Nitrate leaching is one of the most important pathways of nitrogen (N) loss which leads to groundwater contamination or surface water eutrophication. Clarifying the rates, controlling factors and characteristics of nitrate leaching is the pre-requisite for proposing effective mitigation strategies. We investigated the effects of interactions among chemical N fertilizer, straw and manure applications on nitrogen leaching in an intensively managed calcareous Fluvo-aquic soil with winter wheat-summer maize cropping rotations on the North China Plain from October 2010 to September 2013 using ceramic suction cups and seepage water calculations based on a long-term field experiment. Annual nitrate leaching reached 38\uffe2\uff80\uff9360\uffe2\uff80\uff89kg\uffe2\uff80\uff89N ha\uffe2\uff88\uff921 from conventional N managements, but declined by 32\uffe2\uff80\uff9371% due to optimum N, compost manure or municipal waste treatments, respectively. Nitrate leaching concentrated in the summer maize season, and fewer leaching events with high amounts are the characteristics of nitrate leaching in this region. Overuse of chemical N fertilizers, high net mineralization and nitrification, together with predominance of rainfall in the summer season with light soil texture are the main controlling factors responsible for the high nitrate leaching loss in this soil-crop-climatic system.
", "keywords": ["2. Zero hunger", "Agricultural Irrigation", "Nitrates", "Nitrogen", "Rain", "Agriculture", "04 agricultural and veterinary sciences", "15. Life on land", "Zea mays", "Article", "6. Clean water", "Soil", "13. Climate action", "Regression Analysis", "0401 agriculture", " forestry", " and fisheries", "Seasons", "Fertilizers", "Triticum"]}, "links": [{"href": "https://doi.org/10.1038/srep42247"}, {"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/srep42247", "name": "item", "description": "10.1038/srep42247", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/srep42247"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-02-08T00:00:00Z"}}, {"id": "3024869357", "type": "Feature", "geometry": null, "properties": {"license": "Restricted", "updated": "2026-05-30T16:27:22Z", "type": "Journal Article", "created": "2020-05-15", "title": "Can pedotransfer functions based on environmental variables improve soil total nutrient mapping at a regional scale?", "description": "Abstract Numerous pedotransfer functions (PTFs) have been developed to predict the soil properties of interest from other soil properties and, less commonly, from environmental variables. However, only a few PTFs have been developed to predict soil nutrients using environmental variables and to extrapolate them to characterize spatial soil variations at a regional scale. In this study, we attempted to develop PTFs for the total nitrogen (TN), total phosphorus (TP) and total potassium (TK) concentrations in three typical pedo-climatic areas of China (Fujian Province, Jiangsu Province and Qilian Mountains) with diverse climate, terrain and soil types. A series of linear PTFs were developed to quantify the effect of terrain and climate on the predictive relations between the soil nutrients and other measured soil properties and environmental variables. In addition, digital soil mapping (DSM) based on the random forest (RF) technique was performed to test the hypothesis that the best-fit PTFs could be extrapolated, based on soil maps and environmental variables, to describe regional soil variations in the soil nutrients. The root mean square errors (RMSEs) of the best-fit PTFs for TN, TP and TK ranged from 0.21 to 0.79 g kg\u22121, 0.20 to 0.58 g kg\u22121, and 3.68 to 5.00 g kg\u22121, respectively. Different RMSEs were produced by DSM, namely 0.37-1.89 g kg\u22121, 0.19\u22120.56 g kg\u22121 and 3.79-4.83 g kg\u22121 for TN, TP and TK, respectively. PTFs provided a sound basis for database compilation if the soil properties were highly correlated. However, the extrapolation of best-fit PTFs to regional scales yielded greater errors than those produced by DSM. The comparison results reveal the limitations of PTFs and suggest that their performance could be improved by using environmental covariates or by fitting data in areas with relatively homogeneous soil landscapes. The DSM techniques may provide satisfactory alternatives to predict soil data at both regional and plot scales.", "keywords": ["Digital soil mapping", "Total phosphorus", "Total potassium", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "Total nitrogen", "15. Life on land", "Regression analysis", "01 natural sciences", "6. Clean water", "Random forest", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/3024869357"}, {"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": "3024869357", "name": "item", "description": "3024869357", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3024869357"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-08-01T00:00:00Z"}}, {"id": "10.1093/jxb/err133", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-30T16:19:02Z", "type": "Journal Article", "created": "2011-05-18", "title": "Interactive Effects Of Elevated Co2, Warming, And Drought On Photosynthesis Of Deschampsia Flexuosa In A Temperate Heath Ecosystem", "description": "Global change factors affect plant carbon uptake in concert. In order to investigate the response directions and potential interactive effects, and to understand the underlying mechanisms, multifactor experiments are needed. The focus of this study was on the photosynthetic response to elevated CO(2) [CO2; free air CO(2) enrichment (FACE)], drought (D; water-excluding curtains), and night-time warming (T; infrared-reflective curtains) in a temperate heath. A/C(i) curves were measured, allowing analysis of light-saturated net photosynthesis (P(n)), light- and CO(2)-saturated net photosynthesis (P(max)), stomatal conductance (g(s)), the maximal rate of Rubisco carboxylation (V(cmax)), and the maximal rate of ribulose bisphosphate (RuBP) regeneration (J(max)) along with leaf \u03b4(13)C, and carbon and nitrogen concentration on a monthly basis in the grass Deschampsia flexuosa. Seasonal drought reduced P(n) via g(s), but severe (experimental) drought decreased P(n) via a reduction in photosynthetic capacity (P(max), J(max), and V(cmax)). The effects were completely reversed by rewetting and stimulated P(n) via photosynthetic capacity stimulation. Warming increased early and late season P(n) via higher P(max) and J(max). Elevated CO(2) did not decrease g(s), but stimulated P(n) via increased C(i). The T\u00d7CO2 synergistically increased plant carbon uptake via photosynthetic capacity up-regulation in early season and by better access to water after rewetting. The effects of the combination of drought and elevated CO(2) depended on soil water availability, with additive effects when the soil water content was low and D\u00d7CO2 synergistic stimulation of P(n) after rewetting. The photosynthetic responses appeared to be highly influenced by growth pattern. The grass has opportunistic water consumption, and a biphasic growth pattern allowing for leaf dieback at low soil water availability followed by rapid re-growth of active leaves when rewetted and possibly a large resource allocation capability mediated by the rhizome. This growth characteristic allowed for the photosynthetic capacity up-regulations that mediated the T\u00d7CO2 and D\u00d7CO2 synergistic effects on photosynthesis. These are clearly advantageous characteristics when exposed to climate changes. In conclusion, after 1 year of experimentation, the limitations by low soil water availability and stimulation in early and late season by warming clearly structure and interact with the photosynthetic response to elevated CO(2) in this grassland species.", "keywords": ["0301 basic medicine", "2. Zero hunger", "Carbon Isotopes", "0303 health sciences", "Light", "Nitrogen", "Rain", "Temperature", "Water", "Carbon Dioxide", "15. Life on land", "Poaceae", "Research Papers", "Carbon", "6. Clean water", "Droughts", "Soil", "03 medical and health sciences", "13. Climate action", "Plant Stomata", "Regression Analysis", "Seasons", "Photosynthesis", "Ecosystem"]}, "links": [{"href": "https://doi.org/10.1093/jxb/err133"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Experimental%20Botany", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1093/jxb/err133", "name": "item", "description": "10.1093/jxb/err133", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1093/jxb/err133"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2011-05-16T00:00:00Z"}}, {"id": "10.1109/jphotov.2020.3043104", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-30T16:19:13Z", "type": "Journal Article", "created": "2021-01-05", "title": "Improved PV Soiling Extraction Through the Detection of Cleanings and Change Points", "description": "<b>Accepted Manuscript (Postprint): </b>L. Micheli et al., \uffe2\uff80\uff9cImproved PV Soiling Extraction through the Detection of Cleanings and Change Points,\uffe2\uff80\uff9d IEEE Journal of Photovoltaics, Volume: 11, Issue: 2, March 2021.
", "keywords": ["13. Climate action", "0211 other engineering and technologies", "0202 electrical engineering", " electronic engineering", " information engineering", "02 engineering and technology", "Electrical and Electronic Engineering", "monitoring; photovoltaic (PV) systems; regression analysis; soiling; time-series analysis", "Condensed Matter Physics", "6. Clean water", "Electronic", " Optical and Magnetic Materials"]}, "links": [{"href": "https://iris.uniroma1.it/bitstream/11573/1625157/3/Micheli_Improved%20PV_post-print_2021.pdf"}, {"href": "http://xplorestaging.ieee.org/ielx7/5503869/9358028/09312967.pdf?arnumber=9312967"}, {"href": "https://doi.org/10.1109/jphotov.2020.3043104"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IEEE%20Journal%20of%20Photovoltaics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/jphotov.2020.3043104", "name": "item", "description": "10.1109/jphotov.2020.3043104", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/jphotov.2020.3043104"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-01T00:00:00Z"}}, {"id": "10.1111/2041-210X.14392", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:19:17Z", "type": "Journal Article", "created": "2024-08-07", "title": "Sap Flow Analyzer: A tool to standardize sap flow estimation and scaling to whole\u2010tree water use using the HFD method", "description": "Abstract
Sap flow measurements are fundamental to understanding water use in trees and could aid in predicting climate change effects on forest function. Deriving knowledge from such measurements requires empirical calibrations and upscaling methods to translate thermometric recordings to tree water use. Here, we developed a user\uffe2\uff80\uff90friendly open\uffe2\uff80\uff90source application, the Sap Flow Analyzer (SFA), which estimates sap flow rates and tree water use from the heat field deformation (HFD) instruments.
The SFA incorporates four key features to ensure maximum accuracy and reproducibility of sap flow estimates: diagnosis diagrams to assess data patterns visually, regression models implemented to increase accuracy when estimating K (the main HFD parameter), three approaches to upscale sap flow rates to whole\uffe2\uff80\uff90tree water use and visualization of the input parameters' uncertainty. Thirteen participants were given three raw datasets and assigned data processing tasks using the SFA user guide, from estimating sapwood depth to scaling sap flow rates to whole\uffe2\uff80\uff90tree water use to assess the reproducibility and applicability of the SFA.
Participants' results were reasonably consistent and independent of their background in using the SFA, R, or HFD method. The results showed lower variability for high flow rates (SD: mean 1% vs. 10%). K estimates and sapwood depth differentiation were the primary sources of variability, which in turn was mainly caused by the user's chosen scaling method.
The SFA provides an easy way to visualize and process sap flow and tree water use data from HFD measurements. It is the first free and open software tool for HFD users. The ability to trace analysis steps ensures reproducibility, increasing transparency and consistency in data processing. Developing tools such as the SFA and masked trials are essential for more precise workflows and improved quality and comparability of HFD sap flow datasets.
In the short\uffe2\uff80\uff90term heterotrophic soil respiration is strongly and positively related to temperature. In the long\uffe2\uff80\uff90term, its response to temperature is uncertain. One reason for this is because in field experiments increases in respiration due to warming are relatively short\uffe2\uff80\uff90lived. The explanations proposed for this ephemeral response include depletion of fast\uffe2\uff80\uff90cycling, soil carbon pools and thermal adaptation of microbial respiration. Using a >\uffe2\uff80\uff8315\uffe2\uff80\uff83year soil warming experiment in a mid\uffe2\uff80\uff90latitude forest, we show that the apparent \uffe2\uff80\uff98acclimation\uffe2\uff80\uff99 of soil respiration at the ecosystem scale results from combined effects of reductions in soil carbon pools and microbial biomass, and thermal adaptation of microbial respiration. Mass\uffe2\uff80\uff90specific respiration rates were lower when seasonal temperatures were higher, suggesting that rate reductions under experimental warming likely occurred through temperature\uffe2\uff80\uff90induced changes in the microbial community. Our results imply that stimulatory effects of global temperature rise on soil respiration rates may be lower than currently predicted.", "keywords": ["0106 biological sciences", "Hot Temperature", "Physiological", "adaptation", "carbon cycling", "soil respiration", "01 natural sciences", "climate warming", "thermal biology", "Soil", "Biomass", "Adaptation", "Soil Microbiology", "Evolutionary Biology", "Ecology", "temperature", "04 agricultural and veterinary sciences", "Biogeochemistry", "15. Life on land", "Adaptation", " Physiological", "Climate Action", "climate change", "13. Climate action", "Ecological Applications", "Regression Analysis", "0401 agriculture", " forestry", " and fisheries", "CO2", "Seasons", "microbial community", "Acclimation"]}, "links": [{"href": "https://escholarship.org/content/qt1kz5j4pn/qt1kz5j4pn.pdf"}, {"href": "https://doi.org/10.1111/j.1461-0248.2008.01251.x"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Ecology%20Letters", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/j.1461-0248.2008.01251.x", "name": "item", "description": "10.1111/j.1461-0248.2008.01251.x", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/j.1461-0248.2008.01251.x"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2008-11-05T00:00:00Z"}}, {"id": "10.1111/j.1469-8137.2010.03321.x", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-30T16:19:41Z", "type": "Journal Article", "created": "2010-06-11", "title": "Belowground Carbon Allocation By Trees Drives Seasonal Patterns Of Extracellular Enzyme Activities By Altering Microbial Community Composition In A Beech Forest Soil", "description": "*Plant seasonal cycles alter carbon (C) and nitrogen (N) availability for soil microbes, which may affect microbial community composition and thus feed back on microbial decomposition of soil organic material and plant N availability. The temporal dynamics of these plant-soil interactions are, however, unclear. *Here, we experimentally manipulated the C and N availability in a beech forest through N fertilization or tree girdling and conducted a detailed analysis of the seasonal pattern of microbial community composition and decomposition processes over 2 yr. *We found a strong relationship between microbial community composition and enzyme activities over the seasonal course. Phenoloxidase and peroxidase activities were highest during late summer, whereas cellulase and protease peaked in late autumn. Girdling, and thus loss of mycorrhiza, resulted in an increase in soil organic matter-degrading enzymes and a decrease in cellulase and protease activity. *Temporal changes in enzyme activities suggest a switch of the main substrate for decomposition between summer (soil organic matter) and autumn (plant litter). Our results indicate that ectomycorrhizal fungi are possibly involved in autumn cellulase and protease activity. Our study shows that, through belowground C allocation, trees significantly alter soil microbial communities, which may affect seasonal patterns of decomposition processes.", "keywords": ["Nitrogen", "Climate", "Trees", "Soil", "Mycorrhizae", "401902 Soil science", "Fagus", "Biomass", "Phospholipids", "Soil Microbiology", "106022 Mikrobiologie", "Bacteria", "Research", "Temperature", "04 agricultural and veterinary sciences", "15. Life on land", "401902 Bodenkunde", "Carbon", "Solubility", "106030 Pflanzen\u00f6kologie", "106022 Microbiology", "Regression Analysis", "0401 agriculture", " forestry", " and fisheries", "106030 Plant ecology", "Seasons", "Extracellular Space", "Biomarkers"]}, "links": [{"href": "https://doi.org/10.1111/j.1469-8137.2010.03321.x"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/New%20Phytologist", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/j.1469-8137.2010.03321.x", "name": "item", "description": "10.1111/j.1469-8137.2010.03321.x", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/j.1469-8137.2010.03321.x"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2010-07-19T00:00:00Z"}}, {"id": "10.1371/journal.pone.0072019", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:20:16Z", "type": "Journal Article", "created": "2013-08-21", "title": "Predicting Greenhouse Gas Emissions And Soil Carbon From Changing Pasture To An Energy Crop", "description": "Bioenergy related land use change would likely alter biogeochemical cycles and global greenhouse gas budgets. Energy cane (Saccharum officinarum L.) is a sugarcane variety and an emerging biofuel feedstock for cellulosic bio-ethanol production. It has potential for high yields and can be grown on marginal land, which minimizes competition with grain and vegetable production. The DayCent biogeochemical model was parameterized to infer potential yields of energy cane and how changing land from grazed pasture to energy cane would affect greenhouse gas (CO2, CH4 and N2O) fluxes and soil C pools. The model was used to simulate energy cane production on two soil types in central Florida, nutrient poor Spodosols and organic Histosols. Energy cane was productive on both soil types (yielding 46-76 Mg dry mass \u00b7 ha(-1)). Yields were maintained through three annual cropping cycles on Histosols but declined with each harvest on Spodosols. Overall, converting pasture to energy cane created a sink for GHGs on Spodosols and reduced the size of the GHG source on Histosols. This change was driven on both soil types by eliminating CH4 emissions from cattle and by the large increase in C uptake by greater biomass production in energy cane relative to pasture. However, the change from pasture to energy cane caused Histosols to lose 4493 g CO2 eq \u00b7 m(-2) over 15 years of energy cane production. Cultivation of energy cane on former pasture on Spodosol soils in the southeast US has the potential for high biomass yield and the mitigation of GHG emissions.", "keywords": ["Crops", " Agricultural", "Greenhouse Effect", "Science", "Nitrous Oxide", "Models", " Biological", "7. Clean energy", "12. Responsible consumption", "Soil", "11. Sustainability", "Animals", "Biomass", "Ecosystem", "2. Zero hunger", "Q", "R", "04 agricultural and veterinary sciences", "Carbon Dioxide", "15. Life on land", "Carbon", "Saccharum", "13. Climate action", "Biofuels", "Florida", "Medicine", "Regression Analysis", "0401 agriculture", " forestry", " and fisheries", "Cattle", "Gases", "Methane", "Research Article"]}, "links": [{"href": "https://doi.org/10.1371/journal.pone.0072019"}, {"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.0072019", "name": "item", "description": "10.1371/journal.pone.0072019", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1371/journal.pone.0072019"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2013-08-21T00:00:00Z"}}, {"id": "10.1371/journal.pone.0184198", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:20:18Z", "type": "Journal Article", "created": "2017-09-01", "title": "Portfolio optimization for seed selection in diverse weather scenarios", "description": "The aim of this work was to develop a method for selection of optimal soybean varieties for the American Midwest using data analytics. We extracted the knowledge about 174 varieties from the dataset, which contained information about weather, soil, yield and regional statistical parameters. Next, we predicted the yield of each variety in each of 6,490 observed subregions of the Midwest. Furthermore, yield was predicted for all the possible weather scenarios approximated by 15 historical weather instances contained in the dataset. Using predicted yields and covariance between varieties through different weather scenarios, we performed portfolio optimisation. In this way, for each subregion, we obtained a selection of varieties, that proved superior to others in terms of the amount and stability of yield. According to the rules of Syngenta Crop Challenge, for which this research was conducted, we aggregated the results across all subregions and selected up to five soybean varieties that should be distributed across the network of seed retailers. The work presented in this paper was the winning solution for Syngenta Crop Challenge 2017.", "keywords": ["Crops", " Agricultural", "2. Zero hunger", "Models", " Statistical", "Glycine max", "Science", "Climate Change", "Q", "R", "Uncertainty", "Agriculture", "04 agricultural and veterinary sciences", "15. Life on land", "Portfolio optimisation", "Yield prediction", "Midwestern United States", "03 medical and health sciences", "0302 clinical medicine", "Seeds", "Medicine", "Regression Analysis", "0401 agriculture", " forestry", " and fisheries", "data analytics", "Weather", "Research Article"]}, "links": [{"href": "https://doi.org/10.1371/journal.pone.0184198"}, {"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.0184198", "name": "item", "description": "10.1371/journal.pone.0184198", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1371/journal.pone.0184198"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-09-01T00:00:00Z"}}, {"id": "2753196607", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-30T16:27:04Z", "type": "Journal Article", "created": "2017-09-01", "title": "Portfolio optimization for seed selection in diverse weather scenarios", "description": "The aim of this work was to develop a method for selection of optimal soybean varieties for the American Midwest using data analytics. We extracted the knowledge about 174 varieties from the dataset, which contained information about weather, soil, yield and regional statistical parameters. Next, we predicted the yield of each variety in each of 6,490 observed subregions of the Midwest. Furthermore, yield was predicted for all the possible weather scenarios approximated by 15 historical weather instances contained in the dataset. Using predicted yields and covariance between varieties through different weather scenarios, we performed portfolio optimisation. In this way, for each subregion, we obtained a selection of varieties, that proved superior to others in terms of the amount and stability of yield. According to the rules of Syngenta Crop Challenge, for which this research was conducted, we aggregated the results across all subregions and selected up to five soybean varieties that should be distributed across the network of seed retailers. The work presented in this paper was the winning solution for Syngenta Crop Challenge 2017.", "keywords": ["Crops", " Agricultural", "2. Zero hunger", "Models", " Statistical", "Glycine max", "Science", "Climate Change", "Q", "R", "Uncertainty", "Agriculture", "04 agricultural and veterinary sciences", "15. Life on land", "Portfolio optimisation", "Yield prediction", "Midwestern United States", "03 medical and health sciences", "0302 clinical medicine", "Seeds", "Medicine", "Regression Analysis", "0401 agriculture", " forestry", " and fisheries", "data analytics", "Weather", "Research Article"]}, "links": [{"href": "https://doi.org/2753196607"}, {"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": "2753196607", "name": "item", "description": "2753196607", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2753196607"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-09-01T00:00:00Z"}}, {"id": "34998760", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:27:45Z", "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": "PMC5580993", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:29:49Z", "type": "Journal Article", "created": "2017-09-01", "title": "Portfolio optimization for seed selection in diverse weather scenarios", "description": "The aim of this work was to develop a method for selection of optimal soybean varieties for the American Midwest using data analytics. We extracted the knowledge about 174 varieties from the dataset, which contained information about weather, soil, yield and regional statistical parameters. Next, we predicted the yield of each variety in each of 6,490 observed subregions of the Midwest. Furthermore, yield was predicted for all the possible weather scenarios approximated by 15 historical weather instances contained in the dataset. Using predicted yields and covariance between varieties through different weather scenarios, we performed portfolio optimisation. In this way, for each subregion, we obtained a selection of varieties, that proved superior to others in terms of the amount and stability of yield. According to the rules of Syngenta Crop Challenge, for which this research was conducted, we aggregated the results across all subregions and selected up to five soybean varieties that should be distributed across the network of seed retailers. The work presented in this paper was the winning solution for Syngenta Crop Challenge 2017.", "keywords": ["Crops", " Agricultural", "2. Zero hunger", "Models", " Statistical", "Glycine max", "Science", "Climate Change", "Q", "R", "Uncertainty", "Agriculture", "04 agricultural and veterinary sciences", "15. Life on land", "Midwestern United States", "03 medical and health sciences", "0302 clinical medicine", "Seeds", "Medicine", "Regression Analysis", "0401 agriculture", " forestry", " and fisheries", "Weather", "Research Article"]}, "links": [{"href": "https://doi.org/PMC5580993"}, {"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": "PMC5580993", "name": "item", "description": "PMC5580993", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PMC5580993"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-09-01T00:00:00Z"}}, {"id": "bc466e30-b683-4859-ae4f-577cbc6505f7", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[5.87, 47.27], [5.87, 55.06], [15.03, 55.06], [15.03, 47.27], [5.87, 47.27]]]}, "properties": {"license": "CC BY", "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": "2023-12-12", "type": "Service", "created": "2022-09-30", "language": "eng", "title": "Web Map Service of the dataset 'Germany-wide time series of interpolated phenological observations for main crop types between 1993 and 2021'", "description": "This Web Map Service includes spatial information used by the dataset \"Germany-wide time series of interpolated phenological observations for main crop types between 1993 and 2021''", "keywords": ["infoMapAccessService", "agricultural management", "Soil", "agricultural practices", "agricultural practices", "crop rotation", "environmental monitoring", "multiple regression analysis", "plant developmental stages", "phenology", "elevation", "land use", "meteorological geographical features", "geographical grid systems"], "contacts": [{"name": "M\u00f6ller, Markus", "organization": "Julius K\u00fchn Institute (JKI) \u2013 Federal Research Centre for Cultivated Plants, Institute for Crop and Soil Science, Bundesallee 69, D-38116 Braunschweig, Germany", "position": null, "roles": ["author"], "phones": [{"value": null}], "emails": [{"value": "markus.moeller@julius-kuehn.de"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": {"url": "https://orcid.org", "protocol": null, "protocol_url": "", "name": "0000-0002-1918-7747", "name_url": "", "description": "ORCID", "description_url": "", "applicationprofile": null, "applicationprofile_url": "", "function": null}}]}, {"name": "M\u00f6ller, Markus", "organization": "Julius K\u00fchn Institute (JKI) \u2013 Federal Research Centre for Cultivated Plants, Institute for Crop and Soil Science, Bundesallee 58, 38116 Braunschweig, Germany", "position": null, "roles": ["projectLeader"], "phones": [{"value": null}], "emails": [{"value": "markus.moeller@julius-kuehn.de"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": {"url": "https://orcid.org", "protocol": null, "protocol_url": "", "name": "0000-0002-1918-7747", "name_url": "", "description": "ORCID", "description_url": "", "applicationprofile": null, "applicationprofile_url": "", "function": null}}]}, {"name": "BonaRes Data Center", "organization": "Leibniz Centre for Agricultural Landscape Research (ZALF)", "position": "Research Platform 'Data Analysis & Simulation' - Workgroup Research Data Management", "roles": ["publisher"], "phones": [{"value": "+49 33432 82 300"}], "emails": [{"value": "dataservice@zalf.de"}], "addresses": [{"deliveryPoint": ["Eberswalder Strasse 84"], "city": "M\u00fcncheberg", "administrativeArea": "Brandenburg", "postalCode": "15374", "country": "Germany"}], "links": [{"href": null}]}, {"name": "Gerstmann, Henning", "organization": "Federal Agency for Nature Conservation (BfN), Alte Messe 6, 04103 Leipzig", "position": null, "roles": ["author"], "phones": [{"value": null}], "emails": [{"value": "henning.gerstmann@bfn.de"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": null}]}, {"name": "Horney, Peter", "organization": "Julius K\u00fchn Institute (JKI) \u2013 Federal Research Centre for Cultivated Plants, Institute for for Strategies and Technology Assessment, Stahnsdorfer Damm 81, 14532 Kleinmachnow, Germany", "position": null, "roles": ["author"], "phones": [{"value": null}], "emails": [{"value": "peter.horney@julius-kuehn.de"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": null}]}, {"name": "Horney, Peter", "organization": "Julius K\u00fchn Institute (JKI) \u2013 Federal Research Centre for Cultivated Plants, Institute for for Strategies and Technology Assessment, Stahnsdorfer Damm 81, 14532 Kleinmachnow, Germany", "position": null, "roles": ["dataCurator"], "phones": [{"value": null}], "emails": [{"value": "peter.horney@julius-kuehn.de"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": null}]}, {"organization": "Federal Agency for Nature Conservation (BfN), Alte Messe 6, 04103 Leipzig;Julius K\u00fchn Institute (JKI) \u2013 Federal Research Centre for Cultivated Plants, Institute for for Strategies and Technology Assessment, Stahnsdorfer Damm 81, 14532 Kleinmachnow, Germany;Julius K\u00fchn Institute (JKI) \u2013 Federal Research Centre for Cultivated Plants, Institute for Crop and Soil Science, Bundesallee 69, D-38116 Braunschweig, Germany", "roles": ["contributor"]}], "themes": [{"concepts": [{"id": "infoMapAccessService"}, {"id": "agricultural management"}], "scheme": "GEMET - INSPIRE themes, version 1.0"}, {"concepts": [{"id": "Soil"}, {"id": "agricultural practices"}, {"id": "agricultural practices"}, {"id": "crop rotation"}, {"id": "environmental monitoring"}, {"id": "multiple regression analysis"}, {"id": "plant developmental stages"}, {"id": "phenology"}], "scheme": "AGROVOC Multilingual agricultural thesaurus"}, {"concepts": [{"id": "elevation"}, {"id": "land use"}, {"id": "meteorological geographical features"}, {"id": "geographical grid systems"}], "scheme": "INSPIRE Annex 4"}]}, "links": [{"href": "https://maps.bonares.de/mapapps/resources/apps/bonares/index.html?lang=en&mid=33aa2643-5018-4e31-8b88-c2eb0a7a56f8", "rel": "information"}, {"href": "https://maps.bonares.de/wss/service/ags-relay/ags/guest/arcgis/rest/services/Extern/ID_3982_PHASE/MapServer/WMSServer?request=GetCapabilities&service=WMS"}, {"rel": "self", "type": "application/geo+json", "title": "bc466e30-b683-4859-ae4f-577cbc6505f7", "name": "item", "description": "bc466e30-b683-4859-ae4f-577cbc6505f7", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/bc466e30-b683-4859-ae4f-577cbc6505f7"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-12-12T00:00:00Z"}}, {"id": "33aa2643-5018-4e31-8b88-c2eb0a7a56f8", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[5.87, 47.27], [5.87, 55.06], [15.03, 55.06], [15.03, 47.27], [5.87, 47.27]]]}, "properties": {"themes": [{"concepts": [{"id": "farming"}], "scheme": "https://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_TopicCategoryCode"}, {"concepts": [{"id": "Soil"}, {"id": "agricultural practices"}, {"id": "crop rotation"}, {"id": "environmental monitoring"}, {"id": "multiple regression analysis"}, {"id": "plant developmental stages"}, {"id": "phenology"}], "scheme": "AGROVOC Multilingual agricultural thesaurus"}, {"concepts": [{"id": "opendata"}], "scheme": "Individual"}, {"concepts": [{"id": "Boden"}, {"id": "agricultural management"}, {"id": "crop rotation"}, {"id": "environmental monitoring"}, {"id": "interpolation"}, {"id": "seasonal variation"}, {"id": "biological development"}], "scheme": "GEMET - Concepts, version 2.4"}, {"concepts": [{"id": "elevation"}, {"id": "land use"}, {"id": "meteorological geographical features"}, {"id": "geographical grid systems"}], "scheme": "INSPIRE"}, {"concepts": [{"id": "Germany"}], "scheme": "individual"}], "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 Repository www.bonares.de. This data were created as part of the JKI's research activities.\" Although every care has been taken in preparing and testing the data, the JKI and the BonaRes Data Centre cannot guarantee that the data are correct; neither does the JKI and the BonaRes Repository 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 Repository will not be responsible for any direct or indirect use which might be made of the data.", "updated": "2023-12-13", "type": "Dataset", "created": "2022-09-30", "language": "eng", "title": "Germany-wide time series of interpolated phenological observations for main crop types between 1993 and 2021", "description": "The data set documents a Germany-wide and spatio-temporally consistent 1 \u00d7 1 km\u00b2 analysis-ready time series (ARD-TS) of interpolated DOYs (days of the year) covering 56 beginning phenological development stages (phases) of 9 main crop types for the period between 1993 and 2021. The derivation is based on Germany-wide observations of the German Weather Service (DWD), which were statistically filtered and interpolated.
\nThe German Weather Service operates a phenological observation network. About 1200 observers monitor 160 phenological phases of wild and cultivated plants. The PHASE model was developed to interpolate the phenological observations for the entire territory of Germany (Gerstmann et al. (2016) Rel.Identifer TAB 8). The model combines the concept of growing degree days (GDD) with a geostatistical interpolation procedure. The PHASE model was applied to create a Germany-wide and spatio-temporally consistent 1 \u00d7 1 km\u00b2 analysis-ready time series (ARD-TS) of interpolated DOYs (days of the year) covering 56 beginning phenological development stages (phases) of 9 main crop types for the period between 1993 and 2021. The dataset includes the following information:
\n\n\u2022\tGermany-wide interpolated temperature data from the German Weather Service (DWD),
\n\u2022\traster datasets of interpolated crop-specific and Germany-wide incipient phenological development stages for the period between 1993 and 2020. The value in each pixel of these rasters represents the Day Of the Year (DOY) of the respective beginning phenological plant development stage, \n
\n\u2022\taccuracy metrics (RMSE, MSE, MAE, and R\u00b2) for each Germany-wide interpolation result.
The code of the phase model is documented in a software repository (Rel.Identifer TAB 9 and 10). The temporally static model input data are also stored there.
\nResearch question
\nThe dataset allows the spatio-temporal definition of phenological windows for any available year and user-defined region (M\u00f6ller et al. (2020) Rel.Identifer TAB 1). Such information is important for various agricultural issues such as the derivation of weather or biodiversity indices, crop classification, soil erosion or crop yield modeling (Bucheli et al. (2022) Rel.Identifer TAB 5; Gerstmann et al. (2018) ; Rel.Identifer TAB 3; M\u00f6ller et al. (2017, 2018) ; Rel.Identifer TAB 2 and 4; Riedsel et al. (2022) ; Rel.Identifer TAB 6).