Glyphosate [N-(phosphonomethyl) glycine] is the active ingredient in Roundup, which is the most used herbicide around the world. It is a non-selective herbicide with carboxyl, amino, and phosphonate functional groups, and it has a strong affinity to the soil mineral fraction. Sorption plays a major role for the fate and transport of glyphosate in the environment. The sorption coefficient (Kd) of glyphosate, and hence its mobility, varies greatly among different soil types. Determining Kd is laborious and requires the use of wet chemistry. In this study, we aimed to estimate Kd using basic soil properties, and visible near-infrared spectroscopy (vis\u2013NIRS). The latter method is fast, requires no chemicals, and several soil properties can be estimated from the same spectrum. The data set included 68 topsoil samples collected across the South Island of New Zealand, with clay and organic carbon (OC) contents ranging from 0.001 to 0.520 kg kg\u22121 and 0.021 to 0.217 kg kg\u22121, respectively. The Kd was determined with batch equilibration sorption experiments and ranged from 13 to 3810 L kg\u22121. The visible near-infrared spectra were obtained from 400 to 2500 nm. Multiple linear regression was used to correlate Kd to oxalate extractable aluminium and phosphorous and pH, which resulted in an R2 of 0.89 and an RMSE of 259.59 L kg\u22121. Further, interval partial least squares regression with ten-fold cross-validation was used to predict Kd by vis\u2013NIRS, and an R2 of 0.93 and an RMSECV of 207.58 L kg\u22121 were obtained. Thus, these results show that both basic soil properties and vis\u2013NIRS can predict the variation in Kd across these samples with high accuracy and hence, that glyphosate sorption to a soil can be determined with vis\u2013NIRS.
", "keywords": ["2. Zero hunger", "ADSORPTION", "NEAR-INFRARED SPECTROSCOPY", "04 agricultural and veterinary sciences", "DEGRADATION", "15. Life on land", "WATER REPELLENCY", "FIELD-SCALE", "REFLECTANCE SPECTROSCOPY", "MOBILITY", "FACILITATED TRANSPORT", "CONTAMINANTS", "0401 agriculture", " forestry", " and fisheries", "COEFFICIENT"]}, "links": [{"href": "https://doi.org/10.1016/j.geoderma.2019.114009"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoderma", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.geoderma.2019.114009", "name": "item", "description": "10.1016/j.geoderma.2019.114009", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.geoderma.2019.114009"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-02-01T00:00:00Z"}}, {"id": "10.1016/j.envpol.2021.118128", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:16:21Z", "type": "Journal Article", "created": "2021-09-09", "title": "Diagnosis of cadmium contamination in urban and suburban soils using visible-to-near-infrared spectroscopy", "description": "Previous studies have mostly focused on using visible-to-near-infrared spectral technique to quantitatively estimate soil cadmium (Cd) content, whereas little attention has been paid to identifying soil Cd contamination from a perspective of spectral classification. Here, we developed a framework to compare the potential of two spectral transformations (i.e., raw reflectance and continuum removal [CR]), three optimization strategies (i.e., full-spectrum, Boruta feature selection, and synthetic minority over-sampling technique [SMOTE]), and three classification algorithms (i.e., partial least squares discriminant analysis, random forest [RF], and support vector machine) for diagnosing soil Cd contamination. A total of 536 soil samples were collected from urban and suburban areas located in Wuhan City, China. Specifically, Boruta and SMOTE strategies were aimed at selecting the most informative predictors and obtaining balanced training datasets, respectively. Results indicated that soils contaminated by Cd induced decrease in spectral reflectance magnitude. Classification models developed after Boruta and SMOTE strategies out-performed to those from full-spectrum. A diagnose model combining CR preprocessing, SMOTE strategy, and RF algorithm achieved the highest validation accuracy for soil Cd (Kappa = 0.74). This study provides a theoretical reference for rapid identification of and monitoring of soil Cd contamination in urban and suburban areas.", "keywords": ["DIFFUSE-REFLECTANCE SPECTROSCOPY", "HUMAN HEALTH", "PREDICTION", "POTENTIALLY TOXIC ELEMENTS", "Boruta algorithm", "01 natural sciences", "Visible-to-near-infrared spectroscopy", "NIR SPECTROSCOPY", "Soil", "ORGANIC-CARBON", "Machine learning", "11. Sustainability", "Soil Pollutants", "Least-Squares Analysis", "0105 earth and related environmental sciences", "Spectroscopy", " Near-Infrared", "RANDOM FOREST", "Urban and suburban soil Cd contamination", "04 agricultural and veterinary sciences", "15. Life on land", "QUANTITATIVE-ANALYSIS", "6. Clean water", "RIVER DELTA", "13. Climate action", "Earth and Environmental Sciences", "Synthetic minority over-sampling technique", "0401 agriculture", " forestry", " and fisheries", "HEAVY-METAL CONCENTRATIONS", "Cadmium"]}, "links": [{"href": "https://doi.org/10.1016/j.envpol.2021.118128"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Pollution", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.envpol.2021.118128", "name": "item", "description": "10.1016/j.envpol.2021.118128", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.envpol.2021.118128"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-01T00:00:00Z"}}, {"id": "1854/LU-01JV4A4VV9MSQATBRHJD3K77RH", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:26:33Z", "type": "Journal Article", "created": "2025-04-25", "title": "Multi-dimensional evaluation of site-specific tillage using mouldboard ploughing", "description": "Due to the lack of high-resolution data on soil compaction using proximal sensing technology, mouldboard (MB) ploughing is carried out at uniform speed and depth, which does not necessarily respond to tillage needs due to compaction level and depth that are spatially variable across the field area. This study aims at simulating the comparative performance of different site specific tillage (SST) schemes (e.g., speed and depth) and uniform tillage of a MB plough using a high resolution soil packing density (PD) maps. An on-the-go soil sensing platform was used to predict and map topsoil PD in a Luvisol field in Belgium and two Cambisol fields in Spain. All fields were divided into three management zones, to each of which different tillage speed and depth were assigned based on PD maps. A MATLAB simulation code was developed to predict and compare the power efficiency, fuel consumption, emission of carbon dioxide (CO2) from diesel combustion and total operating time of uniform, SST depth, SST speed, and hybrid SST depth and speed MB ploughing schemes. Results revealed that the degree of soil compaction varies from field to field and within fields, which necessitates SST tillage practices. It was found that the depth control was the best performing SST in fields having large areas with low (PD < 1.55) and medium (PD = 1.55 - 1.70) compaction levels, resulting in the largest reduction in draught (33.7 % - 57 %), fuel consumption and CO2 emission (29.6 % - 50.1 %), while using the same operational time as that of the uniform tillage. However, in cases when the majority of the field area was highly compacted (PD > 1.70), potential savings were smaller at 22.5 %, with the speed control emerged as a more effective control scheme. It is recommended to validate the simulation results of SST of MB ploughing in fields to enable assessing the impacts they have on crop responses and soil quality.", "keywords": ["Agriculture and Food Sciences", "CALIBRATION", "NEAR-INFRARED SPECTROSCOPY", "Precision agriculture", "IN-SITU", "SOIL COMPACTION", "Compaction", "LOAM", "Energy consumption", "DENSITY", "ONLINE SENSOR", "On-the-go soil sensing", "Simulation", "TOPSOIL COMPACTION"]}, "links": [{"href": "https://doi.org/1854/LU-01JV4A4VV9MSQATBRHJD3K77RH"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20and%20Tillage%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1854/LU-01JV4A4VV9MSQATBRHJD3K77RH", "name": "item", "description": "1854/LU-01JV4A4VV9MSQATBRHJD3K77RH", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1854/LU-01JV4A4VV9MSQATBRHJD3K77RH"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-10-01T00:00:00Z"}}, {"id": "1854/LU-8720112", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:26:33Z", "type": "Journal Article", "created": "2021-09-09", "title": "Diagnosis of cadmium contamination in urban and suburban soils using visible-to-near-infrared spectroscopy", "description": "Previous studies have mostly focused on using visible-to-near-infrared spectral technique to quantitatively estimate soil cadmium (Cd) content, whereas little attention has been paid to identifying soil Cd contamination from a perspective of spectral classification. Here, we developed a framework to compare the potential of two spectral transformations (i.e., raw reflectance and continuum removal [CR]), three optimization strategies (i.e., full-spectrum, Boruta feature selection, and synthetic minority over-sampling technique [SMOTE]), and three classification algorithms (i.e., partial least squares discriminant analysis, random forest [RF], and support vector machine) for diagnosing soil Cd contamination. A total of 536 soil samples were collected from urban and suburban areas located in Wuhan City, China. Specifically, Boruta and SMOTE strategies were aimed at selecting the most informative predictors and obtaining balanced training datasets, respectively. Results indicated that soils contaminated by Cd induced decrease in spectral reflectance magnitude. Classification models developed after Boruta and SMOTE strategies out-performed to those from full-spectrum. A diagnose model combining CR preprocessing, SMOTE strategy, and RF algorithm achieved the highest validation accuracy for soil Cd (Kappa = 0.74). This study provides a theoretical reference for rapid identification of and monitoring of soil Cd contamination in urban and suburban areas.", "keywords": ["DIFFUSE-REFLECTANCE SPECTROSCOPY", "HUMAN HEALTH", "PREDICTION", "POTENTIALLY TOXIC ELEMENTS", "Boruta algorithm", "01 natural sciences", "Visible-to-near-infrared spectroscopy", "NIR SPECTROSCOPY", "Soil", "ORGANIC-CARBON", "Machine learning", "11. Sustainability", "Soil Pollutants", "Least-Squares Analysis", "0105 earth and related environmental sciences", "Spectroscopy", " Near-Infrared", "RANDOM FOREST", "Urban and suburban soil Cd contamination", "04 agricultural and veterinary sciences", "15. Life on land", "QUANTITATIVE-ANALYSIS", "6. Clean water", "RIVER DELTA", "13. Climate action", "Earth and Environmental Sciences", "Synthetic minority over-sampling technique", "0401 agriculture", " forestry", " and fisheries", "HEAVY-METAL CONCENTRATIONS", "Cadmium"]}, "links": [{"href": "https://doi.org/1854/LU-8720112"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Pollution", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1854/LU-8720112", "name": "item", "description": "1854/LU-8720112", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1854/LU-8720112"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-01T00:00:00Z"}}, {"id": "2987388425", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:27:25Z", "type": "Journal Article", "created": "2019-11-12", "title": "Predicting glyphosate sorption across New Zealand pastoral soils using basic soil properties or Vis\u2013NIR spectroscopy", "description": "Glyphosate [N-(phosphonomethyl) glycine] is the active ingredient in Roundup, which is the most used herbicide around the world. It is a non-selective herbicide with carboxyl, amino, and phosphonate functional groups, and it has a strong affinity to the soil mineral fraction. Sorption plays a major role for the fate and transport of glyphosate in the environment. The sorption coefficient (Kd) of glyphosate, and hence its mobility, varies greatly among different soil types. Determining Kd is laborious and requires the use of wet chemistry. In this study, we aimed to estimate Kd using basic soil properties, and visible near-infrared spectroscopy (vis\u2013NIRS). The latter method is fast, requires no chemicals, and several soil properties can be estimated from the same spectrum. The data set included 68 topsoil samples collected across the South Island of New Zealand, with clay and organic carbon (OC) contents ranging from 0.001 to 0.520 kg kg\u22121 and 0.021 to 0.217 kg kg\u22121, respectively. The Kd was determined with batch equilibration sorption experiments and ranged from 13 to 3810 L kg\u22121. The visible near-infrared spectra were obtained from 400 to 2500 nm. Multiple linear regression was used to correlate Kd to oxalate extractable aluminium and phosphorous and pH, which resulted in an R2 of 0.89 and an RMSE of 259.59 L kg\u22121. Further, interval partial least squares regression with ten-fold cross-validation was used to predict Kd by vis\u2013NIRS, and an R2 of 0.93 and an RMSECV of 207.58 L kg\u22121 were obtained. Thus, these results show that both basic soil properties and vis\u2013NIRS can predict the variation in Kd across these samples with high accuracy and hence, that glyphosate sorption to a soil can be determined with vis\u2013NIRS.
", "keywords": ["2. Zero hunger", "ADSORPTION", "NEAR-INFRARED SPECTROSCOPY", "04 agricultural and veterinary sciences", "DEGRADATION", "15. Life on land", "WATER REPELLENCY", "FIELD-SCALE", "REFLECTANCE SPECTROSCOPY", "MOBILITY", "FACILITATED TRANSPORT", "CONTAMINANTS", "0401 agriculture", " forestry", " and fisheries", "COEFFICIENT"]}, "links": [{"href": "https://doi.org/2987388425"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoderma", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2987388425", "name": "item", "description": "2987388425", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2987388425"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-02-01T00:00:00Z"}}, {"id": "c9a6def1-d330-475f-bf52-4931ae2b8bcf", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[5.81, 47.26], [5.81, 54.76], [15.77, 54.76], [15.77, 47.26], [5.81, 47.26]]]}, "properties": {"themes": [{"concepts": [{"id": "farming"}], "scheme": "https://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_TopicCategoryCode"}, {"concepts": [{"id": "Soil"}, {"id": "soil pH"}], "scheme": "AGROVOC Multilingual agricultural thesaurus"}, {"concepts": [{"id": "opendata"}, {"id": "Proximal Soil Sensing; Near-Infrared Spectroscopy (NIR); Soil pH; Soil Electrical Conductivity; Gamma Sensor."}], "scheme": "Individual"}, {"concepts": [{"id": "Boden"}], "scheme": "GEMET - INSPIRE themes, version 1.0"}], "rights": "Restrictions applied to assure the protection of privacy or intellectual property, and any special restrictions or limitations or warnings on using the resource or metadata. Reports, articles, papers, scientific and non - scientific works of any form, including tables, maps, or any other kind of output, in printed or electronic form, based in whole or in part on the data supplied, must contain an acknowledgement of the form: \"Data reused from the BonaRes Data Centre www.bonares.de. This data were created as part of the Other's research activities.\" Although every care has been taken in preparing and testing the data, the Other and the BonaRes Data Centre cannot guarantee that the data are correct; neither does the Other and the BonaRes Data Centre accept any liability whatsoever for any error, missing data or omission in the data, or for any loss or damage arising from its use. The Other and BonaRes Data Centre will not be responsible for any direct or indirect use which might be made of the data.", "updated": "2022-12-09", "type": "Dataset", "created": "2022-11-18", "language": "eng", "title": "Proximal soil sensing data from the RapidMapper, a novel mobile multi-sensor platform for topsoil mapping [Boo\u00dfen (Brandenburg, Germany), August 2021].", "description": "Proximal soil sensing data were collected by a novel multi-sensor platform (\u201cRapidMapper\u201d) for on-the-go topsoil mapping. This platform was developed within the BonaRes project \u201cI4S (Intelligence for Soil) \u2013 Integrated System for Site-Specific Soil Fertility Management\u201d (https://www.bonares.de/i4s). The sensor data comprise: (i) apparent electrical conductivity (ECa) using the galvanic contact resistivity technique based on the Wenner array configuration, (ii) near-infrared (NIR) spectra covering the nominal range of 860\uf02d2550 nm with a resolution of 1 nm (C11118GA, Hamamatsu Photonics K. K., Shizuoka Pref., Japan), and (iii) gamma spectra from a CsI (Caesium Iodide) scintillator crystal (MS-2000-CsI-MTS, Medusa Radiometrics BV, Groningen, Netherlands)detecting the naturally occurring radionuclides, Potassium-40 (40K), Uranium-238 (238U), Thorium-232 (232Th) and Caesium-137 (137Cs). They were collected from the topsoil at a measurement frequency of 1 Hz during a field mapping campaign in August 2021 conducted on an agricultural field of 15.5 ha in Boo\u00dfen near Frankfurt/Oder (Brandenburg, Germany; 52\u00b023\u201938.688\u2019\u2019N, 14\u00b027\u201938.844\u2019\u2019E). The RapidMapper platform was pulled over the field at an average speed of 2.5 km/h and along parallel tracks being about 18 m apart.", "formats": [{"name": "CSV"}], "keywords": ["Soil", "soil pH", "opendata", "Proximal Soil Sensing; Near-Infrared Spectroscopy (NIR); Soil pH; Soil Electrical Conductivity; Gamma Sensor.", "Boden"], "contacts": [{"name": "Hamed Tavakoli", "organization": "Leibniz Institute for Agricultural Engineering and Bioeconomy e.V. 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