Find research datasets worth reusing

opencc-zeroNov 2023View details →

Hot spots and hot moments of greenhouse gas emissions in agricultural peatlands

Drained agricultural peatlands occupy only 1% of agricultural land but are estimated to be responsible for approximately one-third of global cropland greenhouse gas emissions. However, recent studies show that greenhouse gas fluxes from agricultural peatlands can vary by orders of magnitude over time. The relationship between these hot moments (individual fluxes with disproportionate impact on annual budgets) of greenhouse gas emissions and individual chamber locations (i.e. hot spots with disproportionate observations of hot moments) is poorly understood but may help elucidate patterns and drivers of high greenhouse gas emissions from agricultural peatland soils. We used continuous chamber-based flux measurements across three land uses (corn, alfalfa, and pasture) to quantify the spatiotemporal patterns of soil greenhouse gas emissions from temperate agricultural peatlands in the Sacramento-San Joaquin Delta of California. We found that the location of hot spots of emissions varied over time and were not consistent across annual timescales. Hot moments of nitrous oxide (N2O) and carbon dioxide (CO2) fluxes were more evenly distributed across space than methane (CH4). In the corn system, hot moments of CH4 flux were often isolated to a single location but locations were not consistent across years. Spatiotemporal variability in soil moisture, soil oxygen, and temperature helped explain patterns in N2O fluxes in the annual corn agroecosystem but was less informative for perennial alfalfa N2O fluxes or CH4 fluxes across ecosystems, potentially due to insufficient spatiotemporal resolution of the associated drivers. Overall, our results do not support the concept of persistent hot spots of soil CO2, CH4, and N2O emissions in these drained agricultural peatlands. Hot moments of high flux events generally varied in space and time and thus required high sample densities. Our results highlight the importance of constraining hot moments and their controls to better quantify ecosystem greenhouse gas budgets.

opencc-by-4.0Nov 2023View details →

Anastrepha pests captures and FTD index in protected and agricultural sites

Data on captures and FTD index of  fruit flies Anastrepha ludens, A. obliqua, A. striata and A. serpentina in five monitoring sites within a protected natural area and agricultural zones in southern Tamaulipas state, Mexico.

opencc-by-4.0Nov 2023View details →

Soil grid data for 4 agricultural fields in PT (ECe; soil organic carbon, pH)

Soil data collected in an agricultural area with annual crops in Portugal (Lezíria Grande). The data refers to soil properties of 63 soil samples collected at a depth of 0-20 cm, considering a regular sampling grid, in four fields with varying soil salinity (field areas between 2 and 34 ha). The samples were collected at a period when the soil was bare, following the harvest of the annual crops, and pictures of the soil surface were taken for eventual correction of corresponding remote sensing imaging. The data includes: soil organic carbon (SOC) (Walkley-Black method), soil water content, electric conductivity of the saturated soil paste (ECe), EC1:5, and  pH1:5. The data may be representative of the soil conditions of the area, which is a highly productive agricultural low land, prone to the development of soil salinity as a result of the rise of saline groundwater and/or irrigation. The data can be used to establish relations between soil salinity (ECe) and other soil properties as well as build prediction models of the soil properties from remote sensing namely, for developing models for SOC prediction under the STEROPES project (WP5 (WP5-T3) and WP2 (WP2-T3)).The aim of the collected dataset was to be able to analyze the influence of soil salinity in SOC prediction from remote sensing.Data in the form of MS Excel files (xlsx), pictures of the soil surface in jpg. format.

opencc-by-4.0Nov 2023View details →

Multimodal Agricultural Aerial and Ground Robotics Simulation Dataset

Dataset descriptionThis dataset was generated using an aerial robot and a ground robot in the Webots simulator with the <a href="https://github.com/opendr-eu/opendr/tree/master/projects/python/simulation">OpenDR agricultural dataset generator tool</a>.It consists of 13980 RGB images and their semantic segmentation counterparts taken at different lighting conditions and robot positions in an agricultural field. It also includes the annotation data comprised of the class of the object, x, and y of the top left pixel of the object bounding box, and the width and height of the object bounding box. Furthermore, it includes gps and inertial unit sensor data for UAV and gps, inertial and lidar sensor data for UGV.Folder configurationThe dataset contains 4 folders for different lighting conditions:<ul><li>noon cloudy</li><li>noon stormy</li><li>dawn cloudy</li><li>dusk</li></ul>Each contains UAV and UGV folders. UAV folder includes:<ul><li>annotations: contains segmented images in JPG files and annotations in TXT files.</li><li>camera: contains generated RGB images.</li><li>gps: contains the three-axis location of global positioning sensor saved in TXT files.</li><li>inertial unit: contains the inertial unit date in TXT files.</li></ul>UGV folder includes:<ul><li>annotations: contains segmented images in JPG files and annotations in TXT files.</li><li>front_bottom_camera: contains generated RGB images.</li><li>Hemisphere_v500: contains the three-axis location of the global positioning sensor saved in TXT files.</li><li>imu_robotti: contains the inertial unit date in TXT files.</li><li>velodyne: contains lidar data in PCD files.</li></ul>Data formatThe dataset includes<ul><li>The inertial measurement TXT files include Euler angles in order of Roll, Pitch, and Yaw.</li><li>The GPS measurement TXT files include the robot position in x, y, and z order.</li><li>Object annotation TXT files include the class of the object, x, and y of the top left pixel of the object bounding box, and the width and height of the object bounding box at each line for the corresponding frame.</li></ul>File naming conventionEach data is named "s_i{_segmented, _annotation}.ext", where:<ul><li>s denotes the simulated time in seconds.</li><li>i denotes the index counting every 10ms of simulated time.</li><li>ext denotes the extension, "jpg" for images, "pcd" for lidar, and "txt" for the rest.</li><li>Labels _segmented and _annotation appended to the name for segmentation image and object annotations, respectively.</li></ul>Each segmented image uses the following RGB color mapping:<ul><li>Tree: 0.1, 0.4, 0.0</li><li>Apple Tree: 0.85, 0.49, 0.57</li><li>Cow: 0.380, 0.220, 0.137</li><li>Sheep: 0.937, 0.921, 0.862</li><li>Fox: 0.992, 0.376, 0.086</li><li>Barn: 0.625, 0.293, 0.226</li><li>Cat: 0.870, 0.580, 0.0</li><li>Deer: 0.415, 0.364, 0.302</li><li>Human: 1.0, 0.855, 0.672</li></ul>

opencc-by-4.0Dec 2023View details →

Replication data and code for "The Spatiotemporal Pattern of Surface Ozone and Its Impact on Agricultural Productivity in China"

This dataset contains code and data to replicate the results for "The Spatiotemporal Pattern of Surface Ozone and Its Impact on Agricultural Productivity in China" by Xiaoguang Chen, Jing Gao, Luoye Chen, Madhu Khanna, Binlei Gong, and Maximilian Auffhammer.

opencc-by-4.0Sep 2023View details →

Livestock increasingly drove global agricultural emissions in 1910-2015

supplementary material

opencc-zeroJan 2024View details →

Data from: Urbanisation and agricultural intensification modulate plant-pollinator network structure and robustness

Land use change is a major pressure on pollinator abundance, diversity, and plant-pollinator interactions. Far less is known about how land use alters the structure of plant-pollinator networks and their robustness to plant-pollinator coextinctions. We analyzed the structure of plant-pollinator networks sampled in 12 landscapes along an urbanisation and agricultural intensity gradient, from early spring to late summer 2021, and used a stochastic coextinction model to correlate plant-pollinator coextinction risk with network structure (species and network-level metrics) and landscape context. Networks in intensively managed (i.e. agricultural and urban) landscapes had a lower risk of initiating a coextinction cascade, while networks in less-intensively managed landscapes may be less robust. Network structure modulated the frequency and severity of coextinctions and species loss, while the strength of species interactions increased robustness. Urban networks were more species-rich and symmetrical due to the high diversity of ornamental plants, while intensively managed agricultural landscapes had smaller, more tightly connected, and nested networks. Network structure modulated the frequency of extinctions, which was decreased by greater linkage density, interaction asymmetry, and interaction dependence in the networks, while once an extinction occurred, nestedness and linkage density propagated the degree of the coextinction cascade and species loss. At the species level, species strength was inversely correlated with extinction risk, implying that generalist species with a high number of interactions with specialists had the lowest extinction risk.

opencc-zeroJan 2024View details →

Oregon hydrologic area agricultural field boundaries and field level and hydrologic unit water use data

The data was developed for the USGS Water-Use and Data Research program grant opportunities G20AS00053 and G21AS00258, combined with fundnig from Oregon Water Resources Department to improve estimates of water use from irrigated lands in Oregon. These data contain attributes of irrigation status, irrigation source type, crop type, irrigation method, assumed irrigation efficiency, irrigation water source, evapotranspiration (ET) data from OpenET, and effective precipitation developed using the USBR ET Demands model. Thee data were aggregated in order to further the development of estimates of applied water at the field-scale.

opencc-zeroJan 2024View details →

Selection of a diversionary field and other habitats by large grazing birds in a landscape managed for agriculture and wetland biodiversity

Several populations of cranes, geese, and swans are thriving and increasing in modern agricultural landscapes. Abundant populations are causing conservation conflicts, as they may affect agricultural production and biodiversity negatively. Management strategies involving provisioning of attractive diversionary fields where birds are tolerated can be used to reduce negative impact to growing crops. To improve such strategies, knowledge of how the birds interact with the landscape and respond to current management interventions is key. We used GPS locations from tagged common cranes (Grus grus) and greylag geese (Anser anser) to assess how they use and select differentially managed habitats, such as diversionary fields to decrease impact on agriculture and wetlands protected for biodiversity conservation. Our findings show a high probability of presence of common cranes and greylag geese in the protected area and in the diversionary field, but also on arable fields, potentially causing negative impact on agricultural production and wetland biodiversity. We outline recommendations for how to improve the practice of diversionary fields and complementary management to reduce risk of negative impact of large grazing birds in landscapes tailored for both conservation and conventional agriculture.

opencc-by-4.0Jun 2023View details →

Supplementary Tables for Can leafhoppers help us trace the impact of climate change on agriculture?

Supplementary Tables for the Preprint entitled: Can leafhoppers help us trace the impact of climate change on agriculture? to be posted in bioRxiv.  Table S1. Detailed information on the strawberry fields included in this study. Table S2. Detailed information on the weather stations used to retrieve temperature and precipitation data used in this study  Table S3. Strawberry samples analyzed in this study with symptoms resembling strawberry green petal phytoplasma disease during both growing seasons studied here. Table S4. The geographic location of all the strawberry green petal phytoplasma disease cases reported to the provincial laboratory in expertise in diagnostic and phytopathology in the last decade. Table S5. Leafhopper species and the number of specimens per species analyzed by phytoplasma-specific PCR to detect the presence of the pathogen. Table S6. Detailed information on the leafhoppers incubated with strawberry plants during the phytoplasma transmission assays. Table S7. Detailed information on Macosteles quadrilineatus used to study the leafhopper microbiome. Table S8. Detailed information on the insecticides used by strawberry growers during both grow seasons included in the study and those treatments selected for further statistic analyses. Table S9. Identification and number of leafhopper species captured in strawberry fields in each geographic region screened in this study. Table S10. Detailed information of diversity indexes Shannon and Simpson calculated using the data collected in this study. Table S11. Fixed days and temperature values used during leafhopper populations modelling. Table S12. Detailed information on the taxonomy of the phytoplasma strain SbGPQ affecting strawberry plants in eastern Canada by hybridization and illumine sequencing and by PCR amplification, cloning and Sanger sequencing. Table S13. Detailed information on Macosteles quadrilineatus microbiome including OTUs, reads, and metadata information. Table S14. Detailed information on the core microbiome for Macosteles quadrilineatus captured during each growing season and in common for all the leafhoppers analyzed during this study. Table S15. BIC values for models selection.

opencc-by-4.0Mar 2024View details →

Assessing Heavy Metal Contamination in Agricultural Soils: A Predictive Model Integrating GIS Tools and Probability-Risk Matrix – Case Study: Guarda Region, Portugal

In these files we can find the final risk map of heavy metal contamination for the guarding area in Portugal obtained according to the methodology explained in the paper "Assessing Heavy Metal Contamination in Agricultural Soils: A Predictive Model Instegrating GIS Tools and Probability-Risk Matrix - Case Study: Guarda Region (Portugal) Final Risk Equal.tiff:  GeoTiff with a pixel size of 30m. EPSG:3763 - ETRS89 / Portugal TM06 Also attached is the symbolisation for the image in .qml (Quantum GIS Layer Style File) format. A file called RISK RECLASS is also available, where you can find the risk classification maps for each of the studied factors:  <ul> <li>Proximity to roads</li> <li>Proximity to industrial areas</li> <li>Ph</li> <li>Soil organic content</li> <li>Slope</li> <li>Soil texture</li> <li>Mining extraction areas </li> <li>Drainage</li> </ul> finally a DATABASE file where the data of the 360 points for the calculation of the risk maps can be found.

opencc-by-4.0Mar 2024View details →

Determinants of climate-smart agriculture adoption and crop productivity among smallholder farmers in Nyimba district, Zambia

Data was collected among smallholder farmers' households in the Nyimba district of Zambia in a view to find determinants for crop productivity and adoption of climate-smart agriculture practices.

opencc-zeroMar 2024View details →

Wildfire, prescribed burn, and agricultural burn smoke PM2.5 estimates for CA, WA, and OR 2014-2020

Wildfires, prescribed burns, and agricultural burns all impact ambient air quality across the Western U.S.; however, little is known about how communities across the region are differentially exposed to smoke from each of these fire types. To address this gap, we quantify smoke exposure stemming from wildfire, prescribed, and agricultural burns across Washington, Oregon, and California from 2014-2020 using a fire type-specific biomass burning emissions inventory and the GEOS-Chem chemical transport model. We examine fire type-specific PM2.5 concentration by race/ethnicity, socioeconomic status, and in relation to the Center for Disease Control's Social Vulnerability Index. Overall, population average PM2.5 concentrations are greater from wildfires than from prescribed and agricultural burns. While we found limited evidence of exposure disparities among sub-groups across the full study area, we did observe disproportionately higher exposures to wildfire-specific PM2.5 exposures among Native communities in all three states and, in California, higher agricultural burn-specific PM2.5 exposures among lower socioeconomic groups. We also identified, for all three states, areas of significant spatial clustering of smoke exposures from all fire types and increased social vulnerability. These results provide a first look at the differential contributions of smoke from wildfires, prescribed burns, and agricultural burns to PM2.5 exposures among demographic subgroups, which can be used to inform more tailored exposure reduction strategies across sources.

opencc-by-4.0Dec 2013View details →

Fig. 1 in Carabid beetle (Coleoptera: Carabidae) diversity in agricultural and post-agricultural areas in relation to the surrounding habitats

Fig. 1. Scheme of the research object "Krzywda" (a) and location of the study sites (1-8) (b).

opencc-zeroApr 2024View details →

Soil microbiome dataset from the University of Wisconsin Arlington and Lancaster agricultural research stations and cheese maker and vegetable processor wastewater land application sites

Cheese making and vegetable processing are trillion-dollar industries globally. However, they generate immense volumes of high nitrogen wastewater that must be processed safely and cost effectively. Land application systems are frequently used by rural medium and smaller processing facilities that lack ready access to wastewater resource recovery facilities. This study utilized soil microbial data to determine system differences leading to high denitrification rates observed in incubation studies in agricultural soil collected from University of Wisconsin Agricultural Research Stations (ARS), Arlington and Lancaster stations, compared to industry cheese making and vegetable processing land application water treatment facilities. It was hypothesized that decade long frequent treatment with facility wastewater would alter the microbial communities in the system soils, but this is not the case. No clear correlations were found between soil denitrification rates and biotic or abiotic system factors and the microbial communities observed in the industry systems are similar to the ARS soils under agricultural production and to literature reported denitrifying systems such as wetlands and wastewater resource recovery facilities. Knowing that land application system management does not alter the microbial biome will allow any management advances that increase denitrification efficiency in other denitrifying systems to be readily applied to industry wastewater land application facilities.

Climatologies of agriculture related solar products over Cyprus

This dataset presents a detailed 20-year climatology of solar radiation quantities related to agriculture across Cyprus, covering the period from 2004 to 2023. It includes Photosynthetically Active Radiation, plant damage and plant growth, offering a comprehensive view of solar radiation impacts at agricultural products. The data boasts a high temporal resolution of 15 minutes and a fine spatial resolution of 0.05°x0.05°. The creation of this climatology involved integrates re-analysis data and satellite observations with radiative transfer modeling. This approach allows for a detailed and accurate representation of solar radiation patterns over the island, catering to the needs of researchers and professionals in fields such as climatology, environmental science and agriculture. This dataset is associated with the upcoming publication: K. Fragkos et al., (2024). "Twenty-Year Climatology of Solar UV and PAR in Cyprus: Integrating Satellite Earth Observations with Radiative Transfer Modeling," submitted to the journal Remote Sensing. The study describes the methodology in detail and discusses the implications of these climatologies.

N2O and N2 emission from DE and PA agriculture

N2, N2O, CO2, soil moisture (%), and headspace oxygen (%) data from two corn fields in the northeast US; a coastal site in Delawre and an inland site in Pennsylvania. Emissions were measured using the nitrogen free air recirculation method (NFARM). Measurements were made a three time points over the growing season of 2014 (post-fertilization, mid-season, and pre-harvest) from plots treated with five different of fertilizer treatments (control, urea, manure, compost, and biochar).

Agricultural Fields 2D and 3D Models Dataset

Agricultural Fields 2D and 3D Models Dataset Introduction This dataset was created to address the lack of comprehensive datasets in the literature that provide necessary information to evaluate and validate path planning approaches on both 2D and 3D surfaces of agricultural fields. It comprises 30 manually-selected agricultural fields located in France, chosen to cover a diverse range of shapes and sizes (from 1.83 to 13.21 hectares). The dataset includes simple shapes that do not require field decomposition and more complex shapes that necessitate field decomposition, ensuring a broad representation of real-world scenarios.  This dataset was initially produced to validate our Complete Coverage Path Planning approach, and we are pleased to make these data available for future research. In sharing this dataset, we kindly ask that users cite this dataset in any publications or presentations that make use of the data. This will help acknowledge our contribution and encourage further collaboration and research in this area. Background Agricultural field shapes result from a complex interplay of historical, geographic, and topographic factors, as well as cultural and economic practices. Fields in countries with a more recent history of land ownership and partitioning may have simpler shapes, while those with more complex histories may have irregular shapes. Geography and topography also influence field shapes, with fields in flat, open areas having simpler shapes than those in mountainous or hilly regions. This dataset focuses on French fields due to the variety of field shapes and the availability of high-precision elevation data from the French government. Dataset Content For each of the 30 agricultural fields, this dataset provides the following information in separate files: <ul> <li>Aerial image (PNG)</li> <li>2D polygon (XML)</li> <li>2D triangulated surface (PLY) with a grid resolution of 0.25 m</li> <li>Elevation grid (PLY) with a grid resolution of 5 m</li> <li>3D triangulated surface (PLY) with a grid resolution of 0.25 m</li> <li>Set of 2D line segments representing access segments (XML)</li> <li>Set of dividing lines for fields 20-30 to decompose them into sub-polygons in different ways</li> <li>The obtained result by our "Advanced 3D Hybrid Path Planning with Multiple Objectives for complete coverage of agricultural field by wheeled robots", which includes: <ul> <li>A way-points in a CSV file</li> <li>An illustration of the result projected on the field surface</li> </ul> </li> </ul> Note: All coordinates are represented in Cartesian coordinates with centimeter precision. The table below provides links to the field data in the Géoportail platform and coordinates (longitude and latitude) of a point inside each field for all 30 fields. These links and coordinates can be used to access the data and for visualization purposes. <table> <tbody> <tr> <th>Field</th> <th>Link</th> <th>Lon / Lat</th> </tr> </tbody> <tbody> <tr> <td>1</td> <td><a href="https://bit.ly/3FYtuKu">bit.ly/3FYtuKu</a></td> <td>7.435° / 48.7732°</td> </tr> <tr> <td>2</td> <td><a href="https://bit.ly/3WGAyRI">bit.ly/3WGAyRI</a></td> <td>7.474° / 48.7825°</td> </tr> <tr> <td>3</td> <td><a href="https://bit.ly/3zX1vqJ">bit.ly/3zX1vqJ</a></td> <td>2.9205° / 49.8115°</td> </tr> <tr> <td>4</td> <td><a href="https://bit.ly/3DJL0PI">bit.ly/3DJL0PI</a></td> <td>1.6713° / 47.9864°</td> </tr> <tr> <td>5</td> <td><a href="http://bit.ly/3htb8H3">bit.ly/3htb8H3</a></td> <td>3.3216° / 50.6623°</td> </tr> <tr> <td>6</td> <td><a href="https://bit.ly/3WGTfER">bit.ly/3WGTfER</a></td> <td>7.4311° / 48.8245°</td> </tr> <tr> <td>7</td> <td><a href="https://bit.ly/3DP8vqG">bit.ly/3DP8vqG</a></td> <td>2.4845° / 50.3106°</td> </tr> <tr> <td>8</td> <td><a href="https://bit.ly/3NLmQJf">bit.ly/3NLmQJf</a></td> <td>7.5924° / 48.831°</td> </tr> <tr> <td>9</td> <td><a href="https://bit.ly/3EeTvUo">bit.ly/3EeTvUo</a></td> <td>7.4641° / 48.8146°</td> </tr> <tr> <td>10</td> <td><a href="http://bit.ly/3UOyTrv">bit.ly/3UOyTrv</a></td> <td>1.3491° / 48.012°</td> </tr> <tr> <td>11</td> <td><a href="https://bit.ly/3zW7v30">bit.ly/3zW7v30</a></td> <td>3.4701° / 46.652°</td> </tr> <tr> <td>12</td> <td><a href="http://bit.ly/3UMC6I3">bit.ly/3UMC6I3</a></td> <td>7.5742° / 48.8071°</td> </tr> <tr> <td>13</td> <td><a href="https://bit.ly/3TjkOkA">bit.ly/3TjkOkA</a></td> <td>3.578° / 46.7016°</td> </tr> <tr> <td>14</td> <td><a href="https://bit.ly/3UAmdo0">bit.ly/3UAmdo0</a></td> <td>7.4269° / 48.8194°</td> </tr> <tr> <td>15</td> <td><a href="http://bit.ly/3GpjdXZ">bit.ly/3GpjdXZ</a></td> <td>3.5611° / 46.6875°</td> </tr> <tr> <td>16</td> <td><a href="https://bit.ly/3tcGhRN">bit.ly/3tcGhRN</a></td> <td>2.5127° / 48.2645°</td> </tr> <tr> <td>17</td> <td><a href="https://bit.ly/3zW26sE">bit.ly/3zW26sE</a></td> <td>2.6443° / 48.2546°</td> </tr> <tr> <td>18</td> <td><a href="https://bit.ly/3Trsqlq">bit.ly/3Trsqlq</a></td> <td>7.9196° / 48.9513°</td> </tr> <tr> <td>19</td> <td><a href="http://bit.ly/3DWHgKJ">bit.ly/3DWHgKJ</a></td> <td>2.1269° / 46.8124°</td> </tr> <tr> <td>20</td> <td><a href="https://bit.ly/3NN8pnT">bit.ly/3NN8pnT</a></td> <td>1.5874° / 47.1346°</td> </tr> <tr> <td>21</td> <td><a href="https://bit.ly/3DShkA3">bit.ly/3DShkA3</a></td> <td>0.6254° / 49.191°</td> </tr> <tr> <td>22</td> <td><a href="https://bit.ly/3zZK1dg">bit.ly/3zZK1dg</a></td> <td>2.7067° / 50.3336°</td> </tr> <tr> <td>23</td> <td><a href="https://bit.ly/3TmwcMC">bit.ly/3TmwcMC</a></td> <td>7.4416° / 48.7223°</td> </tr> <tr> <td>24</td> <td><a href="http://bit.ly/3E3l8OK">bit.ly/3E3l8OK</a></td> <td>3.1021° / 48.2449°</td> </tr> <tr> <td>25</td> <td><a href="http://bit.ly/3E0Raeq">bit.ly/3E0Raeq</a></td> <td>1.6183° / 49.9655°</td> </tr> <tr> <td>26</td> <td><a href="http://bit.ly/3tvN0Xg">bit.ly/3tvN0Xg</a></td> <td>3.5476° / 50.1441°</td> </tr> <tr> <td>27</td> <td><a href="https://bit.ly/3A0tZ2D">bit.ly/3A0tZ2D</a></td> <td>3.6644° / 48.0046°</td> </tr> <tr> <td>28</td> <td><a href="https://bit.ly/3fTlQGl">bit.ly/3fTlQGl</a></td> <td>1.7086° / 47.2054°</td> </tr> <tr> <td>29</td> <td><a href="http://bit.ly/3hBeLL2">bit.ly/3hBeLL2</a></td> <td>1.6893° / 47.1421°</td> </tr> <tr> <td>30</td> <td><a href="https://bit.ly/3Edm2cN">bit.ly/3Edm2cN</a></td> <td>3.1018° / 48.5853°</td> </tr> </tbody> </table> Hybrid_CCPP_Result Subdirectory Hybrid_CCPP_Result subdirectory contains the results of our path planning algorithm for complete coverage of agricultural fields by wheeled robots. The provided files include way-points in CSV format and an illustration of the result projected on the field surface. Approach Parameters The results were obtained under the following considerations: The driving direction step size ($\ell_s$), the spacing of access segment discretization ($\ell_a$) and the spacing of working trajectory discretization for slope computation ($\ell_{slp}$). These parameters were respectively set to $3°$, $0.5m$, and $0.5m$. The values of other parameters are listed in the table below: <table> <tbody> <tr> <th>Parameter</th> <th>Description</th> <th>Value</th> </tr> </tbody> <tbody> <tr> <td>$w$</td> <td>working width</td> <td>3m</td> </tr> <tr> <td>$\gamma_{on}$</td> <td>minimum turning radius - implement on</td> <td>10m</td> </tr> <tr> <td>$\gamma_{off}$</td> <td>minimum turning radius - implement off</td> <td>2.8m</td> </tr> <tr> <td>$V_{on}$</td> <td>average speed - implement on</td> <td>4.5m/s</td> </tr> <tr> <td>$V_{gap}$</td> <td>average speed - implement transition</td> <td>1.5m/s</td> </tr> <tr> <td>$V_{off}$</td> <td>average speed - implement off</td> <td>3.5m/s</td> </tr> <tr> <td>$\ell_t$</td> <td>transition trajectory length</td> <td>1.5m</td> </tr> <tr> <td>$\ell_o$</td> <td>robot-implement offset</td> <td>1.0m</td> </tr> <tr> <td>$\Delta_{mwd}$</td> <td>minimum working distance threshold</td> <td>3m</td> </tr> <tr> <td>$p$</td> <td>number of inner trajectories</td> <td>2</td> </tr> <tr> <td>$g$</td> <td>number of gap-covering trajectories</td> <td>1</td> </tr> <tr> <td>$W_{cov}$</td> <td>weight of $S_{cov}$</td> <td>0.30</td> </tr> <tr> <td>$W_{ovl}$</td> <td>weight of $S_{ovl}$</td> <td>0.15</td> </tr> <tr> <td>$W_{nwd}$</td> <td>weight of $S_{nwd}$</td> <td>0.10</td> </tr> <tr> <td>$W_{otm}$</td> <td>weight of $S_{otm}$</td> <td>0.10</td> </tr> <tr> <td>$W_{slp}$</td> <td>weight of $S_{slp}$</td> <td>0.35</td> </tr> <tr> <td>$W_{s0}$</td> <td>weight of $\ell_{s0}$</td> <td>0.00</td> </tr> <tr> <td>$W_{s1}$</td> <td>weight of $\ell_{s1}$</td> <td>0.10</td> </tr> <tr> <td>$W_{s2}$</td> <td>weight of $\ell_{s2}$</td> <td>0.15</td> </tr> <tr> <td>$W_{s3}$</td> <td>weight of $\ell_{s3}$</td> <td>0.20</td> </tr> <tr> <td>$W_{s4}$</td> <td>weight of $\ell_{s4}$</td> <td>0.25</td> </tr> <tr> <td>$W_{s5}$</td> <td>weight of $\ell_{s5}$</td> <td>0.30</td> </tr> </tbody> </table> For an in-depth understanding of these parameters, we kindly invite you to consult our published article: Pour Arab, D., Spisser, M. & Essert, C. (2024) 3D hybrid path planning for optimized coverage of agricultural fields: a novel approach for wheeled robots. Journal of Field Robotics, 1–19. <a href="https://doi.org/10.1002/rob.22422">https://doi.org/10.1002/rob.22422</a> Way-point Structure A way-point is represented by the following format: Point X, Point Y, Point Z, Heading, Type, Move where Heading is in radians, and Type and Move are according to the following structures: <pre><code>enum WayPointType { WORKING = 1, TURN_OFF = 2, TURN_ON = 3, TRANSITION_OFF_TO_ON = 4, TRANSITION_ON_TO_OFF = 5 }; enum RobotMove { FORWARD = 1, REVERSE = -1 };</code></pre> WayPointType <ul> <li>WORKING: The robot implement for driving at this point must be on.</li> <li>TURN_OFF: The robot is performing a turn while its implement is off and elevated from the ground.</li> <li>TURN_ON: The robot is performing a turn while its implement is on.</li> <li>TRANSITION_OFF_TO_ON: The robot is traveling a straight transition trajectory for turning on its implement.</li> <li>TRANSITION_ON_TO_OFF: The robot is traveling a straight transition trajectory for turning off its implement.</li> </ul> RobotMove <ul> <li>FORWARD: The robot is moving forward.</li> <li>REVERSE: The robot is moving in reverse.</li> </ul> Files <ul> <li>CSV file contains the way-points generated by our proposed approach.</li> <li>SVG file providing an illustration of the result projected on the field surface.</li> </ul> Usage This dataset is intended for researchers and developers working on path planning algorithms for agricultural applications. Users can leverage the data to evaluate and validate their path planning approaches in various scenarios, from simple to complex field shapes, and on both 2D and 3D surfaces. Please ensure that you cite this dataset appropriately in any publications or presentations that make use of the data.

Chemical composition of main agricultural plastics articles used for protected cultivation systems: additive characterization

The folder includes chromatographic and mass spectrometry data of the characterization of additives in agricultural plastics, including mulch films (coded as M), mulch fabrics (coded as C and GT), ropes (coded as R), irrigation tapes (coded as I), seed coatings (coded as O), feed sack (coded as K), bale knitted nets and silage nets (coded as S4), biodegradable shelters (coded as SH), ropes & twines for crop production (coded as R), plant tunnels (coded as L), fertilizers sacks (coded as K), silage film /coded as S1). Conventional polymers including polyethylene (PE), polypropylene (PP), and biodegradable polymers (BIO) have been analyzed.

Raw data for the manuscript: Survival and reproduction effects of microplastics from three agricultural mulching films on Folsomia candida, Sinella curviseta, Heteromurus nitidus and Ceratophysella denticulata (Collembola)

Survival and reproduction data from single species tests involving four Collembola species and three types of plastic materials.