Wallumbilla 2024 wheat trials: Impact of Sowing Depth, Coleoptile Traits, and Soil Strength on Emergence and Biomass Across Multiple Field Trials

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.\n\nThe 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.\n\nThe 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.\n\nData 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.\n\nLineage: Field experiment data

本数据集包含澳大利亚昆士兰州罗马市开展的一系列小麦田间试验的详细农艺测量数据，旨在探究播种深度、胚芽鞘类型、土壤紧实度等因素对植株出苗、生长及产量的影响。数据集包含两个主要的Excel（.xlsx）文件：其一为主数据表，收录了多环境试验（MET）、压力试验、种子尺寸试验中各小区的原始与处理后测量数据；其二为分析工作簿，汇总了统计结果与模型筛选结果。此外，配套文件还包含MET Deep Tiny Tag与MET Shallow Tiny Tag 格式的.csv文件。

主数据表记录了各试验的小区级数据，涵盖播种条件（播种深度、日期、不同土层深度的土壤紧实度）、植株性状（胚芽鞘长度与直径）、多个间隔时段（播种后7、14、21天）的出苗计数及最终出苗率；同时包含生物量与籽粒产量指标、收获指数、籽粒品质参数（蛋白质含量、含水率、容重、筛杂物率）以及成熟日期。每个小区通过试验地点、重复、处理方式与品种进行标识，并在相关记录中标注胚芽鞘类型（长胚芽鞘或常规型）与种子尺寸（标准型或大粒型）。

分析工作簿提供了方差分析（Analysis of Variance）与回归模型的统计汇总结果，重点展示了播种深度、品种、胚芽鞘类型与土壤紧实度之间的显著效应与交互作用。其中包含出苗性状与胚芽鞘性状的模型筛选输出，涵盖各类预测变量组合的决定系数（R²）与p值。环境条件数据方面，研究人员采用重量法与压力法，在播种时及播种后多个间隔时段测量了土壤紧实度；同时还收集了无人机影像、EM38土壤电导率勘测数据与气象站数据，以支撑时空分析。

本数据集采用GenStat软件结合固定效应与随机效应模型进行处理，必要时会对数据进行转换以满足分布假设要求。数据集包含70余个变量，变量定义嵌入在列标题与试验文档中。研究人员使用长胚芽鞘小麦（LCW）等代码区分遗传性状类型。该数据集结构可支撑多变量分析，适用于评估不同农艺条件下的基因型-环境互作、出苗动态与产量形成过程。

数据谱系：田间试验数据