Skip to main content

Advertisement

SpringerLink
Log in

Three genetic systems controlling growth, development and productivity of rice (Oryza sativa L.): a reevaluation of the ‘Green Revolution’

  • Original Paper
  • Published:
Theoretical and Applied Genetics Aims and scope Submit manuscript

Abstract

The Green Revolution (GR-I) included worldwide adoption of semi-dwarf rice cultivars (SRCs) with mutant alleles at GA20ox2 or SD1 encoding gibberellin 20-oxidase. Two series of experiments were conducted to characterize the pleiotropic effects of SD1 and its relationships with large numbers of QTLs affecting rice growth, development and productivity. The pleiotropic effects of SD1 in the IR64 genetic background for increased height, root length/mass and grain weight, and for reduced spikelet fertility and delayed heading were first demonstrated using large populations derived from near isogenic IR64 lines of SD1. In the second set of experiments, QTLs controlling nine growth and yield traits were characterized using a new molecular quantitative genetics model and the phenotypic data of the well-known IR64/Azucena DH population evaluated across 11 environments, which revealed three genetic systems: the SD1-mediated, SD1-repressed and SD1-independent pathways that control rice growth, development and productivity. The SD1-mediated system comprised 43 functional genetic units (FGUs) controlled by GA. The SD1-repressed system was the alternative one comprising 38 FGUs that were only expressed in the mutant sd1 backgrounds. The SD1-independent one comprised 64 FGUs that were independent of SD1. GR-I resulted from the overall differences between the former two systems in the three aspects: (1) trait/environment-specific contributions; (2) distribution of favorable alleles for increased productivity in the parents; and (3) different responses to (fertilizer) inputs. Our results suggest that at 71.4 % of the detected loci, a QTL resulted from the difference between a functional allele and a loss-of-function mutant, whereas at the remaining 28.6 % of loci, from two functional alleles with differentiated effects. Our results suggest two general strategies to achieve GR-II (1) by further exploiting the genetic potential of the SD1-repressed and SD1-independent pathways and (2) by restoring the SD1-mediated pathways, or ‘back to the nature’ to fully exploit the genetic diversity of those loci in the SD1-mediated pathways which are virtually inaccessible to most rice-breeding programs worldwide that are exclusively based on sd1.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Ali AJ, Xu JL, Ismail AM, Fu BY, Vijaykumar CHM et al (2006) Hidden diversity for abiotic and biotic stress tolerances in the primary gene pool of rice revealed by a large backcross breeding program. Field Crop Res 97:66–76

    Article  Google Scholar 

  • Asano K, Yamasaki M, Takuno S, Miura K, Katagiri S et al (2011) Artificial selection for a green revolution gene during japonica rice domestication. Proc Natl Acad Sci USA 108:11034–11039

    Article  CAS  PubMed  Google Scholar 

  • Cassman KG (1999) Ecological intensification of cereal production systems: yield potential, soil quality, and precision agriculture. Proc Natl Acad Sci USA 96:5952–5959

    Article  CAS  PubMed  Google Scholar 

  • Cho YG, Eun MY, McCouch SR, Chae YA (1994) The semidwarf gene, sd-1, of rice (Oryza sativa L.). II. Molecular mapping and marker-assisted selection. Theor Appl Genet 89:54–59

    CAS  Google Scholar 

  • Choi YH, Kobayshi M, Fujioka S, Matsuno T, Hirosawa T et al (1995) Fluctuation of endogenous gibberellin levels in the early development of rice. Biosci Biotech Biochem 59:285–288

    Article  CAS  Google Scholar 

  • Conway G (1999) The doubly green revolution: food for all in the twenty-first century. Cornell University Press, Ithaca

    Google Scholar 

  • Evenson RE, Gollin D (2003) Assessing the impact of the green revolution, 1960 to 2000. Science 300:758–762

    Article  CAS  PubMed  Google Scholar 

  • Guan YS, Serraj R, Liu SH, Xu JL, Ali J et al (2010) Simultaneously improving yield under drought stress and non-stress conditions: a case study of rice (Oryza sativa L.). J Exp Bot 61:4145–4156

    Article  CAS  PubMed  Google Scholar 

  • Hedden P (2003) The genes of the green revolution. Trends Genet 19:5–9

    Article  CAS  PubMed  Google Scholar 

  • Hedden P, Phillips AL (2000) Gibberellin metabolism: new insights revealed by the genes. Trends Plant Sci 5:523–530

    Article  CAS  PubMed  Google Scholar 

  • Hirano K, Ueguchi-Tanaka M, Matsuoka M (2008) GID1-mediated gibberellin signaling in plants. Trends Plant Sci 13:192–199

    Article  CAS  PubMed  Google Scholar 

  • Hooley R (1994) Gibberellins: perception, transduction and responses. Plant Mol Biol 26:1529–1555

    Article  CAS  PubMed  Google Scholar 

  • Jiang YH, Cai ZX, Xie WB, Long T, Yu HH et al (2012) Rice functional genomics research: progress and implications for crop genetic improvement. Biotechnol Adv 30:1059–1070

    Article  CAS  PubMed  Google Scholar 

  • Khush GS (1995) Modern varieties—their real contribution to food supply and equity. GeoJournal 35:275–284

    Article  Google Scholar 

  • Khush GS (2001) Green revolution: the way forward. Nat Rev Genet 2:815–822

    Article  CAS  PubMed  Google Scholar 

  • Kuroda E, Ookawa T, Ishihara K (1989) Analysis on difference of dry matter production between rice cultivars with different plant height in relation to gas diffusion inside stands. Jpn J Crop Sci 58:374–382

    Article  Google Scholar 

  • Lafitte HR, Li ZK, Vijayakumar CHM, Gao YM, Shi Y et al (2006) Improvement of rice drought tolerance through backcross breeding: evaluation of donors and selection in drought nurseries. Field Crop Res 97:77–86

    Article  Google Scholar 

  • Lafitte HR, Guan YS, Shi Y, Li ZK (2007) Whole plant responses, key processes, and adaptation to drought stress: the case of rice. J Exp Bot 58:169–175

    Article  CAS  PubMed  Google Scholar 

  • Li ZK, Yu SB, Lafitte HR, Huang N, Courtois B et al (2003) QTL × environment interactions in rice. I. Heading date and plant height. Theor Appl Genet 108:141–153

    Article  CAS  PubMed  Google Scholar 

  • Ma Q, Hedden P, Zhang Q (2011) Heterosis in rice seedlings: its relationship to gibberellin content and expression of gibberellin metabolism and signaling genes. Plant Physiol 156:1905–1920

    Article  CAS  PubMed  Google Scholar 

  • Mackill DJ, Coffman WR, Garrity DP (1996) Rainfed lowland rice improvement. International Rice Research Institute, Los Baños, Metro Manila, The Philippines

  • Matson PA, Parton WJ, Power AG, Swift MJ (1997) Agricultural intensification and ecosystem properties. Science 277:504–509

    Article  CAS  PubMed  Google Scholar 

  • Mei HW, Xu JL, Li ZK, Yu XQ, Guo LB et al (2006) QTLs influencing panicle size detected in two reciprocal introgressive line (IL) populations in rice (Oryza sativa L.). Theor Appl Genet 112:648–656

    Article  CAS  PubMed  Google Scholar 

  • Monna L, Kitazawa N, Yoshino R, Suzuki J, Masuda H et al (2002) Positional cloning of rice semidwarfing gene, sd-1: rice “green revolution gene” encodes a mutant enzyme involved in gibberellin synthesis. DNA Res 9:11–17

    Article  CAS  PubMed  Google Scholar 

  • Murai M, Takamure I, Sato S, Tokutome T, Sato Y (2002) Effects of the dwarfing gene originating from ‘Dee-geo-woo-gen’ on yield and its related traits in rice. Breed Sci 52:95–100

    Article  Google Scholar 

  • Nemhauser JL, Hong F, Chory J (2006) Different plant hormones regulate similar processes through largely nonoverlapping transcriptional responses. Cell 126:467–475

    Article  CAS  PubMed  Google Scholar 

  • Ookawa T, Hobo T, Yano M, Murata K, Ando T et al (2010) New approach for rice improvement using a pleiotropic QTL gene for lodging resistance and yield. Nat Comms 1:132. doi:10.1038/ncomms1132

    Article  Google Scholar 

  • Paterson AH, Li ZK (2011) Paleo-green revolution for rice. Proc Natl Acad Sci USA 108:10931–10932

    Article  CAS  PubMed  Google Scholar 

  • Peng S, Cassman KG, Virmani SS, Sheehy J, Khush GS (1999) Yield potential trends of tropical rice since the release of IR8 and the challenge of increasing rice yield potential. Crop Sci 39:1552–1559

    Article  Google Scholar 

  • Sakamoto T, Miura K, Itoh H, Tatsumi T, Ueguchi-Tanaka M et al (2004) An overview of gibberellin metabolism enzyme genes and their related mutants in rice. Plant Physiol 134:1642–1653

    Article  CAS  PubMed  Google Scholar 

  • SAS Institute (2004) SAS/STAT 9.1 user’s guide. SAS Institute Inc., Cary

  • Sasaki A, Ashikari M, Ueguchi-Tanaka M, Itoh H, Nishimura A et al (2002) A mutant gibberellin-synthesis gene in rice. Nature 416:701–702

    Article  CAS  PubMed  Google Scholar 

  • Shen L, Courtois B, McNally KL, Robin S, Li Z (2001) Evaluation of near-isogenic lines of rice introgressed with QTLs for root depth through marker-aided selection. Theor Appl Genet 103:75–83

    Article  CAS  Google Scholar 

  • Spielmeyer W, Ellis MH, Chandler PM (2002) Semidwarf (sd-1), “green revolution” rice, contains a defective gibberellin 20-oxidase gene. Proc Natl Acad Sci USA 99:9043–9048

    Article  CAS  PubMed  Google Scholar 

  • Tilman D (1998) The greening of the green revolution. Nature 396:211–212

    Article  CAS  Google Scholar 

  • Vitousek PM, Aber JD, Howarth RW, Likens GE, Matson PA et al (1997) Human alteration of the global nitrogen cycle: sources and consequences. Ecol Appl 7:737–750

    Google Scholar 

  • Wang DL, Zhu J, Li ZK, Paterson AH (1999) Mapping QTLs with epistatic effects and QTL × environment interactions by mixed linear model approaches. Theor Appl Genet 99:1255–1264

    Article  Google Scholar 

  • Xing YZ, Tan YF, Hua JP, Sun XL, Xu CG et al (2002) Characterization of the main effects, epistatic effects and their environmental interactions of QTLs on the genetic basis of yield traits in rice. Theor Appl Genet 105:248–257

    Article  CAS  PubMed  Google Scholar 

  • Yamaguchi S (2008) Gibberellin metabolism and its regulation. Annu Rev Plant Biol 59:225–251

    Article  CAS  PubMed  Google Scholar 

  • Zeng ZB (1994) Precision mapping of quantitative trait loci. Genetics 136:1457–1468

    CAS  PubMed  Google Scholar 

  • Zhang Q (2007) Strategies for developing green super rice. Proc Natl Acad Sci USA 104:16402–16409

    Article  CAS  PubMed  Google Scholar 

  • Zhang F, Zhai HQ, Paterson AH, Xu JL, Gao YM et al (2011) Dissecting genetic networks underlying complex phenotypes: the theoretical framework. PLoS ONE 6:e14541

    Article  CAS  PubMed  Google Scholar 

  • Zheng TQ, Wang Y, Ali AJ, Zhu LH, Sun Y et al (2011) Genetic effects of background-independent loci for grain weight and shape identified using advanced reciprocal introgression lines from Lemont × Teqing in rice. Crop Sci 51:2525–2534

    Article  Google Scholar 

  • Zhuang JY, Lin HX, Lu J, Qian HR, Hittalmani S et al (1997) Analysis of QTL × environment interaction for yield components and plant height in rice. Theor Appl Genet 95:799–808

    Article  CAS  Google Scholar 

Download references

Acknowledgments

We thank the co-authors in Li et al. (2003) for their contributions in generating the original data of the DH population (Li et al. 2003). This work was funded by the “863” project (2012AA101101) from the Ministry of Science and Technology of China, the “948” Project (#2011-G2B) from the Ministry of Agriculture of China, a program of the International S & T Cooperation (S2012ZR0160), the Generation Challenge Program project (#12) of CGIAR, and the Bill & Melinda Gates Foundation project (OPP51587). Fan Zhang was also supported by the Monsanto’s Beachell-Borlaug International Scholars Program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Conflict of interest

The authors declare no conflict of interest.

Author information

Author notes

  1. Si-Bin Yu

    Present address: National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China

  2. G. S. Khush

    Present address: University of California, Davis, CA, 95616, USA

  3. R. Lafitte

    Present address: Pioneer Hi-Bred International, 18285 Co. Rd, 96, Woodland, CA, 95616, USA

Authors and Affiliations

  1. Institute of Crop Sciences/National Key Facility for Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing, 100081, China

    Fan Zhang, Jian-Long Xu, Yong-Ming Gao, Bin-Ying Fu & Zhi-Kang Li

  2. Plant Genome Mapping Laboratory, University of Georgia, Athens, GA, 30602, USA

    Fan Zhang & A. H. Paterson

  3. The International Rice Research Institute, DAPO Box 7777, Metro Manila, The Philippines

    Yun-Zhu Jiang, Si-Bin Yu, J. Ali, G. S. Khush, R. Lafitte & Zhi-Kang Li

  4. Chinese National Center for Rice Improvement, China National Rice Research Institute, Hangzhou, 310006, China

    Yun-Zhu Jiang

Authors
  1. Fan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yun-Zhu Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Si-Bin Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. J. Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. A. H. Paterson
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. G. S. Khush
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jian-Long Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yong-Ming Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Bin-Ying Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. R. Lafitte
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Zhi-Kang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhi-Kang Li.

Additional information

Communicated by Y. Xu.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhang, F., Jiang, YZ., Yu, SB. et al. Three genetic systems controlling growth, development and productivity of rice (Oryza sativa L.): a reevaluation of the ‘Green Revolution’. Theor Appl Genet 126, 1011–1024 (2013). https://doi.org/10.1007/s00122-012-2033-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00122-012-2033-1

Keywords

Search

Navigation