{"type": "FeatureCollection", "features": [{"id": "10.5061/dryad.cz8w9gj78", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:48Z", "type": "Dataset", "title": "Soil microbial relative resource limitation exhibited contrasting seasonal patterns along an elevational gradient in Yulong snow mountain", "description": "unspecified", "keywords": ["2. Zero hunger", "mountain ecosystems", "13. Climate action", "microbial metabolic mechanisms", "microbial relative C limitation", "microbial relative P limitation", "C use efficiency", "FOS: Earth and related environmental sciences", "15. Life on land", "elevations"], "contacts": [{"organization": "Zhang, Dandan, Wu, Baoyun, Li, Jinsheng, Cheng, Xiaoli,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.cz8w9gj78"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.cz8w9gj78", "name": "item", "description": "10.5061/dryad.cz8w9gj78", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.cz8w9gj78"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-02-02T00:00:00Z"}}, {"id": "10.1002/ecy.2199", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:14:13Z", "type": "Journal Article", "created": "2018-02-27", "title": "Temperature and aridity regulate spatial variability of soil multifunctionality in drylands across the globe", "description": "Abstract

The relationship between the spatial variability of soil multifunctionality (i.e., the capacity of soils to conduct multiple functions; SVM) and major climatic drivers, such as temperature and aridity, has never been assessed globally in terrestrial ecosystems. We surveyed 236 dryland ecosystems from six continents to evaluate the relative importance of aridity and mean annual temperature, and of other abiotic (e.g., texture) and biotic (e.g., plant cover) variables as drivers of SVM, calculated as the averaged coefficient of variation for multiple soil variables linked to nutrient stocks and cycling. We found that increases in temperature and aridity were globally correlated to increases in SVM. Some of these climatic effects on SVM were direct, but others were indirectly driven through reductions in the number of vegetation patches and increases in soil sand content. The predictive capacity of our structural equation\uffc2\uffa0modelling was clearly higher for the spatial variability of N\uffe2\uff80\uff90 than for C\uffe2\uff80\uff90 and P\uffe2\uff80\uff90related soil variables. In the case of N cycling, the effects of temperature and aridity were both direct and indirect via changes in soil properties. For C and P, the effect of climate was mainly indirect via changes in plant attributes. These results suggest that future changes in climate may decouple the spatial availability of these elements for plants and microbes in dryland soils. Our findings significantly advance our understanding of the patterns and mechanisms driving SVM in drylands across the globe, which is critical for predicting changes in ecosystem functioning in response to climate change.Timely spatially explicit warning of areas with high fire occurrence probability is an important component of strategic plans to prevent and monitor fires within South American (SA) Protected Areas (PAs). In this study, we present a five\uffe2\uff80\uff90level alert system, which combines both climatological and anthropogenic factors, the two main drivers of fires in SA. The alert levels are: High Alert, Alert, Attention, Observation and Low Probability. The trend in the number of active fires over the past three years and the accumulated number of active fires over the same period were used as indicators of intensification of human use of fire in that region, possibly associated with ongoing land use/land cover change (LULCC). An ensemble of temperature and precipitation gridded output from the GloSea5 Seasonal Forecast System was used to indicate an enhanced probability of hot and dry weather conditions that combined with LULCC favour fire occurrences. Alerts from this system were first issued in August 2020, for the period ranging from August to October (ASO) 2020. Overall, 50% of all fires observed during the ASO 2017\uffe2\uff80\uff932019 period and 40% of the ASO 2020 fires occurred in only 29 PAs were all categorized in the top two alert levels. In categories mapped as High Alert level, 34% of the PAs experienced an increase in fires compared with the 2017\uffe2\uff80\uff932019 reference period, and 81% of the High Alert false alarm registered fire occurrence above the median. Initial feedback from stakeholders indicates that these alerts were used to inform resource management in some PAs. We expect that these forecasts can provide continuous information aiming at changing societal perceptions of fire use and consequently subsidize strategic planning and mitigatory actions, focusing on timely responses to a disaster risk management strategy. Further research must focus on the model improvement and knowledge translation to stakeholders.

", "keywords": ["0106 biological sciences", "Atmospheric Science", "Land cover", "Flood Risk", "Precipitation", "01 natural sciences", "Environmental science", "Impact of Climate Change on Forest Wildfires", "Global Flood Risk Assessment and Management", "Meteorology", "Engineering", "Machine learning", "False alarm", "Civil engineering", "0105 earth and related environmental sciences", "Climatology", "Global and Planetary Change", "Tropical Cyclone Intensity and Climate Change", "Geography", "Warning system", "Geology", "FOS: Earth and related environmental sciences", "15. Life on land", "Computer science", "Earth and Planetary Sciences", "13. Climate action", "Environmental Science", "Physical Sciences", "Land use", "Telecommunications", "FOS: Civil engineering"]}, "links": [{"href": "https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/cli2.19"}, {"href": "https://doi.org/10.1002/cli2.19"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Climate%20Resilience%20and%20Sustainability", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/cli2.19", "name": "item", "description": "10.1002/cli2.19", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/cli2.19"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-10-20T00:00:00Z"}}, {"id": "10.1007/s10533-021-00759-x", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:14:43Z", "type": "Journal Article", "created": "2021-01-26", "title": "How much carbon can be added to soil by sorption?", "description": "Abstract

Quantifying the upper limit of stable soil carbon storage is essential for guiding policies to increase soil carbon storage. One pool of carbon considered particularly stable across climate zones and soil types is formed when dissolved organic carbon sorbs to minerals. We quantified, for the first time, the potential of mineral soils to sorb additional dissolved organic carbon (DOC) for six soil orders. We compiled 402 laboratory sorption experiments to estimate the additional DOC sorption potential, that is the potential of excess DOC sorption in addition to the existing background level already sorbed in each soil sample. We estimated this potential using gridded climate and soil geochemical variables within a machine learning model. We find that mid- and low-latitude soils and subsoils have a greater capacity to store DOC by sorption compared to high-latitude soils and topsoils. The global additional DOC sorption potential for six soil orders is estimated to be 107 $$ pm$$ \uffc2\uffb1 13 Pg C to 1\uffc2\uffa0m depth. If this potential was realized, it would represent a 7% increase in the existing total carbon stock.Greenhouse gas (GHGs) emissions from peatlands contribute significantly to ongoing climate change because of human land use. To develop reliable and comprehensive estimates and predictions of GHG emissions from peatlands, it is necessary to have GHG observations, including carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), that cover different peatland types globally. We synthesize published peatland studies with field GHG flux measurements to identify gaps in observations and suggest directions for future research. Although GHG flux measurements have been conducted at numerous sites globally, substantial gaps remain in current observations, encompassing various peatland types, regions and GHGs. Generally, there is a pressing need for additional GHG observations in Africa, Latin America and the Caribbean regions. Despite widespread measurements of CO2 and CH4, studies quantifying N2O emissions from peatlands are scarce, particularly in natural ecosystems. To expand the global coverage of peatland data, it is crucial to conduct more eddy covariance observations for long-term monitoring. Automated chambers are preferable for plot-scale observations to produce high temporal resolution data; however, traditional field campaigns with manual chamber measurements remain necessary, particularly in remote areas. To ensure that the data can be further used for modeling purposes, we suggest that chamber campaigns should be conducted at least monthly for a minimum duration of one year with no fewer than three replicates and measure key environmental variables. In addition, further studies are needed in restored peatlands, focusing on identifying the most effective restoration approaches for different ecosystem types, conditions, climates, and land use histories.Observations of the South Polar Residual Cap suggest a possible erosion of the cap, leading to an increase of the global mass of the atmosphere. We test this assumption by making the first comparison between Viking 1 and InSight surface pressure data, which were recorded 40\uffc2\uffa0years apart. Such a comparison also allows us to determine changes in the dynamics of the seasonal ice caps between these two periods. To do so, we first had to recalibrate the InSight pressure data because of their unexpected sensitivity to the sensor temperature. Then, we had to design a procedure to compare distant pressure measurements. We propose two surface pressure interpolation methods at the local and global scale to do the comparison. The comparison of Viking and InSight seasonal surface pressure variations does not show changes larger than \uffc2\uffb18\uffc2\uffa0Pa in the CO2 cycle. Such conclusions are supported by an analysis of Mars Science Laboratory (MSL) pressure data. Further comparisons with images of the south seasonal cap taken by the Viking 2 orbiter and MARCI camera do not display significant changes in the dynamics of this cap over a 40\uffc2\uffa0year period. Only a possible larger extension of the North Cap after the global storm of MY 34 is observed, but the physical mechanisms behind this anomaly are not well determined. Finally, the first comparison of MSL and InSight pressure data suggests a pressure deficit at Gale crater during southern summer, possibly resulting from a large presence of dust suspended within the crater.

", "keywords": ["Atmospheric sciences", "550", "Astronomy", "Atmosphere (unit)", "FOS: Mechanical engineering", "Library science", "Oceanography", "01 natural sciences", "CO2 ice", "pressure", "Mars Exploration Program", "Engineering", "Surface pressure", "Storm", "Martian Climate", "Space Suit Design and Ergonomics for EVA", "Martian Atmosphere", "Earth and Planetary Astrophysics (astro-ph.EP)", "Climatology", "Global and Planetary Change", "Geography", "Martian Surface", "Physics", "Geology", "Impact crater", "Condensed matter physics", "Anomaly (physics)", "World Wide Web", "Algorithm", "Satellite Observations", "Residual", "Physical Sciences", "Exploration and Study of Mars", "Astrophysics - Instrumentation and Methods for Astrophysics", "Research Article", "FOS: Physical sciences", "Mars", "Aerospace Engineering", "Pressure gradient", "Environmental science", "[SDU] Sciences of the Universe [physics]", "atmospheric mass", "Meteorology", "Orbiter", "0103 physical sciences", "Instrumentation and Methods for Astrophysics (astro-ph.IM)", "Formation and Evolution of the Solar System", "0105 earth and related environmental sciences", "Pressure system", "CO 2 ice", "Astronomy and Astrophysics", "FOS: Earth and related environmental sciences", "Astrobiology", "Computer science", "Physics and Astronomy", "[SDU]Sciences of the Universe [physics]", "13. Climate action", "Global Methane Emissions and Impacts", "Environmental Science", "cap sublimation", "Water on Mars", "Astrophysics - Earth and Planetary Astrophysics"]}, "links": [{"href": "https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2022JE007190"}, {"href": "https://doi.org/10.1029/2022je007190"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Geophysical%20Research%3A%20Planets", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1029/2022je007190", "name": "item", "description": "10.1029/2022je007190", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1029/2022je007190"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-25T00:00:00Z"}}, {"id": "10.1038/ncomms15972", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:16:44Z", "type": "Journal Article", "created": "2017-06-26", "title": "Iron-Mediated Soil Carbon Response To Water-Table Decline In An Alpine Wetland", "description": "Abstract

The tremendous reservoir of soil organic carbon (SOC) in wetlands is being threatened by water-table decline (WTD) globally. However, the SOC response to WTD remains highly uncertain. Here we examine the under-investigated role of iron (Fe) in mediating soil enzyme activity and lignin stabilization in a mesocosm WTD experiment in an alpine wetland. In contrast to the classic \uffe2\uff80\uff98enzyme latch\uffe2\uff80\uff99 theory, phenol oxidative activity is mainly controlled by ferrous iron [Fe(II)] and declines with WTD, leading to an accumulation of dissolvable aromatics and a reduced activity of hydrolytic enzyme. Furthermore, using dithionite to remove Fe oxides, we observe a significant increase of Fe-protected lignin phenols in the air-exposed soils. Fe oxidation hence acts as an \uffe2\uff80\uff98iron gate\uffe2\uff80\uff99 against the \uffe2\uff80\uff98enzyme latch\uffe2\uff80\uff99 in regulating wetland SOC dynamics under oxygen exposure. This newly recognized mechanism may be key to predicting wetland soil carbon storage with intensified WTD in a changing climate.

", "keywords": ["Composite material", "Science", "Soil Science", "Organic chemistry", "Carbon Dynamics in Peatland Ecosystems", "01 natural sciences", "Article", "Environmental science", "Agricultural and Biological Sciences", "Importance of Mangrove Ecosystems in Coastal Protection", "Soil water", "Carbon fibers", "Soil Carbon Sequestration", "Biology", "Groundwater", "Ecosystem", "0105 earth and related environmental sciences", "Soil science", "Ecology", "Q", "Life Sciences", "Composite number", "Geology", "Mesocosm", "FOS: Earth and related environmental sciences", "04 agricultural and veterinary sciences", "15. Life on land", "Soil carbon", "Materials science", "6. Clean water", "Water table", "Chemistry", "Geotechnical engineering", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Physical Sciences", "Wetland", "Environmental chemistry", "0401 agriculture", " forestry", " and fisheries", "Soil Carbon Dynamics and Nutrient Cycling in Ecosystems", "Ferrous"]}, "links": [{"href": "https://doi.org/10.1038/ncomms15972"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Nature%20Communications", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1038/ncomms15972", "name": "item", "description": "10.1038/ncomms15972", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/ncomms15972"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-06-26T00:00:00Z"}}, {"id": "10.1038/s41467-017-00114-5", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:16:45Z", "type": "Journal Article", "created": "2017-07-17", "title": "Recent increases in terrestrial carbon uptake at little cost to the water cycle", "description": "Abstract

Quantifying the responses of the coupled carbon and water cycles to current global warming and rising atmospheric CO2 concentration is crucial for predicting and adapting to climate changes. Here we show that terrestrial carbon uptake (i.e. gross primary production) increased significantly from 1982 to 2011 using a combination of ground-based and remotely sensed land and atmospheric observations. Importantly, we find that the terrestrial carbon uptake increase is not accompanied by a proportional increase in water use (i.e. evapotranspiration) but is largely (about 90%) driven by increased carbon uptake per unit of water use, i.e. water use efficiency. The increased water use efficiency is positively related to rising CO2 concentration and increased canopy leaf area index, and negatively influenced by increased vapour pressure deficits. Our findings suggest that rising atmospheric CO2 concentration has caused a shift in terrestrial water economics of carbon uptake.

", "keywords": ["Atmospheric sciences", "GLOBAL-SCALE", "Climate Change and Variability Research", "02 engineering and technology", "7. Clean energy", "01 natural sciences", "Terrestrial ecosystem", "Carbon fibers", "Climate change", "Terrestrial plant", "Global and Planetary Change", "CLIMATE-CHANGE", "EVAPOTRANSPIRATION", "Evapotranspiration", "Primary production", "Ecology", "Global warming", "Q", "TRANSPIRATION", "Composite number", "Geology", "Carbon cycle", "6. Clean water", "Physical Sciences", "8. Economic growth", "DIOXIDE", "Water-use efficiency", "Composite material", "Atmospheric carbon cycle", "Science", "Carbon dioxide in Earth's atmosphere", "STOMATAL CONDUCTANCE", "0207 environmental engineering", "Article", "Environmental science", "USE EFFICIENCY", "ATMOSPHERIC CO2", "Irrigation", "Biology", "Ecosystem", "0105 earth and related environmental sciences", "Global Forest Drought Response and Climate Change", "FOS: Earth and related environmental sciences", "15. Life on land", "TRENDS", "Materials science", "Carbon dioxide", "13. Climate action", "Earth and Environmental Sciences", "FOS: Biological sciences", "Environmental Science", "Global Methane Emissions and Impacts", "VEGETATION", "Water cycle", "Climate Modeling", "Water use"]}, "links": [{"href": "https://www.nature.com/articles/s41467-017-00114-5.pdf"}, {"href": "https://doi.org/10.1038/s41467-017-00114-5"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Nature%20Communications", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1038/s41467-017-00114-5", "name": "item", "description": "10.1038/s41467-017-00114-5", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/s41467-017-00114-5"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-07-24T00:00:00Z"}}, {"id": "10.1038/s41467-022-31540-9", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:16:46Z", "type": "Journal Article", "created": "2022-07-01", "title": "Global stocks and capacity of mineral-associated soil organic carbon", "description": "Abstract

Soil is the largest terrestrial reservoir of organic carbon and is central for climate change mitigation and carbon-climate feedbacks. Chemical and physical associations of soil carbon with minerals play a critical role in carbon storage, but the amount and global capacity for storage in this form remain unquantified. Here, we produce spatially-resolved global estimates of mineral-associated organic carbon stocks and carbon-storage capacity by analyzing 1144 globally-distributed soil profiles. We show that current stocks total 899 Pg C to a depth of 1\uffe2\uff80\uff89m in non-permafrost mineral soils. Although this constitutes 66% and 70% of soil carbon in surface and deeper layers, respectively, it is only 42% and 21% of the mineralogical capacity. Regions under agricultural management and deeper soil layers show the largest undersaturation of mineral-associated carbon. Critically, the degree of undersaturation indicates sequestration efficiency over years to decades. We show that, across 103 carbon-accrual measurements spanning management interventions globally, soils furthest from their mineralogical capacity are more effective at accruing carbon; sequestration rates average 3-times higher in soils at one tenth of their capacity compared to soils at one half of their capacity. Our findings provide insights into the world\uffe2\uff80\uff99s soils, their capacity to store carbon, and priority regions and actions for soil carbon management.The present work aims to quantify the impact of climate change (CC) on the grain yields of irrigated cereals and their water requirements in the Tensift region of Morocco. The Med-CORDEX (MEDiterranean COordinated Regional Climate Downscaling EXperiment) ensemble runs under scenarios RCP4.5 (Representative Concentration Pathway) and RCP8.5 are first evaluated and disaggregated using the quantile-quantile approach. The impact of CC on the duration of the main wheat phenological stages based on the degree-day approach is then analyzed. The results show that the rise in air temperature causes a shortening of the development cycle of up to 50 days. The impacts of rising temperature and changes in precipitation on wheat yields are next evaluated, based on the AquaCrop model, both with and without taking into account the fertilizing effect of CO2. As expected, optimal wheat yields will decrease on the order of 7 to 30% if CO2 concentration rise is not considered. The fertilizing effect of CO2 can counterbalance yield losses, since optimal yields could increase by 7% and 13% respectively at mid-century for the RCP4.5 and RCP8.5 scenarios. Finally, water requirements are expected to decrease by 13 to 42%, mainly in response to the shortening of the cycle. This decrease is associated with a change in temporal patterns, with the requirement peak coming two months earlier than under current conditions.

", "keywords": ["Water resources", "Atmospheric sciences", "Agricultural Irrigation", "environment/Bioclimatology", "550", "Representative Concentration Pathways", "Adaptation to Climate Change in Agriculture", "Arid", "Rain", "[SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/Agronomy", "Climate Change and Variability Research", "Plant Science", "Precipitation", "02 engineering and technology", "01 natural sciences", "Agricultural and Biological Sciences", "Downscaling", "Climate change", "Quantile", "Triticum", "Climatology", "2. Zero hunger", "Global and Planetary Change", "Ecology", "Geography", "Temperature", "Life Sciences", "Geology", "Morocco", "Phenology", "[SDV.EE.BIO]Life Sciences [q-bio]/Ecology", "Seeds", "Physical Sciences", "Metallurgy", "Desert Climate", "Impacts of Elevated CO2 and Ozone on Plant Physiology", "Climate Change", "0207 environmental engineering", "Yield (engineering)", "Climate model", "Article", "Environmental science", "FOS: Economics and business", "Meteorology", "FOS: Mathematics", "Econometrics", "[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology", "Biology", "Ecology", " Evolution", " Behavior and Systematics", "0105 earth and related environmental sciences", "[SDV.SA.AGRO] Life Sciences [q-bio]/Agricultural sciences/Agronomy", "Water", "FOS: Earth and related environmental sciences", "Carbon Dioxide", "15. Life on land", "Agronomy", "Materials science", "[SDV.EE.BIO] Life Sciences [q-bio]/Ecology", " environment/Bioclimatology", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "[SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology", "Crop Yield", "Mediterranean climate", "Mathematics", "Climate Modeling"]}, "links": [{"href": "https://www.nature.com/articles/s41598-019-55251-2.pdf"}, {"href": "https://doi.org/10.1038/s41598-019-55251-2"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Reports", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1038/s41598-019-55251-2", "name": "item", "description": "10.1038/s41598-019-55251-2", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/s41598-019-55251-2"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-12-16T00:00:00Z"}}, {"id": "10.1038/s41598-019-56868-z", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:16:49Z", "type": "Journal Article", "created": "2020-01-09", "title": "Modelling photovoltaic soiling losses through optical characterization", "description": "Abstract

The accumulation of soiling on photovoltaic (PV) modules affects PV systems worldwide. Soiling consists of mineral dust, soot particles, aerosols, pollen, fungi and/or other contaminants that deposit on the surface of PV modules. Soiling absorbs, scatters, and reflects a fraction of the incoming sunlight, reducing the intensity that reaches the active part of the solar cell. Here, we report on the comparison of naturally accumulated soiling on coupons of PV glass soiled at seven locations worldwide. The spectral hemispherical transmittance was measured. It was found that natural soiling disproportionately impacts the blue and ultraviolet (UV) portions of the spectrum compared to the visible and infrared (IR). Also, the general shape of the transmittance spectra was similar at all the studied sites and could adequately be described by a modified form of the \uffc3\uff85ngstr\uffc3\uffb6m turbidity equation. In addition, the distribution of particles sizes was found to follow the IEST-STD-CC 1246E cleanliness standard. The fractional coverage of the glass surface by particles could be determined directly or indirectly and, as expected, has a linear correlation with the transmittance. It thus becomes feasible to estimate the optical consequences of the soiling of PV modules from the particle size distribution and the cleanliness value.

", "keywords": ["Photovoltaic Arrays", "Cleanliness", "Particle", "PV", "02 engineering and technology", "Oceanography", "7. Clean energy", "soiling; experimental; transmittance; spectrum", "Turbidity", "Size", "Materials Science and Engineering", "\u00c5ngstr\u00f6m turbidity equation", "Transmittance", "0202 electrical engineering", " electronic engineering", " information engineering", "Photovoltaic system", "Ultraviolet", "Microscopy", "Soiling", "Energy", "Ecology", "Physics", "Q", "R", "Imaging and sensing", "Geology", "Particle size", "6. Clean water", "Photovoltaic Efficiency", "Chemistry", "Physical chemistry", "Particle (ecology)", "Physical Sciences", "Sunlight", "Medicine", "Infrared", "570", "Particle-size distribution", "PV System", "Energy science and technology", "Science", "Optical spectroscopy", "Partial Shading", "530", "Modelling", "Article", "Environmental science", "Techniques and instrumentation", "Optical physics", "Meteorology", "Artificial Intelligence", "Machine Learning Methods for Solar Radiation Forecasting", "Optical techniques", "Optoelectronics", "Aerosol", "Biology", "Renewable Energy", " Sustainability and the Environment", "Electronics", " photonics and device physics", "Building Integrated Photovoltaics", "Optics", "Photovoltaic Maximum Power Point Tracking Techniques", "FOS: Earth and related environmental sciences", "Materials science", "Photovoltaics", "Optics and photonics", "13. Climate action", "FOS: Biological sciences", "Computer Science", "Solar Thermal Energy Technologies"]}, "links": [{"href": "https://iris.uniroma1.it/bitstream/11573/1625670/2/Smestad_Modelling_2020.pdf"}, {"href": "https://www.nature.com/articles/s41598-019-56868-z.pdf"}, {"href": "https://doi.org/10.1038/s41598-019-56868-z"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Reports", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1038/s41598-019-56868-z", "name": "item", "description": "10.1038/s41598-019-56868-z", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/s41598-019-56868-z"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-09T00:00:00Z"}}, {"id": "10.1080/09064710.2022.2136583", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:17:09Z", "type": "Journal Article", "created": "2022-10-26", "title": "Exploring structural sediment connectivity via surface runoff in agricultural lands of Finland", "description": "Spatial information on the distribution of erosion areas and sediment transport pathways within agricultural landscapes is limited. Thus, we assess structural sediment connectivity via surface runoff by using a digital elevation model (2 \u00d7 2 m2) and RUSLE-based erosion estimates to compute index of connectivity (IC) and sediment delivery estimates. The variables were analyzed within and between two topographically contrasting subcatchments. We found greater spatial variability of IC within a subcatchment than between the subcatchments. The majority of field parcel areas (65%\u201397%) were structurally connected to adjacent open ditches and streams. Areas with high erosion estimates also tended to be structurally well-connected, both at the pixel (Pearson r = 0.58\u20130.63) and parcel scale (r = 0.49\u20130.67). The IC model was not highly sensitive to parameter variations. In contrast, the magnitude of sediment delivery estimates was highly sensitive to parameter variations. However, based on the high rank correlation (Spearman rs > 0.95) between computed sediment delivery estimates, the tool provided consistent information on potentially high sediment delivery areas. More empirical data and dynamic model applications could be applied to improve the accuracy of the estimates. The method provides a feasible tool to generate open data on connectivity.", "keywords": ["550", "ta1172", "rusle", "SB1-1110", "Inorganic Chemistry", "Sociology", "FOS: Chemical sciences", "FOS: Mathematics", "RUSLE", "ta218", "Connectivity", "Ecology", "connectivity index", "Plant culture", "lowlands", "FOS: Earth and related environmental sciences", "04 agricultural and veterinary sciences", "ta4111", "15. Life on land", "erosion", "59999 Environmental Sciences not elsewhere classified", "FOS: Sociology", "FOS: Biological sciences", "connectivity", "Medicine", "19999 Mathematical Sciences not elsewhere classified", "0401 agriculture", " forestry", " and fisheries", "69999 Biological Sciences not elsewhere classified", "Biotechnology"]}, "links": [{"href": "https://www.tandfonline.com/doi/pdf/10.1080/09064710.2022.2136583"}, {"href": "https://doi.org/10.1080/09064710.2022.2136583"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Acta%20Agriculturae%20Scandinavica%2C%20Section%20B%20%E2%80%94%20Soil%20%26amp%3B%20Plant%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1080/09064710.2022.2136583", "name": "item", "description": "10.1080/09064710.2022.2136583", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1080/09064710.2022.2136583"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-10-26T00:00:00Z"}}, {"id": "10.1093/ismejo/wrae025", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:17:16Z", "type": "Journal Article", "created": "2024-02-12", "title": "Stronger compensatory thermal adaptation of soil microbial respiration with higher substrate availability", "description": "Abstract

Ongoing global warming is expected to augment soil respiration by increasing the microbial activity, driving self-reinforcing feedback to climate change. However, the compensatory thermal adaptation of soil microorganisms and substrate depletion may weaken the effects of rising temperature on soil respiration. To test this hypothesis, we collected soils along a large-scale forest transect in eastern China spanning a natural temperature gradient, and we incubated the soils at different temperatures with or without substrate addition. We combined the exponential thermal response function and a data-driven model to study the interaction effect of thermal adaptation and substrate availability on microbial respiration and compared our results to those from two additional continental and global independent datasets. Modeled results suggested that the effect of thermal adaptation on microbial respiration was greater in areas with higher mean annual temperatures, which is consistent with the compensatory response to warming. In addition, the effect of thermal adaptation on microbial respiration was greater under substrate addition than under substrate depletion, which was also true for the independent datasets reanalyzed using our approach. Our results indicate that thermal adaptation in warmer regions could exert a more pronounced negative impact on microbial respiration when the substrate availability is abundant. These findings improve the body of knowledge on how substrate availability influences the soil microbial community\uffe2\uff80\uff93temperature interactions, which could improve estimates of projected soil carbon losses to the atmosphere through respiration.

In Morocco, cereal production shows high interannual variability due to uncertain rainfall and recurrent drought periods. Considering the socioeconomic importance of cereal for the country, there is a serious need to characterize the impact of drought on cereal yields. In this study, drought is assessed through (1) indices derived from remote sensing data (the vegetation condition index (VCI), temperature condition index (TCI), vegetation health ind ex (VHI), soil moisture condition index (SMCI) and soil water index for different soil layers (SWI)) and (2) key land surface variables (Land Area Index (LAI), soil moisture (SM) at different depths, soil evaporation and plant transpiration) from a Land Data Assimilation System (LDAS) over 2000\u20132017. A lagged correlation analysis was conducted to assess the relationships between the drought indices and cereal yield at monthly time scales. The VCI and LAI around the heading stage (March-April) are highly linked to yield for all provinces (R = 0.94 for the Khemisset province), while a high link for TCI occurs during the development stage in January-February (R = 0.83 for the Beni Mellal province). Interestingly, indices related to soil moisture in the superficial soil layer are correlated with yield earlier in the season around the emergence stage (December). The results demonstrate the clear added value of using an LDAS compared with using a remote sensing product alone, particularly concerning the soil moisture in the root-zone, considered a key variable for yield production, that is not directly observable from space. The time scale of integration is also discussed. By integrating the indices on the main phenological stages of wheat using a dynamic threshold approach instead of the monthly time scale, the correlation between indices and yield increased by up to 14%. In addition, the contributions of VCI and TCI to VHI were optimized by using yield anomalies as proxies for drought. This study opens perspectives for the development of drought early warning systems in Morocco and over North Africa, as well as for seasonal crop yield forecasting.

", "keywords": ["[SDE] Environmental Sciences", "550", "Science", "0207 environmental engineering", "Agricultural drought", "02 engineering and technology", "01 natural sciences", "630", "Environmental science", "remote sensing", "Land data assimilation systems", "Pathology", "assimilation systems", "Biology", "land data assimilation systems", "0105 earth and related environmental sciences", "2. Zero hunger", "Global and Planetary Change", "Vegetation Monitoring", "Water content", "Ecology", "Drought", "Global Forest Drought Response and Climate Change", "Q", "Hydrology (agriculture)", "Geology", "cereal yield", "Remote Sensing in Vegetation Monitoring and Phenology", "FOS: Earth and related environmental sciences", "Remote sensing", "semiarid region", "15. Life on land", "agricultural drought", "Agronomy", "6. Clean water", "Cereal yield", "Geotechnical engineering", "13. Climate action", "FOS: Biological sciences", "[SDE]Environmental Sciences", "Global Drought Monitoring and Assessment", "Environmental Science", "Physical Sciences", "Leaf area index", "Medicine", "Semiarid region", "land data", "Vegetation (pathology)"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/24/4018/pdf"}, {"href": "https://www.mdpi.com/2072-4292/12/24/4018/pdf"}, {"href": "https://doi.org/10.3390/rs12244018"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs12244018", "name": "item", "description": "10.3390/rs12244018", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs12244018"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-12-08T00:00:00Z"}}, {"id": "10.1371/journal.pone.0001299", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:18:15Z", "type": "Journal Article", "created": "2007-12-11", "title": "Increased Litterfall In Tropical Forests Boosts The Transfer Of Soil Co2 To The Atmosphere", "description": "Open AccessLa production de liti\u00e8re a\u00e9rienne dans les for\u00eats est susceptible d'augmenter en raison des concentrations \u00e9lev\u00e9es de dioxyde de carbone atmosph\u00e9rique (CO(2)), de la hausse des temp\u00e9ratures et du changement des r\u00e9gimes de pr\u00e9cipitations. Comme les chutes de liti\u00e8re repr\u00e9sentent un flux majeur de carbone de la v\u00e9g\u00e9tation vers le sol, les changements dans les apports de liti\u00e8re sont susceptibles d'avoir des cons\u00e9quences de grande port\u00e9e sur la dynamique du carbone du sol. De telles perturbations du bilan carbone peuvent \u00eatre particuli\u00e8rement importantes sous les tropiques, car les for\u00eats tropicales stockent pr\u00e8s de 30\u00a0% du carbone mondial du sol, ce qui en fait une composante essentielle du cycle mondial du carbone\u00a0; n\u00e9anmoins, les effets de l'augmentation de la production de liti\u00e8re a\u00e9rienne sur la dynamique du carbone souterrain sont mal compris. Nous avons utilis\u00e9 des traitements mensuels \u00e0 long terme et \u00e0 grande \u00e9chelle d'enl\u00e8vement et d'ajout de liti\u00e8re dans une for\u00eat tropicale de plaine pour \u00e9valuer les cons\u00e9quences de l'augmentation des chutes de liti\u00e8re sur la production souterraine de CO(2). Au cours de la deuxi\u00e8me \u00e0 la cinqui\u00e8me ann\u00e9e de traitement, l'ajout de liti\u00e8re a augment\u00e9 la respiration du sol plus que l'enl\u00e8vement de la liti\u00e8re ne l'a diminu\u00e9\u00a0; la respiration du sol \u00e9tait en moyenne 20\u00a0% plus faible dans l'enl\u00e8vement de la liti\u00e8re et 43\u00a0% plus \u00e9lev\u00e9e dans le traitement d'ajout de liti\u00e8re par rapport aux t\u00e9moins, mais l'ajout de liti\u00e8re n'a pas modifi\u00e9 la biomasse microbienne. Nous avons pr\u00e9dit une augmentation de 9% de la respiration du sol dans les parcelles d'ajout de liti\u00e8re, bas\u00e9e sur la diminution de 20% des parcelles d'enl\u00e8vement de la liti\u00e8re et une r\u00e9duction de 11% due \u00e0 une biomasse racinaire fine plus faible dans les parcelles d'ajout de liti\u00e8re. L'augmentation mesur\u00e9e de 43\u00a0% de la respiration du sol \u00e9tait donc 34\u00a0% plus \u00e9lev\u00e9e que pr\u00e9vu et il est possible que ce CO \u00ab\u00a0suppl\u00e9mentaire\u00a0\u00bb (2) soit le r\u00e9sultat d'effets d'amor\u00e7age, c'est-\u00e0-dire la stimulation de la d\u00e9composition de la mati\u00e8re organique du sol plus ancienne par l'ajout de mati\u00e8re organique fra\u00eeche. Nos r\u00e9sultats montrent que l'augmentation de la production de liti\u00e8re a\u00e9rienne en raison du changement global a le potentiel de provoquer des pertes consid\u00e9rables de carbone du sol dans l'atmosph\u00e8re dans les for\u00eats tropicales.", "keywords": ["570", "Atmospheric sciences", "Science", "Atmosphere (unit)", "Soil Science", "Carbon Loss", "630", "Environmental science", "Plant litter", "Trees", "Agricultural and Biological Sciences", "Impact of Climate Change on Forest Wildfires", "Soil", "Meteorology", "Litter", "Biomass", "Biology", "Ecosystem", "2. Zero hunger", "Tropical Climate", "Global and Planetary Change", "Ecology", "Geography", "Atmosphere", "Global Forest Drought Response and Climate Change", "Q", "R", "Temperature", "Tropics", "Water", "Life Sciences", "Geology", "FOS: Earth and related environmental sciences", "04 agricultural and veterinary sciences", "Carbon Dioxide", "15. Life on land", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Physical Sciences", "Medicine", "0401 agriculture", " forestry", " and fisheries", "Seasons", "Soil Carbon Dynamics and Nutrient Cycling in Ecosystems", "Research Article"]}, "links": [{"href": "http://oro.open.ac.uk/36464/1/Sayer%20et%20al%202007.pdf"}, {"href": "https://eprints.lancs.ac.uk/id/eprint/69199/1/journal.pone.0001299.pdf"}, {"href": "https://doi.org/10.1371/journal.pone.0001299"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PLoS%20ONE", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1371/journal.pone.0001299", "name": "item", "description": "10.1371/journal.pone.0001299", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1371/journal.pone.0001299"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2007-12-12T00:00:00Z"}}, {"id": "10.1371/journal.pone.0056562", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:18:15Z", "type": "Journal Article", "created": "2013-02-20", "title": "Carbon Dioxide Flux From Rice Paddy Soils In Central China: Effects Of Intermittent Flooding And Draining Cycles", "description": "Open AccessSe realiz\u00f3 un experimento de campo para (i) examinar el patr\u00f3n de flujos de di\u00f3xido de carbono (CO(2)) del suelo diurno y estacional en los arrozales en el centro de China y (ii) evaluar el papel del agua de inundaci\u00f3n en el control de las emisiones de CO(2) del suelo y el agua de inundaci\u00f3n en el drenaje intermitente del suelo de los arrozales. Las tasas de flujo de CO(2) del suelo oscilaron entre -0.45 y 8.62 \u00b5mol.m(-2).s(-1) durante la temporada de cultivo de arroz. Los eflujos netos de CO(2) del suelo del arrozal fueron menores cuando se inund\u00f3 el arrozal que cuando se dren\u00f3. Las emisiones de CO(2) para las condiciones de drenaje mostraron una variaci\u00f3n diurna distinta con un eflujo m\u00e1ximo observado en la tarde. Cuando el arrozal se inund\u00f3, los flujos de CO(2) del suelo diurno se invirtieron con un flujo m\u00e1ximo negativo justo despu\u00e9s del mediod\u00eda. En per\u00edodos alternos de drenaje/inundaci\u00f3n, se produjo un evento repentino similar a un pulso de eflujo de CO(2) en r\u00e1pido aumento en respuesta a una nueva inundaci\u00f3n despu\u00e9s del drenaje. El an\u00e1lisis de correlaci\u00f3n mostr\u00f3 una relaci\u00f3n negativa entre el flujo de CO(2) del suelo y la temperatura en condiciones de inundaci\u00f3n, pero se encontr\u00f3 una relaci\u00f3n positiva en condiciones de drenaje. Los resultados mostraron que los ciclos de drenaje e inundaci\u00f3n juegan un papel vital en el control de las emisiones de CO(2) de los suelos de los arrozales.", "keywords": ["Carbon sequestration", "Organic chemistry", "Agricultural and Biological Sciences", "Soil", "Agricultural soil science", "Soil water", "Psychology", "2. Zero hunger", "Global and Planetary Change", "Ecology", "Q", "R", "Temperature", "Life Sciences", "Hydrology (agriculture)", "Geology", "Carbon cycle", "04 agricultural and veterinary sciences", "6. Clean water", "FOS: Psychology", "Chemistry", "Emissions", "Physical Sciences", "Medicine", "Seasons", "Methane", "Research Article", "China", "Science", "Soil Science", "Flooding (psychology)", "Environmental science", "Carbon Cycle", "Humans", "Biology", "Ecosystem", "Soil science", "Soil organic matter", "Oryza", "FOS: Earth and related environmental sciences", "Carbon Dioxide", "15. Life on land", "Soil biodiversity", "Floods", "Agronomy", "Geotechnical engineering", "Carbon dioxide", "13. Climate action", "FOS: Biological sciences", "Global Methane Emissions and Impacts", "Environmental Science", "Flux (metallurgy)", "Psychotherapist", "0401 agriculture", " forestry", " and fisheries", "Soil Carbon Dynamics and Nutrient Cycling in Ecosystems"], "contacts": [{"organization": "Yi Liu, Kaiyuan Wan, Yong Tao, Zhiguo Li, Guoshi Zhang, Shuanglai Li, Fang Chen,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1371/journal.pone.0056562"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PLoS%20ONE", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1371/journal.pone.0056562", "name": "item", "description": "10.1371/journal.pone.0056562", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1371/journal.pone.0056562"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2013-02-20T00:00:00Z"}}, {"id": "10.5061/dryad.fbg79cntt", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-09-21T16:19:48Z", "type": "Dataset", "title": "Tree species of wet tropical forests differ in their tissue biochemistry and effects on soil carbon dynamics", "description": "unspecifiedMissing values are denoted by a period (.). The 'Metadata' sheet contains information about units, references and other notes.", "keywords": ["FOS: Earth and related environmental sciences", "15. Life on land"]}, "links": [{"href": "https://doi.org/10.5061/dryad.fbg79cntt"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.fbg79cntt", "name": "item", "description": "10.5061/dryad.fbg79cntt", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.fbg79cntt"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-04-28T00:00:00Z"}}, {"id": "10.5061/dryad.fj6q57401", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:48Z", "type": "Dataset", "title": "Pathways of glyphosate effects on litter decomposition in grasslands", "description": "unspecifiedStudy site and application of glyphosate The study site was a humid mesophytic grassland in the Flooding Pampa, a vast region of around 9 million hectares in the province of Buenos Aires, Argentina. The mean annual temperature is around 15\u00b0C and the mean annual rainfall is 885 mm (Soriano and Paruelo 1992). The landscape has a treeless physiognomy and an extremely flat topography with periodic flooding during autumn\u2013spring in lowland, except in ridge areas with well-drained sandy soils (Burkart et al. 1998). The field experiment was carried out in a commercial livestock farm (35\u00b0 01\u00b4S, 57\u00b0 50\u00b4 W). The plant community is dominated by C3 and C4 grass species (see details in Druille et al. 2015). Soil is classified as a typical Natracuol (US Soil Taxonomy), characterized by having a nonsaline acid A1 horizon, and a highly alkaline saline B2 horizon (Lavado and Taboada 1988). Glyphosate was not applied at the study site before, even though glyphosate application in the late summer is a common practice in the region with a 3 l/ha dose (Rodriguez and Jacobo 2010). We applied this dose (1440 g of acid equivalent/ha) of a commercial glyphosate formulation Glacoxan\u00ae in field and greenhouse experiments with a 20 l backpack sprayer with a constant pressure of 3 bars. Pathways of single-glyphosate application To evaluate pathways of single application of glyphosate effects through living plants (1), leaf litter (2), and soil (3), we set up a litter decomposition experiment in a greenhouse. For these pathways, we used plant material that is naturally found in the field at the end of the summer when glyphosate is applied in the Flooding Pampa. At that time of the year, Paspalum dilatatum, the native dominant perennial C4 grass, can be found as a living plant and as plant litter. In turn, Lolium multiflorum, which is an introduced annual winter C3 forage grass, is only found dead as plant litter. Considering that in this grassland the vast majority of L. multiflorum plants are endophyte-infected with Epichlo\u00eb occultans (Gundel et al. 2009), in this experiment we used L. multiflorum plants associated with the endophyte. Together, for the living plant pathway we used P. dilatatum (1) and for the leaf litter pathway (2) we used litter produced by P. dilatatum and L. multiflorum with endophyte plants. Paspalum dilatatum and Lolium multiflorum plants were grown in 1 m x 1 m monoculture plots in the experimental campus of the School of Agronomy at the University of Buenos Aires. L. multiflorum plants grew from seeds with naturally high level (82%) of endophyte association (E+), and from seeds without endophyte (E-) obtained experimentally following Omacini et al. (2004). We collected fresh senesced plant litter of both species and, in the lab, sorted leaf litter from other plant organs. Then, in the P. dilatatum plots, we removed all dead plant material to applied glyphosate on living plants. After 15 days, we collected P. dilatatum plants killed by glyphosate and separated the leaf litter from other organs. We determined the total carbon (%C) and nitrogen (%N) content of all types of litter by Dumas combustion with a TruSpec elemental analyzer (LECO, St. Joseph, MI, USA) at the University of Buenos Aires. We prepared litterbags containing leaf litter from P. dilatatum plants killed by glyphosate (Plant Gx) and from naturally senesced P. dilatatum and L. multiflorum E+ plants. Litterbags were made of fiberglass mesh, which is the most common used material for litter decomposition studies (Harmon et al. 1999, Bradford et al. 2002), and that we have successfully used before (Omacini et al. 2004, Vivanco and Austin 2006, 2019).\u00a0 We used 0.5 g of each litter type in 11 cm x 9 cm litterbags with a 3 mm opening on the upper face and a 2 mm opening on the lower face. We prepared plastic containers with 1.2 kg of soil from the study site, which had not received prior glyphosate treatment. Half of the litterbags containing naturally senesced leaves were sprayed with glyphosate (Litter G+) and the other half was sprayed with water (Litter G-). Half of the soil containers were sprayed with glyphosate (Soil G+) and the other half was sprayed with water (Soil G-). We assigned litterbags (Plant Gx, Litter G+, Litter G-) to soil containers (Soil G+, Soil G-) in a factorial design and kept them moistened with regular watering (n=5). We assessed litter decomposition as litter mass loss over time. We collected litterbags after 140 and 270 days of incubation. Litterbags were dried for 48 h at 65\u00b0C; soil and debris were removed from litter and were oven-dried again for determination of dry mass. We estimated the decomposition constant k using a single exponential decay model by regressing the log of the fraction of mass remaining against time. The decomposition constant integrates the dynamics of litter mass loss over time and it is a useful parameter to compare between litter types and treatments (Wieder and Lang 1982). We used ln (Mt/Mo) = \u2013kt, where Mo is the initial dry mass, Mt is the dry mass at time t, and k is the decomposition constant (Swift et al. 1979). Linear regressions were performed by setting the intercept to zero. In the few cases when samples did not fit a significant regression, values were considered outliers and were replaced by the mean of the treatment, following the missing value procedure (Steel and Torrie 1980, Vivanco and Austin 2008). Pathways of repeated annual application of glyphosate in natural grasslands We evaluated pathways of repeated annual application of glyphosate through legacies in ecosystem properties (4) and through the enhancement of endophytic grass (5) (Fig. 1) on decomposition of leaf litter and roots in a field experiment in the Flooding Pampa. In this field experiment, we previously studied the impacts of glyphosate application on beneficial soil microorganisms (Druille et al. 2013, 2015, 2016). We established 10 plots (1.5m x 1.5 m) in an area of similar floristic composition and randomly assigned them to control (Ecosystem G-) or glyphosate application (Ecosystem G+) treatments. Every April (late summer in the southern hemisphere) for three consecutive years, we applied 3 l / ha of water to Ecosystem G- plots and 3 l / ha (1440 g acid equivalent / ha) of commercial glyphosate formulation (Glacoxan\u00ae) to Ecosystem G+ plots. We applied these treatments using a 20 l backpack sprayer with a constant pressure of 3 bars. Cattle grazing was avoided during the experiment by keeping an electric wire around the experimental area. To avoid biomass accumulation and the consequent aging of grasslands, we made a harvest of plant biomass using a lawn mower set to leave 10 cm stubble every year before application of the treatment. To evaluate pathways of repeated annual application of glyphosate, we used litter produced by plants of L. multiflorum with (E+) and without (E-) endophyte that was accumulated above and below ground (leaf and root litter). We prepared 14 cm x 14 cm litterbags made of 2 mm fiberglass mesh. We placed leaf litterbags on the ground and root litterbags buried 5 cm belowground. Considering that the place where the litter was deposited (above and belowground) can interact with the type of litter (leaf and root litter), we placed a common substrate (stem litter) litterbags on the ground and buried at 5 cm to assess the effects of the above and belowground environment. The experiment started 15 days after the third year of application of glyphosate (n = 4) and we collected litterbags at 30, 140 and 260 days. We assessed ash-free dry mass (500\u00b0C oven for 4 h) to estimate the decomposition constant k as described in Section 2.3. Together, this experiment evaluated the relative importance of pathways 4 and 5 and provides information about the effect of an aerial symbiosis on root decomposition of the host, which has not been evaluated previously. We assessed above and belowground ecosystem properties in Ecosystem G- and Ecosystem G+ plots. We measured plant cover in December (when the last litterbag pickup occurred) in 10 plots of each level of glyphosate application. For estimation of plant cover, we used the line intercept method proposed by Canfield (1941). We determined potential water evaporation at ground level by measuring the water loss of wet filter papers. We used preweighted oven-dried filter papers and wet them in the field to full water-holding capacity. Filter papers were weighed immediately before and after incubation on the ground for 1 hour at midday in May to calculate water loss. We measured two filter papers per plot in 5 replicates for each level of glyphosate application. We determined soil gravimetric water content from 10 cm depth soil cores taken in August and December (second and third litterbag harvest dates, respectively). We also determined soil organic matter content and soil potential respiration from soil cores taken in May, approximately one year after the decomposition experiment was installed in the field. Soil organic matter content was determined by total combustion in an oven at 500\u00b0C for 4 hours. We determined soil potential respiration by incubating a 15-g sample at 25\u00b0C, in a 200-ml vial with gastight septum caps. The soil was pre-incubated at water field capacity for 48 h without seedlings or any plants. CO2 production was measured 2, 4 and 7 days after a 24-h incubation period with an infrared gas analyzer (PP Systems EGM-4, Amesbury, Massachusetts, USA). We used five replicates per level of glyphosate application for soil measurements.", "keywords": ["2. Zero hunger", "Glyphosate", "litter C/N", "Pampa Grasslands", "FOS: Earth and related environmental sciences", "litter decomposition", "fungal symbiont", "15. Life on land", "carbon loss", "Endophyte", "forage management", "litter bags", "13. Climate action", "root litter", "herbicide", "soil organic matter", "Epichl\u00f6e occultans", "livestock production"], "contacts": [{"organization": "Vivanco, Luc\u00eda, S\u00e1nchez, Mar\u00eda, Druille, Magdalena, Omacini, Marina,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.fj6q57401"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.fj6q57401", "name": "item", "description": "10.5061/dryad.fj6q57401", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.fj6q57401"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-02-28T00:00:00Z"}}, {"id": "10.5061/dryad.6m905qg4x", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:46Z", "type": "Dataset", "title": "Inconsistent responses of carabid beetles and spiders to land-use intensity and landscape complexity in Northwestern Europe", "description": "Open AccessWe used data on natural enemy communities in 66 paired winter wheat fields in four Northwestern European countries (Germany, the Netherlands, Sweden and United Kingdom) to investigate the response of natural enemy communities to landscape complexity, local land-use intensity and soil organic matter content, and specifically examined whether and how responses differ between dominant and non-dominant species. We focused on carabid beetles and spiders as they represent the two groups of natural enemies in arable fields in Northwestern European and widely used as bioindicators (Lang et al., 1999; Borchard et al., 2014). We used pitfall traps to collect carabids and spiders in field pairs that covered a gradient in land-use intensity and landscape complexity, with fields within pairs having contrasting soil organic carbon content.\u00a0 Pitfall traps (polypropylene beakers 155 mm high and 95 mm across) were used to survey ground-dwelling arthropods during the wheat flowering season (late May to early June). We placed one pitfall trap in the center of each treatment subplot at least 10 m from the field edge and filled it with 200 mL of a mixed solution of 2/3 water and 1/3 glycol and a drop of detergent to lower surface tension. A square aluminum plate was placed approximately 10 cm above each pitfall trap to prevent flooding by rain. Pitfall traps were opened for 10 days. All of the collected arthropods were stored in 70% ethanol solution for later identification. For the purpose of our study, the two most abundant species groups, carabid beetles (Carabidae) and adult spiders (Araneae), were selected as our bioindicators and they were counted and identified to species level using standard keys (Hackston, 2020; Nentwig et al., 2021). We determined the diet preference of each carabid beetle species based on Larochelle (1990) and the hunting strategy of all observed spider species based on Cardoso et al. (2011) following Gall\u00e9 et al. (2019). Furthermore, because the arthropod communities will inevitably differ in composition between countries, we classified the carabids or spiders as nationally dominant and non-dominant species based on whether species made up respectively more or less than 5% of the total number of individuals caught of each species group in a country following Kleijn et al. (2015).", "keywords": ["2. Zero hunger", "soil organic carbon", "ecological intensification", "Earth and related environmental sciences", "pest control service", "evenness", "dominant species", "14. Life underwater", "FOS: Earth and related environmental sciences", "15. Life on land", "natural enemies"], "contacts": [{"organization": "Mei, Zulin, Scheper, Jeroen, Bommarco, Riccardo, de Groot, Gerard Arjen, Garratt, Michael P. D., Hedlund, Katarina, Potts, Simon G., Redlich, Sarah, Smith, Henrik G., Steffan-Dewenter, Ingolf, van der Putten, Wim H., van Gils, Stijn, Kleijn, David,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.6m905qg4x"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.6m905qg4x", "name": "item", "description": "10.5061/dryad.6m905qg4x", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.6m905qg4x"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-01T00:00:00Z"}}, {"id": "10.5194/isprs-archives-xlii-3-w6-9-2019", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:20:06Z", "type": "Journal Article", "created": "2019-07-29", "title": "EVAPOTRANSPIRATION AND EVAPORATION/TRANSPIRATION RETRIEVAL USING DUAL-SOURCE SURFACE ENERGY BALANCE MODELS INTEGRATING VIS/NIR/TIR DATA WITH SATELLITE SURFACE SOIL MOISTURE INFORMATION", "description": "

Abstract. Evapotranspiration is an important component of the water cycle. For the agronomic management and ecosystem health monitoring, it is also important to provide an estimate of evapotranspiration components, i.e. transpiration and soil evaporation. To do so, Thermal InfraRed data can be used with dual-source surface energy balance models, because they solve separate energy budgets for the soil and the vegetation. But those models rely on specific assumptions on raw levels of plant water stress to get both components (evaporation and transpiration) out of a single source of information, namely the surface temperature. Additional information from remote sensing data are thus required. This works evaluates the ability of the SPARSE dual-source energy balance model to compute not only total evapotranspiration, but also water stress and transpiration/evaporation components, using either the sole surface temperature as a remote sensing driver, or a combination of surface temperature and soil moisture level derived from microwave data. Flux data at an experimental plot in semi-arid Morocco is used to assess this potentiality and shows the increased robustness of both the total evapotranspiration and partitioning retrieval performances. This work is realized within the frame of the Phase A activities for the TRISHNA CNES/ISRO Thermal Infra-Red satellite mission.

", "keywords": ["Technology", "Environmental Engineering", "550", "Ecosystem Resilience", "Soil Moisture", "Evaporation", "Energy balance", "Biochemistry", "Environmental science", "Transpiration", "Meteorology", "Artificial Intelligence", "Soil water", "Thermal Infrared", "Applied optics. Photonics", "Machine Learning Methods for Solar Radiation Forecasting", "Photosynthesis", "TRISHNA", "Water balance", "Biology", "Soil science", "Global and Planetary Change", "Water content", "Evapotranspiration", "Geography", "Ecology", "Global Forest Drought Response and Climate Change", "T", "FOS: Environmental engineering", "Geology", "FOS: Earth and related environmental sciences", "Remote sensing", "15. Life on land", "Engineering (General). Civil engineering (General)", "Remote Sensing of Soil Moisture", "6. Clean water", "TA1501-1820", "[SDE.MCG] Environmental Sciences/Global Changes", "Chemistry", "Geotechnical engineering", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Physical Sciences", "Computer Science", "TA1-2040", "Water cycle"]}, "links": [{"href": "https://doi.org/10.5194/isprs-archives-xlii-3-w6-9-2019"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/The%20International%20Archives%20of%20the%20Photogrammetry%2C%20Remote%20Sensing%20and%20Spatial%20Information%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/isprs-archives-xlii-3-w6-9-2019", "name": "item", "description": "10.5194/isprs-archives-xlii-3-w6-9-2019", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/isprs-archives-xlii-3-w6-9-2019"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-07-26T00:00:00Z"}}, {"id": "10.5194/hess-2019-105", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:20:05Z", "type": "Journal Article", "created": "2019-04-23", "title": "An evapotranspiration model self-calibrated from remotely sensed surface soil moisture, land surface temperature and vegetation cover fraction: application to disaggregated SMOS and MODIS data", "description": "

Abstract. Thermal-based two-source energy balance modeling is very useful for estimating the land evapotranspiration (ET) at a wide range of spatial and temporal scales. However, the land surface temperature (LST) is not sufficient for constraining simultaneously both soil and vegetation flux components in such a way that assumptions (on either the soil or the vegetation fluxes) are commonly required. To avoid such assumptions, a new energy balance model (TSEB-SM) was recently developed in Ait Hssaine et al. (2018a) to integrate the microwave-derived near-surface soil moisture (SM), in addition to the thermal-derived LST and vegetation cover fraction (fc). Whereas, TSEB-SM has been recently tested using in-situ measurements, the objective of this paper is to evaluate the performance of TSEB-SM in real-life using 1\u2009km resolution MODIS (Moderate resolution imaging spectroradiometer) LST and fc data and the 1\u2009km resolution SM data disaggregated from SMOS (Soil Moisture and Ocean Salinity) observations by using DisPATCh. The approach is applied during a four-year period (2014\u20132018) over a rainfed wheat field in the Tensift basin, central Morocco, during a four-year period (2014\u20132018). The field was seeded for the 2014\u20132015 (S1), 2016\u20132017 (S2) and 2017\u20132018 (S3) agricultural season, while it was not ploughed (remained as bare soil) during the 2015\u20132016 (B1) agricultural season. The mean retrieved values of (arss, brss) calculated for the entire study period using satellite data are (7.32, 4.58). The daily calibrated \u03b1PT ranges between 0 and 1.38 for both S1 and S2. Its temporal variability is mainly attributed to the rainfall distribution along the agricultural season. For S3, the daily retrieved \u03b1PT remains at a mostly constant value (\u223c\u20090.7) throughout the study period, because of the lack of clear sky disaggregated SM and LST observations during this season. Compared to eddy covariance measurements, TSEB driven only by LST and fc data significantly overestimates latent heat fluxes for the four seasons. The overall mean bias values are 119, 94, 128 and 181\u2009W/m2 for S1, S2, S3 and B1 respectively. In contrast, these errors are much reduced when using TSEB-SM (SM and LST combined data) with the mean bias values estimated as 39, 4, 7 and 62\u2009W/m2 for S1, S2, S3 and B1 respectively.

", "keywords": ["Technology", "Atmospheric sciences", "550", "Soil Moisture", "0208 environmental biotechnology", "02 engineering and technology", "Environmental technology. Sanitary engineering", "01 natural sciences", "Engineering", "Geography. Anthropology. Recreation", "Pathology", "GE1-350", "TD1-1066", "2. Zero hunger", "Global and Planetary Change", "Water content", "Evapotranspiration", "Geography", "Ecology", "T", "Soil Water Retention", "Moderate-resolution imaging spectroradiometer", "Hydrology (agriculture)", "Geology", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "6. Clean water", "Aerospace engineering", "Physical Sciences", "Medicine", "environment", "Vegetation (pathology)", "Latent heat", "Mechanics and Transport in Unsaturated Soils", "Land cover", "Environmental Engineering", "0207 environmental engineering", "Energy balance", "Thermal Effects on Soil", "Environmental science", "[SDU] Sciences of the Universe [physics]", "G", "Meteorology", "Civil engineering", "14. Life underwater", "[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology", "Biology", "Civil and Structural Engineering", "0105 earth and related environmental sciences", "Global Forest Drought Response and Climate Change", "FOS: Environmental engineering", "FOS: Earth and related environmental sciences", "15. Life on land", "Remote Sensing of Soil Moisture", "Environmental sciences", "Geotechnical engineering", "[SDU]Sciences of the Universe [physics]", "Satellite", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Land use", "[SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "FOS: Civil engineering"]}, "links": [{"href": "https://hess.copernicus.org/articles/24/1781/2020/hess-24-1781-2020.pdf"}, {"href": "https://doi.org/10.5194/hess-2019-105"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Hydrology%20and%20Earth%20System%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/hess-2019-105", "name": "item", "description": "10.5194/hess-2019-105", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/hess-2019-105"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-04-23T00:00:00Z"}}, {"id": "10.5194/hess-24-3789-2020", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:20:05Z", "type": "Journal Article", "created": "2020-07-27", "title": "Evapotranspiration partition using the multiple energy balance version of the ISBA-A-gs land surface model over two irrigated crops in a semi-arid Mediterranean region (Marrakech, Morocco)", "description": "

Abstract. The main objective of this work is to question the representation of the energy budget in soil\u2013vegetation\u2013atmosphere transfer\u00a0(SVAT) models for the prediction of the turbulent fluxes in the case of irrigated crops with a complex structure (row) and under strong transient hydric regimes due to irrigation. To this end, the Interaction between Soil, Biosphere, and Atmosphere\u00a0(ISBA-A-gs) is evaluated at a complex open olive orchard and, for the purposes of comparison, on a winter wheat field taken as an example of a homogeneous canopy. The initial version of ISBA-A-gs, based on a composite energy budget (hereafter ISBA-1P for one\u00a0patch), is compared to the new multiple energy balance\u00a0(MEB) version of ISBA that represents a double source arising from the vegetation located above the soil layer. In addition, a patch representation corresponding to two adjacent, uncoupled source schemes (hereafter ISBA-2P for two\u00a0patches) is also considered for the olive orchard. Continuous observations of evapotranspiration\u00a0(ET), with an eddy covariance system and plant transpiration\u00a0(Tr) with sap flow and isotopic methods were used to evaluate the three representations. A preliminary sensitivity analyses showed a strong sensitivity to the parameters related to turbulence in the canopy introduced in the new ISBA\u2013MEB version. For wheat, the ability of the single- and dual-source configuration to reproduce the composite soil\u2013vegetation heat fluxes was very similar; the root mean square error (RMSE) differences between ISBA-1P, ISBA-2P and ISBA\u2013MEB did not exceed 10\u2009W\u2009m\u22122 for the latent heat flux. These results showed that a composite energy balance in homogeneous covers is sufficient to reproduce the total convective fluxes. The two configurations are also fairly close to the isotopic observations of transpiration in spite of a light underestimation (overestimation) of ISBA-1P\u00a0(ISBA\u2013MEB). At the olive orchard, contrasting results are obtained. The dual-source configurations, including both the uncoupled\u00a0(ISBA-2P) and the coupled\u00a0(ISBA\u2013MEB) representations, outperformed the single-source version\u00a0(ISBA-1P), with slightly better results for ISBA\u2013MEB in predicting both total heat fluxes and evapotranspiration partition. Concerning plant transpiration in particular, the coupled approach ISBA\u2013MEB provides better results than ISBA-1P and, to a lesser extent, ISBA-2P with RMSEs of\u00a01.60, 0.90, and 0.70\u2009mm\u2009d\u22121 and R2\u00a0of\u00a00.43, 0.69, and\u00a00.70\u00a0for ISBA-1P, ISBA-2P and ISBA\u2013MEB, respectively. In addition, it is shown that the acceptable predictions of composite convective fluxes by ISBA-2P for the olive orchard are obtained for the wrong reasons as neither of the two patches is in agreement with the observations because of a bad spatial distribution of the roots and a lack of incoming radiation screening for the bare soil patch. This work shows that composite convection fluxes predicted by the SURFace EXternalis\u00e9e (SURFEX) platform and the partition of evapotranspiration in a highly transient regime due to irrigation is improved for moderately open tree canopies by the new coupled dual-source ISBA\u2013MEB model. It also points out the need for further local-scale evaluations on different crops of various geometry (more open rainfed agriculture or a denser, intensive olive orchard) to provide adequate parameterisation to global database, such as ECOCLIMAP-II, in the view of a global application of the ISBA\u2013MEB model.

", "keywords": ["Technology", "Atmospheric Science", "Atmospheric sciences", "550", "[SDV]Life Sciences [q-bio]", "0207 environmental engineering", "02 engineering and technology", "Energy balance", "Eddy covariance", "Environmental technology. Sanitary engineering", "01 natural sciences", "Environmental science", "G", "Meteorology", "Geography. Anthropology. Recreation", "GE1-350", "Biology", "TD1-1066", "Ecosystem", "0105 earth and related environmental sciences", "Soil science", "2. Zero hunger", "Global and Planetary Change", "Evapotranspiration", "Ecology", "Global Forest Drought Response and Climate Change", "T", "Causes and Impacts of Climate Change Over Millennia", "Physics", "Hydrology (agriculture)", "Geology", "FOS: Earth and related environmental sciences", "15. Life on land", "Agronomy", "[SDV] Life Sciences [q-bio]", "Environmental sciences", "Earth and Planetary Sciences", "Geotechnical engineering", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Physical Sciences", "Global Drought Monitoring and Assessment", "Leaf area index", "Thermodynamics", "Global Vegetation Models"]}, "links": [{"href": "https://doi.org/10.5194/hess-24-3789-2020"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Hydrology%20and%20Earth%20System%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/hess-24-3789-2020", "name": "item", "description": "10.5194/hess-24-3789-2020", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/hess-24-3789-2020"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-10-15T00:00:00Z"}}, {"id": "10.3389/fsoil.2023.1240930", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:18Z", "type": "Journal Article", "created": "2023-07-11", "title": "Editorial: Greenhouse gas measurements in underrepresented areas of the world", "description": "Open Access\u0645\u0642\u0627\u0644 \u062a\u062d\u0631\u064a\u0631\u064a Front. Soil Sci., 11 July 2023Sec. \u0627\u0644\u0643\u064a\u0645\u064a\u0627\u0621 \u0627\u0644\u062d\u064a\u0648\u064a\u0629 \u0644\u0644\u062a\u0631\u0628\u0629 \u0648\u0631\u0643\u0648\u0628 \u0627\u0644\u062f\u0631\u0627\u062c\u0627\u062a \u0627\u0644\u063a\u0630\u0627\u0626\u064a\u0629 \u0627\u0644\u0645\u062c\u0644\u062f 3 - 2023 | https://doi.org/10.3389/fsoil.2023.1240930", "keywords": ["Soil nutrients", "Mechanics and Transport in Unsaturated Soils", "representativeness", "Oceanography", "Greenhouse gas", "Environmental science", "climate change mitigation", "12. Responsible consumption", "Impact of Climate Change on Forest Wildfires", "Engineering", "greenhouse gases", "Soil water", "11. Sustainability", "TA703-712", "QD1-999", "Biology", "Civil and Structural Engineering", "Soil science", "2. Zero hunger", "Global and Planetary Change", "nitrous oxide", "Geography", "Ecology", "greenhouse gas emissions", "Global Forest Drought Response and Climate Change", "methane", "carbon dioxide", "Cycling", "Geology", "Forestry", "Engineering geology. Rock mechanics. Soil mechanics. Underground construction", "FOS: Earth and related environmental sciences", "Biogeochemistry", "15. Life on land", "6. Clean water", "livestock", "Chemistry", "climate change", "Global Emissions", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Physical Sciences", "Nutrient"]}, "links": [{"href": "https://doi.org/10.3389/fsoil.2023.1240930"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Soil%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/fsoil.2023.1240930", "name": "item", "description": "10.3389/fsoil.2023.1240930", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/fsoil.2023.1240930"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-07-11T00:00:00Z"}}, {"id": "10.3535/2p4-8vy-qaf", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-09-21T16:19:35Z", "type": "Dataset", "title": "rocks", "description": "Digital Specimen for the physical specimen hosted at Tallinn University of Technology.", "keywords": ["Geology", "FOS: Earth and related environmental sciences", "Earth System"], "contacts": [{"organization": "Tallinn University of Technology", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.3535/2p4-8vy-qaf"}, {"rel": "self", "type": "application/geo+json", "title": "10.3535/2p4-8vy-qaf", "name": "item", "description": "10.3535/2p4-8vy-qaf", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3535/2p4-8vy-qaf"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-03-31T00:00:00Z"}}, {"id": "10.3535/5td-xfj-g5x", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-09-21T16:19:35Z", "type": "Dataset", "title": "igneous rocks", "description": "Digital Specimen for the physical specimen hosted at Tallinn University of Technology.", "keywords": ["Geology", "FOS: Earth and related environmental sciences", "Earth System"], "contacts": [{"organization": "Tallinn University of Technology", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.3535/5td-xfj-g5x"}, {"rel": "self", "type": "application/geo+json", "title": "10.3535/5td-xfj-g5x", "name": "item", "description": "10.3535/5td-xfj-g5x", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3535/5td-xfj-g5x"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-03-31T00:00:00Z"}}, {"id": "10.3390/rs9111155", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:31Z", "type": "Journal Article", "created": "2017-11-10", "title": "Disaggregation of SMOS Soil Moisture to 100 m Resolution Using MODIS Optical/Thermal and Sentinel-1 Radar Data: Evaluation over a Bare Soil Site in Morocco", "description": "

The 40 km resolution SMOS (Soil Moisture and Ocean Salinity) soil moisture, previously disaggregated at a 1 km resolution using the DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) method based on MODIS optical/thermal data, is further disaggregated to 100 m resolution using Sentinel-1 backscattering coefficient (\u03c3\u00b0). For this purpose, three distinct radar-based disaggregation methods are tested by linking the spatio-temporal variability of \u03c3\u00b0 and soil moisture data at the 1 km and 100 m resolution. The three methods are: (1) the weight method, which estimates soil moisture at 100 m resolution at a certain time as a function of \u03c3\u00b0 ratio (100 m to 1 km resolution) and the 1 km DISPATCH products of the same time; (2) the regression method which estimates soil moisture as a function of \u03c3\u00b0 where the regression parameters (e.g., intercept and slope) vary in space and time; and (3) the Cumulative Distribution Function (CDF) method, which estimates 100 m resolution soil moisture from the cumulative probability of 100 m resolution backscatter and the maximum to minimum 1 km resolution (DISPATCH) soil moisture difference. In each case, disaggregation results are evaluated against in situ measurements collected between 1 January 2016 and 11 October 2016 over a bare soil site in central Morocco. The determination coefficient (R2) between 1 km resolution DISPATCH and localized in situ soil moisture is 0.31. The regression and CDF methods have marginal effect on improving the DISPATCH accuracy at the station scale with a R2 between remotely sensed and in situ soil moisture of 0.29 and 0.34, respectively. By contrast, the weight method significantly improves the correlation between remotely sensed and in situ soil moisture with a R2 of 0.52. Likewise, the soil moisture estimates show low root mean square difference with in situ measurements (RMSD= 0.032 m3 m\u22123).

", "keywords": ["soil moisture and ocean salinity satellite (SMOS)", "Atmospheric Science", "Artificial intelligence", "Environmental Engineering", "550", "Science", "Soil Moisture", "0211 other engineering and technologies", "Aerospace Engineering", "FOS: Mechanical engineering", "02 engineering and technology", "01 natural sciences", "Environmental science", "[SDU] Sciences of the Universe [physics]", "Engineering", "Meteorology", "DISPATCH", "Image resolution", "Arctic Permafrost Dynamics and Climate Change", "14. Life underwater", "Moisture", "0105 earth and related environmental sciences", "Soil science", "Water content", "Radar", "Geography", "soil moisture and ocean salinity satellite (SMOS); DISPATCH; radar; Sentinel-1; disaggregation; soil moisture", "Soilmoisture and ocean salinity satellite (SMOS)", "Synthetic Aperture Radar Interferometry", "Q", "FOS: Environmental engineering", "Geology", "FOS: Earth and related environmental sciences", "Remote sensing", "Remote Sensing of Soil Moisture", "Surface Deformation Monitoring", "Computer science", "Earth and Planetary Sciences", "Groundwater Extraction", "Geotechnical engineering", "[SDU]Sciences of the Universe [physics]", "disaggregation", "Environmental Science", "Physical Sciences", "Sentinel-1", "soil moisture", "radar"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/9/11/1155/pdf"}, {"href": "https://doi.org/10.3390/rs9111155"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs9111155", "name": "item", "description": "10.3390/rs9111155", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs9111155"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-11-10T00:00:00Z"}}, {"id": "10.5061/dryad.0cfxpnw4m", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:43Z", "type": "Dataset", "title": "Data from: Decipher soil organic carbon dynamics and driving forces across China using machine learning", "description": "unspecifiedPlease see the ReadMe file.", "keywords": ["2. Zero hunger", "Driving Forces", "13. Climate action", "Machine learning", "cross validation", "FOS: Earth and related environmental sciences", "SOC", "spatiotemporal dynamics", "15. Life on land", "random forest"], "contacts": [{"organization": "Li, Huiwen, Wu, Yiping, Liu, Shuguang, Xiao, Jingfeng, Zhao, Wenzhi, Chen, Ji, Alexandrov, Georgii, Cao, Yue,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.0cfxpnw4m"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.0cfxpnw4m", "name": "item", "description": "10.5061/dryad.0cfxpnw4m", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.0cfxpnw4m"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-04-23T00:00:00Z"}}, {"id": "10.5061/dryad.0k6djhb5k", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:43Z", "type": "Dataset", "created": "2023-08-29", "title": "Empirical data and model simulations of the effect of repeated hurricanes on soil carbon dynamics in a humid tropical forest", "description": "unspecifiedSite description Soils were sampled from the Bisley Experimental Watershed of the LEF, Puerto Rico (18.3157 deg. N, 65.7487 deg W), a Long-Term Ecological Research and Critical Zone Observatory and Network site (https://luq.lter.network). The mean maximum daily temperature at Bisley was 27 \u00baC between 1993 and 2010 (Gonzales, 2020), with little seasonality. The mean annual precipitation at Bisley was 3883 (\u00b1 864 s.d.) mm y-1 from 1988 through 2014 (Gonz\u00e1lez, 2017; Murphy et al., 2017). Rainfall occurs all year, though January through April experience slightly less precipitation than other months (Heartsill-Scalley et al., 2007). The site is a humid tropical forest with a diverse tree community of approximately 170 species > 4 cm diameter at breast height (Weaver & Murphy, 1990), and dominated by tabonuco (Dacryodes excelsa Vahl). Elevation of Bisley spans from 261 m a.s.l. at the base to 450 m a.s.l. on the ridges (Scatena, 1989). Soils in Bisley are derived from volcaniclastic sediments of andesitic parent material (Scatena, 1989).\u00a0 Ridge soils are classified as Ultisols (Typic Haplohumults), while slope soils are classified as Oxisols (inceptic and Aquic Hapludox), and valley soils are classified as Inceptisols (Typic Epiaquaepts) (Hall et al., 2015; McDowell et al., 2012; Scatena, 1989). Detailed site descriptions can be found in Scatena (1989), Heartsill-Scalley et al (2010), and McDowell et al (2012). Here we refer to soil organic C (SOC) and soil C interchangeably because there is no detectable inorganic C in these soils. Hurricane occurrence\u00a0 Figure 1: Timeline of major hurricanes that have affected Luquillo Experimental Forest between sampling dates. Nine major hurricanes (category 3 or higher) have impacted Puerto Rico between 1851 and 2019 (L\u00f3pez-Marrero et al., 2019), and five of these hurricanes have impacted the LEF. Until 1998, hurricanes had historically directly impacted the LEF approximately every 60 years (Scatena & Larsen, 1991). Before the initial sampling campaign of this study, Hurricane San Cipri\u00e1n in 1932 was the most recent storm to cause major disturbance to the LEF (Scatena & Larsen, 1991).\u00a0 However, since sampling in 1988, four major hurricanes have impacted the forest (Figure 1). Hurricane Hugo (Category 3-4) in 1989, Hurricane Georges (Category 3) in 1998, and Hurricanes Irma and Maria (Categories 5 and 4, respectively) within two weeks in 2017. The trajectory and windspeeds of all these hurricanes caused widespread defoliation. Litterfall historically takes over five years to return to pre-hurricane levels (Scatena et al., 1996).\u00a0 Sampling Sample collection occurred in 1988 and again in 2018. In both years, samples were collected from three depths: 0\u201310 cm (the A horizon), 10\u201335 cm (all of the B1 horizon and part of B2), and 35\u201360 cm (B2 to C) using an 8 cm diameter soil auger. Soils in this study were sampled at three separate sites at least 40 m from one another for each of three topographic locations, ridge, slope, and upland valley. Two separate cores were taken from a fourth topographic location in the riparian valley, that characterized a smaller proportion of the area of these watersheds (Scatena & Lugo, 1995). Riparian valley sites were ephemeral streambeds with a high boulder presence that limited sampling to less than 25 cm depth in one case. Sampling sites from 1988 were marked with flags, and samples from 2018 were collected from within 15 m of the same locations as the replicates from 1988, for consistency. Samples collected in 1988 were analyzed for bulk density, pH, soil moisture, and a suite of soil chemical properties (see Silver et al. 1994). Samples were then air-dried and stored in closed Ziploc bags within paper bags in a storage facility in Richmond, CA, USA before density fractionation in 2018. Fresh samples collected in 2018 were also characterized for pH, soil moisture, and soil chemistry. Approximately 3 g subsamples from each fresh sample in 2018 were immediately extracted with 45 mL of 0.2 M sodium citrate/0.5 M ascorbate solution, shaken for 16 hours, then centrifuged and the supernatant decanted to measure concentrations of poorly crystalline iron (Fe) oxides. Within two days of being double-bagged in Ziploc bags, fresh samples were further subsampled and analyzed for pH in a 1:1 soil-to-water slurry (Thomas, 1996) and for gravimetric soil moisture by oven-drying ~10 g subsamples at 105 \u00baC until a constant weight. Soil samples were air-dried before further processing and analysis. Air-dried soils from both sampling years were sieved to 2 mm and large roots were sorted out. Soil Density fractionation Soil was fractionated by density following the method of Swanston et al. (2005), as modified by Marin-Spiotta et al., (2009). Approximately 20 g of air-dried soil was added to centrifuge tubes. Sodium polytungstate (SPT, Na6 [H2W12O40] TC-Tungsten Compounds, Bavaria, Germany) in solution of density 1.85 g cm-3 was added to centrifuge tubes and agitated before centrifuging. The density of the SPT followed previous studies from this and nearby sites to allow direct comparison (Guti\u00e9rrez del Arroyo & Silver, 2018; Hall et al., 2015). Particulate organic matter floating at the surface after centrifugation, the free light fraction (FLF), was aspirated and then rinsed with 100 ml of deionized water 5 times on a 0.8 \u00b5m pore polycarbonate filter (Whatman Nuclepore Track Etch Membrane, Darmstadt, Germany). Rinsed FLF was oven-dried at 65 \u00baC until weight had stabilized. The remainder of the sample was combined with 70 ml of additional SPT and mixed using an electric benchtop mixer (G3U05R, Lightning, New York, NY, USA) at 1700 rpm for 1 min and sonicated in an ice bath for 3 min at 70% pulse (Branson 450 Sonifier, Danbury, CT, USA). Sonication is intended to disrupt soil structure and liberate organic matter that has been occluded in aggregates. The sonicated slurry was centrifuged again, and the light fraction at the surface, the occluded light fraction (OLF), was aspirated, rinsed, and dried using the same method as for the FLF. The remaining soil pellet was considered the heavy fraction (HF), or mineral-associated organic matter fraction. The HF was rinsed by thoroughly mixing with 150 ml of deionized water in the centrifuge tube, centrifuging, and removing the supernatant repeatedly until the fraction had been rinsed 5 times. The rinsed HF was oven-dried at 105 \u00baC until weight stabilized. The average mass recovery was 98%. Soil C and N and \u03b413C Dried bulk and HF soils were homogenized separately using a Spex Ball mill (SPEX Sample Prep Mixer Mill 8000D, Metuchen, NJ). The FLF and OLF were homogenized separately by hand using a mortar and pestle. All homogenized samples were then analyzed at U. C. Berkeley for C and N concentrations on the CE Elantech elemental analyzer (Lakewood, NJ) and for \u03b413C in the Stable Isotope Laboratory at UC Berkeley, using a CHNOS Elemental Analyzer interfaced to an IsoPrime 100 mass spectrometer (Cheadle Hulme, UK), with a long-term external precision of 0.10 %. \u00a0Soil C stocks were calculated by multiplying the C concentrations (%) by the oven-dry mass of bulk soil (< 2 mm) and dividing by depth and the bulk density as measured in 1988 (Silver et al., 1994; Throop et al., 2012). Radiocarbon Homogenized soil samples were combusted to CO2 in sealed glass tubes along with silver (Ag) and copper oxide (CuO) at the Center for Accelerator Mass Spectrometry at Lawrence Livermore National Lab. The CO2 was then graphitized on Fe powder under pressurized hydrogen gas (Vogel et al., 1984). Graphite was pressed into aluminum targets and run on the Compact Accelerator Mass Spectrometer for radiocarbon analysis (Broek et al., 2021). Radiocarbon is reported in \u039414C, following Stuiver & Polach (1977), and calculated based on the fraction of modern isotope composition, corrected for the year of sampling, and corrected for mass-dependent fractionation with observed \u03b413C values of the sample. The compact AMS had an average \u039414C precision of 3.2 %. We report the corrected \u039414C value and \u0394\u039414C, which is calculated as \u039414C of the sample minus \u039414C of the atmosphere, to account for rapidly changing atmospheric \u039414C during the study period. Atmospheric radiocarbon has been decaying nonlinearly since the peak of weapons testing in the 1950s. Radiocarbon signatures in the soil are strongly influenced by the atmospheric D14C signature, making them useful for modeling soil C age and transit time, especially since the 1950s. To compare the contribution of modern C between 1988 and 2018, it is useful to take the difference between soil and atmospheric D14C values, or DD14C, because atmospheric D14C declined between 1988 (98 %) and 2018 (4.4 %) in Northern Hemisphere Zone 2 (Hua et al., 2013). We note that the decline in atmospheric D14C is nonlinear, and thus the DD14C in 2018 soil will be less sensitive to short-term shifts in D14C inputs than the samples from 1988. Carbon age and transit time modeling Transit times and ages of C were modeled with the package \u201cSoilR\u201d (Sierra et al., 2012, 2014) in R, version 4.0.2. The change in C density fractions over time, termed C flow, was modeled using a 3-pool structure with a series flow matrix, under the simplifying assumption that C flows from the litter pool to the FLF, where it is sequentially transferred into the OLF and HF pools (Figure 2). The model structure is depicted in basic form in equation 1, \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 (1)\u00a0 dC(t)/dt = Inputs - k*C \u00a0in matrix form with explicit pools in equation 2, \u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 (2)\u00a0 dC(t)/dt = [Litter Inputs; 0; 0] + [-kFLF, 0, 0 ; a21,\u00a0-kOLF, 0; 0, a32, -kHRF] * [CFLF; COLF; CHF] where k is the first-order decay constant for each pool, a is the C transfer rate between pools (i.e. a21 is the transfer from FLF (pool 1) to OLF (pool 2) and a32 is the transfer from OLF (pool 2) to HF (pool 3)), and C is the C stock of each pool. The transitTime and systemAge functions within the \u201csoilR\u201d package use this model structure to solve for the distribution of ages (time since entry) of each pool, and the distribution of transit times (times between entry and exit from the bulk soil) (Sierra et al 2016). Distributions of age and transit time were time-independent and did not assume a specific distribution (Sierra et al., 2014, 2017). Figure 2: Hypothesized flow of C in soils. Free light fraction (FLF) C (pink) is either decomposed (at cycling rate -kFLF * FLF) or transferred to the occluded light fraction pool (OLF, blue) with the transfer proportion defined by a21. Carbon transfer between the OLF and heavy fraction (HF, purple) is defined by transfer coefficient a32, and is respired from these pools at cycling rates -kOLF* OLF and -kHF* HF, respectively. Figure adapted from Sierra et al. (2012). Soil D14C and C stock mean and standard deviations from each time point, depth, and fraction were used to constrain the matrix model describing the movement of C through three soil pools and losses of C from each pool. Topography was not a strong predictor of patterns in D14C, C stocks, or C fractions, so samples from all topographies were aggregated for model simulations. The model used mean observed C content in each pool for each depth in 1988 as initial conditions for SOC stocks. Above and belowground litter inputs at 0\u201310 cm were assumed to be 900 g C m-2 in non-hurricane or hurricane recovery years, based on observations from the same site (Liu et al., 2018; Scatena et al., 1996; Silver et al., 1996; Vogt et al., 1996). Inputs to the 10\u201335 cm and 35\u201360 cm depths were estimated using observations of live fine roots on the surface and typical root distribution in the forest (Silver & Vogt, 1993). Total root input is approximately threefold the input of fine roots alone (McCormack et al., 2015; Yaffar & Norby, 2020), and live fine roots in the 0\u201310 cm depth had a mean biomass of 80 - 250 g C m-2 \u00a0(Hall et al., 2015), suggesting that total root C inputs of approximately 450 g C m-2 to the surface would be well within the expected range. Root inputs below 0\u201310 cm were estimated assuming that inputs follow the typical distribution of root biomass in Puerto Rican tropical forests, with 60\u201370% of root biomass in 0\u201310 cm, an additional 20-30% of biomass in 10\u201335 cm (~135 g C m-2\u00ad), and 5\u20138% of biomass is in the 35\u201360 cm depth (~40 g C m-2\u00ad) (Silver & Vogt, 1993; Yaffar & Norby, 2020). The model was parameterized under two scenarios for each depth: 1) constant inputs, assuming a steady-state undisturbed forest, and 2) hurricane inputs, which simulated the input fluxes from defoliation during the three major hurricanes, followed by a subsequent reduction in litter inputs and then litterfall increasing linearly to pre-hurricane inputs over 6 years (Scatena et al., 1996; Silver et al., 1996; Vogt et al., 1996). Hurricane inputs were imposed as an additional pulse of litter inputs to each depth interval, declining with depth. \u00a0The 0\u201310 cm interval received 100% of the surface input pulse, the 10\u201335 cm depth received a pulse of root inputs equivalent to 30% of the surface input pulse, and the 35\u201360 cm depth received root inputs equal to 10% of the surface input pulse. Surface litter pulses under hurricanes were specified according to measured litterfall values and were 42.5 g C m-2\u00ad to the surface in 1989 (Hurricane Hugo) and 1998 (Hurricane Georges) (Scatena et al., 1993; Silver et al., 1996) and 1611 g C m-2 \u00a0in 2017 (Hurricanes Irma and Maria) (Liu et al. 2018a). The same soil D14C and C stock observations were used to constrain the model under each scenario, with only the input regime varying. Parameters of the transfer matrix (-k\u00ad\u00adFLF, -k\u00ad\u00adOLF, -k\u00ad\u00adHF, a21, a32) were constrained using a cost function to accept or reject potential parameter sets over 1000 iterations, based on observed D14C and C stock means and standard errors from both time points (1988 and 2018). A Markov chain Monte Carlo (MCMC) simulation initialized with cost-optimized parameters was run to assimilate observed data and optimize parameter choices to the observations using function modMCMC() from R package \u201cFME\u201d (Sierra et al., 2014; Soetaert & Petzoldt, 2010). The MCMC was iterated over at least 20,000 simulations or until parameter solutions converged according to the trace, which was over 100,000 iterations at the 35\u201360 cm depth. The first half of the iterations was considered the burn-in period before the chain started to converge near an equilibrium, and these iterations were discarded in calculations of optimal parameters. The model output for the surface soils of the HF pool was validated using published radiocarbon values from the mineral-associated fraction (the only fraction analyzed) of samples from the site taken in 2012 (Hall et al., 2015).\u00a0 Bulk and pool soil C age and transit time density distributions and mean values were calculated using the systemAge() and transitTime() functions from the \u201cSoilR\u201d package. Mean density distributions were calculated using the mean parameter set given from the MCMC analysis. Standard deviation from the mean was calculated using the systemAge() and transitTime() functions on 200 sets of five parameters selected randomly within one standard deviation of the mean of each parameter given as output from the MCMC. Lower and upper limits of SOC ages and transit times were calculated using the upper and lower ranges of these iterations. Statistics Statistics were run in R, version 4.0.2 (R Core Team, 2020). The statistical model selection followed the recommendations of Zuur et al (2009). Statistical models were chosen using a linear mixed effects model in package \u201clme4\u201d, with random slopes accounting for the influence each core, or sampling site, had on the response variable values as they varied with depth. This random effect of the core site on the depth effect was evaluated using a restricted maximum likelihood approach and was included in the initial evaluation of all model comparisons. Linear mixed effect models included year, topographic position, depth, and interactions as fixed factors, and the depth effect of each core as a random factor for each of the response variables: C concentration, N concentration, d13C, DD14C. In evaluations of some response variables with AIC and BIC criteria, the random effect no longer enhanced the model, and model comparison proceeded using ANOVAs of linear models without random effects. Topographic effects on C concentrations are discussed in the supplemental information. Model assumptions were evaluated using the check_model function in R package \u201cperformance\u201d, to check for multicollinearity, normality of residuals, homoscedasticity, homogeneity of variance, influential observations, and normality of random effects. In the cases when random effects were significant (bulk soil d13C and DD14C, FLF DD14C and HF C and N concentrations), fixed effects were chosen using ANOVA of subsequent models using maximum likelihood estimation, with the random effects held constant. Once fixed effects were established, the model was re-fitted using a restricted maximum likelihood approach to report model estimates, and an ANOVA was run to determine the significance of the response variable. In all cases, P-values were estimated using Tukey\u2019s honest significant post-hoc test to assess significant differences between variables, in the package \u201cagricolae\u201d in R, and contrasts and standard errors of contrasts were estimated using lsmeans() function in package \u201clsmeans\u201d in R. Values of\u00a0P < 0.10 were reported as significant unless otherwise specified. The topographic position was not a significant predictor for most variables, so results are reported as means aggregated across positions.", "keywords": ["soil organic carbon", "Transit time", "Tropical forest soil", "FOS: Earth and related environmental sciences", "Soil R", "density fractions", "Radiocarbon"], "contacts": [{"organization": "Mayer, Allegra", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.0k6djhb5k"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.0k6djhb5k", "name": "item", "description": "10.5061/dryad.0k6djhb5k", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.0k6djhb5k"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-04-01T00:00:00Z"}}, {"id": "10.5061/dryad.41ns1rnjd", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:45Z", "type": "Dataset", "title": "Does long-term soil warming affect microbial element limitation? A test by short-term assays of microbial growth responses to labile C, N and P additions", "description": "Open AccessPlease refer to the accompanying README file and the published paper: Shi, C., Malo, C., Tian, Y., Heinzle, J., Kengdo, S. K., Inselsbacher, E., Borken, W., Schindlbacher, A., & Wanek, W. (2023). Does long-term soil warming affect microbial element limitation? A test by short-term assays of microbial growth responses to labile C, N and P additions. Global Change Biology. Accepted.", "keywords": ["2. Zero hunger", "Temperate forest ecosystem", "13. Climate action", "FOS: Agriculture", " forestry and fisheries", "Microbial element limitation", "Soil Sciences", "18O incorporation into microbial DNA", "FOS: Earth and related environmental sciences", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Shi, Chupei, Urbina-Malo, Carolina, Tian, Ye, Heinzle, Jakob, Kwatcho Kengdo, Steve, Inselsbacher, Erich, Borken, Werner, Schindlbacher, Andreas, Wanek, Wolfgang,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.41ns1rnjd"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.41ns1rnjd", "name": "item", "description": "10.5061/dryad.41ns1rnjd", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.41ns1rnjd"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-09T00:00:00Z"}}, {"id": "10.5061/dryad.41ns1rnjs", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:45Z", "type": "Dataset", "title": "Plant growth strategy determines the magnitude and direction of drought-induced changes in root exudates in subtropical forests", "description": "Root exudates are an important pathway for plant-microbial interactions and are highly sensitive to climate change. However, how extreme drought affects root exudates and the main components, as well as species-specific differences in response magnitude and direction, are poorly understood. In this study, root exudation rates of total carbon (C) and its components (e.g., sugar, organic acid, and amino acid) were measured under the control and extreme drought treatments (i.e., 70% throughfall reduction) by in situ collection of four tree species with different growth rates in a subtropical forest. We also quantified soil properties, root morphological traits, and mycorrhizal infection rates to examine the driving factors underlying variations in root exudation. Our results showed that extreme drought significantly decreased root exudation rates of total C, sugar, and amino acid by 17.8%, 30.8%, and 35.0%, respectively, but increased root exudation rate of organic acid by 38.6%, which were largely associated with drought-induced changes in tree growth rates, root morphological traits, and mycorrhizal infection rates. Specifically, trees with relatively high growth rates were more responsive to drought for root exudation rates compared to those with relatively low growth rates, which were closely related to root morphological traits and mycorrhizal infection rates. These findings highlight the importance of plant growth strategy in mediating drought-induced changes in root exudation rates. The co-ordinations among root exudation rates, root morphological traits, and mycorrhizal symbioses in response to drought could be incorporated into land surface models to improve the prediction of climate change impacts on rhizosphere C dynamics in forest ecosystems.", "keywords": ["2. Zero hunger", "root morphological traits", "Drought", "subtropical forsts", "13. Climate action", "Root exudation", "tree growth", "FOS: Earth and related environmental sciences", "15. Life on land", "Organic acid", "6. Clean water", "amino acid", "mycorrhizal infection"], "contacts": [{"organization": "Jiang, Zheng, Fu, Yuling, Zhou, Lingyan, He, Yanghui, Zhou, Guiyao, Dietrich, Peter, Long, Jilan, Wang, Xinxin, Jia, Shuxian, Ji, Yuhuang, Jia, Zhen, Song, Bingqian, Liu, Ruiqiang, Zhou, Xuhui,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.41ns1rnjs"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.41ns1rnjs", "name": "item", "description": "10.5061/dryad.41ns1rnjs", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.41ns1rnjs"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-03-20T00:00:00Z"}}, {"id": "10.5061/dryad.3bk3j9kt3", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:45Z", "type": "Dataset", "created": "2024-03-31", "title": "Data from: Burrowing crab effects on the properties and functions of coastal soft sediments", "description": "unspecified# Data from: Burrowing crab effects on the properties and functions of coastal soft sediments [https://doi.org/10.5061/dryad.3bk3j9kt3](https://doi.org/10.5061/dryad.3bk3j9kt3) Effect size calculations (including means, sample sizes, and standard deviation) of crab burrowing effects (i.e., high density vs low density) on the properties, nutrient stocks, and functions of coastal sediments. Data comes from studies conducted across Africa, Asia, Australia, North America, and South America. ## Description of the data and file structure **File list:** 1. Rinehart_et_al.202X_Effectsizes CSV file containing the Hedges d effect size calculations (including the raw means, sample sizes, and standard deviations) for each extracted comparison/study from all 59 manuscripts. Additional extracted data (e.g., crab taxa, experimental conditions, habitat, burrow density) are also included for each comparison/study. 2. Rinehart_et_al.202X_Publicationbias CSV file containing the pooled standard deviation and the Hedges d effect size calculation for each comparison/study. This datafile was used to conduct analyses of publication bias for a resulting systematic meta-analysis. **Data-specific information for:** (1) Rinehart_et_al.202X_Effectsizes **Number of variables:** 47 **Number of cases/rows:** 1423 Variable List:\u00a0 1. id: the unique code assigned to each data row. 2. reference: author, year, and journal for each data source. 3. pub_year: year of reference publication. One in preparation study was included in the dataset (Rinehart et al. 20XX), it's publication year is denoted as 20XX. 4. paper id: the unique code assigned to each manuscript included in the dataset. 5. continent: the continent where the data was collected. 6. country: the country where the data was collected. 7. state: the state (united states only) where the data was collected. 8. estuary: the name of the estuary where the data was collected. 9. latitude_dd: the latitude associated with the data collected in decimal degrees (dd). 10. longitude_dd: the longitude associated with the data collected in decimal degrees (dd). 11. ecosystem: the type of ecosystem (e.g., salt marsh, mangrove forest, tidal flat) associated with the collected data. 12. vegetation: categorical variable noting the presence (vegetated) or absence (not unvegetated) of any vegetation. 13. ecosystem_type: categorical variable noting if the ecosystem was restored, created, or natural. 14. relative_salinity: categorical variable noting the relative salinity in the ecosystem where the data was collected. 15. tidal_amplitude_m: the tidal amplitude (in meters) in the ecosystem where the data was collected. 16. tidal_cycle: categorical variable noting the type of tidal cycle (e.g., diurnal) in the ecosystem where the data was collected. 17. soil_type: categorical variable noting the soil type (e.g., sand) in the ecosystem where the data was collected. 18. elevation_m: the elevation (in meters) of the ecosystem where the data was collected. 19. study_duration_d: the length of time (in days) that the study ran (applies mainly to manipulative studies). 20. study_timing: the seasons or months during which the study was run. 21. dominant_plant_genus: the genus of the dominant plant present in the ecosystem where the data was collected. 22. dominant_plant_species: the species of the dominant plant present in the ecosystem where the data was collected. 23. dominant_plant_functional_group: categorical variable noting the functional group (e.g., grass) of the dominant plant species in the ecosystem where the data was collected. 24. crab_genus: the genus of the dominant burrowing crab used in the study. Studies with mixed crab communities are denoted with by 'mixed'. 25. crab_species: the species of the dominant burrowing crab used in the study. Studies with mixed crab communities are denoted with by 'mixed'. 26. crab_diet: categorical variable noting the main feeding strategy (e.g., herbivore, detritivore) used by the dominant crab species. 27. crab_superfamily: the superfamily of the dominant burrowing crab used in the study. Studies with mixed crab communities are denoted with by 'mixed'. 28. mean_burrow_diameter_high_crab_treatment_mm: the mean burrow diameter in the study's high crab treatment in mm. 29. mean_burrow_diameter_low_crab_treatment_mm: the mean burrow diameter in the study's low crab treatment in mm. 30. mean_burrow_depth_cm: the mean burrow depth in cm reported by the study. 31. burrow_density_high_crab_m^2: the mean crab burrow density per meter-squared reported in the study's high crab treatment. 32. burrow_density_low_crab_m^2: the mean crab burrow density per meter-squared reported in the study's low crab treatment. 33. experiment_type: categorical variable noting if the study used observational or manipulative methodologies. 34. experiment_setting: categorical variable noting if the study was conducted in a laboratory or field setting. Laboratory studies also include outdoor mesocosm studies. 35. field_location: categorical variable noting where studies conducted in the field placed their study relative to the shoreline. Specifically, we noted if studied sampled in the ecosystem interior (far from shoreline) or at the ecosystem edge (adjacent to the shoreline). 36. soil_depth_cm: the depth, in cm, within the soil profile from which the sediment samples were collected. 37. soil_characteristic_measured: categorical variable identifying the specific sediment property, nutrient stock, or function that was quantified by the study. 38. soil_characteristic_units: the original units used to quantify the soil characteristic within the study. 39. mean_low_crab: the mean value of the soil characteristic measured in the low crab treatment within the study. 40. sd_low_crab: the standard deviation of the soil characteristic measured in the low crab treatment within the study. 41. n_low_crab: the sample size of the soil characteristic measured in the low crab treatment within the study. 42. mean_high_crab: the mean value of the soil characteristic measured in the high crab treatment within the study. 43. sd_high_crab: the standard deviation of the soil characteristic measured in the high crab treatment within the study. 44. n_high_crab: the sample size of the soil characteristic measured in the high crab treatment within the study. 45. crab_density: categorical variable noting if the study documented relative burrowing crab density within their study using burrow density (burrow) or counts of individuals (individuals). 46. hedges_d: the hedges d effect size calculated for the effects of burrowing crabs on the measured sediment characteristic. Hedges d values were calculated in OpenMee software (see code/software below). Positive effect sizes indicate that burrowing crabs increased the value of the sediment measurement, while negative effect sized indicate that burrowing crabs decreased the value of the sediment measurement. 47. hedges_d_var: the variation of the hedges d effect size calculated for the effects of burrowing crabs on the measured sediment characteristic. Hedges d variation values were calculated in OpenMee software (see code/software below). **Missing data codes:** na Data-specific information for: (2) Rinehart_et_al.202X_Publicationbias ***Number of variables:*** 22 ***Number of cases/rows:*** 1423 Variable List:\u00a0 1. id: the unique code assigned to each data row. 2. reference: author, year, and journal for each data source. 3. pub_year: year of reference publication. One in preparation study was included in the dataset (Rinehart et al. 20XX), it's publication year is denoted as 20XX. 4. paper id: the unique code assigned to each manuscript included in the dataset. 5. ecosystem: the type of ecosystem (e.g., salt marsh, mangrove forest, tidal flat) associated with the collected data. 6. vegetation: categorical variable noting the presence (vegetated) or absence (not unvegetated) of any vegetation. 7. crab_superfamily: the superfamily of the dominant burrowing crab used in the study. Studies with mixed crab communities are denoted with by 'mixed'. 8. burrow_density_high_crab_m^2: the mean crab burrow density per meter-squared reported in the study's high crab treatment. 9. experiment_type: categorical variable noting if the study used observational or manipulative methodologies. 10. experiment_setting: categorical variable noting if the study was conducted in a laboratory or field setting. Laboratory studies also include outdoor mesocosm studies. 11. soil_characteristic_measured: categorical variable identifying the specific sediment property, nutrient stock, or function that was quantified by the study. 12. soil_characteristic_units: the original units used to quantify the soil characteristic within the study. 13. mean_low_crab: the mean value of the soil characteristic measured in the low crab treatment within the study. 14. sd_low_crab: the standard deviation of the soil characteristic measured in the low crab treatment within the study. 15. n_low_crab: the sample size of the soil characteristic measured in the low crab treatment within the study. 16. mean_high_crab: the mean value of the soil characteristic measured in the high crab treatment within the study. 17. sd_high_crab: the standard deviation of the soil characteristic measured in the high crab treatment within the study. 18. n_high_crab: the sample size of the soil characteristic measured in the high crab treatment within the study. 19. pooled_sd: the pooled standard deviation of the high and low crab treatments for each study. 20. crab_density: categorical variable noting if the study documented relative burrowing crab density within their study using burrow density (burrow) or counts of individuals (individuals). 21. hedges_d: the hedges d effect size calculated for the effects of burrowing crabs on the measured sediment characteristic. Hedges d values were calculated in OpenMee software (see code/software below). Positive effect sizes indicate that burrowing crabs increased the value of the sediment measurement, while negative effect sized indicate that burrowing crabs decreased the value of the sediment measurement. 22. hedges_d_var: the variation of the hedges d effect size calculated for the effects of burrowing crabs on the measured sediment characteristic. Hedges d variation values were calculated in OpenMee software (see code/software below). **Missing data codes:** na ## Sharing/Access information All data are included in the provided datafiles. ## Code/Software Hedges\u2019 *d* (hereafter, *d*) effect sizes were calculated using meta-analysis using OpenMEE software (Build date: 26 July 2016; Wallace et al. 2017). Wallace, B. C., M. J. Lajeunesse, G. Dietz, I. J. Dahabreh, T. A. Trikalinos, C. H. Schmid, and J. Gurevitch. 2017. OpenMEE: Intuitive, open-source software for meta-analysis in ecology and evolutionary biology. Methods in Ecology and Evolution 8:941\u2013947.", "keywords": ["coastal wetlands", "density-dependance", "bioturbation", "animal effects", "Burrowing", "functional traits", "FOS: Earth and related environmental sciences", "habitat effects", "zoogeochemistry"], "contacts": [{"organization": "Rinehart, Shelby", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.3bk3j9kt3"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.3bk3j9kt3", "name": "item", "description": "10.5061/dryad.3bk3j9kt3", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.3bk3j9kt3"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-04-29T00:00:00Z"}}, {"id": "10.5061/dryad.fj6q573x9", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:48Z", "type": "Dataset", "title": "A synthesis of nitric oxide emissions across global fertilized croplands from crop-specific emission factors", "description": "Nitrogen (N)-fertilizer application to agricultural soils results in substantial emissions of nitric oxide (NO), a key substance in tropospheric chemistry involved in climate forcing and air pollution. However, estimates of global cropland NO emissions remain uncertain due to a lack of information on direct NO emission factors (EFds) of applied N for variours cropping systems at seasonal or annual scales. Here we quantified the crop-specific seasonal and annual-scale NO EFds through synthesizing 1094 measurements from 125 field-based studies worldwide. The global mean crop-specific seasonal EFd was 0.53%, with the highest for vegetables (0.75%). Among cereal crops, the EFd of maize (0.45%) or wheat (0.47%) was about three-times higher than for rice (0.12%). At annual scale, the mean EFd across all cropping systems was 0.58%, with tea plantations having the highest (1.54%). For other cropping systems, the annual-scale EFds ranged from 0.02% to 1.07%. Besides crop type, also soil organic carbon, total N and pH as well as N fertilizer type were the main factors explaining the variations of NO EFds. Based on obtained specific EFds for each crop type, we estimated that NO emissions due to the use of synthetic fertilizers from global croplands are about 0.42\u20130.62 Tg N yr\u22121. Our budgets are relatively lower if compared to estimates derived by the use of IPCC defaults for NO emissions (0.72\u20131.66 Tg N yr\u22121) or reported elsewhere (0.67\u20131.04 Tg N yr\u22121). In our estimates, cash crops (vegetable, tea and orchard), which cover only 9% of the world cropland area, contributed about 31% to total NO emissions from global fertilized croplands. Overall, our meta-analysis provides improved crop-specific NO EFds reflecting current stage of knowledge. The work also highlights the relative importance of cash crop production as sources for atmospheric NO, i.e., agricultural systems on which mitigation efforts may focus.", "keywords": ["2. Zero hunger", "cropland NO emission", "13. Climate action", "Nitric oxide", "FOS: Earth and related environmental sciences", "15. Life on land", "7. Clean energy"], "contacts": [{"organization": "Wang, Yan, Yao, Zhisheng, Zheng, Xunhua, Subramaniam, Logapragasan, Butterbach-Bahl, Klaus,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.fj6q573x9"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.fj6q573x9", "name": "item", "description": "10.5061/dryad.fj6q573x9", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.fj6q573x9"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-04-07T00:00:00Z"}}, {"id": "10.5061/dryad.ht76hdrnm", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:49Z", "type": "Dataset", "created": "2023-10-19", "title": "Data from: Abiotic legacies mediate plant-soil feedback during early vegetation succession on rare earth element mine tailings", "description": "Open AccessAn increasing number of studies have shown how feedback interactions between plants and soil can influence primary and secondary succession. However, very little is known about the patterns and mechanisms of such plant-soil feedbacks on stressed mine tailings ecosystem, which can be severely contaminated by a range of toxic elements.\u00a0 In a two-phase plant-soil feedback experiment based on the rare earth element (REE) mine tailing soil, we investigated biotic (changes in bacterial and fungal community) and abiotic legacies (changes in chemical properties) of three pioneer grass species, and examined feedback effects of three grasses, two legumes and two woody plants with different root traits. Positive plant-soil feedback was found in Miscanthus sinensis, Paspalum thunbergii and Tephrosia candida, and neutral feedback was observed in other four plants. These effects corresponded with an increase of nutrients and total organic carbon, as well as a decrease of acidity and extractable aluminum and REEs. There were less signs of biotic changes in the conditioned tailings.\u00a0 The correlation analysis suggested a relationship between responses to soil legacies and root traits, as well as root economics spectrum. On the mine tailings, acquisitive species with higher specific root length appeared to have greater potential for positive feedback.\u00a0 Synthesis and application: Our study shows that early succession on contaminated REE mine tailings may lead to more positive plant-soil feedback than predicted based on results of non-contaminated soils, mainly due to the alleviation of abiotic stress in tailings. Therefore, the improvement of specific abiotic soil stress and the trait-based selection of acquisitive plants should be preferentially considered to promote the primary restoration of degraded land.", "keywords": ["plant-soil feedback", "primary succession", "rare earth mine waste soil", "Trait-based approach", "root functional traits", "FOS: Earth and related environmental sciences", "soil legacies", "Ecological restoration"], "contacts": [{"organization": "Zhu, Shi Chen, Liu, Wen Shen, Chen, Zi Wu, Liu, Xiao Rui, Zheng, Hong Xiang, Chen, Bo Yu, Zhi, Xin Yu, Chao, Yuanqing, Qiu, Rong Liang, Chu, Chengjin, Liu, Chong, Morel, Jean Louis, van der Ent, Antony, Tang, Ye Tao,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.ht76hdrnm"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.ht76hdrnm", "name": "item", "description": "10.5061/dryad.ht76hdrnm", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.ht76hdrnm"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-01T00:00:00Z"}}, {"id": "10.5061/dryad.18931zd1m", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:44Z", "type": "Dataset", "title": "Input data to model multiple effects of large-scale deployment of grass in crop-rotations at European scale", "description": "unspecifiedThis is the input dataset to a Python script (https://github.com/oskeng/MF-bio-grass) used to model the effects of widespread deployment of grass in rotations with annual crops to provide biomass while remediating soil organic carbon (SOC) losses and other environmental impacts. For more information about the dataset and the study, see the original article: Englund, O., Mola-Yudego, B., B\u00f6rjesson, P., Cederberg, C., Dimitriou, I., Scarlat, N., Berndes, G. Large-scale deployment of grass in crop rotations as a multifunctional climate mitigation strategy. GCB Bioenergy", "keywords": ["2. Zero hunger", "spatial modelling", "climate mitigation", "grass", "Agriculture", "FOS: Earth and related environmental sciences", "15. Life on land", "Environmental impacts", "Soil carbon", "Europe", "13. Climate action", "environmental benefits", "Land-use", "perennial crops"], "contacts": [{"organization": "Englund, Oskar", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.18931zd1m"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.18931zd1m", "name": "item", "description": "10.5061/dryad.18931zd1m", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.18931zd1m"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-11-17T00:00:00Z"}}, {"id": "10.5061/dryad.bvq83bkbg", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-09-21T16:19:47Z", "type": "Dataset", "title": "Recent photosynthates are the primary carbon source for soil microbial respiration in subtropical forests", "description": "unspecifiedTropical and subtropical forests represent the largest terrestrial carbon pool. Elucidating the carbon sources for soil microbial respiration (Rm) in tropical and subtropical forests is of fundamental importance to the global carbon cycle in a warming world. Based on hourly measurements, we quantified Rm of\u00a0in situ\u00a0forest soil and soil cores from a subtropical forest. We found recent photosynthates, not soil organic carbon (SOC), contributed 88% \u00b1 12% of the carbon source fueling Rm. The control of recent photosynthates on Rm is also supported by the close relationship between Rm and photosynthetically active radiation as well as literature data synthesis results. These results challenge conventional models based on the tenet that Rm is mainly regulated by soil temperature in all forest ecosystems. The results imply that the widely observed warming-induced Rm increases are largely explained by the enhanced input of recent photosynthates in tropical forests, not SOC consumption.", "keywords": ["recent photosynthates", "microbial respiration", "13. Climate action", "FOS: Earth and related environmental sciences", "15. Life on land", "subtropical forest"], "contacts": [{"organization": "Yang, zhijie, Lin, Teng-Chiu, Wang, Lixin, Yang, Yusheng,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.bvq83bkbg"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.bvq83bkbg", "name": "item", "description": "10.5061/dryad.bvq83bkbg", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.bvq83bkbg"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-10-25T00:00:00Z"}}, {"id": "10.5061/dryad.g1jwstqx5", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:48Z", "type": "Dataset", "created": "2023-09-27", "title": "Microbial traits dictate soil neromass accumulation coefficient: A global synthesis", "description": "unspecified# Readme **Title** Microbial necromass carbon accumulation coefficients (NAC) dataset **Author** Bingbing Han, Yanzhong Yao, Yini Wang, Xiaoxuan Su, Lihua Ma, Xinping Chen, Zhaolei Li * **Corresponding author:** Zhaolei Li, Professor E-mail: lizhaolei@swu.edu.cn **Correspondence address:** College of Resources and Environment, and Academy of Agricultural Sciences, Southwest University, Chongqing 400715, China **Data abstract** The accumulation of microbial necromass carbon has drawn mounting attention due to the slow decomposition. However, it remains unclear what determines the microbial necromass carbon accumulation via reiterated community turnover on large spatial scales. This study aimed to explore the characteristics of soil necromass carbon accumulation in terrestrial ecosystems. A dataset was compiled with 993 observations on the coefficient of microbial carbon accumulation in the equilibrium from 82 peer-reviewed papers. The linear mixed-effect models and structural equation models were used to ascertain the controlling factors of NAC on a global scale. The average NAC was higher in croplands (28.2) and forests (26.8) than that in grasslands (21.1). Although the edaphic factors seemingly affect the NAC whereby the NAC lowered in soils with high levels of pH and clay content on a global scale, the biotic factors, particularly for the living microorganism abundance and microbial biomass nitrogen content, were the pivotal drivers of NAC that accounted for approximately 42.5% of the geographic variances in NAC. More organic carbon was likely to be preserved in soil with a higher NAC regardless of ecosystem types. Novel findings on the overriding controls from the living microorganism abundance and microbial biomass nitrogen in driving NAC raise an urgent need for viable strategies in manipulating microbial characteristics for carbon sequestrations. **Data collect** The NACs dataset was compiled from peer-reviewed papers. These peer-reviewed papers were obtained by means of two platforms: the Web of Science ([http://apps.webofkonwledge.com](http://apps.webofkonwledge.com)) and the China National Knowledge Infrastructure Database ([http://www.cnki.net](http://www.cnki.net)). At the same time, the papers were supplemented by Google Scholar. The keywords used to search papers are soil microbial biomass AND microbial necromass AND microbial residue * AND amino sugar * AND PLFAs. The publishing date for the peer-reviewed paper was up to January 20, 2023. The eligible peer-reviewed papers matched the following criteria: (1) Soil microbial necromass was measured using amino sugars as markers; (2) The living microorganisms were determined by phospholipid fatty acid (PLFAs). Finally, the NAC dataset was constructed based on the 82 peer-reviewed papers. The details of the experimental site were also extracted from papers, including the geographic information of the experiment site (i.e., latitude and longitude), climate conditions (i.e., mean annual temperature and mean annual precipitation), and ecosystem types (i.e., grasslands, forests, and croplands). Additionally, soil physicochemical properties [soil pH, the ratio of carbon to nitrogen (soil C: N), total nitrogen (TN), bulk density (BD), clay content, and ammonium content (NH4+)] and the number of replicates were also extracted from the articles. Additionally, in the NAC dataset, the empty cells are representing the data scarcity (i.e., NA values). You should know that not every article will contain all the metrics. **Data analysis** The content of fungal and bacterial necromass carbon was calculated based on the concentrations of amino sugar in microbial cell walls: glucosamine and muramic acid. The bacterial necromass carbon and fungal necromass carbon were calculated using equations (1) and (2). where, MurA is muramic acid and GlcN is glucosamine. In equation (1), 45 is the conversion factor from MurA to bacterial necromass carbon; in equation (2), 9 is the conversion factor from GlcN to fungal necromass carbon; while 179.17 and 251.23 are the molecule weights of GlcN and MurA, respectively. Total microbial necromass carbon was the sum of fungal necromass carbon and bacterial necromass carbon. For the absence of microbial biomass carbon (MBC) in some experimental sites. The NAC functions as the ratio of the microbial necromass carbon to microbial biomass carbon: where MBC is soil microbial biomass carbon. The linear mixed-effect models were used to test the bivariate relationship between the NAC and environmental factor by means of *lme4* packages in R (version 4.2.2., R Core Team). The equation (4) was: where NAC refers to the microbial necromass carbon accumulation coefficient, lnX is the logarithm of each edaphic and climatic factor (except for soil pH and fungi: bacteria ratio), refers to the intercept of this model, refers to the slope value, refers to the random effect of study, refers to the sampling error. **Document Type** We will upload it in data _NAC _2023.csv format to the Dryad database. The main variables collected in the data form were muramic acid (MurA) and glucosamine (GlcN). We perform the calculation of NAC based on equations 1, 2, and 3 above. Total biomass represents the abundance of living microorganisms. Fungal biomass represents the abundance of fungi. Bacterial biomass represents the abundance of bacteria. MBC is microbial biomass nitrogen. SOC is soil organic carbon. **Data processing software** We processed the entire set of data by utilizing the R language, version 4.2.2., R Core Team. **Contact Information** **Corresponding author:** Zhaolei Li, Professor **E-mail:** [lizhaolei@swu.edu.cn](mailto:lizhaolei@swu.edu.cn) **ORCID:** [https://orcid.org/0000-0001-8767-1277](https://orcid.org/0000-0001-8767-1277)", "keywords": ["2. Zero hunger", "microbial abundance", "soil carbon sequestration", "microbial necromass carbon", "living microbes", "FOS: Earth and related environmental sciences", "15. Life on land", "microbial carbon pump", "ecosystem type"], "contacts": [{"organization": "Han, Bingbing, Yao, Yan Zhong, Wang, Yini, Su, Xiaoxuan, Ma, Lihua, Chen, Xinping, Li, Zhaolei,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.g1jwstqx5"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.g1jwstqx5", "name": "item", "description": "10.5061/dryad.g1jwstqx5", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.g1jwstqx5"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-10-05T00:00:00Z"}}, {"id": "10.5061/dryad.r7sqv9shh", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:51Z", "type": "Dataset", "title": "Imprint of tree species mycorrhizal association on microbial-mediated enzyme activity and stoichiometry", "description": "unspecified1. Understanding the effects of tree species and their mycorrhizal association on soil processes is critical for predicting the ecosystem consequences of species shifts owing to global change and forest management decisions. While it is well established that forests dominated by different mycorrhizal types can vary in how they cycle carbon (C), nitrogen (N) and phosphorus (P), the degree to which these patterns are driven by microbial-mediated enzyme activity (EA) and ecoenzymatic stoichiometry (ES) remain elusive. 2. Here, we synthesized the effects of mycorrhizal association on seven soil enzymes involved in microbial C, N and P acquisition and ES using data from 56 peer-reviewed papers. 3. We found that relative to soil in ectomycorrhizal (EcM) trees, soil in arbuscular mycorrhizal (AM) trees exhibited greater activity of some C acquisition enzymes (e.g., beta-glucosidase; BG) and higher ecoenzymatic ratios of BG/NAG (N-acetyl-glucosaminidase) and BG/AP (acid phosphatase). These results supported that AM trees had rapid C and nutrient turnover rates, inorganic nutrient economics and high soil microbial C limitation. We also found evidence for an organic nutrient economy and greater soil microbial demand for nutrients in EcM trees compared to AM trees. In addition, the effect of mycorrhizal association on the activity of certain soil enzymes and enzymatic stoichiometry (i.e., BG and BG/NAG ratio) appeared to be associated with the differences in soil pH, phylogenetic group (i.e., conifers and broadleaves) and leaf habit (i.e., evergreen and deciduous) between AM and EcM trees. 4. The results from the global meta-analysis suggested that soil EA and ES appear to play critical roles in shaping the differences in the nutrient economy between AM and EcM tree species, but leaf morphology and soil conditions should be considered in evaluations of soil processes in forests of different mycorrhizal associations. Given that most of the studies in the database were from the temperate and subtropical regions, further research in other biomes is needed to elucidate the underlying mechanisms driving the mycorrhizal effect at the global scale.", "keywords": ["meta-analysis", "soil enzyme activity", "13. Climate action", "Nutrient economics", "FOS: Earth and related environmental sciences", "15. Life on land", "microbial decomposition", "substrate quality"], "contacts": [{"organization": "Zheng, Haifeng, Phillips, Richard, Rousk, Johannes, Yue, Kai, Schmidt, Inger Kappel, Wang, Senhao, Peng, Yan, Vesterdal, Lars,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.r7sqv9shh"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.r7sqv9shh", "name": "item", "description": "10.5061/dryad.r7sqv9shh", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.r7sqv9shh"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-02-28T00:00:00Z"}}, {"id": "10.5061/dryad.h70rxwdqs", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:49Z", "type": "Dataset", "created": "2023-11-13", "title": "Data from: Microbial carbon use efficiency and soil organic carbon stocks across an elevational gradient in the Peruvian Andes", "description": "unspecifiedSoils of mountain ecosystems are one of the most vulnerable ecosystems to climate change, while the ecosystem services they produce are significant and currently at risk. High altitude soils contain high C stocks, but due to difficult access to sites these areas are understudied. Moreover, how the C and N cycling is changing in response to climate change in these ecosystems, is still unclear. Microbial carbon use efficiency (CUE) and its dependency on the environmental constraints along the altitudinal gradients is one important unknown factor. Here we present results from an altitudinal gradient study (3500 to 4500 m a.s.l.) from a Polylepis forest in the Peruvian Andes. We measured the soil organic carbon (SOC) stocks and microbial metabolic CUE by\u00a013C glucose tracing and microbial resource use efficiency (CUEC:N) based on enzyme activity measurements. We expected to find an increase in SOC stock, microbial nutrient limitations, and lower CUE with elevation. SOC stocks depended on soil development and followed a unimodal curve that peaks at 4000 m in two of the three studied valleys. Neither 13CUE nor CUEC:N changed significantly with altitude. Soil C:N ratio, \u03b2-glucosidase, chitinase, and phosphatase enzyme activities increased with elevation, but peroxidase activity decreased with elevation. We suggest that more labile organic matter left at high elevation could compensate for the increasing nutrient limitation at high elevation, resulting in no noticeable change in CUE with elevation.", "keywords": ["soil organic carbon", "Exoenzyme", "Carbon use efficiency", "FOS: Earth and related environmental sciences", "Stoichiometric modelling", "Elevational gradient"], "contacts": [{"organization": "Martin Vivanco, Angela Katherine, Sieti\u00f6, Outi-Maaria, Meyer, Nele, Mganga, Kevin, Kalu, Subin, Adamczyk, Sylwia, Celis, Susan, Alegre, Julio, Karhu, Kristiina,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.h70rxwdqs"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.h70rxwdqs", "name": "item", "description": "10.5061/dryad.h70rxwdqs", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.h70rxwdqs"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-12-18T00:00:00Z"}}, {"id": "10.5061/dryad.h70rxwdsm", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:49Z", "type": "Dataset", "created": "2024-06-01", "title": "Data from: Impacts of organic matter amendments on urban soil carbon and soil quality: A meta-analysis", "description": "unspecified# Organic Matter Impacts on Urban Soil Meta-Analysis [https://doi.org/10.5061/dryad.h70rxwdsm](https://doi.org/10.5061/dryad.h70rxwdsm) ## Description of the data and file structure This dataset (all the excel files) has information from almost 50 papers that research the use of organic matter amendments (compost, biochar, and biosolids) on urban soils until July of 2023. The focus data collected is soil carbon, nitrogen, phosphorus, potassium, bulk density, and pH. Other data includes sample size, sampling depth, years of application, application rate, type of urban environment, plant types, and publication information for each included study. The file 'Urban Amendments (Y or N) sheet' is the main data file. The has an extra column with a 'Yes' or 'No' depending on if organic matter amendment application occurred. A 'No'' indicates the control samples within studies. This includes data for all the soil properties included in the meta-analysis as well. The main data in included in the first tab titled 'General Info (C,N,BD) Sheet'. Comments are present to give context if unit conversions were made or data pulled from a paper was unclear. The second tab 'Heavy Metals Sheet'\u00a0 contains information on the impacts of organic matter amendments on heavy metals in soils. Data in this second tab was not used in the associated paper. The file 'Application Amount Mg per ha (1 or 0)' has all application rates given as %'s converted to Mg/ha of application (if the information needed for the conversion was available. The 1 indicates organic matter application occurred while a 0 indicates a control (no amending) sample. Data from the first excel file above in included in this data set as well. The file 'Spreadsheet Long and Lat for Urban Amendments' has the latitude and longitude values converted for use in GIS software for mapping. Most data from the first two excel files above in included in this data set as well, however, this file contains no comments for context on data. Missing data code: NA I welcome any inquiries at my email [zmalone@ucmerced.edu\u00a0.](mailto:zmalone@ucmerc.e) ## Sharing/Access information Data was derived from the following sources: * Papers found via searches on Google Scholar and Web of Science.\u00a0African Journals Online and Dialnet were also used for searching.", "keywords": ["Urban Soil", "meta-analysis", "compost", "Organic Matter Amendments", "FOS: Earth and related environmental sciences"], "contacts": [{"organization": "Malone, Zachary, Berhe, Asmeret, Ryals, Rebecca,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.h70rxwdsm"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.h70rxwdsm", "name": "item", "description": "10.5061/dryad.h70rxwdsm", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.h70rxwdsm"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-06-05T00:00:00Z"}}, {"id": "10.5061/dryad.j3tx95xk8", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:49Z", "type": "Dataset", "title": "Patterns and determinants of plant-derived lignin phenols in coastal wetlands: implications for organic C accumulation", "description": "unspecifiedPlease see the README document\u00a0\u00a0('Lignin_content_and_monomer_composition.csv', 'Site_location.csv', 'Soil_organic_carbon_content.csv', 'Soil_properties.csv', 'Vegetation_and_climate.csv') and the accompanying published article: Shaopan Xia, Zhaoliang Song, Weiqi Wang, Yaran Fan, Laodong Guo, Lukas Van Zwieten, Iain P. Hartley, Yin Fang, Yidong Wang, Zhenqing Zhang, Cong-Qiang Liu, and Hailong Wang. 2023. Patterns and determinants of plant-derived lignin phenols in coastal wetlands: implications for organic C accumulation. Functional Ecology. Accepted. DOI: 10.5061/dryad.j3tx95xk8", "keywords": ["lignin biomarker", "salt marsh and mangrove", "13. Climate action", "plant-soil Interactions", "blue carbon", "organic C source apportionment", "14. Life underwater", "FOS: Earth and related environmental sciences", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Song, Zhaoliang, Xia, Shaopan, Wang, Weiqi, Fan, Yaran, Guo, Laodong, Van Zwieten, Lukas, Hartley, Iain P., Fang, Yin, Wang, Yidong, Zhang, Zhenqing, Liu, Cong-Qiang, Wang, Hailong,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.j3tx95xk8"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.j3tx95xk8", "name": "item", "description": "10.5061/dryad.j3tx95xk8", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.j3tx95xk8"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-02-02T00:00:00Z"}}, {"id": "10.5061/dryad.jm63xsj8p", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-09-21T16:19:49Z", "type": "Dataset", "title": "Meta-analysis of glucose tracing studies", "description": "unspecifiedA longstanding assumption of glucose tracing experiments is that all glucose is microbially utilized during short incubations of \u22642\u00a0days to become microbial biomass or carbon dioxide. Carbon use efficiency (CUE) estimates have consequently ignored the formation of residues (non-living microbial products) although such materials could represent an important sink of glucose that is prone to stabilization as soil organic matter. We examined the dynamics of microbial residue formation from a short tracer experiment with frequent samplings over 72\u00a0h, and conducted a meta-analysis of previously published glucose tracing studies to assess the generality of these experimental results. Both our experiment and meta-analysis indicated 30\u201334% of amended glucose-C (13C or\u00a014C) was in the form of residues within the first 6\u00a0h of substrate addition. We expand the conventional efficiency calculation to include residues in both the numerator and denominator of efficiency, thereby deriving a novel metric of the potential persistence of glucose-C in soil as living microbial biomass plus residues (\u2018carbon stabilization efficiency\u2019). This new metric indicates nearly 40% of amended glucose-C persists in soil 180\u00a0days after amendment, the majority as non-biomass residues. Starting microbial biomass and clay content emerge as critical factors that positively promote such long term stabilization of labile C. Rapid residue production supports the conclusion that non-growth maintenance activity can illicit high demands for C in soil, perhaps equaling that directed towards growth, and that residues may have an underestimated role in the cycling and sequestration potential of C in soil.", "keywords": ["2. Zero hunger", "FOS: Earth and related environmental sciences", "15. Life on land"], "contacts": [{"organization": "Geyer, Kevin, Schnecker, Joerg, Grandy, Stuart, Richter, Andreas, Frey, Serita,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.jm63xsj8p"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.jm63xsj8p", "name": "item", "description": "10.5061/dryad.jm63xsj8p", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.jm63xsj8p"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-11-17T00:00:00Z"}}, {"id": "10.5061/dryad.k6djh9wdx", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:50Z", "type": "Dataset", "created": "2024-01-30", "title": "Fluxes and concentrations of dissolved organic carbon in soils", "description": "unspecifiedThe data were compiled from data in our study and those from published sources by searching for \u201cdissolved organic carbon\u201d, \u201csolute\u201d, \u201cflux\u201d, \u201cleaching\u201d, and \u201csoil\u201d in Google Scholar. We compiled the data of DOC fluxes in throughfall and soil profiles from 91 sites, of which the DOC flux data at 18 sites have been published by our group. The climate was classified into four groups [polar climate (MAT < 0 \u00baC), boreal climate (0 \u00baC < MAT < 6 \u00baC), temperate climate (6 \u00baC < MAT < 20 \u00baC), tropical climate (20 \u00baC < MAT)], based on mean annual air temperature. The other parameters include climatic properties [mean annual precipitation and mean annual air temperature], plant litter properties [litterfall C input, C/N ratio, Klason-lignin (residue after digestion with sulfuric acid; Allen et al., 1974), lignin/N ratio, root litter production] and soil properties [soil C stocks (O horizon and mineral soil (0-30 cm depth)), pH (water extraction), clay content, short-range-order (amorphous) aluminum (Al), iron (Fe) (acid ammonium oxalate extractable Al and Fe; McKeague and Day, 1966)]. The sampling and analytical methods are concisely summarized as follows: Throughfall (canopy leaching) samples were collected by precipitation collector, while soil solution samples were collected using tension-free lysimeters for downward flux of water percolating in the soil profiles. Sample solutions were filtered through a 0.45 \u00b5m filter (e.g., PTFE syringe filter) and stored at 1\u00b0C in the dark prior to analyses. The concentrations of DOC were determined using a total organic carbon and nitrogen analyzer (TOC-VCSH, Shimadzu, Japan). The dissolved organic nitrogen (DON) concentrations were calculated by subtracting dissolved inorganic nitrogen (sum of NH4+ and NO3-) from TDN concentrations (DON = TDN - NH4+ - NO3-) to obtain DOC/DON ratios in soil solution. The DOC flux at the depth of 0 cm (the bottom of organic layers) and the bottom of B horizon (the bottom of rooting zone) was estimated by multiplying DOC concentrations in soil solution and water fluxes at each depth. Soil water fluxes were estimated by hydrological models or precipitation-evapotranspiration water budgets. Annual root production was measured by ingrowth core method, net sheet method, or sequential sampling method and estimated to be equal to annual root litter inputs. Proportion of DOC flux from the O horizon relative to C input via both throughfall and litterfall was calculated by dividing DOC flux from the O horizon by C input via both throughfall and litterfall. DOC retention in the mineral soil was calculated as the percentage of net decrease in DOC flux between O and B horizons relative to DOC flux from the O horizon. The apparent turnover time (yr) of soil C was estimated by dividing soil C stocks (Mg C ha\u20131) by C inputs (net DOC inputs and root litter inputs into the mineral soil) (Mg C ha\u20131 yr\u20131).", "keywords": ["tropical forest", "FOS: Earth and related environmental sciences", "Soil pH", "dissolved organic carbon", "dissolved organic nitrogen"], "contacts": [{"organization": "Fujii, Kazumichi", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.k6djh9wdx"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.k6djh9wdx", "name": "item", "description": "10.5061/dryad.k6djh9wdx", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.k6djh9wdx"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-02-19T00:00:00Z"}}, {"id": "10.5061/dryad.jwstqjqbs", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-09-21T16:19:50Z", "type": "Dataset", "title": "Data from: Positron-emitting radiotracers spatially resolve unexpected biogeochemical relationships linked with methane oxidation in Arctic soils", "description": "Arctic soils are marked by cryoturbic features, which impact soil-atmosphere methane (CH4) dynamics vital to global climate regulation. Cryoturbic diapirism alters C/N chemistry within frost boils by introducing soluble organic carbon and nutrients, potentially influencing microbial CH4\u00a0oxidation. CH4\u00a0oxidation in soils, however, requires a spatio-temporal convergence of ecological factors to occur. Spatial delineation of microbial activity with respect to these key microbial and biogeochemical factors at relevant scales is experimentally challenging in inherently complex and heterogeneous natural soil matrices. This work aims to overcome this barrier by spatially linking microbial CH4\u00a0oxidation with C/N chemistry and metagenomic characteristics. This is achieved by using positron-emitting radiotracers to visualize millimeter-scale active CH4\u00a0uptake areas in Arctic soils with and without diapirism. X-ray absorption spectroscopic speciation of active and inactive areas shows CH4\u00a0uptake spatially associates with greater proportions of inorganic N in diapiric frost boils. Metagenomic analyses reveal\u00a0Ralstonia pickettii\u00a0associates with CH4\u00a0uptake across soils along with pertinent CH4\u00a0and inorganic N metabolism associated genes. This study highlights the critical relationship between CH4 and N cycles in Arctic soils, with potential implications for better understanding future climate. Furthermore, our experimental framework presents a novel, widely applicable strategy for unraveling ecological relationships underlying greenhouse gas dynamics under global change.", "keywords": ["13. Climate action", "FOS: Earth and related environmental sciences", "15. Life on land"], "contacts": [{"organization": "Schmidt, Michael, Mamet, Steven, Senger, Curtis, Schebel, Alixandra, Ota, Mitsuaki, Tian, Tony, Aziz, Umair, Stein, Lisa, Regier, Tom, Stanley, Kevin, Peak, Derek, Siciliano, Steven,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.jwstqjqbs"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.jwstqjqbs", "name": "item", "description": "10.5061/dryad.jwstqjqbs", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.jwstqjqbs"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-12T00:00:00Z"}}, {"id": "10.5061/dryad.ksn02v75n", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-21T16:19:50Z", "type": "Dataset", "title": "Tree identity and diversity directly affect soil moisture and temperature but not soil carbon ten years after planting", "description": "1. Soil C is the largest C pool in forest ecosystems that contributes to C sequestration and mitigates climate change. Tree diversity enhances forest productivity, so diversifying the tree species composition, notably in managed forests, could increase the quantity of organic matter being transferred to soils, and alter other soil properties relevant to the C cycle. 2. A ten-year-old tree diversity experiment was used to study the effects of tree identity and diversity (functional and taxonomic) on soils. Surface (0-10 cm) mineral soil was repeatedly measured for soil C concentration, C:N ratio, pH, moisture and temperature in twenty-four tree species mixtures and twelve corresponding monocultures (replicated in four blocks). 3. Soil pH, moisture and temperature responded to tree diversity and identity. Greater productivity in above- and below-ground tree components did not increase soil C concentration. Soil pH increased and soil moisture decreased with functional diversity, more specifically, when species had different growth strategies and shade tolerances. Functional identity affected soil moisture and temperature, such that tree communities with more slow-growing and shade-tolerant species had greater soil moisture and temperature. Higher temperature was measured in communities with broadleaf-deciduous species compared to communities with coniferous-evergreen species. 4. We conclude that long-term soil C cycling in forest plantations will likely respond to changes in soil pH, moisture and temperature that is mediated by tree species composition, since tree species affect these soil properties through their litter quality, water uptake and physical control of soil microclimates.", "keywords": ["FOS: Earth and related environmental sciences", "15. Life on land"], "contacts": [{"organization": "Martin-Guay, Marc-Olivier, Belluau, Micha\u00ebl, C\u00f4t\u00e9, Beno\u00eet, Handa, Ira Tanya, Jewell, Mark, Khlifa, Rim, Munson, Alison, Rivest, Maxime, Whalen, Joann, Rivest, David,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.ksn02v75n"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.ksn02v75n", "name": "item", "description": "10.5061/dryad.ksn02v75n", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.ksn02v75n"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-08T00:00:00Z"}}, {"id": "10.5061/dryad.m0cfxpp9w", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:50Z", "type": "Dataset", "created": "2024-01-03", "title": "Organo-organic interactions dominantly drive soil organic carbon accrual", "description": "unspecifiedOrgano-mineral interactions have been regarded as the primary mechanism for the stabilization of soil organic carbon (SOC) over decadal to millennial timescales, and the capacity for soil carbon (C) storage has commonly been assessed based on soil mineralogical attributes, particularly mineral surface availability. However, it remains contentious whether soil C sequestration is exclusively governed by mineral vacancies, making it challenging to accurately predict SOC dynamics. Here, through a 400-day incubation experiment using 13C-labeled organic materials in two contrasting soils (i.e., Mollisol and Ultisol), we show that despite the unsaturation of mineral surfaces in both soils, the newly incorporated C predominantly adheres to 'dirty' mineral surfaces coated with native organic matter (OM), demonstrating the crucial role of organo-organic interactions in exogenous C sequestration. Such interactions lead to multilayered C accumulation that is not constrained by mineral vacancies, a process distinct from direct organo-mineral contacts. The coverage of native OM by new C, representing the degree of organo-organic interactions, is noticeably larger in Ultisol (~14.2%) than in Mollisol (~5.8%), amounting to the net retention of exogenous C in Ultisol by 0.2\u20131.3 g kg\u22121 and in Mollisol by 0.1\u20131.0 g kg\u22121. Additionally, organo-organic interactions are primarily mediated by polysaccharide-rich microbial necromass. Further evidence indicates that iron oxides can selectively preserve polysaccharide compounds, thereby promoting the organo-organic interactions. Overall, our findings provide direct empirical evidence for an overlooked but critically important pathway of C accumulation, challenging the prevailing \u201cC saturation\u201d concept that emphasizes the overriding role of mineral vacancies. It is estimated that, through organo-organic interactions, global Mollisols and Ultisols might sequester ~0.1\u20131.0 Pg C and ~0.3\u20131.7 Pg C per year, respectively, corresponding to the neutralization of ca. 0.5%\u20133.0% of soil C emissions or 5%\u201330% of fossil fuel combustion globally.", "keywords": ["organo-organic interactions", "mineral-associated organic carbon", "SR-FTIR", "SOC accrual", "NanoSIMS", "FOS: Earth and related environmental sciences", "microbial necromass", "stable C isotope"], "contacts": [{"organization": "Kang, Jie, Qu, Chenchen, Chen, Wenli, Cai, Peng, Chen, Chengrong, Huang, Qiaoyun,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.m0cfxpp9w"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.m0cfxpp9w", "name": "item", "description": "10.5061/dryad.m0cfxpp9w", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.m0cfxpp9w"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-15T00:00:00Z"}}, {"id": "10.5061/dryad.m63xsj45g", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:50Z", "type": "Dataset", "title": "Plant litter chemistry controls coarse-textured soil carbon dynamics", "description": "unspecifiedThe data are archieved as a .csv text file.", "keywords": ["2. Zero hunger", "Decomposition", "Ecosystem function and services", "plant litter", "13. Climate action", "soil organic matter", "soil carbon storage", "Carbon cycle", "FOS: Earth and related environmental sciences", "15. Life on land", "Priming effect"], "contacts": [{"organization": "Huys, Raoul, Poirier, Vincent, Bourget, Malo, Roumet, Catherine, Hattenschwiler, Stephan, Fromin, Nathalie, Munson, Alison, Freschet, Gr\u00e9goire,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.m63xsj45g"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.m63xsj45g", "name": "item", "description": "10.5061/dryad.m63xsj45g", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.m63xsj45g"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-09-15T00:00:00Z"}}, {"id": "10.5061/dryad.mgqnk991r", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-09-21T16:19:50Z", "type": "Dataset", "title": "Increased precipitation and nitrogen addition accelerate the temporal increase of soil respiration during eight-year old-field grassland succession", "description": "Ecological succession after disturbance plays a vital role in influencing ecosystem structure and functioning. However, how global change factors regulate ecosystem carbon (C) cycling in successional plant communities remains largely elusive. As part of an eight-year (2012-2019) manipulative experiment, this study was designed to examine the responses of soil respiration and its heterotrophic component to simulated increases in precipitation and atmospheric nitrogen (N) deposition in an old-field grassland undergoing secondary succession. Over the eight-year experimental period, increased precipitation stimulated soil respiration by 11.6%, but did not affect soil heterotrophic respiration. Nitrogen addition increased both soil respiration (5.1%) and heterotrophic respiration (6.2%). Soil respiration and heterotrophic respiration linearly increased with time in the control plots, resulting from changes in soil moisture and shifts of plant community composition from grass-forb codominance to grass dominance in this old-field grassland. Compared to the control, increased precipitation significantly strengthened the temporal increase of soil respiration through stimulating belowground net primary productivity. By contrast, N addition accelerated temporal increases of both soil respiration and its heterotrophic component by driving plant community shifts and thus stimulating soil organic C. Our findings indicate that increases in water and N availabilities may accelerate soil C release during old-field grassland succession and reduce their potential positive impacts on soil C accumulation under future climate change scenarios.", "keywords": ["2. Zero hunger", "13. Climate action", "FOS: Earth and related environmental sciences", "15. Life on land"], "contacts": [{"organization": "Zhang, Jiajia, Ru, Jingyi, Song, Jian, Li, Heng, Li, Xiaoming, Ma, Yafei, Li, Zheng, Hao, Yuanfang, Chi, Zhensheng, Hui, Dafeng, Wan, Shiqiang,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.mgqnk991r"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.mgqnk991r", "name": "item", "description": "10.5061/dryad.mgqnk991r", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.mgqnk991r"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-03-29T00:00:00Z"}}, {"id": "10.5061/dryad.np5hqc016", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-09-21T16:19:50Z", "type": "Dataset", "created": "2023-12-08", "title": "Protists regulate microbially-mediated organic carbon turnover in soil aggregates", "description": "unspecifiedSoil protists, the major predator of bacteria and fungi, shape the taxonomic and functional structure of soil microbiome via trophic regulation. However, how trophic interactions between protists and their prey influence microbially mediated soil organic carbon turnover remains largely unknown. Here, we investigated the protistan communities and microbial trophic interactions across different aggregates-size fractions in agricultural soil with long-term fertilization regimes. Our results showed that aggregate sizes significantly influenced the protistan community and microbial hierarchical interactions. Bacterivores were the predominant protistan functional group and were more abundant in macroaggregates and silt + clay than in microaggregates, while omnivores showed an opposite distribution pattern. Furthermore, partial least square path modeling revealed positive impacts of omnivores on the C-decomposition genes and soil organic matter (SOM) contents, while bacterivores displayed negative impacts. Microbial trophic interactions were intensive in macroaggregates and silt + clay but were restricted in microaggregates, as indicated by the intensity of protistan-bacterial associations and network complexity and connectivity. Cercozoan taxa were consistently identified as the keystone species in SOM degradation-related ecological clusters in macroaggregates and silt + clay, indicating the critical roles of protists in SOM degradation by regulating bacterial and fungal taxa. Chemical fertilization had a positive effect on soil C sequestration through suppressing SOM degradation-related ecological clusters in macroaggregate and silt + clay. Conversely, the associations between the trophic interactions and SOM contents were decoupled in microaggregates, suggesting limited microbial contributions to SOM turnovers. Our study demonstrates the importance of protists-driven trophic interactions on soil C cycling in agricultural ecosystems.", "keywords": ["soil aggregates", "Soil protists", "FOS: Earth and related environmental sciences", "carbon cycling", "Trophic interactions"], "contacts": [{"organization": "Liao, Hao, Hao, Xiuli, Li, Yiting, Ma, Silin, Gao, Shenghan, Cai, Peng, Chen, Wenli, Huang, Qiaoyun,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.np5hqc016"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.np5hqc016", "name": "item", "description": "10.5061/dryad.np5hqc016", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.np5hqc016"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-12-22T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=FOS%3A+Earth+and+related+environmental+sciences&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=FOS%3A+Earth+and+related+environmental+sciences&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=FOS%3A+Earth+and+related+environmental+sciences&", "hreflang": "en-US"}, {"rel": "next", "type": "application/geo+json", "title": "items (next)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=FOS%3A+Earth+and+related+environmental+sciences&offset=50", "hreflang": "en-US"}], "numberMatched": 132, "numberReturned": 50, "distributedFeatures": [], "timeStamp": "2026-09-22T03:04:09.586109Z"}