Environment and Agrifood
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Item Open Access Data related to Using Bayesian Belief Networks to assess the influence of landscape connectivity on ecosystem service trade-offs and synergies in urban landscapes in the UK(Cranfield University, 2021-08-09 21:50) Dariush Karimi, JamesThis data comprises ten file figures reported in the paper Using Bayesian Belief Networks to assess the influence of landscape connectivity on ecosystem service trade-offs and synergies in urban landscapes in the UK. Fig1a.tif, Fig1b.tif and Fig1c.tif show the study area and land cover classification. Fig1Loc_a.jpg shows the location of the study area. Fig2.png shows the methodological framework to assess the influence of connectivity on ES trade-offs and synergies. Fig3.png shows an example of Bayesian Belief Network model structure for Nutrient retention and Carbon storage trade-offs. All models used a comparable structure. Fig4a.tif, Fig4b.tif and Fig4c.tif show the modelled cumulative current maps for Bedford, Luton and Milton Keynes at 2 m resolution. Fig5.png shows the heat maps that visually depict the conditional probabilities driving each model. The dataset Dataset_PC_maxBA.txt was used for Bayesian modelling to assess whether connectivity affects ES trade-offs and synergies. It contains 116 cases (observations) where each case represents a point observation of counts of bird abundance (within a radius of 200 m), a point observation of bird species richness, data point cumulative current mapped values, data point principal components raster mapped values and patch area metric values found at the same location. The dataset refers to cases (observations) across the combined built-up areas of Bedford, Luton and Milton Keynes. The data point principal component values represent nutrient retention and carbon storage trade-offs(PC 1), habitat quality and pollinator abundance trade-offs (PC 2) and potential soil erosion and water supply synergies(PC 3).Item Open Access Model Results for 'Street-scale dispersion modelling framework of road-traffic derived air pollution in Hanoi, Vietnam'(Cranfield University, 2023-06-30 11:56) Quang Ngo, KhoiADMS-Urban result for street-scale dispersion of air pollution derived from traffic activities in HanoiItem Open Access Underlying data for PhD thesis titled 'Understanding landscape change in support of opium monitoring in Afghanistan'(Cranfield University, 2021-05-04 16:44) Hamer, AlexThe data used in the PhD thesis 'Understanding landscape change in support of opium monitoring in Afghanistan' are outlined in the PDF document.Item Open Access Data related to Bundling ecosystem services at a high resolution in the UK: Trade-offs and synergies in urban landscapes(Cranfield University, 2021-04-30 09:29) Dariush Karimi, JamesThis dataset comprises ten file figures reported in the paper Bundling ecosystem services at a high resolution in the UK: Trade-offs and synergies in urban landscapes. Fig1a.tif, Fig1b.tif and Fig1c.tif show the study area and land cover classification. Fig1Loc_a.jpg shows the location of the study area. Fig2a.tif, Fig2b.tif and Fig2c.tif show the spatial distribution of each ecosystem service bundle for the towns of Bedford, Luton and Milton Keynes Fig3a.png, Fig3b.png, Fig3c.png and Fig3d.png show the radar charts with the average values of each service in the bundle type. a) Potential soil erosion, b) Urban trees and woodland c) Urban grassland and d) Suburban grassland bundle types The dataset Data_test_pc_st.txt was used to analyse the trade-offs and synergies between ecosystem services and in the K-means clustering analysis. It contains the ecosystems services and the principal component values (for PC1 nutrient retention and carbon storage, PC 2 habitat quality and pollinator abundance and PC3 potential soil erosion and water supply).Item Open Access HECRAS 2D model files "A Remote Sensing Based Integrated Approach to Quantify the Impact of Fluvial and Pluvial Flooding in an Urban Catchment"(Cranfield University, 2020-07-10 12:39) Muthusamy, ManoranjanThis HECRAS 2D model setup files and results were produced to compare fluvial and pluvial flood properties at Cockermouth during storm Desmend (2015). For more details please refer the following publication Muthusamy, Manoranjan, Monica Rivas Casado, Gloria Salmoral, Tracy Irvine, and Paul Leinster. 2019. €œA Remote Sensing Based Integrated Approach to Quantify the Impact of Fluvial and Pluvial Flooding in an Urban Catchment.€ Remote Sensing . doi:10.3390/rs11050577. Note: This folder contains DEM data downloaded from Environment Agency, UK. This metadata record is for Approval for Access product AfA458. Attribution statement: (c) Environment Agency copyright and/or database right 2019. All rights reserved.Item Open Access Data supporting 'Understanding the effects of Digital Elevation Model resolution in urban fluvial flood modelling'(Cranfield University, 2023-02-10 17:21) Muthusamy, Manoranjan; Rivas Casado, Monica; Leinster, Paul; Butler, DavidThis HERAS 2D model setup files and results were produced to study the effect of DEM resolution in fluvial flood modelling using Cockermouth storm Desmend flood (2015). -Link to the publication will be added once available- Note: This folder contains DEM data downloaded from Environment Agency, UK. This metadata record is for Approval for Access product AfA458. Attribution statement: (c) Environment Agency copyright and/or database right 2019. All rights reservedItem Open Access Supporting data for 'An insight into the hormonal interplay regulating pigment changes and colour development in the peel of ‘Granny Smith’, ‘Opal®’ and ‘Royal Gala’ apples'(Cranfield University, 2024-03-28 09:47) Alamar Gavidia, Maria del carmen; Teixidó, Neus; Giné-Bordonaba, Jordi; Fernández Cancelo, PabloThis data set contains physiological (colour, size, total soluble content) and biochemical data (including plant hormones, indivicual sugars, anthocyanins) of three different apple cultivars. It also includes the gene expression of gene involved in the ethylene pathway.Item Open Access Non-destructive methods for mango ripening prediction: Visible and near[1]infrared spectroscopy (visNIRS) and laser Doppler vibrometry (LDV): Data(Cranfield University, 2024-03-12 09:10) del carmen Alamar Gavidia, Maria; O'Brien, Ciara; Falagan Sama, Natalia; Landahl, Sandra; Terry, Leon; Kourmpetli, SofiaThis data set includes reference measurements (firmness, colour [lightness, chroma and hue angle], total soluble solids [TSS], individual sugar concentrations [glucose, fructose, sucrose]), as well as visible and near-infrared spectroscopic (vis-NIRS) data (nm) and resonant frequency measured by laser Doppler vibroemetry (LDV) on 'Keitt' and 'Kent' mango fruit.Item Open Access The influence of different abiotic conditions on the concentrations of free and conjugated (masked) deoxynivalenol and zearalenone in stored wheat: data(Cranfield University, 2024-02-09 16:10) Oluwakayode, Abimbola; Greer, brett; Meneely, Julie; He, Qiqi; Sulyok, Michael; Krska, Rudolf; Medina Vaya, AngelThis study aims to examine the impact of storage conditions of water activities 0.93, 0.95, 0.98 aw and temperature 20-25 °C on (a) the concentrations of DON and ZEN and their respective glucosides/conjugates and (b) the concentrations of emerging mycotoxins in both naturally contaminated and irradiated wheat grains inoculated with Fusarium graminearum to ascertain any potential increases in toxicity in the wheat grains.Item Open Access Farm-SAFE v3 - Comparing the financial benefits and costs of arable, forest, and agroforestry systems(Cranfield University, 2024-02-06 13:58) Graves, Anil; Burgess, Paul; Wiltshire, Katy; Giannitsopoulos, Michail; Herzog, Felix; Palma, JoaoAgroforestry systems integrate trees with livestock and/or arable crops on the same parcel of land. Compared to monoculture arable or grass systems, agroforestry systems can enhance soil conservation, carbon sequestration, species and habitat diversity, and provide additional sources of farm income. Farm-SAFE (Financial and Resource use Model for Simulating AgroForestry in Europe) is a spreadsheet-based bio-economic model which has been developed in Microsoft® Excel® to compare the financial benefits and costs of crop-only, tree-only, and agroforestry system over tree rotations of up to 60 years (Graves et al., 2024a). The results are presented in both graphical and tabular form in terms of a net present value and equivalent annual values. A description and user guide is also available (Graves et al., 2024b). Farm-SAFE requires input of tree and crop yields. One way to obtain crop and tree yields in tree-only, agroforestry, and crop-only systems is to use the Yield-SAFE model. Yield-SAFE is a spreadsheet-based biophysical model which has been developed to enable the prediction of the relationship between tree and crop yields over the rotation of the tree component. A copy of the Yield-SAFE model, together with a full description and user guide, is available here. The original Farm-SAFE model was developed with funding from the European Union through the Silvoarable Agroforestry For Europe project (contract number QLK5-CT-2001-00560). The process of creating a default publicly available version of the model has been enabled through the BioForce project funded by the UK Department for Energy Security and Net Zero. Graves, A.R., Burgess, P.J., Wiltshire, C., Giannitsopoulos, M., Herzog, F., Palma, J.H.N. (2024a). Farm-SAFE v3 model in Excel. Cranfield, Bedfordshire, UK: Cranfield University. Graves, A.R., Burgess, P.J., Wiltshire, C., Giannitsopoulos, M., Herzog, F., Palma, J.H.N. (2024b). Description and User Guide for Farm-SAFE v3. January 2024. Cranfield, Bedfordshire, UK: Cranfield University. 42 pp.Item Open Access Data supporting 'Unveiling Biomarkers for Postharvest Resilience: The Role of Canopy Position on Quality and Abscisic Acid Dynamics of 'Nadorcott' Clementine Mandarins'(Cranfield University, 2024-02-28 16:02) del carmen Alamar Gavidia, Maria; Magwaza, Lembe; Terry, LeonPhysiological (colour, respiration rate), and biochemical (individual sugars, organic acids, hormones) data of mandarin during postharevst cold storageItem Open Access Data: Storage duration and temperature affect pathogen load, heavy metals, and nutrient levels in faecal derived fertiliser(Cranfield University, 2024-02-08 15:33) Gbenatey Nartey, Eric; Sakrabani, Ruben; Tyrrel, Sean; Cofie, OlufunkeThis dataset describes the changes in nutrient characteristics and pathogen in two types of stored faecal derived fertiliser over a period of time.Item Open Access Data: Assessing consistency in the aerobic co-composting of faecal sludge and food waste in a municipality in Ghana(Cranfield University, 2023-11-13 11:23) Gbenatey Nartey, Eric; Sakrabani, RubenData on feedstock and co-composting process. DFS means dewatered faecal sludge while FW means food waste.Item Open Access Data - Immobilisation of anaerobic digestate supplied nitrogen into soil microbial biomass is dependent on lability of high organic carbon mat(Cranfield University, 2024-03-11 09:09) Van Midden, Christina; Harris, Jim; Shaw, Liz; Sizmur, Tom; Morgan, Hayden; Pawlett, MarkResearch data for a 150 day incubation study to determine the effects of mixing high organic carbon materials into anaerobic digestate on soil microbial immobilisation of digestate supplied nitrogen and on soil microbial communities. This dateset contains raw data on microbial biomass carbon and nitrogen, soil available nitrogen (ammonium-N and total oxides of nitrate-nitrite), total soil nitrogen, and PLFA biomarkers.Item Open Access Pyrolysis or hydrothermal carbonisation for anaerobic-digested sewage sludge? A comparison of pyrochar and hydrochar structure and stability: data(Cranfield University, 2024-02-07 15:08) Pimenta-Ocampo, Maria; Sakrabani, Ruben; Otten, WilfredThermochemical conversion of sewage sludge was proven as a useful method for waste management. HTC showed the greatest potential to produce a material with higher adsorption capacity (100 cm3 /g for H180-4) but all chars should be subjected to an activation process to be able to compete with other kinds of feedstocks. The reduction of the H:C and O:C from the original SS after the treatments indicated a greater carbonisation degree, but a general reduction of the high heating value (HHV) from 17.94 MJ kgˆ’1 in SS to (14.93 MJ kgˆ’1 ). The torrefied char and hydrochars could be an attractive option to reduce energy of the process and drying stage in the case of HTC.Item Open Access Yield-SAFE v2 - Biophysical model for tree and crop yields in agroforestry(Cranfield University, 2024-01-23 09:50) Burgess, Paul; Graves, AnilAgroforestry systems integrate trees with livestock and/or arable crops on the same parcel of land. Compared to monoculture arable or grass systems, agroforestry systems can enhance soil conservation, carbon sequestration, species and habitat diversity, and provide additional sources of farm income. However, as the trees increase in size, the grass and/or arable yields will tend to decline due to light and water competition with the trees. The form of the tree-crop yield relationship will vary with the level of solar radiation, rainfall, the species being grown, management actions such as choice of planting date and pruning, the soil type, and the time from tree planting. The Yield-SAFE v2 provides the opportunity to model the response of tree-only, agroforestry, and crop-only systems in the same worksheet. Yield-SAFE is a spreadsheet-based biophysical model which has been developed in Microsoft® Excel® to enable the prediction of the relationship between tree and crop yields over the rotation of the tree component. The full name for Yield-SAFE is the €œYIeld trategyEstimator for Long term Design of Silvoarable AgroForestry in Europe€ . The original Yield-SAFE model was developed with funding from the European Union through the Silvoarable Agroforestry For Europe project (contract number QLK5-CT-2001-00560). The process of creating a default publicly available version of the model has been enabled through the BioForce project funded by the UK Department for Energy Security and Net Zero.Item Open Access Very high resolution aerial photography and annotated land cover data of the Peak District National Park(Cranfield University, 2023-11-06 17:08) van der Plas, Thijs; Geikie, Simon; Alexander, David; Simms, DanielLicense Contents of compressed file (zip) from Van der Plas, Geikie, Alexander and Simms, upcoming publication titled Multi-stage semantic segmentation quantifies fragmentation of small habitats at a landscape scale This data set contains the RGB image data and human land cover annotation for 1027 patches of 512 pixels x 512 pixels ( 64 m x 64 m spatial resolution). For more information on how the data can be used, the land cover schema and other details, please see our paper. For code examples of how to use the data, please see the github repository at https://github.com/pdnpa/cnn-land-cover The data is given in two formats: python and tiff. The Python format can be directly loaded by the code in the repository into Pytorch DataLoaders. The tiff format is independent of progamming language and application. This data is released under the CC BY 4.0 license, which means if you use this data set, we ask you to cite along with our paper above.If you use the RGB images, you must acknowledge the following copyright: "Aerial Photography for Great Britain, © Bluesky International Limited and Getmapping Plc [2022]" - README land cover patch data.txt - lc_label_names.json contains mapping from land cover label (integer) to land cover class name - python_format - images_python_all all (train and test) RGB images in .npy format (each of shape (3, 512, 512)) - masks_python_all all (train and test) land cover masks in .npy format (each of shape (512, 512)) - train_test_split_80tiles_2023-03-22-2131.json train/test split in json format - train_test_split_80tiles_2023-03-22-2131.pkl train/test split in pickle format (to be used with the data class in the repository) - tiff_format - images_masks_tiff_train train set only patches, containing both the RGB image (first 3 bands) and the land cover annotation (4th band) (each of shape (4, 512, 512)) - images_masks_tiff_test test set only patches, containing both the RGB image (first 3 bands) and the land cover annotation (4th band) (each of shape (4, 512, 512))Item Open Access Data supporting 'Biomarkers of postharvest resilience: unveiling the role of abscisic acid in table grapes during cold storage'(Cranfield University, 2023-09-28 11:00) del carmen Alamar Gavidia, Maria; Falagan Sama, Natalia; Terry, LeonPhysiological (colour, respiration rate, firmness), pathalogical (mould incidence) and biochemical (individual sugars, hormones, organic acids) data of table grapes during postharevst cold storageItem Open Access The EPSRC's responsible innovation framework: to what extent does it influence research practice?. Survey data(Cranfield University, 2023-04-05 10:24) Rose, David; Shortland, Faye; Smith, Rachel; Schillings, JulietteMethod: We also undertook an online Qualtrics survey of UK EPSRC-funded researchers and PhD students between September and November 2022. The survey was circulated via social media, by emailing interviewees, and by contacting CDT/DTP leads or administrators. Questions are shown in Appendix B. We received 138 responses (103 PhD students, 35 researchers/academics). In total, 30 institutions were represented in the responses with the top five institutions accounting for 76 responses and no more than 20 responses from any one single institution. At least 43[1] disciplines were covered. The gender response split was 87 Male, 43 Female, 2 Non-binary/non-conforming, with 5 preferring not to say or selecting other (no further detail provided). Descriptive statistics are provided for close-ended questions and open-ended answers were thematically coded. Software used: Qualtrics Format: Excel spreadsheet. Q1 and 2 were administrative questions and are not included. Anonymisation: Some open-ended question responses are removed or edited due to the possibility of identification. Where edits have been made, they are marked with xxx. · Q11: part anonymisation due to one answer giving identifiable information. · Q12_2: part anonymisation due to one answer giving identifiable information. · Q14: on training, fully removed as many answers identified their university or CDT. · Q20: giving further information, fully removed as many answers contained identifiable information. Q21: asked to name university. One respondent asked for this to be removed as it was too easy to identify themselves from a combination of discipline and university. Hence, the answers to this were fully removed for all. [1] Taking the first discipline given, as described by respondents (136 gave responses): Aeronautical engineering (1), Aerosol science (1), AI (2), Atmospheric Science (1), Automotive Engineering (1), Behavioural Science (1), Biochemical engineering (5), Biology (2), Biomaterials Science (1), Biomedical Science (4), Chemical Biology (1), Chemical Engineering (1), Chemistry (12), Computational Materials Modelling (1), Computer Science (13), Computing (5), Cybersecurity (2), Data Science (1), Economics (1), Electronic Engineering (3), Engineering (10), Environmental Science (1), Geography (1), Geospatial engineering (1), HCI (2), Health technology (2), Immunology (2), International relations (1), Machine Learning (3), Materials Science (6), Mathematical sciences (1), Mathematics (11), Mechanical engineering (2), Medical imaging (1), Neuroscience (3), Pharmaceutical Sciences (2), Physics (8), Psychology (9), Risk Analysis (1), Robotics (5), Social sciences (1), Sociology (1), Statistics (3).Item Open Access Data Supporting 'Efficacy of bioadmendments in reducing the influence of salinity on the bioremediation of oil-contaminated soil'(Cranfield University, 2023-06-16 15:49) Atai, Emmanuel; Pawlett, Mark; Coulon, FredericData to support the paper: 'Efficacy of bioadmendments in reducing the influence of salinity on the bioremediation of oil-contaminated soil', which include hydrocarbon alkanes and PAHs, PLFA Mol% to give the microbial community dynamics and abundance of the groups, basal and multisubstrate respiration, and the correlation of the entire datasets. Some statistics and Charts were included in the data.