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34 results for “spring_wheat”
Carbon fluxes data over Indian spring wheat agro-ecosystem
<p>The data consists of the following:</p> <ol> <li>Site-scale carbon flux data for an IARI experimental wheat site for the growing season 2013–2014 in New Delhi (28°40' N, 77°12' E).</li> <li>The simulation data in NetCDF format comprises carbon fluxes such as GPP, NPP, Ra, Rh, and NEE.</li> <li>Harvested wheat area of spring wheat across the Indian wheat-growing regions.</li> <li>Site-scale NEP (gC/m2/mon) measured at Meerut (29°05′33″N, 77°41′53″E; growing season 2009-2010) and Saharanpur (29° 52′ 19.139″ N and 077° 34′ 01.621″ E; growing season 2014-15) extracted from published work (Patel et al., 2011; Patel et al., 2021, respectively)</li> </ol>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL spring wheat simulations
<p>This data set contains output data from simulations with the model LPJmL for spring 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 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').</p> <p>Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.</p>
CIMMYT spring wheat product profiles 2022
<p>Detailed target product profiles for CIMMYT spring wheat breeding 2022. The product profiles are the trait specifications for selection and release of material from the five CIMMYT spring wheat breeding pipelines: <em>(1) Hard white, optimum environment; (2) Hard white, heat tolerant, early maturity; (3) Hard white, drought tolerant, normal maturity; (4) Hard white, drought tolerant, early maturity </em>and <em>(5) Hard white/red, high rainfall</em>. These five pipelines target eight global market segments in Latin America and the Caribbean, East Africa, Central Asia, West Asia and North Africa and South Asia. Product profiles are defined as in the Excellence in Breeding Toolbox for “product design and management” (https://excellenceinbreeding.org/toolbox/tools/cgiar-seed-product-market-segment-database).</p> <p>Traits include grain and processing traits, nutritional enhancement, agronomic and disease traits. For spring wheat, no specific production, multiplication, or unique product registration traits apply in 2022 so these fields in the product profiles are marked as not applicable (NA). Traits are listed by category and assigned a measurement scale which is used in selection along with a minimum score. The traits are differentiated by requirement: either as “essential” or “nice to have” as well as the requirement for improvement (vs. maintenance). Indication of a trait as a threshold trait indicates it is a requirement for release of material from the breeding pipeline.</p>
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.
Mildew ratings and yields of winter wheat and spring oat varieties on NIAB and AHDB Recommended Lists, 1972-2022
<p>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 </p>
Pulsed stress hypothesis revisited – A case study of Metopolophium dirhodum and spring wheat
<p>Life table data for the rose-grain aphid, Metopolophium dirhodum, reared on the spring wheat, Triticum sativum, under four regimes of water supply: 40C - continuous drought (40 SWC), 70C - well watered (70 SWC), 40-0, 40-1, 40-2 and 40-3 - pulsed stress timed for one week before aphids were established (40-0), during nymphal development (40-1), and during first (40-2) and second week of reproduction (40-3). </p>
Noah-MP data for modeling Canadian spring wheat study
<p>This zip file contains the simulation results from a Noah-MP crop model for a Canadian spring wheat study.</p> <p>There are two separate folders inside: one for single-point data and one for regional data results.</p> <p>The single-point folder contains three model outputs from the three site-year (2016, 2019SW, 2019SE) and three model treatments (default NoahMP, wheat model, and TAVE for dynamic planting threshold)</p> <p>The regional folder contains the combined agricultural statistics from USDA and StatisCanada (combine_crop_PPR.nc), default wheat model results, and the temperature stress results. </p> <p>Please feel free to contact Dr. Zhe Zhang (zhe.zhang@usask.ca) or Dr. Yanping Li (yanping.li@usask.ca) for further details.</p>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJ-GUESS spring wheat simulations
This data set contains output data from simulations with the model LPJ-GUESS for spring 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 GEPIC spring wheat simulations
This data set contains output data from simulations with the model GEPIC for spring 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 PEPIC spring wheat simulations
This data set contains output data from simulations with the model PEPIC for spring 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-TAMU spring wheat simulations
This data set contains output data from simulations with the model EPIC-TAMU for spring 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 pDSSAT spring wheat simulations
This data set contains output data from simulations with the model pDSSAT for spring 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-IIASA spring wheat simulations
This data set contains output data from simulations with the model EPIC-IIASA for spring 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 PROMET spring wheat simulations
This data set contains output data from simulations with the model PROMET for spring 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, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the ERA-Interim (Dee et al. 2011) 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 CARAIB spring wheat simulations
<p>This data set contains output data from simulations with the model CARAIB for spring 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').</p>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from APSIM-UGOE spring wheat simulations
This data set contains output data from simulations with the model APSIM-UGOE for spring 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 JULES spring wheat simulations
<p>This data set contains output data from simulations with the model JULES for spring 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 WFDEI (Weedon et al. 2014) 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').</p>
Data from: Validation of grain yield QTL from soft winter wheat using a CIMMYT spring wheat panel
Open the record for dataset details and reuse information.
Data from: Leveraging historical trials to predict Fusarium head blight resistance in spring wheat breeding programs
Open the record for dataset details and reuse information.
Data from: Genome-wide association mapping of resistance to Septoria nodorum leaf blotch in a Nordic spring wheat collection
Parastagonospora nodorum is the causal agent of septoria nodorum blotch (SNB) in wheat. It is the most important leaf blotch pathogen in Norwegian spring wheat. Several quantitative trait loci (QTL) for SNB susceptibility have been identified. Some of these QTL are the result of underlying gene-for-gene interactions involving necrotrophic effectors (NEs) and corresponding sensitivity (Snn) genes. A collection of diverse spring wheat lines was evaluated for SNB resistance/susceptibility over seven growing seasons in the field. In addition, wheat seedlings were inoculated and infiltrated with culture filtrates (CFs) from four single spore isolates and infiltrated with semi-purified NEs (SnToxA, SnTox1 and SnTox3) under greenhouse conditions. In adult plants, the most stable SNB resistance QTL were located on 2B, 2D, 4A, 4B, 5A, 6B, 7A and 7B. The QTL on 2D was effective most years in the field. At the seedling stage, the most significant QTL after inoculation were located on 1A, 1B, 3A, 4B, 5B, 6B, 7A and 7B. The QTL on 3A and 6B were significant both after inoculation and CF infiltration, indicating the presence of novel NE-Snn interactions. The QTL on 4B and 7A were significant in both seedlings and adult plants. Correlations between SnToxA sensitivity and disease severity in the field were significant. To our knowledge, this is the first genome wide association mapping study (GWAS) to investigate SNB resistance at the adult plant stage under field conditions.
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