Find research datasets worth reusing

opencc-by-4.0Sep 2018View details →

Figure 3 in Ecomorphological associations and abundance of birds across the agricultural landscape of Pothwar Plateau, Pakistan

Figure 3. The proportion of birds feeding on crops with respect to PC3; birds having long narrow beaks and long tarsi.

opencc-by-4.0Sep 2018View details →

Figure 2 in Ecomorphological associations and abundance of birds across the agricultural landscape of Pothwar Plateau, Pakistan

Figure 2. Scatter plots of the 3 indices of morphology in relation to the feeding habits of birds in the agroecosystem of Pothwar Plateau, Pakistan.

opencc-by-4.0Nov 2018View details →

Fig. 1 in First report of Trissolcus japonicus parasitizing Halyomorpha halys in North American agriculture

Fig. 1. White and black circles indicate the locations of 2 sentinel egg masses that were parasitized by Trissolcus japonicus within a heterogeneous landscape at 1 of the field sites in New Jersey. Black lines indicate the nearest tree line, and the inset map of New Jersey indicates with a star where this farm was located.

Fig. 3 in Isolation of native strains of entomopathogenic fungi from agricultural soils of northeastern Mexico and their virulence on Spodoptera exigua (Lepidoptera: Noctuidae)

Fig. 3. Changes in the metamorphosis of Spodoptera exigua caused by isolates (HEB1, HIB-12) and collection strains (GHA, Ma) of entomopathogenic fungi under laboratory conditions (26 °C, 65 ± 5% RH, 14:10 h [L:D] photoperiod). (A) HEB1 (Beauveria bassiana); (B) GHA (Beauveria bassiana); (C) HIB-12 (Metharizium anisopliae); (D) Ma (Metharizium anisopliae). Lines in the bars indicate the standard error.

Fig. 2 in Isolation of native strains of entomopathogenic fungi from agricultural soils of northeastern Mexico and their virulence on Spodoptera exigua (Lepidoptera: Noctuidae)

Fig. 2. Interruption of the metamorphosis of Spodoptera exigua caused by isolates (HEB1, HIB-12) and collection strains (GHA, Ma) of entomopathogenic fungi under laboratory conditions (26 °C, 65 ± 5% RH, 14:10 h [L:D] photoperiod). Lines in the bars indicate the standard error.

Fig. 1 in Isolation of native strains of entomopathogenic fungi from agricultural soils of northeastern Mexico and their virulence on Spodoptera exigua (Lepidoptera: Noctuidae)

Fig. 1. Phylogenetic tree reconstructed from internal transcribed spacer sequences of the isolates compared with referenced internal transcribed spacer sequences deposited in the NCBI GenBank. The phylogram size bar represents a 1% sequence divergence. Labelled branches represent referenced internal transcribed spacer sequences.

Fig. 2 in Toxoplasma gondii contamination at an animal agriculture facility: Environmental, agricultural animal, and wildlife contamination indicator evaluation

Fig. 2. Soil sampling and animal trapping locations at Little River Animal and Environmental Unit in Walland, Tennessee, United States.

Fig. 1 in Toxoplasma gondii contamination at an animal agriculture facility: Environmental, agricultural animal, and wildlife contamination indicator evaluation

Fig. 1. The geographic location of the study site, Little River Animal and Environmental Unit in Walland, Tennessee, United States.

opencc-by-4.0Aug 2024View details →

Data for Publication - Impact of agricultural systems on arbuscular mycorrhizal fungi community composition in Robusta coffee roots in the Democratic Republic of Congo

Data used for the publication: "Impact of agricultural systems on arbuscular mycorrhizal fungi community composition in Robusta coffee roots in the Democratic Republic of Congo" - Ieben Broeckhoven, Arne Devriese, Olivier Honnay, Roel Merckx, and Bruno Verbist

opencc-by-nc-4.0Aug 2024View details →

DeDuCE: Deforestation and carbon emissions due to agriculture and forestry activities from 2001-2022

<h2>Overview</h2> This dataset provides country-level estimates of agriculture and forestry-driven deforestation and associated carbon emissions for the period 2001-2022. A sub-national level attribution dataset is available for Brazil. Generated by the Deforestation Driver and Carbon Emission (DeDuCE) model, it amalgamates remotely sensed datasets with extensive agricultural statistics to estimate deforestation attributable to agricultural and forestry activities globally. Developed utilizing Google Earth Engine and Python, DeDuCE comprehensively covers over 9300 unique country-commodity footprints across 179 countries and 184 commodities within the specified period, presenting an unmatched scope and granularity of data. <h2>Documentation</h2> The manuscript detailing the dataset is currently archived at EarthArXiV: Singh, C., & Persson, U. M. (2024). Global patterns of commodity-driven deforestation and associated carbon emissions. <a href="https://doi.org/10.31223/X5T69B" target="_blank" rel="noopener">https://doi.org/10.31223/X5T69B</a> The insights from this dataset can also be viewed at: <a href="https://www.deforestationfootprint.earth" target="_blank" rel="noopener">https://www.deforestationfootprint.earth</a> <h2>Repository contents</h2> The input and output/data generated by the model are archived here at Zenodo, and their description is available in 'README (files in the directory).txt'. The columns of the (final) dataset 'DeDuCE_Deforestation_attribution_v1.0.1 (2001-2022).xlsx' in the folder 'Final Attribution Results' represent the following: <ul> <li>Continent/Country group: All countries are divided into 8 geographical regions</li> <li>ISO: Three-letter country codes defined by ISO</li> <li>Producer country: Country of deforestation</li> <li>Year: Year of deforestation, ranges from 2001-2022 </li> <li>Commodity group: All commodities are divided into 11 commodity groups</li> <li>Commodity: Name of commodity aligning with FAOSTAT</li> <li>Deforestation attribution, unamortized (ha): Annual deforestation estimates</li> <li>Deforestation risk, amortized (ha): 5-year amortised deforestation estimates</li> <li>Deforestation emissions excl. peat drainage, unamortized (MtCO2): Annual estimates of carbon emissions (based on AGB, BGB, deadwood, litter, soil organic carbon and carbon stock of replacing commodity)</li> <li>Deforestation emissions excl. peat drainage, amortized (MtCO2): 5-year amortised carbon emission estimates, excluding carbon emissions from peatland drainage </li> <li>Peatland drainage emissions (MtCO2): Annual estimates of carbon emissions from peatland drainage </li> <li>Deforestation emissions incl. peat drainage, amortized (MtCO2): 5-year amortised carbon emission estimates, including emissions from peatland drainage</li> <li>Quality Index: Flagging deforestation estimates </li> </ul> <h2>Contact</h2> If you have any questions, you can contact us at:               Chandrakant Singh and U. Martin Persson          <a href="mailto:chandrakant.singh@chalmers.se;martin.persson@chalmers.se">Email</a>: chandrakant.singh@chalmers.se and martin.persson@chalmers.se          Physical Resource Theory, Department of Space, Earth & Environment,          Chalmers University of Technology, Gothenburg, Sweden

Fig. 5 in An Agricultural Detergent as Co-Adjuvant for Entomopathogenic Fungi and Chlorpyrifos to Control Pseudococcus viburni (Hemiptera: Pseudococcidae)

Fig. 5. Mortality (%) of Pseudococcus viburni females to (A) chlorpyrifos alone, and (B) mixed with a nonlethal concentration of TS-2035.

Fig. 4 in An Agricultural Detergent as Co-Adjuvant for Entomopathogenic Fungi and Chlorpyrifos to Control Pseudococcus viburni (Hemiptera: Pseudococcidae)

Fig. 4. Mycelium growth of (A-C) Beauveria bassiana, and (D-F) Metarhizium anisopliae on Pseudococcus viburni females at 24, 72, and 172 h afer exposure.

Fig. 3 in An Agricultural Detergent as Co-Adjuvant for Entomopathogenic Fungi and Chlorpyrifos to Control Pseudococcus viburni (Hemiptera: Pseudococcidae)

Fig. 3. Mortality (%) of Pseudococcus viburni females to (A) Metarhizium anisopliae alone, and (B) mixed with a nonlethal concentration of TS-2035.

Fig. 1 in An Agricultural Detergent as Co-Adjuvant for Entomopathogenic Fungi and Chlorpyrifos to Control Pseudococcus viburni (Hemiptera: Pseudococcidae)

Fig. 1. Mortality (%) of Pseudococcus viburni females afer exposure to several concentrations of TS-2035.

Fig. 2 in An Agricultural Detergent as Co-Adjuvant for Entomopathogenic Fungi and Chlorpyrifos to Control Pseudococcus viburni (Hemiptera: Pseudococcidae)

Fig. 2. Mortality (%) of Pseudococcus viburni females to (A) Beauveria bassiana alone, and (B) mixed with a nonlethal concentration of TS-2035.

opencc-by-4.0Sep 2021View details →

Fig. 2 in Importance of insect pollinators for Florida agriculture: a systematic review of the literature

Fig. 2. Heat map showing the relative contribution of insect pollinators to agriculture in each county in Florida. Values were calculated as acreage per crop per county*average pollinator contribution value per crop (proportion 0–1), summed across all plant-based crops in each county and divided by the county's total area.

opencc-by-4.0Sep 2021View details →

Fig. 1. Example pollinator contribution value for 3 in Importance of insect pollinators for Florida agriculture: a systematic review of the literature

Fig. 1. Example pollinator contribution value for 3 hypothetical crops including banana (no value), tomatoes (moderate value), and watermelons (high value).

opencc-by-4.0Jun 2024View details →

Exploring the Synergy of Enhanced Weathering and Bacillus subtilis: A Promising Strategy for Sustainable Agriculture

Data: Exploring the Synergy of Enhanced Weathering and Bacillus subtilis: A Promising Strategy for Sustainable Agriculture

opencc-by-4.0Sep 2024View details →

LAND RESOURCES OF YAKUTIA'S AGRICULTURE IN THE LAST DECADE OF SOCIALISM: PECULIARITIES OF LAND ACCOUNTING OF STATE FARMS IN THE ARCTIC AND NORTHERN REGIONS

The article shows the peculiarities of land resources utilization in the traditional economy of the indigenous population of Yakutia in the last decade of the Soviet period with a separate delineation of the state of the land balance and lands used in agriculture in 1990-1991. Including on the basis of archival data on land resources of state farms of the studied 15 arctic and northern regions, peculiarities of their accounting, preliminary results of statistical analysis of land resources of these large farms are obtained. The author introduces into scientific turnover new factual materials on land resources of separate state farms for the last Soviet 1991, in particular on their agricultural lands, reindeer and horse pastures.