데이터셋 상세
미국
Data from: Mapping the Quantitative Field Resistance to Stripe Rust in a Hard Winter Wheat Population ‘Overley’ × ‘Overland’
,Data reported in research published in Crop Science, “Mapping the quantitative field resistance to stripe rust in a hard winter wheat population ‘Overley’ × ‘Overland.’” Authors are Wardah Mustahsan, Mary J. Guttieri, Robert L. Bowden, Kimberley Garland-Campbell, Katherine Jordan, Guihua Bai, Guorong Zhang from USDA Agricultural Research Service and Kansas State University. This study was conducted to identify quantitative trait loci (QTL) associated with field resistance to stripe rust, also known as yellow rust (YR), in hard winter wheat. Stripe rust infection type and severity were rated in recombinant inbred lines (RILs, n=204) derived from a cross between hard red winter wheat cultivars ‘Overley’ and ‘Overland’ in replicated field trials in the Great Plains and Pacific Northwest. RILs (n=184) were genotyped with reduced representation sequencing to produce SNP markers from alignment to the ‘Chinese Spring’ reference sequence, IWGSC v2.1, and from alignment to the reference sequence for ‘Jagger’, which is a parent of Overley. Genetic linkage maps were developed independently from each set of SNP markers. QTL analysis identified genomic regions on chromosome arms 2AS, 2BS, 2BL, and 2DL that were associated with stripe rust resistance using multi-environment best linear unbiased predictors for stripe rust infection type and severity. Results for the two linkage maps were very similar. PCR-based SNP marker assays associated with the QTL regions were developed to efficiently identify these genomic regions in breeding populations.,Field response to YR was evaluated in seven trials: Rossville, KS (2018 and 2019), Hays, KS (2019), Pullman, WA (2019 and 2020) and Central Ferry, WA (2019 and 2020). An augmented experimental design was used at Rossville, KS with highly replicated checks and two full replications of RILs (n=187 in 2018; n=204 in 2019). The field experiment at Hays was arranged in a partially replicated augmented design with one or two replications of each RIL (n=194). The parental checks (Overley and Overland) were represented in three blocks for each of the two field replications at Hays, and RILs were distributed among blocks; not all RILs were present in each replication. RILs were arranged in an augmented design with two replications at Pullman (n=204 RILs) and Central Ferry (n=155 RILs in 2019; n=204 in 2020). At Pullman and Central Ferry.,The trials at Rossville, KS were inoculated using an inoculum consisting of equal parts of four isolates that were all virulent to Yr9. Two isolates were collected in Kansas in 2010 and had virulence to Yr17 but not QYr.tamu-2B. The other two isolates were from Kansas in 2012 and had virulence to QYr.tamu-2B, but not Yr17. Susceptible spreader rows (KS89180B, carrying Yr9) were inoculated several times during the tillering stage in the evenings with an ultra-low volume sprayer using a suspension of 2 mL of fresh urediniospores in 1 L of Soltrol 170 isoparaffin oil. Trials at Pullman, WA and Central Ferry, WA were evaluated under natural inoculum supplemented by a mixture of isolates collected in the previous field season. The trial at Hays, KS was evaluated under natural infection.,Data collection at Rossville, KS began once the susceptible check (KS89180B) had an infection severity coverage of ~10% and continued until senescence. In Rossville, disease ratings (IT and SEV) were collected on 16, 22, and 28th of May 2019. Most ratings in Rossville were taken some time after heading from Zadoks stages 55 to 70. In Pullman, disease ratings were collected on July 1 and 12. In Central Ferry, disease ratings were taken on 12th and 18th of June 2019. The second rating date was used for subsequent statistical analysis. In Hays, disease ratings were taken on June 1, 2019, when the plants were in early booting or heading stages (Zadoks 31-41). Stripe rust evaluations were measured using two disease rating scales: IT (0-9; from no infection to highly susceptible, Line and Qayoum, 1992)
데이터 정보
연관 데이터
Data from: Genome-wide association mapping of resistance to the foliar diseases septoria nodorum blotch and tan spot in a global winter wheat collection
공공데이터포털
,Phenotypic Data A subset of 264 lines from the National Small Grains Collection global hexaploid winter wheat germplasm collection was evaluated under controlled growth chamber conditions for reaction to the pathogens Parastagonospora nodorum and Pyrenophora tritici-repentis. Both infiltrations and inoculations were performed on plants planted in plastic cones and when seedlings were at the second leaf stage. Plants were infiltrated with the P. nodorum necrotrophic effectors (NEs) SnTox1, SnToxA, SnTox3, SnTox267, and SnTox5; and the P. tritici-repentis NE Ptr ToxB. The scoring system was 0-3, with reaction types of 2 and 3 considered sensitive and 0 to 1 were insensitive. Plants were inoculated with the P. nodorum isolates Sn4, Sn2000, AR2-1, SnIr05H71a, and NOR4 and P. tritici-repentis isolates Pti2, 86-124, DW5, and AR CrossB10. After inoculation, plants were placed in a 100 % humidity growth chamber at 21 °C for 24 hours under constant light, then moved to a controlled growth chamber at 21 °C with a 12 h photoperiod. Plants were scored at 7 days post inoculation. For P. nodorum, plants were scored using a 0 to 5 scale, with 0 being highly resistant and 5 being highly susceptible. For P. tritici-repentis, plants were scored using a 1 to 5 scale, with 1 being highly resistance and 5 being highly susceptible. Three homogeneous replicates (determined by Bartlett’s chi squared analysis) were used to calculate an average value for each trait. This value was used for the rest of the analysis.,Genotypic Data DNA of the winter wheat panel was extracted and genotyped using the Illumina iSelect 90k wheat SNP array. Clustering data was analyzed using GenomeStudio 2.0.5 from Illumina, Inc. SNPs were ordered based on their physical position in the Chinese Spring IWGSC RefSeq v2.0. In TASSEL v5.2, SNP markers were filtered with a minor allele frequency greater than 0.01 and missing data less than 50%. For the remaining markers, missing values were imputed using the LD-KNNi method.,Genome-wide association analysis data Association mapping was conducted using the R package GAPIT v.3. The filtered hapmap file was used for the association mapping, along with the average value for each phenotypic trait. The models GLM, MLM, MLMM, FarmCPU, and Blink were run on the averages for each trait. ** Resources in this dataset:,
Data from: Soil resistance under grazed intermediate wheatgrass
공공데이터포털
,Intermediate wheatgrass [Thinopyrum intermedium (Host) Barkw. & D.R. Dewey subsp. intermedium] is a high-yielding cool-season grass with adaptable uses for grazing, haying, and soil restoration. Despite its adaptability, adoption of intermediate wheatgrass has been limited due to inadequate stand longevity under grazing stress. A study was conducted near Mandan, ND USA to investigate if stand longevity of intermediate wheatgrass was affected by changes in soil properties due to grazing. Soil data from this study included measurements of soil bulk density, soil pH, soil organic carbon, and total soil nitrogen on a Wilton silt loam soil (USDA: Fine-silty, mixed, superactive frigid Pachic Haplustoll). Measurements were made in May 1997 (baseline) and again in May 2004 following four years of grazing. Data may be used to understand soil property responses to grazed perennial forages. Data are generally applicable to rainfed conditions under a semiarid Continental climate for the following associated soil types: Temvik, Grassna, Linton, Mandan, and Williams.,Resources in this dataset:,Resource title: Intermediate Wheatgrass Grazing Study Data Dictionary File name: IWGS_Data Dictionary.xlsx Resource description: Data dictionary for associated dataset.,Resource title: Intermediate Wheatgrass Grazing Study_Soil Data for Aggregated Depths File name: IWGS_Soil Data_Aggregated Depths.xlsx Resource description: File includes data for 0-30 cm depth.,Resource title: Intermediate Wheatgrass Grazing Study_Soil Data for Separated Depths File name: IWGS_Soil Data_Separated Depths.xlsx Resource description: Soil data for 0-5, 5-10, 10-20, and 20-30 cm depths.,Resource title: Intermediate Wheatgrass Grazing Study_Soil Data_Aggregated Depths File name: IWGS_Soil Data_Aggregated Depths.csv Resource description: Data for aggregated depths in csv format.,Resource title: Intermediate Wheatgrass Grazing Study_Metadata_Aggregated Depths File name: IWGS_Soil Data_Aggregated Depths_Metadata.csv Resource description: Metadata for aggregated depths.,Resource title: Intermediate Wheatgrass Grazing Study_Soils Data_Separated Depths File name: IWGS_Soil Data_Separated Depths.csv Resource description: Soil data for 0-5, 5-10, 10-20, and 20-30 cm depths.,Resource title: Intermediate Wheatgrass Grazing Study_Metadata_Separated Depths File name: IWGS_Soil Data_Separated Depths_Metadata.csv Resource description: Metadata for soils data separated by depth increment.,
Data from: Similarities among Test Sites Based on the Performance of Advanced Breeding Lines in the Great Plains Hard Winter Wheat Region
공공데이터포털
,USDA-ARS coordinated regional wheat (Triticum aestivum L.) breeding trials examine agronomic performance and adaptation over a wider geographic range than single breeding programs can achieve. The trials provide an evaluation of experimental breeding lines in alternate test sites that are environmentally similar or dissimilar to the program of origin. Data from USDA-ARS Hard Winter Wheat Regional Nurseries grown in 1987 to 2014 were used to identify similarities among Great Plains test sites. Mean correlations of entry grain yields across locations and years were used in principal factor analyses to cluster them into production zones. The procedures used were identical to those of a previously published analysis using test data from 1959 to 1989. Five factors explained 67% of the variance in the correlation matrix among Southern Regional Performance Nursery (SRPN) locations. The analysis divided the SRPN into four major Great Plains production zones, designated Southeast, Northwest, Southwest and Northeast. The remaining minor production zone consisted of only two central South Dakota locations, both outside the typical target area and selection site of SRPN entries. In the Northern Regional Performance Nursery (NRPN), five production zones were established, with location separation predominantly resulting from east–west differences in performance. The SRPN and NRPN wheat production zones closely follow previously described ecological zones of adaptation of native Great Plains plant species. Wheat breeding programs and growers may continue to use the production zones established via the USDA-ARS coordinated winter wheat regional nurseries to target and select germplasm for crossing and for production.,,
Greg Rebetzke - Birchip 2024 Long Coleoptile Wheat Trial: Genotype, Sowing Depth, Presswheel Pressure Effects on Emergence, Growth, and Yield
공공데이터포털
This dataset originates from a comprehensive field trial conducted in Birchip, Victoria, during the 2024 winter cropping season. The trial, identified as 24_Nulla_0124_Long Coleoptile, involved 108 plots across six ranges and 26 rows, with GPS-referenced layout and border plots included. Treatments combined three factors: wheat genotype (including Mace18, Magenta13, Scepter, Calibre), sowing depth (shallow or deep), and presswheel pressure (light, standard, heavy). The dataset comprises multiple Excel sheets detailing agronomic measurements, environmental conditions, and sensor data. Soil water content was assessed gravimetrically and via matric potential across 12 plots at six depth increments (0–12 cm), both pre- and post-sowing. Soil strength was measured using a penetrometer, with readings taken in 2 cm increments and converted to kg/cm². Soil temperature was monitored using sensors placed at the surface, 3–4 cm, and 8–10 cm depths in two plots, with high-frequency time-series data recorded over several days. Emergence was tracked biweekly and then weekly from sowing through six weeks, with counts taken from 1 m sections of seeding rows. Coleoptile length and sowing depth were measured from 20 seedlings per tyne per plot, with values ranging from approximately 1.3 to 8.6 cm and 2.8 to 10.8 cm respectively. NDVI readings were collected using a handheld Greenseeker across 10 dates, showing progressive canopy development with values ranging from 0.09 to 0.79 and CV% from 0.5 to 29.5. Zadoks scores were recorded weekly from early September to October, capturing phenological stages from stem elongation to anthesis (Z43–Z71). Rabbit damage was assessed on 06/07/2024, with severity scores (1–5) and percent chewed recorded per plot. Final harvest data includes grain yield (raw and corrected), moisture content, protein percentage, test weight, screenings, and emergence rates. The dataset is structured for analytical modeling, enabling genotype, depth, pressure comparisons and supporting time-series analysis of emergence, growth, and yield. It includes both raw and processed data, with consistent formatting and minimal missing values. Some metadata corruption is present but does not affect core data usability.
Greg Rebetzke - Wallumbilla 2024 wheat trials: Impact of Sowing Depth, Coleoptile Traits, and Soil Strength on Emergence and Biomass Across Multiple Field Trials
공공데이터포털
This dataset comprises detailed agronomic measurements from a series of wheat field trials conducted in Roma, Queensland, designed to investigate the effects of sowing depth, coleoptile type, soil strength, and other factors on plant emergence, growth, and yield. The collection includes two primary Excel (.xlsx) files: a master data sheet containing raw and processed measurements from individual plots across multiple trials (MET, Pressure, Seed Size), and an analysis workbook summarizing statistical outputs and model selections. These main files are complemented by MET Deep Tiny Tag and MET Shallow Tiny Tag .csv files. The master sheet documents plot-level data for each trial, including sowing conditions (depth, date, soil strength at multiple depths), plant traits (coleoptile length and diameter), emergence counts at multiple intervals (7, 14, 21 days after sowing), and final emergence. It also includes biomass and grain yield metrics, harvest index, grain quality parameters (protein, moisture, test weight, screenings), and maturity dates. Each plot is identified by location, replicate, treatment, and variety, with coleoptile type (long or conventional) and seed size (standard or large) noted where relevant. The analysis workbook provides statistical summaries from ANOVA and regression models, highlighting significant effects and interactions among depth, variety, coleoptile type, and soil strength. It includes model selection outputs for emergence and coleoptile traits, with R² values and p-values for various combinations of predictors. Environmental conditions such as soil strength was measured at sowing and at multiple intervals post-sowing using gravimetric and pressure-based methods. Drone imagery, EM38 surveys, and weather station data were also collected to support spatial and temporal analysis. Data was processed using GenStat with fixed and random effects models, and transformations were applied where necessary to meet distributional assumptions. The dataset includes over 70 variables, with definitions embedded in column headers and trial documentation. Codes such as LCW (long coleoptile wheat) and conventional types are used to distinguish genetic traits. The dataset is structured to support multivariate analysis and is suitable for evaluating genotype by environment interactions, emergence dynamics, and yield formation under varying agronomic conditions.
Greg Rebetzke - 2024 Dookie Long Coleoptile Wheat Trial: Genotype, Sowing Depth and Soil Strength Interactions
공공데이터포털
This dataset originates from the 2024 long coleoptile wheat trial conducted at Dookie College, Victoria, Australia. The trial investigates the interactions between wheat genotype, sowing depth, and presswheel pressure (as a proxy for soil strength) on early crop establishment and development. The core experimental design is a factorial combination of six wheat genotypes (Scepter, Calibre, Mace, Mace18, Magenta, Magenta13), two sowing depths (shallow: 30–40 mm; deep: 80–100 mm), and three presswheel pressures (light, standard, heavy), resulting in 36 treatment combinations replicated across multiple blocks. The dataset is structured across multiple Excel file (.xlsx) sheets, each representing different aspects of the trial, including experimental design, field maps, seed characteristics, soil measurements, and emergence data. Key files include treatment layouts, seed packing details, soil strength and moisture data at various depths and time points (pre-sowing, 0 days after sowing, and 12 days after sowing), temperature logger data, and detailed emergence counts over time. Soil strength was measured using a Geotester penetrometer, while gravimetric moisture and matric potential were assessed through laboratory analysis of soil cores taken at specified depths. Temperature sensors recorded hourly data at 0, 3–4, and 8–10 cm depths in selected plots. Seed characteristics such as thousand seed weight, seed size grading, and germination/vigour assessments were recorded for each genotype. The emergence data includes counts from two seeding rows per plot, tracked over multiple dates post-sowing, allowing for analysis of emergence dynamics. The dataset supports investigations into how genotype and agronomic practices influence wheat establishment under varying soil mechanical resistance and moisture conditions. All data is labelled with consistent identifiers for plot, treatment, genotype, depth, and pressure, facilitating integration across sheets. This comprehensive dataset enables robust analysis of genotype by environment by management interactions relevant to improving wheat establishment under challenging sowing conditions.
Data for: Development and characterization of a wild emmer wheat backcross introgression population for hard winter wheat improvement
공공데이터포털
,This dataset describes a set of 1473 accessions derived from first backcrosses of hexaploid bread wheat (Triticum aestivum L.) to a diversity panel of wild emmer wheat (Triticum turgidum subsp. dicoccoides (Körn) Thell.). Wild emmer is the tetraploid progenitor of hexaploid bread wheat and is known to be a valuable source of genetic variation for wheat improvement. However, direct evaluation of wild emmer diversity for agronomic potential has limited value unless performed in the backgrounds of adapted cultivars. Here, we present a genetic characterization of a population of 1,473 backcross recombinant inbred lines, with an average genome composition of 75% bread wheat and 25% wild emmer. Low coverage whole-genome sequencing allowed introgressions and aneuploidies to be identified at relatively low cost per sample. These data identify the counts of hexaploid and wild emmer alleles in 1 Mb bins and 10 Mb sliding windows along each of the A- and B-genome chromosomes of each accession, using the IWGSC 'Chinese Spring' reference sequence v2.1. Allele proportions in 1 Mb bins and 10 Mb sliding windows also are provided for the introgression lines.,
Data from: Random forest regression to predict Farinograph traits from GlutoPeak output in wheat wild relative backcross lines
공공데이터포털
,Flour quality is a key breeding target in hard winter wheat cultivar development. The Farinograph is perhaps the most important device for assessing quality prior to cultivar release in the United States, but large sample size requirements and long test times make in impracticable for early-stage selection. We used random forest regression to predict key Farinograph parameters from novel features we calculated from the raw data output of the GlutoPeak, which requires less time and less sample, in a winter wheat population containing wild relative introgressions. Here, we present the raw GlutoPeak data and Farinograph data used in model development.,GlutoPeak output for 68 wheat samples, contained in folder "GP_upload". Some lines including wild relative introgressions. Files with the same number prior to the underscore represent multiple replications of the same sample - one file was randomly selected for model construction. FarinoGraph output for 68 wheat samples, some lines including wild relative introgressions.,
목록