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.

This dataset comprises detailed agronomic and phenotypic data from a series of wheat trials conducted in 2024 at the Narrabri, New South Wales, focusing on the performance of long coleoptile wheat genotypes under varying sowing depths, seed sizes, water regimes, and disease treatments. The collection includes multiple Excel (.xlsx) sheets, each representing different aspects of the trial: experimental diary, plot layout, multi-environment trials (MET), durum wheat trials, seed size trials, Victrado trials (irrigated and dryland), temperature logger data, GS32 dry matter biomass, and multiple plant count records across time points. The MET and Durum trials were conducted across two times of sowing (TOS1 and TOS2), with sowing depths of 4 cm and 12 cm, and included genotypes such as Mace, Scepter, Magenta, and Westcourt. Seed size trials compared small and large seeds, while Victrado trials assessed genotype responses under well-watered and dryland conditions with factorial combinations of depth and Fusarium crown rot (FCR) treatments. Data collection included emergence counts, seedling depth and length, biomass at GS32, head counts, plant height, lodging, and final harvest metrics such as grain yield, test weight, protein content, and grain size. Soil moisture was monitored using Tiny Tag and disposable probes, and neutron moisture meters (NMM) at depths from 0 to 150 cm. The dataset includes over 70 variables, with consistent identifiers for site, trial, genotype, depth, treatment, and replication. Codes such as “4cm_Mace” or “12cm_Sunchaser+” denote sowing depth, genotype, and treatment combinations. The data were collected using standard field trial protocols, with sowing conducted using tyned implements and press wheels at 25 cm row spacing. Fertiliser applications included 238 kg/ha urea and 70 kg/ha Granulock Z Extra. Herbicide applications were recorded per TOS. This dataset enables comprehensive analysis of genotype × environment × management interactions, particularly for traits associated with early vigour, drought adaptation, and disease resistance in wheat.

This dataset comprises soil, plant, climatic, and management data from a 2023 field experiment conducted at Ungarra, Eyre Peninsula, South Australia. The trial aimed to evaluate the establishment and performance of long coleoptile wheat genotypes compared to short coleoptile varieties under varying sowing depths and fertiliser regimes. Three distinct experiments were conducted side-by-side: 1. Systems Trial (Water Balance): Investigated water balance at sowing using tarp treatments and sowing moisture assessments. 2. Core Genotype × Sowing Depth Trial: Compared eight wheat genotypes across three sowing depths (shallow, mid, deep). 3. Genotype × Depth × Nutrition Trial: Explored interactions between two genotypes, two sowing depths, and three fertiliser rates (45, 100, 150 kg/ha Monoammonium phosphate). Data was collected through field-based measurements including plant counts, seeding depth, NDVI (via Greenseeker), canopy cover (via Canopeo), biomass, spike counts, and grain yield using a plot header. Soil chemistry was analyzed by Eurofins APAL using standardized test codes, and rainfall data were sourced from a nearby soil moisture probe. All data was manually recorded and digitised for further analysis. The data is presented in an Excel workbook (.xlsx) contains trial details, metadata, raw experimental data and soil chemistry. The sheets are interrelated through shared identifiers such as trial number, sowing depth, genotype, and treatment number. Test variables across trials, include a range of agronomic, physiological, and soil metrics, such as grain yield, harvest ratio, biomass, coleoptile length, plant density, seeding depth, NDVI, estimated canopy cover and soil pH, EC, N, P). Codes and Symbols: - GS: Growth Stage (e.g., GS10–11) - NF: Nil Found (used in plant emergence data) - WB: Water Balance treatment codes (e.g., WB1, WB2)

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.

This dataset originates from the 2024 Lipson Long Coleoptile Wheat Trial conducted at Lipson, Eyre Peninsula, South Australia. It captures a comprehensive multi-factor field experiment designed to evaluate the effects of wheat genotype, sowing depth, and soil strength (via presswheel pressure) on crop emergence, early growth, and yield. The trial includes two core components: a genotype × sowing depth factorial and a systems experiment incorporating soil strength. Six wheat genotypes (Mace, Mace18, Magenta, Magenta13, Calibre, Scepter) were sown at two depths (shallow: 4–6 cm; deep: 8–10 cm) under three presswheel pressures (light, mid, heavy), resulting in 72 treatment combinations replicated across four blocks. The dataset is organised across multiple Excel (.xlsx) sheets, each representing specific data domains: trial metadata, treatment layout, sowing depth and coleoptile measurements, soil moisture and strength profiles, and crop performance metrics. Soil moisture was measured gravimetrically at multiple depths (0–12 cm) and time points (seeding, 6, 14, 21, and 35 days after sowing), while soil strength was assessed using a penetrometer at the same intervals. Crop assessments include emergence counts (29–44 DAS), NDVI and canopy cover (via Greenseeker and Canapeo), booting/head emergence (Zadoks scale), biomass and spike number, and grain yield and quality. Variables include plant density, coleoptile length, sowing depth, biomass, harvest index, grain yield, 1000 grain weight, screenings, test weight, and protein content. Soil and environmental data are linked to treatment identifiers and plot coordinates, enabling integration across sheets. Rainfall data from the Tumby Bay weather station is included to contextualize environmental conditions. The dataset is structured to support genotype by environment by management interaction analysis and is suitable for agronomic modelling, trait evaluation, and soil-plant interaction studies.

,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.,

,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.,,

,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:,

This data set contains raw data and best linear unbiased estimates (BLUEs) for ~300 wheat varieties grown under contrasting day lengths. Two hundred and eighty-six varieties from the OzWheat diversity panel were germinated under ‘long’ (12hr light) and ’short’ (8hr light) photoperiod regimes. Both the long and short-day treatments were carried out in a double coated plastic growth house with temperature control based at the CSIRO Black Mountain Innovation Precinct in Canberra, Australia. For the duration of each day length treatment temperature was maintained at 20oC and plants experienced normal sun light, which was supplemented with artificial lighting under fluorescent lamps delivering 300 µmol m-2 s-1 outside daylight hours. The long and short-day treatments were sown on the 30th November 2015 and 6th of June 2016 and harvested by the 6th of May and 9th of November 2016 respectively. In each treatment 1716 pots were grown across 9 benches within a 12 row and 16 column grid. Plants were watered via an automated irrigation system every 2 days. Seven traits were recorded by manual assessment twice weekly, from the 5th week post sowing which captured variation in phenology staging and spike development. These were number of days to first node detectable (Z31), number of days to anthesis (Z61), height (cm), spike length (cm), spikelets per spike and number of empty spikelets at maturity. Variation in phenology and spike traits was first assessed for incorrect entries and outlying values. Extreme outliers (i.e. less than 1\% of data) were excluded from the analyses. All traits adhered to expectations of normality. Trait data were analysed using a linear mixed model implemented in ASReml-R Release 3 (Butler et al. 2009, R Development Core Team, 2015). As in a typical repeated measures analysis we modelled 'variety' as a random treatment effect. Within each day length experiment trait data were analysed using the general mixed model framework. Broad sense heritability, or repeatability of variety means (Pmr), was estimated as the proportion of the total phenotypic variance explained by the random variety (genetic) effect variance (sigma^2), following Falconer and Mackay (1996). Further, variety level best linear unbiased estimates (BLUEs) were obtained for each trait by re-running the above model while fitting variety as a fixed effect. Adjusted variety means were extracted from this model using the predict() function classifying on 'variety' in ASReml-R. This approach was taken so as to avoid shrinkage of random effect estimates that would subsequently be applied in association analysis.

This dataset will contain phenotypic observations of a large number of wheat genotypes evaluated in 2016-2017 and 2017-2018 at the International Maize and Wheat Improvement Center in Ciudad Obregon, Mexico.