Find research datasets worth reusing

Information flows around agricultural best management practices in central Pennsylvania

This dataset was collected between February and April 2019, to assess the information network of agricultural Best-Management Practices (BMPs) in central Pennsylvania, a sub-region of the Chesapeake Bay watershed. It contains information flows (or "messages") relating to 16 specific BMPs, including: <ul> <li>the BMP it relates to (e.g. riparian buffers, manure management planning, no-till, cover-cropping, etc.);</li> <li>the source and target of the information (actors);</li> <li>the kind of message (e.g. funding, regulation, technical assistance, etc.);</li> <li>the weight (strength) of messages (only for those received by farmers directly).</li> </ul> Over 3900 messages/information flows were recorded, involving 57 actors. This data was used to conduct the study "Navigating agricultural nonpoint source pollution governance: A social network analysis of best management practices in central Pennsylvania".

opencc-zeroNov 2023View details →

Agricultural soil legacy influences multitrophic interactions between crops, their pathogens, and pollinators

Soil legacy influences plant interactions with antagonists and below-ground mutualists. Plant-antagonist interactions can jeopardize, while soil mutualists can enhance, plant-pollinator interactions. This suggests that soil legacy, either directly or mediated through plant symbionts, affects pollinators. However, despite the importance of pollinators to natural and managed ecosystems, there is almost no information on how soil legacy affects plant-pollinator interactions. We assessed effects of soil management legacy (organic vs. conventional) on floral rewards and plant interactions with wild pollinators, herbivores, beneficial fungi, and pathogens. We used an observational dataset and structural equation models to evaluate hypothesized relationships between soil and pollinators, then tested observed correlations in a manipulative experiment. Organic legacy increased mycorrhizal fungal colonization and improved resistance to powdery mildew, which promoted pollinator visitation. Further, soil legacy and powdery mildew independently and interactively impacted plant traits important to pollinators, including floral traits and floral reward nutrients. Our results indicate that pollination could be an overlooked consequence of soil legacy and suggests opportunity to develop long-term soil management plans that benefit pollinators and pollination.

Insight into the impact of viruses on biogeochemical processes in agricultural soils

Open the record for dataset details and reuse information.

Insight into the impact of viruses on biogeochemical processes in agricultural soils

Open the record for dataset details and reuse information.

opencc-by-4.0Nov 2023View details →

From fork to farm: Impacts of more sustainable diets in the EU-27 on the agricultural sector

This work was supported by the European Union's Horizon 2020 project Nutri2Cycle (Grant agreement No. 773682) These datasets are supplementary information to the manuscript titled "<a href="https://onlinelibrary.wiley.com/doi/10.1111/1477-9552.12530">From fork to farm: Impacts of more sustainable diets in the EU-27 on the agricultural sector</a>"

opencc-zeroNov 2023View details →

Data from: Climatic damage cause variations of agricultural insurance loss for the Pacific Northwest region of the United States

Agricultural crop insurance is an important component for mitigating farm risk, particularly given the potential for unexpected climatic events. Using a 2.8 million nationwide insurance claim dataset from the United States Department of Agriculture (USDA), this research study examines spatiotemporal variations of over 31,000 agricultural insurance loss claims across the 24-county region of the inland Pacific Northwest (iPNW) portion of the United States, from 2001 to 2022. Wheat is the dominant insurance loss crop for the region, accounting for over 2.8 billion dollars in indemnities, with over 1.5 billion dollars resulting in claims due to drought (across the 22 year time period). While fruit production generates considerably lesser insurance losses (400 million dollars) as a primary result of freeze, frost, and hail, overall revenue ranks number one for the region, with 2 billion dollars in sales, across the same time range. Principal components analysis of crop insurance claims showed distinct spatial and temporal differentiation in wheat and apple insurance losses using the range of damage causes as factor loadings. The first two factor loadings for wheat accounts for approximately 50 percent of total variance for the region, while a separate analysis of apples accounts for over 60 percent of total variance. These distinct orthogonal differences in losses by year and commodity in relationship to damage causes suggest that insurance loss analysis may serve as an effective barometer in gauging climatic influences.

opencc-by-4.0Dec 2023View details →

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

Supplementary Figures for: Can leafhoppers help us trace the impact of climate change on agriculture? to be posted in bioRxiv.Figure S1. Diversity indexes calculated in this study to compare leafhopper diversity each growing season investigated in this study and the geographic regions where the strawberry fields were located. Statistical analyses were performed for Shannon and Simpson finding that in both cases there is no interaction between years and regions with p = 0.0889 and p = 0.7139, respectively.Figure S2. Distinctive RFLP patterns obtained with CpnClassiPhyR from in silico digestion of cpn60UT from SbGPQ clones and AY-Col. Lanes labelled MW in in silico RFLP represent HaeIII-digested phage ϕX174 DNA.Figure S3. Phylogenetic tree using neighbour-joining method of the 16S, secY, nusA, rp, secA, cpn60 and tuf sequences obtained in this study for the SbGP phytoplasma and sequences retrieved from Genbank. Acholeplasma laidlawii PG8 was used as an outgroup. The phylogenetic tree was bootstrapped 1000 times to achieve reliability. Bar, 1 substitution in 100 or 500 positions. Fig. S3 Panel 1: cpn60UT, tuf, and secY trees.Fig. S3 Panel 2: nusA, rp, and secA trees.Fig. S3 Panel 3: 16S tree with subtree showing heterogeneity of SbGPQ and 'Ca. P. tritici'.Figure S4. Leafhopper feeding-associated damages observed in strawberry plants. A, in the field. B, in the greenhouse after incubation with leafhoppers.Figure S5. Alpha diversity indexes were calculated to study Macrosteles quadrilineatus microbiome observed for each growing season. No statistical difference was observed among the sites for any of the indexes calculated.Figure S6. Effect of insecticides leafhopper population control. Only those with a number of applications higher or equal to five are presented. We did not find statistical differences among the treatments before and after the application of the insecticides with p = 0.8488.Figure S7. Effect of insecticides on Macrosteles quadrilineatus and Empoasca fabae population control. All insecticides (n = 12) are represented but the statistical analysis was only performed with those that the number of applications was higher than 5. We did not find statistical differences among the treatments before and after the application of the insecticides with p = 0.1781 for the aster leafhopper M. quadrilineatus and p = 0.6540 for the potato leafhopper E. fabae.Figure S8. Comparison among the Shannon index obtained for leafhopper populations in vineyards in 2007 and 2008 and for leafhopper populations in strawberry fields in 2021 and 2022 in Quebec.

Data from: Bees go up, flowers go down: Increased resource limitation from late spring to summer in agricultural landscapes

Data underlying the publication "Bees go up, flowers go down: Increased resource limitation from late spring to summer in agricultural landscapes". Site coordinates are excluded from this dataset for data protection.

opencc-by-4.0Jan 2024View details →

RafanoSet: Dataset of raw, manual and automatically annotated Raphanus Raphanistrum weed images for object detection and segmentation in Heterogenous Agriculture Environment

This dataset is a collection of raw and annotated Multispectral (MS) images acquired in a heterogenous agricultural environment with MicaSense RedEdge-M camera. The spectra particularly Green, Blue, Red, Red Edge and Near Infrared (NIR) were acquired at sub-metre level..  The MS images were labelled manually using VIA and automatically using Grounding DINO in combination with Segment Anything Model. The segmentation masks obtained using these two annotation techniqes over as well as the source code to perform necessary image processing operations are provided in the repository. The images are focussed over Horseradish (Raphanus Raphanistrum) infestations in Triticum Aestivum (wheat) crops. The nomenclature of sequecncing and naming images and annotations has been in this format: IMG_<scene number>_<spectral channel number> _1: Blue _2: Green _3: Red _4: Near Infrared _5: RedEdge Example: An image name  IMG_0200_3 represents the scene number 200 in Red channel This dataset 'RafanoSet'is categorized in 6 directories namely 'Raw Images', 'Manual Annotations', 'Automated Annotations', 'Binary Masks - Manual', 'Binary Masks - Automated' and 'Codes'. The sub-directory 'Raw Images' consists of manually acquired 85 images in .PNG format. over 17 different scenes. The sub-directory 'Manual Annotations' consists of annotation file 'region_data' in COCO segmentation format. The sub-directory 'Automated Annotations' consists of 80 automatically annotated images in .JPG format and 80 .XML files in Pascal VOC annotation format. The scientific framework of image acquisition and annotations are explained in the Data in Brief paper which is the course of peer review. This is just a prerequisite to the data article.  Field experimentation roles: The image acquisition was performed by Mariano Crimaldi, a researcher, on behalf of Department of Agriculture and the hosting institution University of Naples Federico II, Italy. Shubham Rana has been the curator and analyst for the data under the supervision of his PhD supervisor Prof. Salvatore Gerbino. They are affiliated with Department of Engineering, University of Campania 'Luigi Vanvitelli'.  Domenico Barretta, Department of Engineering has been associated in consulting and brainstorming role particularly with data validation, annotation management and litmus testing of the datasets.

opencc-zeroDec 2023View details →

Data for: Functional redundancy of weed seed predation is reduced by intensified agriculture

Intensive agriculture, a driver of biodiversity loss, can diminish ecosystem functions and their stability. Biodiversity can increase functional redundancy and is expected to stabilize ecosystem functions. Few studies however have explored how agricultural intensity affects functional redundancy and its link with ecosystem function stability. Here, within a continent-wide study, we assess how the functional redundancy of seed predation is affected by agricultural intensity and landscape simplification. By combining carabid abundances with molecular gut content data, functional redundancy of seed predation was quantified for 65 weed genera across 60 fields in four European countries. Across weed genera, functional redundancy was reduced with high field management intensity and simplified crop rotations. Moreover, functional redundancy increased the spatial stability of weed seed predation within fields. We found that ecosystem functions are vulnerable to disturbance in intensively managed agroecosystems, providing empirical evidence of the importance of biodiversity for stable ecosystem functions across space.

opencc-by-4.0Jan 2024View details →

Data and code from: Unoccupied aerial systems adoption in agricultural research

<div> </div> This repository contains data and code supporting the findings of the study on the adoption of Unoccupied Aerial Systems (UAS) in agricultural research as reported by Lachowiec et al (2024) in The Plant Phenome Journal. We collected data through an online survey as well as through in person interviews. <div> <h2>Description of Repository Contents</h2> </div> <div> <h3>Data</h3> </div> Data are in the <code>/data</code> directory: <ul> <li><code>Ag_Drones_Codebook_14Jun2023.pdf</code>: Codebook providing detailed descriptions of survey questions and coding schemes. This contains detailed descriptions of the content of the two CSV files listed below.</li> <li><code>Results_Ag_Drones_2021_Survey.csv</code>: This is raw survey data collected from agricultural researchers regarding their use of UAS technology.</li> <li><code>countries_code.csv</code>: Country codes used in the survey data for respondent location.</li> <li><code>interviews/</code>: A directory containing interview transcripts and summary provided as both Microsoft Word and plain text (Markdown) formats, specifically: <ul> <li>Notes from nine one on one interviews named <code><interviewee last name>)UAS_Interview.[md|docx]</code></li> <li>A summary document, <code>Feldman_AG2PI_InterviewSummary_2022-08-10.docx</code>.</li> </ul> </li> </ul> <div> <h3>Code</h3> </div> Code used to process data and generate the manuscript's analysis and figures. <ul> <li><code>data_code.R</code>: R Script for preprocessing and cleaning the survey data.</li> <li><code>dataAnalysis.R</code>: R script for statistical analysis and visualization of survey results.</li> </ul> <div> <h2>Citing this work</h2> </div> This repository contains data and code to support the manuscript: <blockquote> Lachowiec, J., Feldman, M.J., Matias, F.I., LeBauer, D., Gregory, A. (2024). Unoccupied aerial systems adoption in agricultural research. Zenodo. The Plant Phenome Journal Volume(Issue), pages 00. doi:DOI </blockquote> If you use the data or code from this repository, please also cite: <blockquote> Lachowiec, J., Feldman, M.J., Matias, F.I., LeBauer, D., Gregory, A. (2024). Data and code from: Unoccupied aerial systems adoption in agricultural research. Zenodo. doi:10.5281/zenodo.10573428 </blockquote> And consider contributing cleaned data and code to this repository. <div> <h2>Acknowlegements and Support</h2> </div> Acknowledgments We thank all survey respondents for their participation. We acknowledge the Montana State University HELPS lab for aiding in the development and implementation of the survey. Funding This research was supported by the intramural research program of the U.S. Department of Agriculture, National Institute of Food and Agriculture, Agricultural Genome to Phenome Initiative (2020-70412-32615 and 2021-70412-35233). The findings and conclusions in this preliminary presentation have not been formally disseminated by the U. S. Department of Agriculture and should not be construed to represent any agency determination or policy.

opencc-zeroDec 2023View details →

Data for: Spillover effects of organic agriculture on pesticide use on nearby fields

The environmental impacts of organic agriculture are only partially understood and whether such practices have spillover effects on pests or pest control activity on nearby fields remains unknown. Using roughly 13,000 field observations per year from 2013-2019 in Kern County, CA , we estimate that organic crop producers benefit from surrounding organic fields, decreasing overall pesticide use and pesticides targeting insect pests. Conventional fields, in contrast, tend to increase pesticide use as the area of surrounding organic production increases.

Oilseed rape plant phytometers in an agricultural landscape in France - LTSER Zone Atelier Plaine & Val de Sèvre

Fruit set as a proxy of pollination efficiency measured using oilseed rape plant phytometers placed in grasslands, cereals and oilseed rape fields in the LTSER Zone Atelier Plaine & Val de Sèvre. The individual contributions of different processes to pollination were determined using a bagging experiment (large-, small- and osmolux) on plant phytometers. Landscape metrics are available upon reasonable request

Impact of viruses on microbial communities and biogeochemical processes in agricultural soils

The source data of microcosm experiment.

opencc-zeroDec 2023View details →

Joint environmental and social benefits from diversified agriculture

Agricultural simplification continues to expand at the expense of more diverse forms of agriculture. This simplification in the form of, for example, intensively-managed monocultures, poses a risk to keeping the world within safe and just Earth system boundaries. Here, we estimate how agricultural diversification simultaneously affects social and environmental outcomes. Drawing from 24 studies in 11 countries across 2,655 farms, we show how five diversification strategies focusing on livestock, crops, soils, non-crop plantings, and water conservation benefit social (human well-being, yields, food security) and environmental (biodiversity, ecosystem services, reduced environmental externalities) outcomes. We find that applying multiple diversification strategies creates more positive outcomes than individual management strategies alone. To realize these benefits, well-designed policies are needed to incentivize the adoption of multiple diversification strategies in unison.

Sensitivity of the global agricultural sector to changes in climate policy - EU countries compared to the rest of the world

The files contain data from the FAOSTAT database used in the article: DOI:10.2478/oszn-2023-0012 File content: Agricultural emissions data for the period 1961-2020 Population data for 1950-2020 Production value from agriculture for the period 1961-2020 Agricultural area for the period 1961-2020 The layout of the tables and the description of the columns is the same as the FAOSTAT database methodology

opencc-by-4.0Dec 2013View details →

Fig. 2 in Carabid beetle (Coleoptera: Carabidae) diversity in agricultural and post-agricultural areas in relation to the surrounding habitats

Fig. 2. Ordination plot based on correspondence analysis (CA) of carabid species (triangles) and study sites (circles).

Estimated Annual Agricultural Pesticide Use for USA48 2000 to 2019 (gridded maps at 250-m resolution)

Estimated low and high pescticide uses based on census data for USA48, provided for the cropland mask at 250-m spatial resolution. The original <a href="https://water.usgs.gov/nawqa/pnsp/usage/maps/">USGS maps</a> are only available as PNGs, so we have prepared here a code to rasterize the values and produce GeoTIFFs. For cropland mask we use the <a href="https://www.usgs.gov/centers/eros/science/national-land-cover-database">National Land Cover Database (NLCD)</a>. All processing is fully documented in: <a href="https://github.com/Envirometrix/pesticide-use-USA48">https://github.com/Envirometrix/pesticide-use-USA48</a>. All maps are projected in the <a href="https://epsg.io/5070-1252">EPSG:5070</a>. Description of the layers: <ul> <li><code>croplands_20**_250m.tif</code> = cropland mask (0–100%) estimated based on NLCD.</li> <li><code>GLYPHOSATE_EPEST.HIGH.KG.KM2_20**_250m.tif</code> = estimated pesticide use per county (high estimate) expressed in kg/km-square.</li> <li><code>diff.GLYPHOSATE_EPEST.HIGH.KG.KM2_250m.tif</code> = annual difference in pesticide use from 2000 to 2019;</li> </ul> Disclaimer: this code is under construction and USGS makes is clearly available that there are some limitations to this data: <ul> <li>These estimates are made by using projected county crop acres from the previous Census of Agriculture and are expected to be revised upon availability of updated crop acreages in the following Census of Agriculture.</li> <li>The files do not include pesticide use estimates for California. Data for California are obtained from the online Department of Pesticide Regulation-Pesticide Use Reporting (DPR-PUR) database and are typically not available at the time the preliminary pesticide use estimates are generated for the rest of the U.S.</li> </ul> Pease have in mind these limitations when using these maps for further modeling.

opencc-by-4.0Apr 2024View details →

Physical characterization of agricultural plastic mulching films

The excel file regards the radiometric properties of the source material mulching films.  The radiometric tests were carried out at the University of Bari. The mulching films were used for the generation of microplastic test materials. The Italian mulching films were buried at the experimental field at the University of Bari.