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Underlying data of soil measurements and analysis by TUC team for the publication ���The Impact of Soil-Improving Cropping Practices on Erosion Rates: A Stakeholder-Oriented Field Experiment Assessment��� https://doi.org/10.3390/land10090964 from the SoilCare project study sites in Crete. Abstract: The risk of erosion is particularly high in Mediterranean areas, especially in areas that are subject to a not so effective agricultural management���or with some omissions���, land abandonment or wildfires. Soils on Crete are under imminent threat of desertification, characterized by loss of vegetation, water erosion, and subsequently, loss of soil. Several large-scale studies have estimated average soil erosion on the island between 6 and 8 Mg/ha/year, but more localized investigations assess soil losses one order of magnitude higher. An experiment initiated in 2017, under the framework of the SoilCare H2020 EU project, aimed to evaluate the effect of different management practices on the soil erosion. The experiment was set up in control versus treatment experimental design including different sets of treatments, targeting the most important cultivations on Crete (olive orchards, vineyards, fruit orchards). The minimum-to-no tillage practice was adopted as an erosion mitigation practice for the olive orchard study site, while for the vineyard site, the cover crop practice was used. For the fruit orchard field, the crop-type change procedure (orange to avocado) was used. The experiment demonstrated that soil-improving cropping techniques have an important impact on soil erosion, and as a result, on soil water conservation that is of primary importance, especially for the Mediterranean dry regions. The demonstration of the findings is of practical use to most stakeholders, especially those that live and work with the local land.

This dataset contains the parameterization of a no-policy baseline scenario of the global 11-regional MESSAGEix-GLOBIOM integrated assessment model. Regions, time periods, commodities, technologies and relations included in this model are described in a separate repository. The dataset relies on the MESSAGEix modeling framework (Huppmann et al. 2019) and can be imported into MESSAGEix via the read_excel() functionality, for which a tutorial is available, or via snapshot.load() as described here. After the import the scenario can be solved and modified to create new scenarios. Note that the published scenario as included in the ENGAGE global scenarios dataset has been run with a release candidate of version 3.4.0 of MESSAGEix. Removed superfluous/unallowed white spaces in unit names. This allows reporting workflows to function with newly created databases. 

Raw data related to Figures 1 to 3 of the publication Botyanszka et al. (2020) Chlorophyll fluorescence kinetics may be useful to identify early drought and irrigation effects on photosynthetic apparatus in field-grown wheat. Agronomy 10, 1275. https://doi.org/10.3390/agronomy10091275

The workshop has been realised in the framework of the H2020-MSCA-RISE-2018 project “LoGov - Local Government and the Changing Urban-Rural Interplay” as part of the implementation phase of the project. The workshops have been conducted with experts in the field of public administration, public law and political science, both researchers and practitioners, with the aim of widening the scope of the Country Report on Germany. To receive more information about the project, please visit: https://www.logov-rise.eu/. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 823961.

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The scenarios are developed based on projected climate and socio-economic changes, following the representative concentration pathways (RCPs) and the shared socioeconomic pathways (SSPs) for the region. The Norrström-Baltic SD model analyzes possible future shifts in the annual average conditions of sectoral and natural water system interactions. Such shifts are evaluated based on recent annual averages reflecting the condition of system components. Parameters taken into account are, amongst others, sectoral water availability, water fluxes between sectors and the corresponding nutrient (nitrogen and phosphorus) exchanges, coastal runoff and nitrogen and phosphorous loads ending up in the Baltic Sea. An overview of the model input variables and the parameters that are identified as system external uncertainties that may affect the behavior of the model: - Precipitation: climate change - Agricultural land: Development policies and market forces, food security and trade regulations, population growth and corresponding food demand/diet changes - Built-up land: Development policies and market forces, population growth, regional urbanization level, tourism expansion level - Forest land: Mitigation policies on climate change (i.e. afforestation and/or reforestation to maintain/enhance carbon capture and storage capacity), socio-economic developments leading to sectoral land competition (i.e. deforestation) - Open lands and wetlands: Policies and market forces supporting social and economic development in the region A total of 5 scenarios were developed for the Norrström/Baltic Sea case. One of them represents the ‘Base case’ conditions, while the rest are rooted in the combination of a certain SSP with a climate scenario linked to a certain RCP. The following overview shows the combinations used during the scenario building process: - Scenario 1: SSP1 + RCP 4.5 - Scenario 2: SSP2 + RCP 4.5 - Scenario 3: SSP4 + RCP 4.5 - Scenario 4: SSP5 + RCP 4.5 - Base Case scenario: Continuation into the future of the past-recent long-term average conditions in relation to hydro-climate and land use variables in the SD model. All the scenarios developed for the Norrström-Baltic region are linked to a climate scenario corresponding with RCP4.5, because projected patterns and changes for climate variables under this climate scenario were found to be more consistent with the observed changes in the region than other RCPs. The period 2010-2100 is compared with the normal mean for the period 1961-1990. Each year is compared separately with the long-term annual average precipitation. The xsls file is organized as follows. It comprises three sheets: Precipitation RCP with annual data of changes in annual precipitation (in percentage), precipitation (in million of m3/year and in mm/year); Land cover RCPs and SSPs with scenario data on land cover, annual change in land cover (in percentage), annual land cover areas for the Norrström water management district area, land dover area average for teh Norrström water management district area and average change in land cover compared to the long-term average (in percentage); Input data model with the four input variables (precipitation change rate in hydro-climate scenarios, urban growth rate in socioeconomic scenarios, forest land change rate in socioeconomic scenarios and agricultural land change rate in socioeconomic scenarios) and their change for each scenario (expressed in percentage).

A database has been developed and delivered during the FAIRWAY project. This database was developed as a response to the need to harmonize datasets and assessment methods related to pressure and state indicators for water quality in the EU member states, in order to compare and assess indicators using a harmonized approach. The dataset that is made available here provides two files: a public version* of the Excel database, which contains all "tabular" (non-GIS) data related to the 13 case studies that was gathered for the purposes of FAIRWAY's Monitoring & Indicators research theme. It is structured as one "data sheet" and one "summary sheet" per case study. The data sheets contain various parameters (ideally time-dependent data series i.e. time series) that were used, wherever possible, to compute relevant Agri-drinking water quality indicators (ADWIs) such as "nitrogen budget" (a compound Pressure indicator) or "lag time" (a statistically-inferred Link indicator). a ZIP folder containing all GIS data gathered for the FAIRWAY's Monitoring & Indicators research theme. The GIS files are grouped in subfolders, by case study, and then by keywords describing the nature of the spatial data. The Excel database contains near 385,000 rows of data from the 13 case study sites, with more than 65 parameters and more than 500 sub-parameters. The dataset also contains spatial information in a GIS-data zipped folder. The spatial mapping information can be made visible using basic QGIS project files (.qgz), so that GIS data from each case study can be explored. The indicators database can be used in several ways. It may be used to explore data or to calculate additional indicators. Depending of the case studies��� interests, the most commonly available State indicators are about nitrate and pesticides concentrations in water. From a practical point of view based on its actual content, the database may notably be used to explore statistical relations (or Links) between related Pressure and State indicators. This database can also be used as a spatial mapping portal for other usages. For more information on the database, follow this link. * Note that this is a public version of the database, which means that all confidential data was removed from the data sheets. This is the PUBLIC version of the database, where all confidential data has been removed from the Excel-file data sheets.

This Data Set is derived from the WP4 of the AgriLink project. It is part of task T4.4 of Work Package (WP) 4 of the H2020 AgriLink project. AgriLink [Agricultural Knowledge: linking farmers, advisors and researchers to boost innovation] aims at better understanding the role of advisory services in farmers��� decision making and at boosting their contribution to innovation for sustainable development of agriculture. WP4 addresses more specifically the governance of farm advisory services. The objective of the research presented in this report is to understand the institutions that influence how farm advisory services function on the ground, and to discuss implications for the support for sustainable development innovation. Data were collected in seven European countries: the Czech Republic, France, Greece, Poland, Portugal, Spain and the UK. Data were collected for a diversity of types of innovation: Market, Technological, Process, and Social Innovation. The Data set was built based on interviews with farm advisory suppliers. In total 170 farm advisory suppliers were interviewed. The table below provides the distribution of interviews according to countries. Country Market innovation (NCRO & RETRO) Technological innovation (TECH) Process innovation (BIOP & SOIL) Social innovation (LABO & COMM) TOTAL Czech Republic 4 16 20 France 14 11 25 Greece 11 10 21 Poland 6 18 24 Portugal 11 20 31 Spain 9 29 38 UK 7 4 11 TOTAL 34 28 75 33 170 The data has two aims. First, to characterise farm advisory suppliers, in terms of (table below): what do they provide? Who is in control of the supplier? What do they provide Farmers NGO Private Public semi-public Total Advice and Bookkeeping 8 4 1 13 Advice and Digital tech 1 3 4 Advice and Education 2 4 3 5 14 Advice and Health services 1 2 3 Advice and Inputs 1 14 15 Advice and Inputs and Outputs 15 5 20 Advice and Machinery 7 7 Advice and Outputs 8 1 8 2 19 Advice and Research 2 2 5 12 1 22 Only advice and training 16 26 10 1 53 Total 53 7 76 32 2 170 Second, we have set a series of variables to characterise the services they provide. The main variables are: Number of advisors of the organisation Number of advisors Number of organisations in that group [0:5] 96 ]10:50] 35 ]5:10] 16 >50 19 n.a. 4 Total 170 Percentage of advisors in the staff of the organisation % of advisors Number of organisations [0:25[ 43 [25:50[ 17 [50:75[ 30 [75:100] 70 n.a. 10 Total 170 Share of back-office activities in the staff of the organisation Share of back-office (%) Number of organisations [0:25[ 41 [25:50[ 26 [50:75[ 66 [75:100] 24 n.a. 13 Total 170 Number of farmers client of the supplier per advisor Number of clients per organisation Number of organisation [0:25[ 31 [25:75[ 43 [50:75[ 3 [75:175[ 28 >175 36 n.a. 29 Total 170 Main advisory method Main Advisory method Number of organisations Group Advice 19 IT tool (app, software���) 2 n.a. 1 One to One Advice 129 Phone or web helpdesk 15 Publications 4 Total 170 Main funding source Main funding source Number of organisations EU funds 15 Fee-for-advice 46 Joint trade 42 Membership 11 Membership fee 6 n.a. 16 Public funding 3 Public funds 4 State budget 27 Total 170 More detailed information about the variables collected can be found in the questionnaire that is available in the appendix of the deliverable D4.2 of AgriLink

For a detailed description of this dataset, based on the Datasheets for Datasets (Gebru, Timnit, et al. "Datasheets for datasets." Communications of the ACM 64.12 (2021): 86-92.), check the ACRE_datasheet.md file. For what purpose was the dataset created? The ACRE dataset was created within the scope of the METRICS project to serve as a benchmark for weed detection models in various tasks, including object detection, semantic segmentation, and instance segmentation. The Agri-Food Competition for Robot Evaluation (ACRE) is a benchmarking competition specifically designed for autonomous robots and smart implements, with a primary focus on agricultural activities like weed removal and field navigation. These capabilities play a vital role in facilitating the transition to Digital Agriculture. The ACRE competition, which can be found at https://metricsproject.eu/agri-food, is part of the METRICS project, an EU-funded initiative dedicated to the metrological evaluation and testing of autonomous robots. What do the instances that comprise the dataset represent? The instances consist of RGB images depicting both crop and weed plants. The crop category encompasses two species: maize (Zea mays) and beans (Phaseolus vulgaris). On the other hand, the weed category encompasses four species: ryegrass (Lolium perenne), mustard (Sinapis arvensis), matricaria (Matricaria chamomilla), and lamb's quarter (Chenopodium album). Is there a label or target associated with each instance? Every image in the dataset is accompanied by an XML file that contains instance segmentation annotations. What mechanisms or procedures were used to collect the data? The data collection process involved the use of a four-wheel skid-steering robot that was equipped with a Basler acA2000-50gc RGB camera. The camera was mounted on the robot in such a way that its principal axis was directed perpendicular to the ground. It had a resolution of 2046 x 1080 pixels. The robot was teleoperated and operated at an average speed of 0.2 m/s. To capture the data, the camera's stream was recorded in rosbag format. For this purpose, the camera was connected to a PC running Ubuntu 18.04 and ROS Melodic via an Ethernet interface.