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Review Articles

Vol. 13 No. sp5 (2026): Recent Advances in Agriculture

Speeding crop resilience: Accelerated breeding strategies for pulse crops

DOI
https://doi.org/10.14719/pst.16081
Submitted
15 June 2026
Published
01-08-2026

Abstract

Pulses, including chickpea (Cicer arietinum L.), lentil (Lens culinaris Medik.), field pea (Pisum sativum L.), mungbean (Vigna radiata (L.) R. Wilczek) and pigeonpea (Cajanus cajan (L.) Huth), are major sources of plant protein, dietary fibre and essential micronutrients, for over one billion people worldwide. However, conventional breeding typically requires 7–12 years to develop improved cultivars, limiting the ability to respond to climate change, emerging biotic and abiotic stresses and increasing food demand. Accelerated breeding (AB) integrates innovative approaches such as speed breeding (SB), rapid generation advancement (RGA), marker-assisted breeding, genomic selection, doubled haploids (DH), high-throughput phenotyping, genome editing and artificial intelligence to shorten breeding cycles and enhance genetic gain. This review summarises recent advances in these technologies, their applications in major pulse crops and their potential to improve breeding efficiency, selection accuracy and cultivar development. It also highlights current challenges, including genotype-dependent transformation, limited phenotyping infrastructure and data integration, while discussing emerging opportunities in multi-omics, predictive breeding and AI-assisted decision support. Integrating these technologies into unified breeding pipelines will accelerate the development of climate-resilient, high-yielding and nutritionally superior pulse cultivars, thereby strengthening global food and nutritional security and promoting sustainable agriculture.

References

  1. 1. FAOSTAT. FAOSTAT [Internet]. 2022 [cited 2026 Apr 16].
  2. 2. Foyer CH, Lam HM, Nguyen HT, Siddique KHM, Varshney RK, Colmer TD, et al. Neglecting legumes has compromised human health and sustainable food production. Nat Plants. 2016;2(8):16112. https://doi.org/10.1038/nplants.2016.112
  3. 3. Roorkiwal M, Jarquin D, Singh MK, Gaur PM, Bharadwaj C, Rathore A, et al. Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype × environment interaction on prediction accuracy in chickpea. Sci Rep. 2018;8(1):11701. https://doi.org/10.1038/s41598-018-30027-2
  4. 4. Varshney RK, Bohra A, Roorkiwal M, Barmukh R, Cowling WA, Chitikineni A, et al. Fast-forward breeding for a food-secure world. Trends Genet. 2021;37(12):1124–36. https://doi.org/10.1016/j.tig.2021.08.002
  5. 5. Annicchiarico P, Nazzicari N, Pecetti L, Romani M, Ferrari B, Wei Y, et al. GBS-based genomic selection for pea grain yield under severe terminal drought. Plant Genome. 2017;10(2):plantgenome2016.07.0072. https://doi.org/10.3835/plantgenome2016.07.0072
  6. 6. Hickey LT, Hafeez AN, Robinson H, Jackson SA, Leal-Bertioli SCM, Tester M, et al. Breeding crops to feed 10 billion. Nat Biotechnol. 2019;37(7):744–54. https://doi.org/10.1038/s41587-019-0152-9
  7. 7. Watson A, Ghosh S, Williams MJ, Cuddy WS, Simmonds J, Rey MD, et al. Speed breeding is a powerful tool to accelerate crop research and breeding. Nat Plants. 2018;4(1):23–9. https://doi.org/10.1038/s41477-017-0083-8
  8. 8. Meuwissen TH, Hayes BJ, Goddard M. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001;157(4):1819–29. https://doi.org/10.1093/genetics/157.4.1819
  9. 9. Cazzola F, Bermejo CJ, Gatti I, Cointry E. Speed breeding in pulses: an opportunity to improve the efficiency of breeding programs. Crop Pasture Sci. 2021;72:165–72. https://doi.org/10.1071/CP20462
  10. 10. Collard BCY, Mackill DJ. Marker-assisted selection: an approach for precision plant breeding in the twenty-first century. Philos Trans R Soc B Biol Sci. 2007;363(1491):557–72. https://doi.org/10.1098/rstb.2007.2170
  11. 11. Forster BP, Heberle-Bors E, Kasha KJ, Touraev A. The resurgence of haploids in higher plants. Trends Plant Sci. 2007;12(8):368–75. https://doi.org/10.1016/j.tplants.2007.06.007
  12. 12. Araus JL, Cairns JE. Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci. 2014;19(1):52–61. https://doi.org/10.1016/j.tplants.2013.09.008
  13. 13. Mishra R, Joshi RK, Zhao K. Base editing in crops: current advances, limitations and future implications. Plant Biotechnol J. 2020;18(1):20–31. https://doi.org/10.1111/pbi.13225
  14. 14. Samineni S, Sen M, Sajja SB, Gaur PM. Rapid generation advance (RGA) in chickpea to produce up to seven generations per year and enable speed breeding. Crop J. 2020;8(1):164–9. https://doi.org/10.1016/j.cj.2019.08.003
  15. 15. Lulsdorf MM, Banniza S. Rapid generation cycling of an F2 population derived from a cross between Lens culinaris Medik. and Lens ervoides (Brign.) Grande after aphanomyces root rot selection. Plant Breed. 2018;137(4):486–91. https://doi.org/10.1111/pbr.12612
  16. 16. Ghosh S, Watson A, Gonzalez-Navarro OE, Ramirez-Gonzalez RH, Yanes L, Mendoza-Suárez M, et al. Speed breeding in growth chambers and glasshouses for crop breeding and model plant research. Nat Protoc. 2018;13(12):2944–63. https://doi.org/10.1038/s41596-018-0072-z
  17. 17. O’Connor DJ, Wright GC, Dieters MJ, George DL, Hunter MN, Tatnell JR, et al. Development and application of speed breeding technologies in a commercial peanut breeding program. Peanut Sci. 2013;40(2):107–14. https://doi.org/10.3146/PS12-12.1
  18. 18. Croser JS, Pazos-Navarro M, Bennett RG, Tschirren S, Edwards K, Erskine W, et al. Time to flowering of temperate pulses in vivo and generation turnover in vivo–in vitro of narrow-leaf lupin accelerated by low red to far-red ratio and high intensity in the far-red region. Plant Cell Tissue Organ Cult. 2016;127(3):591–9. https://doi.org/10.1007/s11240-016-1092-4
  19. 19. Mobini SH, Lulsdorf M, Warkentin TD, Vandenberg A. Plant growth regulators improve in vitro flowering and rapid generation advancement in lentil and faba bean. In Vitro Cell Dev Biol Plant. 2015;51(1):71–9. https://doi.org/10.1007/s11627-014-9647-8
  20. 20. Varshney RK, Saxena RK, Upadhyaya HD, Khan AW, Yu Y, Kim C, et al. Whole-genome resequencing of 292 pigeonpea accessions identifies genomic regions associated with domestication and agronomic traits. Nat Genet. 2017;49(7):1082–8. https://doi.org/10.1038/ng.3872
  21. 21. Bashir A, Abbas A, Li X, Shi Q, Niu D, Zhang L. Harnessing light, photoperiod and temperature for accelerated flowering in speed breeding: Mechanisms, applications and crop diversity. J Plant Physiol. 2025. https://doi.org/10.1016/j.jplph.2025.154548
  22. 22. Wanga MA, Shimelis H, Mashilo J, Laing MD. Opportunities and challenges of speed breeding: A review. Plant Breed. 2021;140:185–94. https://doi.org/10.1111/pbr.12909
  23. 23. Sysoeva MI, Markovskaya EF, Shibaeva TG. Plants under continuous light: a review. Plant Stress. 2010;4(1):5–17.
  24. 24. Weller JL, Ortega R. Genetic control of flowering time in legumes. Front Plant Sci. 2015;6:207. https://doi.org/10.3389/fpls.2015.00207
  25. 25. Samantara K, Bohra A, Mohapatra SR, Prihatini R, Asibe F, Singh L, et al. Breeding more crops in less time: a perspective on speed breeding. Biology. 2022;11:275. https://doi.org/10.3390/biology11020275
  26. 26. Blinkov AO, Kroupin PY, Dmitrieva AR, Kocheshkova AA, Karlov GI, Divashuk MG. Speed breeding: protocols, application and achievements. Front Plant Sci. 2025. https://doi.org/10.3389/fpls.2025.1680955
  27. 27. Gurumurthy S, Ashu A, Kruthika S, Solanke AP, Basavaraja T, Soren KR, et al. An innovative natural speed breeding technique for accelerated chickpea (Cicer arietinum L.) generation turnover. Plant Methods. 2024;20(1). doi:10.1186/s13007-024-01299-9. https://doi.org/10.1186/s13007-024-01299-9
  28. 28. Gangashetty PI, Belliappa SH, Bomma N, Kanuganahalli V, Sajja SB, Choudhary S, et al. Optimizing speed breeding and seed/pod chip based genotyping techniques in pigeonpea: A way forward for high throughput line development. Plant Methods. 2024;20(1). https://doi.org/10.1186/s13007-024-01155-w
  29. 29. Roorkiwal M, Rathore A, Das RR, Singh MK, Jain A, Srinivasan S, et al. Genome-enabled prediction models for yield related traits in chickpea. Front Plant Sci. 2016;7:1666. https://doi.org/10.3389/fpls.2016.01666
  30. 30. Haile TA, Heidecker T, Wright D, Neupane S, Ramsay L, Vandenberg A, et al. Genomic selection for lentil breeding: Empirical evidence. Plant Genome. 2020;13(1). https://doi.org/10.1002/tpg2.20002
  31. 31. Jarquín D, Crossa J, Lacaze X, Du Cheyron P, Daucourt J, Lorgeou J, et al. A reaction norm model for genomic selection using high-dimensional genomic and environmental data. Theor Appl Genet. 2014;127(3):595–607. https://doi.org/10.1007/s00122-013-2243-1
  32. 32. Warsame AO, O’Sullivan DM, Tosi P. Seed storage proteins of faba bean (Vicia faba L.): current status and prospects for genetic improvement. J Agric Food Chem. 2018;66(48):12617–26. https://doi.org/10.1021/acs.jafc.8b04992
  33. 33. Pratap A, Gupta S. The beans and the peas: From orphan to mainstream crops. Cambridge: Woodhead Publishing; 2020.
  34. 34. Rincent R, Laloë D, Nicolas S, Altmann T, Brunel D, Revilla P, et al. Maximizing the reliability of genomic selection by optimizing the calibration set of reference individuals: comparison of methods in two diverse groups of maize inbreds (Zea mays L.). Genetics. 2012;192(2):715–28. https://doi.org/10.1534/genetics.112.141473
  35. 35. Voss-Fels KP, Cooper M, Hayes BJ. Accelerating crop genetic gains with genomic selection. Theor Appl Genet. 2019;132(3):669–86. https://doi.org/10.1007/s00122-018-3270-8
  36. 36. Endelman JB. Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome. 2011;4(3). https://doi.org/10.3835/plantgenome2011.08.0024
  37. 37. Pérez P, de Los Campos G. Genome-wide regression and prediction with the BGLR statistical package. Genetics. 2014;198(2):483–95. https://doi.org/10.1534/genetics.114.164442
  38. 38. Varshney RK, Roorkiwal M, Sun S, Bajaj P, Chitikineni A, Thudi M, et al. A chickpea genetic variation map based on the sequencing of 3,366 genomes. Nature. 2021;599(7886):622–7. https://doi.org/10.1038/s41586-021-04066-1
  39. 39. Varshney RK, Song C, Saxena RK, Azam S, Yu S, Sharpe AG, et al. Draft genome sequence of chickpea (Cicer arietinum) provides a resource for trait improvement. Nat Biotechnol. 2013;31(3):240–6. https://doi.org/10.1038/nbt.2491
  40. 40. Sharpe AG, Ramsay L, Sanderson LA, Fedoruk MJ, Clarke WE, Li R, et al. Ancient orphan crop joins modern era: gene-based SNP discovery and mapping in lentil. BMC Genomics. 2013;14(1):192. https://doi.org/10.1186/1471-2164-14-192
  41. 41. Mahlein AK, Kuska MT, Thomas S, Wahabzada M, Behmann J, Rascher U, et al. Quantitative and qualitative phenotyping of disease resistance of crops by hyperspectral sensors: seamless interlocking of phytopathology, sensors and machine learning is needed! Curr Opin Plant Biol. 2019;50:156–62. https://doi.org/10.1016/j.pbi.2019.06.007
  42. 42. Singh A, Ganapathysubramanian B, Singh AK, Sarkar S. Machine learning for high-throughput stress phenotyping in plants. Trends Plant Sci. 2016;21(2):110–24. https://doi.org/10.1016/j.tplants.2015.10.015
  43. 43. Montes JM, Technow F, Dhillon BS, Mauch F, Melchinger AE. High-throughput non-destructive biomass determination during early plant development in maize under field conditions. Field Crops Res. 2011;121(2):268–73. https://doi.org/10.1016/j.fcr.2010.12.017
  44. 44. Desai SV, Balasubramanian VN, Fukatsu T, Ninomiya S, Guo W. Automatic estimation of heading date of paddy rice using deep learning. Plant Methods. 2019;15(1):76. https://doi.org/10.1186/s13007-019-0457-1
  45. 45. Doudna J, Charpentier E. Genome editing. The new frontier of genome engineering with CRISPR-Cas9. Science. 2014;346(6213). https://doi.org/10.1126/science.1258096
  46. 46. Haun W, Coffman A, Clasen BM, Demorest ZL, Lowy A, Ray E, et al. Improved soybean oil quality by targeted mutagenesis of the fatty acid desaturase 2 gene family. Plant Biotechnol J. 2014;12(7):934–40. https://doi.org/10.1111/pbi.12201
  47. 47. Cai Y, Chen L, Liu X, Guo C, Sun S, Wu C, et al. CRISPR/Cas9-mediated targeted mutagenesis of GmFT2a delays flowering time in soya bean. Plant Biotechnol J. 2018;16(1):176–85. https://doi.org/10.1111/pbi.12758
  48. 48. Badhan S, Ball AS, Mantri N. First report of CRISPR/Cas9-mediated DNA-free editing of 4CL and RVE7 genes in chickpea protoplasts. Int J Mol Sci. 2021;22(1):1–15. https://doi.org/10.3390/ijms22010396
  49. 49. Svitashev S, Schwartz C, Lenderts B, Young JK, Cigan AM. Genome editing in maize directed by CRISPR–Cas9 ribonucleoprotein complexes. Nat Commun. 2016;7(1):13274. https://doi.org/10.1038/ncomms13274
  50. 50. Ishii T, Araki M. A future scenario of the global regulatory landscape regarding genome-edited crops. GM Crops Food. 2017;8(1):44–56. https://doi.org/10.1080/21645698.2016.1261787
  51. 51. Mahto RK, Ambika, Singh C, Chandana BS, Singh RK, Verma S, et al. Chickpea biofortification for cytokinin dehydrogenase via genome editing to enhance abiotic-biotic stress tolerance and food security. Front Genet. 2022;13. https://doi.org/10.3389/fgene.2022.900324
  52. 52. Germana MA. Anther culture for haploid and doubled haploid production. Plant Cell Tissue Organ Cult. 2011;104(3):283–300. https://doi.org/10.1007/s11240-010-9852-z
  53. 53. Gaynor RC, Gorjanc G, Bentley AR, Ober ES, Howell P, Jackson R, et al. A two-part strategy for using genomic selection to develop inbred lines. Crop Sci. 2017;57(5):2372–86. https://doi.org/10.2135/cropsci2016.09.0742
  54. 54. Cooper M, Gho C, Leafgren R, Tang T, Messina C. Breeding drought-tolerant maize hybrids for the US corn-belt: discovery to product. J Exp Bot. 2014;65(21):6191–204. https://doi.org/10.1093/jxb/eru064
  55. 55. Sinha P, Singh VK, Bohra A, Kumar A, Reif JC, Varshney RK. Genomics and breeding innovations for enhancing genetic gain for climate resilience and nutrition traits. Theor Appl Genet. 2021;134(6):1829–43. https://doi.org/10.1007/s00122-021-03847-6
  56. 56. Pandey MK, Roorkiwal M, Singh VK, Ramalingam A, Kudapa H, Thudi M, et al. Emerging genomic tools for legume breeding: current status and future prospects. Front Plant Sci. 2016;7. https://doi.org/10.3389/fpls.2016.00455
  57. 57. Cobb JN, Juma RU, Biswas PS, Arbelaez JD, Rutkoski J, Atlin G, et al. Enhancing the rate of genetic gain in public-sector plant breeding programs: lessons from the breeder’s equation. Theor Appl Genet. 2019;132(3):627–45. https://doi.org/10.1007/s00122-019-03317-0
  58. 58. Huang X, Han B. Natural variations and genome-wide association studies in crop plants. Annu Rev Plant Biol. 2014;65(1):531–51. https://doi.org/10.1146/annurev-arplant-050213-035715
  59. 59. Bayer PE, Golicz AA, Scheben A, Batley J, Edwards D. Plant pan-genomes are the new reference. Nat Plants. 2020;6(8):914–20. https://doi.org/10.1038/s41477-020-0733-0
  60. 60. Varshney RK, Bohra A, Yu J, Graner A, Zhang Q, Sorrells ME. Designing future crops: genomics-assisted breeding comes of age. Trends Plant Sci. 2021;26(6):631–49. https://doi.org/10.1016/j.tplants.2021.03.010
  61. 61. Bohra A, Chand Jha U, Godwin ID, Kumar Varshney R. Genomic interventions for sustainable agriculture. Plant Biotechnol J. 2020;18(12):2388–405. https://doi.org/10.1111/pbi.13472
  62. 62. Kang YJ, Kim SK, Kim MY, Lestari P, Kim KH, Ha BK, et al. Genome sequence of mungbean and insights into evolution within Vigna species. Nat Commun. 2014;5(1):5443. https://doi.org/10.1038/ncomms6443

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