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Crop yield, soil data, pest surveillance, livestock, food composition, precision farming
19,207 datasets
Monthly water quality monitoring data from the Aquafin CRC Salmon Project, collected by CSIRO and TAFI from September 2004 to October 2005. Samples were taken from 11 sites in the D'Entrecasteaux Channel and Huon Estuary, with measurements for nutrients, phytoplankton, chlorophyll, carotenoids, turbidity, dissolved oxygen, and CTD profiles. The dataset includes a correction applied in June 2012 to phytoplankton biovolume units.
A machine learning methodology using remote sensing identifies stands where saplings persist in healthy numbers within an eight-year timeframe since planting. The model was funded by DEFRA through the Natural Capital and Ecosystem Assessment programme and developed by the Forestry Commission. Validation recommends using high-confidence thresholds for restock classification, and future integration with the National Forest Inventory aims to improve national woodland indicators.
UNICEF Data and Analytics provides the Minimum Meal Frequency indicator, measuring the percentage of children aged 6–23 months who consumed solid, semi-solid, or soft foods (including milk feeds for non-breastfed children) at least the minimum recommended number of times the previous day. The dataset is structured for global health monitoring and is available in CSV and XML formats. Its license is CC-BY-3.0-IGO, facilitating open use and redistribution.
Bangladesh shrimp farmers in the southern districts of Bagerhat, Khulna, and Satkhira were surveyed by phone in May-June 2025. The dataset, created by the International Food Policy Research Institute (IFPRI), investigates the survival of 68 shrimp farming clusters following a project's end. It includes responses from cluster leads and forms part of a mixed-methods study.
112 grass samples collected in the Gran Paradiso National Park during 2013 estimate biomass, crude protein, fiber content, and digestibility. Data were used to validate the predictability of forage quality from remotely sensed data. The dataset was created by Luigi Ranghetti and colleagues for a 2016 publication in the European Journal of Remote Sensing.
A survey of 357 university students in Porto Alegre, Brazil, collected personal, anthropometric, and eating behavior data. The study, authored by Rosenir Korpalski de Souza, analyzed the relationship between self-perception of eating habits and adherence to national dietary guidelines. Results indicate 55.5% of students did not consider their eating habits healthy, with low observance of most recommended dietary steps.
36 plots across two Spanish sites were scanned with a Leica HDS6200 scanner at 3.2mm resolution. Individual trees were segmented, zero-centered, and downsampled to 5 cm for species classification using deep learning. Harry Owen from the University of Cambridge created this dataset, which includes species IDs for five tree types and metadata on single or multi-stem status.
Official statistics from the Scottish Government, last updated on 2026-07-08, track greenhouse gas emissions and removals from Land Use, Land Use Change and Forestry (LULUCF). The data likely contains annual estimates for Scotland, quantifying the carbon flux from activities like afforestation, deforestation, and land management. It is designated as Official Statistics not designated as National Statistics.
LAMACLIMA experiments provide climate model outputs for training the TIMBER v0.1 model. The data includes local climate signals extracted from simulations of full cropland and forest expansion scenarios. Datasets were authored by Shruti Nath of ETH Zurich and include surface temperature responses from models like MPI-ESM, EC-EARTH, and CESM2.
OSNI Open Data provides a complete listing of all streets in Northern Ireland with their official names and Irish Grid coordinates. This gazetteer is published for open data use under the LPS Open Government Data Licence. The dataset is accessible via multiple formats including CSV, JSON, and ESRI Shapefile, though its API requires specific geospatial software for access.
NESP TWQ Project 2.1.4 data evaluates gully remediation options in the Burdekin Region from 2016 to 2018. The project, associated with the Australian Ocean Data Network, assesses impacts on downstream water quality, project cost-effectiveness, and agricultural production. This work underpins the Reef Trust Gully Erosion Control Programme, aiming to reduce anthropogenic sediment delivery to the Great Barrier Reef.
Project 25 focuses on a key cane growing region within the Great Barrier Reef catchment area from 2018 to 2020. It uses a bottom-up approach with small, sub-catchment scale water quality monitoring to identify 'hot spot' sub-catchments. The project emphasizes industry ownership of monitoring design to deliver locally targeted data for on-farm decision-making.
NESP TWQ Project 3.1.7 developed field trials to test rehabilitation treatments for large active alluvial gullies, which are major sources of sediment and nutrients to the Great Barrier Reef lagoon. The project was conducted in collaboration with delivery partners like the Reef Trust, Greening Australia, WWF, and GBRF. The dataset, hosted by the Australian Ocean Data Network, was last updated on 2026-07-10.
Over 50 land-sourced pesticides have been detected in the waters of the Great Barrier Reef. This dataset from the Australian Ocean Data Network quantifies the toxicity of 'alternate' pesticides to freshwater and marine species to improve water quality guidelines and risk assessments. The project was active from 2017 to 2019.
Australian sugarcane farming data from the Wet Tropics region, collected between 2017 and 2019 under the NESP TWQ Project 3.1.8. The project developed a framework for commercial insurers to underwrite the risk of nitrogen practice change to reduce DIN exports. It was contributed by the Australian Ocean Data Network and last updated in July 2026.
Ormesby, North of Long Bank Farm in Redcar and Cleveland, England, is covered by this detailed Agricultural Land Classification (ALC) survey. The dataset includes scanned original paper maps and reports from a site survey conducted between 1989 and 1999 by the Ministry of Agriculture, Fisheries and Food. It uses the grading methodology from "Agricultural Land Classification of England and Wales," with Grade 3 subdivided into subgrades 3a and 3b.
Post 1988 Agricultural Land Classification (ALC) site survey data for the Andover, Picket Twenty Farm site in Test Valley. The dataset includes scanned original paper maps and survey reports from detailed site surveys conducted between 1989 and 1999, using the grading methodology from the 'Agricultural Land Classification of England and Wales' guidelines. It contains detailed land grading, including subgrades 3a and 3b, with maps at scales from 1:5,000 to 1:50,000 and unedited sample point soils data for some surveys.
Post-1988 Agricultural Land Classification (ALC) site survey data comprises scanned original paper maps and reports for individual sites surveyed in detail between 1989 and 1999 by the Ministry of Agriculture, Fisheries and Food. Surveys map land grades, including the subdivision of Grade 3 into subgrades 3a and 3b, using the methodology from the 'Agricultural Land Classification of England and Wales' guide. Individual sites were mapped at scales from 1:5,000 to 1:50,000, with unedited sample point soils data and soil pit descriptions available for some surveys.
Scanned original paper maps and survey reports detail the Agricultural Land Classification for a specific site in Congleton Borough, surveyed between 1989 and 1999. The data includes the subdivision of Grade 3 land into subgrades 3a and 3b, following the official UK methodology. Unedited sample point soils data and soil pit descriptions are available for some of these detailed surveys.
Agricultural Land Classification (ALC) survey data for a site near Haydock, England, conducted between 1989 and 1999. The dataset includes scanned original paper maps and survey reports from detailed site surveys by the Ministry of Agriculture, Fisheries and Food, using the grading methodology from the "Agricultural Land Classification of England and Wales" guidelines. It contains unedited sample point soils data and soil pit descriptions for some surveys, with maps at scales ranging from 1:5,000 to 1:50,000.