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1,648 results for “farming”
GRIME AI Water Segmentation Model for the USGS Monitoring Site Discovery Farms Waterway AO1 Near Antigo, WI, 2023-2024
Ground-based observations from fixed-mount cameras have the potential to fill an important role in environmental sensing, including direct measurement of water levels and qualitative observation of ecohydrological research sites. All of this is theoretically possible for anyone who can install a trail camera. Easy acquisition of ground-based imagery has resulted in millions of environmental images stored, some of which are public data, and many of which contain information that has yet to be used for scientific purposes. The goal of this project was to develop and document key image processing and machine learning workflows, primarily related to semi-automated image labeling, to increase the use and value of existing and emerging archives of imagery that is relevant to ecohydrological processes. This data package includes imagery, annotation files, water segmentation model and model performance plots, and model test results (overlay images and masks) for the USGS monitoring location Discovery Farms Waterway AO1 Near Antigo, WI (2023-2024). All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/WI_AO1_STAFF for this specific data set) and/or the National Imagery Management System (NIMS) API. Water segmentation models were created by tuning the open-source Segment Anything Model 2 (SAM2, https://github.com/facebookresearch/sam2) using images that were annotated by team members on this project. The models were trained on the "water" annotations, but annotation files may include additional labels, such as "snow", "sky", and "unknown". Image annotation was done in Computer Vision Annotation Tool (CVAT) and exported in COCO format (.json). All model training and testing was completed in GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI, https://gaugecam.org/) software (Version: Beta 16). Model performance plots were automatically generated during this process.
Raw microclimate data from plots at burned areas from the 2020 Holiday Farm fire in the Andrews Experimental Forest and Hagan Block, 2022-2024
This dataset includes a suite of microclimate sensor data from areas burned by the 2020 Holiday Farm fire within the McKenzie River basin. A total of 42 microclimate sensor suites were installed in July and August of 2022 distributed across RS01, RS08, RS15, WS02, WS09, WS01, HGBK. All sensor locations are within the Holiday Farm Fire footprint and within Permanent Sample Plots (PSPs). An additional sensor was placed within the Primary meteorological station (PRIMET) of the HJ Andrews Experimental Forest for comparison and calibration between open-air measurements. Sites are stratified across three treatment variables including 1) fire severity: high or low; 2) management: managed or unmanaged; 3) water balance: moister or drier topographic positions. This resulted in eight treatment blocks, each with 5 sensor suite replicates. Each microclimate site includes a suite of measurements: Hobo temperature and relative humidity sensor installed at 1.5m within a gill shield (recording at 30 minute interval), one TOMST TMS-4 temperature and soil moisture sensor was located within 1 m of plot center (recording at 15 minute interval). The TOMST sensors include air temperature sensors at 15cm, 2cm, and soil temperature at -6cm in soil near-surface. Soil moisture is measured across the ~10cm near surface zone.
Soil Carbon and Nitrogen at the Harvard Farm at Harvard Forest since 2015
These data represent baseline soil bulk density and total soil carbon and nitrogen concentrations for the three grazing treatments at Harvard Farm: hay (no grazing), rotational grazing, and intensive grazing. These samples were collected at the beginning of the study, so any differences across plots or treatments are due to inherent soil variability at the site rather than the treatments themselves. These data were collected to establish the starting conditions with which to compare potential treatment effects at later sampling dates.
Vascular Flora Surveys of Permanent Plots at the Harvard Farm at Harvard Forest since 2015
The objectives of this study were to: (1) Determine the species richness and cover of vascular flora in 27 permanent plots at the Harvard Farm (established in HF236). (2) Track any changes in species richness and species dominance in the plant communities at each plot over time. (3) Observe any changes in invasive species richness and cover over time. (4) Identify differences between plant community composition of plots in constant grazing, rotational grazing, and mowing areas. These data were collected by Harvard Forest interns and may include some misidentifications, but they should be considered accurate for larger scale analyses of changes in the density of growth forms and invasive species. For more accurate species-level information, please see data collected every five years by Glenn Motzkin (HF236 with data from 2014 and 2019).
PhenoCam Images and Canopy Phenology at the Harvard Forest Farm since 2015
The PhenoCam Network uses imagery from digital cameras to track vegetation phenology and seasonal changes in vegetation activity in diverse ecosystems across North America and around the world. Imagery is uploaded to the PhenoCam server at the University of New Hampshire, where it is made publicly available in near-real time, every 30 minutes from sunrise to sunset, 365 days a year. The data are processed using simple image analysis tools to yield a measure of canopy greenness, from which phenological metrics are extracted, characterizing the start and end of the growing season. These transition dates have been shown to align well with on-the-ground observations of tree phenology at Harvard Forest (HF003). Long-term PhenoCam data can be used to track the impact of climate variability and change on the rhythm of the seasons. This dataset contains one mid-day image for each camera. Please see the PhenoCam Network website (https://phenocam.nau.edu/webcam/) for more information and additional images.
Fish Food on Floodplain Farm Fields, California Central Valley, Seasons 2019 and 2021
2019 Water Year (October 1, 2018 through September 30, 2019) In the winter and spring of 2018-2019, 5,000 acres of agricultural land in Yolo County, California was intentionally flooded. These “dry-side” rice fields, although on the former floodplain of the Sacramento River, are separated from the fish-bearing Sacramento River (the “wet-side”) by high flood levees. Today, levees cut off 95% of the Central Valley’s floodplains from river channels so that Central Valley aquatic ecosystems no longer recruit floodplain the food web resources needed to support robust aquatic food webs, create fish biomass and sustain abundant fish populations. In this experiment we asked whether floodplain food web resources “grown” in intentionally inundated “dry-side” agricultural fields could be exported back to the river via flood drainage infrastructure. If so, we were interested to know whether those resources could improve juvenile salmon foraging success and increase growth rates. In order to test these questions, we caged fish in the floodplain drainage canal, at the location where the floodplain drainage water entered the river and at locations both up- and downstream. We hypothesized that zooplankton abundance and fish growth rates would be elevated at the managed floodplain outfall location, relative to the upstream location. We measured water quality parameters, zooplankton species assemblage and abundance, and juvenile Chinook salmon growth rates with PIT tagged, hatchery-origin fish confined to enclosures at the study locations. The 5,000 acres of managed floodplain was drained over the coarse of 5 weeks in February and March, 2019 at a maximum rate of 1,000 cfs. The Sacramento River flow during the experiment ranged from 20,000-30,000 cfs. Fish growth rates at the floodplain outfall location were up to five times greater than growth rates upstream of the outfall and enclosure fish experienced growth rate benefits at least up to a mile downstream from the managed floodplai
Vascular Flora of the Harvard Farm at Harvard Forest 2014
The objectives of this study were to: (1) Inventory the vascular flora of the former Petersham Country Club (now the Harvard Farm). (2) Collect voucher specimens for the HF Herbarium of species that were not previously documented from Harvard Forest, or that were found historically at HF but were not recorded in 2004–2007 by Jenkins et al. (2008). (3) Establish and sample a series of permanent plots to characterize current vegetation composition, and to enable evaluation of vegetation change over time. (4) Sample soils within permanent plots to document initial conditions and to facilitate future work on soil dynamics and ecosystem processes.
National Phenology Network tree phenology at Crosby Farm Adaptive Silviculture for Climate Change study, 2021-2025
Phenology is the study of relations between climate and periodic biological phenomena, such as bud break or leaf drop in deciduous trees. Phenology is a leading indicator of climate change, and the response of urban tree species to climate can help inform how to manage for a more resilient, and adaptive urban tree canopy. This dataset contains tree phenology data from the The Mississippi National River and Recreation Area (MNRRA) Urban Affiliate Adaptive Silviculture for Climate Change (ASCC) project located at Crosby Farm Regional Park. This dataset includes Individual Phenometrics, Site Phenomentrics, Status and Intensity, and Magnitude Phenometrics. This data was collected through mobile app submissions to Nature's Notebook and downloaded from the National Phenology Network Observation Portal, filtered by date range 01/01/2021 to 02/26/2024 and for Crosby Farm ASCC. Data Attribution: USA National Phenology Network. 2024. Plant and Animal Phenology Data. Data type: Status & Intensity, Individual Phenometrics, Site Phenometrics, Magnitude Phenometricts. 01/01/2021-02/26/2024 for Region: 45.221627°, -92.554965° (UR); 44.599185°, -93.5712° (LL). USA-NPN, St. Paul, Minnesota, USA. Data set accessed 03/19/2024 at http://doi.org/10.5066/F78S4N1
Data supplement for "Alignment of scanning lidars in offshore wind farms" - Wind Energy Science Journal
<p>These data are supplements for the calculations of the methods from the article "Alignment of scanning lidars in offshore wind farms".<br> The data was used to produce the results from the publication and is intended to be used here as sample data for illustrative purposes.</p>
Soil organic carbon and nutrient dynamics in response to anaerobic digestate application to farm fields, Eastern Iowa, 2011-2023
This dataset documents a long-term, field-scale study of anaerobic digestate application on commercial croplands in eastern Iowa, USA. It includes detailed records of digestate composition, application rates, and timing, as well as soil test results collected over a 12-year period (2011–2023) from 14 agricultural fields. The dataset supports analysis of soil organic carbon (SOC), nutrient dynamics, and isotopic composition in response to digestate inputs. It contains 421 georeferenced soil samples, digestate nutrient profiles, field management histories, and spatial boundaries. The data were collected as part of a collaborative effort between researchers at Iowa State University and Sievers Family Farms to evaluate the agronomic and environmental implications of integrating anaerobic digestion into row crop and livestock systems.
PIE LTER, meteorological data, 15 minute intervals, from the Marshview Farm weather station located in Newbury, MA, year 2021
Meteorological measurements for 2021 at MBL Marshview Farm, Newbury, MA. Sensors conduct measurements every 5 seconds and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.
PIE LTER 15-minute meteorological data from the Marshview Farm weather station located in Newbury, MA, year 2022
Meteorological measurements for 2022 at MBL Marshview Farm, Newbury, MA. Sensors conduct measurements every 5 seconds and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.
PIE LTER 15-minute meteorological data from the Marshview Farm weather station located in Newbury, MA, year 2023
Meteorological measurements for 2023 at MBL Marshview Farm, Newbury, MA. Sensors conduct measurements every 5 seconds and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.
Visuo-motor dataset recorded from a micro-farming robot
<p>This is the accompanying dataset of the paper [1] describing algorithms for intrinsic motivation and episodic memory on the Sony LettuceThink microfarming robot.</p> <p>The LettuceThink microfarming robot developed by Sony Computer Science Laboratories consists of an aluminium frame with an X-Carve CNC machine mounted on it. The CNC machine is used to provide 3-axes movements to a depth camera (Sony DepthSense) mounted at the tip of the vertical z-axis (the end-effector camera). In the experiments presented in the paper, the end-effector camera is facing top-down and only two motors are used (x and y).</p> <p>A simulator of the LettuceThink robot has been developed to ease the testing of different configurations of the learning system. The simulator generates sensorimotor data from requested trajectories of the end-effector camera. Knowing the initial position of the CNC machine and the target position, the simulator linearly interpolates the trajectory and returns the intermediate positions of the camera together with the images captured from each specific position. The sensorimotor data returned by the simulator have been prerecorded by performing a full scan of the (x,y) plane of the CNC machine using a resolution of 5mm. This resulted in 24,964 images, each mapped to an (x,y) position of the CNC machine. The dataset published here contains these images.</p> <p>In particular, the dataset consists of a set of images, each named with the specific position of the 2 motors of the robot. A python script for generating visuo-motor trajectories (sequences of data consisting of [image, motor_x, motor_y]) from this dataset is available at the following github page: <a href="https://github.com/guidoschillaci/sonylettucethink_dataset">https://github.com/guidoschillaci/sonylettucethink_dataset</a></p> <p>Provided with the dataset is also a python script that allows to easily read the images and to generate trajectories (returning</p> <p>This work has been supported by the EU-H2020 ROMI Project and by the EU-H2020 Marie Sklodowska Curie project "Predictive Robots" (grant agreement no. 838861)References:</p> <p>[1] Schillaci, G., Villalpando, A. P., Hafner, V. V., Hanappe, P., Colliaux, D., & Wintz, T. (2020). Intrinsic Motivation and Episodic Memories for Robot Exploration of High-Dimensional Sensory Spaces. arXiv preprint arXiv:2001.01982.</p>
Survey Data on Apple Farming in China: Agronomic Management, Advisory Channels, and Profitability
<p>The Survey results and original data are stored in a directory structured as the table:</p> <table style="width: 100%; height: 223.938px;"> <tbody> <tr style="height: 19.5938px;"> <td style="width: 18.8269%; height: 19.5938px;"><strong>Type</strong></td> <td style="width: 21.7597%; height: 19.5938px;"><strong>File Name</strong></td> <td style="width: 59.4134%; height: 19.5938px;"><strong>Description</strong></td> </tr> <tr style="height: 47.5938px;"> <td style="width: 18.8269%; height: 47.5938px;"> <p>Raw_Data_Spearate_Source</p> </td> <td style="width: 21.7597%; height: 47.5938px;">raw_data_english_telephone.xlsx</td> <td style="width: 59.4134%; height: 47.5938px;">Translated data in English corresponding to the Chinese telephone interview data</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 18.8269%; height: 19.5938px;"> </td> <td style="width: 21.7597%; height: 19.5938px;">raw_data_english_wechat.xlsx</td> <td style="width: 59.4134%; height: 19.5938px;">Translated data in English corresponding to the Chinese Wechat Mini Program data</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 18.8269%; height: 19.5938px;">Raw_Data_Total</td> <td style="width: 21.7597%; height: 19.5938px;">raw_data_english_total.xlsx</td> <td style="width: 59.4134%; height: 19.5938px;">Combined data from raw_data_english_telephone.xlsx and raw_data_english_wechat.xlsx</td> </tr> <tr style="height: 39.1875px;"> <td style="width: 18.8269%; height: 39.1875px;">Apple_Statistical_Data</td> <td style="width: 21.7597%; height: 39.1875px;">apple_2022_statistical_data.xlsx</td> <td style="width: 59.4134%; height: 39.1875px;">Contains data on apple planting area, production, and yield sourced from the China Statistics Bureau, along with the number of survey questionnaires collected from various provinces</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 18.8269%; height: 19.5938px;"> </td> <td style="width: 21.7597%; height: 19.5938px;">province_eng.xlsx</td> <td style="width: 59.4134%; height: 19.5938px;">Contains the English version of the provinces' names</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 18.8269%; height: 19.5938px;">Map_Boundary_line</td> <td style="width: 21.7597%; height: 19.5938px;">national_boundary_line.shp</td> <td style="width: 59.4134%; height: 19.5938px;">The country boundaires of China</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 18.8269%; height: 19.5938px;"> </td> <td style="width: 21.7597%; height: 19.5938px;">province_boundary.shp</td> <td style="width: 59.4134%; height: 19.5938px;">The province boundaries of China</td> </tr> </tbody> </table> <p>For privacy reasons, personally identifiable information such as respondents’ names, telephone numbers, and specific addresses has been anonymized in the dataset. The file <em>raw_data_english_total.xlsx</em> contains 96 columns, each corresponding to a question in the questionnaire.</p>
20211118_CircAgro_Primary data of Lungau farms and MDF
<p>For a description of the dataset, please see: Readme file for 20211118_CircAgro_Primary data of Lungau farms and MDF.txt</p>
Online survey among students and teachers in the Swiss farm management course
<p>This dataset contains survey data including the codebook for an online survey conducted in German and French in Switzerland in spring 2021. With this survey, we aimed to find out what students learn and what teachers teach in this course about digital technologies in agriculture. </p>
A multi-level network tool to trace wasted water from farm to fork and backward
<p>NETFLOW - Network-based 13 Evaluation Tool for Food LOss and Waste<br>V 0.1</p> <p>####################################################################################################################################################</p> <p><br>Authors:<br>Francesco Semeria - Politecnico di Torino - francesco.semeria@polito.it<br>Marta Tuninetti - Politecnico di Torino<br>Luca Ridolfi - Politecnico di Torino</p> <p>####################################################################################################################################################</p> <p>CONTENT OF THIS ARCHIVE</p> <p>The listed files contain output data from the NETFLOW tool and assess the impact on water resources of food loss and waste (FLW) for wheat an its main derived products (flour, bran, pasta and bread).</p> <p>In particular, they quantify such impact offering two perspectives: <br> 1. supply-side, from FLW associated to food consumption backwards to the countries of production;<br> 2. utilisation-side, from the countries of production forward to the countries where FLW occurs.</p> <p>It should be noted that the two perspectives allow to identify two different aspects of the FLW issue.</p> <p><br>List of files:</p> <p>data_fig2_ita_supply_vw.xlsx = output data regarding the supply network of Italy.<br>data_fig3_usa_utilisation_vw.xlsx = output data regarding the utilisation network of the United States.<br>data_fig4_global_supply_vw.xlx = output data regarding the global supply network.</p> <p><br>Modelling scripts are currently available upon request.</p>
Costal operating wind farms: two datasets with concurrent SCADA, LiDAR and turbulent fluxes
<p>This data collection consists of two datasets from a micrometeorological experiment conducted in two distinct operating wind farms in a coastal area of the northeast region of Brazil, called Pedra do Sal Wind Farm (UEPS) and Beberibe Wind Farm (UEBB). These wind farms are located on the northeast coast of Brazil where meteorological conditions are strongly influenced by trade winds and sea breeze. Both datasets represent a full-year of measurements from August/2013 to July/2014.</p> <p>On both operating wind farms it was commissioned a fully instrumented IEC-compliant 100m met mast, with five levels of first-class calibrated cup anemometers and one level (100m) with 3D sonic anemometer. Additionally at UEPS there's an extra 3D sonic at 20m height on the met mast, as well as a VAISALA LEOSPHERE Windcube8 doppler wind lidar with a range up to 500m height and located 2.5D upwind of one of the wind turbines.</p> <p>The Pedra do Sal wind farm (UEPS) has an installed capacity of 18MW, with 20 Enercon E-44 installed at 55m a.g.l. At Beberibe wind farm (UEBB) there are 32 Enercon E-48 wind turbines installed at 75m a.g.l. The dataset includes 10min SCADA data for all wind turbines on both wind farms.</p> <p>This dataset has a high-quality combination of meteorological, SCADA and turbulent flux data of two operating wind farms in Brazil. During a full-year of measurements both datasets had a high data recovery rate (see attached tables). The dataset has already been used to assess the impact of atmospheric stability on the wind farm performance, as well as the effect of mesoscale patterns on the wind profile and wind farm power production. Recirculation of the sea breeze and the development of an internal boundary layer upwind the wind turbines were also characterized.</p> <p>For more details on the experimental layout, wind turbine locations, meso and microcale wind conditions and any other information not stated in the NetCDF4 files, please refer to the reference material or contact one of the authors.</p> <p> </p>
Soil resistance and soil moisture data of organic, permaculture and conventional horticultural farms of Central Hungary
<p>This dataset has been produced from the PhD research of Alfréd Szilágyi supervised by Csaba Centeri and Eszter Kovács Tormáné. The study compared permaculture, organic and conventional farming systems regarding their ecosystem-service provision potential and sustainability. Multiple ecological indicators were measured in the field during the field study in 2020, and the basic datasets (soil test results; photo gallery of the studied farms with soil core sample; soil resistance and moisture; decomposition; earthworms; nematodes; soil surface fauna; pollinators; agrobiodiversity and habitat types) are uploaded in Zenodo separately to provide scientific data on permaculture systems. In this way, we hope to contribute to international efforts to evaluate the performance of agroecological agriculture alternatives. These publications also serve as supplements to the PhD thesis. For the sake of further usability of the datasets short description of the used methods is described. For further information please contact the authors.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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