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Wheat Biomass for Kherson and Poltava regions in Ukraine The dataset contains Dry Above Ground Biomass (DAM) estimates over the Kherson and Poltava regions in Ukraine for years 2020,2021 and 2022. - Processing:The processing is done using the AgriCarbon-EOv1.5 processing chain, using the TREX processing centre at CNES France.The input remote sensing data are L2A Sentinel-2 surface reflectances provided by the MAJA processing chain based on the Copernicus Sentinel-2 L1C data.The Landcover maps are provided using ML Deep learning based on the Copernicus L2A data.The daily weather data is extracted from ERA5Land products (C3S). -Geophysical variable:Dry Above ground biomass of winter wheat in g/m2. - Extents: * DAM estimates over the Copernicus Sentinel-2 tile 36TWT cover the Kherson region.* DAM estimates over the Copernicus Sentinel-2 tile 36UVA cover the Poltava region. - Spatial resolution:10m resolution estimlates over wheat plots identified in the landcover map. - Temporal coverage:Estimates are provided at the end of the wheat cycle for cycles:* The year 2020 correspond to cycle: 2019-2020* The year 2021 corresponds to cycle : 2020-2021* The year 2022 corresponds to cycle : 2021-2022 - Projection: EPSG:32636 - File content: Each Raster file has 2 bands containing respectively: * band1: mean value of DAM in g/m2. * band2: standard deviation of DAM in g/m2. - List of maps:* Dry_aboveground_biomass_2020_T36TWT_Kherson_Ukraine.tif* Dry_aboveground_biomass_2020_T36UVA_Poltava_Ukraine.tif* Dry_aboveground_biomass_2021_T36TWT_Kherson_Ukraine.tif* Dry_aboveground_biomass_2021_T36UVA_Poltava_Ukraine.tif* Dry_aboveground_biomass_2022_T36TWT_Kherson_Ukraine.tif* Dry_aboveground_biomass_2022_T36UVA_Poltava_Ukraine.tif

The Global Yield Gap Atlas project (GYGA - http://yieldgap.org ) has undertaken a yield gap assessment following the protocol recommended by van Ittersum et. al. (van Ittersum et. al., 2013). This datafile holds the results for rainfed rice.

The Global Yield Gap Atlas project (GYGA - http://yieldgap.org ) has undertaken a yield gap assessment following the protocol recommended by van Ittersum et. al. (van Ittersum et. al., 2013). This datafile holds the results for rainfed sugarcane.

The Global Yield Gap Atlas project (GYGA - http://yieldgap.org ) has undertaken a yield gap assessment following the protocol recommended by van Ittersum et. al. (van Ittersum et. al., 2013). This datafile holds the results for rainfed soybean.

The Global Yield Gap Atlas project (GYGA - http://yieldgap.org ) has undertaken a yield gap assessment following the protocol recommended by van Ittersum et. al. (van Ittersum et. al., 2013). This datafile holds the results for rainfed sorghum.

Publication list FOR CCAFS Almetrics for the SMO report of 2018

The Agro-Chain Greenhouse Gas Emissions (ACE) calculator is a tool for estimating total greenhouse gas emissions associated to a food product. It addresses the most common stages of ‘linear’ agro-food chains (chains for fresh and simple processed products: canned, frozen, packaged and other minimal processed forms). It combines a calculation framework with datasets containing crops greenhouse gas emission factors and food loss factors along the chain. Combined with user-definition parameters for the product-chain considered, it generates an estimate for greenhouse gas emissions associated to a product when bought by a consumer. The default data that the calculator derives from the dataset may be overruled by the user if more specific data are available; this would make the calculations more case-specific. The calculator works on a personal computer as well as a mobile phone (both in Microsoft Excel). Full instructions are provided on the first worksheet of the calculator. ; Improvements to the calculator in Version 4, May 2021 ; The user-interface now directly supports the comparison of two scenarios. Differences between the scenarios are highlighted, and the main results are shared on top of the calculator sheet. ; At the bottom of the calculator sheet, there is now a summary of the climate impacts per chain stage including FLW-associated impacts for both scenarios. ; Introduction of another method for overriding default data by user-values. ; Addition of new data, including crop GHG intensities for various crops, specified to countries, from the FAOstat database.

Datasets associated with Agostini, S., Houlbreque, F., Biscéré, T., Harvey, B. P., Heitzman, J. M., Takimoto, R., et al. (2020). Greater mitochondrial energy production provides resistance to ocean acidification in ‘winning’ hermatypic corals. Front. Mar. Sci. 7. doi:10.3389/fmars.2020.600836.

Towards large scale adoption of CSA practice, not only in CSVs but also in the whole Vietnam, CCAFS has provided support to documentation and dissemination of information on CSA practices and systems having values for the implementation of Nationally Determined Contributions (NDC) and the National Adaptation Plan (NAP) in different locations in the country. This effort involves: defining the suitability levels of each priority CSA practice of system for large-scale in each location (province or agro-ecological zones); and combining information into a database to serve as an evidence-base for 64 provinces and cities in Vietnam to develop their CSA development plans. Preliminarily, in total, 57 existing CSA practices and systems in Vietnam were identified as priority for promoting adoption by provinces in implementing NAP and NIDC. These could be grouped into (i) pest control and production of quality products, (ii) protection and sustainable use of cultivated land and water resource, (iii) agroforestry and integrated cropping systems, (iv) paddy rice production, (v) livestock and (vi) aquaculture.

This pack is designed to help you produce high quality, reusable and open data from your research activities. It consists of documents, templates and videos covering the different aspects of data management and ranging from the overarching concepts and strategies through to the day-to-day activities. For each of the videos in the pack we have included a transcript of the narrative. The Data Management Support Pack was created to support the implementation of the CCAFS Data Management Strategy.