approach to estimate annual SR (SR annual) from the flux measured at mean soil temperature (SR MAST), but the robustness of this approach needs to be evaluated in diverse ecosystem types and climatic conditions globally. We used a global soil respiration database (SRDB-V4, with 823 SR observations worldwide) to test the capability of SR MAST to predict SR annual. SR MAST estimated using a variety of methods all showed clear relationships with annual SR annual. Two single-rate methods (i.e., using the single SR rate closest to mean annual soil temperature, or the single SR rate closest to mean annual air temperature) showed the most pronounced divergence from the true SR annual , but errors significantly decreased when using multiple SR rates within 1 ℃ of the mean annual soil temperature to estimate SR MAST. SR annual was most closely correlated with SR MAST estimated via a Q 10 relationship, but this method has a potential autocorrelation issue that we explore and discuss. Air temperature data are much more widely available than is soil temperature, and we found that SR at mean annual air temperature (SR MAAT) can be used to predict SR annual as well. This study builds on Bahn et al. (2010) to demonstrate that SR measured at both annual mean soil and air temperature can be used to predict annual SR, with well-quantified errors. This capability could be used to reduce SR measurement frequency required for estimating SR annual and greatly decrease cost, factors that are generally important but especially in lower-income countries and cold, inaccessible regions. [ABSTRACT FROM AUTHOR]

• We analyzed how cover cropping affected 38 soil health indicators. • Cover crop caused significant changes in 28 of 38 soil health indicators. • Based on the results of 13 indicators, we developed a soil health calculator. Many producers use cover crops as a means to increase soil health and agricultural productivity, yet benefits of this practice vary depending on environmental and management conditions. In an effort to objectively evaluate how cover crops affect soil properties and crop production across climates and systems, we compiled data from 269 studies that compared cover crop treatments versus no cover crop controls. We then used t-tests and unbalanced analysis of variation tests to evaluate cover crop-related effects on 38 indicators of soil health and productivity. The t-test analysis indicated that cover cropping caused significant changes in 28 of 38 indicators, with differences seen for all physical parameters and most indicators associated with biological and environmental measurements. The unbalanced analysis of variation test allowed us to identify a hierarchy of most to least important environment and management factors for each indicator. Using this hierarchy, we developed a calculator that allows users to evaluate how cover crop usage affects 13 key indicators, including cash crop yield, weed pressure, soil aggregate stability, soil organic carbon, soil nitrogen, and infiltration rates. The calculator requires only four inputs – climatic region, soil texture group, cash crop rotation, and cover crop type – and as output provides mean percent change for each indicator based on the selected factors. The analyses produced in this study provide new insight into specific soil health and productivity responses to cover cropping, and the corresponding web-based calculator will help to ensure that soil health measurements from the field and laboratory are useful to farmers, planners, and regulators. Further, as more data becomes integrated into the calculator, results will continue to improve in accuracy and realism, ultimately helping to make soil health evaluation a practical outcome for more producers. [ABSTRACT FROM AUTHOR]

Including cover crops within agricultural rotations may increase soil organic carbon (SOC). However, contradictory findings generated by on-site experiments make it necessary to perform a comprehensive assessment of interactions between cover crops, environmental and management factors, and changes in SOC. In this study, we collected data from studies that compared agricultural production with and without cover crops, and then analyzed those data using meta-analysis and regression. Our results showed that including cover crops into rotations significantly increased SOC, with an overall mean change of 15.5% (95% confidence interval of 13.8%–17.3%). Whereas medium-textured soils had highest SOC stocks (overall means of 39 Mg ha−1 with and 37 Mg ha−1 without cover crops), fine-textured soils showed the greatest increase in SOC after the inclusion of cover crops (mean change of 39.5%). Coarse-textured (11.4%) and medium-textured soils (10.3%) had comparatively smaller changes in SOC, while soils in temperate climates had greater changes (18.7%) than those in tropical climates (7.2%). Cover crop mixtures resulted in greater increases in SOC compared to mono-species cover crops, and using legumes caused greater SOC increases than grass species. Cover crop biomass positively affected SOC changes while carbon:nitrogen ratio of cover crop biomass was negatively correlated with SOC changes. Cover cropping was associated with significant SOC increases in shallow soils (≤30 cm), but not in subsurface soils (>30 cm). The regression analysis revealed that SOC changes from cover cropping correlated with improvements in soil quality, specifically decreased runoff and erosion and increased mineralizable carbon, mineralizable nitrogen, and soil nitrogen. Soil carbon change was also affected by annual temperature, number of years after start of cover crop usage, latitude, and initial SOC concentrations. Finally, the mean rate of carbon sequestration from cover cropping across all studies was 0.56 Mg ha−1 yr−1. If 15% of current global cropland were to adopt cover crops, this value would translate to 0.16 ± 0.06 Pg of carbon sequestered per year, which is ~1–2% of current fossil fuels emissions. Altogether, these results indicated that the inclusion of cover crops into agricultural rotations can enhance soil carbon concentrations, improve many soil quality parameters, and serve as a potential sink for atmosphere CO 2. • Cover cropping increases SOC in near-surface soils by an average of 15.5%. • SOC changes were correlated with decreased erosion and increased mineralizable carbon. • SOC change was affected by annual temperature, latitude, and initial SOC concentrations. • Fine-textured soils showed greatest rates of SOC increase after cover cropping. [ABSTRACT FROM AUTHOR]

Background: Deadwood contains a large reservoir of carbon and nutrients in forest ecosystems, its decomposition has considerable effects on forest soil chemistry and biota. Tree functional group and nutrient inputs both have a significant influence on wood decomposition rates. However, little is known about how these factors interactively influence soil biogeochemistry through wood decomposition.We examined the effects of nitrogen (N) and phosphorus (P) addition on wood decomposition of different angiosperm and gymnosperm tree species in a three-year period in a subtropical forest. We explored the outcomes for the underlying soil nutrients, microbial biomass, and saprotrophic fungal communities.We found that P addition, rather than N, significantly increased total C, P, as well as microbial biomass C and P concentrations in the soil beneath deadwood. These effects were particularly pronounced in the soil beneath angiosperm wood compared to gymnosperm wood, likely related to the higher decomposition rates of angiosperm wood and its sensitivity to P. Similarly, the presence and abundance of soil saprotrophic fungal communities was strongly associated with P addition, where specific fungal responses were more pronounced under angiosperm wood than gymnosperm wood.Our study underscores the pivotal role of tree functional group in modulating the response of soil nutrient dynamics and fungal community structure beneath decomposing wood in a subtropical forest. These insights are critical for developing predictive models of soil nutrient cycles, which can help manage forest ecosystems more effectively in the face of global environmental changes.Methods: Deadwood contains a large reservoir of carbon and nutrients in forest ecosystems, its decomposition has considerable effects on forest soil chemistry and biota. Tree functional group and nutrient inputs both have a significant influence on wood decomposition rates. However, little is known about how these factors interactively influence soil biogeochemistry through wood decomposition.We examined the effects of nitrogen (N) and phosphorus (P) addition on wood decomposition of different angiosperm and gymnosperm tree species in a three-year period in a subtropical forest. We explored the outcomes for the underlying soil nutrients, microbial biomass, and saprotrophic fungal communities.We found that P addition, rather than N, significantly increased total C, P, as well as microbial biomass C and P concentrations in the soil beneath deadwood. These effects were particularly pronounced in the soil beneath angiosperm wood compared to gymnosperm wood, likely related to the higher decomposition rates of angiosperm wood and its sensitivity to P. Similarly, the presence and abundance of soil saprotrophic fungal communities was strongly associated with P addition, where specific fungal responses were more pronounced under angiosperm wood than gymnosperm wood.Our study underscores the pivotal role of tree functional group in modulating the response of soil nutrient dynamics and fungal community structure beneath decomposing wood in a subtropical forest. These insights are critical for developing predictive models of soil nutrient cycles, which can help manage forest ecosystems more effectively in the face of global environmental changes.Result: Deadwood contains a large reservoir of carbon and nutrients in forest ecosystems, its decomposition has considerable effects on forest soil chemistry and biota. Tree functional group and nutrient inputs both have a significant influence on wood decomposition rates. However, little is known about how these factors interactively influence soil biogeochemistry through wood decomposition.We examined the effects of nitrogen (N) and phosphorus (P) addition on wood decomposition of different angiosperm and gymnosperm tree species in a three-year period in a subtropical forest. We explored the outcomes for the underlying soil nutrients, microbial biomass, and saprotrophic fungal communities.We found that P addition, rather than N, significantly increased total C, P, as well as microbial biomass C and P concentrations in the soil beneath deadwood. These effects were particularly pronounced in the soil beneath angiosperm wood compared to gymnosperm wood, likely related to the higher decomposition rates of angiosperm wood and its sensitivity to P. Similarly, the presence and abundance of soil saprotrophic fungal communities was strongly associated with P addition, where specific fungal responses were more pronounced under angiosperm wood than gymnosperm wood.Our study underscores the pivotal role of tree functional group in modulating the response of soil nutrient dynamics and fungal community structure beneath decomposing wood in a subtropical forest. These insights are critical for developing predictive models of soil nutrient cycles, which can help manage forest ecosystems more effectively in the face of global environmental changes.Conclusion: Deadwood contains a large reservoir of carbon and nutrients in forest ecosystems, its decomposition has considerable effects on forest soil chemistry and biota. Tree functional group and nutrient inputs both have a significant influence on wood decomposition rates. However, little is known about how these factors interactively influence soil biogeochemistry through wood decomposition.We examined the effects of nitrogen (N) and phosphorus (P) addition on wood decomposition of different angiosperm and gymnosperm tree species in a three-year period in a subtropical forest. We explored the outcomes for the underlying soil nutrients, microbial biomass, and saprotrophic fungal communities.We found that P addition, rather than N, significantly increased total C, P, as well as microbial biomass C and P concentrations in the soil beneath deadwood. These effects were particularly pronounced in the soil beneath angiosperm wood compared to gymnosperm wood, likely related to the higher decomposition rates of angiosperm wood and its sensitivity to P. Similarly, the presence and abundance of soil saprotrophic fungal communities was strongly associated with P addition, where specific fungal responses were more pronounced under angiosperm wood than gymnosperm wood.Our study underscores the pivotal role of tree functional group in modulating the response of soil nutrient dynamics and fungal community structure beneath decomposing wood in a subtropical forest. These insights are critical for developing predictive models of soil nutrient cycles, which can help manage forest ecosystems more effectively in the face of global environmental changes. [ABSTRACT FROM AUTHOR]

Soil respiration (Rs), the efflux of CO2 from soils to the atmosphere, is a major component of the terrestrial carbon cycle, but is poorly constrained from regional to global scales. The global soil respiration database (SRDB) is a compilation of in situ Rs observations from around the globe that has been consistently updated with new measurements over the past decade. It is unclear whether the addition of data to new versions has produced better‐constrained global Rs estimates. We compared two versions of the SRDB (v3.0 n = 5173 and v5.0 n = 10,366) to determine how additional data influenced global Rs annual sum, spatial patterns and associated uncertainty (1 km spatial resolution) using a machine learning approach. A quantile regression forest model parameterized using SRDBv3 yielded a global Rs sum of 88.6 Pg C year−1, and associated uncertainty of 29.9 (mean absolute error) and 57.9 (standard deviation) Pg C year−1, whereas parameterization using SRDBv5 yielded 96.5 Pg C year−1 and associated uncertainty of 30.2 (mean average error) and 73.4 (standard deviation) Pg C year−1. Empirically estimated global heterotrophic respiration (Rh) from v3 and v5 were 49.9–50.2 (mean 50.1) and 53.3–53.5 (mean 53.4) Pg C year−1, respectively. SRDBv5's inclusion of new data from underrepresented regions (e.g., Asia, Africa, South America) resulted in overall higher model uncertainty. The largest differences between models parameterized with different SRDVB versions were in arid/semi‐arid regions. The SRDBv5 is still biased toward northern latitudes and temperate zones, so we tested an optimized global distribution of Rs measurements, which resulted in a global sum of 96.4 ± 21.4 Pg C year−1 with an overall lower model uncertainty. These results support current global estimates of Rs but highlight spatial biases that influence model parameterization and interpretation and provide insights for design of environmental networks to improve global‐scale Rs estimates. [ABSTRACT FROM AUTHOR]

Grasslands, a vital ecosystem and component of the global carbon cycle, play a significant role in evaluating ecosystem health and monitoring the global carbon balance. In this study, based on the Carnegie–Ames–Stanford Approach (CASA) model, we estimated the Net Primary Productivity (NPP) of grasslands in northern Shaanxi from 2000 to 2020. Employing trend analysis, stability analysis, multiple regression analysis, and residual analysis, the research examined the dynamic changes of grassland NPP and its response to climatic and human factors. Key findings include: (1) Grassland NPP showed a significant increasing trend during 2000–2020, with high-coverage grasslands showing a higher rate of increase than medium and low-coverage grasslands. (2) Most grasslands (>90%) exhibited unstable growth and high NPP fluctuation. (3) While temperature, precipitation, and radiation undulate, the trends were not significant. Rainfall and radiation emerged as dominant factors affecting NPP, with temperature suppressing NPP increase to some extent. (4) Policies like returning farmland to grassland had a positive impact on grassland recovery, vegetation productivity, and regional ecosystem health. [ABSTRACT FROM AUTHOR]

Conservation agriculture (CA) has the potential to sustain soil productivity and benefit agroecosystems, yet it is not fully understood how yield responses of different cropping systems are affected by inherent soil characteristics, for example, texture and dynamic soil properties, such as aggregation, nutrients and erosion. In this study, we conducted a systematic review to compare crop yield from cropland with conventional management versus different CA practices, specifically reduced‐ or no‐tillage, agroforestry, organic farming and cover crops. The data were first analysed for different climatic regions, soil textures and cash crop types. We then quantified how yield responses correlated with soil properties change under different CA practices. The results showed that CA practices were associated with an overall mean crop yield increase of 12%. This response was primarily driven by corn, which had a mean yield increase of almost 41% after CA implementation, whereas other cash crops did not have significant yield responses or showed slight decreases, as rotation with mixtures of multiple cash crops had a mean decrease of 6% when using CA. The increase in corn yield after CA may be related to the enhanced ability of that crop to absorb nutrient elements (e.g. nitrogen) and reduce nutrient leaching. Agroforestry increased crop yield by 66% and cover cropping increased yield by 11%, likely due to increases in soil water content and nutrient availability and decreases in erosion and surface runoff. However, other agricultural systems showed no significant increase after CA compared with conventional row cropping practices. Using CA practices had the greatest yield benefit in tropical climates and when farming in coarse‐textured soils. In addition, legumes and grass‐legume mixtures resulted in significant cash crop yield increases, possibly because legumes promoted the increase of soil nitrogen and depleted soil moisture less compared with other cover crops. The results provide new insight into how interactions between soil properties and CA practices affect crop yield and at the same time can help guide the development of practical, evidence‐based guidelines for using conservation practices to improve yield in corn and other cash crops. [ABSTRACT FROM AUTHOR]

• Water erosion on the Qinghai-Tibet Plateau decreased from south to north. • Water erosion decreased from 1982 to 2015 but slightly increased since 2007. • Climate variability enhanced erosion and human activity reduced it from 1982 to 2015. Climate variability and human activities cause changes in precipitation and underlying surface conditions, which are the main driving forces of changes in water erosion, especially in the Qinghai-Tibet Plateau (QTP). In this paper, the water erosion on the QTP from 1982 to 2015 was quantified by the Revised Universal Soil Loss Equation (RUSLE), and the response of water erosion changes to climate variability and human activities was investigated. The results indicated that the distribution of water erosion gradually decreased from south to north during 1982–2015. Areas of severe erosion were mainly distributed along the Yarlung Tsangpo River and in the western Hengduan Mountains. The years of the abrupt change in water erosion were 1989 and 2006. Compared to 1982–1989, the amount of water erosion was mitigated in both 1990–2006 and 2007–2015, with a 19.5% and 6.2% decrease, respectively. The contribution of human activities to the decrease in water erosion was 25.4% and 16.7% for each of these periods, respectively, and the contribution of climate variability to the increase in water erosion was 5.9% and 10.5%. However, water erosion increased by 16.5% from 2007 to 2015 compared to 1990–2006. The contribution of climate variability was 4.9% and the contribution of human activities 8.4%. The increase in water erosion due to human activities may be attributed to the construction of the Qinghai-Tibet Railway and associated human activities. It is therefore important to draw attention to the influence of human activities on the QTP on erosion, to prevent potential future risks. This study is the first attempt to research the relative contributions of variation in climate and human activities to changes in water erosion on the QTP. It would be beneficial for carrying out ecological environmental protection work on the QTP. [ABSTRACT FROM AUTHOR]

• 91 rain-induced flood hazard events (RFHEs) at the Loess Plateau (LP) in 2004–2020. • The central part of the southern LP experienced the greatest impact from RFHEs. • RFHEs are predominantly happed in June-September on the LP. • Rainfall and elevation prominently influence spatial distribution of RFHEs on the LP. • High-risk intervals for RFHEs on the LP and its ecological sub-regions vary widely. Rain-induced flooding hazards are prevalent on the Loess Plateau (LP). Descriptive statistics, kernel density estimation, and geographical detector methods were used to explore the spatial and temporal distribution, driving factors, and their high-risk intervals of rain-induced flooding hazard events (RFHEs) on the LP and whether they differ across the entire LP and its ecological subregions. The findings showed that 91 RFHEs occurred mainly in the south-central LP during 2004–2020. The daily rainfall, surface relief amplitude (SRA), elevation, normalized difference vegetation index (NDVI), soil texture, and population were identified as the driving factors of RFHEs on the LP. However, the driving factors of RFHEs in the Sandy and Agricultural Irrigation Regions (Subregion C), and Earth-rocky Mountainous Region and River Valley Plain Region (Subregion D) all had an added soil texture and population factor compared to the entire LP, but they lacked NDVI and SRA factors, respectively. The driving factors for the Loess Plateau Gully Region (Subregion A) lacked SRA and soil texture factors. The Loess Hilly and Gully Region (Subregion B) lacked NDVI, soil texture, and population factors. There were also differences between high-risk intervals on the LP and its subregions. The high-risk daily rainfall for the entire LP was 64.5 mm, while it was 64.5, 82.3, 14.7, and 50.0 mm for subregions A, B, C, and D, respectively. Therefore, adopting uniform standards on the LP may over-estimate or under-estimate RFHE occurrence in ecological subregions. These findings contribute to guiding decision-makers involved in ecosystem management and hazard prevention. [ABSTRACT FROM AUTHOR]

• Road erosion intensity ranged from 2.16 × 102 to 1.02 × 104 t/ha during the rainstorm. • The impacts of hydrological connections on road erosion were evaluated. • Soil erosion in roads during the rainstorm is dominated by landslides and collapses. • Upslope-Road connections evidently intensify road erosion. Road network extension is closely associated with soil erosion in watersheds during frequent extreme rainfall events. Identifying potential risk areas that actively deliver surface runoff to road structures and channel networks is critical for comprehending hydrological processes and controlling road erosion. In this study, the road erosion (landslides, collapses, scour pits, and gullies on monitored roads) on the Loess Plateau during a heavy rainstorm was analyzed using field survey and unmanned aerial vehicle (UAV) data. We defined hydrological connections as the intersections of roads and channel networks, and the activated hydrological connections represent hydrological connectivity. The results of this study highlight the exacerbating effect of hydrological connectivity on road erosion during a rainstorm in an agricultural watershed in the Loess Plateau region. The results showed that the road erosion intensity ranged from 2.16 × 102 to 1.02 × 104 t/ha. Strong hydrological connectivity resulted in average soil erosion amounts of landslides, collapses, scour pits, and gullies in the downslope area that were 2.16, 1.78, 5.46, and 6.98 times greater than those without hydrological connectivity, respectively. The presence of hydrological connectivity generated a 1.88 times higher road surface erosion intensity than that without hydrological connectivity. The Upslope-Road connections created the most significant enhancement effect on road erosion, and up to 65.37 % landslide erosion and 41.97 % collapse erosion amounts were influenced by these connections. The enhancement effect of Upslope–Road connections on road erosion varied with different road types, this impact of Upslope–Road connections on road erosion were only observed on the landslide erosion of petroleum transport roads and the collapse erosion of agricultural roads. Conversely, Road–Downslope connections did not significantly impact road erosion (p > 0.05). To effectively control the negative effect of road erosion, hydrological connections should be identified and regulated to impede hydrological connectivity activation during rainstorms by modifying tillage activities, improving road maintenance, and strengthening drainage systems. [ABSTRACT FROM AUTHOR]

• The effects of rainfall types and maintenance on road erosion were evaluated. • LH-R could generate road erosion several times higher than ordinary erosive events. • SL-R should be paid attention due to its cumulative impacts on rill erosion. • Drainage systems were confirmed to weaken road erosion. Although road erosion represents a threat to regional environmental and ecological security, limited evidence exists regarding the relationship between road erosion, rainfall, and road maintenance. In this study, we investigated the effects of different rainfall types on rill erosion and the degree of rill dissection of roads involving petroleum transportation and agricultural production in the hilly and gully region on the Loess Plateau of China from 2019 to 2021. During this period, 30 natural rainfall events occurred that were classified into four rainfall types using K-means clustering analysis. The results indicated that road erosion caused by large amount of rainfall with high intensity (LH-R) was 4.31, 3.82, and 3.48 times higher than that with small amount of rainfall with high intensity (SH-R), small amount of rainfall with low intensity (SL-R), and large amount with low intensity (LL-R), respectively. This highlighted the devastating impact of the LH-R on road erosion (the most severe of all types), with an average erosion rate of 527.04 t ha−1. Road erosion caused by SL-R events (most frequent) during the rainy season also deserves attention because of its cumulative impact on rill erosion. Moreover, compared to bare runoff plots and sloping farmlands, unpaved roads experienced a greater erosion rate due to upslope drainage areas and frequent human disturbance. In addition, the drainage system mitigates road erosion under LH-R, thereby reducing the average erosion rate by 56.42 %. We also found that road rehabilitation impedes the development of rills and diminishes the rate of road erosion compared to road segments without road rehabilitation. Collectively, the effects of rainfall types and maintenance activities on road erosion were assessed. The findings indicate that strengthening water and land management of upslope drainage areas, complementing the drainage system of unpaved roads, maintaining appropriate maintenance activities, and promoting vegetation coverage on the downslope of roads mitigate the resistance of unpaved roads to LH-R. [ABSTRACT FROM AUTHOR]

Tracking fecal contamination in surface waters is critical to remediating water quality; however, general and source-specific fecal indicators often provide conflicting results. To understand the spatial and temporal dynamics of multiple fecal indicators and the sources they represent, we measured weekly concentrations of two general fecal indicator bacteria (FIB), a genetic indicator of human-associated Bacteroides (HF183), and surface water chemistry in nine mixed land-use watersheds in southwest Virginia, USA. At the watershed scale, general and source-specific indicators were decoupled, with distinct spatial, temporal, and chemical patterns. Random Forest analysis of individual sample variability identified temperature, watershed, nutrients, and cations as top predictors of indicator concentrations. However, these patterns – and the specific nutrients and cations identified – varied by indicator type. Among watersheds, FIB increased with developed land cover and during the summer months, while HF183 increased during the winter and only in urban watersheds. Nutrients generally related poorly to FIB and HF183, except E. coli , which correlated with total nitrogen. In contrast, all fecal indicators showed strong correlations with cations. FIB were more strongly related to calcium, magnesium, and potassium concentrations, while HF183 was related to sodium. These results suggest that, even at the watershed scale, 1) HF183 detects mainly human fecal contamination, while FIB detect broader ecosystem fecal inputs, and 2) poor correlation between specific and generalist fecal indicators is caused by unique spatial, temporal, and transport dynamics of different fecal sources in watersheds. Unlabelled Image • Top predictors of fecal indicators included temperature, site, nutrients, and cations. • Seasonal patterns varied by fecal indicator type and were specific to land cover • Human-specific fecal indicator concentrations correlated with urban land cover • Cations and leaking sewage lines may be under-credited drivers of water quality [ABSTRACT FROM AUTHOR]