Skip to main content
SpringerLink
Log in

Efficient Breeding of Crop Plants

  • Chapter
  • First Online:
Fundamentals of Field Crop Breeding

Abstract

In crop breeding programs, the rate of genetic gain which is achieved using the traditional breeding is insufficient to meet the increased demand of food for the rapidly expanding global population. The main constraint with the conventional breeding is the time which is required in developing crosses, followed by selection and testing of the experimental cultivars. Although, using this technique, lot of progress has been made in increasing the productivity, the time has come to think beyond this and integrate the recent advances in the area of genomics, phenomics and computational biology into the conventional breeding program for increasing its efficiency. While doing this emphasis on proper characterization and use of plant genetic resources, defining the breeding objectives and use of recent advances in holistic way are also essential. Therefore, in this chapter, we first highlight the importance of plant breeding followed by significance of the plant genetic resources in the breeding program, need of ideotype breeding and the breeding objectives for important traits including resistance against various biotic and abiotic stresses. We then discuss the limitations of conventional breeding and advantages of genomics-assisted breeding. While doing this, we also discuss various molecular breeding tools and genomic resources as well as different approaches for efficient breeding including marker-assisted selection, marker-assisted recurrent selection and genomic selection. This is followed by importance of other non-conventional approaches including the recent one on gene editing, speed breeding and role of integrated data management and bioinformatics in the breeding programs. We also discuss the significance of phenomics and phenotyping platforms in the crop breeding as well as role of computational techniques like artificial intelligence and machine learning in analysing the huge data which is being generated in the breeding programs. Finally, we conclude with a brief note on the emerging challenges in breeding which need to be addressed and the thrust areas of research for the future.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • Agarwal R, Narayan J (2015) Unraveling the impact of bioinformatics and omics in agriculture. Int J Plant Biol Res 3:1039

    Google Scholar 

  • Ahmar S, Gill RA, Jung KH et al (2020) Conventional and molecular techniques from simple breeding to speed breeding in crop plants: recent advances and future outlook. Int J Mol Sci 21:2590

    CAS  PubMed  PubMed Central  Google Scholar 

  • Aslam Z, Khattak JZK, Ahmed M et al (2004) A role of bioinformatics in agriculture. In: Ahmed M, Stockle CO (eds) Quantification of climate variability, adaptation and mitigation for agricultural sustainability, pp 414–430

    Google Scholar 

  • Bailey-Serres J, Fukao T, Ronald P et al (2010) Submergence tolerant rice: SUB1’s journey from landrace to modern cultivar. Rice 3:138–147

    Google Scholar 

  • Bainsla NK, Meena HP (2016) Breeding for resistance to biotic stresses in plants. In: Yadav P, Kumar S, Jain V (eds) Recent advances in plant stress physiology. Daya Publishing House, New Delhi, pp 379–411

    Google Scholar 

  • Balconi C, Stevanato P, Motto M, Biancardi E (2012) Breeding for biotic stress resistance/tolerance in plants. In: Ashraf M et al (eds) Crop production for agricultural improvement. Springer, pp 57–114

    Google Scholar 

  • Batley J, Edwards D (2008) Bioinformatics: fundamentals and applications in plant genetics and breeding. In: Kole C, Abbott AG (eds) Principles and practices of plant molecular mapping and breeding. Science Publishers, Enfield, pp 269–302

    Google Scholar 

  • Batley J, Edwards D (2009) Mining for single nucleotide polymorphism (SNP) and simple sequence repeat (SSR) molecular genetic markers. In: Posada D (ed) Bioinformatics for DNA sequence analysis. Humana Press, New York, pp 303–322

    Google Scholar 

  • Beans C (2020) Inner workings: crop researchers harness artificial intelligence to breed crops for the changing climate. Proc Natl Acad Sci U S A 117:27066–27069

    CAS  PubMed  PubMed Central  Google Scholar 

  • Bernardo R, Charcosset A (2006) Usefulness of gene information in marker-assisted recurrent selection: a simulation appraisal. Crop Sci 46:614–621

    Google Scholar 

  • Bertioli DJ, Cannon SB, Froenicke L et al (2016) The genome sequences of Arachis duranensis and Arachis ipaensis, the diploid ancestors of cultivated peanut. Nat Genet 48:438

    CAS  PubMed  Google Scholar 

  • Bertioli DJ, Jenkins J, Clevenger J et al (2019) The genome sequence of peanut (Arachis hypogaea), a segmental allotetraploid. Nat Genet 51:877–884

    CAS  PubMed  Google Scholar 

  • Beyene Y, Semagn K, Crossa J et al (2016) Improving maize grain yield under drought stress and non-stress environments in sub-Saharan Africa using marker-assisted recurrent selection. Crop Sci 56:344–353

    CAS  Google Scholar 

  • Bharadwaj C, Tripathi S, Soren KR et al (2021) Introgression of “QTL-hotspot” region enhances drought tolerance and grain yield in three elite chickpea cultivars. Plant Genome 14:e20076

    CAS  PubMed  Google Scholar 

  • Bhatta M, Sandro P, Smith MR et al (2019) Need for speed: manipulating plant growth to accelerate breeding cycles. Curr Opin Plant Biol 60:101986. https://doi.org/10.1016/j.pbi.2020.101986

    Article  Google Scholar 

  • Bidinger FR (1992) Utilization of plant genetic resources in crop breeding programmes. Dinteria 23:70–81

    Google Scholar 

  • Bohra A, Chand Jha U, Godwin ID et al (2020) Genomic interventions for sustainable agriculture. Plant Biotechnol J 18:2388–2405

    PubMed  PubMed Central  Google Scholar 

  • Bošković J, Zečević V, Coghill TG et al (2012) The importance of plant genetic resources in agroecosystem. Rev Agric Rural Dev 1:302–308

    Google Scholar 

  • Cermak T, Doyle EL, Christian M et al (2011) Efficient design and assembly of custom TALEN and other TAL effector-based constructs for DNA targeting. Nucleic Acids Res 39:e82

    CAS  PubMed  PubMed Central  Google Scholar 

  • Charmet G, Robert N, Perretant MR et al (2001) Marker assisted recurrent selection for cumulating QTLs for bread-making related traits. Euphytica 119:89–93

    CAS  Google Scholar 

  • Chen X, Li H, Pandey MK et al (2016) Draft genome of the peanut A-genome progenitor (Arachis duranensis) provides insights into geocarpy, oil biosynthesis, and allergens. Proc Natl Acad Sci U S A 113:6785–6790

    CAS  PubMed  PubMed Central  Google Scholar 

  • Cobb JN, Biswas PS, Platten JD (2019) Back to the future: revisiting MAS as a tool for modern plant breeding. Theor Appl Genet 132:647–667

    CAS  PubMed  Google Scholar 

  • Crossa J, Pérez-Rodríguez P, Cuevas J et al (2017) Genomic selection in plant breeding: methods, models, and perspectives. Trends Plant Sci 22:961–975

    CAS  PubMed  Google Scholar 

  • Datta SK, Baisakh N, Ramanathan V et al (2004) Transgenics in crop improvement plant. In: Jain HK, Kharkwal MC (eds) Breeding—Mendelian to molecular approaches, pp 333–371

    Google Scholar 

  • Desta ZA, Ortiz R (2014) Genomic selection: genome-wide prediction in plant improvement. Trends Plant Sci 19:592–601

    CAS  PubMed  Google Scholar 

  • Dickmann DI (1985) The ideotype concept applied to forest trees. In: Cannell MGR, Jackson JE (eds) Attributes of trees as crop plants. Institute of Terrestrial Ecology, Penicuik, Scotland, pp 89–101

    Google Scholar 

  • Dickmann DI, Gold MA, Flore JA (1994) The ideotype concept and the genetic improvement of tree crops. Plant Breed Rev 12:163–193

    Google Scholar 

  • Donald CM (1968) The breeding of crop ideotypes. Euphytica 17:385–403

    Google Scholar 

  • Dong OX, Ronald PC (2019) Genetic engineering for disease resistance in plants: recent progress and future perspectives. Plant Physiol 180:26–38

    CAS  PubMed  PubMed Central  Google Scholar 

  • Dormatey R, Sun C, Ali K et al (2020) Gene pyramiding for sustainable crop improvement against biotic and abiotic stresses. Agronomy 10:1255

    CAS  Google Scholar 

  • Finkel E (2009) With ‘phenomics,’ plant scientists hope to shift breeding into overdrive. Science 325:380–381

    CAS  PubMed  Google Scholar 

  • Galluzzi G, Seyoum A, Halewood M et al (2020) The role of genetic resources in breeding for climate change: the case of public breeding programmes in eighteen developing countries. Plan Theory 9:1129. https://doi.org/10.3390/plants9091129

    Article  Google Scholar 

  • Gauffreteau A (2018) Using ideotypes to support selection and recommendation of varieties. OCL 25(6):D602. https://doi.org/10.1051/ocl/2018042

    Article  Google Scholar 

  • 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

    CAS  PubMed  Google Scholar 

  • Godfray HCJ, Beddington JR, Crute IR et al (2010) Food security: the challenge of feeding 9 billion people. Science 327:812–818

    CAS  PubMed  Google Scholar 

  • Godiki Y, Bhanu AN, Singh MN (2016) Marker assisted recurrent selection: an overview. Adv Life Sci 5:6493–6499

    Google Scholar 

  • Gupta PK, Rustgi S, Mir RR (2008) Array-based high-throughput DNA markers for crop improvement. Heredity 101:5–18

    CAS  PubMed  Google Scholar 

  • Gupta PK, Balyan HS, Mir RR et al (2011) QTL analysis, association mapping and marker-assisted selection for some quality traits in bread wheat—an overview of the work done at CCS university, Meerut. J Wheat Res 3:1–11

    CAS  Google Scholar 

  • Gupta PK, Balyan HS, Gahlaut V et al (2012) Phenotyping, genetic dissection, and breeding for drought and heat tolerance in common wheat: status and prospects. Plant Breed Rev 36:85

    Google Scholar 

  • Gupta PK, Kulwal PL, Mir RR (2013a) QTL mapping: methodology and applications in cereal breeding. In: Gupta PK, Varshney RK (eds) Cereal genomics II. Springer, Dordrecht, pp 275–318

    Google Scholar 

  • Gupta PK, Rustgi S, Mir RR (2013b) Array-based high-throughput DNA markers and genotyping platforms for cereal genetics and genomics. In: Gupta PK, Varshney RK (eds) Cereal genomics II. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6401-9_2

    Chapter  Google Scholar 

  • Gupta PK, Kulwal PL, Jaiswal V (2014) Association mapping in crop plants: opportunities and challenges. Adv Genet 85:109–147

    CAS  PubMed  Google Scholar 

  • Gupta PK, Kulwal PL, Jaiswal V (2019) Association mapping in plants in the post-GWAS genomics era. Adv Genet 104:75–154. https://doi.org/10.1016/bs.adgen.2018.12.001

    Article  PubMed  Google Scholar 

  • Halewood M, Chiurugwi T, Hamilton RS, Kurtz B, Marden E, Welch E, Michiels F, Mozafari J, Sabran M, Patron N, Kersey P, Bastow R, Dorius S, Dias S, McCouch S, Powel W (2018) Plant genetic resources for food and agriculture: opportunities and challenges emerging from the science and information technology revolution. New Phytol 217:1407–1419

    PubMed  Google Scholar 

  • Harfouche AL, Jacobson DA, Kainer D et al (2019) Accelerating climate resilient plant breeding by applying next-generation artificial intelligence. Trends Biotechnol 37(11):1217–1235

    CAS  PubMed  Google Scholar 

  • Haussmann BI, Parzies HK, Presterl T et al (2004) Plant genetic resources in crop improvement. Plant Genet Resour 2:3–21

    Google Scholar 

  • Heffner EL, Lorenz AJ, Jannink J-L et al (2010) Plant breeding with genomic selection: gain per unit time and cost. Crop Sci 50:1681–1690

    Google Scholar 

  • Hickey LT, Germa SE, Diaz JE et al (2017) Speed breeding for multiple disease resistance in barley. Springer, New York

    Google Scholar 

  • Hickey LT, Hafeez AN, Robinson H et al (2019) Breeding crops to feed 10 billion. Nat Biotechnol 37:744–754

    CAS  PubMed  Google Scholar 

  • Husaini AM, Rashid Z, Mir RR et al (2011) Approaches for gene targeting and targeted gene expression in plants. GM Crops 2:150–162

    PubMed  Google Scholar 

  • Jain M, Misra G, Patel RK et al (2013) A draft genome sequence of the pulse crop chickpea (Cicer arietinum L.). Plant J 74:715–729

    CAS  PubMed  Google Scholar 

  • Jha UC, Chaturvedi SK, Bohra A et al (2014) Abiotic stresses, constraints and improvement strategies in chickpea. Plant Breed 133:163–178

    Google Scholar 

  • Jiang GL (2015) Molecular marker-assisted breeding: a plant breeder’s review. In: Advances in plant breeding strategies: breeding, biotechnology and molecular tools. Springer, Cham, pp 431–472

    Google Scholar 

  • Johnson R (2004) Marker assisted selection. Plant Breed Rev 24:293–309

    Google Scholar 

  • Kärki L, Tigerstedt PMA (1985) Definition and exploitation of forest tree ideotypes in Finland. In: Cannell MGR, Jackson JE (eds) Attributes of trees as crop plants. Institute of Terrestrial Ecology, Penicuik, Scotland, pp 102–109

    Google Scholar 

  • Khan AW, Garg V, Roorkiwal M et al (2020) Super-pangenome by integrating the wild side of a species for accelerated crop improvement. Trends Plant Sci 25:148–158

    CAS  PubMed  PubMed Central  Google Scholar 

  • Khanday I, Skinner D, Yang B et al (2019) A maleexpressed rice embryogenic trigger redirected for asexual propagation through seeds. Nature 565:91–95

    CAS  PubMed  Google Scholar 

  • Kim MY, Lee S, Van K et al (2010) Whole-genome sequencing and intensive analysis of the undomesticated soybean (Glycine soja Sieb. and Zucc.) genome. Proc Natl Acad Sci U S A 107:2032–22037

    Google Scholar 

  • Kulwal PL (2016) Association mapping and genomic selection—where does sorghum stand? In: Rakshit S, Wang Y-H (eds) The sorghum genome. Springer, Cham, pp 137–148

    Google Scholar 

  • Kulwal PL (2018) Trait mapping approaches through linkage mapping in plants. Adv Biochem Eng Biotechnol 164:53–82. https://doi.org/10.1007/10_2017_49

    Article  CAS  PubMed  Google Scholar 

  • Kulwal PL, Mhase LB (2017) Protein content exhibits a significant positive correlation with seed weight in chickpea germplasm collection. Plant Genet Resour 15:283

    CAS  Google Scholar 

  • Kulwal PL, Singh R (2021) Association mapping in plants. In: Tripodi P (ed) Crop breeding. Methods in molecular biology, vol 2264. Humana Press, New York. https://doi.org/10.1007/978-1-0716-1201-9_8

    Chapter  Google Scholar 

  • Kulwal P, Kumar N, Kumar A et al (2005) Gene networks in hexaploid wheat: interacting quantitative trait loci for grain protein content. Funct Integr Genomics 5:254–259

    CAS  PubMed  Google Scholar 

  • Kulwal PL, Mir RR, Kumar S et al (2010) QTL analysis and molecular breeding for seed dormancy and pre-harvest sprouting tolerance in bread wheat. J Plant Biol 37:59–74

    Google Scholar 

  • Kulwal PL, Mahendar T, Varshney RK (2012) Genomics interventions in crop breeding for sustainable agriculture. Encycl Sustain Sci Technol 1:2527–2540

    Google Scholar 

  • Kumar S, Kumar M, Mir RR et al (2021) Advances in molecular markers and their use in genetic improvement of wheat. In: Wani SH, Mohan A, Singh GP (eds) Physiological, molecular, and genetic perspectives of wheat improvement, pp 139–174

    Google Scholar 

  • Kuriakose SV, Pushker R, Hyde EM (2020) Data-driven decisions for accelerated plant breeding. In: Gosal SS, Wani SH (eds) Accelerated plant breeding, vol vol 1. Springer, Cham, pp 89–119

    Google Scholar 

  • Kushwaha UKS, Deo I, Jaiswal JP et al (2017) Role of bioinformatics in crop improvement. GJSFR-D 17:2249–4626

    Google Scholar 

  • Li JF, Norville JE, Aach J et al (2013) Multiplex and homologous recombination-mediated genome editing in Arabidopsis and Nicotiana benthamiana using guide RNA and Cas9. Nat Biotechnol 31:688–691

    CAS  PubMed  PubMed Central  Google Scholar 

  • Lloyd A, Plaisier CL, Carroll D et al (2005) Targeted mutagenesis using zinc-finger nucleases in Arabidopsis. Proc Natl Acad Sci U S A 102:2232–2237

    CAS  PubMed  PubMed Central  Google Scholar 

  • Lu Q, Li H, Hong Y et al (2018) Genome sequencing and analysis of the peanut B-genome progenitor (Arachis ipaensis). Front Plant Sci 3:604

    Google Scholar 

  • Mahfouz MM, Li LX, Shamimuzzaman M et al (2011) De novo engineered transcription activator-like effector (TALE) hybrid nuclease with novel DNA binding specificity creates double-strand breaks. Proc Natl Acad Sci U S A 108:2623–2628

    CAS  PubMed  PubMed Central  Google Scholar 

  • Mascher M, Gundlach H, Himmelbach A et al (2017) A chromosome conformation capture ordered sequence of the barley genome. Nature 544:427–433

    CAS  PubMed  Google Scholar 

  • McCouch S, Navabi ZK, Abberton M et al (2020) Mobilizing crop biodiversity. Mol Plant 13:1341–1344

    CAS  PubMed  Google Scholar 

  • Meena HP, Bainsla NK, Yadav DK (2017) Breeding for abiotic stress tolerance in crop plants. In: Yadav P, Kumar S, Jain V (eds) Recent advances in plant stress physiology, pp 329–377

    Google Scholar 

  • Meuwissen T (2007) Genomic selection: marker assisted selection on a genomewide scale. J Anim Breed Genet 124:321–322

    PubMed  Google Scholar 

  • Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genomewide dense marker maps. Genetics 157:1819–1829

    CAS  PubMed  PubMed Central  Google Scholar 

  • Mir RR, Varshney RK (2013) Future prospects of molecular markers in plants. In: Henry RJ (ed) Molecular markers in plants. Blackwell, Oxford, pp 169–190. ISBN 9781118473023

    Google Scholar 

  • Mir RR, Kumar N, Jaiswal V et al (2012a) Genetic dissection of grain weight (GW) in bread wheat through QTL interval and association mapping. Mol Breed 29:963–972

    Google Scholar 

  • Mir RR, Zaman-Allah M, Sreenivasulu N et al (2012b) Integrated genomics, physiology and breeding approaches for improving drought tolerance in crops. Theor Appl Genet 125:625–645

    CAS  PubMed  PubMed Central  Google Scholar 

  • Mir RR, Hiremath PJ, Riera-Lizarazu O et al (2013) Evolving molecular marker technologies in plants: from RFLPs to GBS. In: Lübberstedt T, Varshney RK (eds) Diagnostics in plant breeding. Springer Science+Business, New York, pp 229–247. ISBN 978–94–007-5686-1; 978–94–007-5687-8

    Google Scholar 

  • Mir RR, Choudhary N, Singh B et al (2015) Harnessing genomics through phenomics. In: Jitendar K et al (eds) Phenomics in crop plants: trends, options and limitations. Springer, New Delhi

    Google Scholar 

  • Mir RR, Reynolds M, Pinto F et al (2019) High-throughput phenotyping for crop improvement in the genomics era. Plant Sci 282:60–72

    CAS  PubMed  Google Scholar 

  • Mir AH, Bhat MA, Dar SA et al (2021a) Assessment of cold tolerance in chickpea (Cicer spp.) grown under cold/freezing weather conditions of north-western Himalayas of Jammu and Kashmir, India. Physiol Mol Biol Plants 27:1105–1118

    CAS  PubMed  PubMed Central  Google Scholar 

  • Mir RR, Kumar S, Shafi S (2021b) Genetic dissection for yield and yield-related traits in bread wheat (Triticum aestivum L.). In: Wani SH, Mohan A, Singh GP (eds) Physiological, molecular, and genetic perspectives of wheat improvement, pp 209–227

    Google Scholar 

  • Munck L (2009) Breeding for quality traits in cereals: a revised outlook on old and new tools for integrated breeding. In: Carena MJ (ed) Cereals: handbook of plant breeding. Springer Publishers, New York, pp 333–366

    Google Scholar 

  • Murphy KP (2012) Machine learning: a probabilistic perspective. MIT Press, Cambridge

    Google Scholar 

  • Nekrasov V, Staskawicz B, Weigel D et al (2013) Targeted mutagenesis in the model plant Nicotiana benthamiana using Cas9 RNA-guided endonuclease. Nat Biotechnol 31:691–693

    CAS  PubMed  Google Scholar 

  • Niazian M, Niedbała G (2020) Machine learning for plant breeding and biotechnology. Agriculture 10:436

    CAS  Google Scholar 

  • Pandey MK, Chaudhari S, Jarquin D et al (2020) Genome-based trait prediction in multi-environment breeding trials in groundnut. Theor Appl Genet 133:3101–3117

    CAS  PubMed  PubMed Central  Google Scholar 

  • Parmley KA, Higgins RH, Ganapathysubramanian B et al (2019) Machine learning approach for prescriptive plant breeding. Sci Rep 9:17132

    PubMed  PubMed Central  Google Scholar 

  • Rahim MS, Bhandawat A, Rana N et al (2020) Genomic selection in cereal crops: methods and applications. In: Gosal SS, Wani SH (eds) Accelerated plant breeding, vol 1. Springer, Cham, pp 51–88

    Google Scholar 

  • Rahman M, Davies P, Bansal U et al (2020) Marker-assisted recurrent selection improves the crown rot resistance of bread wheat. Mol Breed 40:1–4

    Google Scholar 

  • Ramstein GP, Jensen SE, Buckler ES (2019) Breaking the curse of dimensionality to identify causal variants in breeding 4. Theor Appl Genet 132:559–567

    PubMed  Google Scholar 

  • Rani A, Devi P, Jha UC et al (2020) Developing climate-resilient chickpea involving physiological and molecular approaches with a focus on temperature and drought stresses. Front Plant Sci 10:1759

    PubMed  PubMed Central  Google Scholar 

  • Rasmusson DC (1987) An evaluation of ideotype breeding. Crop Sci 27:1140–1146

    Google Scholar 

  • Ribaut JM, Vicente MC, Delannay X (2010) Molecular breeding in developing countries: challenges and perspectives. Curr Opin Plant Biol 13:1–6

    Google Scholar 

  • Richardson KL, Vales MI, Kling JG et al (2006) Pyramiding and dissecting disease resistance QTL to barley stripe rust. Theor Appl Genet 113:485–495

    CAS  PubMed  Google Scholar 

  • Rodriguez-Leal D, Lemmon ZH, Man J et al (2017) Engineering quantitative trait variation for crop improvement by genome editing. Cell 171:470–480

    CAS  PubMed  Google Scholar 

  • Roorkiwal M, Bharadwaj C, Barmukh R et al (2020) Integrating genomics for chickpea improvement: achievements and opportunities. Theor Appl Genet 133:1703–1720

    PubMed  PubMed Central  Google Scholar 

  • Schmutz J, Cannon SB, Schlueter J et al (2010) Genome sequence of the palaeopolyploid soybean. Nature 463:178–183

    CAS  PubMed  Google Scholar 

  • Schmutz J, McClean PE, Mamidi S et al (2014) A reference genome for common bean and genome-wide analysis of dual domestications. Nat Genet 46:707–713

    CAS  PubMed  PubMed Central  Google Scholar 

  • Schnable PS, Ware D, Fulton RS et al (2009) The B73 maize genome: complexity, diversity, and dynamics. J Sci 326:1112–1115

    CAS  Google Scholar 

  • Shan QW, Wang YP, Li J et al (2013) Targeted genome modification of crop plants using a CRISPR-Cas system. Nat Biotechnol 31:686–688

    CAS  PubMed  Google Scholar 

  • Siddique KHM, Sedgley RH (1987) Canopy development modifies the water economy of chickpea (Cicer arietinum L.). Aust J Agr Res 37:599–610

    Google Scholar 

  • Singh NK, Gupta DK, Jayaswal PK et al (2012) The first draft of the pigeonpea genome sequence. J Plant Biochem Biotechnol 21:98–11

    PubMed  Google Scholar 

  • Tan Y-y, Du H, Wu X et al (2020) Gene editing: an instrument for practical application of gene biology to plant breeding. J Zhejiang Univ Sci B (Biomed Biotechnol) 21:460–473

    CAS  Google Scholar 

  • Tester M, Langridge P (2010) Breeding technologies to increase crop production in a changing world. Science 327:818–822

    CAS  PubMed  Google Scholar 

  • Thudi M, Palakurthi R, Schnable JC et al (2021) Genomic resources in plant breeding for sustainable agriculture. J Plant Physiol 257:153351

    CAS  PubMed  PubMed Central  Google Scholar 

  • Tyagi S, Sharma S, Ganie SA et al (2019) Plant microRNAs: biogenesis, gene silencing, web-based analysis tools and their use as molecular markers. 3 Biotech 9:413

    PubMed  PubMed Central  Google Scholar 

  • Tyagi S, Kumar A, Gautam T et al (2021) Development and use of miRNA-derived SSR markers for the study of genetic diversity, population structure, and characterization of genotypes for breeding heat tolerant wheat. PLoS One 16:e0231063

    CAS  PubMed  PubMed Central  Google Scholar 

  • Varshney RK, Glaszmann JC, Leung H, Ribaut JM (2010) More genomic resources for less-studied crops. Trends Biotechnol 28:452–460

    CAS  PubMed  Google Scholar 

  • Varshney RK, Chen W, Li Y et al (2012) Draft genome sequence of pigeonpea (Cajanus cajan), an orphan legume crop of resource-poor farmers. Nat Biotechnol 30:83–89

    CAS  Google Scholar 

  • Varshney RK, Mohan SM, Gaur PM et al (2013a) Achievements and prospects of genomics-assisted breeding in three legume crops of the semi-arid tropics. Biotechnol Adv 31(8):1120–1134

    PubMed  Google Scholar 

  • Varshney RK, Song C, Saxena RK et al (2013b) Draft genome sequence of chickpea (Cicer arietinum) provides a resource for trait improvement. Nat Biotechnol 31:240–246

    CAS  PubMed  Google Scholar 

  • Varshney RK, Thudi M, Nayak SN, Gaur PM, Kashiwagi J, Krishnamurthy L, Jaganathan D, Koppolu J, Bohra A, Tripathi S, Rathore A, Jukanti AK, Jayalakshmi V, Vemula A, Singh SJ, Yasin M, Sheshshayee MS, Viswanatha KP (2014) Genetic dissection of drought tolerance in chickpea (Cicer arietinum L.). Theor Appl Genet 127:445–462

    CAS  PubMed  Google Scholar 

  • Varshney RK, Godwin ID, Mohapatra T et al (2019) A SWEET solution to rice blight. Nat Biotechnol 37:1280–1282

    CAS  PubMed  Google Scholar 

  • Varshney RK, Bohra A, Yu J et al (2021) Designing future crops: genomics-assisted breeding comes of age. Trends Plant Sci 26(6):631–649. https://doi.org/10.1016/j.tplants.2021.03.010

    Article  CAS  PubMed  Google Scholar 

  • Vassilev D, Leunissen J, Atanassov A et al (2005) Application of bioinformatics in plant breeding. Biotechnol Biotechnol Equip 19(sup3):139–152

    Google Scholar 

  • Wallace JG, Rodgers-Melnick E, Buckler ES (2018) On the road to breeding 4.0: unraveling the good, the bad, and the boring of crop quantitative genomics. Annu Rev Genet 52:421–444

    CAS  PubMed  Google Scholar 

  • Wang C et al (2019) Clonal seeds from hybrid rice by simultaneous genome engineering of meiosis and fertilization genes. Nat Biotechnol 37:283–286

    CAS  PubMed  Google Scholar 

  • 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

    PubMed  Google Scholar 

  • Wong CK, Bernardo R (2008) Genome-wide selection in oil palm: increasing selection gain per unit time and cost with small populations. Theor Appl Genet 116:815–824

    CAS  PubMed  Google Scholar 

  • Xu Y, Liu X, Fu J et al (2020) Enhancing genetic gain through genomic selection: from livestock to plants. Plant Commun 1:100005

    PubMed  Google Scholar 

  • Yin D, Ji C, Ma X et al (2018) Genome of an allotetraploid wild peanut Arachis monticola: a de novo assembly. Gigascience 7:giy66

    Google Scholar 

  • Zeng X, Long H, Wang Z et al (2015) The draft genome of Tibetan hulless barley reveals adaptive patterns to the high stressful Tibetan plateau. Proc Natl Acad Sci U S A 112:1095–1100

    CAS  PubMed  PubMed Central  Google Scholar 

  • Zeng X, Guo Y, Xu Q et al (2018) Origin and evolution of qingke barley in Tibet. Nat Commun 9:1–11

    Google Scholar 

  • Zhang Y, Massel K, Godwin ID, Gao C (2018) Applications and potential of genome editing in crop improvement. Genome Biol 19:210

    CAS  PubMed  PubMed Central  Google Scholar 

  • Zhang YX, Malzahn AA, Sretenovic S et al (2019) The emerging and uncultivated potential of CRSIPR technology in plant science. Nat Plants 5:778–794

    PubMed  Google Scholar 

  • Zhang Y, Pribil M, Palmgren M et al (2020) A CRISPR way for accelerating improvement of food crops. Nat Food 1(4):200–205. https://doi.org/10.1038/s43016-020-0051-8

    Article  CAS  Google Scholar 

  • Zhuang W, Chen H, Yang M et al (2019) The genome of cultivated peanut provides insight into legume karyotypes, polyploid evolution and crop domestication. Nat Genet 51:865–876

    CAS  PubMed  PubMed Central  Google Scholar 

  • Zimny T, Sowa S, Tyczewska A et al (2019) Certain new plant breeding techniques and their marketability in the context of EU GMO legislation—recent developments. N Biotechnol 51:49–56

    CAS  PubMed  Google Scholar 

  • Zong Y et al (2018) Efficient C-to-T base editing in plants using a fusion of nCas9 and human APOBEC3A. Nat Biotechnol 36:950–953

    CAS  Google Scholar 

Download references

Acknowledgements

PLK would like to thank Department of Biotechnology, Govt. of India for the research grants during the course of writing this article. RKV thanks Bill and Melinda Gates Foundation, USA, for supporting several projects related to genomics-assisted breeding at ICRISAT and acknowledges Science and Engineering Research Board (SERB), Department of Science and Technology, Government of India, for awarding JC Bose National Fellowship.

Author information

Authors and Affiliations

  1. Biotechnology Centre, Mahatma Phule Krishi Vidyapeeth (Agricultural University), Rahuri, Maharashtra, India

    Pawan L. Kulwal

  2. Division of Genetics and Plant Breeding, Faculty of Agriculture, SKUAST-Kashmir, Kashmir, Jammu and Kashmir, India

    Reyazul Rouf Mir

  3. Centre of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics, Patancheru, Hyderabad, Telangana, India

    Rajeev K. Varshney

  4. State Agricultural Biotechnology Centre, Crop Research Innovation Centre, Food Futures Institute, Murdoch University, Murdoch, WA, Australia

    Rajeev K. Varshney

Authors
  1. Pawan L. Kulwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Reyazul Rouf Mir
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rajeev K. Varshney
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Crop Science Division, Indian Council of Agricultural Research, Delhi, India

    Devendra Kumar Yadava

  2. Division of Genetics, Indian Agricultural Research Institute, Delhi, India

    Harsh Kumar Dikshit

  3. Division of Genetics, Indian Agricultural Research Institute, Delhi, India

    Gyan Prakash Mishra

  4. Division of Genetics, Indian Agricultural Research Institute, Delhi, India

    Shailesh Tripathi

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kulwal, P.L., Mir, R.R., Varshney, R.K. (2022). Efficient Breeding of Crop Plants. In: Yadava, D.K., Dikshit, H.K., Mishra, G.P., Tripathi, S. (eds) Fundamentals of Field Crop Breeding. Springer, Singapore. https://doi.org/10.1007/978-981-16-9257-4_14

Download citation

Publish with us

Policies and ethics

Search

Navigation