{"type": "FeatureCollection", "features": [{"id": "10.1016/j.agrformet.2018.04.010", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:15:44Z", "type": "Journal Article", "created": "2018-04-19", "title": "A phenomenological model of soil evaporative efficiency using surface soil moisture and temperature data", "description": "Abstract Modeling soil evaporation has been a notorious challenge due to the complexity of the phenomenon and the lack of data to constrain it. In this context, a parsimonious model is developed to estimate soil evaporative efficiency (SEE) defined as the ratio of actual to potential soil evaporation. It uses a soil resistance driven by surface (0\u20135\u202fcm) soil moisture, meteorological forcing and time (hour) of day, and has the capability to be calibrated using the radiometric surface temperature derived from remotely sensed thermal data. The new approach is tested over a rainfed semi-arid site, which had been under bare soil conditions during a 9-month period in 2016. Three calibration strategies are adopted based on SEE time series derived from (1) eddy-covariance measurements, (2) thermal measurements, and (3) eddy-covariance measurements used only over separate drying periods between significant rainfall events. The correlation coefficients (and slopes of the linear regression) between simulated and observed (eddy-covariance-derived) SEE are 0.85, 0.86 and 0.87 (and 0.91, 0.87 and 0.91) for calibration strategies 1, 2 and 3, respectively. Moreover, the correlation coefficient (and slope of the linear regression) between simulated and observed SEE is improved from 0.80 to 0.85 (from 0.86 to 0.91) when including hour of day in the soil resistance. The reason is that, under non-energy-limited conditions, the receding evaporation front during daytime makes SEE decrease at the hourly time scale. The soil resistance formulation can be integrated into state-of-the-art dual-source surface models and has calibration capabilities across a range of spatial scales from spaceborne microwave and thermal data.", "keywords": ["550", "0207 environmental engineering", "Soil resistance", "02 engineering and technology", "Remote sensing", "15. Life on land", "calibration", "surface temperature", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "Surface temperature", "remote sensing", "Calibration", "[SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology", "soil resistance", "Soil moisture", "[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "soil moisture", "environment", "Soil evaporation"]}, "links": [{"href": "https://doi.org/10.1016/j.agrformet.2018.04.010"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agricultural%20and%20Forest%20Meteorology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agrformet.2018.04.010", "name": "item", "description": "10.1016/j.agrformet.2018.04.010", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agrformet.2018.04.010"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-06-01T00:00:00Z"}}, {"id": "10.1016/j.renene.2021.02.003", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:17:01Z", "type": "Journal Article", "created": "2020-11-05", "title": "Virtual fatigue diagnostics of wake-affected wind turbine via Gaussian Process Regression", "description": "

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.

", "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": "10.1002/hyp.14966", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:14:09Z", "type": "Journal Article", "created": "2023-09-15", "title": "Testing CASE: A new event\u2010based Morgan\u2010Morgan\u2010Finney\u2010type erosion model for different rainfall experimental scenarios", "description": "Abstract

Every application of soil erosion models brings the need of proper parameterisation, that is, finding physically or conceptually plausible parameter values that allow a model to reproduce measured values. No universal approach for model parameterisation, calibration and validation exists, as it depends on the model, spatial and temporal resolution and the nature of the datasets used. We explored some existing options for parameterisation, calibration and validation for erosion modelling exemplary with a specific dataset and modelling approach. A new Morgan\uffe2\uff80\uff90Morgan\uffe2\uff80\uff90Finney (MMF)\uffe2\uff80\uff90type model was developed, representing a balanced position between physically\uffe2\uff80\uff90based and empirical modelling approaches. The resulting model termed \uffe2\uff80\uff98calculator for soil erosion\uffe2\uff80\uff99 (CASE), works in a spatially distributed way on the timescale of individual rainfall events. A dataset of 142 high\uffe2\uff80\uff90intensity rainfall experiments in Central Europe (AT, HU, IT, CZ), covering various slopes, soil types and experimental designs was used for calibration and validation with a modified Monte\uffe2\uff80\uff90Carlo approach. Subsequently, model parameter values were compared to parameter values obtained by alternative methods (measurements, pedotransfer functions, literature data). The model reproduced runoff and soil loss of the dataset in the validation setting with R2adj of 0.89 and 0.76, respectively. Satisfactory agreement for the water phase was found, with calibrated saturated hydraulic conductivity (ksat) values falling within the interquartile range of ksat predicted with 14 different pedotransfer functions, or being within one order of magnitude. The chosen approach also well reflected specific experimental setups contained in the dataset dealing with the effects of consecutive rainfall and different soil water conditions. For the sediment phase of the tested model agreement between calibrated cohesion, literature values and field measurements were only partially in line. The methods we explored may specifically be interesting for use with other MMF\uffe2\uff80\uff90type models, or with similar datasets.Our understanding of plant-microbe interactions in soil is limited by the difficulty of observing processes at the microscopic scale throughout plants\uffe2\uff80\uff99 large volume of influence. Here, we present the development of 3D live microscopy for resolving plant-microbe interactions across the environment of an entire seedling growing in a transparent soil in tailor-made mesocosms, maintaining physical conditions for the culture of both plants and microorganisms. A tailor made dual-illumination light-sheet system acquired scattering signals from the plant whilst fluorescence signals were captured from transparent soil particles and labelled microorganisms, allowing the generation of quantitative data on samples approximately 3600 mm3in size with as good as 5 \uffce\uffbcm resolution at a rate of up to one scan every 30 minutes. The system tracked the movement ofBacillus subtilispopulations in the rhizosphere of lettuce plants in real time, revealing previously unseen patterns of activity. Motile bacteria favoured small pore spaces over the surface of soil particles, colonising the root in a pulsatile manner. Migrations appeared to be directed towards the root cap, the point \uffe2\uff80\uff9cfirst contact\uffe2\uff80\uff9d, before subsequent colonisation of mature epidermis cells. Our findings show that microscopes dedicated to live environmental studies present an invaluable tool to understand plant-microbe interactions.Tomato is an important commercial product which is perishable by nature and highly susceptible to fungal incidence once it is harvested. Not all tomatoes are equally vulnerable to pathogenic fungi, and an early detection of the vulnerable ones can help in taking timely preventive actions, ranging from isolating tomato batches to adjusting storage conditions, but also in making right business decisions like dynamic pricing based on quality or better shelf life estimate. More importantly, early detection of vulnerable produce can help in taking timely actions to minimize potential post-harvest losses. This paper investigates Near-infrared (NIR) hyperspectral imaging (1000\uffe2\uff80\uff931700\uffc2\uffa0nm) and machine learning to build models to automatically predict the susceptibility of sepals of recently harvested tomatoes to future fungal infections. Hyperspectral images of newly harvested tomatoes (cultivar Brioso) from 5 different growers were acquired before the onset of any visible fungal infection. After imaging, the tomatoes were placed under controlled conditions suited for fungal germination and growth for a 4-day period, and then imaged using normal color cameras. All sepals in the color images were ranked for fungal severity using crowdsourcing, and the final severity of each sepal was fused using principal component analysis. A novel hyperspectral data processing pipeline is presented which was used to automatically segment the tomato sepals from spectral images with multiple tomatoes connected via a truss. The key modelling question addressed in this research is whether there is a correlation between the hyperspectral data captured at harvest and the fungal infection observed 4 days later. Using 10-fold and group k-fold cross-validation, XG-Boost and Random Forest based regression models were trained on the features derived from the hyperspectral data corresponding to each sepal in the training set and tested on hold out test set. The best model found a Pearson correlation of 0.837, showing that there is strong linear correlation between the NIR spectra and the future fungal severity of the sepal. The sepal specific predictions were aggregated to predict the susceptibility of individual tomatoes, and a correlation of 0.92 was found. Besides modelling, focus is also on model interpretation, particularly to understand which spectral features are most relevant to model prediction. Two approaches to model interpretation were explored, feature importance and SHAP (SHapley Additive exPlanations), resulting in similar conclusions that the NIR range between 1390\uffe2\uff80\uff931420\uffc2\uffa0nm contributes most to the model\uffe2\uff80\uff99s final decision.Our understanding of plant-microbe interactions in soil is limited by the difficulty of observing processes at the microscopic scale throughout plants\uffe2\uff80\uff99 large volume of influence. Here, we present the development of 3D live microscopy for resolving plant-microbe interactions across the environment of an entire seedling growing in a transparent soil in tailor-made mesocosms, maintaining physical conditions for the culture of both plants and microorganisms. A tailor made dual-illumination light-sheet system acquired scattering signals from the plant whilst fluorescence signals were captured from transparent soil particles and labelled microorganisms, allowing the generation of quantitative data on samples approximately 3600 mm3in size with as good as 5 \uffce\uffbcm resolution at a rate of up to one scan every 30 minutes. The system tracked the movement ofBacillus subtilispopulations in the rhizosphere of lettuce plants in real time, revealing previously unseen patterns of activity. Motile bacteria favoured small pore spaces over the surface of soil particles, colonising the root in a pulsatile manner. Migrations appeared to be directed towards the root cap, the point \uffe2\uff80\uff9cfirst contact\uffe2\uff80\uff9d, before subsequent colonisation of mature epidermis cells. Our findings show that microscopes dedicated to live environmental studies present an invaluable tool to understand plant-microbe interactions.

", "keywords": ["0301 basic medicine", "570", "Microscopy", "Silicon", "0303 health sciences", "Temperature", "root-microbe interactions", "Equipment Design", "Biological Sciences", "Environment", "15. Life on land", "Plant Roots", "630", "Fluorescence", "Soil", "03 medical and health sciences", "Seedlings", "Calibration", "Rhizosphere", "Image Processing", " Computer-Assisted", "environmental imaging", "rhizosphere", "Soil Microbiology", "Bacillus subtilis", "Lactuca"]}, "links": [{"href": "https://eprints.whiterose.ac.uk/178939/18/e2109176118.full.pdf"}, {"href": "https://pnas.org/doi/pdf/10.1073/pnas.2109176118"}, {"href": "https://doi.org/10.1073/pnas.2109176118"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Proceedings%20of%20the%20National%20Academy%20of%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1073/pnas.2109176118", "name": "item", "description": "10.1073/pnas.2109176118", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1073/pnas.2109176118"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-13T00:00:00Z"}}, {"id": "10.1088/1748-9326/abb62d", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:18:54Z", "type": "Journal Article", "created": "2020-09-08", "title": "Overlooked organic vapor emissions from thawing Arctic permafrost", "description": "Abstract

Volatile organic compounds (VOCs) play an essential role in climate change and air pollution by modulating tropospheric oxidation capacity and providing precursors for ozone and aerosol formation. Arctic permafrost buries large quantities of frozen soil carbon, which could be released as VOCs with permafrost thawing or collapsing as a consequence of global warming. However, due to the lack of reported studies in this field and the limited capability of the conventional measurement techniques, it is poorly understood how much VOCs could be emitted from thawing permafrost and the chemical speciation of the released VOCs. Here we apply a Vocus proton-transfer-reaction time-of-flight mass spectrometer (PTR-TOF) in laboratory incubations for the first time to examine the release of VOCs from thawing permafrost peatland soils sampled from Finnish Lapland. The warming-induced rapid VOC emissions from the thawing soils were mainly attributed to the direct release of old, trapped gases from the permafrost. The average VOC fluxes from thawing permafrost were four times as high as those from the active layer (the top layer of soil in permafrost terrain). The emissions of less volatile compounds, i.e. sesquiterpenes and diterpenes, increased substantially with rising temperatures. Results in this study demonstrate the potential for substantive VOC releases from thawing permafrost. We anticipate that future global warming could stimulate VOC emissions from the Arctic permafrost, which may significantly influence the Arctic atmospheric chemistry and climate change.Soil spectroscopy is a widely used method for estimating soil properties that are important to environmental and agricultural monitoring. However, a bottleneck to its more widespread adoption is the need for establishing large reference datasets for training machine learning (ML) models, which are called soil spectral libraries (SSLs). Similarly, the prediction capacity of new samples is also subject to the number and diversity of soil types and conditions represented in the SSLs. To help bridge this gap and enable hundreds of stakeholders to collect more affordable soil data by leveraging a centralized open resource, the Soil Spectroscopy for Global Good has created the Open Soil Spectral Library (OSSL). In this paper, we describe the procedures for collecting and harmonizing several SSLs that are incorporated into the OSSL, followed by exploratory analysis and predictive modeling. The results of 10-fold cross-validation with refitting show that, in general, mid-infrared (MIR)-based models are significantly more accurate than visible and near-infrared (VisNIR) or near-infrared (NIR) models. From independent model evaluation, we found that Cubist comes out as the best-performing ML algorithm for the calibration and delivery of reliable outputs (prediction uncertainty and representation flag). Although many soil properties are well predicted, total sulfur, extractable sodium, and electrical conductivity performed poorly in all spectral regions, with some other extractable nutrients and physical soil properties also performing poorly in one or two spectral regions (VisNIR or Neospectra NIR). Hence, the use of predictive models based solely on spectral variations has limitations. This study also presents and discusses several other open resources that were developed from the OSSL, aspects of opening data, current limitations, and future development. With this genuinely open science project, we hope that OSSL becomes the driver of the soil spectroscopy community to accelerate the pace of scientific discovery and innovation.Worldwide climate change will most likely lead to drastic changes in hydrology and food production. In this study, the impact of climate change on the hydrological regime and the fate of pesticides in the Guayas River basin is investigated using the Soil and Water Assessment Tool. Four general circulation models and three representative concentration pathways (RCP 4.5, RCP 6.0 and RCP 8.5) for three future periods were used to assess impact of climate change. Future projections showed a maximum increase in the average monthly precipitation of 40% in June, as well as an increase in an average minimum temperature of 3.85\uffc2\uffb0C for July and an average maximum temperature of 4.5\uffc2\uffb0C for August in 2080s. The model simulations based on RCP 8.5 scenario predict an increase in potential evapotranspiration by 11%, surface runoff of 39% and water yield of 33% in 2080s. The pesticide simulation showed the highest water concentrations during the wet season. Projections of trends in pesticide concentration indicate a similar trend to the current situation given the application rate remains the same. The results can be beneficial for the management and planning of the basin to mitigate flood and water quality-related impacts of food production and climate change.

In this paper, the possibility of detecting polymers in plastic mixtures and extruded blends has been investigated. Pyrolysis\u2013gas chromatography/mass spectrometry (py-GC/MS) allows researchers to identify multicomponent mixtures and low amounts of polymers without high spatial resolution, background noise and constituents mix interfering, as with molecular spectrometry techniques normally used for this purpose, such as Fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy and differential scanning calorimetry (DSC). In total, 15 solid mixtures of low-density polyethylene (LDPE), polypropylene (PP), polystyrene (PS), polyamide (PA) and polycarbonate (PC) in various combinations have been qualitatively analyzed after choosing their characteristic pyrolysis products and each polymer has been detected in every mix; thus, in extruded blends of high-density polyethylene (HDPE), PP and PS had varying weight percentages of the individual constituents ranging from 10 up to 90. Moreover, quantitative analysis of these polymers has been achieved in every blend with a trend that can be considered linear with coefficients of determination higher than 0.9, even though the limits of quantification are lower with respect to the ones reported in the literature, probably due to the extrusion process.

", "keywords": ["blends; calibration curves; polymers; py-GC/MS", "01 natural sciences", "Article", "0104 chemical sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://iris.polito.it/bitstream/11583/2984964/1/Identification_of_Plastics_in_Mixtures_and_Blends_.pdf"}, {"href": "https://doi.org/10.3390/polym16010071"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Polymers", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/polym16010071", "name": "item", "description": "10.3390/polym16010071", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/polym16010071"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-12-26T00:00:00Z"}}, {"id": "10.3390/w13162238", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:22:10Z", "type": "Journal Article", "created": "2021-08-18", "title": "Multi-Step Calibration Approach for SWAT Model Using Soil Moisture and Crop Yields in a Small Agricultural Catchment", "description": "

The quantitative prediction of hydrological components through hydrological models could serve as a basis for developing better land and water management policies. This study provides a comprehensive step by step modelling approach for a small agricultural watershed using the SWAT model. The watershed is situated in Petzenkirchen in the western part of Lower Austria and has total area of 66 hectares. At present, 87% of the catchment area is arable land, 5% is used as pasture, 6% is forested and 2% is paved. The calibration approach involves a sequential calibration of the model starting from surface runoff, and groundwater flow, followed by crop yields and then soil moisture, and finally total streamflow and sediment yields. Calibration and validation are carried out using the r-package SWATplusR. The impact of each calibration step on sediment yields and total streamflow is evaluated. The results of this approach are compared with those of the conventional model calibration approach, where all the parameters governing various hydrological processes are calibrated simultaneously. Results showed that the model was capable of successfully predicting surface runoff, groundwater flow, soil profile water content, total streamflow and sediment yields with Nash-Sutcliffe efficiency (NSE) of greater than 0.75. Crop yields were also well simulated with a percent bias (PBIAS) ranging from \u221217% to 14%. Surface runoff calibration had the highest impact on streamflow output, improving NSE from 0.39 to 0.77. The step-wise calibration approach performed better for streamflow prediction than the simultaneous calibration approach. The results of this study show that the step-wise calibration approach is more accurate, and provides a better representation of different hydrological components and processes than the simultaneous calibration approach.

", "keywords": ["Step-wise calibration", "2. Zero hunger", "step-wise calibration", "Crop yields", "soil erosion model", "Sequential calibration", "Sediment yield", "0207 environmental engineering", "HOAL", "crop yields", "Streamflow", "SWATplusR", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "sediment yield", "6. Clean water", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "SWAT", "Soil erosion model", "streamflow", "Soil moisture", "soil moisture", "sequential calibration"]}, "links": [{"href": "http://www.mdpi.com/2073-4441/13/16/2238/pdf"}, {"href": "https://www.mdpi.com/2073-4441/13/16/2238/pdf"}, {"href": "https://doi.org/10.3390/w13162238"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Water", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/w13162238", "name": "item", "description": "10.3390/w13162238", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/w13162238"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-08-17T00:00:00Z"}}, {"id": "10.3390/w11091918", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:22:10Z", "type": "Journal Article", "created": "2019-09-16", "title": "Modeling Sugar Beet Responses to Irrigation with AquaCrop for Optimizing Water Allocation", "description": "

Process-based crop models such as AquaCrop are useful for a variety of applications but must be accurately calibrated and validated. Sugar beet is an important crop that is grown in regions under water scarcity. The discrepancies and uncertainty in past published calibrations, together with important modifications in the program, deemed it necessary to conduct a study aimed at the calibration of AquaCrop (version 6.1) using the results of a single deficit irrigation experiment. The model was validated with additional data from eight farms differing in location, years, varieties, sowing dates, and irrigation. The overall performance of AquaCrop for simulating canopy cover, biomass, and final yield was accurate (RMSE = 11.39%, 2.10 t ha\u22121, and 0.85 t ha\u22121, respectively). Once the model was properly calibrated and validated, a scenario analysis was carried out to assess the crop response in terms of yield and water productivity to different irrigation water allocations in the two main production areas of sugar beet in Spain (spring and autumn sowing). The results highlighted the potential of the model by showing the important impact of irrigation water allocation and sowing time on sugar beet production and its irrigation water productivity.

", "keywords": ["2. Zero hunger", "Water productivity", "Sugar beet", "sugar beet", "04 agricultural and veterinary sciences", "15. Life on land", "calibration", "irrigation water allocation", "Modelling", "AquaCrop", "6. Clean water", "Irrigation water allocation", "modelling", "Calibration", "water productivity", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "http://www.mdpi.com/2073-4441/11/9/1918/pdf"}, {"href": "https://www.mdpi.com/2073-4441/11/9/1918/pdf"}, {"href": "https://doi.org/10.3390/w11091918"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Water", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/w11091918", "name": "item", "description": "10.3390/w11091918", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/w11091918"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-09-14T00:00:00Z"}}, {"id": "10261/253007", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:25:56Z", "type": "Journal Article", "created": "2021-08-17", "title": "Multi-Step Calibration Approach for SWAT Model Using Soil Moisture and Crop Yields in a Small Agricultural Catchment", "description": "

The quantitative prediction of hydrological components through hydrological models could serve as a basis for developing better land and water management policies. This study provides a comprehensive step by step modelling approach for a small agricultural watershed using the SWAT model. The watershed is situated in Petzenkirchen in the western part of Lower Austria and has total area of 66 hectares. At present, 87% of the catchment area is arable land, 5% is used as pasture, 6% is forested and 2% is paved. The calibration approach involves a sequential calibration of the model starting from surface runoff, and groundwater flow, followed by crop yields and then soil moisture, and finally total streamflow and sediment yields. Calibration and validation are carried out using the r-package SWATplusR. The impact of each calibration step on sediment yields and total streamflow is evaluated. The results of this approach are compared with those of the conventional model calibration approach, where all the parameters governing various hydrological processes are calibrated simultaneously. Results showed that the model was capable of successfully predicting surface runoff, groundwater flow, soil profile water content, total streamflow and sediment yields with Nash-Sutcliffe efficiency (NSE) of greater than 0.75. Crop yields were also well simulated with a percent bias (PBIAS) ranging from \u221217% to 14%. Surface runoff calibration had the highest impact on streamflow output, improving NSE from 0.39 to 0.77. The step-wise calibration approach performed better for streamflow prediction than the simultaneous calibration approach. The results of this study show that the step-wise calibration approach is more accurate, and provides a better representation of different hydrological components and processes than the simultaneous calibration approach.

", "keywords": ["Step-wise calibration", "2. Zero hunger", "step-wise calibration", "Crop yields", "soil erosion model", "Sequential calibration", "Sediment yield", "0207 environmental engineering", "HOAL", "crop yields", "Streamflow", "SWATplusR", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "sediment yield", "6. Clean water", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "SWAT", "Soil erosion model", "streamflow", "Soil moisture", "soil moisture", "sequential calibration"]}, "links": [{"href": "http://www.mdpi.com/2073-4441/13/16/2238/pdf"}, {"href": "https://www.mdpi.com/2073-4441/13/16/2238/pdf"}, {"href": "https://doi.org/10261/253007"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Water", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10261/253007", "name": "item", "description": "10261/253007", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10261/253007"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-08-17T00:00:00Z"}}, {"id": "11583/2984964", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:26:22Z", "type": "Journal Article", "created": "2023-12-26", "title": "Identification of Plastics in Mixtures and Blends through Pyrolysis-Gas Chromatography/Mass Spectrometry", "description": "

In this paper, the possibility of detecting polymers in plastic mixtures and extruded blends has been investigated. Pyrolysis\u2013gas chromatography/mass spectrometry (py-GC/MS) allows researchers to identify multicomponent mixtures and low amounts of polymers without high spatial resolution, background noise and constituents mix interfering, as with molecular spectrometry techniques normally used for this purpose, such as Fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy and differential scanning calorimetry (DSC). In total, 15 solid mixtures of low-density polyethylene (LDPE), polypropylene (PP), polystyrene (PS), polyamide (PA) and polycarbonate (PC) in various combinations have been qualitatively analyzed after choosing their characteristic pyrolysis products and each polymer has been detected in every mix; thus, in extruded blends of high-density polyethylene (HDPE), PP and PS had varying weight percentages of the individual constituents ranging from 10 up to 90. Moreover, quantitative analysis of these polymers has been achieved in every blend with a trend that can be considered linear with coefficients of determination higher than 0.9, even though the limits of quantification are lower with respect to the ones reported in the literature, probably due to the extrusion process.

", "keywords": ["blends; calibration curves; polymers; py-GC/MS", "01 natural sciences", "Article", "0104 chemical sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://iris.polito.it/bitstream/11583/2984964/1/Identification_of_Plastics_in_Mixtures_and_Blends_.pdf"}, {"href": "https://doi.org/11583/2984964"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Polymers", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "11583/2984964", "name": "item", "description": "11583/2984964", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11583/2984964"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-12-26T00:00:00Z"}}, {"id": "10.5281/zenodo.832877", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:25:00Z", "type": "Dataset", "title": "Observed monthly N2O emission dataset used for model carlibaration and validation", "description": "The N2O emission dataset for calibration sites was extracted from the published figures and tables using GetData Graph Digitizer version 2.24; the other information such as biome, geographic location, experimental period, soil organic carbon content, soil pH and soil texture was selected from corresponding literature. If the data related to soil was not avaliable, we extracted them from the soil database(IGBP-DIS)", "keywords": ["13. Climate action", "15. Life on land", "N2O emissions; model calibration; model validation"], "contacts": [{"organization": "Kerou Zhang, Changhui Peng", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.832877"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.832877", "name": "item", "description": "10.5281/zenodo.832877", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.832877"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-06-03T00:00:00Z"}}, {"id": "3130873339", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:27:41Z", "type": "Journal Article", "created": "2021-02-13", "title": "Plant-environment microscopy tracks interactions of Bacillus subtilis with plant roots across the entire rhizosphere", "description": "Abstract

Our understanding of plant-microbe interactions in soil is limited by the difficulty of observing processes at the microscopic scale throughout plants\uffe2\uff80\uff99 large volume of influence. Here, we present the development of 3D live microscopy for resolving plant-microbe interactions across the environment of an entire seedling growing in a transparent soil in tailor-made mesocosms, maintaining physical conditions for the culture of both plants and microorganisms. A tailor made dual-illumination light-sheet system acquired scattering signals from the plant whilst fluorescence signals were captured from transparent soil particles and labelled microorganisms, allowing the generation of quantitative data on samples approximately 3600 mm3in size with as good as 5 \uffce\uffbcm resolution at a rate of up to one scan every 30 minutes. The system tracked the movement ofBacillus subtilispopulations in the rhizosphere of lettuce plants in real time, revealing previously unseen patterns of activity. Motile bacteria favoured small pore spaces over the surface of soil particles, colonising the root in a pulsatile manner. Migrations appeared to be directed towards the root cap, the point \uffe2\uff80\uff9cfirst contact\uffe2\uff80\uff9d, before subsequent colonisation of mature epidermis cells. Our findings show that microscopes dedicated to live environmental studies present an invaluable tool to understand plant-microbe interactions.Volatile organic compounds (VOCs) play an essential role in climate change and air pollution by modulating tropospheric oxidation capacity and providing precursors for ozone and aerosol formation. Arctic permafrost buries large quantities of frozen soil carbon, which could be released as VOCs with permafrost thawing or collapsing as a consequence of global warming. However, due to the lack of reported studies in this field and the limited capability of the conventional measurement techniques, it is poorly understood how much VOCs could be emitted from thawing permafrost and the chemical speciation of the released VOCs. Here we apply a Vocus proton-transfer-reaction time-of-flight mass spectrometer (PTR-TOF) in laboratory incubations for the first time to examine the release of VOCs from thawing permafrost peatland soils sampled from Finnish Lapland. The warming-induced rapid VOC emissions from the thawing soils were mainly attributed to the direct release of old, trapped gases from the permafrost. The average VOC fluxes from thawing permafrost were four times as high as those from the active layer (the top layer of soil in permafrost terrain). The emissions of less volatile compounds, i.e. sesquiterpenes and diterpenes, increased substantially with rising temperatures. Results in this study demonstrate the potential for substantive VOC releases from thawing permafrost. We anticipate that future global warming could stimulate VOC emissions from the Arctic permafrost, which may significantly influence the Arctic atmospheric chemistry and climate change.

Process-based crop models such as AquaCrop are useful for a variety of applications but must be accurately calibrated and validated. Sugar beet is an important crop that is grown in regions under water scarcity. The discrepancies and uncertainty in past published calibrations, together with important modifications in the program, deemed it necessary to conduct a study aimed at the calibration of AquaCrop (version 6.1) using the results of a single deficit irrigation experiment. The model was validated with additional data from eight farms differing in location, years, varieties, sowing dates, and irrigation. The overall performance of AquaCrop for simulating canopy cover, biomass, and final yield was accurate (RMSE = 11.39%, 2.10 t ha\u22121, and 0.85 t ha\u22121, respectively). Once the model was properly calibrated and validated, a scenario analysis was carried out to assess the crop response in terms of yield and water productivity to different irrigation water allocations in the two main production areas of sugar beet in Spain (spring and autumn sowing). The results highlighted the potential of the model by showing the important impact of irrigation water allocation and sowing time on sugar beet production and its irrigation water productivity.

", "keywords": ["2. Zero hunger", "Water productivity", "Sugar beet", "sugar beet", "04 agricultural and veterinary sciences", "15. Life on land", "calibration", "irrigation water allocation", "Modelling", "AquaCrop", "6. Clean water", "Irrigation water allocation", "modelling", "Calibration", "water productivity", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "http://www.mdpi.com/2073-4441/11/9/1918/pdf"}, {"href": "https://www.mdpi.com/2073-4441/11/9/1918/pdf"}, {"href": "https://doi.org/10261/205841"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Water", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10261/205841", "name": "item", "description": "10261/205841", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10261/205841"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-09-14T00:00:00Z"}}, {"id": "10396/18990", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:26:04Z", "type": "Journal Article", "created": "2019-09-16", "title": "Modeling Sugar Beet Responses to Irrigation with AquaCrop for Optimizing Water Allocation", "description": "

Process-based crop models such as AquaCrop are useful for a variety of applications but must be accurately calibrated and validated. Sugar beet is an important crop that is grown in regions under water scarcity. The discrepancies and uncertainty in past published calibrations, together with important modifications in the program, deemed it necessary to conduct a study aimed at the calibration of AquaCrop (version 6.1) using the results of a single deficit irrigation experiment. The model was validated with additional data from eight farms differing in location, years, varieties, sowing dates, and irrigation. The overall performance of AquaCrop for simulating canopy cover, biomass, and final yield was accurate (RMSE = 11.39%, 2.10 t ha\u22121, and 0.85 t ha\u22121, respectively). Once the model was properly calibrated and validated, a scenario analysis was carried out to assess the crop response in terms of yield and water productivity to different irrigation water allocations in the two main production areas of sugar beet in Spain (spring and autumn sowing). The results highlighted the potential of the model by showing the important impact of irrigation water allocation and sowing time on sugar beet production and its irrigation water productivity.

", "keywords": ["2. Zero hunger", "Water productivity", "Sugar beet", "sugar beet", "04 agricultural and veterinary sciences", "15. Life on land", "calibration", "irrigation water allocation", "Modelling", "AquaCrop", "6. Clean water", "Irrigation water allocation", "modelling", "Calibration", "water productivity", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "http://www.mdpi.com/2073-4441/11/9/1918/pdf"}, {"href": "https://www.mdpi.com/2073-4441/11/9/1918/pdf"}, {"href": "https://doi.org/10396/18990"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Water", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10396/18990", "name": "item", "description": "10396/18990", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10396/18990"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-09-14T00:00:00Z"}}, {"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-01J9NQCTA3B39X0MAC0P804GF5", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:26:33Z", "type": "Journal Article", "created": "2024-09-20", "title": "Model-based analysis of the impact of climate change on hydrology in the Guayas River basin (Ecuador)", "description": "ABSTRACT

Worldwide climate change will most likely lead to drastic changes in hydrology and food production. In this study, the impact of climate change on the hydrological regime and the fate of pesticides in the Guayas River basin is investigated using the Soil and Water Assessment Tool. Four general circulation models and three representative concentration pathways (RCP 4.5, RCP 6.0 and RCP 8.5) for three future periods were used to assess impact of climate change. Future projections showed a maximum increase in the average monthly precipitation of 40% in June, as well as an increase in an average minimum temperature of 3.85\uffc2\uffb0C for July and an average maximum temperature of 4.5\uffc2\uffb0C for August in 2080s. The model simulations based on RCP 8.5 scenario predict an increase in potential evapotranspiration by 11%, surface runoff of 39% and water yield of 33% in 2080s. The pesticide simulation showed the highest water concentrations during the wet season. Projections of trends in pesticide concentration indicate a similar trend to the current situation given the application rate remains the same. The results can be beneficial for the management and planning of the basin to mitigate flood and water quality-related impacts of food production and climate change.Every application of soil erosion models brings the need of proper parameterisation, that is, finding physically or conceptually plausible parameter values that allow a model to reproduce measured values. No universal approach for model parameterisation, calibration and validation exists, as it depends on the model, spatial and temporal resolution and the nature of the datasets used. We explored some existing options for parameterisation, calibration and validation for erosion modelling exemplary with a specific dataset and modelling approach. A new Morgan\uffe2\uff80\uff90Morgan\uffe2\uff80\uff90Finney (MMF)\uffe2\uff80\uff90type model was developed, representing a balanced position between physically\uffe2\uff80\uff90based and empirical modelling approaches. The resulting model termed \uffe2\uff80\uff98calculator for soil erosion\uffe2\uff80\uff99 (CASE), works in a spatially distributed way on the timescale of individual rainfall events. A dataset of 142 high\uffe2\uff80\uff90intensity rainfall experiments in Central Europe (AT, HU, IT, CZ), covering various slopes, soil types and experimental designs was used for calibration and validation with a modified Monte\uffe2\uff80\uff90Carlo approach. Subsequently, model parameter values were compared to parameter values obtained by alternative methods (measurements, pedotransfer functions, literature data). The model reproduced runoff and soil loss of the dataset in the validation setting with R2adj of 0.89 and 0.76, respectively. Satisfactory agreement for the water phase was found, with calibrated saturated hydraulic conductivity (ksat) values falling within the interquartile range of ksat predicted with 14 different pedotransfer functions, or being within one order of magnitude. The chosen approach also well reflected specific experimental setups contained in the dataset dealing with the effects of consecutive rainfall and different soil water conditions. For the sediment phase of the tested model agreement between calibrated cohesion, literature values and field measurements were only partially in line. The methods we explored may specifically be interesting for use with other MMF\uffe2\uff80\uff90type models, or with similar datasets.

Process-based crop models such as AquaCrop are useful for a variety of applications but must be accurately calibrated and validated. Sugar beet is an important crop that is grown in regions under water scarcity. The discrepancies and uncertainty in past published calibrations, together with important modifications in the program, deemed it necessary to conduct a study aimed at the calibration of AquaCrop (version 6.1) using the results of a single deficit irrigation experiment. The model was validated with additional data from eight farms differing in location, years, varieties, sowing dates, and irrigation. The overall performance of AquaCrop for simulating canopy cover, biomass, and final yield was accurate (RMSE = 11.39%, 2.10 t ha\u22121, and 0.85 t ha\u22121, respectively). Once the model was properly calibrated and validated, a scenario analysis was carried out to assess the crop response in terms of yield and water productivity to different irrigation water allocations in the two main production areas of sugar beet in Spain (spring and autumn sowing). The results highlighted the potential of the model by showing the important impact of irrigation water allocation and sowing time on sugar beet production and its irrigation water productivity.

", "keywords": ["2. Zero hunger", "Water productivity", "Sugar beet", "sugar beet", "04 agricultural and veterinary sciences", "15. Life on land", "calibration", "irrigation water allocation", "Modelling", "AquaCrop", "6. Clean water", "Irrigation water allocation", "modelling", "Calibration", "water productivity", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "http://www.mdpi.com/2073-4441/11/9/1918/pdf"}, {"href": "https://www.mdpi.com/2073-4441/11/9/1918/pdf"}, {"href": "https://doi.org/2972466247"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Water", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2972466247", "name": "item", "description": "2972466247", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2972466247"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-09-14T00:00:00Z"}}, {"id": "3195029335", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:27:47Z", "type": "Journal Article", "created": "2021-08-18", "title": "Multi-Step Calibration Approach for SWAT Model Using Soil Moisture and Crop Yields in a Small Agricultural Catchment", "description": "

The quantitative prediction of hydrological components through hydrological models could serve as a basis for developing better land and water management policies. This study provides a comprehensive step by step modelling approach for a small agricultural watershed using the SWAT model. The watershed is situated in Petzenkirchen in the western part of Lower Austria and has total area of 66 hectares. At present, 87% of the catchment area is arable land, 5% is used as pasture, 6% is forested and 2% is paved. The calibration approach involves a sequential calibration of the model starting from surface runoff, and groundwater flow, followed by crop yields and then soil moisture, and finally total streamflow and sediment yields. Calibration and validation are carried out using the r-package SWATplusR. The impact of each calibration step on sediment yields and total streamflow is evaluated. The results of this approach are compared with those of the conventional model calibration approach, where all the parameters governing various hydrological processes are calibrated simultaneously. Results showed that the model was capable of successfully predicting surface runoff, groundwater flow, soil profile water content, total streamflow and sediment yields with Nash-Sutcliffe efficiency (NSE) of greater than 0.75. Crop yields were also well simulated with a percent bias (PBIAS) ranging from \u221217% to 14%. Surface runoff calibration had the highest impact on streamflow output, improving NSE from 0.39 to 0.77. The step-wise calibration approach performed better for streamflow prediction than the simultaneous calibration approach. The results of this study show that the step-wise calibration approach is more accurate, and provides a better representation of different hydrological components and processes than the simultaneous calibration approach.

", "keywords": ["Step-wise calibration", "2. Zero hunger", "step-wise calibration", "Crop yields", "soil erosion model", "Sequential calibration", "Sediment yield", "0207 environmental engineering", "HOAL", "crop yields", "Streamflow", "SWATplusR", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "sediment yield", "6. Clean water", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "SWAT", "Soil erosion model", "streamflow", "Soil moisture", "soil moisture", "sequential calibration"]}, "links": [{"href": "http://www.mdpi.com/2073-4441/13/16/2238/pdf"}, {"href": "https://www.mdpi.com/2073-4441/13/16/2238/pdf"}, {"href": "https://doi.org/3195029335"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Water", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3195029335", "name": "item", "description": "3195029335", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3195029335"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-08-17T00:00:00Z"}}, {"id": "3215851315", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:27:50Z", "type": "Journal Article", "created": "2021-11-30", "title": "Predicting sensitivity of recently harvested tomatoes and tomato sepals to future fungal infections", "description": "Abstract

Tomato is an important commercial product which is perishable by nature and highly susceptible to fungal incidence once it is harvested. Not all tomatoes are equally vulnerable to pathogenic fungi, and an early detection of the vulnerable ones can help in taking timely preventive actions, ranging from isolating tomato batches to adjusting storage conditions, but also in making right business decisions like dynamic pricing based on quality or better shelf life estimate. More importantly, early detection of vulnerable produce can help in taking timely actions to minimize potential post-harvest losses. This paper investigates Near-infrared (NIR) hyperspectral imaging (1000\uffe2\uff80\uff931700\uffc2\uffa0nm) and machine learning to build models to automatically predict the susceptibility of sepals of recently harvested tomatoes to future fungal infections. Hyperspectral images of newly harvested tomatoes (cultivar Brioso) from 5 different growers were acquired before the onset of any visible fungal infection. After imaging, the tomatoes were placed under controlled conditions suited for fungal germination and growth for a 4-day period, and then imaged using normal color cameras. All sepals in the color images were ranked for fungal severity using crowdsourcing, and the final severity of each sepal was fused using principal component analysis. A novel hyperspectral data processing pipeline is presented which was used to automatically segment the tomato sepals from spectral images with multiple tomatoes connected via a truss. The key modelling question addressed in this research is whether there is a correlation between the hyperspectral data captured at harvest and the fungal infection observed 4 days later. Using 10-fold and group k-fold cross-validation, XG-Boost and Random Forest based regression models were trained on the features derived from the hyperspectral data corresponding to each sepal in the training set and tested on hold out test set. The best model found a Pearson correlation of 0.837, showing that there is strong linear correlation between the NIR spectra and the future fungal severity of the sepal. The sepal specific predictions were aggregated to predict the susceptibility of individual tomatoes, and a correlation of 0.92 was found. Besides modelling, focus is also on model interpretation, particularly to understand which spectral features are most relevant to model prediction. Two approaches to model interpretation were explored, feature importance and SHAP (SHapley Additive exPlanations), resulting in similar conclusions that the NIR range between 1390\uffe2\uff80\uff931420\uffc2\uffa0nm contributes most to the model\uffe2\uff80\uff99s final decision.Soil spectroscopy is a widely used method for estimating soil properties that are important to environmental and agricultural monitoring. However, a bottleneck to its more widespread adoption is the need for establishing large reference datasets for training machine learning (ML) models, which are called soil spectral libraries (SSLs). Similarly, the prediction capacity of new samples is also subject to the number and diversity of soil types and conditions represented in the SSLs. To help bridge this gap and enable hundreds of stakeholders to collect more affordable soil data by leveraging a centralized open resource, the Soil Spectroscopy for Global Good has created the Open Soil Spectral Library (OSSL). In this paper, we describe the procedures for collecting and harmonizing several SSLs that are incorporated into the OSSL, followed by exploratory analysis and predictive modeling. The results of 10-fold cross-validation with refitting show that, in general, mid-infrared (MIR)-based models are significantly more accurate than visible and near-infrared (VisNIR) or near-infrared (NIR) models. From independent model evaluation, we found that Cubist comes out as the best-performing ML algorithm for the calibration and delivery of reliable outputs (prediction uncertainty and representation flag). Although many soil properties are well predicted, total sulfur, extractable sodium, and electrical conductivity performed poorly in all spectral regions, with some other extractable nutrients and physical soil properties also performing poorly in one or two spectral regions (VisNIR or Neospectra NIR). Hence, the use of predictive models based solely on spectral variations has limitations. This study also presents and discusses several other open resources that were developed from the OSSL, aspects of opening data, current limitations, and future development. With this genuinely open science project, we hope that OSSL becomes the driver of the soil spectroscopy community to accelerate the pace of scientific discovery and innovation.Tomato is an important commercial product which is perishable by nature and highly susceptible to fungal incidence once it is harvested. Not all tomatoes are equally vulnerable to pathogenic fungi, and an early detection of the vulnerable ones can help in taking timely preventive actions, ranging from isolating tomato batches to adjusting storage conditions, but also in making right business decisions like dynamic pricing based on quality or better shelf life estimate. More importantly, early detection of vulnerable produce can help in taking timely actions to minimize potential post-harvest losses. This paper investigates Near-infrared (NIR) hyperspectral imaging (1000\uffe2\uff80\uff931700\uffc2\uffa0nm) and machine learning to build models to automatically predict the susceptibility of sepals of recently harvested tomatoes to future fungal infections. Hyperspectral images of newly harvested tomatoes (cultivar Brioso) from 5 different growers were acquired before the onset of any visible fungal infection. After imaging, the tomatoes were placed under controlled conditions suited for fungal germination and growth for a 4-day period, and then imaged using normal color cameras. All sepals in the color images were ranked for fungal severity using crowdsourcing, and the final severity of each sepal was fused using principal component analysis. A novel hyperspectral data processing pipeline is presented which was used to automatically segment the tomato sepals from spectral images with multiple tomatoes connected via a truss. The key modelling question addressed in this research is whether there is a correlation between the hyperspectral data captured at harvest and the fungal infection observed 4 days later. Using 10-fold and group k-fold cross-validation, XG-Boost and Random Forest based regression models were trained on the features derived from the hyperspectral data corresponding to each sepal in the training set and tested on hold out test set. The best model found a Pearson correlation of 0.837, showing that there is strong linear correlation between the NIR spectra and the future fungal severity of the sepal. The sepal specific predictions were aggregated to predict the susceptibility of individual tomatoes, and a correlation of 0.92 was found. Besides modelling, focus is also on model interpretation, particularly to understand which spectral features are most relevant to model prediction. Two approaches to model interpretation were explored, feature importance and SHAP (SHapley Additive exPlanations), resulting in similar conclusions that the NIR range between 1390\uffe2\uff80\uff931420\uffc2\uffa0nm contributes most to the model\uffe2\uff80\uff99s final decision.Our understanding of plant-microbe interactions in soil is limited by the difficulty of observing processes at the microscopic scale throughout plants\uffe2\uff80\uff99 large volume of influence. Here, we present the development of 3D live microscopy for resolving plant-microbe interactions across the environment of an entire seedling growing in a transparent soil in tailor-made mesocosms, maintaining physical conditions for the culture of both plants and microorganisms. A tailor made dual-illumination light-sheet system acquired scattering signals from the plant whilst fluorescence signals were captured from transparent soil particles and labelled microorganisms, allowing the generation of quantitative data on samples approximately 3600 mm3in size with as good as 5 \uffce\uffbcm resolution at a rate of up to one scan every 30 minutes. The system tracked the movement ofBacillus subtilispopulations in the rhizosphere of lettuce plants in real time, revealing previously unseen patterns of activity. Motile bacteria favoured small pore spaces over the surface of soil particles, colonising the root in a pulsatile manner. Migrations appeared to be directed towards the root cap, the point \uffe2\uff80\uff9cfirst contact\uffe2\uff80\uff9d, before subsequent colonisation of mature epidermis cells. Our findings show that microscopes dedicated to live environmental studies present an invaluable tool to understand plant-microbe interactions.