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41 results for “Crop type”
GEO-Bench: m-SA-crop-type
<p><strong>GEO-Bench: m-SA-crop-type dataset</strong></p> <p> This dataset has been modified to be included in the <a href="https://zenodo.org/communities/geo-bench/">GEO-Bench dataset</a>. All changes with respect to the original version are documented at <a href="https://github.com/ServiceNow/geo-bench">https://github.com/ServiceNow/geo-bench</a>. </p> <p> The original version of this dataset is due to the Western Cape Department of Agriculture and the Radiant Earth Foundation (2021) and is available at: <a href="https://mlhub.earth/data/ref_south_africa_crops_competition_v1">https://mlhub.earth/data/ref_south_africa_crops_competition_v1</a>. </p> <p> See the LICENSE file provided alongside this dataset for applicable licensing information. </p>
GEO-Bench: m-SA-crop-type
<p><strong>GEO-Bench: m-SA-crop-type dataset</strong></p> <p> This dataset has been modified to be included in the <a href="https://zenodo.org/communities/geo-bench/">GEO-Bench dataset</a>. All changes with respect to the original version are documented at <a href="https://github.com/ServiceNow/geo-bench">https://github.com/ServiceNow/geo-bench</a>. </p> <p> The original version of this dataset is due to the Western Cape Department of Agriculture and the Radiant Earth Foundation (2021) and is available at: <a href="https://mlhub.earth/data/ref_south_africa_crops_competition_v1">https://mlhub.earth/data/ref_south_africa_crops_competition_v1</a>. </p> <p> See the LICENSE file provided alongside this dataset for applicable licensing information. </p>
Barn Swallow (Hirundo rustica) fledglings use crop habitat more frequently in relation to its availability than pasture and other habitat types
Open the record for dataset details and reuse information.
Data from: Pest control potential of adjacent agri-environment schemes varies with crop type and is shaped by landscape context and within-field position
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The effects of microplastics on crop variation depend on polymer types and their interactions with soil nutrient availability and weed competition
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Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021)
<p>The dataset contains maps of the main classes of agricultural land use (dominant crop types and other land use types) in Germany, which are produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire open landscape, i.e., the agriculturally used area (UAA) and e.g., uncultivated areas. The map was derived from time series of Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data. Map production is based on the methods described in <a href="https://doi.org/10.1016/j.rse.2021.112831">Blickensdörfer et al. (2022)</a>.</p> <p>All optical satellite data were managed, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software <a href="https://force-eo.readthedocs.io/en/latest/">FORCE </a>- Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019), in which SAR and environmental data were integrated.</p> <p>The map extent covers all areas in Germany that are defined in the respective year as cropland, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020). </p> <p>Version v201:<br>Post-processing of the maps included a sieve filter as well as a ruleset for the reduction of non-plausible areas using the Basis-DLM and the digital terrain model of Germany (Geobasisdaten: © GeoBasis-DE / BKG, 2015).</p> <p>Version v202:<br>Additional post-processing was performed to detect and mask additional non-plausible areas that were not adequately covered by the first post-processing (e.g., areas with sparse vegetation, montane forests) based on the „Ökosystematlas Deutschland“ (© Statistisches Bundesamt, Deutschland, 2024). As a consequence, the current version includes a new class “Small woody features on other land”. Furthermore, the class "permanent grassland" was refined. Each pixel that was classified as "cultivated grassland" in at least five years (between 2017 and 2022) was translated to "permanent grassland" in the annual maps.</p> <p>The maps are available as cloud optimized GeoTiffs, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the provided URL to the datasets (right click on the respective data set --> “copy link address”). By doing so the entire map area or only the regions of interest can be accessed. QGIS legend files for data visualization can be downloaded separately.</p> <p>Class-specific accuracies for each year are provided in the respective tables. We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability. </p> <p> </p> <p><strong>References:<br></strong><br><em>Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831.</em></p> <p><em>BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022).</em></p> <p><em>BKG, Bundesamt für Kartographie und Geodäsie (2020). Digitales Basis-Landschaftsmodell. </em><br><em>https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022).</em></p> <p><em>Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.</em></p> <p><em>Statistisches Bundesamt, Deutschland (2024). Ökosystematlas Deutschland <br>https://oekosystematlas-ugr.destatis.de/ (last accessed: 08.02.2024).</em></p> <p>___________________________________________________________________________<br>National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) © 2024 by Schwieder, Marcel; Tetteh, Gideon Okpoti; Blickensdörfer, Lukas; Gocht, Alexander; Erasmi, Stefan; licensed under CC BY 4.0. </p> <p>Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (<a href="https://www.agrarmonitoring-monvia.de/en/">MonViA</a>, Monitoring of biodiversity in agricultural landscapes).</p> <p>The study was financially supported by the European Environment Agency and the European Union’s Horizon Europe Research and Innovation programme under Grant Agreement No 101060423 (LAMASUS).</p>
Data from: Arthropod abundance is most strongly driven by crop and semi-natural habitat type rather than management in an intensive agricultural landscape in the Netherlands
<p>The dataset supporting the publication "Arthropod abundance is most strongly driven by crop and semi-natural habitat type rather than management in an intensive agricultural landscape in the Netherlands" is provided. Methods of data collection can be found in the respective publication.</p>
Data from: Biochar from "Kon Tiki" flame curtain and other kilns: effects of nutrient enrichment and kiln type on crop yield and soil chemistry
Biochar application to soils has been investigated as a means of improving soil fertility and mitigating climate change through soil carbon sequestration. In the present work, the invasive shrub "Eupatorium adenophorum" was utilized as a sustainable feedstock for making biochar under different pyrolysis conditions in Nepal. Biochar was produced using several different types of kilns; four sub types of flame curtain kilns (deep-cone metal kiln, steel shielded soil pit, conical soil pit and steel small cone), brick-made traditional kiln, traditional earth-mound kiln and top lift up draft (TLUD). The resultant biochars showed consistent pH (9.1 ± 0.3), cation exchange capacities (133 ± 37 cmolc kg-1), organic carbon contents (73.9 ± 6.4 %) and surface areas (35 to 215 m2/g) for all kiln types. A pot trial with maize was carried out to investigate the effect on maize biomass production of the biochars made with various kilns, applied at 1% and 4% dosages. Biochars were either pretreated with hot or cold mineral nutrient enrichment (mixing with a nutrient solution before or after cooling down, respectively), or added separately from the same nutrient dosages to the soil. Significantly higher CEC (P< 0.05), lower Al/Ca ratios (P< 0.05), and high OC% (P<0.001) were observed for both dosages of biochar as compared to non-amended control soils. Importantly, the study showed that biochar made by flame curtain kilns resulted in the same agronomic effect as biochar made by the other kilns (P > 0.05). At a dosage of 1% biochar, the hot nutrient-enriched biochar led to significant increases of 153% in above ground biomass production compared to cold nutrient-enriched biochar and 209% compared to biochar added separately from the nutrients. Liquid nutrient enhancement of biochar thus improved fertilizer effectiveness compared to separate application of biochar and fertilizer.
Data for: Cover crop functional types differentially alter the content and composition of soil organic carbon in particulate and mineral-associated fractions
<p>Cover crops (CCs) can increase soil organic carbon (SOC) sequestration by providing additional OC residues, recruiting beneficial soil microbiota, and improving soil aggregation and structure. The various CC species that belong to distinct plant functional types (PFTs) may differentially impact SOC formation and stabilization. Biogeochemical theory suggests that selection of PFTs with distinct litter quality (C:N ratio) should influence the pathways and magnitude of SOC sequestration. Yet, we lack knowledge on the effect of CCs from different PFTs on the quantity and composition of physiochemical pools of SOC. We sampled soils under monocultures of three CC PFTs (legume [crimson clover]; grass [triticale]; and brassica [canola]) and a mixture of these three species, from a long-term CC experiment in Pennsylvania, USA. We measured C content in bulk soil and C content and composition in contrasting physical fractions: particulate organic matter, POM; and mineral-associated organic matter, MAOM. The bulk SOC content was higher in all CC treatments compared to the fallow. Compared to the legume, monocultures of grass and brassica with lower litter quality (wider C:N) had higher proportion of plant-derived C in POM, indicating selective preservation of complex structural plant compounds. In contrast, soils under legumes had greater accumulation of microbial-derived C in MAOM. Our results for the first time, revealed that the mixture contributed to a higher concentration of plant-derived compounds in POM relative to the legume, and a greater accumulation of microbial-derived C in MAOM compared to monocultures of grass and brassica. Mixtures with all three PFTs can thus increase the short- and long-term SOC persistence balancing the contrasting effects on the chemistries in POM and MAOM imposed by monoculture CC PFTs. Thus, despite different cumulative C inputs in CC treatments from different PFTs, the total SOC stocks did not vary between CC PFTs, rather PFTs impacted whether C accumulated in POM or MAOM fractions. This highlights that CCs of different PFTs may shift the dominant SOC formation pathways (POM vs. MAOM), subsequently impacting short- and long-term SOC stabilization and stocks. Our work provides a strong applied field test of biogeochemical theory linking litter quality to pathways of C accrual in soil.</p>
Data from: Biochar from "Kon Tiki" flame curtain and other kilns: effects of nutrient enrichment and kiln type on crop yield and soil chemistry
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Data for: Cover crop functional types differentially alter the content and composition of soil organic carbon in particulate and mineral-associated fractions
Open the record for dataset details and reuse information.
Figure 1 from: Fanfarillo E, Latini M, Iberite M, Abbate G (2020) The segetal flora of Italy: an occurrence dataset from relevés in winter cereals and allied crop types. PhytoKeys 161: 107-118. https://doi.org/10.3897/phytokeys.161.53915
Figure 1 Geographical coverage of the "Segetal flora of Italy" dataset. Different colours express the different number of records per cell (darker colours = higher number of records).
Data from: Plant domestication disrupts biodiversity effects across major crop types
Plant diversity fosters productivity in natural ecosystems. Biodiversity effects might increase agricultural yields at no cost in additional inputs. However, the effects of diversity on crop assemblages are inconsistent, probably because crops and wild plants differ in a range of traits relevant to plant-plant interactions. We tested whether domestication has changed the potential of crop mixtures to over-yield by comparing the performance and traits of major crop species and those of their wild progenitors under varying levels of diversity. We found stronger biodiversity effects in mixtures of wild progenitors, due to larger selection effects. Variation in selection effects was partly explained by within-mixture differences in leaf size. Our results indicate that domestication might disrupt the ability of crops to benefit from diverse neighbourhoods via reduced trait variance. These results highlight potential limitations of current crop mixtures to over-yield and the potential of breeding to re-establish variance and increase mixture performance.
Adjacent crop type impacts potential pollinator communities and their pollination services in remnants of natural vegetation.
<p><em><span>Aim:</span></em><span> Pollination plays a crucial role in the conservation of many plant species persisting in fragmented, human-dominated landscapes. Pollinators are known to be instrumental in maintaining genetic diversity and metapopulation dynamics for many plant species and are important for providing ecological services that are essential in agricultural landscapes where populations of native plants are highly isolated. Numerous studies have explored the value of remnant native vegetation for supporting pollination services to crop species; yet the effect of mass-flowering crops on the pollinator communities and the pollination services they provide to native plant communities persisting in fragmented landscapes are less well-understood. Here, we</span><span> assess the influence of the presence and phenology of a mass-flowering crop to pollinator community structure, abundance and pollen load composition in remnant vegetation in complex agricultural landscapes. </span></p> <p><em><span>Location:</span></em><span> South-west Western Australia, Australia.</span></p> <p><em><span>Methods:</span></em><span> </span>We recorded the composition and abundance of insect flower visitors and their pollen loads in isolated remnants of York gum-Jam woodlands adjacent to canola (insect-attracting) or wheat (non-insect-attracting) fields over two years. </p> <p><em>Results:</em> All bees were much more sensitive to adjacent crop type (neighbouring canola or wheat) than non-bee pollinators. Honeybees were the most abundant pollinators in canola fields during peak flowering. Honeybee abundance increased in canola-adjacent reserves post-canola bloom, potentially indicating a movement into reserves as crop flowering waned. Native bees were the most diverse in remnant vegetation. Pollen loads of native bees were more mixed (increased pollen richness and evenness) when sampled next to canola fields compared to wheat fields. </p> <p><em>Main conclusion:</em> The availability of potential insect pollinators to remnant wildflower communities in agricultural landscapes is context dependent. Whether sampled communities were adjacent to wheat or canola in a landscape significantly impacted the abundance of potential pollinators in certain landscape elements, but not others, and the composition of pollen loads carried by these insects. Results offer novel insights about the influence of landscape context on <span>pollinator communities and the potential pollination services available for the conservation of native plant species in highly fragmented agricultural landscapes.</span></p>
Data from: Plant domestication disrupts biodiversity effects across major crop types
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Data from: Type of fitness cost influences the rate of evolution of resistance to transgenic Bt crops
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Adjacent crop type impacts potential pollinator communities and their pollination services in remnants of natural vegetation.
Open the record for dataset details and reuse information.
Figure 3 from: Fanfarillo E, Latini M, Iberite M, Abbate G (2020) The segetal flora of Italy: an occurrence dataset from relevés in winter cereals and allied crop types. PhytoKeys 161: 107-118. https://doi.org/10.3897/phytokeys.161.53915
Figure 3 Temporal coverage of the "Segetal flora of Italy" dataset (occurrences per month).
Figure 2 from: Fanfarillo E, Latini M, Iberite M, Abbate G (2020) The segetal flora of Italy: an occurrence dataset from relevés in winter cereals and allied crop types. PhytoKeys 161: 107-118. https://doi.org/10.3897/phytokeys.161.53915
Figure 2 Temporal coverage of the "Segetal flora of Italy" dataset (occurrences per year).
National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data (2020)
<p>This data set contains information on the agricultural land use in Germany for the year 2020.<br> The map was derived from dense time series of Sentinel-2 and Landsat 8 data, Sentinel-1 monthly composites and addtional environmental data. It is based on the methods described in <a href="https://www.sciencedirect.com/science/article/pii/S0034425721005514">Blickensdörfer et al. 2022</a> and can be seen as a continuation of the dataset provided under: <a href="http://zenodo.org/record/5153047#.YWFyXn1CREZ">https://zenodo.org/record/5153047#.YWFyXn1CREZ</a>.<br> The maps can be explored online in a <a href="https://ows.geo.hu-berlin.de/webviewer/landwirtschaft/">webviewer</a>.</p> <p>Due to specific user needs the class catalogue was slightly modified but a translation key (Table 1) and a translated map version (*_V1.tif) is provided. However, it has to be noted that some rather small classes in the previous maps were not differentiated anymore (e.g., onions, carrots, asparagus).Thus, the classes 34, 43, 92, 130, 140, 181 and 182 were excluded from the raster and legend files.</p> <p> </p> <p>Table 1: Updated class catalogue and translation key to the class catalogue used in Blickensdörfer et al. 2022.</p> <table> <tbody> <tr> <td> <p><strong>New class code (V2) </strong></p> </td> <td> <p><strong>Class name (V2)</strong></p> </td> <td> <p><strong>Class code (V1)</strong></p> </td> <td> <p><strong>Class name (V1)</strong></p> </td> </tr> <tr> <td> <p>1101</p> </td> <td> <p>Winter wheat</p> </td> <td> <p>31</p> </td> <td> <p>Winter wheat</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p>34</p> </td> <td> <p>Other winter cereals</p> </td> </tr> <tr> <td> <p>1102</p> </td> <td> <p>Winter barley</p> </td> <td> <p>33</p> </td> <td> <p>Winter barley</p> </td> </tr> <tr> <td> <p>1103</p> </td> <td> <p>Winter rye</p> </td> <td> <p>32</p> </td> <td> <p>Winter rye</p> </td> </tr> <tr> <td> <p>1201</p> </td> <td> <p>Spring barley</p> </td> <td> <p>41</p> </td> <td> <p>Spring barley</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>43</p> </td> <td> <p>Other spring cereals</p> </td> </tr> <tr> <td> <p>1202</p> </td> <td> <p>Oat</p> </td> <td> <p>42</p> </td> <td> <p>Spring oat</p> </td> </tr> <tr> <td> <p>1300</p> </td> <td> <p>Maize</p> </td> <td> <p>91</p> </td> <td> <p>Maize (silage)</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>92</p> </td> <td> <p>Maize (grain)</p> </td> </tr> <tr> <td> <p>1401</p> </td> <td> <p>Potatoe</p> </td> <td> <p>100</p> </td> <td> <p>Potatoe</p> </td> </tr> <tr> <td> <p>1402</p> </td> <td> <p>Sugar beet</p> </td> <td> <p>80</p> </td> <td> <p>Sugar beet</p> </td> </tr> <tr> <td> <p>1501</p> </td> <td> <p>Rapeseed</p> </td> <td> <p>50</p> </td> <td> <p>Winter rapeseed</p> </td> </tr> <tr> <td> <p>1502</p> </td> <td> <p>Sunflower</p> </td> <td> <p>70</p> </td> <td> <p>Sunflower</p> </td> </tr> <tr> <td> <p>1611</p> </td> <td> <p>Peas</p> </td> <td> <p>60</p> </td> <td> <p>Legume</p> </td> </tr> <tr> <td> <p>1612</p> </td> <td> <p>Broad beans</p> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>1613</p> </td> <td> <p>Lupine</p> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>1614</p> </td> <td> <p>Soy</p> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>1603</p> </td> <td> <p>Vegetables</p> </td> <td> <p>120</p> </td> <td> <p>Strawberry</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>130</p> </td> <td> <p>Asparagus</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>140</p> </td> <td> <p>Onion</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>181</p> </td> <td> <p>Carrot</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>182</p> </td> <td> <p>Other leafy vegetables</p> </td> </tr> <tr> <td> <p>1602</p> </td> <td> <p>Cultivated grassland</p> </td> <td> <p>10</p> </td> <td> <p>Grassland</p> </td> </tr> <tr> <td> <p>200</p> </td> <td> <p>Permanent grassland</p> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>3003</p> </td> <td> <p>Fallow land</p> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>3001</p> </td> <td> <p>Small woody features</p> </td> <td> <p>555</p> </td> <td> <p>Small woody features</p> </td> </tr> <tr> <td> <p>3002</p> </td> <td> <p>Other areas</p> </td> <td> <p>999</p> </td> <td> <p>Other agricultural areas</p> </td> </tr> <tr> <td> <p>4001</p> </td> <td> <p>Grapevine</p> </td> <td> <p>110</p> </td> <td> <p>Grapevine</p> </td> </tr> <tr> <td> <p>4002</p> </td> <td> <p>Hops</p> </td> <td> <p>150</p> </td> <td> <p>Hops</p> </td> </tr> <tr> <td> <p>4003</p> </td> <td> <p>Orchard</p> </td> <td> <p>160</p> </td> <td> <p>Orchards</p> </td> </tr> </tbody> </table> <p> </p> <p>All optical satellite data were downloaded, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019; <a href="https://force-eo.readthedocs.io/en/latest/">https://force-eo.readthedocs.io/en/latest/</a> last accessed: 12. April 2022), before environmental and SAR data were included in the ARD cube. </p> <p>The models were trained in FORCE and applied to all areas in Germany that were defined as agricultural land, small woody features, heathland or peatland in ATKIS DLM 2020 (Geobasisdaten: © GeoBasis-DE / BKG (2020)). Post-processing of the final maps included applying a sieve filter, the exclusion of classes other than grasslands and small woody features above 900 m (based on the Digital Elevation Model for Germany BKG (2015)) and the exclusion of grapevine and hops areas that were not labelled as the respective permanent crop in ATKIS DLM (BKG (2020); labelled as other agricultural areas in the final map). <br> </p> <p>The maps are provided as GeoTiff files together with QGIS legend files for visualization. </p> <p> </p> <p>References:</p> <p>Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831</p> <p>BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022). </p> <p>BKG, Bundesamt für Kartographie und Geodäsie (2018). Digitales Basis-Landschaftsmodell. <br> https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022).</p> <p>Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.</p> <p> </p> <p><a href="https://zenodo.org/record/5153047#.YhYwgpYxmUn">National-scale crop type maps for Germany </a>© 2022 by Schwieder, Marcel; Erasmi, Stefan; Nendel, Claas; Hostert, Patrick is licensed under <a href="http://creativecommons.org/licenses/by/4.0/?ref=chooser-v1">CC BY 4.0. </a></p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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