{"type": "FeatureCollection", "features": [{"id": "10.1016/j.fcr.2007.12.011", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:16:02Z", "type": "Journal Article", "created": "2008-02-06", "title": "Productivity And Sustainability Of A Spring Wheat-Field Pea Rotation In A Semi-Arid Environment Under Conventional And Conservation Tillage Systems", "description": "A long-term rotation experiment was established in 2001 to compare conservation tillage techniques with conventional tillage in a semi-arid environment in the western Loess Plateau of China. We examined resource use efficiencies and crop productivity in a spring wheat (Triticum aestivum L.)-field pea (Pisum arvense L.) rotation. The experimental design included a factorial combination of tillage with different ground covers (complete stubble removal, stubble retained and plastic film mulch). Results showed that there was more soil water in 0-30 cm at sowing under the no-till with stubble retained treatment than the conventional tillage with stubble removed treatment for both field pea (60 mm vs. 55 mm) and spring wheat (60 mm vs. 53 mm). The fallow rainfall efficiency was up to 18% on the no-till with stubble retained treatment compared to only 8% for the conventional tillage with stubble removed treatment. The water use efficiency was the highest in the no-till with stubble retained treatment for both field pea (10.2 kg/ha mm) and spring wheat (8.0 kg/ha mm), but the lowest on the no-till with stubble removed treatment for both crops (8.4 kg/ha mm vs. 6.9 kg/ha mm). Spring wheat also had the highest nitrogen use efficiency on the no-till with stubble retained treatment (24.5%) and the lowest on the no-till with stubble removed treatment (15.5%). As a result, grain yields were the highest under no-till with stubble retained treatment, but the lowest under no-till with no ground cover treatment for both spring wheat (2.4 t/ha vs. 1.9 t/ha) and field pea (1.8 t/ha vs. 1.4 t/ha). The important finding from this study is that conservation tillage has to be adopted as a system, combining both no-tillage and retention of crop residues. Adoption of a no-till system with stubble removal will result in reductions in grain yields and a combination of soil degradation and erosion. Plastic film mulch increased crop yields in the short-term compared with the conventional tillage practice. However, use of non-biodegradable plastic film creates a disposal problem and contamination risk for soil and water resources. It was concluded that no-till with stubble retained treatment was the best option in terms of higher and more efficient use of water and nutrient resources and would result in increased crop productivity and sustainability for the semi-arid region in the Loess Plateau. The prospects for adoption of conservation tillage under local conditions were also discussed.", "keywords": ["0106 biological sciences", "070301 - Agro-ecosystem Function and Prediction", "571", "pea", "rotation", "01 natural sciences", "630", "12. Responsible consumption", "wheat", "Physical Sciences and Mathematics", "Productivity", "conventional", "2. Zero hunger", "spring", "conservation", "arid", "04 agricultural and veterinary sciences", "15. Life on land", "sustainability", "field", "6. Clean water", "semi", "tillage", "systems", "0401 agriculture", " forestry", " and fisheries", "environment", "under"]}, "links": [{"href": "https://doi.org/10.1016/j.fcr.2007.12.011"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Field%20Crops%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.fcr.2007.12.011", "name": "item", "description": "10.1016/j.fcr.2007.12.011", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.fcr.2007.12.011"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2008-04-01T00:00:00Z"}}, {"id": "10.1016/j.renene.2019.03.134", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:16:35Z", "type": "Journal Article", "created": "2018-09-04", "title": "Scoping the potential usefulness of seasonal climate forecasts for solar power management", "description": "Solar photovoltaic energy is widespread worldwide and particularly in Europe, which became in 2016 the first region in the world to pass the 100\u202fGW of installed capacity. As with all the renewable energy sources, for an effective management of solar power, it is essential to have reliable and accurate information about weather/climate conditions that affect the production of electricity. Operations in the solar energy industry are normally based on daily (or intra-daily) forecasts. Nevertheless, information about the incoming months can be relevant to support and inform operational and maintenance activities. This paper discusses a methodology to assess whether a seasonal climate forecast can provide a useful prediction for a specific sector, in this paper the European solar power industry. After evaluating the quality of the forecasts in providing probabilistic information for solar radiation, we describe how to assess their potential usefulness for a generic user by proposing an approach that takes into account not only their accuracy but also other potentially relevant factors. This approach is called index of opportunity and is then illustrated by presenting an example for the European solar power sector. The index of opportunity provides indications about where and when seasonal climate forecasts can benefit the decision-making in the photovoltaic sector. Even more importantly, it suggests an approach on how to evaluate their usefulness for the user's decision-making. This approach has the advantage of not limiting the definition of the usefulness only to the quality of the forecasts but rather considering, in an explicit way, all the factors that must be combined with the forecast's quality to define what is useful or not for the user.", "keywords": ["bepress|Physical Sciences and Mathematics", "330", "EarthArXiv|Physical Sciences and Mathematics|Environmental Sciences|Sustainability", "EarthArXiv|Physical Sciences and Mathematics|Environmental Sciences", "bepress|Physical Sciences and Mathematics|Earth Sciences", "02 engineering and technology", "EarthArXiv|Physical Sciences and Mathematics|Earth Sciences", "7. Clean energy", "01 natural sciences", "EarthArXiv|Physical Sciences and Mathematics", "Sustainability", "13. Climate action", "Physical Sciences and Mathematics", "Earth Sciences", "0202 electrical engineering", " electronic engineering", " information engineering", "bepress|Physical Sciences and Mathematics|Environmental Sciences", "bepress|Physical Sciences and Mathematics|Environmental Sciences|Sustainability", "Environmental Sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://eprints.whiterose.ac.uk/149508/1/usefulness-climate-fcsts-solar-power.revised_3rd.pdf"}, {"href": "https://ueaeprints.uea.ac.uk/id/eprint/70701/1/Accepted_Manuscript.pdf"}, {"href": "https://doi.org/10.1016/j.renene.2019.03.134"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Renewable%20Energy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.renene.2019.03.134", "name": "item", "description": "10.1016/j.renene.2019.03.134", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.renene.2019.03.134"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-09-04T00:00:00Z"}}, {"id": "10.1016/j.renene.2021.02.003", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:16:35Z", "type": "Journal Article", "created": "2020-11-05", "title": "Virtual fatigue diagnostics of wake-affected wind turbine via Gaussian Process Regression", "description": "<p>We propose a data-driven model to predict the short-term fatigue Damage Equivalent Loads (DEL) on a wake-affected wind turbine based on wind field inflow sensors and/or loads sensors deployed on an adjacent up-wind wind turbine. Gaussian Process Regression (GPR) with Bayesian hyperparameters calibration is proposed to obtain a surrogate from input random variables to output DELs in the blades and towers of the up-wind and wake-affected wind turbines. A sensitivity analysis based on the hyperparameters of the GPR and Kullback-Leibler divergence is conducted to assess the effect of different input on the obtained DELs. We provide qualitative recommendations for a minimal set of necessary and sufficient input random variables to minimize the error in the DEL predictions on the wake-affected wind turbine. Extensive simulations are performed comprising different random variables, including wind speed, turbulence intensity, shear exponent and inflow horizontal skewness. Furthermore, we include random variables related to the blades lift and drag coefficients with direct impact on the rotor aerodynamic induction, which governs the evolution and transport of the meandering wake. In addition, different spacing between the wind turbines and W\u00f6hler exponents for calculation of DELs are considered. The maximum prediction normalized mean squared error, obtained in the tower base DELs in the fore-aft direction of the wake affected wind turbine, is less than 4%. In the case of the blade root DELs, the overall prediction error is less than 1%. The proposed scheme promotes utilization of sparse structural monitoring (loads) measurements for improving diagnostics on wake-affected turbines.</p>", "keywords": ["bepress|Physical Sciences and Mathematics|Physics|Engineering Physics", "engrXiv|Engineering|Risk Analysis", "engrXiv|Engineering|Other Engineering", "bepress|Engineering", "engrXiv|Engineering|Mechanical Engineering|Fluid Mechanics", "bepress|Engineering|Mechanical Engineering", "engrXiv|Engineering|Mechanical Engineering", "bepress|Engineering|Mechanical Engineering|Applied Mechanics", "Gaussian Process Regression", "02 engineering and technology", "7. Clean energy", "Virtual sensing", "wind turbine", "bepress|Engineering|Computational Engineering", "engrXiv|Engineering|Civil and Environmental Engineering", "0202 electrical engineering", " electronic engineering", " information engineering", "uncertainty", "Fatigue", "wake", "engrXiv|Engineering|Civil and Environmental Engineering|Structural Engineering", "Uncertainty", "engrXiv|Engineering|Mechanical Engineering|Applied Mechanics", "Bayesian Calibration", "engrXiv|Engineering|Engineering Physics", "bepress|Engineering|Risk Analysis", "engrXiv|Engineering", "bepress|Engineering|Civil and Environmental Engineering", "engrXiv|Engineering|Computational Engineering", "Wake", "bepress|Engineering|Aerospace Engineering|Aerodynamics and Fluid Mechanics", "bepress|Engineering|Civil and Environmental Engineering|Structural Engineering", "fatigue", "bepress|Engineering|Other Engineering", "Sensitivity analysis", "Wind turbine", "Bayesian Gaussian process regression"]}, "links": [{"href": "https://doi.org/10.1016/j.renene.2021.02.003"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Renewable%20Energy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.renene.2021.02.003", "name": "item", "description": "10.1016/j.renene.2021.02.003", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.renene.2021.02.003"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-11-05T00:00:00Z"}}, {"id": "10.1111/j.1466-8238.2009.00512.x", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:18:39Z", "type": "Journal Article", "created": "2010-02-05", "title": "A Biogeographic Model Of Fire Regimes In Australia: Current And Future Implications", "description": "ABSTRACT<p>Aim\uffe2\uff80\uff82 Patterns of fire regimes across Australia exhibit biogeographic variation in response to four processes. Variations in area burned and fire frequency result from differences in the rates of \uffe2\uff80\uff98switching\uffe2\uff80\uff99 of biomass growth, availability to burn, fire weather and ignition. Therefore differing processes limit fire (i.e. the lowest rate of switching) in differing ecosystems. Current and future trends in fire frequency were explored on this basis.</p><p>Location\uffe2\uff80\uff82 Case studies of forests (cool temperate to tropical) and woodlands (temperate to arid) were examined. These represent a broad range of Australian biomes and current fire regimes.</p><p>Methods\uffe2\uff80\uff82 Information on the four processes was applied to each case study and the potential minimum length of interfire interval was predicted and compared to current trends. The potential effects of global change on the processes were then assessed and future trends in fire regimes were predicted.</p><p>Results\uffe2\uff80\uff82 Variations in fire regimes are primarily related to fluctuations in available moisture and dominance by either woody or herbaceous plant cover. Fire in woodland communities (dry climates) is limited by growth of herbaceous fuels (biomass), whereas in forests (wet climates) limitation is by fuel moisture (availability to burn) and fire weather. Increasing dryness in woodland communities will decrease potential fire frequency, while the opposite applies in forests. In the tropics, both forms of limitation are weak due to the annual wet/dry climate. Future change may therefore be constrained.</p><p>Main conclusions\uffe2\uff80\uff82 Increasing dryness may diminish fire activity over much of Australia (dominance of dry woodlands), though increases may occur in temperate forests. Elevated CO2 effects may confound or reinforce these trends. The prognosis for the future fire regime in Australia is therefore uncertain.</p>", "keywords": ["13. Climate action", "Physical Sciences and Mathematics", "Life Sciences", "15. Life on land", "Social and Behavioral Sciences", "01 natural sciences", "0105 earth and related environmental sciences"], "contacts": [{"organization": "Bradstock, Ross A", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1111/j.1466-8238.2009.00512.x"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Global%20Ecology%20and%20Biogeography", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/j.1466-8238.2009.00512.x", "name": "item", "description": "10.1111/j.1466-8238.2009.00512.x", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/j.1466-8238.2009.00512.x"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2010-02-05T00:00:00Z"}}, {"id": "10.31219/osf.io/jfdb9", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:20:26Z", "type": "Journal Article", "created": "2021-03-15", "title": "Overcoming the challenges to enhancing experimental plant biology with computational modeling", "description": "<p>The study of complex biological systems necessitates computational modeling approaches that are currently underutilized in plant biology. Many plant biologists have trouble identifying or adopting modeling methods to their research, particularly mechanistic mathematical modeling. Here we address challenges that limit the use of computational modeling methods, particularly mechanistic mathematical modeling. We divide computational modeling techniques into either pattern models (e.g., bioinformatics, machine learning, or morphology) or mechanistic mathematical models (e.g., biochemical reactions, biophysics, or population models), which both contribute to plant biology research at different scales to answer different research questions. We present arguments and recommendations for the increased adoption of modeling by plant biologists interested in incorporating more modeling into their research programs. As some researchers find math and quantitative methods to be an obstacle to modeling, we provide suggestions for easy-to-use tools for non-specialists and for collaboration with specialists. This may especially be the case for mechanistic mathematical modeling, and we spend some extra time discussing this. Through a more thorough appreciation and awareness of the power of different kinds of modeling in plant biology, we hope to facilitate interdisciplinary, transformative research.</p>", "keywords": ["Other Physical Sciences and Mathematics", "computational modeling", "0301 basic medicine", "2. Zero hunger", "0303 health sciences", "experimental design", "Systems Biology", "Plant Sciences", "Research Methods in Life Sciences", "mathematical modeling", "Life Sciences", "Plant culture", "bioinformatics", "Plant Science", "collaboration", "SB1-1110", "03 medical and health sciences", "Other Life Sciences", "Physical Sciences and Mathematics"]}, "links": [{"href": "https://doi.org/10.31219/osf.io/jfdb9"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Plant%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.31219/osf.io/jfdb9", "name": "item", "description": "10.31219/osf.io/jfdb9", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.31219/osf.io/jfdb9"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-15T00:00:00Z"}}, {"id": "10.3390/s6080648", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:20:51Z", "type": "Journal Article", "created": "2008-10-25", "title": "Solid State pH Sensor Based on Light Emitting Diodes (LED) As Detector Platform", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>A low-power, high sensitivity, very low-cost light emitting diode (LED)-baseddevice developed for low-cost sensor networks was modified with bromocresol greenmembrane to work as a solid-state pH sensor. In this approach, a reverse-biased LEDfunctioning as a photodiode is coupled with a second LED configured in conventionalemission mode. A simple timer circuit measures how long (in microsecond) it takes for thephotocurrent generated on the detector LED to discharge its capacitance from logic 1 ( 5 V)to logic 0 ( 1.7 V). The entire instrument provides an inherently digital output of lightintensity measurements for a few cents. A light dependent resistor (LDR) modified withsimilar sensor membrane was also used as a comparison method. Both the LED sensor andthe LDR sensor responded to various pH buffer solutions in a similar way to obtainsigmoidal curves expected of the dye. The pKa value obtained for the sensors was found toagree with the literature value.</p></article>", "keywords": ["670", "ph", "led", "TP1-1185", "02 engineering and technology", "01 natural sciences", "7. Clean energy", "solid-state pH sensor", "state", "platform", "Engineering", "sensor", "Physical Sciences and Mathematics", "emitting", "detector", "Chemical technology", "diodes", "colorimetric sensor", "pH sensing", "light dependent resistor.", "light emitting diode", "0104 chemical sciences", "optical sensing", "solid", "light", "0210 nano-technology"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/6/8/848/pdf"}, {"href": "https://doi.org/10.3390/s6080648"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/s6080648", "name": "item", "description": "10.3390/s6080648", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/s6080648"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2006-08-23T00:00:00Z"}}, {"id": "1893/33794", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:24:51Z", "type": "Journal Article", "created": "2021-12-30", "title": "Global maps of soil temperature", "description": "Abstract<p>Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2\uffc2\uffa0m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1\uffe2\uff80\uff90km2resolution for 0\uffe2\uff80\uff935 and 5\uffe2\uff80\uff9315\uffc2\uffa0cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1\uffe2\uff80\uff90km2pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse\uffe2\uff80\uff90grained air temperature estimates from ERA5\uffe2\uff80\uff90Land (an atmospheric reanalysis by the European Centre for Medium\uffe2\uff80\uff90Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10\uffc2\uffb0C (mean\uffc2\uffa0=\uffc2\uffa03.0\uffc2\uffa0\uffc2\uffb1\uffc2\uffa02.1\uffc2\uffb0C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6\uffc2\uffa0\uffc2\uffb1\uffc2\uffa02.3\uffc2\uffb0C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (\uffe2\uff88\uff920.7\uffc2\uffa0\uffc2\uffb1\uffc2\uffa02.3\uffc2\uffb0C). The observed substantial and biome\uffe2\uff80\uff90specific offsets emphasize that the projected impacts of climate and climate change on near\uffe2\uff80\uff90surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil\uffe2\uff80\uff90related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.</p", "keywords": ["0106 biological sciences", "Bioclimatic variables; Global maps; Microclimate; Near-surface temperatures; Soil temperature; Soil-dwelling organisms; Temperature offset; Weather stations; Climate change; Temperature; Ecosystem; Soil", "791", "550", ":Zoology and botany: 480 [VDP]", "VDP::Zoologiske og botaniske fag: 480", "551", "Q1", "7. Clean energy", "01 natural sciences", "41 Environmental sciences", "Global map", "SDG 13 - Climate Action", "Soil temperature", "MICROCLIMATE", "bepress|Physical Sciences and Mathematics|Environmental Sciences", "soil-dwelling organism", "bioclimatic variables; global maps; microclimate; near-surface temperatures; soil temperature; soil-dwelling organisms; temperature offset; weather stations", "weather station", "GB", "http://aims.fao.org/aos/agrovoc/c_34836", "Geology", "16. Peace & justice", "Settore BIOS-01/C - Botanica ambientale e applicata", "6. Clean water", "Near-surface soil temperature", "international", "[SDE]Environmental Sciences", "551: Geologie und Hydrologie", "Near-surface temperature", "Near-surface temperatures", "soil temperature", "P40 - M\u00e9t\u00e9orologie et climatologie", "577", "bepress|Physical Sciences and Mathematics|Earth Sciences", "MITIGATION", "bepress|Life Sciences|Ecology and Evolutionary Biology", "12. Responsible consumption", "near-surface temperatures", "bepress|Physical Sciences and Mathematics|Oceanography and Atmospheric Sciences and Meteorology|Climate", "bioclimatic variables", "Bioclimatic variables", "Settore BIO/07 - ECOLOGIA", "temperature offset", "global maps", "http://aims.fao.org/aos/agrovoc/c_1344", "577: \u00d6kologie", "global map", "Biology", "Ecosystem", "Ekologi", "http://aims.fao.org/aos/agrovoc/c_24894", "Science & Technology", "ddc:550", "9. Industry and infrastructure", "31 Biological sciences", "Biology and Life Sciences", "Microclimate", "06 Biological Sciences", "15. 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Education", "Temperature", "Biological Sciences", "bioclimatologie", "FOREST", "Weather station", "Chemistry", "Biodiversity Conservation", "Life Sciences & Biomedicine", "bepress|Physical Sciences and Mathematics", "Technology and Engineering", "http://aims.fao.org/aos/agrovoc/c_1669", "bioclimatic variable", "Climate Change", "soil-dwelling organisms", "Environmental Sciences & Ecology", "MOISTURE", "LITTER DECOMPOSITION", "PERMAFROST", "near-surface temperature", "temp\u00e9rature du sol", "bepress|Physical Sciences and Mathematics|Oceanography and Atmospheric Sciences and Meteorology", "SUITABILITY", "G1", "VDP::Mathematics and natural scienses: 400::Zoology and botany: 480", "Global maps", "http://aims.fao.org/aos/agrovoc/c_1666", ":Zoologiske og botaniske fag: 480 [VDP]", "Soil-dwelling organisms", "0105 earth and related environmental sciences", "info:eu-repo/classification/ddc/550", "r\u00e9chauffement global", "Climate Change; Ecosystem; Microclimate; Soil; Temperature; bioclimatic variables; global maps; microclimate; near-surface temperatures; soil temperature; soil-dwelling organisms; temperature offset; weather stations", "http://aims.fao.org/aos/agrovoc/c_9260", "P30 - Sciences et am\u00e9nagement du sol", "Aquatic Ecology", "Bioclimatic variable", "SNOW-COVER", "Climate Science", "37 Earth sciences", "Climate Action", "bepress|Physical Sciences and Mathematics|Earth Sciences|Soil Science", "[SDE.BE] Environmental Sciences/Biodiversity and Ecology", "Earth sciences", "variation saisonni\u00e8re", "PLANT-RESPONSES", "CLIMATIC CONTROLS", "Soil-dwelling organism", "Settore BIOS-05/A - Ecologia", "13. 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In this approach, a reverse-biased LEDfunctioning as a photodiode is coupled with a second LED configured in conventionalemission mode. A simple timer circuit measures how long (in microsecond) it takes for thephotocurrent generated on the detector LED to discharge its capacitance from logic 1 ( 5 V)to logic 0 ( 1.7 V). The entire instrument provides an inherently digital output of lightintensity measurements for a few cents. A light dependent resistor (LDR) modified withsimilar sensor membrane was also used as a comparison method. Both the LED sensor andthe LDR sensor responded to various pH buffer solutions in a similar way to obtainsigmoidal curves expected of the dye. The pKa value obtained for the sensors was found toagree with the literature value.</p></article>", "keywords": ["670", "ph", "led", "TP1-1185", "02 engineering and technology", "7. 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