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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.

This dataset contains files produced for and generated from the CCAFS Household Baseline Study carried out in sites in Latin America (Trifinio in Honduras/Guatemala, and Cauca in Colombia) and in South-East Asia (a site in each of Cambodia, Laos and Vietnam) in the latter months of 2014 and the early months of 2015. There are six sites in all (two sites from Trifinio). Before downloading any of the files, particularly the data files, please download and read the CCAFS ReadMe file which is prefixed by the code 0000. To gain access to the GPS coordinates from the restricted files please download and complete the Non-disclosure agreement from the file "0002 Non-Disclosure Agreement 2013-01-20.pdf" and send this to Wiebke Foerch at w.foerch@cgiar.org The study was based on earlier baseline studies carried out in sites in West and East Africa and in South Asia. Data and other files from these earlier studies are available in a separate dataset in this Dataverse archive. (CCAFS Household Baseline Survey 2010-2012). If you are intending to use data from both studies together we suggest you read the file "0001 Questionnaire Differences & Recoding Details 2015-10-29.pdf" which explains differences between the two studies.

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.

This dataset gathers data used to determine the temporal variability of couplings between pelagic and benthic habitats for fish assemblages at five periods. Organic matter fluxes were assessed using stable isotopes analysis. Species relative biomass was considered to explore energy fluxes within the fish assemblage

This document contains a review of global digital resources relevant to climate-informed agroecological transitions. The purpose of the review was to catalog relevant digital resources and assess their role in inclusive knowledge development, with special attention to farmers’ co-creation of knowledge for on-the-ground practices. To this end, we identified existing digital tools relevant to technical advisory and performance assessment and reviewed their functions (i.e., the purpose of using a tool) against indicators for exemplary features (i.e., the channels in which a user can engage with the tool) that could support socially inclusive, climate-informed agroecological transitions.<br><br> Metodology:To evaluate exemplary features of agricultural digital tools – defined here as an app, online resource (not platforms), or other software available on a digital device (e.g., phone, smartphone, computer, etc.), including those that are language, audio, and visually based – we developed 87 indicators (see Indicators Explained) relating to seven categories: (i) performance assessment, (ii) technological specifications, (iii) social inclusion and co-creation, (iv) scaling, (v) climate change adaptation, mitigation and whether the tool calculates greenhouse gas emissions, and (vi) agroecological principles Exemplariness was defined in this review as best fitting the requirements of the target users and best addressing agroecological and climate change mitigation and/or adaption outcomes. All indicators for categories i-v were developed and validated via several rounds of internal reviews and expert consultations. The 12 agroecological indicators (i.e., principles) were adopted and refined from the FAO 10 Elements of Agroecology (FAO, 2018), HLPE (HLPE, 2019) and TAPE (FAO, 2019) reports. Sixty (60) tools were selected for a full review against the 87 indicators based on their applicability to provide technical advisory and/or performance assessment on climate-informed agroecological transitions. Tools were classified as technical advisory resources if they delivered any recommendations regarding farming practices and as performance assessment resources if they included review of farm status or operations. Tools were mostly identified via Google searches, expert interviews, and platforms such as the CGIAR Evidence Clearing House and Digital Agri Hub. Each tool was reviewed by one analyst by accessing the tool, when available, or by reviewing materials online. A second analyst validated individual indicator responses when subjectivity in responses was an issue. Indicators were marked as ‘unknown’ when subjectivity in responses persisted or when information was not available.

Abstract Background In the near future, the incidence of mosquito-borne diseases may expand to new sites due to changes in temperature and rainfall patterns caused by climate change. Therefore, there is a need to use recent technological advances to improve vector surveillance methodologies. Unoccupied Aerial Vehicles (UAVs), often called drones, have been used to collect high-resolution imagery to map detailed information on mosquito habitats and direct control measures to specific areas. Supervised classification approaches have been largely used to automatically detect vector habitats. However, manual data labelling for model training limits their use for rapid responses. Open-source foundation models such as the Meta AI Segment Anything Model (SAM) can facilitate the manual digitalization of high-resolution images. This pre-trained model can assist in extracting features of interest in a diverse range of images. Here, we evaluated the performance of SAM through the Samgeo package, a Python-based wrapper for geospatial data, as it has not been applied to analyse remote sensing images for epidemiological studies. Results We tested the identification of two land cover classes of interest: water bodies and human settlements, using different UAV acquired imagery across five malaria-endemic areas in Africa, South America, and Southeast Asia. We employed manually placed point prompts and text prompts associated with specific classes of interest to guide the image segmentation and assessed the performance in the different geographic contexts. An average Dice coefficient value of 0.67 was obtained for buildings segmentation and 0.73 for water bodies using point prompts. Regarding the use of text prompts, the highest Dice coefficient value reached 0.72 for buildings and 0.70 for water bodies. Nevertheless, the performance was closely dependent on each object, landscape characteristics and selected words, resulting in varying performance. Conclusions Recent models such as SAM can potentially assist manual digitalization of imagery by vector control programs, quickly identifying key features when surveying an area of interest. However, accurate segmentation still requires user-provided manual prompts and corrections to obtain precise segmentation. Further evaluations are necessary, especially for applications in rural areas.

AbstractBio-syngas from gasification of sugarcane bagasse is one of the most promising sources for renewable energy. As an agriculture-based biomass, sugarcane bagasse has a high content of moisture (46-52%), fibrous (43-52%) and low bulk density (80-120kg/m3). This quality of bagasse will tend to initiate agglomeration and cause de-fluidization. It will disturb the gasification process and finally will decrease yield and quality of syn-gas. Its chracteristics in low quality can be improved by pretreatment, i.e., torrefaction process, addressed by slow heating of biomass on wet or dry conditions on atmosphere pressure for 1hour before it is used as feedstock gasification.This preliminary work features an experimental investigation of torrefaction process of Indonesian sugarcane cane bagasse. Temperature of torrefaction varies from 150, 175, 200, 225, 250 and 300°C. For bagasse gasification process, the optimum temperature of dry torrefaction is 150°C. At this temperature, yield of syngas will higher than other torrefaction temperature. Temperature of dry torrefaction will give energy saving opportunities than that's of wet torrefaction (180°C, 1 hr).Analysis ultimate and proximate also indicate that sugarcane bagasse with temperature torrefaction 150°C give better result than other torrefaction's temperature in high content of hydrogen and low content of carbon.

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.