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opencc-zeroDec 2017View details →

Data from: Genomic analysis and prediction within a US public collaborative winter wheat regional testing nursery

The development of inexpensive, whole-genome profiling enables a transition to allele-based breeding using genomic prediction models. These models consider alleles shared between lines to predict phenotypes and select new lines based on estimated breeding values. This approach can leverage highly-unbalanced datasets common to breeding programs. The Southern Regional Performance Nursery (SRPN) is a public nursery established by the USDA-ARS in 1931 to characterize performance and quality of near-release wheat varieties from breeding programs in the US Central Plains. New entries are submitted annually and can be reentered only once. The trial is grown at more than 30 locations each year and lines are evaluated for grain yield, disease resistance, and agronomic traits. Overall genetic gain is measured across years by including common check cultivars for comparison. We have generated whole-genome profiles via genotyping-by-sequencing for 939 SPRN entries dating back to 1992. We measured the diversity within the nursery and have explored its potential use as a GS training population. GS prediction models across years (average r= 0.33) outperformed year-to-year phenotypic correlation for yield (r=0.27) for a majority of the years evaluated, suggesting that genomic selection has the potential to outperform low heritability selection on yield in these highly variable environments. We also examined the predictability of programs using both program-specific and whole-set training populations. Generally, the predictability of a program was similar with both approaches. These results suggest that wheat breeding programs can collaboratively leverage the immense datasets that are generated from regional testing networks.

opencc-zeroDec 2017View details →

Data from: Validation of grain yield QTL from soft winter wheat using a CIMMYT spring wheat panel

Validation of quantitative trait loci (QTLs) is an essential step in marker-assisted breeding. The objectives of this study were to validate grain yield (GY) QTLs previously identified in soft red winter wheat (Triticum aestivum L.) through biparental and association mapping using the spring wheat association mapping initiative (WAMI) panel from CIMMYT, Mexico, and to identify allele combinations of the validated QTLs that resulted to the highest GY. Linked single-nucleotide polymorphisms for IWA3560 (3A), IWA1818 (4B), and IWA755 (6B) were significantly associated (P < 0.001) with GY, grain number, and thousand-grain weight in the WAMI. Lines possessing the favorable allele for the QTL at the 3A, 4B, and 6B loci (ACG allele combination) validated on the WAMI had the highest mean GY at 4.55 t ha−1, but three other haplotypes (ACA, GCA, and GCG) differing by one or two alleles in the validated QTL regions were not significantly different. These results validate GY QTLs across winter and spring wheat through genome-wide association analysis and further demonstrate the potential for pyramiding favorable alleles for the genetic improvement of wheat breeding populations.

opencc-zeroNov 2023View details →

QTL mapping for seedling and adult plant resistance to stripe and leaf rust in two winter wheat populations

The two recombinant inbred lines (RIL) populations developed by crossing Almaly × Avocet S (206 RILs) and Almaly × Anza (162 RILs) were used to detect the novel genomic regions associated with adult plant resistance (APR) and seedling or all-stage resistance (ASR) to yellow rust (YR) and leaf rust (LR). Both the populations were evaluated for YR APR in two environments (2018 and 2019) and LR APR in three environments (2018, 2019, and 2020) in the Anza population and two environments (2018 and 2019) in the Avocet population; both the populations were phenotyped for one environment during 2020 for LR and YR ASR and genotyped using high throughput DArTseq technology. A set of 51 QTLs including 22 for YR APR, nine for LR APR, nine for YR ASR, and 11 for LR ASR were identified. Also, a set of 13 stable QTLs including nine QTLs (QYR-APR-2A.1, QYR-APR-2A.2, QYR-APR-4D.2, QYR-APR-1B, QYR-APR-2B.1, QYR-APR-2B.2, QYR-APR-3D, QYR-APR-4D.1, and QYR-APR-4D.2) for YR APR and four QTLs (QLR-APR-4A, QLR-APR-2B, QLR-APR-3B, and QLR-APR-5A.2) for LR APR were identified. In silico analysis revealed that the key putative candidate genes such as Cytochrome P450, Protein kinase-like domain superfamily, Zinc-binding ribosomal protein, SANT/Myb domain, WRKY transcription factor, Nucleotide-sugar transporter, and NAC domain superfamily were in the QTL regions and involved in the regulation of host response towards the pathogen infection. The stable QTLs identified in this study are useful for developing rust-resistant varieties through marker-assisted selection (MAS).

opencc-by-4.0Oct 2023View details →

Carbon isotope discrimination and yield of winter wheat in an agrivoltaic system (Heggelbach, Herdwangen-Schönach, Germany) from 2016/17 - 2019/20

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opencc-by-4.0Feb 2022View details →

Data supporting "Can Long-Term Experiments Predict Real Field N and P Balance and System Sustainability? Results from Maize, Winter Wheat, and Soybean Trials Using Mineral and Organic Fertilisers"

Data supporting "Can Long-Term Experiments Predict Real Field N and P Balance and System Sustainability? Results from Maize, Winter Wheat, and Soybean Trials Using Mineral and Organic Fertilisers" by Piccoli et al. (2021) Agronomy 2021, 11, 1472. https://doi.org/10.3390/agronomy11081472

opencc-by-4.0Feb 2022View details →

Mildew ratings and yields of winter wheat and spring oat varieties on NIAB and AHDB Recommended Lists, 1972-2022

Data on powdery mildew ratings and yields in fungicide-treated trials relative to controls, for winter wheat and spring oat varieties on UK Recommended Lists from 1972 to 2022. These data are used in the graphs in Figure 1 of Brown & Wulff (2022) 'Diversifying the menu for crop powdery mildew resistance', Cell, DOI https://doi.org/10.1016/j.cell.2022.02.003. Data are compiled from published information. (c) NIAB for data from 1972 to 2001. (c) Agriculture and Horticulture Development Board

opencc-zeroOct 2022View details →

Determining haploblocks and haplotypes in the MAGIC winter wheat population WM-800 based on the wheat 15k Infinium and the 135k Affymetrix SNP arrays

Haplotypes are derived from single nucleotide polymorphisms (SNPs). They are beneficial (i) to remove redundant sequence information in genetic populations and, more important, (ii) to distinguish more than two variants/alleles at a genomic locus. A haploblock locus, made of multiple haplotypes, is very useful in multiparent-advanced-generation-intercross (MAGIC) populations, where, ideally, multiple founder alleles need to be distinguished at each locus to subsequently carry out efficient genome-wide association analysis studies (GWAS). In this regard, the dataset contains genotype matrices (made of SNP, haploblock and haplotype data) for 800 lines of the MAGIC WHEAT population WM-800 (Sannemann et al. 2018). The datasets are based on genotyping the lines with both the already published wheat 15k Infinium SNP array (Sannemann et al. 2018) and the new wheat 135k Affymetrix SNP array.

opencc-by-4.0Dec 2022View details →

PPS Winter wheat trials 2014 - 2015

This data set consists of observations that were done during the PPS winter wheat field trials in Wageningen (The Netherlands).  The experiment was conducted by the chair group Plant Production Systems (PPS) of Wageningen University and Research. The experiment consisted of two growing seasons (2013-2014 and 2014-2015), three nitrogen fertilization treatments (N1, N2, N3) and three cultivars (cv Julius, cv Ritmo, cv Tabasco). The experiment has been described in detail by Berghuijs et al (2023). The uploaded files are: 20231228_pps_winterwheat_trials_crop_nitrogen_and_phosphor.xlsx: contains measurements of crop N and crop P concentrations 20231228_pps_winterwheat_trials_dry_matter.xlsx: contains measurements of dry matter, dry matter partitioning and specific leaf area 20231228_pps_winterwheat_trials_phenology.xlsx: contains the sowing dates, dates of anthesis and harvest dates. Note that there were no separate measurements of phenology done per cultivar; only per growing season. References: Berghuijs, H. N. C., Silva, J. V., Rijk, H. C. A., van Ittersum, M. K., van Evert, F. K. & Reidsma, P. (2023). Catching-up with genetic progress: Simulation of potential production for modern wheat cultivars in the Netherlands. Field Crops Research 296: 108891. URL: https://doi.org/10.1016/j.fcr.2023.108891

opencc-by-4.0Dec 2020View details →

Fig. 1 in Effectiveness Of Doubled Haploids Production By Anther Culture From Selected Winter Wheat Hybrids

Fig. 1. Efficiency of formation of embryos and plants-regenerants.

opencc-zeroJun 2024View details →

High buffering potential of winter wheat composite cross populations to rapidly changing environmental conditions

A winter wheat composite cross population (CCP), created in the UK in 2001, has been grown in Germany, Hungary and the UK since 2005 (F5 generation). In 2008/9 (F8), a cycling pattern for the populations was developed between partners to test the effects of rapidly changing environments on agronomic performance. One CCP was grown by eight partners for one year and subsequently sent to the next partner, creating "cycling CCPs" with different histories. In 2013, all eight cycling CCPs and the three non-cycling CCPs (from Germany, Hungary and UK) were included in a two-year experiment in Germany with three line varieties to compare agronomic performance and morphological characteristics. Differing seed weight of the F13 at sowing affected some agronomic parameters under drought conditions in 2014/15, but not under less stressful conditions in 2013/14. In both experimental years, the CCPs were comparable to the line varieties in terms of agronomic performance, with some CCPs outyielding the varieties under drought conditions of 2015. The results highlight the potential of CCPs to compete with line varieties while the overall similarity of the CCPs based on their origin and cycling history for agronomic traits indicate a high buffering potential under highly variable environmental conditions.

opencc-by-4.0Dec 2011View details →

Fig. 2 in The Impact Of Sowing Time On Sugar Content And Snow Mould Development In Winter Wheat

Fig. 2. The content of carbohydrates depending on the year and the sowing time.

opencc-by-4.0Dec 2011View details →

Fig. 1 in The Impact Of Sowing Time On Sugar Content And Snow Mould Development In Winter Wheat

Fig. 1. The average temperature of ten-day periods during overwintering.

AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJ-GUESS winter wheat simulations

This data set contains output data from simulations with the model LPJ-GUESS for winter wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above groun biomass, plant day, maturity day, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simlations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season').

AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from pDSSAT winter wheat simulations

This data set contains output data from simulations with the model pDSSAT for winter wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').

AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from EPIC-TAMU winter wheat simulations

This data set contains output data from simulations with the model EPIC-TAMU for winter wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').

AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from ORCHIDEE-crop winter wheat simulations

This data set contains output data from simulations with the model ORCHIDEE-crop for winter wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, anthesis day . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').

AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from PEPIC winter wheat simulations

This data set contains output data from simulations with the model PEPIC for winter wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').

AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from EPIC-IIASA winter wheat simulations

This data set contains output data from simulations with the model EPIC-IIASA for winter wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').

AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from GEPIC winter wheat simulations

This data set contains output data from simulations with the model GEPIC for winter wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').