Advances in Breeding for Abiotic Stress Tolerance in Wheat

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dissertation heat stress

  • Suchismita Mondal 2 ,
  • Ahmed Sallam 3 ,
  • Deepmala Sehgal 2 ,
  • Sivakumar Sukumaran 2 ,
  • Md Farhad 4 , 5 ,
  • J. Navaneetha Krishnan 4 , 6 ,
  • Uttam Kumar 2 , 7 &
  • Akshaya Biswal 2  

590 Accesses

17 Citations

Wheat is a key economically important cereal crop that is consumed globally. While the grain yield increase is steady at around 1%, it is not enough to meet the growing global demands of the next decades. One the major factor that affects wheat production is the uncertainty in climatic patterns. High temperature, drought, frost, and salinity are some of the abiotic stresses known to affect wheat production significantly. Developing wheat varieties with stable and high grain yield is the crucial for sustainable wheat production. Though, diversity for tolerance to abiotic stress exists within the wheat gene pools and elite germplasms, there is a need to rapidly introgress and breed for stress adapted lines. Optimization of the breeding process, through use of effective screening technologies, faster generation advance, and recycling of parents could impact the varietal development process significantly. The advances in genomic technologies, such as better and cheaper molecular markers and improved prediction models for genomic selection could further contribute to breeding for stress tolerant germplasm. Opportunities exists to increase the grain yield trends under abiotic stresses, which need to be effectively and efficiently utilized.

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Acuña-Galindo MA, Mason RE, Subramanian NK, Hays DB (2015) Meta-analysis of wheat QTL regions associated with adaptation to drought and heat stress. Crop Sci 55:477. https://doi.org/10.2135/cropsci2013.11.0793

Article   Google Scholar  

Afzal F, Li H, Gul A et al (2019) Genome-wide analyses reveal footprints of divergent selection and drought adaptive traits in synthetic-derived wheats. G3 Genes Genomes Genet 9:1957–1973. https://doi.org/10.1534/g3.119.400010

Ahmad MQ, Khan SH, Khan AS, et al (2014) Identification of QTLs for drought tolerance traits on wheat chromosome 2A using association mapping. Intl J Agric Biol 16

Google Scholar  

Ahmadi A, Baker DA (2001) The effect of water stress on the activities of key regulatory enzymes of the sucrose to starch pathway in wheat. Plant Growth Regul 35:81–91. https://doi.org/10.1023/A:1013827600528

Article   CAS   Google Scholar  

Ain QU, Rasheed A, Anwar A et al (2015) Genome-wide association for grain yield under rainfed conditions in historical wheat cultivars from Pakistan. Front Plant Sci 6:743. https://doi.org/10.3389/fpls.2015.00743

Article   PubMed   PubMed Central   Google Scholar  

Alexander LM, Kirigwi FM, Fritz AK, Fellers JP (2012) Mapping and quantitative trait loci analysis of drought tolerance in a Spring wheat population using amplified fragment length polymorphism and diversity array technology markers. Crop Sci 52:253. https://doi.org/10.2135/cropsci2011.05.0267

Allahverdiyev T (2015) Effect of drought stress on some physiological traits of durum ( Triticum durum Desf.) and bread ( Triticum aestivum L.) wheat genotypes. J Stress Physiol Biochem 11:29–38

Amoah JN, Ko CS, Yoon JS, Weon SY (2019) Effect of drought acclimation on oxidative stress and transcript expression in wheat ( Triticum aestivum L.). J Plant Interact 14:492–505. https://doi.org/10.1080/17429145.2019.1662098

Arafa Y, Wasif EA, Mehawed HE (2009) Maximizing water use efficiency in wheat yields based on drip irrigation systems. Aust J Basic Appl Sci 790–796

Asseng S, Ewert F, Martre P et al (2015) Rising temperatures reduce global wheat production. Nat Clim Chang 5:143–147. https://doi.org/10.1038/nclimate2470

Ayalew H, Liu H, Yan G (2016) Quantitative analysis of gene actions controlling root length under water stress in spring wheat ( Triticum aestivum L.) genotypes. Crop Pasture Sci 67. https://doi.org/10.1071/CP15244

Babar MA, Reynolds MP, Van Ginkel M et al (2006a) Spectral reflectance to estimate genetic variation for in-season biomass, leaf chlorophyll, and canopy temperature in wheat. Crop Sci 46:1046. https://doi.org/10.2135/cropsci2005.0211

Babar MA, Reynolds MP, Van Ginkel M et al (2006b) Spectral reflectance indices as a potential indirect selection criteria for wheat yield under irrigation. Crop Sci 46:578. https://doi.org/10.2135/cropsci2005.0059

Bai C, Liang Y, Hawkesford MJ (2013) Identification of QTLs associated with seedling root traits and their correlation with plant height in wheat. J Exp Bot 64:1745. https://doi.org/10.1093/jxb/ert041

Article   CAS   PubMed   PubMed Central   Google Scholar  

Bennett D, Reynolds M, Mullan D et al (2012a) Detection of two major grain yield QTL in bread wheat ( Triticum aestivum L.) under heat, drought and high yield potential environments. Theor Appl Genet 125. https://doi.org/10.1007/s00122-012-1927-2

Bennett D, Reynolds M, Mullan D et al (2012b) Detection of two major grain yield QTL in bread wheat ( Triticum aestivum L.) under heat, drought and high yield potential environments. Theor Appl Genet 125:1473–1485. https://doi.org/10.1007/s00122-012-1927-2

Article   PubMed   Google Scholar  

Bharti S, Balyan HS, Gupta P (2014) Quantitative trait loci analysis for some root traits in bread wheat ( Triticum aestivum l.). Intl J Agric Sci 4:214–221

Bhatta M, Morgounov A, Belamkar V, Baenziger PS (2018) Genome-wide association study reveals novel genomic regions for grain yield and yield-related traits in drought-stressed synthetic hexaploid wheat. Intl J Mol Sci 19:3011. https://doi.org/10.3390/ijms19103011

Bhusal N, Sarial AK, Sharma P, Sareen S (2017) Mapping QTLs for grain yield components in wheat under heat stress. PLoS ONE 12:e0189594. https://doi.org/10.1371/journal.pone.0189594

Biswal AK, Mangrauthia SK, Reddy MR, Yugandhar P (2019) CRISPR mediated genome engineering to develop climate smart rice: challenges and opportunities. Semin Cell Dev Biol 96:100–106. https://doi.org/10.1016/j.semcdb.2019.04.005

Article   CAS   PubMed   Google Scholar  

Borrell AK, van Oosterom EJ, Mullet JE et al (2014) Stay-green alleles individually enhance grain yield in sorghum under drought by modifying canopy development and water uptake patterns. New Phytol 203:817. https://doi.org/10.1111/nph.12869

Boyd LA, Smith PH, Hart N (2006) Mutants in wheat showing multipathogen resistance to biotrophic fungal pathogens. Plant Pathol 55:475–484. https://doi.org/10.1111/j.1365-3059.2006.01402.x

Burgueño J, de los Campos G, Weigel K, Crossa J (2012) Genomic prediction of breeding values when modeling genotype × environment interaction using pedigree and dense molecular markers. Crop Sci 52:707–719. https://doi.org/10.2135/cropsci2011.06.0299

Caverzan A, Casassola A, Brammer SP (2016) Antioxidant responses of wheat plants under stress. Genet Mol Biol 39:1–6. https://doi.org/10.1590/1678-4685-GMB-2015-0109

Cericola F, Jahoor A, Orabi J et al (2017) Optimizing training population size and genotyping strategy for genomic prediction using association study results and pedigree information. A case of study in advanced wheat breeding lines. PLoS One 12. https://doi.org/10.1371/journal.pone.0169606

Cheong BE, Ho WWH, Biddulph B et al (2019) Phenotyping reproductive stage chilling and frost tolerance in wheat using targeted metabolome and lipidome profiling. Metabolomics 15:144. https://doi.org/10.1007/s11306-019-1606-2

Cheong BE, Onyemaobi O, Wing Ho Ho W et al (2020) Phenotyping the chilling and freezing responses of young microspore stage wheat spikes using targeted metabolome and lipidome profiling. Cells 9(5):1309. https://doi.org/10.3390/cells9051309

Article   CAS   PubMed Central   Google Scholar  

Chopra RK, Shukla S, Singh K et al (2012) Characterization of high yielding and drought tolerant RILs identified from wheat cross WL711 x C306 RIL mapping population using drought susceptibility index (DSI) as selection criteria. Indian J Plant Genet Resour 26(1):25–31

Cobb JN, Juma RU, Biswas PS et al (2019) Enhancing the rate of genetic gain in public-sector plant breeding programs: lessons from the breeder’s equation. Theor Appl Genet 132:627–645. https://doi.org/10.1007/s00122-019-03317-0

Cossani CM, Reynolds MP (2012) Physiological traits for improving heat tolerance in wheat. Plant Physiol 160:1710–1718. https://doi.org/10.1104/pp.112.207753

Crain J, Mondal S, Rutkoski J et al (2018a) Combining high-throughput phenotyping and genomic information to increase prediction and selection accuracy in wheat breeding. Plant Genome 11:170043. https://doi.org/10.3835/plantgenome2017.05.0043

Crain J, Mondal S, Rutkoski J et al (2018b) Combining high-throughput phenotyping and genomic information to increase prediction and selection accuracy in wheat breeding. Plant Genome 11:1–14. https://doi.org/10.3835/plantgenome2017.05.0043

Crespo-Herrera LA, Crossa J, Huerta-Espino J et al (2017) Genetic yield gains in CIMMYT’S international elite spring wheat yield trials by modeling the genotype × environment interaction. Crop Sci 57:789. https://doi.org/10.2135/cropsci2016.06.0553

Crespo-Herrera LA, Crossa J, Huerta-Espino J et al (2018) Genetic gains for grain yield in cimmyt’s semi-arid wheat yield trials grown in suboptimal environments. Crop Sci 58:1890–1898. https://doi.org/10.2135/cropsci2018.01.0017

Crossa J, De Los Campos G, Pérez P et al (2010) Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics. https://doi.org/10.1534/genetics.110.118521

Crossa J, Vargas M, Cossani CM et al (2015) Evaluation and interpretation of interactions. Agron J 107:736–747. https://doi.org/10.2134/agronj2012.0491

Crossa J, Jarquín D, Franco J et al (2016) Genomic prediction of gene bank wheat landraces. G3 Genes Genomes Genet 6. https://doi.org/10.1534/g3.116.029637

Crossa J, Martini JWR, Gianola D et al (2019) Deep kernel and deep learning for genome-based prediction of single traits in multienvironment breeding trials. Front Genet 10:1–13. https://doi.org/10.3389/fgene.2019.01168

Czyczyło-Mysza I, Marcińska I, Skrzypek E et al (2014) QTL mapping for germination of seeds obtained from previous wheat generation under drought. Cent Eur J Biol 9:374. https://doi.org/10.2478/s11535-013-0273-y

Dar EA, Brar AS, Singh KB (2017) Water use and productivity of drip irrigated wheat under variable climatic and soil moisture regimes in North-West, India. Agric Ecosyst Environ 248:9–19. https://doi.org/10.1016/j.agee.2017.07.019

Daryanto S, Wang L, Jacinthe P-A (2016) Global synthesis of drought effects on maize and wheat production. PLoS ONE 11:1–15. https://doi.org/10.1371/journal.pone.0156362

Dawood MFA, Abeed AHA, Aldaby EES (2019) Titanium dioxide nanoparticles model growth kinetic traits of some wheat cultivars under different water regimes. Plant Physiol Reports 24:129–140. https://doi.org/10.1007/s40502-019-0437-5

de los Campos G, Vazquez AI, Fernando R et al (2013) Prediction of complex human traits using the genomic best linear unbiased predictor. PLoS Genet 9:e1003608. https://doi.org/10.1371/journal.pgen.1003608

Debernardi JM, Tricoli DM, Ercoli MF et al (2020) A GRF–GIF chimeric protein improves the regeneration efficiency of transgenic plants. Nat Biotechnol 38:1274. https://doi.org/10.1038/s41587-020-0703-0

Denčić S, Kastori R, Kobiljski B, Duggan B (2000) Evaluation of grain yield and its components in wheat cultivars and landraces under near optimal and drought conditions. Euphytica 113:43. https://doi.org/10.1023/A:1003997700865

Deng XP, Shan L, Inanaga S, Inoue M (2005) Water-saving approaches for improving wheat production. J Sci Food Agric 85:1379–1388. https://doi.org/10.1002/jsfa.2101

Djanaguiraman M, Boyle DL, Welti R et al (2018) Decreased photosynthetic rate under high temperature in wheat is due to lipid desaturation, oxidation, acylation, and damage of organelles. BMC Plant Biol 18:1–17. https://doi.org/10.1186/s12870-018-1263-z

Dong H, Wang R, Yuan Y et al (2018) Evaluation of the potential for genomic selection to improve spring wheat resistance to Fusarium head blight in the Pacific Northwest. Front Plant Sci 9:911. https://doi.org/10.3389/fpls.2018.00911

Edae EA, Byrne PF, Manmathan H et al (2013) Association mapping and nucleotide sequence variation in five drought tolerance candidate genes in spring wheat. Plant Genome 6:2013. https://doi.org/10.3835/plantgenome2013.04.0010

Edae EA, Byrne PF, Haley SD et al (2014) Genome-wide association mapping of yield and yield components of spring wheat under contrasting moisture regimes. Theor Appl Genet 127:791–807. https://doi.org/10.1007/s00122-013-2257-8

El Habti A, Fleury D, Jewell N et al (2020) Tolerance of combined drought and heat stress is associated with transpiration maintenance and water soluble carbohydrates in wheat grains. Front Plant Sci 11:1–13. https://doi.org/10.3389/fpls.2020.568693

Enghiad A, Ufer D, Countryman AM, Thilmany DD (2017) An overview of global wheat market fundamentals in an era of climate concerns. Intl J Agron 2017. https://doi.org/10.1155/2017/3931897

Fahad S, Hussain S, Matloob A et al (2015) Phytohormones and plant responses to salinity stress: a review. Plant Growth Regul 75:391–404. https://doi.org/10.1007/s10725-014-0013-y

Fahad S, Bajwa AA, Nazir U et al (2017) Crop production under drought and heat stress: plant responses and management options. Front Plant Sci 8:1–16. https://doi.org/10.3389/fpls.2017.01147

Farooq M, Hussain M, Wahid A, Siddique KHM (2012) Drought stress in plants: an overview. Plant responses to drought stress: from morphological to molecular features. Springer, Berlin, pp 1–33

Farooq M, Hussain M, Siddique KHM (2014) Drought stress in wheat during flowering and grain-filling periods. Crit Rev Plant Sci 33:331–349

Foulkes MJ, Snape JW, Shearman VJ et al (2007) Genetic progress in yield potential in wheat: recent advances and future prospects. J Agric Sci 145:17–29. https://doi.org/10.1017/s0021859607006740

Gahlaut V, Jaiswal V, Tyagi BS et al (2017) QTL mapping for nine drought-responsive agronomic traits in bread wheat under irrigated and rain-fed environments. PLoS ONE 12:e0182857. https://doi.org/10.1371/journal.pone.0182857

Gaudelli NM, Komor AC, Rees HA et al (2017) Programmable base editing of A•T to G•C in genomic DNA without DNA cleavage. Nature 551:464–471. https://doi.org/10.1038/nature24644

Gaudelli NM, Lam DK, Rees HA et al (2020) Directed evolution of adenine base editors with increased activity and therapeutic application. Nat Biotechnol 38:892–900. https://doi.org/10.1038/s41587-020-0491-6

Genc Y, Oldach K, Taylor J, Lyons GH (2016) Uncoupling of sodium and chloride to assist breeding for salinity tolerance in crops. New Phytol 210:145. https://doi.org/10.1111/nph.13757

Genetic Literacy Project: Global gene editing regulation tracker and index. https://crispr-gene-editing-regs-tracker.geneticliteracyproject.org/ . Accessed 20 Oct 2020

Ghosh S, Watson A, Gonzalez-Navarro OE et al (2018) Speed breeding in growth chambers and glasshouses for crop breeding and model plant research. Nat Protoc 13:2944–2963. https://doi.org/10.1038/s41596-018-0072-z

González-Camacho JM, Ornella L, Pérez-Rodríguez P et al (2018) Applications of machine learning methods to genomic selection in breeding wheat for rust resistance. Plant Genome 11:170104. https://doi.org/10.3835/plantgenome2017.11.0104

Gonzalez-Sanchez A, Frausto-Solis J, Ojeda-Bustamante W (2014) Predictive ability of machine learning methods for massive crop yield prediction. Span J Agric Res 12. https://doi.org/10.5424/sjar/2014122-4439

Guo J, Xu W, Yu X et al (2016) Cuticular wax accumulation is associated with drought tolerance in wheat near-isogenic lines. Front Plant Sci 7:1809. https://doi.org/10.3389/fpls.2016.01809

Gupta P, Balyan H, Gahlaut V (2017) QTL Analysis for drought tolerance in wheat: present status and future possibilities. Agronomy 7:5. https://doi.org/10.3390/agronomy7010005

Habbasha E, Ramadan A (2014) Wheat Production in the arid regions by using drip irrigation system. Intl J Adv Res 2:84–96

Hamada A, Nitta M, Nasuda S et al (2012) Novel QTLs for growth angle of seminal roots in wheat ( Triticum aestivum L.) 395–405. https://doi.org/10.1007/s11104-011-1075-5

Hammer K, Knüpffer H (2015) Genetic resources of triticum. In: Ogihara Y, Takumi S, Handa H (eds) Advances in wheat genetics: from genome to field. Springer Japan, Tokyo, pp 23–31

Chapter   Google Scholar  

Hasheminasab H, Taghi Assad M, Aliakbari A, Rasoul Sahhafi S (2012) Influence of drought stress on oxidative damage and antioxidant defense systems in tolerant and susceptible wheat genotypes. J Agric Sci 4:20–30. https://doi.org/10.5539/jas.v4n8p20

Hassan NU, Mahmood Q, Waseem A et al (2013) Assessment of heavy metals in wheat plants irrigated with contaminated wastewater. Polish J Environ Stud 22

Hazratkulova S, Sharma RC, Alikulov S et al (2012) Analysis of genotypic variation for normalized difference vegetation index and its relationship with grain yield in winter wheat under terminal heat stress. Plant Breed 131:716. https://doi.org/10.1111/pbr.12003

Heffner EL, Jannink J-L, Sorrells ME (2011a) Genomic selection accuracy using multifamily prediction models in a wheat breeding program. Plant Genome J 4. https://doi.org/10.3835/plantgenome.2010.12.0029

Heffner EL, Jannink J-L, Sorrells ME (2011b) Genomic selection accuracy using multifamily prediction models in a wheat breeding program. Plant Genome 4. https://doi.org/10.3835/plantgenome2010.12.0029

Hein NT, Wagner D, Bheemanahalli R et al (2019) Integrating field-based heat tents and cyber-physical system technology to phenotype high night-time temperature impact on winter wheat. Plant Methods 15:1–15. https://doi.org/10.1186/s13007-019-0424-x

Heslot N, Akdemir D, Sorrells ME, Jannink J-L (2014) Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions. Theor Appl Genet 127:463–480. https://doi.org/10.1007/s00122-013-2231-5

Hoffstetter A, Cabrera A, Huang M, Sneller C (2016) Optimizing training population data and validation of genomic selection for economic traits in soft winter wheat. G3 Genes Genomes Genet 6. https://doi.org/10.1534/g3.116.032532

Hoisington D, Khairallah M, Reeves T et al (1999) Plant genetic resources: what can they contribute toward increased crop productivity? Proc Natl Acad Sci USA 96:5937–5943

Hong MJ, Kim DY, Nam BM et al (2019) Characterization of novel mutants of hexaploid wheat ( Triticum aestivum L.) with various depths of purple grain color and antioxidant capacity. J Sci Food Agric 99:55–63. https://doi.org/10.1002/jsfa.9141

Hoover DL, Wilcox KR, Young KE (2018) Experimental droughts with rainout shelters: a methodological review. Ecosphere 9. https://doi.org/10.1002/ecs2.2088

Hu P, Zheng Q, Luo Q et al (2020) Genome-wide association study of yield and related traits in common wheat under salt-stress conditions 1–34. https://doi.org/10.21203/rs.3.rs-22191/v1

Iqbal MJ (2018) Role of osmolytes and antioxidant enzymes for drought tolerance in wheat. InTech. http://dx.doi.org/10.5772/intechopen.75926

Jarquín D, Crossa J, Lacaze X et al (2014) A reaction norm model for genomic selection using high-dimensional genomic and environmental data. Theor Appl Genet. https://doi.org/10.1007/s00122-013-2243-1

Jinek M, Chylinski K, Fonfara I et al (2012) A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science (80)337:816–821. https://doi.org/10.1126/science.1225829

Joukhadar R, Daetwyler HD, Bansal UK et al (2017) Genetic diversity, population structure and ancestral origin of Australian wheat. Front Plant Sci 8:1–15. https://doi.org/10.3389/fpls.2017.02115

Juliana P, Singh RP, Singh PK et al (2017) Comparison of models and whole-genomeprofiling approaches for genomic-enabled prediction of Septoria tritici blotch, Stagonospora nodorum blotch, and Tan spot resistance in wheat. Plant Genome 10. https://doi.org/10.3835/plantgenome2016.08.0082

Juliana P, Montesinos-López OA, Crossa J et al (2019a) Integrating genomic-enabled prediction and high-throughput phenotyping in breeding for climate-resilient bread wheat. Theor Appl Genet 132:177. https://doi.org/10.1007/s00122-018-3206-3

Juliana P, Poland J, Huerta-Espino J et al (2019b) Improving grain yield, stress resilience and quality of bread wheat using large-scale genomics. Nat Genet 51:1530–1539. https://doi.org/10.1038/s41588-019-0496-6

Kadioglu A, Terzi R, Saruhan N, Saglam A (2012) Current advances in the investigation of leaf rolling caused by biotic and abiotic stress factors. Plant Sci 182:42–48. https://doi.org/10.1016/j.plantsci.2011.01.013

Kaur R, Bhardwaj R, Sharma R et al (2016) Hormonal regulation of drought stress in plants. Water Stress and Crop Plants A Sustain Approach. Wiley, Chichester, UK, pp 582–599

Kaya C, Tuna AL, Yokaş I (2009) The role of plant hormones in plants under salinity stress 45–50. https://doi.org/10.1007/978-1-4020-9065-3_5

Khadka RB, Raut P (2011) Title : system of wheat intensification (SWI): A new concept on low input technology for increasing wheat yield in marginal land. Authors: Ram Bahadur Khadka Program Coordinator, European Union Food Facility Project Forum for Awareness and Youth Acti

Kim D, Alptekin B, Budak H (2018) CRISPR/Cas9 genome editing in wheat. Funct Integr Genom 18:31–41. https://doi.org/10.1007/s10142-017-0572-x

Kristensen PS, Jahoor A, Andersen JR et al (2018) Genome-wide association studies and comparison of models and cross-validation strategies for genomic prediction of quality traits in advanced winter wheat breeding lines. Front Plant Sci 9:69. https://doi.org/10.3389/fpls.2018.00069

Kumar M, Hasan M, Arora A et al (2015) Sodium chloride-induced spatial and temporal manifestation in membrane stability index and protein profiles of contrasting wheat ( Triticum aestivum L.) genotypes under salt stress. Indian J Plant Physiol 20. https://doi.org/10.1007/s40502-015-0157-4

La Russa MF, Qi LS (2015) The new state of the art: CRISPR for gene activation and repression. Mol Cell Biol MCB.00512-15. https://doi.org/10.1128/MCB.00512-15

Ladejobi O, Mackay IJ, Poland J et al (2019) Reference genome anchoring of high-density markers for association mapping and genomic prediction in European winter wheat. Front Plant Sci 10:1278. https://doi.org/10.3389/fpls.2019.01278

Laxa M, Liebthal M, Telman W et al (2019) The role of the plant antioxidant system in drought tolerance. Antioxidants 8:94. https://doi.org/10.3390/antiox8040094

Lehnert H, Serfling A, Friedt W, Ordon F (2018) Genome-wide association studies reveal genomic regions associated with the response of wheat ( Triticum aestivum l.) to mycorrhizae under drought stress conditions. Front Plant Sci 871. https://doi.org/10.3389/fpls.2018.01728

Li Y, Liang X, Huang Q et al (2019) Inhibition of Cd accumulation in grains of wheat and rice under rotation mode using composite silicate amendment. RSC Adv 9:35539. https://doi.org/10.1039/c9ra07137g

Liang Z, Chen K, Li T et al (2017) Efficient DNA-free genome editing of bread wheat using CRISPR/Cas9 ribonucleoprotein complexes. Nat Commun 8:6–10. https://doi.org/10.1038/ncomms14261

Liu B, Asseng S, Müller C et al (2016) Similar estimates of temperature impacts on global wheat yield by three independent methods. Nat Clim Chang 6:1130–1137. https://doi.org/10.1038/nclimate3115

Liu C, Pinto F, Cossani CM et al (2019) Spectral reflectance indices as proxies for yield potential and heat stress tolerance in spring wheat: Heritability estimates and marker-trait associations. Front Agric Sci Eng 6:296–308. https://doi.org/10.15302/J-FASE-2019269

Lobell DB, Sibley A, Ivan Ortiz-Monasterio J (2012) Extreme heat effects on wheat senescence in India. Nat Clim Chang 2:186–189. https://doi.org/10.1038/nclimate1356

Lonbani M, Arzani A (2011) Morpho-physiological traits associated with terminal drought-stress tolerance in triticale and wheat. Agron Res 9:315–329

Lopez-Cruz M, Crossa J, Bonnett D et al (2015) Increased prediction accuracy in wheat breeding trials using a marker x environment interaction genomic selection model. G3 Genes Genomes Genet Genes|Genomes|Genetics 569–582. https://doi.org/10.1534/g3.114.016097

Loutfy N, El-Tayeb MA, Hassanen AM et al (2012) Changes in the water status and osmotic solute contents in response to drought and salicylic acid treatments in four different cultivars of wheat ( Triticum aestivum ). J Plant Res 125:173–184. https://doi.org/10.1007/s10265-011-0419-9

Lu Y, Ye X, Guo R et al (2017) Genome-wide targeted mutagenesis in rice using the CRISPR/Cas9 system. Mol Plant 10:1242–1245. https://doi.org/10.1016/j.molp.2017.06.007

Ma X, Zhang Q, Zhu Q et al (2015) A robust CRISPR/Cas9 system for convenient, high-efficiency multiplex genome editing in monocot and dicot plants. Mol Plant 8:1274–1284. https://doi.org/10.1016/j.molp.2015.04.007

Maes B, Trethowan RM, Reynolds MP et al (2001) The influence of glume pubescence on spikelet temperature of wheat under freezing conditions. Aust J Plant Physiol 28:141–148. https://doi.org/10.1071/pp00049

Marcussen T, Sandve SR, Heier L et al (2014) A chromosome-based draft sequence of the hexaploid bread wheat ( Triticum aestivum ) genome. Science 345:1250092. https://doi.org/10.1126/science.1251788

Mason RE, Mondal S, Beecher FW, et al (2010a) QTL associated with heat susceptibility index in wheat ( Triticum aestivum L.) under short-term reproductive stage heat stress. Euphytica 174. https://doi.org/10.1007/s10681-010-0151-x

Mason RE, Mondal S, Beecher FW, et al (2010b) QTL associated with heat susceptibility index in wheat ( Triticum aestivum L.) under short-term reproductive stage heat stress. Euphytica 174. https://doi.org/10.1007/s10681-010-0151-x

Mason RE, Hays DB, Mondal S et al (2013) QTL for yield, yield components and canopy temperature depression in wheat under late sown field conditions. Euphytica 194:243. https://doi.org/10.1007/s10681-013-0951-x

Maulana F, Ayalew H, Anderson JD et al (2018) Genome-wide association mapping of seedling heat tolerance in winter wheat. Front Plant Sci 9:1272. https://doi.org/10.3389/fpls.2018.01272

Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829. https://doi.org/11290733

Miransari M, Smith D (2019) Sustainable wheat ( Triticum aestivum L.) production in saline fields: a review. Crit Rev Biotechnol 39:999–1014. https://doi.org/10.1080/07388551.2019.1654973

Mishra R, Joshi RK (2020) Base editing in crops : current advances, limitations and future implications 20–31. https://doi.org/10.1111/pbi.13225

Mishra A, Singh A, Sharma M et al (2016) Development of EMS-induced mutation population for amylose and resistant starch variation in bread wheat ( Triticum aestivum ) and identification of candidate genes responsible for amylose variation. BMC Plant Biol 16:1–15. https://doi.org/10.1186/s12870-016-0896-z

Mohammady S, Aminian R, Hoshmand S, Khodombashi M (2012) Genomic analysis of carbon isotope discrimination, photosynthesis rate, stomatal conductance, and grain yield in wheat ( Triticum aestivum L.) under water-stressed conditions. Crop Pasture Sci 63. https://doi.org/10.1071/CP12050

Molero G, Joynson R, Pinera-Chavez FJ et al (2019) Elucidating the genetic basis of biomass accumulation and radiation use efficiency in spring wheat and its role in yield potential. Plant Biotechnol J 1–13:1276. https://doi.org/10.1111/pbi.13052

Mondal S, Mason RE, Huggins T, Hays DB (2015a) QTL on wheat (Triticum aestivum L.) chromosomes 1B, 3D and 5A are associated with constitutive production of leaf cuticular wax and may contribute to lower leaf temperatures under heat stress. Euphytica 201. https://doi.org/10.1007/s10681-014-1193-2

Mondal S, Mason RE, Huggins T, Hays DB (2015b) QTL on wheat ( Triticum aestivum L.) chromosomes 1B, 3D and 5A are associated with constitutive production of leaf cuticular wax and may contribute to lower leaf temperatures under heat stress. Euphytica 201. https://doi.org/10.1007/s10681-014-1193-2

Mondal S, Rutkoski JE, Velu G et al (2016a) Harnessing diversity in wheat to enhance grain yield, climate resilience, disease and insect pest resistance and nutrition through conventional and modern breeding approaches. Front Plant Sci 7:1–15. https://doi.org/10.3389/fpls.2016.00991

Mondal S, Singh RP, Mason ER et al (2016b) Grain yield, adaptation and progress in breeding for early-maturing and heat-tolerant wheat lines in South Asia. Field Crop Res 192:78. https://doi.org/10.1016/j.fcr.2016.04.017

Mondal S, Dutta S, Crespo-Herrera L et al (2020) Fifty years of semi-dwarf spring wheat breeding at CIMMYT: grain yield progress in optimum, drought and heat stress environments. Field Crop Res 250:107757. https://doi.org/10.1016/j.fcr.2020.107757

Morgounov A, Abugalieva A, Akan K et al (2017) High-yielding winter synthetic hexaploid wheats resistant to multiple diseases and pests. Plant Genet Resour 1–6:273. https://doi.org/10.1017/S147926211700017X

Mujeeb-Kazi A, Rosas V, Roldan S (1996) Conservation of the genetic variation of Triticum tauschii (Coss.) Schmalh. ( Aegilops squarrosa auct. non L.) in synthetic hexaploid wheats ( T. turgidum L. s.lat. x T. tauschii; 2n = 6x = 42, AABBDD) and its potential utilization for wheat improvement. Genet Resour Crop Evol 43:129–134. https://doi.org/10.1007/BF00126756

Muleta KT, Bulli P, Zhang Z et al (2017) Unlocking diversity in germplasm collections via genomic selection: a case study based on quantitative adult plant resistance to stripe rust in spring wheat. Plant Genome 10:2016. https://doi.org/10.3835/plantgenome2016.12.0124

Mwadzingeni L, Figlan S, Shimelis H et al (2017) Genetic resources and breeding methodologies for improving drought tolerance in wheat. J Crop Improv 31:648. https://doi.org/10.1080/15427528.2017.1345816

Nakaya A, Isobe SN (2012) Will genomic selection be a practical method for plant breeding? Ann Bot 110(6):1303–1316

Naz AA, Dadshani S, Ballvora A et al (2019) Genetic analysis and transfer of favorable exotic QTL alleles for grain yield across D genome using two advanced backcross wheat populations. Front Plant Sci 10:1–9. https://doi.org/10.3389/fpls.2019.00711

Nuttall JG, Perry EM, Delahunty AJ et al (2019) Frost response in wheat and early detection using proximal sensors. J Agron Crop Sci 205:220–234. https://doi.org/10.1111/jac.12319

Odilbekov F, Armoniené R, Koc A et al (2019) GWAS-assisted genomic prediction to predict resistance to Septoria tritici blotch in Nordic winter wheat at seedling stage. Front Genet 10:1224. https://doi.org/10.3389/fgene.2019.01224

Ogbonnaya FC, Abdalla O, Mujeeb-Kazi A et al (2013) Synthetic hexaploids: harnessing species of the primary gene pool for wheat improvement. Plant Breed Rev 37:35–122. https://doi.org/10.1002/9781118497869.ch2

ONU (2019) World population prospects 2019

Ornella L, González-Camacho JM, Dreisigacker S, Crossa J (2017) Applications of genomic selection in breeding wheat for rust resistance. Methods Mol Biol 1659:173–182. https://doi.org/10.1007/978-1-4939-7249-4_15

Oyiga BC, Sharma RC, Baum M et al (2018) Allelic variations and differential expressions detected at quantitative trait loci for salt stress tolerance in wheat. Plant Cell Environ 41:919. https://doi.org/10.1111/pce.12898

Paliwal R, Röder MS, Kumar U et al (2012) QTL mapping of terminal heat tolerance in hexaploid wheat ( T. aestivum L.). Theor Appl Genet 125:561–575. https://doi.org/10.1007/s00122-012-1853-3

Pantazi XE, Moshou D, Alexandridis T et al (2016) Wheat yield prediction using machine learning and advanced sensing techniques. Comput Electron Agric 121:57–65. https://doi.org/10.1016/j.compag.2015.11.018

Pérez P, de los Campos G (2014) Genome-wide regression and prediction with the BGLR statistical package. Genetics 198:483–495. https://doi.org/10.1534/genetics.114.164442

Pérez-Rodríguez P, Crossa J, Rutkoski J et al (2017) Single-step genomic and pedigree genotype × environment interaction models for predicting wheat lines in international environments. Plant Genome 10(2). https://doi.org/10.3835/plantgenome2016.09.0089

Pestsova E, Röder M (2002) Microsatellite analysis of wheat chromosome 2D allows the reconstruction of chromosomal inheritance in pedigrees of breeding programmes. Theor Appl Genet 106:84. https://doi.org/10.1007/s00122-002-0998-x

Pinto RS, Reynolds MP, Mathews KL et al (2010) Heat and drought adaptive QTL in a wheat population designed to minimize confounding agronomic effects. Theor Appl Genet 121:1001–1021. https://doi.org/10.1007/s00122-010-1351-4

Poland J, Endelman J, Dawson J et al (2012) Genomic selection in wheat breeding using genotyping-by-sequencing. Plant Genome 5. https://doi.org/10.3835/plantgenome2012.06.0006

Pradhan GP, Prasad PVV, Fritz AK et al (2012) Effects of drought and high temperature stress on synthetic hexaploid wheat. Funct Plant Biol 39:190–198. https://doi.org/10.1071/FP11245

Qaseem MF, Qureshi R, Muqaddasi QH et al (2018) Genome-wide association mapping in bread wheat subjected to independent and combined high temperature and drought stress. PLoS ONE 13:e0199121. https://doi.org/10.1371/journal.pone.0199121

Rawson HM, Macpherson HG (2000) Irrigated wheat : managing your crop. Food and Agriculture Organization of the United Nations, Rome, Italy

Ray DK, Mueller ND, West PC, Foley JA (2013) Yield trends are insufficient to double global crop production by 2050. PLoS ONE 8: https://doi.org/10.1371/journal.pone.0066428

Rebetzke GJ, Condon AG, Farquhar GD et al (2008) Quantitative trait loci for carbon isotope discrimination are repeatable across environments and wheat mapping populations. Theor Appl Genet 118:123. https://doi.org/10.1007/s00122-008-0882-4

Reif JC, Zhang P, Dreisigacker S et al (2005) Wheat genetic diversity trends during domestication and breeding. Theor Appl Genet 110:859–864. https://doi.org/10.1007/s00122-004-1881-8

Ren S, Qin Q, Ren H et al (2019) Heat and drought stress advanced global wheat harvest timing from 1981–2014. Remote Sens 11:971. https://doi.org/10.3390/rs11080912

Reynolds MP, Langridge P (2016) Physiological breeding. Curr Opin Plant Biol 31:162–171. https://doi.org/10.1016/j.pbi.2016.04.005

Reynolds MP, Pierre C Saint, Saad ASI et al (2007) Evaluating potential genetic gains in wheat associated with stress-adaptive trait expression in elite genetic resources under drought and geat Stress. Crop Sci 47:S-172. https://doi.org/10.2135/cropsci2007.10.0022IPBS

Reynolds MP, Pask AJD, Hoppitt WJE et al (2017) Strategic crossing of biomass and harvest index—source and sink—achieves genetic gains in wheat. Euphytica 213:1. https://doi.org/10.1007/s10681-017-2040-z

Roca Paixão JF, Gillet F-X, Ribeiro TP et al (2019) Improved drought stress tolerance in Arabidopsis by CRISPR/dCas9 fusion with a histone acetyltransferase. Sci Rep 9:8080. https://doi.org/10.1038/s41598-019-44571-y

Röder MS, Huang XQ, Börner A (2008) Fine mapping of the region on wheat chromosome 7D controlling grain weight. Funct Integr Genom 8:79–86. https://doi.org/10.1007/s10142-007-0053-8

Rosyara U, Kishii M, Payne T et al (2019) Genetic contribution of synthetic hexaploid wheat to CIMMYT’s spring bread wheat breeding germplasm. Sci Rep 9:1–11. https://doi.org/10.1038/s41598-019-47936-5

Royo A, Abió D (2003) Salt tolerance in durum wheat cultivars. Span J Agric Res 1:27. https://doi.org/10.5424/sjar/2003013-32

Ruelland E, Zachowski A (2010) How plants sense temperature. Environ Exp Bot 69

Ruqiang X, Qixin S, Shuzhen Z (1996) Chromosomal location of genes for heat tolerance as measured by membrane thermostability of common wheat cv. Hope. Yi Chuan = Hereditas 18(4):1–3

Rutkoski JE, Poland JA, Singh RP et al (2015) Genomic selection for quantitative adult plant stem rust resistance in wheat. Plant Genome 7:0. https://doi.org/10.3835/plantgenome2014.02.0006

Rutkoski J, Poland J, Mondal S et al (2016) Canopy temperature and vegetation indices from high-throughput phenotyping improve accuracy of pedigree and genomic selection for grain yield in wheat. G3 Genes Genomes Genet 6. https://doi.org/10.1534/g3.116.032888

Saint Pierre C, Burgueño J, Crossa J et al (2016) Genomic prediction models for grain yield of spring bread wheat in diverse agro-ecological zones. Sci Rep 6:1. https://doi.org/10.1038/srep27312

Sallam A, Amro A, EL-Akhdar A et al (2018) Genetic diversity and genetic variation in morpho-physiological traits to improve heat tolerance in spring barley. Mol Biol Rep 45:2441. https://doi.org/10.1007/s11033-018-4410-6

Sallam A, Alqudah AM, Dawood MFA et al (2019) Drought stress tolerance in wheat and barley: advances in physiology, breeding and genetics research. Intl J Mol Sci 20:3137. https://doi.org/10.3390/ijms20133137

Sansaloni C, Franco J, Santos B et al (2020) Diversity analysis of 80,000 wheat accessions reveals consequences and opportunities of selection footprints. Nat Commun 11:4572. https://doi.org/10.1038/s41467-020-18404-w

Sarinelli JM, Murphy JP, Tyagi P et al (2019) Training population selection and use of fixed effects to optimize genomic predictions in a historical USA winter wheat panel. Theor Appl Genet 132:1247. https://doi.org/10.1007/s00122-019-03276-6

Sattar A, Sher A, Ijaz M et al (2020) Terminal drought and heat stress alter physiological and biochemical attributes in flag leaf of bread wheat. PLoS ONE 15:e0232974. https://doi.org/10.1371/journal.pone.0232974

Savicka M, Škute N (2010) Effects of high temperature on malondialdehyde content, superoxide production and growth changes in wheat seedlings ( Triticum aestivum L.) Aukštos temperatūros poveikis kviečių ( Triticum aestivum L.). Ekologija 56:26–33. https://doi.org/10.2478/v10055-010-0004-x

Schmidt J, Claussen J, Wörlein N et al (2020) Drought and heat stress tolerance screening in wheat using computed tomography. Plant Methods 16:1–12. https://doi.org/10.1186/s13007-020-00565-w

Sehgal D, Autrique E, Singh R et al (2017) Identification of genomic regions for grain yield and yield stability and their epistatic interactions. Sci Rep 7:41578. https://doi.org/10.1038/srep41578

Sehgal D, Rosyara U, Mondal S et al (2020) Incorporating genome-wide association mapping results into genomic prediction models for grain yield and yield stability in CIMMYT spring bread wheat. Front Plant Sci 11:197. https://doi.org/10.3389/fpls.2020.00197

Semenov MA, Halford NG (2009) Identifying target traits and molecular mechanisms for wheat breeding under a changing climate. J Exp Bot 60(10):2791–2804

Shah NH, Paulsen GM (2003) Interaction of drought and high temperature on photosynthesis and grain-filling of wheat. Plant Soil 257:219–226. https://doi.org/10.1023/A:1026237816578

Sharma R, Morgounov A, Braun H et al (2012) Yield stability analysis of winter wheat genotypes targeted to semi-arid environments in the international winter wheat improvement program. Intl J Plant Breed 6:7–13

Sharma DK, Torp AM, Rosenqvist E et al (2017) QTLs and potential candidate genes for heat stress tolerance identified from the mapping populations specifically segregating for Fv/Fm in wheat. Front Plant Sci 8:1668. https://doi.org/10.3389/fpls.2017.01668

Sharma A, Shahzad B, Kumar V et al (2019) Phytohormones regulate accumulation of osmolytes under abiotic stress. Biomolecules 9:285. https://doi.org/10.3390/biom9070285

Shaul O (2015) Unique aspects of plant nonsense-mediated mRNA decay. Trends Plant Sci 20:767–779. https://doi.org/10.1016/j.tplants.2015.08.011

Shi J, Gao H, Wang H et al (2017) ARGOS8 variants generated by CRISPR-Cas9 improve maize grain yield under field drought stress conditions. Plant Biotechnol J 15:207–216. https://doi.org/10.1111/pbi.12603

Simeonov V, Stratis JA, Samara C et al (2003) Assessment of the surface water quality in Northern Greece. Water Res 37:4119. https://doi.org/10.1016/S0043-1354(03)00398-1

Singh RK, Mishra B, Chauhan MS et al (2002) Solution culture for screening rice varieties for sodicity tolerance. J Agric Sci 139:327. https://doi.org/10.1017/S0021859602002447

Singh D, Wang X, Kumar U et al (2019a) High-throughput phenotyping enabled genetic dissection of crop lodging in wheat. Front Plant Sci 10:394. https://doi.org/10.3389/fpls.2019.00394

Singh N, Wu S, Raupp WJ et al (2019b) Efficient curation of genebanks using next generation sequencing reveals substantial duplication of germplasm accessions. Sci Rep 9:1–10. https://doi.org/10.1038/s41598-018-37269-0

Skinner DZ, Garland-Campbell KA (2008) The relationship of LT50 to prolonged freezing survival in winter wheat. Can J Plant Sci 88:885–889. https://doi.org/10.4141/CJPS08007

Snyder RL, Melo-Abreu JP, Matulich S (2005). Frost Protection: fundamentals, practice and economics volume 1 and 2. FAO Environ Nat Res Service Series, No. 10—FAO, Rome

Soriano JM, Alvaro F (2019) Discovering consensus genomic regions in wheat for root-related traits by QTL meta-analysis. Sci Rep 9:1–4. https://doi.org/10.1038/s41598-019-47038-2

Sreenivasulu N, Sopory SK, Kavi Kishor PB (2007) Deciphering the regulatory mechanisms of abiotic stress tolerance in plants by genomic approaches. Gene 388:1–13. https://doi.org/10.1016/j.gene.2006.10.009

Sukumaran S, Yu J (2014) Association mapping of genetic resources: Achievements and future perspectives. In: Tuberosa R, Graner A, Frison E (eds) Genomics of plant genetic resources: volume 1. Managing, sequencing and mining genetic resources. https://doi.org/10.1007/978-94-007-7572-5_9 . Springer, New York

Sukumaran S, Reynolds MP, Lopes MS, Crossa J (2015) Genome-wide association study for adaptation to agronomic plant density: a component of high yield potential in spring wheat. Crop Sci 55:2609. https://doi.org/10.2135/cropsci2015.03.0139

Sun QX, Quick JS (1991) Chromosomal locations of genes for heat tolerance in tetraploid wheat. Cereal Res Commun 19(4):431–437

Talukder SK, Babar MA, Vijayalakshmi K et al (2014) Mapping QTL for the traits associated with heat tolerance in wheat ( Triticum aestivum L.). BMC Genet 15:97. https://doi.org/10.1186/s12863-014-0097-4

Tattaris M, Reynolds MP, Chapman SC (2016) A direct comparison of remote sensing approaches for high-throughput phenotyping in plant breeding. Front Plant Sci 7:1131. https://doi.org/10.3389/fpls.2016.01131

Thomas H, Ougham H (2014) The stay-green trait. J Exp Bot 65(14):3889–3900. https://doi.org/10.1093/jxb/eru037 Epub 2014 Mar 5

Tiwari C, Wallwork H, Kumar U et al (2013) Molecular mapping of high temperature tolerance in bread wheat adapted to the Eastern Gangetic Plain region of India. Field Crop Res 154:201. https://doi.org/10.1016/j.fcr.2013.08.004

Trnka M, Feng S, Semenov MA et al (2019) Mitigation efforts will not fully alleviate the increase in water scarcity occurrence probability in wheat-producing areas. Sci Adv 5:eaau2406. https://doi.org/10.1126/sciadv.aau2406

Tshikunde NM, Mashilo J, Shimelis H, Odindo A (2019) Agronomic and physiological traits, and associated quantitative trait loci (QTL) affecting yield response in wheat ( Triticum aestivum L.): a review. Front Plant Sci 10

United Nations, Department of Economic and Social Affairs, Population Division (2019). World population prospects 2019: Highlights (ST/ESA/SER.A/423)

VanGinkel M, Ortiz R (2018) Cross the best with the best, and select the best: HELP in breeding selfing crops. Crop Sci 58:17–30. https://doi.org/10.2135/cropsci2017.05.0270

Verma V, Foulkes MJ, Worland AJ et al (2004) Mapping quantitative trait loci for flag leaf senescence as a yield determinant in winter wheat under optimal and drought-stressed environments. Euphytica 135:255. https://doi.org/10.1023/B:EUPH.0000013255.31618.14

Véry A-A, Sentenac H (2003) Molecular mechanisms and regulation of K + transport in higher plants. Annu Rev Plant Biol 54:575–603. https://doi.org/10.1146/annurev.arplant.54.031902.134831

Vijayalakshmi K, Fritz AK, Paulsen GM et al (2010) Modeling and mapping QTL for senescence-related traits in winter wheat under high temperature. Mol Breed 26:163. https://doi.org/10.1007/s11032-009-9366-8

Villareal RL, Sayre K, Banuelos O, Mujeeb-Kazi A (2001) Registration of four synthetic hexaploid wheat ( Triticum turgidum / Aegilops tauschii ) germplasm lines tolerant to waterlogging. Crop Sci 41:274. https://doi.org/10.2135/cropsci2001.411274x

Voss-Fels KP, Robinson H, Mudge SR et al (2018) VERNALIZATION1 modulates root system architecture in wheat and barley. Mol Plant 11:226–229. https://doi.org/10.1016/j.molp.2017.10.005

Wang Y, Zhang X, Liu X et al (2013) The effects of nitrogen supply and water regime on instantaneous WUE, time-integrated WUE and carbon isotope discrimination in winter wheat. Field Crop Res 144:236–244. https://doi.org/10.1016/j.fcr.2013.01.021

Wang Y, Cheng X, Shan Q et al (2014) Simultaneous editing of three homoeoalleles in hexaploid bread wheat confers heritable resistance to powdery mildew. Nat Biotechnol 32:947–951. https://doi.org/10.1038/nbt.2969

Wang JY, Xiong YC, Li FM et al (2017) Effects of drought stress on morphophysiological traits, biochemical characteristics, yield, and yield components in different ploidy wheat: a meta-analysis. In: Advances in agronomy. Academic Press Inc., pp 139–173

Wardlaw IF, Willenbrink J (2000) Mobilization of fructan reserves and changes in enzyme activities in wheat stems correlate with water stress during kernel filling. New Phytol 148:413–422. https://doi.org/10.1046/j.1469-8137.2000.00777.x

Watson A, Ghosh S, Williams M, et al (2017) Speed breeding: a powerful tool to accelerate crop research and breeding. bioRxiv 1–17. https://doi.org/10.1101/161182

Watson A, Ghosh S, Williams MJ et al (2018) Speed breeding is a powerful tool to accelerate crop research and breeding. Nat Plants 4:23–29. https://doi.org/10.1038/s41477-017-0083-8

Xie K, Minkenberg B, Yang Y (2015) Boosting CRISPR/Cas9 multiplex editing capability with the endogenous tRNA-processing system. Proc Natl Acad Sci USA 112:3570–3575. https://doi.org/10.1073/pnas.1420294112

Xu W, Jia L, Shi W et al (2013) Abscisic acid accumulation modulates auxin transport in the root tip to enhance proton secretion for maintaining root growth under moderate water stress. New Phytol 197:139–150. https://doi.org/10.1111/nph.12004

Yang J, Sears RG, Gill BS, Paulsen GM (2002) Growth and senescence characteristics associated with tolerance of wheat-alien amphiploids to high temperature under controlled conditions. Euphytica 126:185–193. https://doi.org/10.1023/A:1016365728633

Yin X, Biswal AK, Dionora J et al (2017) CRISPR-Cas9 and CRISPR-Cpf1 mediated targeting of a stomatal developmental gene EPFL9 in rice. Plant Cell Rep 36:745–757. https://doi.org/10.1007/s00299-017-2118-z

Yu X, Li X, Guo T et al (2016) Genomic prediction contributing to a promising global strategy to turbocharge gene banks. Nat Plants 2:16150. https://doi.org/10.1038/nplants.2016.150

Yu S, Wu J, Wang M et al (2020) Haplotype variations in QTL for salt tolerance in Chinese wheat accessions identified by marker-based and pedigree-based kinship analyses. Crop J. https://doi.org/10.1016/j.cj.2020.03.007

Zhang H, Cui F, Wang L et al (2013) Conditional and unconditional QTL mapping of drought-tolerance-related traits of wheat seedling using two related RIL populations. J Genet 92:213. https://doi.org/10.1007/s12041-013-0253-z

Zhao C, Liu B, Piao S et al (2017) Temperature increase reduces global yields of major crops in four independent estimates. Proc Natl Acad Sci USA 201701762:9326. https://doi.org/10.1073/pnas.1701762114

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Mondal, S. et al. (2021). Advances in Breeding for Abiotic Stress Tolerance in Wheat. In: Kole, C. (eds) Genomic Designing for Abiotic Stress Resistant Cereal Crops. Springer, Cham. https://doi.org/10.1007/978-3-030-75875-2_2

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