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.

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 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 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.

,This data set contains 32 million annotated SNPs having an average SNP density of 30 SNPs per kb and 12 non-synonymous SNPs per gene model. These SNPs were identified from a genetically diverse, worldwide, collection of soybean germplasm representing wild, landrace, and improved cultivars. A combination of new and publicly available re-sequencing data was used in this analysis. The accession genotypes and their annotations are described in the manuscript titled: "Analysis and characterization of 1500 diverse genome sequences as a versatile resource for post-genomics research".,,

Single nucleotide polymorphism genotypes for ~300 varieties from the OzWheat diversity panel generated with alternative platforms combined in a single matrix. Original platform source files are also provided. Transcript SNPs: 63,222 SNP loci. 298 wheat lines. File=OzWheat_Transsnp_filtered_CS1.0_231130.hmp.txt 90K Illumina SNP array: 23362 SNP loci. 286 wheat lines from OzWheat panel 1 and 300 lines from OzWheat panel 2. File=V1_V2_commonSNP_merged.hmp 40K Illumina SNP array: 10670 SNP loci. 286 wheat lines. File=OW1_40K_filtered.hmp.txt DARTseq: 56467 SNP loci. 282 wheat lines. File=Report_DW21-6152_SNP_mapping_2.csv Combined platform SNP - joins SNPs called from transcriptome data, 90K and 40K array and DARTseq. 115,260 SNP loci. 296 wheat lines. File=OzWheat_Transsnp40k90kDArT_filtered_CS1.0_alleleinfo_240102.hmp.txt Keeble-Gagnère G, Pasam R, Forrest KL, et al. Novel Design of Imputation-Enabled SNP Arrays for Breeding and Research Applications Supporting Multi-Species Hybridization. Front Plant Sci. 2021;12:756877. Published 2021 Dec 22. doi:10.3389/fpls.2021.756877

,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.,

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 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.