{"type": "FeatureCollection", "facets": {"type": {"type": "terms", "property": "type", "buckets": [{"value": "Journal Article", "count": 47}, {"value": null, "count": 7}, {"value": "Dataset", "count": 6}]}, "soil_chemical_properties": {"type": "terms", "property": "soil_chemical_properties", "buckets": [{"value": "soil organic carbon", "count": 9}, {"value": "soil organic matter", "count": 3}, {"value": "carbon", "count": 1}, {"value": "nitrate", "count": 1}, {"value": "urea", "count": 1}, {"value": "nitrous oxide", "count": 1}, {"value": "potassium", "count": 1}, {"value": "sulphur", "count": 1}]}, "soil_biological_properties": {"type": "terms", "property": "soil_biological_properties", "buckets": [{"value": "respiration", "count": 1}]}, "soil_physical_properties": {"type": "terms", "property": "soil_physical_properties", "buckets": [{"value": "water", "count": 7}, {"value": "bulk density", "count": 2}, {"value": "soil stability", "count": 1}]}, "soil_classification": {"type": "terms", "property": "soil_classification", "buckets": []}, "soil_functions": {"type": "terms", "property": "soil_functions", "buckets": [{"value": "crop yields", "count": 60}, {"value": "soil fertility", "count": 10}, {"value": "food security", "count": 2}, {"value": "productivity", "count": 2}]}, "soil_threats": {"type": "terms", "property": "soil_threats", "buckets": [{"value": "soil erosion", "count": 3}, {"value": "soil compaction", "count": 1}, {"value": "soil degradation", "count": 1}]}, "soil_processes": {"type": "terms", "property": "soil_processes", "buckets": []}, "soil_management": {"type": "terms", "property": "soil_management", "buckets": [{"value": "compost", "count": 4}, {"value": "plant residues", "count": 3}, {"value": "cultivation", "count": 1}, {"value": "digestate", "count": 1}]}, "ecosystem_services": {"type": "terms", "property": "ecosystem_services", "buckets": []}}, "features": [{"id": "34998760", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-04T16:26:59Z", "type": "Journal Article", "created": "2022-01-06", "title": "Exploring the potential role of environmental and multi-source satellite data in crop yield prediction across Northeast China", "description": "Open AccessLe d\u00e9veloppement d'un syst\u00e8me pr\u00e9cis de pr\u00e9diction du rendement des cultures \u00e0 grande \u00e9chelle est d'une importance primordiale pour la gestion des ressources agricoles et la s\u00e9curit\u00e9 alimentaire mondiale. L'observation de la Terre fournit une source unique d'informations pour surveiller les cultures \u00e0 partir d'une diversit\u00e9 de gammes spectrales. Cependant, l'utilisation int\u00e9gr\u00e9e de ces donn\u00e9es et de leurs valeurs dans la pr\u00e9diction du rendement des cultures est encore peu \u00e9tudi\u00e9e. Ici, nous avons propos\u00e9 la combinaison de donn\u00e9es environnementales (climat, sol, g\u00e9ographie et topographie) avec de multiples donn\u00e9es satellitaires (indices de v\u00e9g\u00e9tation optiques, fluorescence induite par le soleil (SIF), temp\u00e9rature de surface du sol (LST) et profondeur optique de la v\u00e9g\u00e9tation micro-ondes (VOD)) dans le cadre pour estimer le rendement des cultures de ma\u00efs, de riz et de soja dans le nord-est de la Chine, et leur valeur unique et leur influence relative sur la pr\u00e9diction du rendement ont \u00e9t\u00e9 \u00e9valu\u00e9es. Deux m\u00e9thodes de r\u00e9gression lin\u00e9aire, trois m\u00e9thodes d'apprentissage automatique (ML) et un mod\u00e8le d'ensemble ML ont \u00e9t\u00e9 adopt\u00e9s pour construire des mod\u00e8les de pr\u00e9diction de rendement. Les r\u00e9sultats ont montr\u00e9 que les m\u00e9thodes individuelles de ML surpassaient les m\u00e9thodes de r\u00e9gression lin\u00e9aire, le mod\u00e8le d'ensemble de ML a encore am\u00e9lior\u00e9 les mod\u00e8les de ML uniques. De plus, les mod\u00e8les avec plus d'intrants ont obtenu de meilleures performances, la combinaison de donn\u00e9es satellitaires avec des donn\u00e9es environnementales, qui expliquaient respectivement 72\u00a0%, 69\u00a0% et 57\u00a0% de la variabilit\u00e9 du rendement du ma\u00efs, du riz et du soja, a d\u00e9montr\u00e9 des performances de pr\u00e9diction du rendement sup\u00e9rieures \u00e0 celles des intrants individuels. Alors que les donn\u00e9es satellitaires ont contribu\u00e9 \u00e0 la pr\u00e9diction du rendement des cultures principalement au d\u00e9but de la pointe de la saison de croissance, les donn\u00e9es climatiques ont fourni des informations suppl\u00e9mentaires principalement \u00e0 la pointe de la fin de la saison. Nous avons \u00e9galement constat\u00e9 que l'utilisation combin\u00e9e de l'IVE, du LST et du SIF a am\u00e9lior\u00e9 la pr\u00e9cision du mod\u00e8le par rapport au mod\u00e8le d'IVE de r\u00e9f\u00e9rence. Cependant, les indices de v\u00e9g\u00e9tation bas\u00e9s sur l'optique partageaient des informations similaires et ne fournissaient pas beaucoup d'informations suppl\u00e9mentaires au-del\u00e0 de l'IVE. Les pr\u00e9visions de rendement en cours de saison ont montr\u00e9 que les rendements des cultures peuvent \u00eatre pr\u00e9vus de mani\u00e8re satisfaisante deux \u00e0 trois mois avant la r\u00e9colte. La g\u00e9ographie, la topographie, la VOD, l'IVE, les param\u00e8tres hydrauliques du sol et les param\u00e8tres nutritifs sont plus importants pour la pr\u00e9diction du rendement des cultures.", "keywords": ["Atmospheric sciences", "Climate", "Multi-source satellite data", "Normalized Difference Vegetation Index", "Engineering", "Pathology", "Climate change", "Urban Heat Islands and Mitigation Strategies", "Linear regression", "2. Zero hunger", "Global and Planetary Change", "Vegetation Monitoring", "Ecology", "Geography", "Statistics", "Agriculture", "Geology", "Remote Sensing in Vegetation Monitoring and Phenology", "04 agricultural and veterinary sciences", "Remote sensing", "Aerospace engineering", "Archaeology", "Physical Sciences", "Metallurgy", "Medicine", "Seasons", "Global Vegetation Models", "Biomass Estimation", "Regression analysis", "Vegetation (pathology)", "Crops", " Agricultural", "Environmental Engineering", "Environmental data", "Yield (engineering)", "Zea mays", "Environmental science", "Machine learning", "FOS: Mathematics", "Crop yield", "Biology", "Global Forest Drought Response and Climate Change", "FOS: Environmental engineering", "Predictive modelling", "Food security", "FOS: Earth and related environmental sciences", "15. Life on land", "Agronomy", "Materials science", "Yield prediction", "Satellite", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Growing season", "0401 agriculture", " forestry", " and fisheries", "Mathematics"], "contacts": [{"organization": "Zhenwang Li, Lei Ding, Donghui Xu,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/34998760"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science%20of%20The%20Total%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "34998760", "name": "item", "description": "34998760", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/34998760"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-04-01T00:00:00Z"}}, {"id": "50|od______2659::6cd4c5a070cdf75f4201430203a356b2", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-04T16:27:24Z", "type": "Dataset", "title": "Spatial relationships among cereal yields and selected soil physical andchemical properties", "description": "Sandy soils occupy large area in Poland (about 50%) and in the world. This study aimed at determining spatialrelationships of cereal yields and the selected soil physical and chemical properties in three study years (2001\u22122003) on low productive sandy Podzol soil (Podlasie, Poland). The yields and soil properties in plough and sub-soil layers were determined at 72\u2013150 points. The test crops were: wheat, wheat and barley mixture and oats. Toexplore the spatial relationship between cereal yields and each soil property spatial statistics was used. The bestfitting models were adjusted to empirical semivariance and cross-semivariance, which were used to draw mapsusing kriging. Majority of the soil properties and crop yields exhibited low and medium variability (coefficient ofvariation 5\u201370%). The effective ranges of the spatial dependence (the distance at which data are autocorrelated)for yields and all soil properties were 24.3\u201358.5 m and 10.5\u2013373 m, respectively. Nugget to sill ratios showed thatcrop yields and soil properties were strongly spatially dependent except bulk density. Majority of the pairs incross-semivariograms exhibited strong spatial interdependence. The ranges of the spatial dependence variedin plough layer between 54.6 m for yield \u00d7 pH up to 2433 m for yield \u00d7 silt content. Corresponding ranges in sub-soil were 24.8 m for crop yield \u00d7 clay content in 2003 and 1404 m for yield \u00d7 bulk density. Kriging maps allowedseparating sub-field area with the lowest yield and soil cation exchange capacity, organic carbon content and pH.This area had lighter color on the aerial photograph due to high content of the sand and low content of soil or-ganic carbon. The results will help farmers at identifying sub-field areas for applying localized management prac-tices to improve these soil properties and further spatial studies in larger scale.", "keywords": ["2. Zero hunger", "Crop yields", "Kriging map", "Cross-semivariograms", "15. Life on land", "Soil variability", "Low productive area"], "contacts": [{"organization": "Lipiec Jerzy, Usowicz Bogus\u0142aw,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/50|od______2659::6cd4c5a070cdf75f4201430203a356b2"}, {"rel": "self", "type": "application/geo+json", "title": "50|od______2659::6cd4c5a070cdf75f4201430203a356b2", "name": "item", "description": "50|od______2659::6cd4c5a070cdf75f4201430203a356b2", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/50|od______2659::6cd4c5a070cdf75f4201430203a356b2"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-04-04T00:00:00Z"}}, {"id": "PMC6934453", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:29:02Z", "type": "Journal Article", "created": "2019-12-27", "title": "Determining the effect of exogenous organic materials on spatial distribution of maize yield", "description": "Abstract

Knowledge on spatial distribution of crop yield in relation to fixed soil fertilisation with exogenous organic materials is essential for improving precise crop and soil management practices within a field. This study assessed the effect of various application rates and types of exogenous (recycled) organic materials (EOMs) containing different organic matter and nitrogen contents vs. mineral nitrogen on the yield of maize by means of linear regressions (trends), spatial kriging-interpolated maps, and Bland-Altman statistics. The experiments were conducted in 2013 and 2014 on two soils, i.e. loam silt in Braszowice (Poland) and clay silt loam in Pust\uffc3\uffa9 Jakartice (Czech Republic) under a cross-border cooperation project. The organic materials included compost from manure, slurry, and straw (Ag), industrial organic compost from sewage sludge (Ra), animal meal from animal by-products (Mb), and digestate from a biogas fries factory (Dg). The following 3 application rates of each EOM were adjusted according to the reference 100%\uffe2\uff80\uff89=\uffe2\uff80\uff89200\uffe2\uff80\uff89kg\uffe2\uff80\uff89N ha\uffe2\uff88\uff921: 50 (50% N from EOM and 50% mineral N), 75 (75% N from EOM and 25% mineral N), and 100 (100% N from EOM). 100% mineral N was applied on control plots. All treatments were carried out in 4 replicates. The linear regressions between the EOM application rates and the maize yield were in general ascending in the Braszowice soil and descending in the more productive Pust\uffc3\uffa9 Jakartice soil. The spatial kriging-interpolated maps allowed separating zones of lower and higher yields with EOMs compared to the control. They were attributed in part to the different EOM application rates and soil water contents. The Bland-Altaman statistics showed that addition of 50% of N from EOMs in 2013 caused a decrease and an increase in the maize grain yield in Braszowice and Pust\uffc3\uffa9 Jakartice, respectively, whereas the inverse was true with the 75 and 100% EOM additions. In 2014, the yield of maize for silage increased with the increasing EOM application rate in Braszowice and decreased in Pust\uffc3\uffa9 Jakartice, but it was smaller on all EOM-amended plots than in the control. As shown by the limits of agreement lines, the maize yields were more even in Pust\uffc3\uffa9 Jakartice than Braszowice. These results provide helpful information for selection of the most yield-producing EOM rates depending on the site soil conditions and prevalent weather conditions.