Crop Sequence Boundaries (CSB) is a vector-based geospatial dataset which are fully synthetic representations of agricultural fields, their acreage, and cropping rotation history. These new data are primarily derived from a historical stack of Cropland Data Layers over a set time frame based on an algorithm of geospatial functions. It was developed in cooperation with the Economic Research Service.

Click here for Original Metadata: 2021 Crop Sequence Boundaries.html

Crop Sequence Boundaries (CSB) is a vector-based geospatial dataset which are fully synthetic representations of agricultural fields, their acreage, and cropping rotation history. These new data are primarily derived from a historical stack of Cropland Data Layers over a set time frame based on an algorithm of geospatial functions. It was developed in cooperation with the Economic Research Service.

Click here for Original Metadata: 2015 Crop Sequence Boundaries.html

Crop type map covering the main agricultural areas of Argentina for growing season 2021/2022. This version includes two different maps for winter and summer crops. Maps were generated using supervised classification methods with samples obtained from on-road surveys and Landsat satellite images along the growing season. Files provided include a Geotiff version of each map with a resolution of 30 m. The report includes the methodological details, map legend and accuracy assessments (in Spanish). A web visualizer can be accessed through the following link: https://intalulc.users.earthengine.app/view/mnc21-22

Crop Sequence Boundaries (CSB) is a vector-based geospatial dataset which are fully synthetic representations of agricultural fields, their acreage, and cropping rotation history. These new data are primarily derived from a historical stack of Cropland Data Layers over a set time frame based on an algorithm of geospatial functions. It was developed in cooperation with the Economic Research Service.

Click here for Original Metadata: 2022 Crop Sequence Boundaries.html

Assessing the benefits of crop diversification – a pillar of the agroecological transition – on a large scale requires a description of current crop sequences as a baseline, which is lacking at the scale of the European Union (EU). This work is based on the Harmonised LUCAS in-situ land cover and use database for field surveys from 2006 to 2018 in the European Union (doi: 10.2905/f85907ae-d123-471f-a44a-8cca993485a2) to fill this gap, We completed this dataset with a crop sequence type information for each point under non-perennial agricultural land cover in 2012, 2015 and 2018.

The dataset lucas_classified.csv includes 31 159 points. Variables "point_id", "nuts0", "nuts2", "th_lat", "th_long", "LC1_2012", "LC1_2015", "LC1_2018" are inherited from the Harmonised LUCAS databse. Variables "cereals", "corn", "rapeseed", "sunflower", "pulses", "rootCrops", "forageLeg", "grassland" correspond to the temporal frequencies of respectively cereals, corn, rapeseed, sunflower, pulses, root crops, forage legumes and grassland within the 2012, 2015 and 2018 crop sequence for each point. Variable "crop_sequence_type" is the crop sequence type assigned to each point, among eight options: cereals, corn and cereals, forage legumes and cereals, pulses and cereals, rapeseed and cereals, root crops and cereals, sunflower and cereals, temporary grasslands.

This dataset could be used to map current dominant crop sequences in the European Union, as illustrated in the map attached, and to assess the benefits of future crop diversification.

Crop type map covering the main agricultural areas of Argentina for growing season 2018/2019. This version includes a unique map for the complete growing season considering single and double crops. Map was generated using supervised classification methods with samples obtained from on-road surveys and Landsat satellite images along the growing season. Files provided include a Geotiff version with a resolution of 30 m. The report includes the methodological details, map legend and accuracy assessments (in Spanish). A web visualizer can be accessed through the following link: https://deabelle.users.earthengine.app/view/mncv1r1

Spatial data on soils, land use, and topography, combined with knowledge of conservation effectiveness can be used to identify alternatives to reduce nutrient discharge from small watersheds. This database was developed to be used in conjunction with the Agricultural Conservation Planning Framework Toolkit. Data comprise soil survey information and land use. Soil characterization data were extracted from the Natural Resources Conservation Service (NRCS) Web Soil Survey (Soil Survey Staff, 2013). Land use coverages were developed to represent agricultural fields and the types and rotations of agricultural crops and other land cover types. Land use boundaries were produced by editing a publicly available USDA field boundaries dataset (pre-2008), with all ownership and county-level attributes removed. To ensure these field polygons were consistent with recent land use, the 2009 Cropland Data Layer (USDA-NASS, 2013) was examined for all fields larger than 16 ha. For those fields with multiple cover types, 2009 National Agricultural Imagery Program (NAIP) aerial photography was used as a basis to manually edit field boundaries. A field was considered to have multiple cover types and was edited if the dominant cover occupied <75% of the field, as indicated by the 2009 Cropland Data Layer. Updated field boundaries were then overlaid with data from USDA-National Agricultural Statistics Service (2013) Cropland Data Layer for 2000 – 2014, and each field was classified to represent crop rotations and land cover using the most recent six-year (2009-2014) sequence of land cover. Six-year land-cover strings (e.g., corn-corn-soybean-corn-soybean-corn) generated for each field were classified to represent major crop rotations, which were dominantly comprised of corn (Zea mays L.) and soybean (Glycine max (L.) Merr) annual row crops. The database does not include high-resolution digital elevation models (DEMs) derived from LiDAR (light detection and ranging) survey data, although these are needed by the Agricultural Conservation Planning Framework Toolkit and must be obtained independently. Database is scheduled to become available on October 1, 2015. Resources in this dataset:Resource Title: Land Use and Soils data, viewing and downloading page. File Name: Web Page, url: https://www.nrrig.mwa.ars.usda.gov/st40_huc/dwnldACPF.html Recent land use, field boundary, and soil survey information for individual HUC12 watersheds in Iowa, Illinois, and southern Minnesota. With this land use viewer web page, users may navigate to individual HUC12 watersheds, view land-use maps, and download land use and soils data that can be directly used as input data for the ACPF toolbox. Before developing information on conservation priorities and opportunities using the ACPF toolbox, users will need to obtain elevation data for their watershed, which is usually available from your state government.

IntroductionIn the Central Plains of the United States (US), wheat (Triticum aestivum L.) is predominantly grown as a monocrop, limiting profits, and compromising environmental sustainability. In the context of recent reports on crop yield stagnation and the increased frequency and intensity of climate extremes, this study aims to i) evaluate the economic feasibility of double cropping sorghum (Sorghum bicolor L.) with winter wheat; ii) identify regional environmental drivers for yield; and iii) map the spatial distribution of the most profitable crop sequences.MethodsThe APSIM classic model was used to simulate the baseline wheat and sorghum monocrops and the diversified crop sequence (sorghum-wheat) over 30 years of climatology (1990 to 2020), across 194 sites in Kansas, United States. Each site was characterized in APSIM, with the predominant soil type and current farming crop management practices. Using terciles of historical input costs for all crop sequences we calculated three cost scenarios low, intermediate, and high. A fuzzy-C means algorithm was used to classify regions based on crop sequences’ profits, resulting in four clusters.Results and discussionResults included two regions where sorghum-wheat was more profitable than the monocrops i.e., one with lower profits (S+W lower), and a second one with higher profits (S+W higher); a third cluster where wheat monocrop was most profitable (W), and lastly one cluster showing no difference between the sorghum-wheat sequence and the wheat monocrop (S+W or W). Principal component analyses were used to identify environmental drivers of profit in each cluster. Results showed that the profitability of the sorghum-wheat sequence was higher in counties in the south-east and south-central of Kansas. Wheat monocrops were the most profitable option for counties of the west and central regions. Counties from the north-east of the state showed similar patterns amongst scenarios. These results highlight potential avenues for diversifying and intensifying the current wheat monocrop sequence while maintaining or increasing profitability. Lastly, this study delineates a map in Kansas with areas where it would be more profitable for farmers to expand their rotations by adding a second crop per year.

This dataset includes soil health, crop biomass, and crop yield data for a 13-year corn stover harvest trial in central Iowa. Following the release in 2005 of the Billion Ton Study assessment of biofuel sources, several soil health assessments associated with harvesting corn stover were initiated across ARS locations to help provide industry guidelines for sustainable stover harvest. This dataset is from a trial conducted by the National Laboratory for Agriculture and Environment from 2007-2021 at the Iowa State University Ag Engineering and Agronomy farm. Management factors evaluated in the trial included the following. Stover harvest rate at three levels: No, moderate (3.5 ± 1.1 Mg ha-1 yr-1), or high (5.0 ± 1.7 Mg ha-1 yr-1) stover harvest rates. No-till versus chisel-plow tillage. Originally, the 3 stover harvest rates were evaluated in a complete factorial design with tillage system. However, the no-till, no-harvest system performed poorly in continuous corn and was discontinued in 2012 due to lack of producer interest. Cropping sequence. In addition to evaluating continuous corn for all stover harvest rates and tillage systems, a corn-alfalfa rotation, and a corn-soybean-wheat rotation with winter cover crops were evaluated in a subset of the tillage and stover harvest rate treatments. One-time additions of biochar in 2013 at rates of either 9 Mg/ha or 30 Mg/ha were evaluated in a continuous corn cropping system. The dataset includes: 1) Crop biomass and yields for all crop phases in every year. 2) Soil organic carbon, total carbon, total nitrogen, and pH to 120 cm depth in 2012, 2016, and 2017. Soil cores from 2005 (pre-study) were also sampled to 90 cm depth. 3) Soil chemistry sampled to 15 cm depth every 1-2 years from 2007 to 2017. 4) Soil strength and compaction was assessed to 60 cm depth in April 2021. These data have been presented in several manuscripts, including Phillips et al. (in review), O'Brien et al. (2020), and Obrycki et al. (2018). Resources in this dataset:Resource Title: R Script for Phillips et al. 2022. File Name: Field 70-71 Analysis Script_AgDataCommons.RResource Description: This R script includes analysis and figures for Phillips et al. "Thirteen-year Stover Harvest and Tillage Effects on Soil Compaction in Iowa". It focuses primarily on the soil compaction and strength data found in "Field 70-71 ConeIndex_BulkDensityDepths_2021". It also includes analysis of corn yields from "Field 70-71 CornYield_2008-2021" and weather conditions from "PRISM_MayTemps" and "Rainfall_AEA".Resource Software Recommended: R version 4.1.3 or higher,url: https://cran.r-project.org/bin/windows/base/ Resource Title: Field 70-71 ConeIndex_BulkDensityDepths_2021. File Name: Field 70-71 ConeIndex_BulkDensityDepths_2021.csvResource Description: This dataset provides an assessment of soil strength (penetration resistance) and soil compaction (bulk density) to 60 cm depth, in continuous corn plots. Penetration resistance was measured in most-trafficked and least-trafficked areas of the plots to assess compaction from increased traffic associated with stover harvest. This spreadsheet also has associated data, including soil water, carbon, and organic matter content. Data were collected in April 2021 and are described in Phillips et al. (in review, 2022).Resource Title: Field 70-71 CornYield_2008-2021. File Name: Field 70-71 CornYield_2008-2021_ForR.csvResource Description: This dataset provides corn stover biomass and grain yields from 2008-2021. Note that this dataset is just for corn, which were presented in Phillips et al., 2022. Yields for all crop phases, including soybeans, wheat, alfalfa, and winter cover crops, are in the file "Field 70-71 Crop Yield File 2008-2020".Resource Title: PRISM_MayTemps. File Name: PRISM_MayTemps.csvResource Description: Average May temperatures during the study period, obtained from interpolation of regional weather stations using the PRISM climate model (https://prism.oregonstate.edu/). These data were used to evaluate how spring temperatures may have impacted corn establishment.Resource Title: Rainfall_AEA. File Name: Rainfall_AEA.csvResource Description: Daily rainfall for the study location, 2008-2021. Data were obtained from the Iowa Environmental Mesonet (https://mesonet.agron.iastate.edu/rainfall/). Title: Field 70-71 Plot Status 2007-2021. File Name: Field 70-71 Plot Status 2007-2021.xlsxResource Description: This file contains descriptions of experimental treatments and diagrams of plot layouts as they were modified through several phases of the trial. Also includes an image of plot locations relative to NRCS soil survey map units.Resource Title: Field 70-71 Deep Soil Cores 2012-2017. File Name: Field 70-71 Deep Soil Cores 2012-2017.xlsxResource Description: Soil carbon, nitrogen, organic matter, and pH to 120 cm depth in 2012, 2016, and 2017.Resource Title: Field 70-71 Baseline Deep Soil Cores 2005. File Name: Field 70-71 Baseline Deep Soil Cores 2005.csvResource Description: Baseline soil carbon, nitrogen, and pH data from an earlier trial in 2005, prior to stover trial establishment.Resource Title: Field 70-71 Crop Yield File 2008-2020. File Name: Field 70-71 Crop Yield File 2008-2020.xlsxResource Description: Yields for all crops in all cropping sequences, 2008-2020. Some of the crop sequences have not been summarized in publications.Resource Title: Field 70-71 Surface Soil Test Data 2007-2021. File Name: Field 70-71 Surface Soil Test Data 2007-2021.xlsxResource Description: Soil chemistry data, 0-15 cm, collect near-annually from 2007 to 2021. Most analyses were performed by Harris Laboratories (now AgSource) in Lincoln, Nebraska, USA. Resource Title: Iowa Stover Harvest Trial Data Dictionary. File Name: Field 70-71 Data Dictionary.xlsxResource Description: Data dictionary for all data files.

The dataset contains a map of the main classes of agricultural land use (dominant crop types and other land use types) in Germany for the year 2023. It complements a series of maps that 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 Blickensdörfer et al. (2022).

All optical satellite data were managed, 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), in which SAR and environmental data were integrated.

The map extent covers all areas in Germany that are defined as agricultural land, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020).

Version v201:
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).

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 URL that will be provided on request. 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.

Class-specific accuracies for each year are proveded in the respective tables. We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability.

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References:

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.

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).

BKG, Bundesamt für Kartographie und Geodäsie (2020). Digitales Basis-Landschaftsmodell.
https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022).

Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.

Statistisches Bundesamt, Deutschland (2024). Ökosystematlas Deutschland
https://oekosystematlas-ugr.destatis.de/ (last accessed: 08.02.2024).

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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.

Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (MonViA, Monitoring of biodiversity in agricultural landscapes).

Crop type map covering the main agricultural areas of Argentina for growing season 2019/2020. This version includes two different maps for winter and summer crops. Maps were generated using supervised classification methods with samples obtained from on-road surveys and Landsat satellite images along the growing season. Files provided include a Geotiff version of each map with a resolution of 30 m. The report includes the methodological details, map legend and accuracy assessments (in Spanish). A web visualizer can be accessed through the following link: https://intalulc.users.earthengine.app/view/mnc19-20

Web-based GIS for spatiotemporal crop climate niche mapping Interactive Google Earth Engine Application—Version 2, July 2020 https://cropniche.cartoscience.com https://cartoscience.users.earthengine.app/view/crop-niche Google Earth Engine Code /* ---------------------------------------------------------------------------------------------------------------------- # CropSuit-GEE Authors: Brad G. Peter (bpeter@ua.edu), Joseph P. Messina, and Zihan Lin Organizations: BGP, JPM - University of Alabama; ZL - Michigan State University Last Modified: 06/28/2020 To cite this code use: Peter, B. G.; Messina, J. P.; Lin, Z., 2019, "Web-based GIS for spatiotemporal crop climate niche mapping", https://doi.org/10.7910/DVN/UFC6B5, Harvard Dataverse, V1 ------------------------------------------------------------------------------------------------------------------------- This is a Google Earth Engine crop climate suitability geocommunication and map export tool designed to support agronomic development and deployment of improved crop system technologies. This content is made possible by the support of the American People provided to the Feed the Future Innovation Lab for Sustainable Intensification through the United States Agency for International Development (USAID). The contents are the sole responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government. Program activities are funded by USAID under Cooperative Agreement No. AID-OAA-L-14-00006. ------------------------------------------------------------------------------------------------------------------------- Summarization of input options: There are 14 user options available. The first is a country of interest selection using a 2-digit FIPS code (link available below). This selection is used to produce a rectangular bounding box for export; however, other geometries can be selected with minimal modification to the code. Options 2 and 3 specify the complete temporal range for aggregation (averaged across seasons; single seasons may also be selected). Options 4–7 specify the growing season for calculating total seasonal rainfall and average season temperatures and NDVI (NDVI is for export only and is not used in suitability determination). Options 8–11 specify the climate parameters for the crop of interest (rainfall and temperature max/min). Option 12 enables masking to agriculture, 13 enables exporting of all data layers, and 14 is a text string for naming export files. ------------------------------------------------------------------------------------------------------------------------- ••••••••••••••••••••••••••••••••••••••••••• USER OPTIONS ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• */ // CHIRPS data availability: https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_PENTAD // MOD11A2 data availability: https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD11A2 var country = 'MI' // [1] https://en.wikipedia.org/wiki/List_of_FIPS_country_codes var startRange = 2001 // [2] var endRange = 2017 // [3] var startSeasonMonth = 11 // [4] var startSeasonDay = 1 // [5] var endSeasonMonth = 4 // [6] var endSeasonDay = 30 // [7] var precipMin = 750 // [8] var precipMax = 1200 // [9] var tempMin = 22 // [10] var tempMax = 32 // [11] var maskToAg = 'TRUE' // [12] 'TRUE' (default) or 'FALSE' var exportLayers = 'TRUE' // [13] 'TRUE' (default) or 'FALSE' var exportNameHeader = 'crop_suit_maize' // [14] text string for naming export file // ••••••••••••••••••••••••••••••••• NO USER INPUT BEYOND THIS POINT •••••••••••••••••••••••••••••••••••••••••••••••••••• // Access precipitation and temperature ImageCollections and a global countries FeatureCollection var region = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017') .filterMetadata('country_co','equals',country) var precip = ee.ImageCollection('UCSB-CHG/CHIRPS/PENTAD').select('precipitation') var temp = ee.ImageCollection('MODIS/006/MOD11A2').select(['LST_Day_1km','LST_Night_1km']) var ndvi = ee.ImageCollection('MODIS/006/MOD13Q1').select(['NDVI']) // Create layers for masking to agriculture and masking out water bodies var waterMask = ee.Image('UMD/hansen/global_forest_change_2015').select('datamask').eq(1) var agModis = ee.ImageCollection('MODIS/006/MCD12Q1').select('LC_Type1').mode() .remap([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17], [0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0]) var agGC = ee.Image('ESA/GLOBCOVER_L4_200901_200912_V2_3').select('landcover') .remap([11,14,20,30,40,50,60,70,90,100,110,120,130,140,150,160,170,180,190,200,210,220,230], [1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]) var cropland = ee.Image('USGS/GFSAD1000_V1').neq(0) var agMask = agModis.add(agGC).add(cropland).gt(0).eq(1) // Modify user input options for processing with raw data var years = ee.List.sequence(startRange,endRange) var bounds = region.geometry().bounds() var tMinMod = (tempMin+273.15)/0.02 var tMaxMod = (tempMax+273.15)/0.02 //...

Genomic, Genetic and Breeding Resources for Pulse Crop Improvement. Crops supported include Adzuki bean, Bambara bean, Chickpea, Common bean, Cowpea, Faba bean, Lentil, Lupin, Pea, Pigeon pea, Vetch, and others. The Pulse Crop Database (PCD), formerly the Cool Season Food Legume Database (CSFL), is being developed by the Main Bioinformatics Laboratory at Washington State University in collaboration with the USDA-ARS Grain Legume Genetics and Physiology Research Unit, the USDA-ARS Plant Germplasm Introduction and Testing Unit, the USA Dry Pea and Lentil Council, Northern Pulse Growers and allied scientists in the US and across the world, to serve as a resource for Genomics-Assisted Breeding (GAB). GAB offers tools to identify genes related to traits of interest among other methods to optimize plant breeding efficiency and research, by providing relevant genomic, genetic and breeding information and analysis. Therefore, tools such as JBrowse and MapViewer can be found in this database, as well as key resources to provide the access to the annotation of available transcriptome data, helping pulse breeders and researchers to succeed in their programs. Resources in this dataset:Resource Title: Pulse Crop Database Resources. File Name: Web Page, url: https://www.pulsedb.org/ Resources include data submission and download, and search by gene and transcript, germplasm, map, marker, publication, QTL, sequence, megasearch, and trait/descriptor. A User Manual describes how to access data and use the tools on the Pulse Crop Database. Tools supported: BLAST, JBrowse, PathwayCyc, MapViewer, and Synteny Viewer

CottonGen (https://www.cottongen.org) is a curated and integrated web-based relational database providing access to publicly available genomic, genetic and breeding data to enable basic, translational and applied research in cotton. Built using the open-source Tripal database infrastructure, CottonGen supersedes CottonDB and the Cotton Marker Database, which includes sequences, genetic and physical maps, genotypic and phenotypic markers and polymorphisms, quantitative trait loci (QTLs), pathogens, germplasm collections and trait evaluations, pedigrees, and relevant bibliographic citations, with enhanced tools for easier data sharing, mining, visualization, and data retrieval of cotton research data. CottonGen contains annotated whole genome sequences, unigenes from expressed sequence tags (ESTs), markers, trait loci, genetic maps, genes, taxonomy, germplasm, publications and communication resources for the cotton community. Annotated whole genome sequences of Gossypium raimondii are available with aligned genetic markers and transcripts. These whole genome data can be accessed through genome pages, search tools and GBrowse, a popular genome browser. Most of the published cotton genetic maps can be viewed and compared using CMap, a comparative map viewer, and are searchable via map search tools. Search tools also exist for markers, quantitative trait loci (QTLs), germplasm, publications and trait evaluation data. CottonGen also provides online analysis tools such as NCBI BLAST and Batch BLAST. This project is funded/supported by Cotton Incorporated, the USDA-ARS Crop Germplasm Research Unit at College Station, TX, the Southern Association of Agricultural Experiment Station Directors, Bayer CropScience, Corteva/Agriscience, Dow/Phytogen, Monsanto, Washington State University, and NRSP10. Resources in this dataset:Resource Title: Website Pointer for CottonGen. File Name: Web Page, url: https://www.cottongen.org/ Genomic, Genetic and Breeding Resources for Cotton Research Discovery and Crop Improvement organized by : Species (Gossypium arboreum, barbadense, herbaceum, hirsutum, raimondii, others), Data (Contributors, Download, Submission, Community Projects, Archives, Cotton Trait Ontology, Nomenclatures, and links to Variety Testing Data and NCBISRA Datasets), Search options (Colleague, Genes and Transcripts, Genotype, Germplasm, Map, Markers, Publications, QTLs, Sequences, Trait Evaluation, MegaSearch), Tools (BIMS, BLAST+, CottonCyc, JBrowse, Map Viewer, Primer3, Sequence Retrieval, Synteny Viewer), International Cotton Genome Initiative (ICGI), and Help sources (User manual, FAQs). Also provides Quick Start links for Major Species and Tools.

This data set contains information on the agricultural land use in Germany for the year 2020.
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 Blickensdörfer et al. 2022 and can be seen as a continuation of the dataset provided under: https://zenodo.org/record/5153047#.YWFyXn1CREZ.
The maps can be explored online in a webviewer.

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.

Table 1: Updated class catalogue and translation key to the class catalogue used in Blickensdörfer et al. 2022.

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; https://force-eo.readthedocs.io/en/latest/ last accessed: 12. April 2022), before environmental and SAR data were included in the ARD cube.

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).

The maps are provided as GeoTiff files together with QGIS legend files for visualization.

References:

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

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).

BKG, Bundesamt für Kartographie und Geodäsie (2018). Digitales Basis-Landschaftsmodell.
https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022).

Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.

National-scale crop type maps for Germany © 2022 by Schwieder, Marcel; Erasmi, Stefan; Nendel, Claas; Hostert, Patrick is licensed under CC BY 4.0.

Primer details for grass/rice comparative mappingThe file gives details of all the primers used to physically map SNPs on the Lolium/Festuca introgression maps. These primers were generated from rice coding sequences. The file therefore also gives the accession number and locus identifier of the rice coding sequence used to generate the primers.Supplementary information 1.xlsGenetic mapping dataGenetic mapping data for the 283 bin mapped SNP markers segregating in a Lolium x Lolium (Perma x Aurora) mapping population.Supplementary information 2.xlsx

BackgroundBanana (genus Musa) is a crop of major economic importance worldwide. It is a monocotyledonous member of the Zingiberales, a sister group of the widely studied Poales. Most cultivated bananas are natural Musa inter-(sub-)specific triploid hybrids. A Musa acuminata reference nuclear genome sequence was recently produced based on sequencing of genomic DNA enriched in nucleus.Methodology/Principal FindingsThe Musa acuminata chloroplast genome was assembled with chloroplast reads extracted from whole-genome-shotgun sequence data. The Musa chloroplast genome is a circular molecule of 169,972 bp with a quadripartite structure containing two single copy regions, a Large Single Copy region (LSC, 88,338 bp) and a Small Single Copy region (SSC, 10,768 bp) separated by Inverted Repeat regions (IRs, 35,433 bp). Two forms of the chloroplast genome relative to the orientation of SSC versus LSC were found. The Musa chloroplast genome shows an extreme IR expansion at the IR/SSC boundary relative to the most common structures found in angiosperms. This expansion consists of the integration of three additional complete genes (rps15, ndhH and ycf1) and part of the ndhA gene. No such expansion has been observed in monocots so far. Simple Sequence Repeats were identified in the Musa chloroplast genome and a new set of Musa chloroplastic markers was designed.ConclusionThe complete sequence of M. acuminata ssp malaccensis chloroplast we reported here is the first one for the Zingiberales order. As such it provides new insight in the evolution of the chloroplast of monocotyledons. In particular, it reinforces that IR/SSC expansion has occurred independently several times within monocotyledons. The discovery of new polymorphic markers within Musa chloroplast opens new perspectives to better understand the origin of cultivated triploid bananas.

Detailed maps of agricultural landscapes are a valuable data source for manifold applications, such as environmental modelling, biodiversity monitoring or the support of agricultural statistics. Satellites from the European Copernicus program, especially, Sentinel-1 and Sentinel-2, as well as the Landsat missions operated by NASA/USGS, acquire data with a spatial resolution (10 m to 30 m) that is sufficient to identify field structures in complex agricultural landscapes. Time series of combined Sentinel-2 and Landsat data facilitate to differentiate crop types with a high thematic detail based on differences in land surface phenology. However, large data gaps due to frequent cloud cover may hamper such classification approaches.

We thus combined dense interpolated times series of Sentinel-2A/B and Landsat data with monthly composites of Sentinel-1 backscatter data to overcome periods with high cloud contamination. To further account for regional variations along the agroecological gradient within Germany, we additionally included a broad set of spatially explicit environmental data in a random forest classification model.

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; https://force-eo.readthedocs.io/en/latest/ last accessed: 19. August 2021), before environmental and SAR data were included in the ARD cube.

For each year (2017, 2018 and 2019) we trained an individual random forest model with 24 agricultural classes. Each model was independently validated with area adjusted overall accuracies of 80% (2017), 79% (2018), and 78% (2019). Further details regarding the data and methods used as well as class wise accuracies can be found in Blickensdörfer et al. (2022).

The final models were applied to areas in Germany that were defined as agricultural land in ATKIS DLM 2018 (Geobasisdaten: © GeoBasis-DE / BKG (2018)). 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/hops areas that were not labelled as the respective permanent crop in ATKIS DLM (labelled as other agricultural areas in the final map).

The maps are provided as GeoTiff files together with a QGIS legend file for visualization.

Class catalogue:

10 Grassland
31 Winter wheat
32 Winter rye
33 Winter barley
34 Other winter cereal
41 Spring barley
42 Spring oat
43 Other spring cereal
50 Winter rapeseed
60 Legume
70 Sunflower
80 Sugar beet
91 Maize
92 Maize (grain)
100 Potato
110 Grapevine
120 Strawberry
130 Asparagus
140 Onion
150 Hops
160 Orchard
181 Carrot
182 Other vegetables
555 Small woody features
999 Other agricultural areas

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

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: 19. August 2021).

BKG, Bundesamt für Kartographie und Geodäsie (2018). Digitales Basis-Landschaftsmodell.
https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 19. August 2021).

Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.

National-scale crop type maps for Germany © 2021 by Blickensdörfer, Lukas; Schwieder, Marcel; Pflugmacher, Dirk; Nendel, Claas; Erasmi, Stefan; Hostert, Patrick is licensed under CC BY 4.0.

The relationship between LD and map distance was modeled by fitting two alternate non-linear regression models: a drift-recombination equilibrium model [36] or a modified recombination-drift model including low level of mutation and an adjustment for sample size [37]. Map distance at r2 = 0.1 was shown. Both average distance across six bi-parental mapping population and minimum distance from available mapping populations were used. Ne, effective population size; SE, standard error.

According to Cognitive Market Research, the global Molecular Breeding market size will be USD XX million in 2023 and will expand at a compound annual growth rate (CAGR) of XX% from 2023 to 2031.

The global Molecular Breeding market will expand significantly by XX% CAGR between 2023 to 2031.
North America held the major market of more than XX% of the global revenue with a market size of USD XX million in 2023 and will grow at a compound annual growth rate (CAGR) of XX% from 2023 to 2031.
Research institutions held the highest Molecular Breeding market revenue share in 2023, reflecting their pivotal role in developing and applying molecular breeding techniques.

Market Dynamics of the Molecular Breeding Market

Key Drivers of the Molecular Breeding Market

Increased Demand for Food Security and Crop Yield Drive the Molecular Breeding Market Growth

The growing global population and the consequent rise in food demand have significantly driven the molecular breeding market. Enhanced crop yield and quality are vital in addressing food security challenges. Molecular breeding offers precise, efficient, and rapid means of improving agricultural outputs by incorporating desirable traits into crop genomes, such as disease resistance and drought tolerance. This technology not only ensures a sustainable increase in food production but also helps in adapting crops to changing climatic conditions. Governments and agricultural organizations worldwide are therefore increasingly investing in molecular breeding to secure food supplies and promote agricultural sustainability.

For instance, He & Li states that the increasing global demand for food, driven by population growth, necessitates improved agricultural outputs. Molecular breeding offers efficient and precise methods for enhancing crop traits like disease resistance and drought tolerance, crucial for adapting to changing climates and ensuring food security. https://doi.org/10.1016/j.cj.2020.04.005

Key Restraints of the Molecular Breeding Market

Complexity and High Cost of Molecular Breeding Techniques Restrict Market Growth

Molecular breeding involves sophisticated genetic analysis and high-throughput genotyping technologies that can be prohibitively expensive and complex. The need for specialized knowledge and equipment restricts the adoption of molecular breeding, particularly in developing countries with limited technological infrastructure. Additionally, the high cost of setting up and maintaining laboratory facilities capable of conducting advanced genetic research can be a significant barrier for smaller research institutions and private companies. These factors limit the accessibility and widespread implementation of molecular breeding methods, thereby restraining market growth.

For instance, Delannay, McLaren, & Ribaut suggested the sophisticated and costly nature of molecular breeding technologies, requiring high levels of expertise and advanced equipment, limits its adoption, especially in developing countries. This restricts access to the benefits of molecular breeding and its widespread implementation. https://doi.org/10.1007/s11032-011-9611-9

Impact of COVID-19 on the Molecular Breeding Market

The COVID-19 pandemic initially disrupted the molecular breeding market due to logistical constraints and reduced research activities. However, the pandemic also highlighted the importance of robust and resilient food systems, accelerating interest and investment in agricultural biotechnologies like molecular breeding. As research resumed with adjusted safety protocols, the sector witnessed a quick recovery, with increased funding for agricultural innovation aimed at ensuring food security against such global disruptions. The pandemic has underlined the critical role of molecular breeding in developing stress-resistant and high-yield crop varieties, likely leading to sustained growth in this market post-pandemic.

For instance, He & Li reported that COVID-19 disruptions emphasized the need for resilient agricultural systems. Increased interest and investment in molecular breeding have been observed post-pandemic, focusing on developing stress-resistant and high-yield crop varieties to enhance food security during global disruptions. https://doi.org/10.1016/j.cj.2020.04.005 Introduction of the Molecular Breeding Market

The Molecular Breeding Market encompasses advanced genetic ...