wheat research paper 2022

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Meeting the challenges facing wheat production: the strategic research agenda of the global wheat initiative.

1. Introduction

2. background, 2.1. why wheat, 2.2. impact of climate change, 2.3. the wheat initiative, 2.4. global wheat research, 3. existing strategic research agenda—work in progress.

3.1. Objective 1: To Increase Yield Potential

3.2. objective 2: to protect ‘on farm’ yield, 3.3. objective 3: ensuring the supply of high-quality safe wheat, 3.4. objective 4: enabling technologies and the sharing of resources, 3.5. objective 5: germplasm accessibility, 3.6. objective 6: knowledge exchange, education and training, 4. major issues and challenges facing wheat production and research, 4.1. inconsistencies in regulatory environment, 4.2. access to staff with the necessary skills in both new and old technologies, 4.3. data access and standards, 4.4. support for multinational research and public–private partnerships, 5. research priorities, 5.1. strengthen existing research activities, 5.2. enhance agronomy in its broadest definition (crop production and soil management), 5.3. increase genetic diversity.

• A broad series of activities can be undertaken to address this research priority:
• Revise and update the Global Wheat Conservation Strategy prepared in 2007 [ 26 ].
• Encourage the large-scale genotyping and phenotypic characterisation of germplasm held in the major genebanks.
• Advocate for the free and open exchange of germplasm and associated data.
• Encourage the utilisation of existing specialist germplasm collections collated by EWGs and share the outcomes: ◦ Tetraploid collections developed by the Durum EWG ▪ Durum elite and landrace collection in conjunction with a tetraploid core collection (GDP: Global Durum wheat Panel) capturing about 80% of the AABB haplotypes [ 27 ] of the collection (TGC: Tetraploid wheat Global Collection) described in [ 28 ]. ◦ Heat and drought tolerant germplasm collections developed by HeDWIC. ◦ Wheat quality assessment panels developed by the Quality EWG.
• Support research aimed at the enhanced utilisation of unadapted germplasm: ◦ Development of introgression populations. ◦ Re-domestication. ◦ Exploration of novel germplasm evaluation strategies. ◦ Development of efficient methods for gene editing.

5.4. Understanding Root and Soil Biology

• Continuing improvement of root phenotyping techniques, particularly in the field.
• Expand information of soil–microbe–plant interactions.
• Integration of data and information on roots and the microbiome in the analysis of wheat production with the full cropping system. It will also be important to emphasise the differences between low and high input systems and organic farming.

6. Wheat Initiative Structure and Organisation

6.1. develop educational and training programs.

• Ensure the full and rapid implementation of the postgraduate and ECR plan for involvement in the EWGs.
• Establish an exchange program that provides partial funding for students to work in other laboratories.
• Encourage EWGs to deliver training workshops and courses, and link to existing options offered by other organisations, such as universities, CIMMYT and ICARDA.
• Develop an online Wheat Initiative seminar program.
• Develop mentoring programs to support students and link to industry.

6.2. The Wheat Initiative as an Advocacy and Lobby Organisation

• Produce public explanatory documents and videos covering the Wheat Initiative activities, major topics and issues affecting wheat production, such as the role of germplasm exchange, gene editing, hybrid wheat, and crop protection.
• Participate in relevant G20 workshops and meetings and develop links to government agencies and international organisations.
• Advocate and lobby for the support of transnational research.
• Develop links to the wheat grower and processing industry organisations.
• Promote wheat resources such as WheatIS and WheatVIVO.

6.3. Expand Engagement

• The Institutions’ Coordination Committee has established a sub-committee to work through the options to build membership.
• Develop and distribute documentation explaining the value to industry from joining the WI—Industry.
• Increase industry participation in WI activities, particularly in training and mentorship: a component would be to identify platforms and capabilities that could be used by industry.
• Identify and target government and institutional organisations in major wheat producing and wheat-importing countries to seek greater engagement in the WI.
• Target early career researchers in under-represented countries to encourage the membership of EWGs. In addition, provide support to allow key people from these regions to participate in WI activities.

6.4. Supporting Multinational Research

• Stage 1 —Coordination across existing research to capture synergies, prevent duplication and identify gaps—low incremental costs but a proactive coordination is instrumental and essential.
• Stage 2 —Project alignment and leverage of existing investments: initially focus on the twinning of existing projects or building on a call(s) for proposals by one or more national funders joining (e.g., recent AAFC (Canada)/BBSRC (UK) IWYP-aligned call-linked consecutive calls for proposals in each country).
• Stage 3 —Scaling-up joint investment: under the key areas of interest to all funders, funding can be allocated to a common/centrally managed pot/program or managed nationally by a lead funder, still aligned under a broad umbrella theme.

7. Conclusions

• Boost research and technology delivery capabilities by investing in staff and student training and encourage and support the exchange of personnel between research organisations and building research infrastructure. This can be achieved if national research programmes place priority on activities with strong international linkages. Financial or organisational support from national agencies to research groups seeking participation in international partnerships would be beneficial.
• Provide support, both financial and organisational, to international activities aiming to facilitate the exchange of resources, particularly germplasm, and support the evaluation and delivery of research outcomes.
• Actively participate in Wheat Initiative research alliances that gather the capabilities and resources targeting global research challenges. These include the work of the Expert Working Groups and the three current alliances: The International Wheat Yield Partnership (boosting wheat yield potential), the Alliance for Wheat Adaptation to Heat and Drought (producing heat- and drought-tolerant germplasm) and the Wheat Initiative Crop Health Alliance (diagnosis and monitoring of wheat diseases).

Author Contributions

Data availability statement, acknowledgments, conflicts of interest, abbreviations.

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Share and Cite

Langridge, P.; Alaux, M.; Almeida, N.F.; Ammar, K.; Baum, M.; Bekkaoui, F.; Bentley, A.R.; Beres, B.L.; Berger, B.; Braun, H.-J.; et al. Meeting the Challenges Facing Wheat Production: The Strategic Research Agenda of the Global Wheat Initiative. Agronomy 2022 , 12 , 2767. https://doi.org/10.3390/agronomy12112767

Langridge P, Alaux M, Almeida NF, Ammar K, Baum M, Bekkaoui F, Bentley AR, Beres BL, Berger B, Braun H-J, et al. Meeting the Challenges Facing Wheat Production: The Strategic Research Agenda of the Global Wheat Initiative. Agronomy . 2022; 12(11):2767. https://doi.org/10.3390/agronomy12112767

Langridge, Peter, Michael Alaux, Nuno Felipe Almeida, Karim Ammar, Michael Baum, Faouzi Bekkaoui, Alison R. Bentley, Brian L. Beres, Bettina Berger, Hans-Joachim Braun, and et al. 2022. "Meeting the Challenges Facing Wheat Production: The Strategic Research Agenda of the Global Wheat Initiative" Agronomy 12, no. 11: 2767. https://doi.org/10.3390/agronomy12112767

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ORIGINAL RESEARCH article

Improving wheat yield prediction using secondary traits and high-density phenotyping under heat-stressed environments.

• 1 Department of Plant Pathology, Throckmorton Plant Sciences Center, Kansas State University, Manhattan, KS, United States
• 2 Stakman-Borlaug Center for Sustainable Plant Health, University of Minnesota, St Paul, MN, United States
• 3 International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
• 4 Department of Plant Pathology, Wheat Genetics Resource Center, Throckmorton Plant Sciences Center, Kansas State University, Manhattan, KS, United States

A primary selection target for wheat ( Triticum aestivum ) improvement is grain yield. However, the selection for yield is limited by the extent of field trials, fluctuating environments, and the time needed to obtain multiyear assessments. Secondary traits such as spectral reflectance and canopy temperature (CT), which can be rapidly measured many times throughout the growing season, are frequently correlated with grain yield and could be used for indirect selection in large populations particularly in earlier generations in the breeding cycle prior to replicated yield testing. While proximal sensing data collection is increasingly implemented with high-throughput platforms that provide powerful and affordable information, efficient and effective use of these data is challenging. The objective of this study was to monitor wheat growth and predict grain yield in wheat breeding trials using high-density proximal sensing measurements under extreme terminal heat stress that is common in Bangladesh. Over five growing seasons, we analyzed normalized difference vegetation index (NDVI) and CT measurements collected in elite breeding lines from the International Maize and Wheat Improvement Center at the Regional Agricultural Research Station, Jamalpur, Bangladesh. We explored several variable reduction and regularization techniques followed by using the combined secondary traits to predict grain yield. Across years, grain yield heritability ranged from 0.30 to 0.72, with variable secondary trait heritability (0.0–0.6), while the correlation between grain yield and secondary traits ranged from −0.5 to 0.5. The prediction accuracy was calculated by a cross-fold validation approach as the correlation between observed and predicted grain yield using univariate and multivariate models. We found that the multivariate models resulted in higher prediction accuracies for grain yield than the univariate models. Stepwise regression performed equal to, or better than, other models in predicting grain yield. When incorporating all secondary traits into the models, we obtained high prediction accuracies (0.58–0.68) across the five growing seasons. Our results show that the optimized phenotypic prediction models can leverage secondary traits to deliver accurate predictions of wheat grain yield, allowing breeding programs to make more robust and rapid selections.

Introduction

Wheat accounts for 26% of world cereal production and 44% of total cereal consumption ( McGuire, 2015 ). Rapid economic and income growth, urbanization, and globalization are leading to dramatic dietary shifts, especially in Asia as consumers are increasing their consumption of wheat products ( Pingali, 2007 ). Wheat production needs to increase to meet the combined growing population and expanding demand by the middle of this century ( Tilman et al., 2011 ). Currently, wheat yield gains are estimated to be 0.9% per year, much less than the 1.5% per year, which is required to meet the projected 60% increase in global production needed by 2050 ( Reserach Program on Wheat, 2016 ). At the current rate, the global production of wheat may only increase by 38%, which is far short of the projected demand. Additionally, the effect of climate change, such as less favorable growing conditions, may even further reduce wheat production ( Gammans et al., 2017 ). Up to 6% yield declines are projected in wheat for each degree Celsius temperature increase if adaptive measures such as improved germplasm are not realized ( Zhao et al., 2017 ).

While wheat is globally distributed and faces a variety of biotic and abiotic challenges, in South Asia, heat is the most important stress and critical yield limitation. Terminal heat stress is also a common problem in temperate regions where 40% of the world's wheat is produced. In these areas, the temperature that ranges from 32 to 38°C can cause up to a 50% grain yield reduction ( Asseng et al., 2011 ). Heat stress is a regulated physiological process that can affect a range of plant phenotypes such as canopy temperature (CT) ( Ayeneh et al., 2002 ). Fundamental research has shown that this response is highly complex and differs at the tissue ( Thomason et al., 2018 ), species ( Kotak et al., 2007 ), and developmental stage ( Tricker et al., 2018 ), suggesting that heat tolerance is a physiologically and genetically complex trait.

Temperatures above the optimum level are deleterious and cause irreversible damage, with the duration and magnitude of temperature exposure determining the severity of yield loss. In controlled studies with supraoptimal temperatures, a 3–5% yield loss for every 1°C increase of mean temperature above 15°C has been observed ( Gibson and Paulsen, 1999 ). In addition to reducing grain yield, high temperatures can reduce individual grain mass by up to 23% ( Stone and Nicolas, 1994 ), further impairing grain yield and quality ( Teixeira et al., 2013 ). Many of the global wheat production areas already have supraoptimal temperature conditions, and global temperatures are predicted to further increase between 1.7 and 4.8°C by the end of the century ( Pachauri et al., 2014 ). Thus, increasing grain yield under heat stress is a major global objective, and more efficient breeding methods and technology are needed to increase the rate of genetic gain in heat-stressed environments.

The complexity of heat stress means that the breeding programs cannot use a single strategy to improve heat tolerance. Some plant adaption mechanisms to avoid and minimize heat stress include early flowering ( Ishimaru et al., 2010 ) and stomatal closure ( Liu et al., 2018 ). The difference in the expression of these traits provides an opportunity to improve wheat if this beneficial genetic variation can be accurately measured. Traditionally, before the discovery of DNA and molecular markers, plant breeders selected promising lines only on the basis of phenotype. By generating large numbers of crosses and evaluating successive generations in a wide range of environments, superior individuals could be identified. While great improvements have been made in this fashion, as the number of lines to evaluate increases, breeders are faced with the challenge of precisely phenotyping large populations within a short time to identify the best progeny.

With the advent of low-cost, high-throughput genotyping technologies, breeders have access to high-density genomic data ( Morrell et al., 2012 ). While molecular markers have aided in breeding objectives ( Bernardo, 2008 ), breeding programs continue to face a combined challenge of characterizing breeding lines precisely and rapidly ( McMullen et al., 2009 ; Araus and Cairns, 2014 ). Unraveling complex traits, such as heat stress, requires precise, and accurate phenotypic data to connect the phenotype to the genotypic data ( Cobb et al., 2013 ). Phenotyping is now considered the bottleneck of crop improvement, but it is crucial to fully realize the benefits of plant breeding ( Araus and Cairns, 2014 ).

Increasing grain yield, especially under extreme terminal heat stress, is a primary goal of the national breeding program in Bangladesh. While grain yield is the primary trait of interest, it can be estimated using remote or proximal sensing data ( Lillesand et al., 2014 ). Any trait that is correlated with the primary trait can be considered a secondary trait in selection and can potentially be used to reduce evaluation time and cost ( Rutkoski et al., 2016 ). If the secondary traits can be accurately phenotyped within the breeding program, these secondary traits can be used to predict the primary trait and to improve genetic gain particularly earlier in the breeding cycle before advancement to replicated yield trials. Two potential secondary traits that are amendable to high-throughput measurements include spectral reflectance and canopy temperature (CT) ( Pask et al., 2012 ).

Remote sensing of spectral reflectance is based on the ability to measure the electromagnetic reflectance of plants. The cells and tissues of plants have wavelength-specific absorbance and reflectance properties that make spectral reflectance a trait that can be rapidly and quantitatively measured ( Montesinos-López et al., 2017 ). Remote sensing has been widely used in agriculture with different vegetation indices providing a non-destructive, real-time measure of crop growth. The normalized difference vegetation index (NDVI) is one of the most commonly used vegetation indices based on the reflectance of red and near-infrared light. It can be used to characterize crop growth stages, evaluate crop density, and predict crop yield ( Rutkoski et al., 2016 ). In crops, such as maize, wheat, sorghum, and barley, scientists have identified significant correlations between biomass and NDVI with some correlation coefficients above 0.70 ( Chen et al., 2011 ). The values of NDVI, especially 2–3 weeks before and after heading, are highly correlated with grain yield in wheat ( Babar et al., 2006 ).

Another trait that can be used to evaluate crop status is CT. Crop CT is the surface temperature of the plant canopy and is related to the amount of transpiration that results in evaporative cooling. CT plays an important role in the observation of the crop-water relationship, which is a factor of crop yield, and CT has been shown to have the potential for selecting heat- and drought-tolerant genotypes in stressed environments ( Reynolds et al., 2009 ). Several important biological factors such as root length and biomass, stomatal conductance, number of stomata, metabolic activities, and photosynthate translocation result in variation in CT between different genotypes ( Reynolds et al., 2012 ). Mason et al. (2013) suggested that CT is a complex trait controlled by loci of small effect with most of the loci having pleiotropic effects on traits such as plant height (PH) and days to heading (DTHD). Even though the exact mechanism of CT difference is unresolved, research has shown that the correlation between CT and grain yield in wheat is generally negative under heat-stressed environments providing selection strategies to identify heat-tolerant lines ( Amani et al., 1996 ; Gutierrez et al., 2010 ; Mason and Singh, 2014 ).

While CT can be easily measured using handheld infrared radiometers ( Pask et al., 2012 ) and often has moderate heritability ( Lopes et al., 2012 ), the application of CT in breeding has been limited due to the inconsistent nature of the CT measurements. CT is impacted by a variety of environmental factors such as solar radiation intensity, atmospheric temperature, humidity, soil moisture, and wind speed, which can quickly change throughout the day ( Reynolds et al., 2012 ). The complexities of CT measurements suggest that it is important to determine how to effectively use CT to select better yielding lines in large wheat breeding programs under heat-stressed environments.

Both CT and NDVI can be measured multiple times throughout the growing season that gives a powerful approach to capture the temporal dynamics of the growing crop. Using just a single measurement to evaluate lines in a breeding program neglects the temporal dynamics of plant growth and development ( Crain et al., 2018 ). Incorporating a combination of multiple variables that show a strong correlation between secondary and primary traits can be used to develop precise inferences about crop phenotypes such as grain yield prediction using secondary traits ( Guo et al., 2014 ). While NDVI and CT have been advocated for plant selection, minimal work has been carried out on incorporating multiple measurements into selection decisions.

As precision phenotyping becomes more routine in breeding programs, new challenges include how to best utilize and translate these data into improved prediction models and selection strategies ( Tester and Langridge, 2010 ). The objective of our study was to evaluate how dense, temporal phenotypic measurements from the proximal sensing of NDVI and CT as well as other agronomic traits could be used within the national plant breeding programs of Bangladesh to assess line performance in heat-stressed environments. Additionally, an emphasis was placed on statistical modeling that could account for highly correlated measurements of secondary traits.

Materials and Methods

Experimental design and field management.

We evaluated different sets of 540 advanced lines from the International Maize and Wheat Improvement Center (CIMMYT) in each of the five growing seasons (i.e., 2015–16, 2016–17, 2017–18, 2018–19, and 2019–20) in Bangladesh. Each year, the sets of 540 lines from CIMMYT were evaluated as new heat-tolerant material became available, and additionally, there were seven different local checks including BARI Gom 26 or BARI Gom 30, which served as the benchmark check variety of Bangladesh. All lines were evaluated in the high heat-stressed environment at the Regional Agricultural Research Station (RARS), Bangladesh Agricultural Research Institute (BARI), Jamalpur, Bangladesh (N 24.93, E 89.93, 23 masl). The climate of this region is hot and humid leading to an overall heat-stressed environment, classified as ME5A according to the CIMMYT wheat mega-environment classification system ( Rajaram et al., 1993 ).

To manage spatial variability, the lines were placed in multiple trials each growing season. Each trial consisted of 60 entries including 53 breeding lines and 7 check varieties. Complete trials were planted within a given day each year with planting dates for each season of December 4–8, 2015; November 25–28, 2016; November 29–30, 2017; November 28, 2018; and December 05, 2019. The trials were arranged in an alpha lattice design with two replications for a total of 120 plots in each trial. Each replication was composed of 12 blocks with 5 entries randomly assigned to each block. The plots were composed of 6 rows of 4.17-m length and on 20-cm row spacing for a total experimental plot size of 5 m 2 . Plots were separated by a 40-cm alley. The 2015–16 season had a total of 10 trials. Subsequent years had a total of 11 trials, with the 11th trial representing the second-year testing of the highest performing lines from the previous season.

The recommended agronomic practices of the Bangladesh Wheat Research Center were followed during the growing season. Fertilizer application consisted of 100:26:50:20:5:1 kg/ha of N:P:K:S:Zn:B, respectively, each year. Irrigation was applied as needed to prevent water deficit. In the 2015–16 growing season, three irrigations were applied at tillering, heading, and grain filling, while from 2016–17 to 2019–20, two irrigations were applied at tillering and booting ( Zadoks et al., 1974 ). Manual weeding was completed every season to keep the plots weed-free. No pesticides were applied during the growing seasons.

Trait Measurement

We considered grain yield as the primary trait, CT and NDVI as sensor-based secondary traits, and all other traits as agronomic traits. The total grain yield of each of the plots was harvested, dried, weighed, and divided by the plot size (5 m 2 ) to get yield (kg/m 2 ) and then converted into metric tons per hectare. Throughout the growing season, phenotypic data were recorded for agronomic traits such as ground coverage (GrndCov), DTHD, days to maturity (DAYSMT), PH, grains per spike (GRNSPK), leaf blight disease due to Helminthosporium severity (HELSPSEV), number of spikes per unit area (SN), number of spikelets per spike (SPLN), spike length (SPKLNG), and thousand grain weight (TGW). GrndCov was a visual estimation of ground covered by the biomass of the crop beginning 30 days after sowing and continuing at 15-day intervals. DTHD was recorded as the number of days to when 50% of total plants in a plot had extended a spike from the leaf sheath. DAYSMT was recorded when 80% of the plants in a plot had peduncles that had turned from green to golden. Plant height was measured as the length from ground level to the apex of the spike excluding awns. The total number of grains from five spikes was counted and divided by five to get the number of GRNSPK. The HELSPSEV was scored according to the scale for appraising foliar intensity of wheat diseases ( Saari and Prescott, 1975 ). The number of total heads per square meter (i.e., SN) was assessed by measuring the number of spikes counted from a 3.5-m-long 20-cm spacing (0.7 m 2 ) and converted into the number of spikes per square meter. SPKLNG was measured on a representative spike within the plot as the length from the base to the tip of a spike excluding awns.

Secondary traits of CT and NDVI data were collected from 8 to 15 times during the growing seasons (8, 14, 12, 13, and 15 time points for the 2016–17, 2017–18, 2018–19, and 2019–20 seasons, respectively). The measurements represented plant growth from tillering through senescence ( Zadoks et al., 1974 ) with measurements taken from 11 a.m. to 2 p.m. corresponding to solar noon on each day of observation. CT was measured using a handheld infrared thermometer (IRT) (Apogee, Logan, UT, USA), which provided a high accuracy, non-contact surface temperature measurement from −30 to 65°C with a precision of ±0.124°C. The IRT readings were taken at a 30° angle from the horizon for measurement and 70 cm above the crop canopy ( Pask et al., 2012 ). The IRT functions at 0.6 hertz, but only the average CT was recorded for each measurement. NDVI was collected using a GreenSeeker handheld sensor (Trimble Inc. Sunnyvale, CA, USA). The GreenSeeker was used by passing the sensor 75 cm over the crop canopy. Two-person teams were employed for CT and NDVI collection, with one person operating the instrument and the other person recording the data. It took ~3 h with two teams (i.e., four people) to measure CT and NDVI of all plots. The data were recorded in the Field Book program ( Rife and Poland, 2014 ).

Data Analysis

All analyses were completed in R software ( Team, 2017 ) by using packages including lme4 ( Bates et al., 2015 ), leaps ( Lumley, 2017 ), tidyverse ( Wickham et al., 2019 ), glmnet ( Friedman et al., 2010 ), plyr ( Wickham, 2011 ), ggplot2 ( Wickham, 2016 ), caret ( Williams et al., 2018 ), PerformanceAnalytics ( Peterson et al., 2014 ), and readr ( Wickham et al., 2017 ).

Statistical Analysis

A mixed model to account for the trial design was used to obtain the best linear unbiased estimators (BLUEs) for each genotype using the following model fit separately for each trial:

where y ij is the observed phenotypic response variable (GRYLD, CT, …, NDVI) for the i th genotype, j th replicate; μ is the overall mean of the individual trial; g i is the fixed effect of i th genotype (line) with i taking the values 1–60; r j is the random effect of j th replicate with j corresponding to 1 or 2 with a normal distribution N (0, σ r 2 ); b n is the random effect of n th block, nested within replicate j , where n ranges from 1 to 12 distributed as N (0, σ n 2 ); and e ij is the residual effect for genotype i in replicate j with a normal distribution N (0, σ e 2 ). BLUEs were calculated for each site year individually.

To estimate heritability for each trial, a random term for genotype was used in equation (1), resulting in variance components used to calculate broad-sense heritability. The heritability was estimated using the following formula ( Holland et al., 2003 ):

where σ g 2 is genotypic variance, σ e 2 is residual model variance, and r is the number of replications, which is two. The heritability estimates were calculated for all agronomic traits during the growing season and for each of the time points of NDVI and CT observations. In addition to calculating heritability on a trial basis, we estimated BLUEs and variance components across the full experiment each year for each trait using the following model:

where y ijk is the phenotype of the trait of interest for i th genotype, j th replicate, and k th trial; μ is the overall mean of the population; t k is the random effect of the trial with k taking values 1–11 with a normal distribution N (0, σ k 2 ); g i is the random effect of i th genotype (line) nested within trial with i taking the values 1–60 with a normal distribution N (0, σ i 2 ); r j is the random effect of j th replicate nested within trial with j corresponding to 1 or 2 with a normal distribution N (0, σ j 2 ); b l is the random effect of n th block, nested within trial i and replicate j , with n from 1 to 12 distributed as N (0, σ n 2 ); and ε ijk is the residual effect for the i th genotype j th replicate in the k th trial with normal distribution N (0, σ e 2 ).

Statistical Models for Grain Yield Prediction

Using the BLUEs for each trait, four different statistical models were used to predict grain yield using multiple measurements of NDVI, CT, and agronomic traits. The models included stepwise regression and three shrinkage regression models of ridge regression, least absolute shrinkage and selection operator (LASSO) regression, and ElasticNet regression ( Hastie et al., 2001 ). In all models, we used all the secondary traits and agronomic traits collected from the field to predict grain yield. The stepwise regression performed forward selection followed by the backward elimination ( Friedman et al., 2010 , pp. 58–60). The shrinkage models function by shrinking the estimated effects toward zero. These models add a penalty that allows variables to have a coefficient close to or equal to zero. The tuning parameter lambda thus determines the amount of shrinkage. The LASSO regression model performs L1 regularization (i.e., the absolute value of the residual error term), and it can select variables by eliminating variables with a coefficient of zero ( Hastie et al., 2001 , p. 68). The ridge regression performs L2 regularization (i.e., the squared value of residual error term), and the coefficients cannot be zero, thus retaining all variables in the model ( Friedman et al., 2010 , pp. 61–68). The penalty for the ElasticNet regression is a combination of ridge and LASSO regression, allowing for both variable shrinkage and feature selection ( Hastie et al., 2001 , pp. 72–73; James et al., 2013 ). The models were built in an iterative process; for each year, we evaluated models with NDVI only, CT only, and all secondary and agronomic traits together.

For each model, a cross-validation approach was evaluated to determine the predictive ability for yield using the trial structure of the CIMMYT trials. As related lines (e.g., full sibs) are evaluated in the same trial, this approach prevents highly related, full- or half-sibling lines, from predicting their own performance. In the cross-validation scheme, all entries from 10 (9 in 2015–16 and 2018–19 seasons) trials were used to fit the model, and the prediction was completed on the 11th (10th in 2015–16 and 2018–19 seasons) trial. This process was repeated by dropping a single trial fitting the model and predicting the left-out trial until all entries had been predicted. The reported prediction accuracy was assessed as the correlation between the predicted value and the BLUEs for grain yield.

Data Availability Statement

All phenotypic data and code for analysis have been placed in the Dryad Digital Repository available at: https://doi.org/10.5061/dryad.vdncjsxrz .

Over five seasons where we evaluated ~2,700 lines along with a local check variety for grain yield, which ranged from a low of 2.4 to a high of 3.5 ton ha −1 . Overall, the 2020 field season had the highest average yield whereas 2016 was the lowest yield ( Supplementary Figure S1 ). In general, these yields are lower than experienced in most global areas where the mean global wheat yield is estimated to be 3.4 ton ha −1 ( Ritchie and Roser, 2013 ). This is likely due to the high heat stress found in the Bangladesh environments. To identify new candidate varieties for farmers, we evaluated the CIMMYT germplasm compared to the local check varieties. Within the CIMMYT germplasm, each year there were lines that exceeded the local check, with some lines being highly superior. For each season of the 540 lines evaluated, 24% to 56% of the lines were higher yielding than the check varieties ( Supplementary Figure S2 ). Based on these tests and observations, there are opportunities to improve wheat yield in Bangladesh and heat-stressed areas.

Broad-Sense Heritability

We observed moderate-to-high broad-sense heritability (repeatability) for grain yield and other agronomic traits, across the five seasons from 2015–16 to 2019–20 when considering the entire experiment (all trials together) ( Table 1 ) and also on an individual trial basis ( Supplementary Tables S1–S5 ). For the agronomic traits such as DTHD, DAYSMT, and PH, we observed a consistent and high heritability. The highest heritability was recorded from DTHD ( H 2 = 0.97; followed by DAYSMT, H 2 = 0.90) across the trials and growing seasons.

Table 1 . Broad-sense heritability of agronomic traits and correlation between agronomic traits and grain yield (GRYLD) for five growing seasons from 2015–16 to 2019–20 for wheat grown in Bangladesh.

For secondary trait measurements, the sensor-based NDVI and CT had heritability ranging from low to high (i.e., from 0 to 0.74). The CT showed a narrower range of heritability compared to that of the heritability of NDVI ( Figure 1 ), but the heritability of CT was almost always lower than that of NDVI. The highest value of heritability was calculated as 0.56 for CT and that for NDVI was 0.74. We observed that the values of heritability for both NDVI and CT were higher at the grain filling stage (i.e., mid-February–mid-March, indicated as two vertical lines on Figures 1 , 2 ) than the early growth stages.

Figure 1 . Broad-sense heritability of the normalized difference vegetation index (NDVI) and canopy temperature (CT) for days after sowing in five growing seasons from 2015–16 to 2019–20. The horizontal dotted lines represent the heritability of grain yield. The vertical dashed lines indicate average days to heading, and the dotted lines represent the average days to physiological maturity.

Figure 2 . Correlation between grain yield and sensor-based secondary traits of NDVI and CT for observations on days after sowing in five wheat growing seasons from 2015–16 to 2019–20. The horizontal dotted lines represent the correlation value of 0. The vertical dashed lines indicate average days to heading, and the vertical dotted lines represent the average days to physiological maturity.

Correlations Between the Measured Traits

The phenotypic correlations were calculated for all measured agronomic traits, considering all trials together to determine the relationship between them and GRYLD ( Table 1 ). We also calculated the correlations between yield and other agronomic traits for individual trials ( Supplementary Tables S6–S10 ). DTHD showed a moderate but negative correlation with grain yield in all the seasons. DAYSMT also showed a negative correlation in three of the five growing seasons. The highest correlation was observed between TGW and GRYLD ( r = 0.49) followed by GRYLD and SN ( r = 0.41) in the 2017–18 season. The most consistent correlation of grain yield was observed for PH and TGW across the growing seasons.

The correlation between the measured CTs at individual time points and GRYLD ranged widely with a trend of being strongly negative at the start of the season to a positive correlation at the final measurement ( Figure 2 ). The strongest correlations were recorded from the CT measurement taken during the grain filling stage (i.e., mid-February–mid-March, indicated as two vertical lines on Figures 1 , 2 ). The correlation between CT and GRYLD was more consistent in the 2017–18 season and had the least consistency in the 2015–16 season.

Generally, NDVI tended to show positive correlations with GRYLD at early to middle growth stages ( Figure 2 ). Out of a total of 63 individual days of NDVI measurement at five growing seasons, 58 days showed a significant correlation with GRYLD. The positive correlation, however, changed at the later crop growth stages of all the seasons, where the correlations between NDVI and GRYLD were negative and the correlations between CT and GRYLD were positive.

There were strong correlations between multiple days of secondary trait measurements across seasons, and it was common for the correlation between different time points of NDVI to have correlations of 0.3. Relationships between different CT time points were often not as highly correlated as NDVI.

Yield Prediction Using Univariate Model

Yield predictions were developed by implementing a prediction model tested for accuracy with a cross-fold validation strategy. Overall, using a single secondary or agronomic trait, the results were inconsistent with the prediction accuracies ranging from 0 to 0.59. The prediction accuracy of individual secondary traits varied greatly depending on the trait and the time of measurement ( Figure 3 ), with traits measured around grain filling providing the highest values, while traits early or late in the growing season had inconsistent values.

Figure 3 . Correlation between predicted grain yield and observed grain yield (prediction accuracy) for five wheat growing seasons in Bangladesh from 2015–16 to 2019–20. Each prediction has been made by using a univariate model with one variable of the phenotypic data.

Yield Prediction Using Multivariate Models

Using four different multivariate models, the accuracy of grain yield prediction was estimated by using a cross-validation strategy where the accuracy was the correlation of the predicted value and the genotypic BLUE. The yield prediction accuracy of the models varied widely from 0.17 to 0.68 ( Table 2 ). When using all traits as predictor variables, it was apparent that the stepwise regression performed similar to shrinkage models, but the proportion of variance explained by the model was always substantially higher than other models. The stepwise regression was consistently the best among the models deployed with LASSO regression, ridge regression, and ElasticNet regression performing similarly.

Table 2 . Yield prediction accuracies for five wheat growing seasons from 2016 to 2020 in Jamalpur, Bangladesh, using four different multivariate models.

Difference in the Sensor-Based Secondary Trait Selection

Grain yield prediction models were developed iteratively with two distinct secondary traits, namely, NDVI and CT, and other agronomic traits with prediction accuracy in the range of 0.17–0.45 for using CT only ( Table 2 ). Using NDVI, the prediction accuracy was usually higher than using CT alone ranging from 0.32 to 0.58. When we incorporated both NDVI and CT into the model, the prediction accuracy further increased ranging from 0.37 to 0.58. Incorporating all traits together resulted in the highest overall prediction accuracies ranging from 0.43 to 0.68 across the experiment years.

Phenotypic Evaluation

The national priorities for wheat breeding programs in Bangladesh are focused on improving heat tolerance to develop early maturing varieties with improved yield and superior grain quality. Such breeding efforts necessitate selecting promising lines from large breeding trials. Precise phenotyping is the most important prerequisite to decide which individuals should be selected. The observed heritability for the evaluated physiological high-throughput traits of NDVI and CT was consistent with the previous literature ( Reynolds et al., 1994 ). Most of the CT showed negative correlation, while most days of NDVI observations showed positive correlation and as such should be the useful parameters for selection of superior breeding lines ( Babar et al., 2007 ; Crain et al., 2017 ). Overall, the sensor-based traits had higher correlations than other agronomic traits and in the context of breeding are amendable to much higher throughput and rapid measurements. However, we also noted that caution should be taken during CT and NDVI data collection as weed population and irrigation management timing could influence the data. Higher weed population could increase NDVI values, and the higher transpiration after irrigation could increase CT expression. Such breeding trial management should be taken into consideration when using these proximal sensing measurements and developing prediction models and selection criteria.

Modeling Yield Prediction

We evaluated how measured traits could be used to predict grain yield through a variety of statistical models. We used a univariate model to predict grain yield using the phenotypic data as we intended to compare the univariate model to more complex multivariate prediction models. We observed that the univariate models had lower prediction accuracies than any of the multivariate models tested in this study. Using a cross-fold validation, the multivariate stepwise model performed well, with the addition of more variables increasing the power of yield prediction. We found that the stepwise regression was the best among the four multivariate models deployed in predicting grain yield using secondary traits in wheat. The stepwise regression model worked as forward selection and backward elimination processes and finally provides the number of variables that should be included in the regression model. We found that the stepwise regression model excluded some of the secondary traits as they had multicollinearity and were excluded from the model ( Supplementary Table S11 ).

Application to National Breeding Programs

In a developing country like Bangladesh, genotyping facilities are not yet available. However, field-based phenotyping protocols are available, and these approaches can be implemented across national programs. Hence, within Bangladesh, the phenotypic modeling is directly applicable for the implementation in applied breeding programs for yield prediction and more tractable than selection based only on genomic profiling. Our study supports that large amounts of phenotypic data can be collected with low-cost phenotyping tools.

While the ability to incorporate high-throughput phenotyping (HTP) data in breeding programs is anticipated to increase genetic gains ( Haghighattalab et al., 2016 ; Crain et al., 2018 ; Krause et al., 2019 ; Singh et al., 2019 ; Wang et al., 2020 ), many of these studies relied on large amounts of resources for both phenotyping and computing. For example, Wang et al. (2019a) used unmanned aerial vehicles to collect HTP imagery. These images were then computationally stitched together followed by the trait extraction using high-performance computers. In the studies by Crain et al. (2018) , Rutkoski et al. (2016) , and Volpato et al. (2021) , expensive phenotyping equipment [i.e., global positioning system (GPS) or multispectral scanners] was used to evaluate plants. To our knowledge, this is the first study that was conducted with low-cost tools and analysis that could be completed with a personal computer (i.e., resources available to many national breeding programs). These methods should be approachable for any breeding program, enabling the data of secondary traits to predict the primary trait of interest and increase selection accuracy. As HTP data collection improves, we anticipate that unmanned aerial vehicle imagery may be able to replace the phenotyping employed in this study. While current results are promising ( Krause et al., 2019 ; Wang et al., 2020 ), the resources such as skilled technicians, hardware, and software are not at a level that is currently practical in many national breeding programs. While we envision the resources becoming more affordable and user-friendly in the future, the methods we utilized are immediately applicable and eliminate the need to have entire phenotyping research teams that are often suggested for HTP.

In these breeding trials, we evaluated a large diversity of elite breeding germplasm that showed much promise in identifying superior performing candidate varieties for Bangladesh. Overall, there was a high proportion (24–57%) of the evaluated lines that outperformed the local check varieties such as BARI Gom 26 and BARI Gom 30 ( Supplementary Figure S2 ). In addition, the average yield of selected entries (i.e., top 10% of evaluated lines) each year was ≥1 ton above the yield of the benchmark local checks ( Supplementary Table S12 ). These observations and favorable selection results support the upward prospects of continued selection of heat-tolerant breeding materials and the development of new, superior candidate varieties for the supraoptimal temperatures found in Bangladesh. The combined use of more rapid selections with the proposed phenotyping tools and selection methods can further accelerate the identification of these superior candidate varieties.

Our goal was to improve the wheat yield prediction by using secondary traits and statistical models that could accommodate highly correlated variables ( Supplementary Table S11 ). While we investigated models with secondary and agronomic data, the sensor-based data of NDVI and CT can be measured easier than agronomic traits that can require more time and often cannot be measured until the end of the season. Supporting the value of these physiological sensor measurements in breeding, the yield prediction with only the sensor-based data showed prediction power almost as high as the prediction using all traits together. These sensor-based traits are easy to measure repeatedly during the season. This allows breeders to use the sensor-based traits to predict grain yield with flexibility depending on the available equipment and to implement yield prediction on small observation plots. If facilities are limited, NDVI could be used instead of CT for yield prediction. Regardless of the exact type of the sensor-based measurement, breeders will have the ability to increase prediction power by incorporating secondary traits. Breeders can use secondary trait measurements, which are obtained during the growing season, to increase selection accuracies prior to harvesting the plots and ensure that the high-yielding plots are harvested. This is of particular interest if these secondary traits can be measured on smaller plots at earlier generations in the breeding cycle enabling more intense selection prior to lines entering into replicated yield testing ( Krause et al., 2020 ).

Overall, we found that the proximal sensing of NDVI and CT data was valuable in developing prediction models for yield. When multiple measurements were obtained throughout the growing season, the multivariate prediction models were much more accurate than the models using a single time measurement. Grain yield prediction was also improved by the incorporation of agronomic traits such as DTHD, DAYSMT, and tiller numbers. While less tractable to measure the full suite of agronomic traits (e.g., spikelet number), the incorporation of the routine agronomic measurements into prediction models can be useful for predictions in the breeding program. If future high-throughput technology allows simple image-based measurement of the agronomic traits ( Wang et al., 2019a , b ), these traits could be measured on large populations and incorporated into prediction models.

This study demonstrated that high prediction accuracy for grain yield can be obtained using the full combination of proximal sensing and agronomic traits with multivariate models. These traits can be measured on small (e.g., <1 m 2 ) plots that are used for early generations in the breeding program. Using these same prediction models, it could be possible to generate accurate predictions of grain yield at this stage, where current labor and time constraints prevent harvest assessment. Additionally, using new HTP platforms and unmanned aerial vehicles that can capture NDVI and CT, these measurements can potentially be expanded to tens of thousands of plots. By making predictions and more accurate selections much earlier in the breeding cycle, there is considerable potential to increase genetic gain, particularly for difficult and complex selection targets such as grain yield under heat stress.

The datasets presented in this study can be found in Dryad Digital Repository at https://doi.org/10.5061/dryad.vdncjsxrz .

Author Contributions

JP, RS, and MR conceived and designed the study. MR collected and analyzed data. JC analyzed data. AH contributed methods and analysis. RS contributed germplasm. MR, JC, and JP wrote the manuscript. All authors edited and approved the manuscript.

This study was based on the support provided by Feed the Future through the U.S. Agency for International Development, under the terms of Contract No. AID-OAA-A-13-00051, by the National Science Foundation under Grant No. 1238187 and 1543958 and the NIFA International Wheat Yield Partnership Grant No. 2017-67007-25933/project accession No. 1011391. MR was supported through the Borlaug Higher Education for Agricultural Research and Development (BHEARD) program.

Author Disclaimer

Any opinions, findings, and conclusions or recommendations expressed in this study are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, the U.S. Agency for International Development, or the U.S. Department of Agriculture.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

We thank the Bill & Melinda Gates Foundation (BMGF) and the Foreign, Commonwealth and Development Office (FCDO) of the UK (formerly UK aid from the UK Government's Department for International Development, DfID) for supporting the wheat breeding activities of CIMMYT through the Delivering Genetic Gains in Wheat (DGGW) Project (OPPGD1389) managed by the Cornell University and the Accelerating Genetic Gains in Maize and Wheat (AGG) Project (INV-003439), and the CGIAR Research Program-WHEAT funders. The support of the BARI field staff was instrumental in completing this research.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2021.633651/full#supplementary-material

Abbreviations

BLUE, best linear unbiased estimator; NDVI, normalized difference vegetation index; CT, canopy temperature; DTHD, days to heading; DAYSMT, days to maturity; GRNSPK, grains per spike; GRYLD, grain yield; HELSPSEV, Helminthosporium severity; PH, plant height; SN, number of spikes per square meter; SPKLNG, spike length; SPLN, number of spikelets per spike.

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Keywords: canopy temperature, grain yield prediction, heat-stress, high-throughput phenotyping, normalized difference vegetation index, wheat

Citation: Rahman MM, Crain J, Haghighattalab A, Singh RP and Poland J (2021) Improving Wheat Yield Prediction Using Secondary Traits and High-Density Phenotyping Under Heat-Stressed Environments. Front. Plant Sci. 12:633651. doi: 10.3389/fpls.2021.633651

Received: 25 November 2020; Accepted: 19 August 2021; Published: 27 September 2021.

Reviewed by:

Copyright © 2021 Rahman, Crain, Haghighattalab, Singh and Poland. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jesse Poland, jpoland@ksu.edu

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Measuring the Effects of Climate Change on Wheat Production: Evidence from Northern China

Associated data.

The data will be available on request.

The current study examines the long-run effects of climatic factors on wheat production in China’s top three wheat-producing provinces (Hebei, Henan, and Shandong). The data set consists of observations from 1992 to 2020 on which several techniques, namely, fully modified OLS (FMOLS), dynamic OLS (DOLS), and canonical co-integrating regression (CCR) estimators, and Granger causality, are applied. The results reveal that climatic factors, such as temperature and rainfall, negatively influenced wheat production in Henan Province. This means that Henan Province is more vulnerable to climate change. In contrast, it is observed that climatic conditions (via temperature and rainfall) positively contributed to wheat production in Hebei Province. Moreover, temperature negatively influenced wheat production in Shandong Province, while rainfall contributed positively to wheat production. Further, the results of Granger causality reveal that climatic factors and other determinants significantly influenced wheat production in the selected provinces.

1. Introduction

China cultivates only eight percent of the world’s arable land to feed eighteen percent of the global population. It is expected that China’s population will peak around 2030 [ 1 ]; hence, the food security problem in China has always been a concern. In the “Outline of the 14th Five-Year Plan for National Economic and Social Development of the People’s Republic of China and the Long-term Goals in 2035,” the Chinese government, for the first time, incorporated the food security strategy into the planning system and set the goal of ensuring that grain output will remain stable at over 650 million tons over the 14th Five-Year Plan period. Climate has a strong influence on food production. Climate change and more frequent bad weather events around the world exacerbate the insecurity of China’s grain production [ 2 , 3 ]. As a result, understanding the impact of climate change on China’s grain crop productivity and devising countermeasures to implement China’s food security strategy are critical.

The impact of climate change on grain crop production has both advantages and disadvantages. Nevertheless, the disadvantages outweigh the advantages overall, and different climate variables have different impacts on different crops and regions [ 4 , 5 , 6 ]. In recent years, the increased heat caused by climate change has been conducive to expanding the grain sown area and producing more grain [ 7 ]. Increasing rainfall and CO 2 concentrations are beneficial for crop production to some extent, but high temperatures may negate this effect in some areas [ 8 , 9 ]. Similarly, climate change had a negative impact on grain production by expanding pest and disease occurrence areas, shortening crop growth cycles, and increasing the frequency of extreme weather events [ 10 , 11 ].

The global climatic variations are a sensitive topic being discussed in China. According to the National Meteorological Administration, China’s temperature has increased by 0.3 °C every 10 years (higher than the global average during the same period), and its annual precipitation has increased by 5.1 mm every 10 years between 1961 and 2020 [ 12 ]. Climate change is causing a “double increase in water and heat.” By evaluating the expected impact of global warming on the yields of China’s main crops (wheat, rice, and corn), it was discovered that the crop yield effect emanating from climate change is primarily due to an increase in air temperature [ 13 ]. Grain production has been impacted by significant deviations in China’s agricultural climate resources. The regional space of grain production is also changing, with the emphasis shifting to the main production areas in the north [ 14 ]. Researchers incorporate technological progress factors into the research system of food production under climate change [ 15 , 16 , 17 , 18 ]. Food security is a technical issue in terms of production mode, and the level of technology determines the ability to ensure food security. As a result, technological advancement can be used as an effective tool for grain production to cope with climate change [ 19 , 20 ].

Many studies have found that technological progress significantly impacts grain production, which is primarily reflected in the two factors listed below. First, technological advancement has improved the crops’ ability to withstand disasters. The advancement of bio-pesticides due to technological advancement has increased the agricultural departments’ ability to prevent and control major agricultural pests and diseases while causing less environmental impact [ 21 , 22 ]. Simultaneously, the monitoring technology system of major sudden agricultural meteorological disasters constructed by the agricultural sector is also conducive to the agricultural sector’s better response to extreme weather events [ 23 ]. Second, technological advancement is a driving force in changing the mode of production. Modern agricultural mechanization can promote rapid agricultural production growth while producing good environmental results [ 24 , 25 ]. Fertilizer use can improve crop yield, but excessive use reduces crop yield [ 26 ]. Improved seed varieties, fertilizers, pesticides, technical equipment, and infrastructure are used as proxies for technological progress in agricultural production [ 18 , 27 , 28 , 29 ]. Hence, this study also considers fertilizer use a proxy for technological advancement, as it is a crucial factor in crop production.

Wheat is China’s second most important food crop, after rice, and its planting area accounts for 22~30% of the total cultivated land [ 30 ]. Winter wheat is the predominant crop in China, accounting for more than 90 percent of the total output [ 31 ]. The regions of China that produce winter wheat can be divided into Southern and Northern cultivation areas. The northern winter wheat producing region is the most concentrated wheat growing and consuming region in China, and its sown area and wheat output account for around two-thirds of the country. In recent years, the top three provinces in northern China for winter wheat production were Henan, Shandong, and Hebei. Henan Province has an alternating temperate monsoon and subtropical climate, whereas Shandong and Hebei Provinces have a temperate monsoon climate. Figure 1 and Figure 2 show their geographical location, wheat yield, temperature, and rainfall.

The distribution of wheat production (10,000 tons) in Hebei, Henan, and Shandong Provinces.

The distribution of temperature (°C) and rainfall (mm) in Hebei, Henan, and Shandong Provinces.

Previous studies in various parts of the world have extensively documented the impact of changing climate on wheat crop yield. Most existing research on China discussed the relationship between the two at the national and regional levels [ 31 , 32 , 33 ] or only focused on a specific province, such as Henan Province, which has the highest wheat yield [ 30 , 34 ]. However, climate change causes food production variability in regions with varying climate resources. The current paper assesses the long-term effects of changing climate on wheat production in the top three provinces in northern China to further analyze the heterogeneous influence of changing climate on wheat production, propose targeted measures to deal with climate change for the main grain-producing areas in China, and contribute to China’s food security strategy. Figure 3 shows the dynamic nexus between climatic factors, other determinants, and wheat production.

The study’s conceptual framework.

2. Literature Review

Wheat may be one of the crops most susceptible to the effects of climate change, but its substantial impact on crop production is profound. Numerous research works have investigated the impacts of climate change on wheat development and harvest in major wheat-producing regions in Asia, Europe, and northern Africa. However, it is important to note that varied temperature conditions and precipitation patterns affect wheat growth and yield differently in different regions.

Several researchers, for example, Zhai et al. [ 18 ], Abbas [ 27 ], Gul et al. [ 28 ], Ali et al. [ 35 ], and Warsame et al. [ 36 ], have explored the short-term and long-term climate change effects on food crop production by applying the autoregressive distributed lag (ARDL) methodology and reported mix outcomes related to climate variables. While, a study by You et al. [ 37 ] revealed that climate warming lowered wheat yield growth, a 1 °C rise in wheat growing temperature reduced wheat production by 3–10% in China. However, the findings of Zhai et al. [ 18 ] from 1970 to 2014 in China evaluated that temperature did not significantly influence the amount of wheat produced per unit of land in the short run and long run, while farm mechanization and fertilizer usage increased wheat output in the long run.

More recently, the research by Gul et al. [ 5 ] from 1985 to 2016 in Pakistan established the long-term link between climate variables and main food crop production. The findings showed that temperature negatively affects key food crop production, while rainfall improves food production. Similarly, the study of Chandio et al. [ 38 ] from 1977 to 2014 in Pakistan revealed that climate change and CO 2 have a detrimental short- and long-term influence on grain productivity, reducing cereal production and causing food security issues in the country.

Another similar study by Warsame et al. [ 39 ] for the period of 1980–2017 in Somalia examined the effects of climate change along with political instability on the productivity of sorghum by using various estimation techniques (i.e., FMOLS, DOLS, and CCR). The findings revealed that political instability and temperature significantly declined the productivity of sorghum, while rainfall and cultivated area enhanced the production in the long term. The long-term findings are also verified by the CCR approach. In the case of India, Bhardwaj et al. [ 40 ] reported that climate variables negatively contributed to wheat and paddy production; moreover, excessive rainfall had a detrimental influence on wheat and rice yields.

In the case of Ghana, Ntiamoah et al. [ 41 ] used a novel dynamic simulated autoregressive distributed lag (ARDL) model to examine the impact of CO 2 emissions, rainfall, credit supply, and fertilizer on the productivity of maize and soybean, covering the period from 1990 to 2020. The findings revealed that CO 2 emissions, as well as rainfall, have a significant and positive impact on crop production, while the supply of credit and fertilizer negatively influenced maize production. In the context of Asia, Ozdemir [ 42 ] studied the effects of climate change on agricultural output by using various estimation techniques (i.e., PMG and CCEMG). The outcomes showed that temperature and CO 2 affected agricultural output negatively and significantly in the long term, while precipitation improved productivity. In addition, other factors, such as power consumption for agricultural machinery and fertilizer, significantly enhanced agricultural output in the same period.

According to research by Akhtar and Masud [ 4 ] on the influence of climatic factors on rice and cereal production from the period 1985–2016, it was found that temperature and energy usage severely influence rice and vegetable output, although their effect on cereal productivity is minor. However, CO 2 emissions negatively influenced coffee production, and the temperature, energy use, and fossil fuel usage induced climate change, which had a negative impact on Malaysian agriculture. The empirical study of Kumar et al. [ 43 ] in selected lower-middle-income nations from 1971 to 2016 assessed the climate change–cereal crops production association. The authors’ findings revealed that rising temperatures diminish crop productivity. Rainfall and CO 2 emissions boosted crop yields, and a bidirectional causation between grain output, temperature, and CO 2 emissions was discovered. Rainfall and temperature affect grain production uni-directionally and might threaten the food security of the rural populations.

In particular, a study in China by Pickson et al. [ 44 ] from 1998 to 2017 examined the effects of climate change on rice cultivation. The findings showed that the climate variable, such as the temperature, adversely influences rice cultivation, while average rainfall influences the rice output but is insignificant. The farmed area positively affected short-term crop output. At the same time, fertilizer use had little effect and bidirectional causation between rice production and the cultivated area. Similarly, the investigation of Pickson et al. [ 6 ] in China over the period 1990Q1–2013Q4 found that the average temperature and its variability associated with cereal production were negative but significant in the long run. Additionally, rainfall variability and cereal production linkage showed no significant effect in the long run, but two variables (CO 2 and temperature variability) had a negatively significant association in the short run.

Likewise, Pickson et al. [ 45 ] investigated the impact of global warming on the main food crops (i.e., rice and maize) production in the case of China over the periods 1978Q1–2015Q4. The outcomes revealed a significantly positive trend in average temperature and seasonal temperature increases during the spring, summer, and fall with the insignificant change in the monthly, seasonal, and annual precipitation. The impact of temperature decreases maize and rice production at higher quantiles.

Due to the diverse time scales, geographic locations, and techniques, the following research work has not yet formed consistent findings on climate change’s influence on wheat growth and yield. These research works failed to combine the climatic conditions and agricultural progress elements into crop yield–climate functions to investigate their influence, and the long-run impacts on wheat must be studied. In this work, we employed the FMOLS method to assess the long-run climate variations’ influence on wheat yield in the northern region of China. The findings of the FMOLS approach are verified by the DOLS and CCR estimators.

3. Materials and Methods

The present study intends to investigate the long-run impact of temperature, rainfall, fertilizer usage, power usage, farming area, and labor on wheat production in the context of top 3 wheat-producing provinces of northern China. Wheat production is used as the dependent variable, and it is measured in 10,000 tons, while climatic factors, namely, temperature measured in degrees Celsius, rainfall measured in millimeters, fertilizer usage measured in 10,000 tons, power consumption measured in 1000 kWh, the cultivated area measured in 1000 hectares, and labor measured in 10,000 people, are used as the independent variables. The data were extracted from the China Rural Statistical Yearbook ( https://data.cnki.net/Yearbook/Navi?type=type&code=A# (accessed on 1 June 2022)) and the National Weather Science Data Center ( http://data.cma.cn/ (accessed on 1 June 2022)).

3.2. Econometric Modeling

This study examines the long-run impact and the causal relationship between temperature, rainfall, fertilizer usage, power consumption, cultivated area, labor, and wheat production in the selected provinces of China. Several investigation tests were carried out to achieve the research objective, including the co-integration test, FMOLS, DOLS, and CCR estimators, and the Granger causality test. The basic model is constructed as shown below:

The logarithmic arrangement of Equation (1) can be developed as follows:

where WP indicates wheat production, TEMP denotes average annual temperature, RF represents average annual rainfall, FER indicates fertilizer usage, PC shows the power consumption, WA stands for wheat cultivated area, LF indicates rural labor force.

3.3. FMOLS Long-Run Estimator

This paper uses the fully modified OLS (FMOLS) proposed by Phillips and Hansen [ 46 ] to estimate the co-integration coefficient. Based on the OLS, this method uses the semi-parametric two-stage estimation method to correct the equalization error and the explained variable, which can effectively eliminate the endogeneity caused by the co-integration relationship and the sequence correlation of error terms, thus obtaining the consistent estimator of co-integration parameter estimator and the asymptotic normal distribution of FMOLS estimator. Suppose the model is

Let μ t ′ = μ 1 t ′ + μ 2 t ′ . First, perform OLS estimation on Equation (3) to obtain the θ ^ and μ t ^ of OLS estimators. Second, estimate the long-term variance of μ t . Let Ω and ∆ represent long-term variance and one-sided long-term variance, respectively, and the estimates are shown in Formulae (5) and (6).

By adjusting the endogeneity, we can obtain Equation (7).

By adjusting the sequence correlation, we can obtain Equation (8).

The final FMOLS estimator is obtained as shown in Equation (9).

Furthermore, we use the dynamic OLS (DOLS) proposed by Stock and Watson [ 47 ] and the canonical co-integrating regressions (CCR) proposed by Park [ 48 ] to verify the robustness of FMOLS estimation results. The DOLS estimation model contains the lag term of explanatory variables, and the standard deviation of its estimator has a normal asymptotic distribution, which is also better than the OLS estimation [ 40 ]. The idea of the CCR model is the same as the FMOLS model, but the difference is that it transforms the data stationarity to obtain the least-square estimation and then eliminates the dependency between the co-integration equation and the random correction equation of the explanatory variables.

4. Results and Discussion

We use the FMOLS, DOLS, and CCR estimators to examine the long-term influence of temperature, rainfall, fertilizer use, power consumption, wheat farming area, and labor on wheat production in the selected three provinces of China. Table 1 reports the statistical summary of the dependent and independent variables for the Hebei, Henan, and Shandong Provinces. The J-B test confirms that all the studied variables are normally distributed. Figure 4 shows the trend of wheat production and climatic factors in the selected provinces of China.

Trend of wheat production, temperature, and rainfall in Hebei, Henan, and Shandong Provinces of China.

Statistical summary for Hebei, Henan, and Shandong Provinces.

Note: LWP, LTEMP, LRF, LFER, LPC, LWA, LLF signify the natural log of wheat production, average annual temperature, average annual rainfall, fertilizer usage, power consumption, wheat cultivated area, and rural labor force, while J-B denotes the Jarque–Bera test.

The outcomes of the correlation matrix for Hebei, Henan, and Shandong Provinces are presented in Table 2 . The findings for Hebei Province reveal that temperature, rainfall, fertilizer usage, power consumption, and labor are significantly and positively associated with wheat production, while the cultivated area is negatively associated. Further, the findings for Henan Province show that all the studied variables are significantly linked with wheat production, except rainfall. In addition, the outcomes for Shandong Province indicate that temperature, cultivated area, and labor are significant and interrelated with wheat production, whereas fertilizer is negatively associated.

Results of the correlation matrix for Hebei, Henan, and Shandong Provinces.

Note: *** p value < 0.01, ** p value < 0.05, and * p value < 0.1.

We employ the Johansen and Juselius co-integration procedure to explore the long-term association between the explained variables, such as wheat production and its explanatory variables. We test the research hypothesis, as the null hypothesis states that the explained variables, wheat production and its explanatory variables, are not co-integrated in the long term. In contrast, the alternative hypothesis mentions that the considered variables are co-integrated in the long term. To reach a decision about the hypothesis, we use the Trace t-statistic test, and the findings for Hebei, Henan, and Shandong Provinces are reported in Table 3 . The findings reveal that wheat production, temperature, rainfall, fertilizer usage, power consumption, cultivated area, and labor force are co-integrated in the long term in China’s selected wheat-producing provinces.

Co-integration outcomes for Hebei, Henan, and Shandong Provinces.

Note: TS indicates the trace statistic, * signifies rejection of the hypothesis at the 0.05 level.

Table 4 reports the estimated results of long-term effect of climate variables and other control variables on wheat yield in Hebei, Henan, and Shandong Provinces, respectively.

Results of FMOLS estimator for top three provinces in northern China.

In the case of Hebei Province, the climate variables (i.e., temperature and rainfall) have a positive, significant impact on wheat production. This means the climate conditions are more favorable for wheat cultivation in Hebei Province. Specific to the North China Plain, where this study area is located, some research evidence shows that in the north of this plain, the impact of rainfall on wheat production is positive, while in the south of this plain, the impact of rainfall turns negative [ 49 ]. Similarly, for temperature, the increase in temperature increases the winter wheat yield in the northern part of the North China Plain but decreases the wheat yield produced in winter in the south of the North China Plain [ 50 ]. The top three wheat-producing provinces are selected for this investigation. Hebei, Shandong, and Henan Provinces are distributed in the North China Plain. The three provinces’ yearly mean temperature and yearly mean precipitation are ranked from low to high in Hebei, Shandong, and Henan (see Figure 2 ). The temperature and rainfall in Hebei Province are low, and the impacts of temperature and precipitation on wheat yield are positive.

Further results reveal that fertilizer use, cultivated area, and labor force also have a positive, significant influence on wheat production. The long-run coefficients of fertilizer use, cultivated area, and labor force indicate that a 1% increase in fertilizer treatment use, cultivated area, and labor force wheat production improved by 0.17%, 0.58%, and 1.36%, respectively.

In the case of Henan Province, the climatic factors (i.e., temperature and rainfall) and wheat production relationship was significant and negative. This means that climatic factors severely impact wheat production in Henan Province. The long-run coefficient of both climate variables, temperature and rainfall, indicates that with a 1% increase in both climate variables (i.e., temperature and rainfall), wheat production decreases by 0.51%, and 0.05%. Geng et al. [ 51 ] reported that high temperatures will be detrimental to wheat production by shortening the growth cycle of the wheat crop. Further, Song et al. [ 33 ] stated that excessive rainfall causes excessive water accumulation, which will aggravate the wet damage of wheat and negatively affect wheat production.

Moreover, the results show that fertilizer usage also significantly negatively impacts wheat production. The long-term coefficient of fertilizer usage reveals that if a farmer overuses the fertilizer by 1%, wheat production declines by 0.61%. Fertilization can not only supplement the nutrients needed by wheat but also improve the utilization rate of water, thus increasing the yield of wheat [ 52 , 53 ]. However, unreasonable and excessive use of chemical fertilizers will cause soil degradation and adversely affect wheat yield. This shows that the rational use of chemical fertilizers is very important for wheat production, and Henan Province should pay more attention to improving chemical fertilizer use efficiency.

In contrast, these variables (power usage, wheat farming area, and labor force) and the wheat production relationship were significant and positive. The long-run coefficient of power usage, wheat farming area, and labor force reveals that a 1% increase in power usage, wheat farming area, and labor force increases wheat production by 0.48%, 2.98%, and 0.21%, respectively.

In the case of Shandong Province, the climate variables, temperature, and wheat production displayed a diverse relationship. At the same time, rainfall had a significant and positive influence, suggesting that with a 1% increase in temperature and rainfall, wheat production decreased by 0.07% and improved by 0.08%. The heterogeneous effect of the climate variables on regional wheat yield is verified by some existing studies. For example, the evidence from Mexico and China verified that the sensitivity of wheat yield to climate variables is uneven in space [ 54 , 55 ]. Tao et al. [ 56 ] studied climate change’s influence on wheat productivity and found the prospective consequences of climate change on winter wheat output in northern China under 10 climatic scenarios and concluded that environmental variability might enhance wheat yield by 37.7% (18.6%), 67.8% (23.1%), and 87.2% (34.4%), with (without) CO 2 fertilization effects in the 2020s, 2050s, and 2080s, respectively, in the future. The temperature and rainfall in Shandong Province are in the middle of the three provinces, and the impact of temperature on wheat yield is negative, but the impact of rainfall is positive. In Henan Province, it is observed that the temperature is higher, and the rainfall is higher; the influence of temperature and rainfall on wheat is negative. Moreover, the results show that these variables (fertilizer use, cultivated area, and labor force) and wheat production association was significant and positive, suggesting that a 1% increase in fertilizer usage, cultivated area, and labor force enhanced wheat production by 0.29%, 1.16%, and 0.23%, respectively.

This study applied the DOLS and CCR long-run estimators as a robust check approach for the FMOLS findings. Table 5 shows that climate variables positively affect wheat production in the context of Hebei Province. The estimated coefficients of DOLS and CCR are consistent with the findings of the FMOLS model. Likewise, in Henan Province’s case, climatic factors negatively influence wheat production. These outcomes are also consistent with the outcomes of the FMOLS model. In addition, climatic factors, such as temperature, only have a negative impact on wheat production. Meanwhile, rainfall has a significant and positive linkage with wheat production. Hence, the results of both techniques, such as DOLS and CCR, are similar to the results of the FMOLS method.

Robustness check.

Although the long-run impact of the variables concerned was explored through the FMOLS, DOLS, and CCR estimators, the causal connection between the underlying variables is still in question. Therefore, we further apply the Granger causality method. The findings for Hebei, Henan, and Shandong Provinces are presented in Table 6 . A bidirectional causality between precipitation and fertilizer usage with wheat production in the context of Hebei Province can be observed. This means that rainfall and fertilizer usage significantly contributed to Hebei Province’s wheat production.

Granger causality test outcomes for Hebei, Henan, and Shandong Provinces.

Note: ⇏   indicates “does not cause Granger”, *** p value < 0.01, ** p value < 0.05, and * p value < 0.1.

Further, the results only discover a unidirectional causality association between wheat production and temperature. In the context of Henan Province, it is revealed that a unidirectional causality association runs from precipitation and fertilizer usage to wheat production. In contrast, a bidirectional causality exists between power consumption and wheat production. This depicts that climate change factors, such as rainfall, and other inputs also positively influence wheat production. In addition, a bidirectional causality is established between the farming area and wheat production, while a unidirectional causality is detected from precipitation and labor to wheat production. These results imply that the cultivated area, rainfall, and labor significantly improve wheat production in the context of Shandong Province.

5. Conclusions

The current study assesses the climate variables’ long-run impact on wheat production in China’s top three wheat-producing provinces. The other important factors considered in this paper include fertilizer usage, cultivated area, power consumption, and labor. The data set consists of observations from 1992 to 2020 on which several time-series techniques, namely, the DOLS, FMOLS, CCR, and Granger causality, were applied. Based on the estimations, the findings revealed that wheat production is negatively affected by climate change in Henan Province. In contrast, climate change is more favorable for wheat production in Hebei Province.

On the other hand, temperature negatively influenced wheat production but was not significant, while rainfall significantly contributed positively to wheat production in Shandong Province. Further findings showed that fertilizer usage, cultivated area, and labor positively and significantly improved wheat production in Hebei and Shandong Provinces. In contrast, power usage, wheat farming area, and labor force significantly and positively enhanced wheat production in Henan Province. In addition, the findings of the Granger causality test reported a bidirectional causality between rainfall and fertilizer use with wheat production in Hebei Province, while a unidirectional causality connection was revealed between wheat production and temperature. In the context of Henan Province, it was discovered that a unidirectional causality link was observed from rainfall and fertilizer use to wheat production. In contrast, a bidirectional causality existed between power consumption and wheat production. Moreover, a bidirectional causality was established between the cultivated area and wheat production, while a unidirectional causality was detected from the rainfall and labor to wheat production in Shandong Province.

Based on the estimated outcomes, the current paper offers several policy implications:

With both advantages and disadvantages, China’s wheat production is affected by global warming. To mitigate the effects of a changing climate on China’s wheat yield, it is vital to increase the adaptability of wheat production. First, modify wheat’s sowing date and area in a reasonable manner. Adjust the sowing date of crops, rationally plan the planting areas, fully utilize the additional heat resources brought about by climate change, decrease the impact of meteorological disasters, and increase the stability of wheat production based on the climatic conditions of various regions.

Second, agricultural technology advancement will continue to be important in ensuring wheat yield stability. On the one hand, the Chinese government must prioritize research and develop seed resources resistant to extreme weather conditions. It is crucial to develop and store wheat germplasm resources that can respond to adverse weather conditions, given the prevalence of extreme weather events (high-temperature resistance, waterlogging resistance, low-temperature resistance, etc.). On the other hand, it is essential to continue using advanced agricultural technologies to produce wheat. For instance, more fertilizer use techniques should be implemented to increase the input effectiveness of chemical fertilizers and ensure the sustainability of agricultural production.

Furthermore, there are regional differences in wheat planting varieties and methods in China, making it difficult to continuously improve wheat production levels by relying solely on a single technology. As a result, it is necessary to promote improved varieties in conjunction with good methods, agricultural machinery, and agronomy, as well as to further tap the potential of science and technology to increase production.

Funding Statement

This research was funded by the National Social Science Fund of China (Grant number: 19CSH029).

Author Contributions

Conceptualization, A.A.C. and H.Z.; methodology, A.A.C.; software, A.A.C. and Y.T.; validation, G.R.S.; formal analysis, A.A.C. and Y.T.; investigation, A.A.C. and Y.T.; resources, H.Z.; data curation, Y.T.; writing—original draft preparation, A.A.C. and Y.T.; writing—review and editing, M.A.T. and G.R.S.; visualization, H.Z.; supervision, A.A.C.; project administration, H.Z.; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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• Published: 25 November 2020

Multiple wheat genomes reveal global variation in modern breeding

• Sean Walkowiak 1 , 2   na1 ,
• Liangliang Gao   ORCID: orcid.org/0000-0002-3864-0631 3   na1 ,
• Cecile Monat   ORCID: orcid.org/0000-0002-0574-3976 4   na1 ,
• Georg Haberer 5 ,
• Mulualem T. Kassa 6 ,
• Jemima Brinton 7 ,
• Ricardo H. Ramirez-Gonzalez 7 ,
• Markus C. Kolodziej   ORCID: orcid.org/0000-0003-3155-2935 8 ,
• Emily Delorean 3 ,
• Dinushika Thambugala 9 ,
• Valentyna Klymiuk   ORCID: orcid.org/0000-0001-9308-7886 1 ,
• Brook Byrns 1 ,
• Heidrun Gundlach   ORCID: orcid.org/0000-0002-6757-0943 5 ,
• Venkat Bandi 10 ,
• Jorge Nunez Siri 10 ,
• Kirby Nilsen   ORCID: orcid.org/0000-0002-9477-5549 1 , 11 ,
• Catharine Aquino 12 ,
• Axel Himmelbach 4 ,
• Dario Copetti   ORCID: orcid.org/0000-0002-2680-2568 13 , 14 ,
• Tomohiro Ban 15 ,
• Luca Venturini 16 ,
• Michael Bevan   ORCID: orcid.org/0000-0001-8264-2354 7 ,
• Bernardo Clavijo 17 ,
• Dal-Hoe Koo 3 ,
• Jennifer Ens   ORCID: orcid.org/0000-0003-2663-1006 1 ,
• Krystalee Wiebe 1 ,
• Amidou N’Diaye   ORCID: orcid.org/0000-0001-7769-7801 1 ,
• Allen K. Fritz 3 ,
• Carl Gutwin 10 ,
• Anne Fiebig 4 ,
• Christine Fosker   ORCID: orcid.org/0000-0002-3799-9474 17 ,
• Bin Xiao Fu 2 ,
• Gonzalo Garcia Accinelli   ORCID: orcid.org/0000-0001-9647-7935 17 ,
• Keith A. Gardner   ORCID: orcid.org/0000-0002-4890-301X 18 ,
• Nick Fradgley 18 ,
• Juan Gutierrez-Gonzalez   ORCID: orcid.org/0000-0002-6795-6192 19 ,
• Gwyneth Halstead-Nussloch 13 ,
• Masaomi Hatakeyama 12 , 13 ,
• Chu Shin Koh 20 ,
• Jasline Deek   ORCID: orcid.org/0000-0002-5584-306X 21 ,
• Alejandro C. Costamagna 22 ,
• Pierre Fobert 6 ,
• Darren Heavens   ORCID: orcid.org/0000-0001-5418-7868 17 ,
• Hiroyuki Kanamori 23 ,
• Kanako Kawaura 15 ,
• Fuminori Kobayashi 23 ,
• Ksenia Krasileva 17 ,
• Tony Kuo 24 , 25 ,
• Neil McKenzie 7 ,
• Kazuki Murata 26 ,
• Yusuke Nabeka   ORCID: orcid.org/0000-0002-8692-5981 26 ,
• Timothy Paape 13 ,
• Sudharsan Padmarasu   ORCID: orcid.org/0000-0003-3125-3695 4 ,
• Lawrence Percival-Alwyn 18 ,
• Sateesh Kagale 6 ,
• Uwe Scholz   ORCID: orcid.org/0000-0001-6113-3518 4 ,
• Jun Sese 25 , 27 ,
• Philomin Juliana   ORCID: orcid.org/0000-0001-6922-0173 28 ,
• Ravi Singh   ORCID: orcid.org/0000-0002-4676-5071 28 ,
• Rie Shimizu-Inatsugi 13 ,
• David Swarbreck 17 ,
• James Cockram   ORCID: orcid.org/0000-0002-1014-6463 18 ,
• Hikmet Budak   ORCID: orcid.org/0000-0002-2556-2478 29 ,
• Toshiaki Tameshige 15 ,
• Tsuyoshi Tanaka 23 ,
• Hiroyuki Tsuji   ORCID: orcid.org/0000-0002-6444-559X 15 ,
• Jonathan Wright   ORCID: orcid.org/0000-0001-6471-8749 17 ,
• Jianzhong Wu   ORCID: orcid.org/0000-0002-4033-852X 23 ,
• Burkhard Steuernagel 7 ,
• Ian Small   ORCID: orcid.org/0000-0001-5300-1216 30 ,
• Sylvie Cloutier 31 ,
• Gabriel Keeble-Gagnère 32 ,
• Gary Muehlbauer 19 ,
• Josquin Tibbets 32 ,
• Shuhei Nasuda 26 ,
• Joanna Melonek   ORCID: orcid.org/0000-0003-4471-2520 30 ,
• Pierre J. Hucl 1 ,
• Andrew G. Sharpe   ORCID: orcid.org/0000-0002-1832-4009 20 ,
• Matthew Clark   ORCID: orcid.org/0000-0002-8049-5423 16 ,
• Erik Legg 33 ,
• Arvind Bharti 33 ,
• Peter Langridge   ORCID: orcid.org/0000-0001-9494-400X 34 ,
• Anthony Hall   ORCID: orcid.org/0000-0002-1806-020X 17 ,
• Cristobal Uauy   ORCID: orcid.org/0000-0002-9814-1770 7 ,
• Martin Mascher   ORCID: orcid.org/0000-0001-6373-6013 4 , 35 ,
• Simon G. Krattinger   ORCID: orcid.org/0000-0001-6912-7411 8 , 36 ,
• Hirokazu Handa 23 , 37 ,
• Kentaro K. Shimizu   ORCID: orcid.org/0000-0002-6483-1781 13 , 15 ,
• Assaf Distelfeld 38 ,
• Ken Chalmers 34 ,
• Beat Keller   ORCID: orcid.org/0000-0003-2379-9225 8 ,
• Klaus F. X. Mayer   ORCID: orcid.org/0000-0001-6484-1077 5 , 39 ,
• Jesse Poland   ORCID: orcid.org/0000-0002-7856-1399 3 ,
• Nils Stein   ORCID: orcid.org/0000-0003-3011-8731 4 , 40 ,
• Curt A. McCartney   ORCID: orcid.org/0000-0002-9482-3133 9 ,
• Manuel Spannagl   ORCID: orcid.org/0000-0003-0701-7035 5 ,
• Thomas Wicker   ORCID: orcid.org/0000-0002-6777-7135 8 &
• Curtis J. Pozniak   ORCID: orcid.org/0000-0002-7536-3856 1  

Nature volume  588 ,  pages 277–283 ( 2020 ) Cite this article

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• Comparative genomics
• Plant breeding

Structural variation

Advances in genomics have expedited the improvement of several agriculturally important crops but similar efforts in wheat ( Triticum spp.) have been more challenging. This is largely owing to the size and complexity of the wheat genome 1 , and the lack of genome-assembly data for multiple wheat lines 2 , 3 . Here we generated ten chromosome pseudomolecule and five scaffold assemblies of hexaploid wheat to explore the genomic diversity among wheat lines from global breeding programs. Comparative analysis revealed extensive structural rearrangements, introgressions from wild relatives and differences in gene content resulting from complex breeding histories aimed at improving adaptation to diverse environments, grain yield and quality, and resistance to stresses 4 , 5 . We provide examples outlining the utility of these genomes, including a detailed multi-genome-derived nucleotide-binding leucine-rich repeat protein repertoire involved in disease resistance and the characterization of Sm1 6 , a gene associated with insect resistance. These genome assemblies will provide a basis for functional gene discovery and breeding to deliver the next generation of modern wheat cultivars.

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Wheat is a staple food across all parts of the world and is one of the most widely grown and consumed crops 7 . As the human population continues to grow, wheat production must increase by more than 50% over current levels by 2050 to meet demand 7 . Efforts to increase wheat production may be aided by comprehensive genomic resources from global breeding programs to identify within-species allelic diversity and determine the best allele combinations to produce superior cultivars 2 , 8 .

Two species dominate current global wheat production: allotetraploid (AABB) durum wheat ( Triticum turgidum ssp. durum ), which is used to make couscous and pasta 9 , and allohexaploid (AABBDD) bread wheat ( Triticum aestivum ), used for making bread and noodles. A, B and D in these designations correspond to separate subgenomes derived from three ancestral diploid species with similar but distinct genome structure and gene content that diverged between 2.5 and 6 million years ago 10 . The large genome size (16 Gb for bread wheat), high sequence similarity between subgenomes and abundance of repetitive elements (about 85% of the genome) hampered early wheat genome-assembly efforts 3 . However, chromosome-level assemblies have recently become available for both tetraploid 11 , 12 and hexaploid wheat 1 , 13 . Although these genome assemblies are valuable resources, they do not fully capture within-species genomic variation that can be used for crop improvement, and comparative genome data from multiple individuals is still needed to expedite bread wheat research and breeding. Until now, comparative genomics of multiple bread wheat lines have been limited to exome-capture sequencing 4 , 5 , 14 , low-coverage sequencing 2 and whole-genome scaffolded assemblies 13 , 15 , 16 , 17 . Here we report multiple reference-quality genome assemblies and explore genome variation that, owing to past breeder selection, differs greatly between bread wheat lines. These genome assemblies usher a new era for bread wheat and equip researchers and breeders with the tools needed to improve bread wheat and meet future food demands.

Global variation in wheat genomes

To expand on the genome assembly of wheat for Chinese Spring 1 , we generated ten reference-quality pseudomolecule assemblies (RQAs) and five scaffold-level assemblies of hexaploid wheat (Supplementary Note  1 , Supplementary Tables 1 – 3 ). For each RQA, we performed de novo assembly of contigs (contig N50 > 48 kb) that were combined into scaffolds (N50 > 10 Mb) spanning more than 14.2 Gb (Supplementary Note  1 ). The completeness of the genomes was supported by a universal single-copy orthologue (BUSCO) analysis that identified more than 97% of the expected gene content in each genome (Supplementary Note  1 ). More than 94% of the scaffolds were ordered, oriented and curated using 10X Genomics linked reads and three-dimensional chromosome conformation capture sequencing (Hi-C) to generate 21 pseudomolecules, as done previously for wheat 1 , 12 and barley ( Hordeum vulgare ) 18 . The size and structure of the genomes were similar to that of Chinese Spring, and we observed high collinearity between the pseudomolecules (Extended Data Fig. 1 ). We also independently validated the scaffold placement and orientation in the pseudomolecule assembly of CDC Landmark by Oxford Nanopore long-read sequencing (Extended Data Fig. 2a , Supplementary Note  2 ). To complement the RQAs, we generated scaffold-level assemblies of five additional bread wheat lines (Supplementary Note  1 ). To determine the global context of the 15 assemblies, we combined our data with existing datasets 4 , 5 , 19 (Fig. 1a , Supplementary Table 4 ). The genetic relationships were in agreement with those reported in previous studies 4 , 5 and reflected pedigree, geographical location and growth habit (that is, spring versus winter type). There was also a clear separation between the newly assembled genomes and Chinese Spring, supporting that they capture geographical and historical variation not represented in the Chinese Spring assembly.

a , Principal component analysis of polymorphisms from exome-capture sequencing of about 1,200 lines (grey markers), 16 lines from whole-genome shotgun resequencing (orange markers) and our new assemblies (black markers). Text colours reflect different geographical locations and winter or spring growth. b , Dendrogram of pairwise Jaccard similarities for gene PAV between all RQA assemblies. c , Number of unique NLRs at different per cent identity cut-offs as the number of genomes increases. Dashed vertical lines represent 90% of the NLR complement. Markers indicate the mean values of all permutations of the order of adding genomes. Whiskers show maximum and minimum values based on one million random permutations. d , Chromosomal location versus insertion age distribution of unique to (reading downward) increasingly shared syntenic full-length LTR retrotransposons.

Polyploidy and CNV drive gene diversification

Single-nucleotide polymorphisms (SNPs), insertions or deletions (indels), presence/absence variation (PAV) and gene copy number variation (CNV) influence agronomically important traits. This is particularly true for polyploid species such as wheat, in which gene redundancy can buffer the effect of genome variation 17 . To assess gene content, we projected around 107,000 high-confidence gene models from Chinese Spring 1 onto the RQAs (Supplementary Note  3 ). The total number of projected genes exhibited a narrow range, between 118,734 and 120,967 (Supplementary Table 5 ). We identified orthologous groups among projected genes and used the alignment of the orthologous groups to examine SNPs in coding sequences (Supplementary Note  3 ). The peak positions of nucleotide diversity across the three subgenomes were highly similar to those reported in previous studies 20 , supporting a strong representation of breeding diversity within the RQAs (Extended Data Fig. 3a, b ). The correlation of synonymous nucleotide diversity π ( r  = 0.11–0.29) and Tajima’s D ( r  = 0.02–0.06) between homeologues was low (Supplementary Tables 6 – 8 ). This suggested that polyploidization increased the number of targets of selection and contributed to broad adaptation of bread wheat, as in wild polyploid plant species 20 , 21 , 22 . Further investigation of orthologous groups indicated that 88.1% were unambiguous (clusters containing at most one member in each cultivar) (Extended Data Fig. 3c , Supplementary Table 5 ). Orthologous groups comprising exactly one gene in each line (‘complete’) were the most frequent (approximately 73.5% of genes per cultivar), suggesting strong retention of orthologous genes within the ten RQAs. The residual genes represented either singleton genes with no reciprocal best BLAST hits or genes located in complex clusters in at least one cultivar. Roughly 12% of genes showed PAVs, and their clustering resulted in relationships (Fig. 1b ) that were consistent with SNP-based phylogenetic similarities (Fig. 1a ). In addition, approximately 26% of the projected genes were found in tandem duplications, indicating that CNV is a strong contributor of genetic variation in wheat.

To provide an example of gene expansion on emerging breeding targets, we performed a more detailed analysis of the restorer of fertility ( Rf ) gene families (Supplementary Note  4 ). Rf genes are involved in restoring pollen fertility in hybrid breeding programs 23 , and we identified a previously undescribed clade within the mitochondrial transcription termination factor (mTERF) family (Supplementary Table 9 ), which has recently been implicated in fertility restoration in barley 24 . Of note, this clade shows evolutionary patterns similar to those of Rf- like pentatricopeptide repeat (PPR) proteins, representatives of which are associated with Rf3 , a major locus used in hybrid wheat breeding programs (Extended Data Fig. 4 ). Although wheat is currently not a hybrid crop, there is substantial interest in Rf genes and their potential application in hybrid wheat production systems 25 . To our knowledge, no Rf genes have been cloned in wheat and our analysis of Rf genes in multiple RQAs and identification of an Rf clade in wheat is an important step forward in tackling the challenges of hybrid wheat breeding.

The wheat NLR repertoire

To further exemplify the use of multi-genome comparisons for characterizing agronomically relevant gene families, we examined gene expansion in nucleotide-binding leucine-rich repeat (NLR) proteins, which are major components of the innate immune system and are often causal genes for disease resistance in plants 26 , 27 . We performed de novo annotation of loci that contain conserved NLR motifs (NB-ARC–leucine-rich repeat) and identified around 2,500 loci with NLR signatures in each RQA (Supplementary Tables 10 , 11 ). A redundancy analysis showed that only 31–34% of the NLR signatures are shared across all genomes, and the number of unique signatures ranged from 22 to 192 per wheat cultivar. We estimated the number of unique NLR signatures that can be detected by incrementally adding more wheat genomes to the dataset; this revealed that 90% of the NLR complement is reached at between 8 (considering 95% sequence identity) and 11 wheat lines (considering 100% protein sequence identity) (Fig. 1c ). The total NLR complement of all wheat lines consisted of 5,905 (98% identity) to 7,780 (100% identity) unique NLR signatures, highlighting the size and complexity of the repertoire of receptors involved in disease resistance.

Transposon signatures identify introgressions

Transposable elements make up a large majority of the wheat genome and have a critical role in genome structure and gene regulation. We characterized the overall transposable element content (81.6%) and its composition (69% long terminal-repeat retrotransposons (LTR) and 12.5% DNA transposons) in the RQAs (Supplementary Table 5 ). Across all RQAs, we annotated 1.22 × 10 6 full length (fl)-LTRs, which clustered lines into the same groups we observed from our analysis of PAV and SNPs (Fig. 1a, b , Extended Data Fig. 3d ). Generally, unique fl-LTRs (147,450) were young (median of 0.9 million years) and were enriched in the highly recombining, more distal chromosomal regions (Fig. 1d ). By contrast, shared fl-LTRs were older (median of 1.3 million years) and were more evenly distributed across the pericentric regions (Fig. 1d ). The RLC- Angela fl-LTRs were the most abundant (21,000–27,000 full-length copies per genome) and analysis of variant patterns identified several chromosomal segments that contained numerous unique or rare retrotransposon insertions (Extended Data Fig. 5 ), which, on the basis of breeding history, we hypothesize to represent introgressions. For example, the LongReach Lancer RQA revealed two unique regions, a pericentric region on chromosome 2B and a segment on the end of chromosome 3D (Fig. 2a, b ), both of which affect chromosome length (Extended Data Fig. 5 ). We used pedigree analysis to postulate the source of the introgressions and performed whole-genome sequencing of multiple accessions of putative donors. LongReach Lancer carries the stem rust resistance gene Sr36 , derived from an introgression from Triticum timopheevii , and the resistance genes Lr24 (leaf rust) and Sr24 (stem rust), derived from tall wheatgrass 28 , 29 ( Thinopyrum ponticum ). We generated whole-genome sequence reads from multiple T. ponticum and T. timopheevii accessions (Supplementary Table 12 ) and alignment to the LongReach Lancer RQA confirmed a T. ponticum introgression spanning a region of approximately 60 Mb of chromosome 3D (Fig. 2a ), whereas T. timopheevii aligned to the majority (427 Mb) of chromosome 2B (Fig. 2b ). Overall, we identified 341 chromosomal segments larger than 20 Mb with unique or rare fl-LTR insertion patterns that were present in only 1 to 4 of the RQA genomes, of which 273 insertion patterns were uniquely associated with a single genome (Supplementary Tables 13 – 16 ). The majority of unique regions were in PI190962 (spelt wheat; Triticum aestivum ssp. spelta ), which was expected, given that it diverged from modern bread wheat several thousand years ago.

a – c , T. ponticum introgression on chromosome 3D in LongReach Lancer ( a ), T. timopheevi introgression on chromosome 2B in LongReach Lancer ( b ) and A. ventricosa introgression on chromosome 3D in Jagger ( c ). Track i, map of polymorphic RLC- Angela retrotransposon insertions (legend at bottom); track ii, density of projected gene annotations from Chinese Spring (blue bars, scaled to maximum value); track iii, per cent identity to Chinese Spring based on chromosome alignment (yellow; scale is 0–100%); track iv, read depth of wheat wild relatives (blue–yellow heat map; legend at bottom). d , Dot plot alignment showing chromosome-level collinearity (black) with relative density of CENH3 ChIP–seq mapped to 100-kb bins for Chinese Spring (blue) and Julius (red); the arrow indicates a centromere shift. e , Robertsonian translocation between chromosomes 5B and 7B in Arina LrFor . f , g , Cytology ( f ) and Hi-C ( g ) confirm the 5B/7B translocation in SY Mattis (left) compared with the non-carrier Norin 61 (right). In f , five independent cells were observed; the translocation was confirmed independently ten times. Scale bar, 10 μm.

A similar strategy was used to confirm RLC- Angela variation at the telomeric region of chromosome 2A in Jagger, Mace, SY Mattis and CDC Stanley (Fig. 2c ), which corresponds to the 2NvS introgression from Aegilops ventricosa (Supplementary Note  5 ). This introgression is a well-known source of resistance to wheat blast 30 , and contains the Lr37–Yr17–Sr38 gene cluster, which provides resistance to several rust diseases 31 . Sequencing of A. ventricosa accessions (Supplementary Table 12 ) followed by comparison of chromosomes with the RQAs confirmed that Jagger, Mace, SY Mattis and CDC Stanley carry the 2NvS introgression, which spans about 33 Mb on chromosome 2A (Fig. 2c , Extended Data Fig. 6a ). We annotated the coding genes within this region and identified 535 high-confidence genes; more than 10% were predicted to be associated with disease resistance, including genes that encode putative NB-ARC and NLRs (Extended Data Fig. 6b , Supplementary Tables 17 , 18 ). Furthermore, we used genotyping by sequencing to detect the 2NvS segment in three wheat panels and discovered that its frequency has been increasing in breeding germplasm and its presence is consistently associated with higher grain yield (Extended Data Fig. 6c, d , Supplementary Tables 19 , 20 ). Of note, we identified about 60 genes belonging to the cytochrome P450 superfamily, which have been implicated in abiotic and biotic stress tolerance 32 and have been functionally validated to influence grain yield in wheat 33 . Together, these data indicate that the modern wheat gene pool contains many chromosomal segments of diverse ancestral origins, which can be identified by their transposable-element signatures. We also confirmed the wild-relative origins of three introgressions within the RQA assemblies—a first step towards characterizing causal genes for breeding targets, such as resistance to wheat blast and rust fungi.

Centromere dynamics

Centromeres are vital for cell division and chromosome pairing during meiosis. In plants, functional centromeres are defined by the epigenetic placement of the modified histone CENH3 34 . We therefore used CENH3 chromatin immunoprecipitation and sequencing (ChIP–seq) 35 to determine the positions and sizes (about 7.5–9.6 Mb) of the centromeres for each RQA (Supplementary Tables 21 , 22 ), which were consistent with previous estimates for wheat 1 . Furthermore, all chromosomes showed a single active site, implying that previous reports of multiple active centromeres in Chinese Spring 1 were artefacts of misoriented scaffolds. However, we found examples in which the relative position of the centromere was shifted owing to several pericentric inversions, including inversions on chromosomes 4B and 5B (Extended Data Fig. 7a, b ). We also observed one instance in which the centromeric position changed, but was not associated with a structural event. Specifically, on chromosome 4D in Chinese Spring, the centromere is shifted by around 25 Mb relative to the consensus position (Fig. 2d ). This shift was previously recognized by cytology but was hypothesized to result from a pericentric inversion 36 . However, the high degree of collinearity between genomes supports the hypothesis that Cen4D in Chinese Spring has shifted to a non-homologous position; this shifting of centromeres to non-homologous sites has also been reported in maize 37 . By characterizing the centromere positions for these diverse wheat lines, we provide strong evidence for changes in centromere position caused by structural rearrangements and centromere shifts.

Large-scale structural variation between genomes

Structural variants are common in wheat 38 , and impact genome structure and gene content. We characterized large structural variants using pairwise genome alignments (Extended Data Fig. 1 ), changes in three-dimensional topology of chromosomes revealed by Hi-C conformation capture directionality biases along the genome 39 , 40 (Extended Data Fig. 8 , Supplementary Table 23 ), which were confirmed by Oxford Nanopore long-read sequencing (Extended Data Fig. 2 ) and cytological karyotyping (Extended Data Fig. 7c , Supplementary Table 24 , Supplementary Note  6 ). The most prominent event was a translocation between chromosomes 5B and 7B, observed in Arina LrFor , SY Mattis (Fig. 2e–g ) and Claire. Normally, chromosomes 5B and 7B are approximately 737 and 762 Mb long, respectively, and we estimated that the recombined chromosomes are 488 Mb (5BS/7BS) and 993 Mb (7BL/5BL) long, making 7BL/5BL the largest wheat chromosome (Extended Data Fig. 9a ). In Arina LrFor and SY Mattis, the 7BL/5BL breakpoint resides within an approximately 5-kb GAA microsatellite, which we were able to span using polymerase chain reaction (PCR) (Extended Data Fig. 9b, c ). By contrast, the breakpoint on 5BS/7BS was less syntenic, and we detected polymorphic fluorescence in situ hybridization signals between Arina LrFor and SY Mattis on the 5BS portion of the translocated chromosome segment, suggesting that the regions adjacent to the translocation events differ on 5BS/7BS (Supplementary Note  6 ). To determine the stability of the translocation in breeding, we genotyped for the translocation event in a panel of 538 wheat lines that represent most of the UK wheat gene pool grown since the 1920s 41 . The translocation occurred in 66% of the lines and was selectively neutral (Supplementary Note  7 ). Notably, the Ph1 locus on chromosome 5B, which controls the pairing of homeologous chromosomes during meiosis 42 , is near the translocation breakpoint, but remained highly syntenic between translocation carriers and non-carriers. Genetic mapping and analysis of short-read sequencing data indicated that the 5B/7B translocated chromosomes recombine freely with 5B and 7B chromosomes (Extended Data Fig. 9d ), suggesting that chromosome pairing is not affected by the translocation.

Haplotype-based gene mapping

To develop improved wheat cultivars, breeders shuffle allelic variants by making targeted crosses and exploiting the recombination that occurs during meiosis. These alleles, however, are not inherited independently, but rather as haplotype blocks that often extend across multiple genes that are in genetic linkage 43 , 44 . We quantified haplotype variation along chromosomes across the assemblies, and developed visualization software to support its utility (Supplementary Note  8 ). We used these haplotypes to characterize a locus that provides resistance to the orange wheat blossom midge (OWBM, Sitodiplosis mosellana Géhin), one of the most damaging insect pests of wheat, which is endemic in Europe, North America, west Asia and the Far East. Upon hatching, the first-instar larvae feed on the developing grains and damage the kernels (Fig. 3a ). Sm1 is the only gene in wheat known to provide resistance to OWBM 6 . CDC Landmark, Robigus and Paragon are all resistant to the OWBM, and all three carry the same 7.3-Mb haplotype within the Sm1 locus on chromosome 2B (Fig. 3b ). To identify Sm1 gene candidates, we used high-resolution genetic mapping and refined the locus to a 587-kb interval in the CDC Landmark RQA (Fig. 3c , Extended Data Fig. 10a , Supplementary Table 25 ). Through extensive genotyping of diverse breeding lines, we found an OWBM-susceptible line, Waskada, that displayed a resistant haplotype except near one gene, which we annotated in CDC Landmark to encode a canonical NLR with kinase and major sperm protein (MSP) integrated domains (Fig. 3c ). Oxford Nanopore long-read sequencing further confirmed the structure of the gene in CDC Landmark (Extended Data Fig. 10b ). By contrast, the remaining assemblies (susceptible to OWBM) lacked the NB-ARC domain, but the kinase and MSP domains remained intact (Fig. 3c ). We sequenced the Waskada allele and found it contains the NB-ARC domain, but an alternative haplotype within the kinase domain (Fig. 3c , Extended Data Fig. 10c ). This gene is expressed in wheat kernels and seedlings of Sm1 carrier lines, and the lack of cDNA amplification of the NB-ARC domain for non-carrier lines further supported an alternative gene structure (Extended Data Fig. 10c ). We generated two knockout-mutant lines of this candidate gene in the Sm1 carrier line Unity 45 , and both were consistently rated as susceptible to OWBM (Supplementary Table 26 ). Sequencing of the candidate gene in these two mutants revealed a single point mutation in each line: a G>A mutation resulting in a Gly>Arg (G182R) amino acid substitution in the NB-ARC domain, and a G>A mutation, resulting in a stop codon (W98*) before the NB-ARC domain (Fig. 3c ). The kinase domain encoded by Sm1 belongs to the serine/threonine class 46 , similar to those of Rpg5 , which provides stem rust resistance 47 , and Tsn1 , which encodes sensitivity to the necrotrophic effector ToxA produced by Parastagonospora nodorum and Pyrenophora tritici-repentis 48 ; however, both Rpg5 and Tsn1 lack the MSP domain. To our knowledge, this is the first report of an NB-ARC-LRR-kinase-MSP coding gene associated with insect resistance. Additional research is needed to functionally validate these domains and their putative role in OWBM resistance using tools such as gene editing. Nevertheless, we developed a high-throughput and low-cost competitive allele-specific PCR marker (KASP) that discriminates between OWBM-susceptible and OWBM-resistant lines with perfect accuracy (Extended Data Fig. 10d , Supplementary Table 27 ). Our analyses, along with the haplotype and synteny viewers ( https://kiranbandi.github.io/10wheatgenomes/ , http://10wheatgenomes.plantinformatics.io/ and http://www.crop-haplotypes.com/ ), laid the foundation for identifying haplotypes for Sm1 . Haplotypes can now be genotyped in breeding programs using single-marker or high-throughput-sequencing-based approaches, which can integrate desirable genes into improved cultivars more efficiently.

a , The orange wheat blossom midge oviposits eggs on wheat spikes and the larvae feed on developing wheat grains, resulting in moderate to severe damage to mature kernels. b , Top, sections of chromosome 2B of the same colour in the same position share haplotypes (based on 5-Mb bins), with the exception of those in grey, which indicates a line-specific haplotype. The position of Sm1 is indicated with respect to the CDC Landmark assembly. Bottom, zoomed-in view of haplotype blocks (based on 250-kb bins) from 5 to 25 Mb positions on chromosome 2B, surrounding Sm1 . CDC Landmark, Robigus and Paragon all carry the same haplotype surrounding Sm1 (teal). c , Top, anchoring of the Sm1 fine map to the physical maps of Chinese Spring and CDC Landmark and graphical genotypes of three haplotypes critical to localizing the Sm1 candidate gene. Bottom, annotation of the Sm1 candidate gene, which encodes NB-ARC and LRR motifs in addition to the integrated serine/threonine (S/T) kinase and MSP domains. Two independent ethyl-methanesulfonate-induced mutations (W98* and G182R) result in loss of function and susceptibility to the orange wheat blossom midge (light blue lines). An alternative haplotype was observed in the kinase region of Waskada (black).

We have built on the genome-sequence resources available for wheat and related species to produce ten RQAs and five scaffolded assemblies that represent hexaploid wheat lines from different regions, growth habits and breeding programs 1 , 11 , 12 , 18 , 20 , 49 . We have identified and characterized SNPs, PAV, CNV, centromere shifts, large-scale structural variants and introgressions from wild relatives of wheat that can be used to identify and characterize important breeding targets. This was complemented by a transposable-element-analysis approach to identify candidate introgressions from wild relatives of wheat, for which we provided high-quality assemblies of segments already used in global breeding programs. Together, these RQAs present an opportunity for breeders and researchers to perform high-resolution manipulation of genomic segments and pave the way to identifying genes responsible for in-demand traits, as we demonstrated for resistance to the insect pest OWBM. Functional gene studies will also be facilitated by comparative gene analyses, as exemplified by our analyses of orthologous groups, Rf genes and NLR immune receptors 26 . Finally, we highlight haplotype blocks, which will facilitate marker development for applied breeding 43 , 50 . Equipped with multiple layers of data describing variation in wheat, we now have powerful tools to increase the rate of wheat improvement to meet future food demands.

No statistical methods were used to predetermine sample size. The field experiments were randomized, but the wheat lines sequenced and assembled were not selected at random. The investigators were not blinded to allocation during experiments and outcome assessment.

Assemblies and annotation

Genome assemblies.

We assembled the genomes of 15 diverse wheat lines using two approaches (Supplementary Table 1 ). The RQA approach used the DeNovoMAGIC v.3.0 assembly pipeline, previously used for the wild emmer wheat 11 , durum wheat 12 and Chinese Spring RefSeqv1.0 assemblies. In brief, high-molecular-weight DNA was extracted from wheat seedlings as described previously 51 . Illumina 450-bp paired-end (PE), 800-bp PE and mate-pair (MP) libraries of three different sizes (3 kb, 6 kb and 9 kb) were generated. Sequencing was performed at the University of Illinois Roy J. Carver Biotechnology Center. 10X Genomics Chromium libraries were prepared and sequenced at the Genome Canada Genome Innovation Centre using the manufacturers’ recommendations to achieve a minimum of 30 × coverage. Hi-C libraries were prepared using previously described methods 40 . Using the Illumina PE, MP, 10X Genomics Chromium, and Hi-C, chromosome scale assemblies were prepared as described previously 18 . For cultivars assembled to a scaffold level, we used the W2RAP-contigger using k  = 200 (Supplementary Note  1 ). Two MP libraries (10 kb and 13 kb) were produced for each line except Weebill 1, for which two additional MP libraries were used. Mate pairs were processed, filtered and used to scaffold contigs as described in the W2RAP pipeline ( https://github.com/bioinfologics/w2rap ). Scaffolds of less than 500 bp were removed from the final assemblies. Additionally, we performed Oxford Nanopore sequencing of CDC Landmark using R9 flow cells and the GridION sequencing technology (Supplementary Note  2 ).

Nucleotide diversity analysis

The variant call format data files from two wheat exome-capture studies 4 , 5 were retrieved, combined, and filtered to retain hexaploid accessions and polymorphisms detected in both studies. The 10X Genomics Chromium sequencing data for each of the RQA lines were aligned to Chinese Spring RefSeqv1.0 using the LongRanger v.2.1.6 software. Alignment files from the accessions assembled here and 16 Bioplatforms Australia lines 19 with alignments obtained from the DAWN project 52 were then used for variant calling by GATK v.3.8 at the same genomic positions identified by exome-capture sequencing. The variant files from the exome-capture studies, DAWN project and 10+ Wheat Genomes lines were then merged and subjected to principal component analysis (PCA) using the prcomp function in R v.3.6.1.

Gene projections

We used the previously published high-confidence gene models for Chinese Spring to assess the gene content in each assembly. Representative coding sequences of each informant locus were aligned to pseudomolecules of each line separately using BLAT 53 v.3.5 with the ‘fine’ parameter and a maximal intron size of 70 kb. BLAT matches seeded an additional alignment by exonerate 54 in the genomic neighbourhood encompassing 20 kb upstream and downstream of the match position. Exonerate alignments required a minimal and maximal intron sizes of 30 bp and 20 kb, respectively. A linear regression of colocalized matches with complete alignments of the informant were computed for 10,000 such pairs to derive a normalization function and to render comparable scoring schemes for both methods. Subsequently, we selected the top-scoring match for each mapping pair as the locus for the gene projection. Projections were then filtered by alignment coverage (Supplementary Note  3 ), the open reading frame (ORF) contiguity, the observed mapping frequency of the informant, coverage of start and stop codons, and the orthology or potential dislocation of the match scaffold relative to its informant chromosome. Identification of orthologous groups was analogous to the approach used previously 55 . Reciprocal best BLAST hit (RBH) graphs were derived from pairwise all-against-all BLASTn v2.8 transcript searches (minimal e -value ≤ 1 × 10 −30 ). Hits were assigned to homeologous groups on the basis of gene models of Chinese Spring following a previously described homeologue classification 9 . Multiple sequence alignments for the population genetics analysis were performed using MUSCLE v.3.8 with default parameters (Supplementary Note  3 ). Using the gene projections, we quantified average pairwise genetic diversity ( π ), polymorphism (Watterson’s θ W ), and Tajima’s D using compute and polydNdS in the libsequence v.1.0.3-1 package 56 . We retained diversity estimates for genes that were in all of the genomes and had ≤100 segregating sites. PAV was determined from the orthologous groups limited to one-to-one relations where there was no match in at least one genome.

Analysis of the Rf -like gene family

For Rf genes, the genome sequences were scanned for ORFs in six frame translations with the getorf program of the EMBOSS v.6.6.0 package. ORFs longer than 89 codons were searched for the presence of PPR motifs using hmmsearch from the HMMER v.3.2.1 package ( http://hmmer.org ) and the hidden Markov models defined previously. The PF02536 profile from the Pfam v32.0 database ( http://pfam.xfam.org ) was used to screen for ORFs carrying mTERF motifs. Downstream processing of the hmmsearch results followed the pipeline described previously 57 . ORFs with low hmmsearch scores were removed from the analysis as they are unlikely to represent functional PPR proteins. Only genes encoding mTERF proteins longer than 100 amino acids were included in the analysis. RFL -PPR sequences were identified as described 23 . The phylogenetic analyses were performed as described previously 23 . Conserved, non-PPR genes delimiting the borders of analysed RFL clusters were identified in the Chinese Spring RefSeqv1.0 reference genome and used to search for syntenic regions in the remaining wheat accessions with BLAST v.2.8. See Supplementary Note  4 for more details.

NLR repertoire

NLR signatures were annotated using NLR-Annotator 58 , 59 ( https://github.com/steuernb/NLR-Annotator ) with the option -a. We estimated redundancy of NLR signatures between genomes at different thresholds of identity: 95%, 98% and 100%. For the 165 amino acids in the consensus of all NB-ARC motifs, this translates to 8, 3 and 0 mismatches of a concatenated motif sequence. To calculate the overall redundancy in all genomes, we counted the number of LR signatures added to a non-redundant set by adding genomes iteratively. This was done for 1 million random permutations.

Repeat annotation

Transposons were detected and classified by a homology search against the REdat_9.7_Poaceae section of the PGSB transposon library 60 using vmatch ( http://www.vmatch.de ) with the following parameters: identity ≥70%, minimal hit length 75 bp, seedlength 12 bp (exact command line: -d -p -l 75 -identity 70 -seedlength 12 -exdrop 5). To remove overlapping annotations, the output was filtered for redundant hits via a priority-based approach in which higher-scoring matches where assigned first and lower-scoring hits at overlapping positions were either shortened or removed if there was ≥90% overlap with a priority hit or if <50 bp remained. Tandem repeats where identified with TandemRepeatFinder v.4.09 under default parameters 61 and subjected to overlap removal as described above. Full-length LTR retrotransposons were identified with LTRharvest ( http://genometools.org/documents/ltrharvest.pdf ). All candidates were subsequently annotated for PfamA domains using HMMER v.3.0 and filtered to remove false positives, non-canonical hybrids and gene-containing elements. The inner domain order served as a criterion for the LTR retrotransposon superfamily classification, either Gypsy (RLG: RT-RH-INT), Copia (RLC: INT-RT-RH) or undetermined (RLX). The insertion age of fl-LTRs was calculated from the divergence between the 5′ and 3′ long terminal repeats, which are identical upon insertion. The genetic distance was calculated with EMBOSS v.6.6.0 distmat (Kimura2-parameter correction) using a random mutation rate of 1.3 × 10 −8 .

Analysis of centromeric regions

For each line with a RQA, ChIP was performed according to previous methods 62 with slight modification using a wheat-specific CENH3 antibody 36 . An antigen with the peptide sequence RTKHPAVRKTKALPKK, corresponding to the N terminus of wheat CENH3, was used to produce an antibody using the custom-antibody production facility provided by Thermo Fisher Scientific. The customized antibody was purified and obtained as pellets. The antibody pellet (0.396 mg) was dissolved in 2 ml PBS buffer, pH 7.4, resulting in a working concentration of 198 ng μl −1 . Nuclei were isolated from 2-week-old seedlings, digested with micrococcal nuclease and incubated overnight at 4 °C with 3 μg of antibody or rabbit serum (control). Antibodies were captured using Dynabeads Protein G and the chromatin eluted using 100 μl of 1% sodium dodecyl sulfate, 0.1 M NaHCO 3 preheated to 65 °C. DNA isolation was then performed using ChIP DNA Clean & Concentrator Kit, and ChIP–seq libraries were constructed using TruSeq ChIP Library Preparation Kit and sequenced with a NovoSeq S4, which generated 150-bp paired-end reads.

For Chinese Spring, we used two datasets, SRR1686799 63 (dataset 1) and the dataset generated in this study (dataset 2). Sequence reads were de-multiplexed, trimmed and aligned to each of the respective RQAs using HISAT2 v.2.1.0 64 . Alignments were sorted, filtered for minimum alignment quality of 30, counted in 100-kb bins using samtools v.1.10 and BEDtools v.2.29, and visualized in R v.3.6.1. To define the midpoint of each centromere, we identified the highest density of CENH3 ChIP–seq reads using a smoothing spline in R v.3.6.1 with smooth.spline function (number of knots = 1,000) and identified the peak of the smooth spline as the centre of the respective centromere for a given chromosome. To compare centromeric positions of different genomes, the CENH3 ChIP–seq density was plotted along with MUMmer v.4.0 chromosome alignments. To determine the overall size of wheat centromeres, we considered each 100-kb bin with CENH3 ChIP–seq read density that was greater than three times the background (genome average) level of read density to be an active centromeric bin. The number of enriched bins for each genome were counted and averaged to a total of 21 chromosomes. This calculation included counting of unanchored bins.

Analysis of introgressions

Identification of full-length rlc- angela retrotransposons.

Retrotransposon profiles were created for each genome using the RLC- Angela family 65 and consensus sequences obtained from the TREP database ( www.botinst.uzh.ch/en/research/genetics/thomasWicker/trep-db.html ). First, BLASTn was used to compare the ~1,700-bp LTR of RLC- Angela to each genome. Matching elements and 500 bp of flanking sequences were aligned to identify precise LTR borders as well as different sub-families and/or sequences variants. We then used BLASTn to compare the 18 consensus LTR sequences against each genome and then screened for pairs of full-length LTRs that are found in the same orientation within a window of 7.5–9.5 kb (RLC- Angela elements are ~8.7 kb long). These initial candidate full-length elements were screened for the presence of RLC- Angela polyprotein sequences by BLASTx, as well as for the typical 5-bp target-site duplications. We allowed a maximum of two mismatches between the two target-site duplications. All identified full-length RLC- Angela copies were then aligned to a RLC- Angela consensus sequence with the program Water from the EMBOSS v.6.6.0 package ( www.ebi.ac.uk/Tools/emboss/ ). These alignments were used to compile all nucleotide polymorphisms into a single file. The variant call file was then used for PCA using the snpgdsPCA function in the R package SNPrelate v.3.11.

Sequencing of the tertiary gene pool of wheat

Genomic DNA (gDNA) was extracted and purified from young leaf tissue collected from multiple accessions of T. timopheevii , A. ventricosa and T. ponticum (Supplementary Table 12 ) following a standard CTAB–chloroform extraction method. Yield and integrity were evaluated by fluorometry (Qubit 2.0) and agarose gel electrophoresis. Paired-end libraries were prepared following the Nextera DNA Flex protocol. In brief, 500 ng gDNA from each accession was fragmented and amplified with a limited-cycle PCR. Each library was uniquely dual-indexed with a distinct 10-bp index code (IDT for Illumina Nextera DNA UD) for multiplexing, and quantified by qPCR (Kapa Biosystems). Final average library size was estimated on a Tapestation 2200. Libraries were normalized and pooled for sequencing on an Illumina NovaSeq 6000 S4 to generate ~5× coverage per genotype. Sequencing data were de-multiplexed and aligned to appropriate RQAs (Supplementary Table 12 ) in semi-perfect mode using the BBMap v.38 short-read alignment software ( https://sourceforge.net/projects/bbmap/ ).

We karyotyped the lines using mitotic metaphase chromosomes prepared by the conventional acetocarmine-squash method. Non-denaturing fluorescence in situ hybridization (ND-FISH) of three repetitive sequence probes, Oligo-pSc119.2-1, Oligo-pTa535 and Oligo-pTa713, was performed as described 66 , 67 (Supplementary Note  6 ). Chromosomes were counterstained with DAPI. Chromosome images were captured with an Olympus BX61 epifluorescence microscope and a CCD camera DP80. Images were processed and pseudocoloured with ImageJ v.1.51n in the Fiji package. For karyotyping, at least four chromosomes per accession were examined and compared to the karyotype of Chinese Spring as described previously 68 . Hierarchal clustering of karyotype polymorphisms was performed using the Ward method in R v.3.0.2, which was used to estimate distance. Next, we applied Hi-C analysis for inversion calling as described previously 40 . In brief, adapters were removed and reads were mapped to Chinese Spring using minimap2 v.2.10 69 as we have done previously 21 . The raw Hi-C link counts were calculated in 1 Mb non-overlapping sliding windows and then normalized as described in our previous work 40 . Finally, the normalized Hi-C link matrix was subjected to inversion calling using R.

We performed flow cytometry of wheat cultivars Arina and Forno as previously described 70 , except that we used a FACSAria SORP flow cytometer and cell sorter (Becton Dickinson). The 5B/7B translocation breakpoints were identified by comparison of chromosomes 5B and 7B from Arina LrFor and Julius. Sequence collinearity between Arina LrFor and Julius was detected by BLASTn searches of 1,000-bp sequence windows every 100 kb along the chromosomes. Once an interruption of synteny was detected, sequence segments at the positions of synteny loss were extracted and used for local alignments to determine the precise breakpoint positions. PCR amplification of the 5BS/7BS and 7BL/5BL translocation sites was performed using standard PCR cycling conditions.

Characterization of haplotypes

Development of a wheat genome haplotype database.

To identify haplotypes, pairwise chromosome alignments were performed between the RQA using MUMmer v.4.0, which were combined with pairwise nucleotide BLASTn analyses of the genes ± 2,000 bp using custom scripts in R v.3.6.1 ( https://github.com/Uauy-Lab/pangenome-haplotypes ) 71 (Supplementary Note 8). The resultant haplotypes were uploaded to an interactive viewer ( http://www.crop-haplotypes.com/ ). Pairwise BLASTn comparisons of the genes were also used to identify structural variants, and were uploaded into AccuSyn ( https://accusyn.usask.ca/ ) and SynVisio ( https://synvisio.github.io/#/ ) to create a wheat-specific database ( https://kiranbandi.github.io/10wheatgenomes/ ). Pretzel ( https://github.com/plantinformatics/pretzel ) was also used to visualize and compare the RQA and the projected gene annotations ( http://10wheatgenomes.plantinformatics.io/ ).

Characterization of Sm1

Sm1 -linked markers 6 were located in RQAs using BLAST v.2.8.0. Two high-resolution mapping populations were developed, 99B60-EJ2D/Thatcher and 99B60-EJ2G/Infinity. Progeny heterozygous for crossover events near Sm1 were identified in the F 2 generation, and the crossovers were fixed in the F 3 generation. The resulting F 2 -derived F 3 families were analysed with KASP markers within the Sm1 region and tested for resistance to OWBM in field nurseries to identify markers associated with Sm1 . Ethyl methanesulfonate was used to develop knockout mutants in the Sm1 gene. Approximately 3,200 seeds of the Canadian spring wheat variety Unity (an Sm1 carrier) were soaked in a 0.2% (v/v) aqueous ethyl methanesulfonate solution for 22 h at 22 °C. The seed was then rinsed in distilled water and sown in a field nursery. The M 1 seed was grown to maturity and bulk harvested. Approximately 6,000 M 2 seeds were space planted in two field nurseries located in Brandon and Glenlea, Manitoba, Canada. Spikes were collected on a per-plant basis at maturity and were classified as resistant, susceptible or undamaged as done previously 6 , 72 . Putative Sm1 -knockout mutants were re-tested for OWBM resistance in indoor cage tests 73 in the M 3 and M 4 generations. M 4 -derived families were tested for resistance to OWBM in field nurseries (randomized complete block design, six environments, and eight replicates per environment).

Candidate genes were identified between Sm1 flanking markers on the CDC Landmark assembly using the projected gene annotations and FGENESH v.2.6 ( http://www.softberry.com/ ), which were compared to the projected genes of non-carriers. Both 5′ and 3′ rapid amplification of cDNA ends (5′ and 3′ RACE) were used to verify the transcription initiation and termination sites of the gene candidate, whose structure was predicted by FGENESH v.2.6. In brief, RNA was extracted from the leaves of Unity ( Sm1 carrier) seedlings (using the Qiagen RNeasy kit), RACE PCR performed (Invitrogen GeneRacer kit), and the PCR product cloned (Invitrogen TOPO TA Cloning kit for sequencing) and sequenced by Sanger sequencing. Prediction of the conserved domains was done using the NCBI Conserved Domain Search tool ( https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi ) and PROSITE (release 2020_01; https://prosite.expasy.org/ ). The LRR domain was defined on the basis of the presence of 2–42 LRR motif repeats of 20–30 amino acids each. LRR motifs were manually annotated 74 . Prediction of transmembrane regions and orientation was performed using the program TMpred NCBI Conserved Domain Search tool ( https://embnet.vital-it.ch/software/TMPRED_form.html ).

To study the expression of Sm1 , total RNA was extracted from four biological replicates from four wheat genotypes (Unity, CDC Landmark, Waskada and Thatcher) from two different tissues; seedling leaves and developing kernels (five days post anthesis) using NucleoSpin RNA Plant kit (Macherey-Nagel) according to the manufacturer’s instructions. RNA was treated with RNase-free DNase (rDNase) (Macherey-Nagel) and reversed transcribed into cDNA using SuperScript IV Reverse Transcriptase kit (Invitrogen) according to the manufacturer’s instructions and the NB-ARC domain amplified by PCR.

Reporting summary

Further information on research design is available in the  Nature Research Reporting Summary linked to this paper.

Data availability

All sequence reads assemblies have been deposited into the National Center for Biotechnology Information sequence read archive (SRA) (see Supplementary Table 1 for accession numbers). Sequence reads for the RQAs, T. ponticum , A. ventricosa and T. timopheevii have been deposited into the SRA (accession no. PRJNA544491 ) and ChIP–seq short read-data used for centromere characterization is deposited under accession no. PRJNA625537 . All Hi-C data have been deposited in the European Nucleotide Archive (Supplementary Table 1). The RQAs are available for direct user download at https://wheat.ipk-gatersleben.de/ . All assemblies and projected annotations are available for comparative analysis at Ensembl Plants ( https://plants.ensembl.org/index.html ). Comparative analysis viewers are also online for synteny ( https://kiranbandi.github.io/10wheatgenomes/ , http://10wheatgenomes.plantinformatics.io/ ) and haplotypes ( http://www.crop-haplotypes.com/ ). Seed stocks of the assembled lines are available at the UK Germplasm Resources Unit ( https://www.seedstor.ac.uk/ ).

Code availability

Code for custom genome visualizers have been deposited in the public domain for haplotype viewer ( https://github.com/Uauy-Lab/pangenome-haplotypes ), Pretzel ( https://github.com/plantinformatics/pretzel ), AccuSyn ( https://github.com/jorgenunezsiri/accusyn ) and SynVisio ( https://github.com/kiranbandi/synvisio ). Additional scripts used for ChIP–seq analysis of the centromeres are provided at https://github.com/wheatgenetics/centromere .

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Acknowledgements

We are grateful for funding from the Canadian Triticum Applied Genomics research project (CTAG2) funded by Genome Canada, Genome Prairie, the Western Grains Research Foundation, Government of Saskatchewan, Saskatchewan Wheat Development Commission, Alberta Wheat Commission, Viterra and Manitoba Wheat and Barley Growers Association. Funding was also provided by the Biotechnology and Biological Sciences Research Council (BBSRC) via the projects Designing Future Wheat (BB/P016855/1), sLOLA (BB/J003557/1) and MAGIC Pangenome (BB/P010741/1, BB/P010733/1 and BB/P010768/1), by AMED NBRP (JP17km0210142), the German Federal Ministry of Education and Research (FKZ 031B0190, WHEATSeq, 2819103915 and 2819104015), German Network for Bioinformatics and Infrastructure de.NBI (FKZ 031A536A, 031A536B), German Federal Ministry of Food and Agriculture (BMEL FKZ 2819103915 WHEATSEQ), Israel Science Foundation (Grant 1137/17), JST CREST (JPMJCR16O3), US National Science Foundation (1339389), Kansas Wheat Commission and Kansas State University, MEXT KAKENHI, The Birth of New Plant Species (JP16H06469, JP16H06464, JP16H06466 and JP16K21727), National Agriculture and Food Research Organization (NARO) Vice President Fund, Swiss Federal Office of Agriculture (NAP-PGREL), Agroscope, Delley Seeds and Plants, ETH Zurich Institute of Agricultural Sciences, Fenaco Co-operative, IP-SUISSE, swisssem, JOWA, SGPV-FSPC, Swiss National Science Foundation (31003A_182318 and CRSII5_183578), University of Zurich Research Priority Program Evolution in Action, King Abdullah University of Science and Technology, Grains Research and Development Corporation (GRDC), Australian Research Council (CE140100008) and Groupe Limagrain. We are grateful for the computational support of the Functional Genomics Center Zurich, the Molecular Plant Breeding Group—ETH Zurich, and the Global Institute of Food Security (GIFS), Saskatoon. We acknowledge the contribution of the Australian Wheat Pathogens Consortium ( https://data.bioplatforms.com/organization/edit/bpa-wheat-cultivars ) in the generation of data used in this publication. The Initiative is supported by funding from Bioplatforms Australia through the Australian Government National Collaborative Research Infrastructure Strategy (NCRIS). We thank S. Wu for DNA preparations for assembly and ChIP–seq library preparations; O. Francisco-Pabalan and J. Santos, T. Wisk and S. Wolfe for their provision of OWBM images; M. Knauft, I. Walde, S. König, T. Münch, J. Bauernfeind and D. Schüler for their contribution to Hi-C data generation and sequencing, DNA sequencing and IT administration and sequence data management; J. Vrána for karyotyping the wheat cultivars Arina and Forno; and R. Regier for project management, administration and support.

Author information

These authors contributed equally: Sean Walkowiak, Liangliang Gao, Cecile Monat

Authors and Affiliations

Crop Development Centre, University of Saskatchewan, Saskatoon, Saskatchewan, Canada

Sean Walkowiak, Valentyna Klymiuk, Brook Byrns, Kirby Nilsen, Jennifer Ens, Krystalee Wiebe, Amidou N’Diaye, Pierre J. Hucl & Curtis J. Pozniak

Grain Research Laboratory, Canadian Grain Commission, Winnipeg, Manitoba, Canada

Sean Walkowiak & Bin Xiao Fu

Department of Plant Pathology, Kansas State University, Manhattan, KS, USA

Liangliang Gao, Emily Delorean, Dal-Hoe Koo, Allen K. Fritz & Jesse Poland

Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, Germany

Cecile Monat, Axel Himmelbach, Anne Fiebig, Sudharsan Padmarasu, Uwe Scholz, Martin Mascher & Nils Stein

Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany

Georg Haberer, Heidrun Gundlach, Klaus F. X. Mayer & Manuel Spannagl

Aquatic and Crop Resource Development, National Research Council Canada, Saskatoon, Saskatchewan, Canada

Mulualem T. Kassa, Pierre Fobert & Sateesh Kagale

John Innes Centre, Norwich Research Park, Norwich, UK

Jemima Brinton, Ricardo H. Ramirez-Gonzalez, Michael Bevan, Neil McKenzie, Burkhard Steuernagel & Cristobal Uauy

Department of Plant and Microbial Biology, University of Zurich, Zurich, Switzerland

Markus C. Kolodziej, Simon G. Krattinger, Beat Keller & Thomas Wicker

Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, Manitoba, Canada

Dinushika Thambugala & Curt A. McCartney

Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada

Venkat Bandi, Jorge Nunez Siri & Carl Gutwin

Brandon Research and Development Centre, Agriculture and Agri-Food Canada, Brandon, Manitoba, Canada

Kirby Nilsen

Genomics/Transcriptomics group, Functional Genomics Center Zurich, Zurich, Switzerland

Catharine Aquino & Masaomi Hatakeyama

Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland

Dario Copetti, Gwyneth Halstead-Nussloch, Masaomi Hatakeyama, Timothy Paape, Rie Shimizu-Inatsugi & Kentaro K. Shimizu

Institute of Agricultural Sciences, ETHZ, Zurich, Switzerland

Dario Copetti

Kihara Institute for Biological Research, Yokohama City University, Yokohama, Japan

Tomohiro Ban, Kanako Kawaura, Toshiaki Tameshige, Hiroyuki Tsuji & Kentaro K. Shimizu

Life Sciences Department, Natural History Museum, London, UK

Luca Venturini & Matthew Clark

Earlham Institute, Norwich Research Park, Norwich, UK

Bernardo Clavijo, Christine Fosker, Gonzalo Garcia Accinelli, Darren Heavens, Ksenia Krasileva, David Swarbreck, Jonathan Wright & Anthony Hall

The John Bingham Laboratory, NIAB, Cambridge, UK

Keith A. Gardner, Nick Fradgley, Lawrence Percival-Alwyn & James Cockram

Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, MN, USA

Juan Gutierrez-Gonzalez & Gary Muehlbauer

Global Institute for Food Security, University of Saskatchewan, Saskatoon, Saskatchewan, Canada

Chu Shin Koh & Andrew G. Sharpe

School of Plant Sciences and Food Security, Tel Aviv University, Ramat Aviv, Israel

Jasline Deek

Department of Entomology, University of Manitoba, Winnipeg, Manitoba, Canada

Alejandro C. Costamagna

Institute of Crop Science, NARO, Tsukuba, Japan

Hiroyuki Kanamori, Fuminori Kobayashi, Tsuyoshi Tanaka, Jianzhong Wu & Hirokazu Handa

Centre for Biodiversity Genomics, University of Guelph, Guelph, Ontario, Canada

National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan

Tony Kuo & Jun Sese

Laboratory of Plant Genetics, Graduate School of Agriculture, Kyoto University, Kyoto, Japan

Kazuki Murata, Yusuke Nabeka & Shuhei Nasuda

Humanome Lab, Tokyo, Japan

Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico

Philomin Juliana & Ravi Singh

Montana BioAg, Missoula, MT, USA

Hikmet Budak

Australian Research Council Centre of Excellence in Plant Energy Biology, School of Molecular Sciences, University of Western Australia, Perth, Western Australia, Australia

Ian Small & Joanna Melonek

Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, Ontario, Canada

Sylvie Cloutier

Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, Victoria, Australia

Gabriel Keeble-Gagnère & Josquin Tibbets

Syngenta, Durham, NC, USA

Erik Legg & Arvind Bharti

School of Agriculture, Food and Wine, University of Adelaide, Adelaide, South Australia, Australia

Peter Langridge & Ken Chalmers

German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany

Martin Mascher

Biological and Environmental Science & Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

Simon G. Krattinger

Graduate School of Life and Environmental Sciences, Kyoto Prefectural University, Kyoto, Japan

Hirokazu Handa

Institute of Evolution and Department of Evolutionary and Environmental Biology, University of Haifa, Haifa, Israel

Assaf Distelfeld

School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany

Klaus F. X. Mayer

Center for Integrated Breeding Research (CiBreed), Georg-August-University Göttingen, Göttingen, Germany

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Contributions

Project establishment: K.C., A.D., A. Hall, B.K., S.G.K., E.L., P.L., K.F.X.M., J.P., C.J.P., K.K.S., M.S. and N.S. Project coordination: A. Hall, C.J.P. and N.S. Genome assemblies were contributed as follows: CDC Stanley and CDC Landmark: P.J.H., C.J.P., A.G.S., B.B., C.S.K., A.N., K.N. and S.W.; Julius: K.F.X.M., N.S., M.M., C.M. and U.S.; Jagger: G.M., J.P. and L.G.; Arina LrFor : B.K., S.G.K. and M.C.K.; Mace and LongReach Lancer: K.C., P.L., G.K.-G. and J.T.; Norin 61: K.K.S., H.H., S.N., J.S., K. Kawaura, H.T., T. Tameshige, T.B., D.C., M.H., R.S.-I., C.A., F.K., J.G.-G. and N.S.; SY Mattis: E.L. and A.B.; spelt (PI190962): A.D., C.J.P. and J.D.; Robigus, Claire, Paragon and Cadenza: M.B., M.C., B.C., C.F., N.F. and D.H.; Weebill 1: M.C., B.C., J.C., K.A.G., L.P.-A. and L.V. Sequencing, assembly and analysis were contributed by WRA2P computational assembly: A. Hall, B.C., G.G.A., K. Krasileva, N.M., D.S. and J. Wright; 10X Genomics: H.B., C.J.P., J.E., S.K. and K.W.; Hi-C and structural analysis: M.M., N.S., A. Himmelbach, C.M., S.P. and L.G.; pseudomolecule assemblies: M.M., C.M. and N.S.; gene projections and TE analysis: K.F.X.M., M.S., H.G. and G.H.; diversity and polymorphism analysis: K.K.S., E.D., T.P., G.H.-N., D.C., M.H., G.H., H.H., H.K., M.S., K.M., T. Tameshige, T. Tanaka, J.S. and J. Wu; centromere diversity: J.P. and D.H.K.; 5B/7B translocation: S.G.K., T.W., J.C. and M.C.K; 2N v S introgression: J.P., A.K.F., L.G., P.J., C.J.P., R.S. and S.W.; TE-based introgressions: T.W., B.B., J.E., M.C.K., J.P., C.J.P., J.T. and S.W; cytological karyotyping: S.N., K.M., Y.N., J.S. and T.K.; diversification of Rf genes: J.M. and I.S.; NLR repertoire: S.G.K. and B.S.; Sm1 gene cloning: C.A.M., C.J.P., C.U., J.B., A.C.C., S.C., P.F., M.T.K., V.K., D.T. and K.W.; haplotype database: C.U., J.B. and R.H.R.-G.; visualization software: C.G., V.B., G.K.-G., J.N.S., J.T. and J.M.; BLAST server: M.M., A.F. and U.S.; C.J.P and S.W. drafted the manuscript with input from all authors. All co-authors contributed to and edited the final version.

Corresponding authors

Correspondence to Curt A. McCartney , Manuel Spannagl , Thomas Wicker or Curtis J. Pozniak .

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Extended data figures and tables

Extended data fig. 1 chromosome-scale collinearity between the rqa..

Genomes were aligned chromosome by chromosome using MUMmer and are represented as dot plots. The introgression on chromosome 2B of LongReach Lancer (red rectangles) and 5B/7B translocation in SY Mattis and Arina LrFor (purple rectangles) are indicated.

Extended Data Fig. 2 Evaluation of the CDC Landmark RQA using Oxford Nanopore Long Reads.

a , Scaffold-scaffold long read contact map showing shared read IDs between scaffold ends along the ordered scaffolds in the CDC Landmark pseudomolecules. The diagonal pattern indicates that adjacent scaffolds share the same long reads and are therefore properly ordered and oriented by Hi-C in the RQA. b , Characterization of inversion events on chromosomes 2A, 3A, and 3D. The directionality biases estimated from alignments of Hi-C data against Chinese Spring (left, top), and chromosome alignment of the inversion events between CDC Landmark and Chinese Spring RQAs (left, bottom) are shown. Long reads spanning the inversion events and magnified views of the reads aligning to the left and right boundaries of the inversions (right) are provided.

Extended Data Fig. 3 Diversity of genes and TEs.

a , Average pairwise genetic diversity of the homeologues (coding sequences only) of the A, B and D subgenomes. The mode of the A, B and D subgenome is 0.00057, 0.00082, and 0.0002, respectively. b , Tajima’s D estimates of coding sequences for each wheat subgenome. The lower and upper range of the boxplot hinges correspond to the first and third quartiles (the 25th and 75th percentiles). Boxplots show centre line, median; box limits, upper and lower quartiles; whiskers, 1.5 × interquartile range. c , Total gene counts and orthologues for the RQA. Genes in orthologous groups with exactly one gene for each line (Complete; dark brown), genes contained in unambiguous orthologous groups missing an orthologue for at least one line, that is, PAV (2-10 Lines; light brown), and genes with ambiguous orthologues or CNV (Other; pink) are indicated. d , Per cent of pairwise shared syntenic fl-LTRs between wheat lines.

Extended Data Fig. 4 Evolutionary relationships among PPR and mTERF gene sequences.

a , The RFL clade is in blue and all remaining P-class PPRs are in green. b , Clustered mTERF sequences are in blue and the remaining mTERFs are shown in green. The scale bar represents number of substitutions per site. c , Sequence inversions and copy number variation at the Rf3 locus on chromosome 1B. RFL genes are shown as light pink triangles above the chromosome scale. Conserved non-PPR genes used as syntenic anchors are shown on the chromosome scale as coloured triangles. The total number (T) and the number of putatively functional RFL genes with 10 or more PPR motifs (F) are indicated on the right side of each panel.

Extended Data Fig. 5 Identification of alien introgressions from wheat relatives.

A feature of foreign chromosomal introgressions is that they contain unique patterns of TE insertions. Shown are stretches of >20 Mb containing multiple polymorphic RLC- Angela retrotransposons that are found only in one or a few (≤4) of the sequenced lines. One representative chromosome for each wheat subgenome is shown. Individual polymorphic retrotransposons are indicated as coloured vertical lines. Colours correspond to the number of cultivars a foreign segment is found in. Regions of particular interest are indicated by black rectangles. These include the 2N v S alien introgression from A. ventricosa at the end of chromosome 2A in Jagger, Mace, SY Mattis and CDC Stanley, as well as introgression in the central region of chromosome 2B from T. timopheevi in LongReach Lancer, and introgression at the end of chromosome 3D from T. ponticum in LongReach Lancer.

Extended Data Fig. 6 Detailed characterization of the 2N v S introgression from A. ventricosa .

a , Pairwise alignments of the first 50 Mb of chromosome 2A. The black arrow indicates a possible unique haplotype within spelt. b , Orthologous genes between the 2N v S introgression from A. ventricosa in Jagger and the genes on chromosomes 2A, 2B, and 2D in Chinese Spring. c , Frequency of 2N v S introgression carriers in North American datasets from CIMMYT, Kansas State, and the USDA Winter Wheat Regional Performance Nursery (RPN) over time. d , Per cent yield difference in lines that carry the 2N v S introgression. Two sided t -tests were performed to test for the significance of the impact of the 2N v S introgression. ** P  < 0.01; *** P  < 0.001.

Extended Data Fig. 7 Centromere positions and karyotype variation.

Functional centromere positions in the RQA have undergone structural and positional rearrangement. Chromosome alignments showing collinearity (black scaffolds in same orientation, grey scaffolds in opposite orientation) with relative density of CENH3 ChIP–seq mapped to 100 kb genomic bins for Chinese Spring (blue) and a representative genome of comparison (red) for chromosome 4B of CDC Stanley ( a ), and chromosome 5B of Julius ( b ). c , Detailed list and clustering of cytological features carried by each wheat line (Supplementary Note  6 ). Features that are identical (dark grey) or have a gain (black) or loss (light grey) relative to Chinese Spring are indicated.

Extended Data Fig. 8 Hi-C validates inversions identified from pairwise chromosome alignments.

Pairwise alignments of chromosome 6B from the RQA and Chinese Spring are shown. Above each alignment dot plot, the directionality biases estimated from alignments of Hi-C data against Chinese Spring are shown. Boundaries of diagonal segments are indicative of inversions and coincide with inversion boundaries identified from the chromosome alignments.

Extended Data Fig. 9 Characterization of a translocation involving wheat chromosomes 5B and 7B.

a , Cytogenetic karyotypes of Forno (left) and Arina (right), the parents of Arina LrFor . Note that the large recombinant chromosome 7B is represented by a distinct peak. b , Sequence of the translocation breakpoint on chromosome 7B of Arina LrFor . Note that the exact breakpoint lies in a sequence gap (stretch of Ns). The bp positions are indicated at the left. Forward PCR primers are shown in red and reverse primers in blue. The overlap of the two reverse primers is shown in purple. The outer primer pair was used for PCR, while the inner pair was used for a nested PCR. c , PCR amplification of the fragment spanning the translocation breakpoint. The nested PCR yielded a ~5 kb fragment that spanned the translocation breakpoint and its identity was confirmed by sequencing. Both PCR and nested PCR were performed in duplicate; both replicates of the nested PCR were sequenced using the Sanger method. For gel source data, see Supplementary Fig. 1 . d , Mapping of Illumina reads from the cultivars Arina and Forno on to the pseudomolecules of Arina LrFor . Sequence derived from Forno is shown in blue, while sequenced derived from Arina is in red. Note that chromosomes 5B and 7B are derived from both parents, indicating that these parental chromosomes can recombine freely, despite the presence of a large 5B/7B translocation in Arina.

Extended Data Fig. 10 Confirmation of gene expression and gene structure for Sm1 .

a , Critical recombinants from the 99B60-EJ2G/Infinity and 99B60-EJ2D/Thatcher populations used to fine map Sm1 . The 99B60-EJ2G/Infinity cross had 5,170 F 2 plants, while 99B60-EJ2D/Thatcher cross had 5,264 F 2 plants; only recombinant haplotypes between orange wheat blossom midge resistant (R) and susceptible (S) genotypes are shown. b , Oxford Nanopore long read confirmation of the Sm1 gene candidate in the CDC Landmark RQA (left), and alternative haplotype in Chinese Spring (right). Vertical coloured lines indicate sequence variants. c , Amplification of cDNA for the NB-ARC domain of the Sm1 gene candidate (top) and actin control (bottom) derived from RNA isolated from developing kernels (left) and wheat seedlings (right). Unity and CDC Landmark are carriers of Sm1 . Waskada carries an alternative haplotype and does not carry Sm1 (see main text). Thatcher was used as a susceptible parent for fine mapping of Sm1 and does not contain the associated NB-ARC domain. The experiment was replicated on four independent biological samples for each condition. d , Distribution of an Sm1 allele-specific PCR marker in a diverse panel of >300 wheat lines.

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Supplementary Figure 1. Original gel source data used for spanning the breakpoint for the 7B/5B translocation.

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Assessment of climate change impact and potential adaptation measures on wheat yield using the DSSAT model in the semi-arid environment

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• Volume 111 , pages 2077–2096, ( 2022 )

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• Devesh Sharma 2 ,
• Aditya Sharma 2 &
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Crop simulation models are essential tools to facilitate the evaluation and application of crop production practices under different climate scenarios. The present study analyzed the impact of climate change on wheat production in the semi-arid regions of western India by using the decision support system for the agrotechnology transfer (DSSAT-CERES) simulation model. We used ensemble and bias correction data of the coordinated regional downscaling experiment for South Asia (CORDEX-SA) driving global climate model (GCM) experiments for the future climate. The study considered the historical (1981–2010), experimental period (2014–2017), and future (2021–2050 and 2051–2080) climatic data to simulate grain yield. We used a randomized complete block design for different crop treatments, followed by a comparison of the simulated crop yield with the historical yield to evaluate the selected adaptation measures to reduce the impact of future climate scenarios. We observed that early sowing dates and medium planting density were the significant factors for achieving high wheat production. The simulation results revealed that the wheat yields would decrease in the near and far future under RCP 4.5 and RCP 8.5 scenarios. Our findings emphasize the requirement to adapt best measurements to improve yield, which involves early sowing by two weeks and maintaining a planting density of 150 plants per square meter. Experimentally, our results suggest that the DSSAT model, if calibrated carefully, can serve as a valuable tool for decision-making on adaptation practices of winter wheat under changing climates.

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1. Introduction

Wheat ( Triticum aestivum ) is known as one of the most important cereal crops and is extensively grown worldwide [ 1 ]. Wheat contributes to 50% and 30% of the global grain trade and production respectively [ 2 ]. Wheat is also known as a staple food in more than 40 countries of the world. Wheat provides 82% of basic calories and 85% of proteins to the world population [ 3 , 4 ]. Wheat-based food is rich in fiber contents than meat-based food. Dough produced from bread wheat flour has different viscoelastic properties than other cereals. It is considered a higher fiber food. Therefore, its positive effects on controlling cholesterol, glucose, and intestinal functions in the body were observed [ 5 ]. Primarily, wheat is being used to make Chapatti (Bread) but it also contributes to other bakery products. Wheat utility and high nutritional value made it the staple food for more than 1/3rd population of the world. Wheat grain is separated from the chaff and stalks after the harvesting of wheat. Stalks of wheat are further used in animal bedding and construction material. Globally, the need for wheat production is enhancing even in countries having unfavorable climates for its production. Global climate changes are badly affecting the production of wheat and it raised the concern for food security.

It is estimated that annual cereal production should be increased by 1 billion tons to feed the expected population of 9.1 billion by 2050 [ 1 ]. The current scenario demands an increase in crop productivity to meet the increased requirements of food supply [ 6 ]. Wheat is grown in tropical and subtropical regions which experiences a lot of stress. These stresses result in a reduction of yield [ 7 ]. Major environmental stresses include cold, salinity, heat, and drought which are drastically affecting its yield. However, water and heat are considered as the key environmental stresses which caused in reduction of the wheat yield globally [ 8 , 9 ]. So, genetic improvements related to yield and stress tolerance are mandatory to enhance the production of wheat [ 10 , 11 ].

2. Genetically modified wheat plants

Genetically modified wheat plants have been produced by the use of bacteria. Wheat plants were inoculated with the plant-growth-promoting bacteria (PGPB) which resulted in the higher expression of abiotic stress (mainly drought and salinity) tolerant genes [ 12 ]. PGPB inoculated wheat cultivars also showed the higher expression of genes encoding antioxidant-enzymes, such as catalase (CAT), peroxidase , ascorbate peroxidase (APX), and glutathione peroxidase (GPX). So, it was concluded that PGPB used in wheat plants resulted in increased tolerance to abiotic stresses [ 12 ]. Cold shock proteins increase the survival of bacteria in severe environmental conditions. CspA and CspB genes from bacteria were transformed into wheat. Transgenic wheat plants expressing SeCspA and SeCspB were observed to have decreased water loss rate, increased proline and chlorophyll contents under salinity, and less water-stress conditions [ 13 ]. It was further investigated that SeCsp transgenic wheat plants resulted in enhanced weight and yield of grain than the control plants. SeCspA transgenic wheat plants were observed to have an improved water-stress tolerance than the control plants ( Table 1 , [ 13 ]).

Development of transgenic wheat having various traits/phenotypes.

Gluten is a protein comprised of gliadins found in wheat. Gluten is the main cause of coeliac disease in individuals. Bread-making quality of wheat is determined by the gluten proteins. Wheat varieties with less gliadin contents were produced using gene-editing technologies and RNAi (RNA interference). Wheat lines lacking immunogenic gluten were produced. Low immunogenic gluten and more nutritional values were added in one wheat line named E82. A better microbiota profile (protection microorganisms available in the gut) was observed in the NCWS patients using the bread made with E82 [ 28 ]. Plant cuticle has a positive role in the protection of plant against biotic and abiotic stresses. Wheat plants transformed with TaSHN1 resulted in increased water-stress tolerance by reducing the leaf stomatal density and changing the composition of the cuticle [ 29 ].

3. Biotic stress tolerance in wheat

Wheat is considered an excessive contributor toward the human calorie intake [ 30 ]. Pests and pathogens cause yield losses in wheat up to 21.5% of the total losses and could be reached to 28.1% [ 31 ]. Wheat is affected by the fungal disease, powdery mildew caused by Blumeria graminis f. sp. tritici (Bgt). Powdery mildew is a damaging disease that resulted in greater loss of wheat [ 32 ]. Broad-spectrum resistant genes (BSR) are considered to have the most significant role to control powdery mildew. CMPG1-V gene was cloned from the Hynaldia villosa and it was observed that higher expression of CMPG1-V gene resulted in the Broad-spectrum resistance against powdery mildew [ 33 , 34 ]. Barley chi26 gene could also be used to enhance the resistance against powdery mildew and rust through genetic modification [ 35 ]. Some epigenetic regulators were determined to have a role in wheat powdery mildew resistance. TaHDT701 is a histone deacetylase that was found as a negative regulator of wheat defense against powdery mildew. TaHDT701 was observed to be associated with the one repeat protein (TaHOS15) and RPD3 type histone deacetylase TaHDA6. Knockdown of this histone deacetylase complex ( TaHDT701 , TaHDA6 , TaHOS15 ) in wheat resulted in increased powdery mildew tolerance [ 36 ].

Fusarium graminearum is a plant fungal pathogen that causes a devastating disease called Fusarium head blight in wheat. It results in the reduction of wheat production. Genetic techniques were used to increase the FHB (Fusarium head blight) resistance in wheat. Transgenic wheat plants expressing barley class II chitinase gene 2 were observed to have a higher resistance against Fusarium graminearum [ 37 ]. Lr10 and Lr21 were cloned and transformed into wheat. The transgenic plants were reported to be resistant to leaf rust disease. Evolution and diversification of HIPPs (heavy metal-associated isoprenylated plant proteins) genes were studied in Triticeae [ 38 ]. HIPPs genes of Hynaldia villosa were cloned through homology-based cloning. Transgenic wheat having HIPP1-V was developed and the role of HIPP1-V in cadmium stress was characterized. It was observed that higher expression of this gene resulted in increased tolerance to cadmium stress. Therefore, HIPP1-V could be used to increase the tolerance in wheat against cadmium [ 39 ].

4. Abiotic stress tolerance in wheat

Grain number, weight, and size are greatly reduced under the negative effects of environmental stresses. However, the timing, duration, and intensity of stress determine the severity of the negative effects [ 40 , 41 ]. Wheat is a major source of protein and calories for the human diet. High temperature is badly affecting the yield of wheat which is a main concern worldwide. Drought and heat stresses are the two main abiotic stresses which are playing a greater role in the reduction of wheat yield. Reduction in starch contents, photosynthetic activity, grain number, and chlorophyll contents in the endosperm is caused due to rise in temperature. Heat stress results in the accumulation of reactive oxygen species (ROS) which is the main reason for higher oxidative damage to the plant. Heat stress also results in the variation of wheat biochemistry, morphology, and physiology. Tolerance, avoidance, and escape are known as the three major mechanisms that support the plant to grow in a heat-stress environment. Major heat tolerance mechanisms in wheat are known as stay green, heat shock proteins, and antioxidant defense [ 42 ]. Protein synthesis and folding were observed to be interrupted during heat stress. Heat stress also resulted in the production of several stress agents badly affecting transcription, translation, and DNA replication in plants [ 43 ]. Plants speed up the production of heat shock proteins as a defense mechanism [ 44 ]. Higher activity of antioxidants, such as peroxidases, catalase, and superoxide dismutase, was observed under heat stress. Wheat cultivar showing greater tolerance to heat stress was observed to have higher activity of catalase, ascorbate peroxidase, and S-transferase [ 45 ].

Salt stress greatly affects the growth of wheat plants. Salinity stress has a higher impact on the morphology and physiology of wheat plants. Plants having less tolerance to salinity are not suitable for cropping. Potassium transporter ( HKT ) genes have a greater role in achieving salinity tolerance in wheat. Sodium (Na + ) exclusion through HKT genes is a major mechanism in wheat to have a salinity tolerance. OsMYBSs and AtAB14 are the transcription factors having a role in regulating HKT genes, which are considered as the candidate targets for increasing salinity tolerance in wheat [ 46 ]. Wheat transformed with a mutated transcription factor, HaHB4 showed higher water-use efficiency and was more yielding under drought stress [ 26 ]. Transgenic wheat expressing GmDREB1 gene from soybean was also observed to have higher drought tolerance under water-stress conditions [ 47 ]. DREB1A gene from Arabidopsis thaliana was introduced to bread wheat and increased tolerance against water stress in the transgenic wheat was observed. Bread wheat under drought stress was observed to have a higher level of WRKY proteins [ 48 ]. Higher expression of AtHDG11 gene in transgenic wheat resulted in increased water-stress tolerance during drought-stress conditions. Enhanced TaNAC69 expression in root and leaf of wheat during drought stress was observed [ 49 ]. Researchers are working to develop transgenic wheat having various traits/phenotypes by using advanced approaches of biotechnology for the last several decades ( Table 1 ). Numbers of transgenic wheat cultivars are being grown in the fields and several more are under trial.

5. CRISPR/Cas9 system in wheat

Gliadins and glutenins are known as the gluten proteins and ingestion of these proteins from barley, rye, and wheat could cause the disease called coeliac disease in humans. The only remedy is to develop gluten-free food. Transgenic wheat which retains baking quality and is safe for coeliac could not be produced using conventional methods because of the complexity of the wheat genome. Coeliac disease (CD) is activated by the immunogenic isotopes mainly gliadins. Gliadin families were downregulated by the use of RNA interference. CRISPR/Cas9 is a targeted gene manipulation tool considered to have a potential role in genetic modification ( Table 2 , [ 60 , 61 ]). CRISPR/Cas9 system was recently used for gene editing of gliadins. Offsprings with deleted, edited, or silenced gliadins were produced by CRISPR/Cas9. They helped to decrease the exposure of the patient to the CD epitopes [ 62 ]. This technology has been used to develop wheat cultivars having gluten genes with inactivated CD epitopes [ 62 , 63 ].

Genome edited wheat developed by CRISPR/Cas9 system.

CRISPR/Cas9 system and TALENS (transcription activator-like effector nuclease) were used in the bread wheat to generate the mutations in three homoeoalleles that encode MLO locus proteins against mildew. Mutations in all three TaMLO were generated by using TALENS which resulted in resistance against powdery mildew. The MLO homoeoalleles ( TaMLOA1 , TaMLOB1, and TaMLOD1 ) of bread wheat contributed to the mildew infection. Mutation of MLO alleles resulted in powdery mildew tolerance in wheat [ 50 ]. Genome editing was reported in which pds (phytoene desaturase) and inox (inositol oxygenase) genes in the cell suspension-culture of wheat were targeted. It was demonstrated that the genome-editing technique could also be applied in the cell suspension of wheat [ 64 ]. Very recently, various research groups are involved to develop transgenic wheat by using genome-editing technology. Some of the experiments are listed in Table 2 .

6. Wheat computational analysis

A comprehensive resource for wheat reference genome was developed by International Wheat Genome Sequencing Consortium. The URGI portal ( https://wheat-urgi.versailles.inra.fr/ ) was developed for the breeders and researchers to access the genome sequence data of bread-wheat. InterMine tools, genome browser, and BLAST were established for the exploration of genome sequences together with the additional linked datasets, including gene expression, physical maps, and sequence variation. Portal provided the higher browser and search features that facilitated the use of the latest genomic resources required for the upgradation of wheat [ 65 ].

DNA binding with one finger (Dof) transcription factors is known to have an important role in abiotic stress tolerance as well as the growth of plants. Ninety-six TaDof members of the gene family have been studied using computational approaches. By qPCR analysis, it was revealed that TaDof genes were upregulated under heavy metal and heat stress in wheat. Consequently, it could be concluded that detection of amino acid sites, genome-wide analysis, and identification of the Dof transcription factor family could provide us the new insight into the function, structure, and evolution of the Dof gene family [ 66 ].

Acknowledgments

This work was supported by funds from the Higher Education Commission of Pakistan.

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