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154 results for “croplands”
Effects of drying temperature on potential carbon mineralization and water-extractable organic carbon in Iowa cropland and riparian buffer soils
Measuring carbon dioxide (CO2) produced after re-wetting a previously dried soil is an increasingly popular soil health assay, but there is disagreement on the optimal soil drying temperature. We tested whether soil drying temperature impacts water-extractable organic carbon (WEOC) and soil CO2 emissions (potential carbon mineralization) following rewetting of dried soil. Samples were collected at four sites in north-central Iowa, US, and each site had soils planted to corn/soybean or perennial vegetation. The dataset includes measurements of WEOC prior to the incubation experiment, and measurements of CO2 flux and its stable carbon isotope ratio over the course of a 28-day incubation. The manuscript describing these data is under review in Geoderma.
Togo Cropland Map and Labeled Dataset
<p>This dataset provides a 10 m resolution map of cropland in Togo (togo_cropland_2019.zip). Each pixel represents a posterior probability (ranging 0 to 1) that the pixel contains crops, predicted using an LSTM classifier and multi-spectral time series of Sentinel-2 satellite observations. For more details on the method, please see Kerner and Tseng, et al. (full reference below).</p> <p>This dataset also provides the hand-labeled polygons used for training (crop_merged_v2.zi, noncrop_merged_v2.zip) and testing (togo_test_majority.zip) the model, which were created by experts based on photointerpretation of high-resolution imagery (primarily SkySat and PlanetScope) in QGIS and Google Earth Pro.</p> <p>If you use any part of this dataset, please cite the following paper: Hannah Kerner, Gabriel Tseng, Inbal Becker-Reshef, Catherine Nakalembe, Brian Barker, Blake Munshell, Madhava Paliyam, and Mehdi Hosseini. 2020. Rapid Response Crop Maps in Data Sparse Regions. In review for <em>KDD ’20: ACMSIGKDD Conference on Knowledge Discovery and Data Mining Workshops</em>, August 22–27, 2020, San Diego, CA. </p>
Data associated with Lark et al. 2020: U.S. cropland conversion (2008-16)
<p>Maps of cropland conversion classes, year of conversion, and pre- and post-conversion land cover associated with Lark et al. (2020). This repository also includes maps of 'local' and 'national' yield differentials for corn, soybeans, and wheat that are associated with the same publication. Code used to generate these data can be found <strong><a href="https://zenodo.org/record/3905556#.XvLXQ21Kipo">here</a></strong>.</p> <ul> <li>Lark, T.J., S.A. Spawn, M.F. Bougie, H.K. Gibbs. Cropland expansion in the United States produces marginal yields with disproportionate costs to wildlife. <em>Nature Communications </em>(In review)</li> </ul> <p>Cropland conversion maps are included in a zipped ESRI Geodatabase titled "US_land_conversion_2008-16.gdb". Each feature layer encompasses all of the conterminous United States at a 30m spatial resolution. Feature layers include:</p> <ul> <li><em><strong>mtr</strong></em> = "Multi-temporal results"; Classifies land as being one of five broad land use change classes during the 2008-16 study period: <ol> <li>"<em>stable non-cropland</em>" -- areas of consistent non-cropland throughout the duration of the study period.</li> <li>"<em>stable cropland</em>" -- areas of consistent cropland throughout the duration of the study period.</li> <li>"<em>cropland expansion</em>" -- areas converted to crop production between 2008 and 2016.</li> <li>"<em>cropland abandonment</em>" -- areas converted away from crop production between 2008 and 2016.</li> <li>"<em>intermittent cropland/confusion</em>" -- areas that were cropped for at least two years but show no clear trend towards or away from cropland. These could include areas under a crop-pasture rotation, fallow rotations, or simply areas with repeated classifier confusion. </li> </ol> </li> <li><em><strong>ytc</strong></em> = "year to cropland"; Indicates the year in which pixels with an <em>mtr</em> classification of "3" (i.e. "cropland expansion") were converted from non-cropland to cropland. e.g., a value of 2009 represents land that was converted between the 2008 growing season and the 2009 growing season.</li> <li><em><strong>yfc</strong></em> = "year from cropland"; Indicates the year in which pixels with an <em>mtr</em> classification of "4" (i.e. "cropland abandonment") were converted from cropland to non-cropland. e.g., a value of 2009 represents land that was still cropped in 2008 and no longer cropped during the 2009 growing season. </li> <li><em><strong>bfc</strong></em> = "before first crop"; Indicates the last land cover class before a non-crop pixel was converted to cropland. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table<strong> <a href="https://developers.google.com/earth-engine/datasets/catalog/USDA_NASS_CDL#bands">here</a>.</strong></li> <li><em><strong>fc</strong></em> = "first crop"; Indicates the class of the first crop planted after a non-crop pixel was converted to cropland. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table<strong> <a href="https://developers.google.com/earth-engine/datasets/catalog/USDA_NASS_CDL#bands">here</a>.</strong></li> <li><em><strong>bfnc</strong></em> = "before first non-crop"; Indicates the last cropland class of a pixel before it was abandoned to non-crop land cover. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table<strong> <a href="https://developers.google.com/earth-engine/datasets/catalog/USDA_NASS_CDL#bands">here</a>.</strong></li> <li><em><strong>fnc</strong></em> = "first non-crop"; Indicates the first non-crop class of a pixel after it was abandoned to non-crop land cover. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table<strong> <a href="https://developers.google.com/earth-engine/datasets/catalog/USDA_NASS_CDL#bands">here</a>.</strong></li> </ul> <p>Yield differential maps are included in the "yieldDifferentials.zip" folder as GeoTIFF rasters with a ~10km spatial resolution. Raster values represent relative (%) differences between the representative yields of new croplands (<em>mtr</em> = 3) and those of stable croplands (<em>mtr </em>= 1) planted to that crop within either (i) the larger 10km x 10km gridcell in which those fields are situated ("local" differentials) or (ii) the entire nation ("national" differentials).</p> <ul> <li><strong>corn_relDiff_local.tif </strong>= local yield differential (%) of corn grain.</li> <li><strong>corn_relDiff_national.tif</strong> = national yield differential (%) of corn grain.</li> <li><strong>soy_relDiff_local.tif</strong> = local yield differential (%) of soybeans.</li> <li><strong>soy_relDiff_national.tif</strong> = national yield differential (%) of soybeans.</li> <li><strong>wheat_relDiff_local.tif</strong> = local yield differential (%) of wheat.</li> <li><strong>wheat_relDiff_national.tif</strong> = national yield differential (%) of wheat.</li> </ul>
Kenya Cropland Map and Non-Crop Labelled dataset
<p>This dataset provides a 10m resolution map of cropland in Kenya for the 2019-2020 growing season (kenya_cropland_binary_2019.tif.zip) and a 10m resolution map of cropland in Busia County, Kenya for the 2020-2021 growing season (busia_cropland_binary_2020.tif.zip). Each pixel has a binary value, 0 if it does not contain crops and 1 if it does. These values were obtained by thresholding the predictions of an LSTM classifier trained on multi-spectral time series of Sentinel-2 satellite observations. A thresholding value of 0.5 was used.</p> <p>This dataset also provides the hand-labelled non-crop points used for training, which were created by labelling high-resolution satellite imagery in QGIS and Google Earth Pro.</p> <p>For more information, or if you use any part of this dataset, please refer to / cite the following paper: Gabriel Tseng, Hannah Kerner, Catherine Nakalembe and Inbal Becker-Reshef. 2020. Annual and in-season mapping of cropland at field scale with sparse labels. Tackling Climate Change with Machine Learning workshop at NeurIPS ’20: December 11th, 2020</p>
ludemannc/fao_cnb: FAO cropland nutrient budget analysis
<p>Data and code used for creating Cropland Nutrient Budget (1961-2020) related figures and tables in the Ludemann et al Earth Systems Science Data article available at: https://doi.org/10.5194/essd-2023-206.</p>
Comparison of Cropland Maps Derived from Land Cover Maps in Sub-Saharan Africa
<p>This data repository provides the datasets associated with analysis conducted in Kerner et al. (2024), citation below.</p> <p>The CSV file `intercomparison-results.csv` gives a table metrics for each of 11 land cover maps (plus a majority vote ensemble of all maps) evaluated using the reference dataset for each of 8 countries (Kenya, Rwanda, Uganda, Tanzania, Mali, Malawi, Togo, and Zambia). The reference datasets are provided as zip files. To ensure these datasets can be used for independent evaluation and comparison between maps in the future, the reference datasets should ONLY be used for final, independent evaluation of data products/model outputs; they should NOT be used for training models, tuning hyperparameters, or any other decisions during model/map development. </p> <p>The provided tif files contain the consensus maps (sum of all 11 maps) used to compute consensus statistics in Kerner et al. (2024).</p> <p>Kerner, H., Nakalembe, C., Yang, A., Zvonkov, I., McWeeny, R., Tseng, G., and Becker-Reshef, I. (2024). How accurate are existing land cover maps for agriculture in Sub-Saharan Africa? <em>Under review</em>. </p>
Output of Optimized gross primary productivity over the croplands within the BEPS particle filtering data assimilation system (BEPS_PF v1.0)
<p>Output of Optimized gross primary productivity over the croplands within the BEPS particle filtering data assimilation system (BEPS_PF v1.0)</p>
Supplementary Table for Earth observation data-driven cropland soil monitoring: A review
<p>Table including 46 manuscripts written in English referring to topsoil monitoring related to Earth observation data-driven cropland soil monitoring: A review paper.</p>
Volunteer classifications of images from the Cropland Capture game
<p>Each entry represents a single classification of a single image by a volunteer rater.</p> <p>The dataset contains six columns:</p> <p>imgid: The unique identifier for each image used in the Cropland Capture campaign<br> userid: The unique identifier for each volunteer in the Cropland Capture campaign<br> rating: The answer provided; can be only one of the following:<br> 1: yes cropland<br> 2: no cropland<br> 0: maybe <br> date: Timestamp of the rating<br> ratingid: The unique identifier of the rating (this is different for each data row)<br> platform: What interface did the volunteer use to provide this rating?<br> 1: iPhone5<br> 2: iPhone, other models<br> 3: iPad<br> 4: Browser<br> >100: Android; different numbers indicate the screen size in pixels </p> <p>For more information, please see the following publications:</p> <p>Salk, CF, T Sturn, L See, S Fritz (2017). Limitations of majority agreement in crowdsourced image interpretation. <em>Transactions in GIS</em>, 21: 207–223.</p> <p>Salk, CF, T Sturn, L See, S Fritz (2016). Local knowledge and professional background have a minimal impact on volunteer citizen science performance in a land-cover classification task. <em>Remote Sensing</em>, 8: 744.</p> <p>Salk, CF, T Sturn, L See, S Fritz and C Perger (2016). Assessing quality of volunteer crowdsourcing contributions: Lessons from the Cropland Capture game. <em>International Journal of Digital Earth</em>, 9(4): 410-426.</p>
Global inventory of potentially cultivable land and potentially available cropland under different scenarios and policies
<p><strong>Global inventory of potentially cultivable land and potentially available cropland under different scenarios and policies</strong></p> <p>To identify and investigate potential land-use conflicts and emerging trade-offs between different Sustainable Development Goals, such as food security, climate protection and biodiversity conservation, it is important to identify where land-use change and particularly the expansion of cropland could potentially take place in the future. Therefore, we provide a consistent global dataset of land potentially cultivable and potentially available for agricultural use for past and future time periods from 1980 until 2100. Based on the agricultural suitability of land for 23 globally important food, feed, fiber and first- and second-generation bioenergy crops, and high resolution land cover data, the potentially cultivable land is defined by its agricultural suitability and the (technical) feasibility of agriculture. The potentially available cropland additionally considers potential nature protection policies restricting agriculture in forests, wetlands and strictly protected areas, thereby reflecting key aims of the Sustainable Development goals and recent efforts to stop deforestation, protect the climate and preserve biodiversity.</p> <p>The spatially explicit global datasets of potentially cultivable land (pcl) and potentially available cropland (pac) are available for four different time periods (1980-2009, 2010-20,39, 2040-2069, 2070-2099) under RCP2.6 and RCP8.5. The impact of irrigation on the agricultural suitability is considered by referring to current irrigations patters. However, to enable different assumptions on the irrigation of land potentially cultivable or available for cropland use, all datasets are also available for rainfed and irrigated conditions separately. Moreover, we provide a subset-version of all dataset which excludes land that is solely suitable for second-generation bioenergy crops. All datasets are available at 30 arc-seconds and 30 arc-minutes spatial resolution and aggregated at country level to enable the application in models that use aggregated data.</p> <p>By serving as an input for land-use models, the data could improve the comparability of the models and their output, and increase the consistency within interdisciplinary research and integrated model coupling approaches that investigate land-use change.</p> <p> </p> <p><strong>Further information:</strong></p> <p>A detailed description on the methods and underlying data is available in:</p> <p>Schneider. J.M., Zabel, F., Mauser, W. (2022): Global inventory of suitable, cultivable and available cropland under different scenarios and policies. Scientific Data.<em> </em><a href="https://doi.org/10.1038/s41597-022-01632-8">https://doi.org/10.1038/s41597-022-01632-8</a></p> <p><strong>Contact:</strong></p> <p>Please contact: Julia M. Schneider (Schneider.ju@lmu.de)<br>Department of Geography, Ludwig-Maximilians-Universität München (LMU), Munich, Germany.</p>
Long-term simulation of snow cover and its potential impacts on seasonal frost dynamics in croplands across southern Canada
<p><em>In northern climes, accurate simulation of thermal and hydrological budgets for farmlands during overwintering conditions is crucial to both an accurate prediction of spring flooding and the successful management of nutrient losses. As snow cover influences soil freezing dynamics, it has been hypothesized that reduced snow cover due to warmer winters might increase the depth and duration of frozen soil conditions. Nonetheless, such impacts remain poorly understood and, given the difficulty in measuring the depth of frozen soil, no long-term field experiment has documented these potential effects. The present study was designed to test this hypothesis. Drawing upon observed snow depth and soil temperature data collected from six research farms across Southern Canada over various time spans from 1989 to 2020, the Root Zone Water Quality Model, integrated with the Simultaneous Heat and Water model, was calibrated and validated. The potential influence of warmer winter on shifts in soil frost dynamics was evaluated by estimating the depth and duration of frozen soil for each farmland site under various RCP temperature scenarios using the RZ-SHAW model. Soil frozen depth in Eastern site increased with the increase of RCP temperature scenarios in some years, but decreased under the highest RCP temperature scenario. The monthly relationship between snow depth and soil frozen depth was determined through partial correlation analysis. Snow was most effective in alleviating soil freezing in the months of January and February, a period when snow cover depth was least affected by warming air temperatures. This paper suggests that Global warming induced-snow cover reduction would be site-specific and is </em>more likely to occur in <em>regions where energy lost through reduced snow cover would outweigh the energy gained through warmer air temperature.</em></p>
Dataset - Identification of early abandonment in cropland through radar-based coherence data and application of a Random-Forest model
<p>This dataset accompanies the manuscript titled "Identification of early abandonment in cropland through radar-based coherence data and application of a Random-Forest model", submitted by co-authors to the journal Global Change Biology (GCB) Bioenergy.</p> <p>Wouter Meijninger<sup>1</sup>, Berien Elbersen<sup>1</sup>, Michiel van Eupen<sup>1</sup>, Stephan Mantel<sup>2</sup>, Pilar Ciria Ciria<sup>3</sup>, Andrea Parenti<sup>4</sup>, Marina Sanz Gallego<sup>3</sup> and Paloma Perez Ortiz<sup>3</sup>, Marco Acciai<sup>4</sup>,and Andrea Monti<sup>4</sup><br> Institutes: 1) Wageningen University & Research, 2) ISRIC, 3) CIEMAT, 4) Bologna University,</p> <p><strong>Abstract (Manuscript)</strong></p> <p>In the context of increased pressures on land for food and non-food production it is relevant to understand better, which land resources have become unused and abandoned and where these lands are. Data on where these lands are and what their extend is are not collected in regular statistics. In this paper we present an approach to detect signs of abandonment in cropping land using radar coherence data. The methodology was tested in the Spanish regions of Albacete and Soria where agricultural land abandonment is a common process. The results show that land abandonment detection using radar coherence data works well for the region of Albacete in arable lands. The radar-based analysis is a relatively simple method to detect land abandonment in an early to longer-term state and can therefore be applied once developed and tested further in other regions to larger areas of the EU where land abandonment is serious and needs monitoring and policy response. The applicability of the method to Soria and Emilia Romagna (Italy) regions show that there are still challenges to overcome to make the method more widely applicable for detecting land abandonment in other environmental zones of Europe. Lack of reliable training and validation data, like LPIS data, in regions is one of the challenges in this respect.</p> <p><strong>Readme data files</strong></p> <p><em>Coherence_quarterly_statisitcs_2017_to_2020.zip</em></p> <p>Radar coherence quarterly statistics - Albacete (Spain)</p> <p>Radar coherence data is based on Sentinel-1B<br> Period: 2017 to 2020</p> <p>File naming (.tif files) per year (<em>YYYY</em>):</p> <ul> <li>Mean coherence: <em>mean_YYYY_1to4.tif</em></li> <li>Standard deviation coherence: <em>std_YYYY_1to4.tif</em></li> <li>Range coherence: <em>range_YYYY_1to4.tif</em></li> <li>Mean delta coherence: <em>mean_delta_YYYY_1to4.tif</em></li> <li>Standard deviation delta coherence: <em>std_delta_YYYY_1to4.tif</em></li> <li>Maximum delta coherence: <em>max_delta_YYYY_1to4.tif</em></li> </ul> <p>Each file consists of 4 bands:</p> <ul> <li>band 1: 1st quarter [Jan-Feb-March]</li> <li>band 2: 2nd quarter [April-May-June]</li> <li>band 3: 3rd quarter [July-Aug-Sept]</li> <li>band 4: 4th quarter [Oct-Nov-Dec]</li> </ul> <p>Statistics are based on radar coherence data, which is scaled between >0 and 1<br> No data: 0-values</p> <p>Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m</p> <p><em>SIGPAC_data_Albacete_2018_to_2020.zip</em></p> <ul> <li>More than 5 year fallow (20m raster files)</li> <li>Land Use Land Cover LULC (20m raster files)</li> </ul> <p>More than 5 year fallow (according to SIGPAC)<br> Period: 2018 to 2020<br> File naming (ENVI files):</p> <ul> <li>Albacete_SIGPAC_MoreThan5YrsFallowAreas_2018_20m.dat (+ Albacete_SIGPAC_MoreThan5YrsFallowAreas_2018_20m.hdr)</li> <li>Albacete_SIGPAC_MoreThan5YrsFallowAreas_2019_20m.dat (+ Albacete_SIGPAC_MoreThan5YrsFallowAreas_2019_20m.hdr)</li> <li>Albacete_SIGPAC_MoreThan5YrsFallowAreas_2020_20m.dat (+ Albacete_SIGPAC_MoreThan5YrsFallowAreas_2020_20m.hdr)</li> </ul> <p>Pixel values:<br> 0: Not fallow<br> 1: Fallow more than 5 years</p> <p>Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m</p> <p>Land Use Land Cover LULC (according to SIGPAC)<br> Period: 2018 to 2020<br> File naming (ENVI files):</p> <ul> <li>LULC_SIGPAC_Albacete_2018_20m.dat (+ LULC_SIGPAC_Albacete_2018_20m.hdr)</li> <li>LULC_SIGPAC_Albacete_2019_20m.dat (+ LULC_SIGPAC_Albacete_2019_20m.hdr)</li> <li>LULC_SIGPAC_Albacete_2020_20m.dat (+ LULC_SIGPAC_Albacete_2020_20m.hdr)</li> </ul> <p>Pixel values:</p> <ul> <li>0 - Nan</li> <li>1 - Arable land</li> <li>2 - Vineyards</li> <li>3 - Olives</li> <li>4 - Fruits</li> <li>5 - Nuts</li> <li>6 - Citrus</li> <li>7 - Permanent grassland</li> <li>8 - Forest</li> <li>9 - Rest, small elements</li> <li>10 - Built-up areas</li> <li>11 - Water</li> <li>12 - Roads</li> <li>13 - Unproductive land</li> </ul> <p>Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m</p> <p><em>Annual_unused_used_land_maps_Albacete_2017_to_2020.zip</em></p> <p>Derived annual unused/used land maps - Albacete (Spain), based on Random-Forest model<br> Period: 2017-2020<br> File naming (ENVI files):</p> <ul> <li>predict_RF_Albacete_2017_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2017_quarterly_stats_LU1_v181920.hdr)</li> <li>predict_RF_Albacete_2018_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2018_quarterly_stats_LU1_v181920.hdr)</li> <li>predict_RF_Albacete_2019_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2019_quarterly_stats_LU1_v181920.hdr)</li> <li>predict_RF_Albacete_2020_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2020_quarterly_stats_LU1_v181920.hdr)</li> </ul> <p>Pixel values:<br> 0 - Used (and/or Nan)<br> 1 - Unused</p> <p>Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m</p> <p><em>Four_year_abandoned_land_Albacete_2017_to_2020.zip</em></p> <p>Four-year abandonment map is based on the 4 annual unused/used land maps<br> File naming (ENVI):</p> <ul> <li>Four_year_abandoned_land_Albacete_2017_to_2020.dat (+ Four_year_abandoned_land_Albacete_2017_to_2020.hdr)</li> </ul> <p>Pixel values:<br> 0 - (Nan)<br> 1 - Used (1 year unused in period 2017 - 2020)<br> 2 - Used (2 year unused in a row in period 2017 - 2020)<br> 3 - Abandoned (3 year unused in a row in period 2017 - 2020)<br> 4 - Abandoned (4 year unused in a row in period 2017 - 2020)</p> <p>Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m</p>
Rising agriculture water scarcity in China is driven by expansion of irrigated cropland in water-scarce regions
<p>The datasets contain original data from the article titled" Rising agriculture water scarcity in China is driven by expansion of irrigated cropland in water-scarce regions "</p>
Reference data set used to validate the hybrid cropland map at 500m (Fritz, S. 2024)
<p>This is a reference data set for validation of the hybrid cropland map at 500m resolution for the year 2019 (Fritz, 2024, map <a title="Hybrid cropland map (GLAD/WorldCereal)" href="../doi/10.5281/zenodo.10818823" target="_blank" rel="noopener">available here</a>)</p> <p>Sampling design: random whithin areas of improvement, where the WorldCereal map is performing better (less errors) than the GLAD cropland map 2019. </p> <p>Number of sample sites: 500</p> <p>Method of data collection: visual interpreation of various sources of information, including very high resolution images and photos. </p> <p><br>Tool for data collection: <a href="https://www.geo-wiki.org" target="_blank" rel="noopener">Geo-Wiki</a></p>
Figure 1 in Morphometry And Eye Morphology Of Harpalus (Proteonus) Distinguendus (Duftschmid, 1812) And H. (Amblystus) Rufipalpis (Sturm, 1818) (Coleoptera: Carabidae), Two Congeners Inhabiting Abandoned Croplands
Figure 1. Measured traits of Harpalus distinguendus female and male individuals. Trait units in table 1.
Annual crop-specific management history of phosphorus fertilizer input (CMH-P) in the croplands of United States from 1850 to 2022: Application rate, timing, and method
<p>This dataset presents spatiotemporal dynamics of phosphorus (P) fertilizer management (application rate, timing, and method) at a 4km × 4 km resolution in agricultural land of the contiguous U.S. from 1850 to 2022. By harmonizing multiple data sources, we reconstructed the county-level crop-specific P fertilizer use history. We then spatialized and resampled P fertilizer use data to 4 km × 4 km gridded maps based on historical U.S. cropland distribution and crop type database developed by Ye et al. (2024).</p> <p>This dataset contains (1) P fertilizer total consumption and mean application rate at the national level (Tabular); (2) P fertilizer consumption of 11 crops at the state level (Tabular); (3) P fertilizer consumption of permanent pasture (Tabular); (4) P fertilizer consumption of non-farm at the state level (Tabular); (5) P fertilizer application rate of 11 crop types at the state level (Tabular); (6) P fertilizer application rate of 11 crop types at the county level (Tabular); (7) P fertilizer application timing ratio at the state level (Tabular); (8) P fertilizer application method ratio at the state level (Tabular); (9) Gridded maps of P fertilizer application rate based on state-level data; (10) and (11) Gridded maps of P fertilizer application rate based on county-level data; (12)-(20) Gridded maps of P fertilizer application rate for each crop.</p> <p>A detailed description of the data development processes, key findings, and uncertainties can be found in Cao, P., Yi, B., Bilotto, F., Gonzalez Fischer, C., Herrero, M., Lu, C.: Crop-specific Management History of Phosphorus fertilizer input (CMH-P) in the croplands of United States: Reconciliation of top-down and bottom-up data sources, is under review for the journal Earth System Science Data (ESSD). https://essd.copernicus.org/preprints/essd-2024-67/#discussion. </p> <p>This work is supported by the Iowa Nutrient Research Center, the ISU College of Liberal Arts and Sciences Dean's Faculty Fellowship, and NSF CAREER grant (1945036).</p> <p> </p>
Global warming effects of cropland expansion by reduction of biogenic secondary organic aerosols
<p><span>This is the dataset to support our paper title of “Global warming effects of cropland expansion by reduction of biogenic secondary organic aerosols”. Cropland expansion has been the most significant global land use change since industrialization. However, evaluations of radiative forcing from land use changes have often neglected the radiative effects of secondary organic aerosols (SOA) linked to cropland expansion. Sensitivity experiments using an Earth system model that incorporates advanced SOA processes reveal approximately a 10% reduction in the global biogenic SOA burden due to cropland expansion since industrialization. This reduction weakens SOA</span><span>’</span><span>s role in scattering radiation and forming clouds, leading to a decline in its cooling effect by 146 mW m⁻², which is equivalent to 8% of the warming caused by CO₂ emissions since industrialization. This effect is expected to increase by nearly half under future climate warming and reduced emissions scenarios. Therefore, policies addressing food security and climate change must consider the radiative impacts of biogenic SOA associated with cropland expansion.</span></p> <p><span> </span></p> <p><span>The dataset consists of three zip files, which include model code and output from sensitivity simulations conducted with the Community Earth System Model (CESM) version 1.2.2, using the IMPACT aerosol module and an offline radiative model. The files are described as follows:</span></p> <p><span> </span></p> <p><strong><span>Model code.zip:</span></strong><span> Contains the source code for the IMPACT aerosol module, which was integrated as an additional aerosol module within CESM version 1.2.2, available from the NCAR repository.</span></p> <p><span> </span></p> <p><strong><span>PD_Cases.zip:</span></strong><span> This file contains two folders (<strong>Concentration and Radiation</strong>), which hold the simulation results for concentration and radiation in present-day cases. The Concentration folder includes 13 subfolders. The Radiation folder contains four subfolders representing different radiation simulation results. These include simulations with and without the direct radiative effects of SOA (labeled <strong>ADEwSOA</strong> and <strong>ADEwoSOA</strong> respectively) and simulations with and without the indirect radiative effects of SOA (labeled <strong>AIEwSOA</strong> and <strong>AIEwoSOA</strong> respectively). Each of these subfolders contains 13 additional subfolders, which share the same names as those in the Concentration folder. These 13 subfolders correspond to specific sensitivity experiments, the details of which are explained below. Within each subfolder, you will find the five-year averaged model output for all 12 months.</span></p> <p><strong><span>18L20E20C</span></strong><span> represents simulations with pre-industrial land use, and present-day emissions and climate conditions; </span></p> <p><strong><span>20L20E20C</span></strong><span> represents simulations with present-day land use, emissions, and climate conditions.</span></p> <p><span>Eight subfolders for single vegetation type transition experiments include model output for cases where land use transitions from deciduous broadleaf forest to cropland (<strong>DBF2CRO</strong>), evergreen broadleaf forest to cropland (<strong>EBF2CRO</strong>), evergreen needleleaf forest to cropland (<strong>ENF2CRO</strong>), grassland to cropland (<strong>GRA2CRO</strong>), shrubland to cropland (<strong>SHR2CRO</strong>), deciduous broadleaf forest to grassland (<strong>DBF2GRA</strong>), evergreen broadleaf forest to grassland (<strong>EBF2GRA</strong>), and evergreen needleleaf forest to grassland (<strong>ENF2GRA</strong>).</span></p> <p><span>Three subfolders for latitude-specific experiments cover conversions for all vegetation types in tropical (20</span><span>°</span><span>S</span><span>–</span><span>20</span><span>°</span><span>N, <strong>LLAT</strong>), mid-latitude (50</span><span>°</span><span>S</span><span>–</span><span>20</span><span>°</span><span>S and 20</span><span>°</span><span>N</span><span>–</span><span>50</span><span>°</span><span>N, <strong>MLAT</strong>), and high-latitude (south of 50</span><span>°</span><span>S and north of 50</span><span>°</span><span>N, <strong>HLAT</strong>) regions.</span></p> <p><span> </span></p> <p><strong><span>FU_Cases.zip:</span></strong><span> This file contains two folders (<strong>Concentration and Radiation</strong>), which hold the simulation results for concentration and radiation in future cases. The Concentration folder includes 4 subfolders. The Radiation folder contains four subfolders representing different radiation simulation results. These include simulations with and without the direct radiative effects of SOA (labeled <strong>ADEwSOA</strong> and <strong>ADEwoSOA</strong> respectively) and simulations with and without the indirect radiative effects of SOA (labeled <strong>AIEwSOA</strong> and <strong>AIEwoSOA</strong> respectively). Each of these subfolders contains 4 additional subfolders, which share the same names as those in the Concentration folder. These 4 subfolders correspond to specific sensitivity experiments, the details of which are explained below. Within each subfolder, you will find the five-year averaged model output for all 12 months.</span></p> <p><strong><span>18L20E21C</span></strong><span> represents simulations with pre-industrial land use, and present-day emissions, and future climate conditions; </span></p> <p><strong><span>18L21E21C</span></strong><span> represents simulations with pre-industrial land use, future emissions, and future climate conditions;</span></p> <p><strong><span>20L20E21C</span></strong><span> represents simulations with present-day land use, and present-day emissions, and future climate conditions; </span></p> <p><strong><span>20L21E21C</span></strong><span> represents simulations with present-day land use, future emissions, and future climate conditions.</span></p>
ValLAI_Crop: Validation dataset for coarse-resolution satellite LAI product over Chinese Cropland
<p>Numerous validation campaigns have been conducted over the last decade to assess the accuracy of the global leaf area index (LAI) products. Accurate and comprehensive validations for coarse-resolution LAI products are still very difficult due to lack of enough high-quality field measurements. Here we developed a fine resolution LAI dataset, consisting of 80 sample plots with an area of 3 km × 3 km in four major agricultural regions in China collected from 2003 to 2017. Instead of the indirect optical measurement method employed in most validation campaigns, the direct destructive method was employed to measure LAI of cropland for all the field experiments to avoid the measurement uncertainties, especially for crops at early growth stages with low height. Fine resolution reference LAI maps were derived from Landsat-5 TM and Landsat-8 OLI surface reflectance products based on the semi-empirical inversion model, which were calibrated using field measurements for each growth stage with an RMSE ranging from 0.22 to 0.95, and a relative root mean square error (RRMSE) ranging from 7.58% to 44.42%. Then, 80 sample plots with an area of 3 km × 3 km were selected as the fine resolution validation dataset from the fine resolution reference LAI maps with a proportion of cropland larger than 75% and one or more in-situ samples were contained in each 3 km × 3 km reference map.</p>
High resolution Cropland Extent for Karamoja Uganda 2010/11
<p>This dataset is a shapefile of Cropland extent for Karamoja derived for 2010/2011 from WorldView 1&2 panchromatic and multispectral data at 0.5 m and 1.8 m resolution respectively. The satellite images were acquired between 2010 and 2011 and obtained from The National Geospatial-Intelligence Agency (NGA) through an agreement with NASA under the NextView License (For information about the NextView License, please visit: http://cad4nasa.gsfc. nasa.gov/.). Description of the method to derive the map are included in the reference below.</p> <p>Nakalembe, C., Dempewolf, J., & Justice, C. (2017). Agricultural land use change in Karamoja Region , Uganda. <em>Land Use Policy</em>, <em>62</em>, 2–12. https://doi.org/10.1016/j.landusepol.2016.11.029</p>
GloCAB: Global Cropland Area Burned
<p>Please note this dataset is undergoing peer review and may be subject to changes. </p> <p>The Global Cropland Area Burned (GloCAB) dataset represents the first attempt at a global cropland-focused burned area product. The dataset provides monthly, cropland burned area files at 0.25-degree spatial resolution from July 2002 - December 2020 for our upper (high) and lower (low) estimates. The data is provided in annual GeoTIFF stacks, where the 2002 tiff file only contains 6 layers (July - December), compared to 12 layers for 2003 - 2020 (January - December). </p> <p>Please refer to the readme file for more details about the dataset. </p> <p> </p>
ScienceDex guides
Understand access before you commit
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.