The Innovation Lab for Applied Wheat Genomics aims to develop heat tolerant, high yielding, and farmer-accepted varieties for South Asia, while simultaneously increasing the research for development capacity of the global wheat improvement system through application of cutting-edge genomics and high-throughput phenotyping in applied wheat improvement.

,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.,,

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.

,Data from this project focuses on the evaluation of breeding lines. Significant progress was made in advancing breeding populations directed towards release of improved varieties in Tanzania. Thirty promising F4:7, 1st generation 2014 PIC (Phaseolus Improvement Cooperative) and ~100 F4:6, 2nd generation 2015 PIC breeding lines were selected. In addition, ~300 F4:5, 3rd generation 2016 PIC single plant selections were completed in Arusha and Mbeya. These breeding lines, derived from 109 PIC populations specifically developed to combine abiotic and biotic stress tolerance, showed superior agronomic potential compared with checks and local landraces. The diversity, scale, and potential of the material in the PIC breeding pipeline is invaluable and requires continued support to ensure the release of varieties that promise to increase the productivity of common bean in the E. African region.,Data available includes databases, spreadsheets, and images related to the project.,,

국립식량과학원에서 매년 개발되는 품종에 대해 지역별 적응성을 알아보기 위한 품종별 지역별 지적시험성적을 제공함

,SHOOTGRO emphasizes the development and growth of the shoot apex of small-grain cereals such as winter and spring wheat (Triticum aestivum L.) and spring barley (Hordeum vulgare L.). To better incorporate the variability typical in the field, up to six cohorts, or age classes, of plants are followed using a daily time step.,Assessing the influence of nitrogen and water availability on development and growth of individual organs of winter wheat (Triticum aestivum L.) is critical in evaluating the response of wheat to environmental conditions. We constructed a simulation model (SHOOTGRO 2.0) of shoot vegetative development and growth from planting to early boot by adding nitrogen and water balances and response functions for seedling emergence, tiller and leaf appearance, leaf and internode growth, and leaf and tiller senescence to the existing wheat development and growth model, SHOOTGRO 1.0. Model inputs include daily maximum and minimum air temperature, rainfall, daily photosynthetically active radiation, soil characteristics necessary to compute soil N and water balances, and several factors describing the cultivar and soil conditions at planting. The model provides information on development and growth characteristics of up to six cohorts of plants within the canopy (cohort groupings are based on time of emergence). The cohort structure allows SHOOTGRO 2.0 to provide output on the frequency of occurrence of plants with specific features (tillers and leaves) within the canopy. The model was constructed so that only water availability limited seedling emergence. Resource availability (nitrogen and water) does not influence time of leaf appearance. Leaf and internode growth, and leaf and tiller senescence processes are limited by the interaction of N and water availability. Tiller appearance is influenced by the correspondence to: W.W. Wilhelm, USDA-ARS, Department of Agronomy, University of Nebraska-Lincoln, Lincoln, Nebraska 68583-0934, USA. 0304-3800/93/$06.00 0 1993 - Elsevier Science Publishers B.V. All rights reserved 184 W.W. WILHELM ET AL. interaction of N, radiation and water availability. Predicted and observed dates of emergence and appearance of the first tiller had correlation coefficients of 0.98 and 0.93, respectively. However, these events were, on average, predicted 3.2 and 5.2 days later than observed. SHOOTGRO 2.0 generally under-predicted the number of culms per unit land area, partially because the simulation is limited to a maximum of 16 culms/plant. Model output shows that the simulation is sensitive to N and water inputs. The model provides a tool for predicting vegetative development and growth of the winter wheat with individual culms identified and followed from emergence through boot. SHOOTGRO 2.0 can be used in evaluating alternative crop management strategies.,,

This dataset comprises detailed agronomic measurements from a multi-factor wheat trial conducted in Emerald, Queensland, designed to evaluate the effects of sowing depth (shallow vs deep), time of sowing (TOS 1: April 17 and TOS 2: May 17), and genotype type (Conventional vs LCW) across 16 wheat varieties. The trial employed a split-split plot design with three replicates per treatment combination. Data were collected for emergence (plants/m²), phenology (days to flowering and maturity), tiller counts at GS65 and GS90, total biomass, grain yield (from biomass cuts and machine harvest), harvest index, and grain quality traits including protein content, test weight, screenings percentage, and 300 seed weight. The dataset is structured across five Excel (.xlsx and .csv), each sheet corresponding to a specific trait or analysis. Each sheet includes raw measurements, statistical summaries, and model outputs from REML-based linear mixed models fitted in GenStat. Fixed effects include TOS, depth, type, variety, and their interactions, while random effects account for replication and nested plot structures. Environmental conditions were consistent across plots, with sowing depth and timing being the primary experimental variables. Soil strength measurements and emergence counts were taken at multiple intervals post-sowing. Data transformations and residual diagnostics were applied where necessary to meet model assumptions. The dataset includes over 150 unique plot-level observations per trait, with some plots excluded due to missing or questionable data. Variable definitions include emergence counts (plants/m²), DTF and DTM (days), tiller counts (tillers/m²), biomass (kg/ha), grain yield (kg/ha at 12.5% moisture), HI (unitless ratio), protein (%), test weight (g), screenings (% arcsine-transformed), and seed weight (g). Codes and abbreviations are consistent across sheets, and all measurements are aligned to standard agronomic protocols. This dataset enables robust analysis of genotype performance under varying sowing conditions and supports genotype selection for improved emergence and yield stability.

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)

One of 20+ trials implemented during 2018/19, 2019/20, and 2020/21 under the USAID-funded Grain Research and Innovation (GRAIN) project implemented by Michigan State University, in partnership with partnership with Afghanistan’s Ministry of Agriculture, Irrigation, and Livestock (MAIL), the Agricultural Research Institute of Afghanistan (ARIA), the International Center for Agricultural Research in the Dry Areas (ICARDA), and the International Maize and Wheat Improvement Center (CIMMYT).