• Rural Digital Europe
• 2018-2022close
• Research data close
• Research software close

Hai He basin is located in the densely populated North China Plain that is providing food to more than 100 million people. The fast developing agriculture and urbanization in Hai He basin have resulted in discharging nutrient-rich wastewater into lakes and reservoirs, leading to eutrophication and water scarcity such as in Guanting reservoir and Baiyangdian lake. In this study we analyzed future trends in nutrient inputs into Guanting reservoir and Baiyangdian lake by applying the MARINA (Model to Assess River Inputs of Nutrients to seAs) model. We analyzed trends between 2012 and 2050 for a business-as-usual scenario (SSP3) and a scenario based on Current Environmental Policies (CEP). In addition, we assessed future impacts of two important events on river export of nutrients: the 2022 Olympic Winter Games in the Guanting basin (OLY scenario), and the development of Xiong’an in the Baiyangdian basin (URB scenario). Finally, we assumed implementation of advanced technologies to reduce nutrients in rivers (OLY+ and URB+). Our study has five main findings. First, nutrients in Guanting reservoir and Baiyangdian lake were mainly from agriculture in 2012. Second, nutrient export doubles between 2012 and 2050 in SSP3. Third, effective implementation of current environmental policies could reduce the future pollution to levels below that in 2012. Fourth, improved sewage systems associated with the 2022 Winter Olympic Games could not reduce nutrient pollution effectively in Guanting reservoir, indicating that reducing nutrient losses from agriculture may be more effective to improve water quality than urban waste water treatment. Fifth, urbanization in the Baiyangdian basin may increase river export of nutrients to the lake by 28-43 % compared to the CEP scenario (URB scenario). Highly effective waste treatment is needed not only in Xiong’an but also in surrounding areas to ensure the availability of clean water (URB+ scenario). Our results could improve our understanding of nutrient management for specific lakes and reservoirs, and highly relevant for policy making for effective environmental policies.

International trade of agricultural products has complicated and far-reaching impacts on land and nitrogen use efficiencies. We analysed the productivity of cropland and livestock and associated use of feed and fertilizer efficiency for over 240 countries, and estimated these countries’ cumulative contributions to imports and exports of 190 agricultural products for the period 1961–2017. Crop trade has increased global land and partial fertilizer nitrogen productivities in terms of protein production, which equalled savings of 2,270 Mha cropland and 480 Tg synthetic fertilizer nitrogen over the analysed period. However, crop trade decreased global cropland productivity when productivity is expressed on an energy (per calorie) basis. Agricultural trade has generally moved towards optimality, that is, has increased global land and nitrogen use efficiencies during 1961–2017, but remains at a relatively low level. Overall, mixed impacts of trade on resource use indicate the need to rethink trade patterns and improve their optimality.

Dataset of manuscript entitled “Germination of crop species in response to whole-soil inoculants that originate from conventional vs organic farming systems”. This manuscript includes the results of WP2 from the SOFT project (ref. 890874).

Dados de medições dendrométricas coletados em três testes de procedências e progênies de K. senegalensis no Brasil. THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

Dataset of manuscript entitled “Comparison of crop productivity (NDVI index) in response to conventional and organic farming”. This manuscript includes the results of WP1 from the SOFT project (ref. 890874).

The sample files can be imported by a user to test the DSS, the link to install the tool (https://gitlab.com/nivaeu/uc1a_docker ). Please follow the instructions provided in the overview and instruction file.

The file ‘GlobalCoconutLayer_2020_v1-1.zip’ contains 886 raster tiles of 100x100 km in geotiff format. The raster files are the result of a convolutional neural network that classified Sentinel-1 and Sentinel-2 annual composites into a coconut layer for the year 2020. The images have a spatial resolution of 20 meters and contain two classes: [0] Other land covers that are not coconut. [1] Coconut. The file ‘GlobalCoconutLayer_2020_densityMap_1km_v1-1.zip’ contains the 20-meter coconut classification aggregated to 1 km. The value of each pixel represents the coconut area (in squared meters) within the 1-km pixel. The file ‘Validation_points_GlobalCoconutLayer_2020_v1-1.shp’ includes the 10,200 points that were used to validate the product. Each point includes the attribute ‘Class’, which is the class assigned by visual interpretation of sub-meter resolution images, and the attribute ‘predClass’, which reflects the predicted class by the convolutional neural network. The ‘predClass’ values are the same as the raster files: [0] Other land covers that are not coconut. [1] Coconut. The attribute ‘Class’ contains the following values: [0] Land cover could not be determined because sub-meter resolution data was not available. [1] Other land covers that are not coconut. [2] Sparse coconut. Low density of coconut trees; between 1 and 4 coconut trees within the 20-meter pixel. [3] Dense open-canopy coconut; more than 4 coconut trees within the 20-meter pixel but coconut trees do not reach the full canopy closure. [4] Closed -canopy coconut; more than 4 coconut trees within the 20-meter pixel and coconut trees fully cover the ground. [5] Palm species that are not coconut.

These annexes refer to a report which describes and maps the main existing structures and systematic initiatives and academic activities for surveillance in the EU for transboundary, emerging and re-emerging zoonoses in domestic animals, wildlife, and the environment, developed by the different sectors, namely human, domestic animal, wildlife and environmental, under One Health approach. This is essential to provide scientific and technical advice and improve future schemes of surveillance. A questionnaire was compiled by MSs and the information collected was complemented by literature reviews about (i) the main existing structures and systematic initiatives or activities, and (ii) academic activities for surveillance in the EU for zoonoses in domestic animals and wildlife. The annexes to the report are as follows: Annex 1. Questionnaire survey on official zoonotic disease surveillance activities in the EU and neighbouring countries. - Sheet 1: PART 1 – Surveillance. This part explores the general organization of the surveillance plan - Sheet 2: PART 2 – Pathogens. This part aims to identify target pathogen and species and methods for surveillance Annex 2. Characteristic of surveillance plans. - Sheet “Pathogens”, where not primary but also a wide range of hosts are summarized. - Sheet “active/passive surveillance” by country - Sheet “origin of funding” (the proportion and number) of surveillance plans Annex 3. More detailed distribution of active and passive surveillance planaccording to countries and pathogen is presented in this Annex. Annex 4. Standardized data model (to extract key information to characterize the surveillance systems in the literature review on systematic surveillance. The data model was divided into two parts: - Sheet 1: PART 1 – Surveillance system (explores the general organization) - Sheet 2: PART 2 – Pathogens (identifies the target pathogen, species, and methods) Annex 5. Standardized data model used during the literature review to extract key information to characterize the surveillance performed by the academia EU, xlsx, biohaw@efsa.europa.eu