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Euphytica

, 216:20 | Cite as

Genetic control of maize plant architecture traits under contrasting plant densities

  • Salvador Juan Pablo IncognitoEmail author
  • Gustavo Ángel Maddonni
  • César Gabriel López
Article
  • 40 Downloads

Abstract

Plant architecture has played an important role in the adaptation of maize (Zea mays L.) hybrids to historical increases in plant density in order to maximize yields per unit area. At high density, a compact plant structure would allow for less interference by light among plants of the row and a deeper penetration of the radiation towards the lowest canopy layers, without compromising the capture of radiation at crop level. The genetic control of plant architecture traits of maize under contrasting plant densities remains poorly understood. In this work, traits related to leaf and stem architecture were phenotypically analyzed and QTLs were mapped using 160 RILs from the IBM B73 × Mo17 Syn4 population cultivated at low density and high density during 2013–2014 and 2014–2015 growing seasons in Buenos Aires province, Argentina. Forty-nine QTLs were detected on chromosomes 1, 3, 4, 5, 9 and 10. Most QTLs of vertical insertion angle of leaves and leaf orientation value (i.e., vertical angle affected by the curvature of leaves) were detected on chromosome 5 at high density and showed a high percentage of co-location. Detected QTLs for plant and ear height, and the relationship between them were concentrated on chromosome 9, with consistent effect under different density × environment combinations. These regions had large-effect QTLs and constitute hot spots that need to be studied in more detail to determine their potential use in breeding programs.

Keywords

Leaf morphology Canopy structure Stem traits Intraspecific competition RILs population QTL mapping 

Notes

Acknowledgements

The authors wish to thank J. Fuentes and M. Rodriguez for their valuable assistance during the experiments. We also thank Dr. Santiago Alvarez Prado, Dr. Lucas Borrás and Dr. Maia Fradkin for his valuable collaboration with the QTL analysis, for supplying seeds of RILs population and their parental lines and for helping with the elaboration of the figures, respectively. This work was supported by the Universidad Nacional de Lomas de Zamora (Lomas CyT Program), the University of Buenos Aires (UBACyT 2014- 20020130100493BA), and the National Agency for the promotion of Science and Technology (PICT 2012-1260). S.J.P. Incognito had a graduate’s scholarship from the National Council of Research (CONICET) of Argentina. G.A. Maddonni is a member of CONICET.

Supplementary material

10681_2019_2552_MOESM1_ESM.docx (34 kb)
Supplementary material 1 (DOCX 33 kb)

References

  1. Alvarez Prado S, López CG, Gambín BL, Abertondo VJ, Borrás L (2013) Dissecting the genetic basis of physiological processes determining maize kernel weight using the IBM (B73 × Mo17) Syn4 population. Field Crops Res 145:33–43.  https://doi.org/10.1016/j.fcr.2013.02.002 CrossRefGoogle Scholar
  2. Amelong A, Gambín BL, Severini AD, Borrás L (2015) Predicting maize kernel number using QTL information. Field Crops Res 172:119–131CrossRefGoogle Scholar
  3. Argenta G, Silva PRFd, Sangoi L (2001) Arranjo de plantas em milho: análise do estado-da-arte. Ciência rural Santa Maria Vol 31, n 6 (nov/dez 2001), p 1075-1084CrossRefGoogle Scholar
  4. Bai W, Zhang H, Zhang Z, Teng F, Wang L, Tao Y, Zheng Y (2010) The evidence for non-additive effect as the main genetic component of plant height and ear height in maize using introgression line populations. Plant Breed 129(4):376–384Google Scholar
  5. Ballaré CL, Sánchez RA, Scopel AL, Casal JJ, Ghersa CM (1987) Early detection of neighbour plants by phytochrome perception of spectral changes in reflected sunlight. Plant Cell Environ 10(7):551–557.  https://doi.org/10.1111/1365-3040.ep11604091 CrossRefGoogle Scholar
  6. Beavis W, Grant D, Albertsen M, Fincher R (1991) Quantitative trait loci for plant height in four maize populations and their associations with qualitative genetic loci. Theor Appl Genet 83(2):141–145PubMedCrossRefGoogle Scholar
  7. Best NB, Hartwig T, Budka J, Fujioka S, Johal GS, Schulz B, Dilkes BP (2016) Nana plant2 encodes a maize ortholog of the Arabidopsis brassinosteroid biosynthesis protein Dwarf1, identifying developmental interactions between brassinosteroids and gibberellins. Plant Physiol.  https://doi.org/10.1104/pp.16.00399 CrossRefPubMedPubMedCentralGoogle Scholar
  8. Cai H, Chu Q, Gu R, Yuan L, Liu J, Zhang X, Chen F, Mi G, Zhang F (2012) Identification of QTLs for plant height, ear height and grain yield in maize (Zea mays L.) in response to nitrogen and phosphorus supply. Plant Breed 131(4):502–510.  https://doi.org/10.1111/j.1439-0523.2012.01963.x CrossRefGoogle Scholar
  9. Chang L, He K, Liu J, Xue J (2016) Mapping of QTLs for leaf angle in maize under different environments. J Maize Sci 4:49–55Google Scholar
  10. Chen X, Xu D, Liu Z, Yu T, Mei X, Cai Y (2015) Identification of QTL for leaf angle and leaf space above ear position across different environments and generations in maize (Zea mays L.). Euphytica 204(2):395–405.  https://doi.org/10.1007/s10681-015-1351-1 CrossRefGoogle Scholar
  11. Cochran WG (1952) The χ2 test of goodness of fit. Ann Math Stat 23(3):315–345CrossRefGoogle Scholar
  12. Cook WB, Miles D (1988) Transposon mutagenesis of nuclear photosynthetic genes in Zea mays. Photosynth Res 18(1):33–59.  https://doi.org/10.1007/bf00042979 CrossRefPubMedGoogle Scholar
  13. Danilevskaya ON, Meng X, Selinger DA, Deschamps S, Hermon P, Vansant G, Gupta R, Ananiev EV, Muszynski MG (2008) Involvement of the MADS-Box gene ZMM4 in floral induction and inflorescence development in maize. Plant Physiol 147(4):2054–2069.  https://doi.org/10.1104/pp.107.115261 CrossRefPubMedPubMedCentralGoogle Scholar
  14. Debernardi JM, Mecchia MA, Vercruyssen L, Smaczniak C, Kaufmann K, Inze D, Rodriguez RE, Palatnik JF (2014) Post-transcriptional control of GRF transcription factors by microRNA miR396 and GIF co-activator affects leaf size and longevity. Plant J 79(3):413–426.  https://doi.org/10.1111/tpj.12567 CrossRefPubMedGoogle Scholar
  15. Demerec M (1926) Notes on Linkages in Maize. Am Nat 60(667):172–176.  https://doi.org/10.1086/280083 CrossRefGoogle Scholar
  16. Ding J, Zhang L, Chen J, Li X, Li Y, Cheng H, Huang R, Zhou B, Li Z, Wang J (2015) Genomic dissection of leaf angle in maize (Zea mays L.) using a four-way cross mapping population. PLoS ONE 10(10):e0141619PubMedPubMedCentralCrossRefGoogle Scholar
  17. Drouet J-L, Moulia B (1997) Spatial re-orientation of maize leaves affected by initial plant orientation and density. Agric For Meteorol 88(1–4):85–100CrossRefGoogle Scholar
  18. Duncan WG (1971) Leaf angles, leaf area, and canopy photosynthesis. Crop Sci 11(4):482–485.  https://doi.org/10.2135/cropsci1971.0011183X001100040006x CrossRefGoogle Scholar
  19. Duvick DN (2005) The Contribution of Breeding to Yield Advances in maize (Zea mays L.). In: Elsevier Inc (ed) Advances in agronomy, vol 86. Academic Press, Netherlands, pp 83–145.  https://doi.org/10.1016/s0065-2113(05)86002-x CrossRefGoogle Scholar
  20. Dzievit MJ, Li X, Yu J (2019) Dissection of leaf angle variation in maize through genetic mapping and meta-analysis. Plant Genome.  https://doi.org/10.3835/plantgenome2018.05.0024 CrossRefPubMedGoogle Scholar
  21. Girardin P, Tollenaar M (1994) Effects of intraspecific interference on maize leaf azimuth. Crop Sci 34:151–155CrossRefGoogle Scholar
  22. Gonzalo M, Vyn TJ, Holland JB, McIntyre L (2006) Mapping density response in maize: a direct approach for testing genotype and treatment interactions. Genetics 173:331–348PubMedPubMedCentralCrossRefGoogle Scholar
  23. Gonzalo M, Holland J, Vyn T, McIntyre L (2010) ) Direct mapping of density response in a population of B73 × Mo17 recombinant inbred lines of maize (Zea mays L.). Heredity 104(6):583PubMedCrossRefGoogle Scholar
  24. Gou L, Xue J, Qi B, Ma B, Zhang W (2017) Morphological variation of maize cultivars in response to elevated plant densities. Agron J 109(4):1443–1453CrossRefGoogle Scholar
  25. Hou X, Liu Y, Xiao Q, Wei B, Zhang X, Gu Y, Wang Y, Chen J, Hu Y, Liu H, Zhang J, Huang Y (2015) Genetic analysis for canopy architecture in an F2:3 population derived from two-type foundation parents across multi-environments. Euphytica 205(2):421–440.  https://doi.org/10.1007/s10681-015-1401-8 CrossRefGoogle Scholar
  26. Huang S, Gao Y, Li Y, Xu L, Tao H, Wang P (2017) Influence of plant architecture on maize physiology and yield in the Heilonggang River valley. Crop J 5(1):52–62CrossRefGoogle Scholar
  27. Kebrom TH, Brutnell TP (2007) The molecular analysis of the shade avoidance syndrome in the grasses has begun. J Exp Bot 58(12):3079–3089.  https://doi.org/10.1093/jxb/erm205 CrossRefPubMedGoogle Scholar
  28. Kosambi DD (1943) The estimation of map distances from recombination values. Ann Hum Genet 12:172–175Google Scholar
  29. Kraja A, Dudley J (2000) QTL analysis of two maize inbred line crosses. Maydica 45(1):1–12Google Scholar
  30. Ku LX, Zhao WM, Zhang J, Wu LC, Wang CL, Wang PA, Zhang WQ, Chen YH (2010) Quantitative trait loci mapping of leaf angle and leaf orientation value in maize (Zea mays L.). Theor Appl Genet 121(5):951–959.  https://doi.org/10.1007/s00122-010-1364-z CrossRefPubMedGoogle Scholar
  31. Ku LX, Zhang J, Guo SL, Liu HY, Zhao RF, Chen YH (2011) Integrated multiple population analysis of leaf architecture traits in maize. J Exp Bot 63(1):261–274.  https://doi.org/10.1093/jxb/err277 CrossRefPubMedGoogle Scholar
  32. Ku L, Zhang L, Tian Z, Guo S, Su H, Ren Z, Wang Z, Li G, Wang X, Zhu Y (2015) Dissection of the genetic architecture underlying the plant density response by mapping plant height-related traits in maize (Zea mays L.). Mol Genet Genomics 290(4):1223–1233PubMedCrossRefGoogle Scholar
  33. Ku L, Ren Z, Chen X, Shi Y, Qi J, Su H, Wang Z, Li G, Wang X, Zhu Y (2016) Genetic analysis of leaf morphology underlying the plant density response by QTL mapping in maize (Zea mays L.). Mol Breed 36(5):63CrossRefGoogle Scholar
  34. Lashkari M, Madani H, Ardakani MR, Golzardi F, Zargari K (2011) Effect of Plant Density on Yield and Yield Components of Different Corn (Zea mays L.). Am Eurasian J Agric Environ Sci 10(3):450–457Google Scholar
  35. Lee M, Sharopova N, Beavis WD, Grant D, Katt M, Blair D, Hallauer A (2002) Expanding the genetic map of maize with the intermated B73 × Mo17 (IBM) population. Plant Mol Biol 48(5–6):453–461PubMedCrossRefPubMedCentralGoogle Scholar
  36. Li C, Li Y, Shi Y, Song Y, Zhang D, Buckler ES, Zhang Z, Wang T, Li Y (2015) Genetic control of the leaf angle and leaf orientation value as revealed by ultra-high density maps in three connected maize populations. PLoS ONE 10(3):e0121624PubMedPubMedCentralCrossRefGoogle Scholar
  37. Li X, Zhou Z, Ding J, Wu Y, Zhou B, Wang R, Ma J, Wang S, Zhang X, Xia Z (2016) Combined linkage and association mapping reveals QTL and candidate genes for plant and ear height in maize. Front Plant Sci 7:833PubMedPubMedCentralGoogle Scholar
  38. Lima MdLA, de Souza CL, Bento DAV, de Souza AP, Carlini-Garcia LA (2006) Mapping QTL for grain yield and plant traits in a tropical maize population. Mol Breed 17(3):227–239CrossRefGoogle Scholar
  39. Liu Z, Yu T, Mei X, Chen X, Wang G, Wang J, Liu C, Wang X, Cai Y (2014) QTL mapping for leaf angle and leaf space above ear position in maize. J Agric Biotechnol 22(2):177–187Google Scholar
  40. Liu X, Hao L, Kou S, Su E, Zhou Y, Wang R, Mohamed A, Gao C, Zhang D, Li Y, Li C, Song Y, Shi Y, Wang T, Li Y (2018) High-density quantitative trait locus mapping revealed genetic architecture of leaf angle and tassel size in maize. Mol Breed 39(1):7.  https://doi.org/10.1007/s11032-018-0914-y CrossRefGoogle Scholar
  41. Lorieux M (2007) MapDisto, a free user-friendly program for computing genetic maps. In: Computer demonstration given at the Plant and Animal Genome XV conference, San DiegoGoogle Scholar
  42. Lu M, Zhou F, Xie C-X, Li M-S, Yunbi X, Warburton ML, Zhang S-H (2007a) Construction of a SSR linkage map and mapping of quantitative trait loci (QTL) for leaf angle and leaf orientation with an elite maize hybrid. Hereditas 29(9):1131–1138PubMedCrossRefGoogle Scholar
  43. Lu M, Zhou F, Xie C, Li M, Xu M (2007b) Construction of a SSR linkage map and mapping of quantitative trait loci (QTL) for leaf angle and leaf orientation with an elite maize hybrid. Hereditas 29:1131–1138PubMedCrossRefGoogle Scholar
  44. Maddonni GA, Otegui ME (1996) Leaf area, light interception, and crop development in maize. Field Crops Res 48(1):81–87.  https://doi.org/10.1016/0378-4290(96)00035-4 CrossRefGoogle Scholar
  45. Maddonni GA, Chelle M, Drouet J-L, Andrieu B (2001a) Light interception of contrasting azimuth canopies under square and rectangular plant spatial distributions: simulations and crop measurements. Field Crops Res 70:1–13CrossRefGoogle Scholar
  46. Maddonni GA, Otegui ME, Cirilo AG (2001b) Plant population density, row spacing and hybrid effects on maize canopy architecture and light attenuation. Field Crops Res 71(3):183–193.  https://doi.org/10.1016/S0378-4290(01)00158-7 CrossRefGoogle Scholar
  47. Maddonni GA, Otegui ME, Andrieu B, Chelle M, Casal JJ (2002) Maize leaves turn away from neighbors. Plant Physiol 130(3):1181–1189.  https://doi.org/10.1104/pp.009738 CrossRefPubMedPubMedCentralGoogle Scholar
  48. Malosetti M, Ribaut JM, Vargas M, Crossa J, Van Eeuwijk FA (2008) A multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress trials in maize (Zea mays L.). Euphytica 161(1):241–257CrossRefGoogle Scholar
  49. Mansfield BD, Mumm RH (2014) Survey of plant density tolerance in U.S. maize germplasm. Crop Sci 54(1):157–173.  https://doi.org/10.2135/cropsci2013.04.0252 CrossRefGoogle Scholar
  50. Margarido G, Pastina M, Souza A, Garcia A (2015) Multi-trait multi-environment quantitative trait loci mapping for a sugarcane commercial cross provides insights on the inheritance of important traits. Mol Breed 35(8):175PubMedPubMedCentralCrossRefGoogle Scholar
  51. Mickelson S, Stuber C, Senior L, Kaeppler S (2002) Quantitative trait loci controlling leaf and tassel traits in a B73 × Mo17 population of maize. Crop Sci 42(6):1902–1909CrossRefGoogle Scholar
  52. Mikel MA, Dudley JW (2006) Evolution of North American dent corn from public to proprietary germplasm. Crop Sci 46(3):1193–1205CrossRefGoogle Scholar
  53. Montgomery EG (1911) Correlation studies in corn. Lincoln, NE 108–159Google Scholar
  54. Montoliu L, Puigdomènech P, Rigau J (1990) The Tubα3 gene from Zea mays: structure and expression in dividing plant tissues. Gene 94(2):201–207.  https://doi.org/10.1016/0378-1119(90)90388-8 CrossRefPubMedGoogle Scholar
  55. Nardmann J, Ji J, Werr W, Scanlon MJ (2004) The maize duplicate genes narrow sheath1 and narrow sheath2 encode a conserved homeobox gene function in a lateral domain of shoot apical meristems. Development 131(12):2827–2839.  https://doi.org/10.1242/dev.01164 CrossRefPubMedGoogle Scholar
  56. Neuffer G, England D (1995) Induced mutations with confirmed locations. Maize Genet Coop Newsl 69:43–46Google Scholar
  57. Pan Q, Xu Y, Li K, Peng Y, Zhan W, Li W, Li L, Yan J (2017) The genetic basis of plant architecture in 10 maize recombinant inbred line populations. Plant Physiol 175(2):858–873.  https://doi.org/10.1104/pp.17.00709 CrossRefPubMedPubMedCentralGoogle Scholar
  58. Park KJ, Sa KJ, Kim BW, Koh H-J, Lee JK (2014) Genetic mapping and QTL analysis for yield and agronomic traits with an F2:3 population derived from a waxy corn × sweet corn cross. Genes Genomics 36(2):179–189.  https://doi.org/10.1007/s13258-013-0157-6 CrossRefGoogle Scholar
  59. Peiffer JA, Romay MC, Gore MA, Flint-Garcia SA, Zhang Z, Millard MJ, Gardner CA, McMullen MD, Holland JB, Bradbury PJ (2014) The genetic architecture of maize height. Genetics.  https://doi.org/10.1534/genetics.113.159152 CrossRefPubMedPubMedCentralGoogle Scholar
  60. Pelleschi S, Leonardi A, Rocher J-P, Cornic G, De Vienne D, Thevenot C, Prioul J-L (2006) Analysis of the relationships between growth, photosynthesis and carbohydrate metabolism using quantitative trait loci (QTLs) in young maize plants subjected to water deprivation. Mol Breed 17(1):21–39CrossRefGoogle Scholar
  61. Pepper GE, Pearce RB, Mock JJ (1977) Leaf orientation and yield of maize1. Crop Sci 17(6):883–886.  https://doi.org/10.2135/cropsci1977.0011183X001700060017x CrossRefGoogle Scholar
  62. Potts S (2014) Identifiation of QTL and candidate genes for plant density. Ph.D. dissertation, University of Illinois at Urbana-ChampaignGoogle Scholar
  63. Raihan MS, Liu J, Huang J, Guo H, Pan Q, Yan J (2016) Multi-environment QTL analysis of grain morphology traits and fine mapping of a kernel-width QTL in Zheng58 × SK maize population. Theor Appl Genet 129(8):1465–1477PubMedCrossRefGoogle Scholar
  64. Reymond M, Muller B, Leonardi A, Charcosset A, Tardieu F (2003) Combining quantitative trait loci analysis and an ecophysiological model to analyze the genetic variability of the responses of maize leaf growth to temperature and water deficit. Plant Physiol 131(2):664–675.  https://doi.org/10.1104/pp.013839 CrossRefPubMedPubMedCentralGoogle Scholar
  65. Reymond M, Muller B, Tardieu F (2004) Dealing with the genotype × environment interaction via a modelling approach: a comparison of QTLs of maize leaf length or width with QTLs of model parameters. J Exp Bot 55(407):2461–2472PubMedCrossRefGoogle Scholar
  66. Rieseberg LH, Archer MA, Wayne RK (1999) Transgressive segregation, adaptation and speciation. Heredity 83(4):363–372PubMedCrossRefGoogle Scholar
  67. Rieseberg LH, Widmer A, Arntz AM, Burke JM (2002) Directional selection is the primary cause of phenotypic diversification. Proc Natl Acad Sci 99(19):12242–12245.  https://doi.org/10.1073/pnas.192360899 CrossRefPubMedGoogle Scholar
  68. Ritchie S, Hanway J, Benson G, Herman J (1993) How a corn plant develops. Iowa state university cooperative extension service. Special report 48Google Scholar
  69. Sa KJ, Park JY, Woo SY, Ramekar RV, Jang C-S, Lee JK (2015) Mapping of QTL traits in corn using a RIL population derived from a cross of dent corn × waxy corn. Genes Genomics 37(1):1–14.  https://doi.org/10.1007/s13258-014-0223-8 CrossRefGoogle Scholar
  70. Sangoi L, Gracietti MA, Rampazzo C, Bianchetti P (2002) Response of Brazilian maize hybrids from different eras to changes in plant density. Field Crops Res 79(1):39–51.  https://doi.org/10.1016/S0378-4290(02)00124-7 CrossRefGoogle Scholar
  71. SAS Institute Inc (2009) SAS OnlineDoc 9.4. SAS Institute, CaryGoogle Scholar
  72. Scanlon MJ, Schneeberger RG, Freeling M (1996) The maize mutant narrow sheath fails to establish leaf margin identity in a meristematic domain. Development 122(6):1683–1691PubMedGoogle Scholar
  73. Sibov ST, De Souza Lopes, Jr C, Garcia AAF, Silva AR (2003) Molecular mapping in tropical maize (Zea mays L.) using microsatellite markers. 2. Quantitative trait loci (QTL) for grain yield, plant height, ear height and grain moisture. Hereditas 139:107–115PubMedCrossRefGoogle Scholar
  74. Song Y, Rui Y, Bedane G, Li J (2016) Morphological characteristics of maize canopy development as affected by increased plant density. PLoS ONE 11(4):e0154084PubMedPubMedCentralCrossRefGoogle Scholar
  75. Subedi K, Ma B, Smith D (2006) Response of a leafy and non-leafy maize hybrid to population densities and fertilizer nitrogen levels. Crop Sci 46(5):1860–1869CrossRefGoogle Scholar
  76. Tang D, Chen Z, Ni J, Jiang Q, Li P, Wang L, Zhou J, Li C, Liu J (2018) QTL mapping of leaf angle on eight nodes in maize enable the optimize canopy by differential operating of leaf angle at different levels of plant. bioRxiv:499665. https://doi.org/10.1101/499665Google Scholar
  77. Tanksley SD, McCouch SR (1997) Seed banks and molecular maps: unlocking genetic potential from the wild. Science 277(5329):1063–1066PubMedCrossRefPubMedCentralGoogle Scholar
  78. Tetio-Kagho F, Gardner F (1988) Responses of maize to plant population density. I. Canopy development, light relationships, and vegetative growth. Agron J 80(6):930–935CrossRefGoogle Scholar
  79. Tian F, Bradbury PJ, Brown PJ, Hung H, Sun Q, Flint-Garcia S, Rocheford TR, McMullen MD, Holland JB, Buckler ES (2011) Genome-wide association study of leaf architecture in the maize nested association mapping population. Nat Genet 43(2):159PubMedCrossRefPubMedCentralGoogle Scholar
  80. Tollenaar M, Lee EA (2002) Yield potential, yield stability and stress tolerance in maize. Field Crops Res 75:161–169CrossRefGoogle Scholar
  81. Troyer AF (1996) Breeding widely adapted, popular maize hybrids. Euphytica 92(1–2):163–174CrossRefGoogle Scholar
  82. Van Ooijen JW (1999) LOD significance thresholds for QTL analysis in experimental populations of diploid species. Heredity 83(5):613PubMedCrossRefGoogle Scholar
  83. Wang S, Basten CJ, Zeng ZB (2011) Windows QTL Cartographer 2.5. North Carolina State University, RaleighGoogle Scholar
  84. Wang H, Liang Q, Li K, Hu X, Wu Y, Wang H, Liu Z, Huang C (2017) QTL analysis of ear leaf traits in maize (Zea mays L.) under different planting densities. Crop J 5(5):387–395.  https://doi.org/10.1016/j.cj.2017.05.001 CrossRefGoogle Scholar
  85. Wassom JJ (2013) Quantitative trait loci for leaf angle, leaf width, leaf length, and plant height in a maize (Zea mays L) B73 × Mo17 population. Maydica 58(3–4):318–321Google Scholar
  86. Wei X, Wang B, Peng Q, Wei F, Mao K, Zhang X, Sun P, Liu Z, Tang J (2015) Heterotic loci for various morphological traits of maize detected using a single segment substitution lines test-cross population. Mol Breed 35(3):94.  https://doi.org/10.1007/s11032-015-0287-4 CrossRefGoogle Scholar
  87. Williams W, Loomis R, Duncan W, Dovrat A, Nunez A (1968) Canopy architecture at various population densities and the growth and grain yield of corn2. Crop Sci 8(3):303–308CrossRefGoogle Scholar
  88. Winkler R, Helentjaris T (1993) Mu tagging of dwarfs. Maize Genet Coop Newsl 67:111Google Scholar
  89. Yang C, Tang D, Qu J, Zhang L, Zhang L, Chen Z, Liu J (2016) Genetic mapping of QTL for the sizes of eight consecutive leaves below the tassel in maize (Zea mays L.). Theor Appl Genet 129(11):2191–2209PubMedCrossRefGoogle Scholar
  90. Yi Q, How X, Liu Y et al (2019) QTL analysis for plant architecture-related traits in maize under two different plant density conditions. Euphytica 215:148CrossRefGoogle Scholar
  91. Yu Y, Zhang J, Shi Y, Song Y, Wang T, Li Y (2006) QTL analysis for plant height and leaf angle by using different populations of maize. J Maize Sci 14(2):88–92Google Scholar
  92. Zhang X, Huang C, Wu D, Qiao F, Li W, Duan L, Wang K, Xiao Y, Chen G, Liu Q, Xiong L, Yang W, Yan J (2017) High-throughput phenotyping and qtl mapping reveals the genetic architecture of maize plant growth. Plant Physiol 173(3):1554–1564.  https://doi.org/10.1104/pp.16.01516 CrossRefPubMedPubMedCentralGoogle Scholar
  93. Zhao X, Fang P, Zhang J, Peng Y (2018) QTL mapping for six ear leaf architecture traits under water-stressed and well-watered conditions in maize (Zea mays L.). Plant Breed 137(1):60–72.  https://doi.org/10.1111/pbr.12559 CrossRefGoogle Scholar

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© Springer Nature B.V. 2020

Authors and Affiliations

  1. 1.Cátedra de Mejoramiento Genético, Facultad de Ciencias AgrariasUniversidad Nacional de Lomas de Zamora, IIPAAS-CICBuenos AiresArgentina
  2. 2.Departamento de Producción Vegetal, Facultad de AgronomíaUniversidad de Buenos AiresCiudad Autónoma de Buenos AiresArgentina
  3. 3.Instituto de Fisiología y Ecología Vinculado a la AgriculturaConsejo Nacional de Investigaciones Científicas y Técnicas (IFEVA-CONICET)Ciudad Autónoma de Buenos AiresArgentina

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