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Figure 2 in Generalist ground-nesting bees dominate diversity survey in intensively managed agricultural land
Figure 2. Species richness compared between sampling periods. Dark grey bars: species from the genus Andrena Fabricius (Andrenidae); light grey bars: species from the genera: Halictus Latreille, Lasioglossum Curtis (Halictidae), Osmia Panzer (Megachilidae), and Nomada Scopoli (Apidae); black bars: species from the genus Bombus Latreille (Apidae). Different letters above the dark grey bars indicate a significant statistical difference between sampling periods in total species richness of all sampled genera (F (3, 42) = 20.01, p<0.001).
Figure 1 in Generalist ground-nesting bees dominate diversity survey in intensively managed agricultural land
Figure 1. Total number of bees sampled in this study at the four different sampling periods. Dark grey bars: individuals from the genus Andrena Fabricius (Andrenidae); light grey bars: individuals from the genera: Halictus Latreille, Lasioglossum Curtis (Halictidae), Osmia Panzer (Megachilidae), and Nomada Scopoli (Apidae); black bars: individuals from the genus Bombus Latreille (Apidae). Different letters above the dark grey bars indicate a significant statistical difference between sampling periods in activity-density of individuals from all sampled genera (F (3, 42) = 18.89, p<0.001).
Figure 3 in Ant (Hymenoptera: Formicidae) species diversity in secondary forest and three agricultural land uses of the Colombian Pacific Coast
Figure 3. Number of shared ant species and total number of specimens caught (pitfall and Winkler sack) between four areas of different land use. Two oil palm plots of three and seven years of age were pooled. El Mira Research Center, Tumaco, Pacific Coast of Colombia. / Número de especies de hormigas compartidas y número total de individuos capturados (Pitfall y sacos Winkler) entre cuatro áreas con diferente uso de tierra. Las dos parcelas de palma de aceite de tres y siete años fueron agrupadas. Centro de Investigación El Mira, Tumaco, Nariño, costa pacÍfica de Colombia.
Figure 1 in Ant (Hymenoptera: Formicidae) species diversity in secondary forest and three agricultural land uses of the Colombian Pacific Coast
Figure 1. Map of El Mira Research Center of the Corporación Colombiana de Investigación Agropecuaria, Tumaco, Nariño, Pacific Coast of Colombia, with the location (arrows) of the pitfall trap transects. Yellow hybrid oil palm 7 years old; red hybrid oil palm 3 years old; black peach palm; white secondary forest. / Mapa del Centro de Investigación El Mira de la Corporación Colombiana de Investigación Agropecuaria, Tumaco, Nariño, costa pacÍfica de Colombia con la ubicación (flechas) de las trampas pitfall en los transectos. Amarillo palma de aceite hÍbrido 7 años; rojo palma de aceite hÍbrido 3 años; negro palma de chontaduro; blanco bosque secundario.
Figure 2 in Ant (Hymenoptera: Formicidae) species diversity in secondary forest and three agricultural land uses of the Colombian Pacific Coast
Figure 2. Variation in 0D diversity (species number) of Formicidae between four areas of different land use: El Mira Research Center, Tumaco, Pacific Coast of Colombia. SF: secondary forest, PP: Peach palm, OP7: Oil palm 7 years old, OP3: Oil palm 3 years old. / Variación en la diversidad 0D (número de especies) de Formicidae entre cuatro áreas con diferente uso de tierra. Centro de Investigación El Mira de la Corporación Colombiana de Investigación Agropecuaria, Tumaco, Nariño, costa pacÍfica de Colombia.
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
<p><span>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.</span></p>
Initial Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the agricultural land at Demmin, Germany
<p>The HYPERNETS project (www.hypernets.eu) aims to ensure that high-quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument-pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site in Demmin, Germany [53°52'5.80"N,13°16'6.80"E] (DEGE). It is a subset of the complete data record, consisting of the measurements withEthaturements which could be used for satellite validation. </p> <p>The provided NetCDF files are the L2A hypernets products with surface reflectances, their associated uncertainties and error-correlation information. The reflectance in the L2A products is the Hemispherical-directional Reflectance Factor (HDRF) defined as HDRF = π L / E where L is the directional upwelling radiance (with the field o, view of 5 degrees), and E is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). These reflectances have dimensions of wavelength and series, where each series is a set of measurements for a given geometry (combination of viewing zenith and azimuth angle). In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, number of valid scans used, and quality flags (typically, no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info.</p> <p>The HYPSTAR®-XR sensor was installed on 22 July 2021 at the top of a 10m mast on an extended 5 m horizontal boom to minimise interruption of the field of view. The boom faces South at the right angle towards bare soil. The mast is located at 53.868278°N, 13.268556°E. Data are collected every 30 minutes between 9:00 and 17:00 (UTC) from different zenith and azimuth angles.</p> <p>The HYPSTAR®-XR (eXtended Range) instruments deployed at each land HYPERNETS site consist of a VNIR and a SWIR sensor and autonomously collect data between 380-1700 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with an FWHM of 3 nm, and the SWIR sensor has 220 channels between 1000 and 1700 nm with an FWHM of 10 nm. The hypernets_processor (Goyens et al. 2021; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. All products have associated uncertainties (divided into random and systematic uncertainties, including error-correlation information) propagated using the CoMet toolkit (www.comet-toolkit.org). </p> <p>To obtain this dataset, we start from the full DEGE data record and omit all the data that do not pass all quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was also developed to remove outliers and supply the best quality data suitable for satellite validation. To remove the outliers, a sigma-clipping method is used. First, reflectances are extracted in separate 2-hour windows throughout the day (to account for BRDF differences due to different solar positions) for four different wavelengths (500, 900, 1100 and 1600 nm). Outliers in these reflectances are then identified by iteratively calculating the mean reflectance trend with time (by binning the data per maximum of 30 data points), calculating the standard deviation from this trend, and masking any data that is more than three standard deviations away from the trend. This process is repeated on the unmasked data until the standard deviation does not vary by more than 5% between two iterations. The masks for the four different wavelengths are then combined (keeping only measurements for which none of the four wavelengths is an outlier). The reflectances and associated uncertainties for any masked series (i.e. a geometry that is masked either by the sigma-clipping procedure or from the masks of the hypernets_processor) are replaced by NaNs. Any sequence that has more than half of its series masked is removed entirely. </p>
Dataset on Article: River ecological status is shaped by agricultural land use intensity across Europe: Establishing a typology of farming-driven freshwater impacts
<p>This repository contains raw data from the article "River ecological status is shaped by agricultural land use intensity across Europe: Establishing a typology of farming-driven freshwater impacts" which is currently under review.</p> <p>It contains data to allocate the pressures (<strong>Data_pressure_allocation.csv</strong>) and calculate the Pressure Index (<strong>Data_pressure_index.csv</strong>) for Table 1, and for the Spearman correlations for Figure 2 (<strong>Data_Spearman_correlations.csv</strong>).</p> <p> </p> <p>Also available is the Shapefile used for the different agricultural maps (Figure 1 and Figure S1-S4):</p> <p><strong>Shapefile Schürings_et_al._2023</strong> (Coordinate system: ETRS 1989 UTM Zone 32N)</p> <p><strong>Attribute description</strong></p> <p>Id - Identifier of polygons</p> <p>gridcode - Code of agricultural archetypes of Levers et al., (2018)</p> <p>M_ZHYD: Unique identifier of corresponding FEC</p> <p>mars_bt12: River types</p> <p>eco_stat_2: Ecological status</p> <p>Biogeoregi: Biogeographical Regions - AN = Northern and Highland, Temp = Temperate, Mediterranean = Mediterranean</p> <p>Cum_pressu: Agricultural pressure index</p> <p>Nitrogen: Agricultural nitrogen pressure</p> <p>Pesticides: Agricultural pesticide pressure</p> <p>Hydromorph: Agricultural hydromorphological pressure</p> <p>Water_abst: Agricultural water abstraction</p>
Saffron-cowled Blackbirds' reduced nest success in Argentina's agricultural land highlights the importance of non-agricultural habitat for its conservation
Open the record for dataset details and reuse information.
Agricultural land use and ensuing eutrophication both shape parasitic trematode communities in rural African lakes
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Agriculture land-use change seasonally rewires stream food webs: A case study from headwater streams in the Lake Erie watershed
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Agricultural intensification and land use change: assessing country-level induced intensification, land sparing and rebound effect
<p><span><span><span><span><span><span><span><span><span><span><span>In the context of growing societal demands for land based products, crop production can be increased through expanding cropland or intensifying production on cultivated land. Intensification can allow sparing land for nature, but it can also drive further expansion of cropland, i.e. a rebound effect. Conversely, constraints on cropland expansion may induce intensification. We tested those hypotheses by investigating the bidirectional relations between changes in cropland area and intensity, using a global cross-country panel dataset over 1961-2016. We used a cointegration approach with additional tests to disentangle long and short-run causal relations between variables, and total factor productivity and yields as two measures of intensification. Over the long run we found support for the induced intensification thesis for low income countries. In the short run, intensification resulted in a rebound effect in middle-income countries, which include many key agricultural producers strongly competitive in global agricultural commodity markets. This rebound effect manifested for commodities with high price-elasticity of demand, including rubber, flex crops (sugarcane, palm oil and soybean), and tropical fruits. Over the long run, strong rebound effects remained for key commodities such as flex crops and rubber. Staple cereals such as wheat and rice manifested significant land sparing. In low-income countries, intensification driven by increases in total factor productivity was associated with a stronger rebound effect than yields increases. Agglomeration economies may drive yields increases for key tropical commodity crops. Our study design could allow addressing other complex long and short run causal dynamics in land and social-ecological systems.</span></span></span></span></span></span></span></span></span></span></span></p>
Data from: Agricultural land-use history and restoration impact soil microbial biodiversity
<ol> <li>Human land uses, such as agriculture, can leave long-lasting legacies as ecosystems recover. As a consequence, active restoration may be necessary to overcome land-use legacies; however, few studies have evaluated the joint effects of agricultural history and restoration on ecological communities. Those that have studied this joint effect have largely focused on plants and ignored other communities, such as soil microbes.</li> <li>We conducted a large-scale experiment to understand how agricultural history and restoration tree thinning affect soil bacterial and fungal communities within longleaf pine savannas of the southern United States. This experiment contained 64 pairs of remnant (no history of tillage agriculture) and post-agricultural (reforested following abandonment from tillage agriculture >60 years prior) longleaf pine savanna plots. Plots were each 1-ha and arranged into 27 blocks to minimize land-use decision making biases. We experimentally restored half of the remnant and post-agricultural plots by thinning trees to reinstate open-canopy savanna conditions and collected soils from all plots five growing seasons after tree thinning. We then evaluated soil bacterial and fungal communities using metabarcoding.</li> <li>Agricultural history increased bacterial diversity but decreased fungal diversity, while restoration increased both bacterial and fungal diversity. Both bacterial and fungal richness were correlated with a range of environmental variables including aboveground variables like leaf litter and plant diversity, and belowground variables such as soil nutrients, pH, and organic matter, many of which were also impacted by agricultural history and restoration.</li> <li>Fungal and bacterial community compositions were shaped by restoration and agricultural history resulting in four distinct communities across the four treatment combinations.</li> <li>Past agricultural land use left persistent legacies on soil microbial biodiversity, even over half a century after agricultural abandonment and after intensive restoration activities. The impacts of these changes on soil microbe biodiversity could play important roles in the functioning of ecosystems following agricultural abandonment and during restoration.</li> </ol>
Soil microbiome dataset from the University of Wisconsin Arlington and Lancaster agricultural research stations and cheese maker and vegetable processor wastewater land application sites
<p>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. </p>
Data for: Carbon benefits through fallow agricultural land transitions: the case of multi-strata agroforestry in Hawaiʻi
<p>Bremer, L.L. (1,2), McGuire, G. (3,4), DeMaagd, N. (1,5), Trauernicht, C. (5)</p> <p><strong> </strong></p> <p>1 University of Hawaiʻi Economic Research Organization, University of Hawaiʻi at Mānoa, Honolulu, HI, 96822</p> <p>2 Water Resources Research Center, University of Hawaiʻi at Mānoa, Honolulu, HI, 96822</p> <p>3 Department of Geography and Environment, University of Hawaiʻi at Mānoa, Honolulu, HI, 96822</p> <p>4 Institute of Pacific Islands Forestry, USDA Forest Service, Hilo, HI, 96720</p> <p>5 Department of Natural Resources and Environmental Management, University of Hawaiʻi at Mānoa, Honolulu, HI, 96822</p> <p> </p> <p><strong>Bremer, L.L., McGuire, G., Hastings Silao, Z., Kurashima, N., Ticktin, T., Crow, S.E., Giardina, P.C., Winter, K.B., DeMaagd, N., and C. Trauernicht. Carbon benefits through fallow agricultural land transitions: the case of multi-strata agroforestry in Hawaiʻi</strong></p> <p> </p> <p>Multi-strata agroforestry land use scenarios were created using data from the: 2020 State of Hawaiʻi Agricultural Baseline (Perroy and Collier 2020), the Hawaiʻi Carbon Assessment (Jacobi et al. 2017), current (Giambelluca et al. 2013) and RCP 8.5 mid-century projected (Ellison-Timm et al. 2015) rainfall rasters, state land use zoning (SLUC, 2020), slope, elevation, and historical colluvial agroforestry maps (Kurashima et al. 2019). The following rasters display multi-strata agroforestry land-use scenarios. </p> <ul> <li> <p>“MS_currentclimate_landuse_scenario” represents potential multi-strata agroforestry under current rainfall.</p> </li> <li> <p>“MS_RCP85_midcentury_landuse_scenario” represents potential multi-strata agroforestry under projected RCP 8.5 mid-century rainfall. </p> </li> </ul> <p>For both scenarios: 1 = dry (550-1500 mm) multistrata agroforestry; 2 = mesic (1500-3000 mm) multi-strata agroforestry; 3 = wet (>3000 mm) multi-strata agroforestry.</p> <p>Estimates of projected changes in above-ground carbon were estimated by comparing modeled AGC in agroforestry scenarios to baseline AGC for current forest (Asner et al. 2016) and estimates of AGC in non-forest vegetation (Selmants et al. 2017). The following rasters display projected directional changes in soil carbon with agroforestry under current and RCP 8.5 mid century rainfall.</p> <ul> <li> <p>“AGC_change_currentclimate”</p> </li> <li> <p>“AGC_change_RCP85_midcentury”</p> </li> </ul> <p>For both scenarios: -2 = significant decrease (projected maximum < baseline); -1 = trend decrease (projected maximum > baseline > projected mean; 1 = weak increase (projected minimum < baseline < projected mean); 2 = strong increase (projected minimum > baseline).</p> <p>Estimates of projected changes in soil carbon under each scenario were estimated using global meta-analyses (Cardinael et al. 2018; Chaterjee et al. 2018; De Stefano and Jacobson 2017), studies of multi-strata agroforestry transitions in similar climates and soil types, and land-use change studies in Hawaiʻi. The following rasters display projected directional changes in soil carbon with agroforestry under current and RCP 8.5 mid-century rainfall. </p> <ul> <li> <p>“SoilC_currentclimate”</p> </li> <li> <p>“SoilC_RCP85_midcentury”</p> </li> </ul> <p>For both scenarios, 1 = increase low confidence; 10 = increase medium confidence; 100 = increase high confidence; 2 = no change low confidence; 20 = no change medium confidence; 200 = no change high confidence; 3 =unclear (insufficient data); 4 = unclear (mixed evidence).</p> <p>Projected synergies and tradeoffs in AGC and soil C under each scenario were estimated by combining the soil C and AGC results. The following rasters display projected synergies and tradeoffs in soil C and AGC under current and RCP 8.5 mid-century rainfall. </p> <ul> <li> <p>“AGC_Soil_Bivariate_currentclimate”</p> </li> <li> <p>“AGC_Soil_Bivariate_RCP85_midcentury”</p> </li> </ul> <p>For both scenarios: the first value is soil C category: 7 = unknown/uncertain; 8= increase; 9= no change; and the second value is AGC category: 0 = decrease; 1 = no change; 2 = increase.</p> <p><strong> </strong></p> <h2>References:</h2> <p>Asner, G. P., Sousan, S., Knapp, D. E., Selmants, P. C., Martin, R. E., Hughes, R. F., & Giardina, C. P. (2016). Rapid forest carbon assessments of oceanic islands: A case study of the Hawaiian archipelago. 11(1). <a href="https://doi.org/10.1186/s13021-015-0043-4">https://doi.org/10.1186/s13021-015-0043-4</a></p> <p>Cardinael, R., Umulisa, V., Toudert, A., Olivier, A., Bockel, L., & Bernoux, M. (2018). Revisiting IPCC Tier 1 coefficients for soil organic and biomass carbon storage in agroforestry systems. Environmental Research Letters, 13. <a href="https://doi.org/10.1088/1748-9326/aaeb5f/meta">https://doi.org/10.1088/1748-9326/aaeb5f/meta</a></p> <p>Chaterjee, N., Nair, P. K. R., Chakraborty, S., & Nair, V. D. (2018). Changes in soil carbon stocks across the forest-agrofoest-agriculture/pasture continuum in various agroecological regions: A meta-analysis. Agriculture, Ecosystems & Environment, 266, 55–67. <a href="https://doi.org/10.1016/j.agee.2018.07.014">https://doi.org/10.1016/j.agee.2018.07.014</a></p> <p>De Stefano, A., & Jacobson, M. G. (2017). soil carbon sequestration in agroforestry systems: A meta-analysis. Agroforestry Systems. <a href="https://doi.org/10.1007/s10457-017-0147-9">https://doi.org/10.1007/s10457-017-0147-9</a></p> <p>Elison Timm, O., Giambelluca, T. W., & Diaz, H. F. (2015). Statistical downscaling of rainfall changes in Hawaiʻi based on the CMIP5 global model projections. Journal of Geophysical Research: Atmospheres. <a href="https://doi.org/10.1002/2014JD22059">https://doi.org/10.1002/2014JD22059</a></p> <p>Giambelluca, T. W., Chen, Q., Frazier, A. G., Price, J. P., Chen, Y. L., Chu, P. S., Eischeid, J. K., & Delparte, D. M. (2013). Online Rainfall Atlas of Hawaiʻi. Bulletin Of the American Meteorological Society, 94, 313–316. <a href="https://doi.org/10.1175/BAMS-D-11-00228.1">https://doi.org/10.1175/BAMS-D-11-00228.1</a></p> <p>Jacobi, J. D., Price, J. P., Fortini, L. B., Gon III, S. M., & Berkowitz, P. (2017). Carbon Assessment of Hawaiʻi Land Cover Map [Map]. USGS. </p> <p><a href="https://www.sciencebase.gov/catalog/item/592dee56e4b092b266efeb6b">https://www.sciencebase.gov/catalog/item/592dee56e4b092b266efeb6b</a></p> <p>Kurashima, N., Fortini, L., & Ticktin, T. (2019). The potential of indigenous agricultural food production under climate change in Hawaiʻi. Nature Sustainability. <a href="https://doi.org/10.1038/s41892-019-0226-1">https://doi.org/10.1038/s41892-019-0226-1</a></p> <p>Selmants, P. C., Giardina, C. P., Sousan, S., Knapp, D. E., Kimball, H., Hawbaker, T. J., Moreno, A., Seirer, J., Running, S. W., Miura, T., Bergstrom, R., Hughes, R. F., Litton, C. M., & Asner, G. P. (2017). Baseline Carbon Storage and Carbon Fluxes in Terrestrial Ecosystems of Hawaiʻi. USGS.</p> <p>State Land Use Commission. (2020). State Land Use District Boundaries [Map]. Hawaiʻi Statewide GIS Program.</p>
Data from: Extrapolating potential crop damage by insect pests based on land use data: examining inter-regional generality in agricultural landscapes_210907
<p>DamagePrediction_data_2021_210907 Data from: Extrapolating potential crop damage by insect pests based on land use data: examining inter-regional generality in agricultural landscapes</p>
Data from: From microbes to mammals: pond biodiversity homogenization across different land-use types in an agricultural landscape
<p>Local biodiversity patterns are expected to strongly reflect variation in topography, land use, dispersal boundaries, nutrient supplies, contaminant spread, management practices and other anthropogenic influences. In contrast, studies focusing on specific taxa revealed a biodiversity homogenization effect in areas subjected to long-term intensive industrial agriculture. We investigated whether land use affects biodiversity levels and community composition (α & β diversity) in 67 kettle holes (KH) representing small aquatic islands embedded in the patchwork matrix of a largely agricultural landscape comprising grassland, forest, and arable fields. These KH, similar to millions of standing water bodies of glacial origin, spread across northern Europe, Asia, and North America, are physico-chemically diverse, differ in the degree of coupling with their surroundings. We assessed biodiversity patterns of eukaryotes, <i>Bacteria</i> and <i>Archaea</i> in relation to environmental features of the KH, using deep-amplicon-sequencing of environmental DNA (eDNA). First, we asked whether deep sequencing of eDNA provides a representative picture of KH biodiversity across the <i>Bacteria</i>, <i>Archaea</i>, and Eukaryotes. Second, we investigated if and to what extent KH biodiversity is influenced by the surrounding land-use. Our data shows that deep eDNA amplicon sequencing is useful for in-depth assessments of cross-domain biodiversity comprising both micro- and macro-organisms, but, has limitations with respect to single-taxa conservation studies. Using this broad method, we show that sediment eDNA, integrating several years to decades, depicts the history of agricultural land-use intensification. The latter, coupled with landscape wide nutrient enrichment (including by atmospheric deposition), groundwater connectivity between KH and organismal (active and passive) dispersal in the tight network of ponds, resulted in a biodiversity homogenization in the KH water, levelling off today's detectable differences in KH biodiversity between land-use types.</p>
Land capability for agriculture (partial cover)
<p>The Land capability for agriculture (partial cover) spatial dataset provides information on the types of crops that may be grown in different areas dependent on environmental and soil characteristics. This map covers much of the productive agricultural land in Scotland and it can be used to determine the areas most suited to growing crops or grazing livestock. </p> <p>The Land capability for agriculture map (partial cover) was originally mapped at 1:50 000 scale by field survey and was subsequently digitised. It shows the distribution of the different land classes across virtually all of Scotland’s cultivated agricultural land and adjacent uplands. The map should be cited as: 'Soil Survey of Scotland Staff (1984-87). Land Capability for Agriculture maps of Scotland at a scale of 1:50 000. Macaulay Institute for Soil Research, Aberdeen. 10.5281/zenodo.6322760'.</p> <p>The digital dataset contains information on the 'class' of soil. Soil classes range from Class 1 (land capable of producing a wide range of crops) to Class 7 (land of very little agricultural value). Land within Class 3 is subdivided to provide further information on potential yields; Classes 4 and 5 are further divided to provide information on grasslands; Class 6 is divided on the quality of the natural vegetation for grazing. Classes 1 to 3.1 are known as prime agricultural land.</p> <p>There is an accompanying booklet that describes the classification in more detail and set out the rules and guidelines to be used. This booklet should be referenced as: Bibby, J.S., Douglas, H.A., Thomasson, A.J. and Robertson, J.S. (1991) Land capability classification for agriculture. Soil Survey of Scotland Monograph. The Macaulay Institute for Soil Research. Aberdeen. ISBN -0-7084-0508-8.</p> <p>The spatial dataset is provided under the James Hutton Institute open data licence included within the zipped dataset.</p> <p>The maintenance of this dataset is funded by the Rural & Environment Science & Analytical Services Division of the Scottish Government. The data can also be downloaded from or viewed at <a href="https://www.hutton.ac.uk/learning/natural-resource-datasets/soilshutton/soils-maps-scotland/download">https://www.hutton.ac.uk/learning/natural-resource-datasets/soilshutton/soils-maps-scotland/download </a>or viewed at https://soils.environment.gov.scot.</p> <p>THE CLASSES<br> Class 1. Land capable of producing a very wide range of crops with high yields<br> Class 2. Land capable of producing a wide range of crops with yields less high than Class 1.<br> Class 3. Land capable of producing good yields from a moderate range of crops.<br> Class 4. Land capable of producing a narrow range of crops.<br> Class 5. Land suited only to improved grassland and rough grazing.<br> Class 6. Land capable only of use as rough grazing.<br> Class 7. Land of very limited agricultural value.</p>
Land Capability for Agriculture (LCA)
<p>The National scale land capability for agriculture spatial dataset provides information on the types of crops that may be grown in different areas dependent on environmental and soil characteristics. This map covers the entire country and it can be used to determine the areas most suited to growing crops or grazing livestock. </p> <p>The digital dataset contains information on the 'class' of soil. Soil classes range from Class 1 (land capable of producing a wide range of crops) to Class 7 (land of very little agricultural value). Land within Class 3 is subdivided to provide further information on potential yields; Classes 4 and 5 are further divided to provide information on grasslands; Class 6 is divided on the quality of the natural vegetation for grazing. Classes 1 to 3.1 are known as prime agricultural land.</p> <p>The Land Capability for Agriculture assessment was carried out in 1981 using data collected between 1978 and 1981. The National scale land capability for agriculture map was then created in 1983 at a scale of 1:250 000. The map should be cited as: 'Soil Survey of Scotland Staff (1981). Land Capability for Agriculture maps of Scotland at a scale of 1:250 000. Macaulay Institute for Soil Research, Aberdeen.10.5281/zenodo.6322683'.</p> <p>There is an accompanying booklet that describes the classification in more detail and set out the rules and guidelines to be used. This booklet should be referenced as: Bibby, J.S., Douglas, H.A., Thomasson, A.J. and Robertson, J.S. (1991) Land capability classification for agriculture. Soil Survey of Scotland Monograph. The Macaulay Institute for Soil Research. Aberdeen. ISBN -0-7084-0508-8.</p> <p>The spatial dataset is provided under the James Hutton Institute open data licence included within the zipped dataset.</p> <p>The maintenance of this dataset is funded by the Rural & Environment Science & Analytical Services Division of the Scottish Government. The data can also be downloaded from or viewed at https://www.hutton.ac.uk/soil-maps/ or viewed at https://soils.environment.gov.scot.</p> <p>THE CLASSES<br>Class 1. Land capable of producing a very wide range of crops with high yields<br>Class 2. Land capable of producing a wide range of crops with yields less high than Class 1.<br>Class 3. Land capable of producing good yields from a moderate range of crops.<br>Class 4. Land capable of producing a narrow range of crops.<br>Class 5. Land suited only to improved grassland and rough grazing.<br>Class 6. Land capable only of use as rough grazing.<br>Class 7. Land of very limited agricultural value.</p>
Effects of land clearing for agriculture on soil organic carbon stocks in drylands: A meta-analysis
<p><span>To improve our understanding of clearing natural ecosystems for cropland on soil organic carbon stocks in drylands, we searched for related peer-reviewed research papers published from 1980 to 2022 on the Web of Science (<a href="https://www.webofscience.com">https://www.webofscience.com</a>) and the Scopus Database (<a href="https://www.scopus.com">https://www.scopus.com</a>) (accessed on 30th April 2022). Then, we screened papers for </span><span>integrity, relevance, and scientific merit under the following criteria: (1) We made sure all studies were independent and based on field-measured data; (2) Each study had to report paired SOC stocks of cropland and adjacent natural ecosystems with the same or a similar suite of environmental factors; (3) Studies need to explicitly present results on SOC stocks or concentrations for certain depths and areas; (4) Studies have specified the types of natural ecosystems that were converted to cropland, which are used as criteria for defining CNEC types. Finally, we winnowed results to a total of 159 scientific journal articles, comprising 242 sites with 1379 paired soil layer observations from 601 paired soil profiles.</span></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.