Skip to main content
SpringerLink
Log in

Genetic analysis for canopy architecture in an F2:3 population derived from two-type foundation parents across multi-environments

  • Published:
Euphytica Aims and scope Submit manuscript

Abstract

Canopy architecture improvements are a major focus in modern maize (Zea mays L.) breeding because appropriate canopy architecture could allow for the adaptation to high-density planting and high utilisation efficiency of solar energy. Therefore, understanding the genetic basis of canopy architecture-related traits is important for maize breeding. In this study, an F2:3 population derived from a cross between R08 (representing a breeding pattern of lower planting density with large ears breeding pattern) × Ye478 (representing a breeding pattern of high planting density) was evaluated for nine canopy architecture-related traits in six environments, including Nanning, Ya’an, and Jinghong, in 2012 and 2013. Mixed linear model-based composite interval mapping was used to dissect the genetic basis of canopy architecture-related traits. Sixty-five quantitative trait loci (QTL) were identified for all nine traits through a joint analysis across all environments. More than 80 % of the QTL in this study did not show significant QTL × environment interactions, but epistasis played an important role in architecture-related trait inheritance. Nine chromosome segments were identified that affected multiple canopy architecture-related traits.

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

Similar content being viewed by others

References

  • Almeida GD, Makumbi D, Magorokosho C, Nair S, Borém A, Ribaut JM, Bänziger M, Prasanna BM, Crossa J, Babu R (2013) QTL mapping in three tropical maize populations reveals a set of constitutive and adaptive genomic regions for drought tolerance. Theor Appl Genet 126(3):583–600

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Bernardo R (2008) Molecular markers and selection for complex traits in plants: learning from the last 20 years. Crop Sci 48(5):1649–1664

    Article  Google Scholar 

  • Buckler ES, Holland JB, Bradbury PJ, Acharya CB, Brown PJ, Browne C, Ersoz E, Flint-Garcia S, Garcia A, Glaubitz JC, Goodman MM, Harjes C, Guill K, Kroon DE, Larsson S, Lepak NK, Li H, Mitchell SE, Pressoir G, Peiffer JA, Rosas MO, Rocheford TR, Romay MC, Romero S, Salvo S, Villeda HS, Silva HS, Sun Q, Tian F, Upadyayula N, Ware D, Yates H, Yu J, Zhang Z, Kresovich S, McMullen MD (2009) The genetic architecture of maize flowering time. Science 325:714–718

    Article  CAS  PubMed  Google Scholar 

  • Chen DH, Ronald PC (1999) A rapid DNA minipreparation method suitable for AFLP and other PCR applications. Plant Mol Biol Report 17:53–57

    Article  CAS  Google Scholar 

  • Chen FB, Yang KC, Rong TZ, Pan GT (2007) Studies on major characters of maize hybrids in the regional tests of Sichuan and southwest China and the countermeasures for maize breeding. J Maize Sci 15(4):41–45

    Google Scholar 

  • Chen J, Xu L, Cai Y, Xu J (2009) Identification of QTLs for phosphorus utilization efficiency in maize (Zea mays L.) across P levels. Euphytica 167(2):245–252

    Article  CAS  Google Scholar 

  • Ci XK, Xu JS, Lu ZY, Bai PF, Ru GL, Liang XL, Zhang DG, Li XH, Bai L, Xie CX, Hao ZF, Zhang SH, Dong ST (2012) Trends of grain yield and plant traits in Chinese maize cultivars from the 1950s to the 2000s. Euphytica 185(3):395–406

    Article  Google Scholar 

  • Doebley J (2004) The genetics of maize evolution. Annu Rev Genet 38:37–59

    Article  CAS  PubMed  Google Scholar 

  • Duvick DN (1997) What is yield? In developing drought- and low n-tolerant maize. CIMMYT, Mexico, pp 332–335

    Google Scholar 

  • Duvick DN (2004) Long-term selection in a commercial hybrid maize breeding program. In: Janick J (ed) Plant breeding reviews 24, Part 2. Wiley, Hoboken, pp 109–151

    Google Scholar 

  • Duvick DN (2005) The contribution of breeding to yield advances in maize. Adv Agron 86:83–145

    Article  Google Scholar 

  • Fan JB, Gunderson KL, Bibikova M, Yeakley JM, Chen J, Wickham-Garcia E, Lebruska LL, Laurent M, Shen R, Barker D (2006) Illumina universal bead arrays. Methods Enzymol 410:57–73

    Article  CAS  PubMed  Google Scholar 

  • Foulkes MJ, Reynolds MP, Sylvester-Bradley R (2009) Genetic improvement of grain crops: yield potential. In: Sadras V, Calderini D (eds) Crop physiology: applications for genetic improvement and agronomy. Elsevier, Amsterdam, pp 355–385

    Chapter  Google Scholar 

  • Frascaroli E, Canè MA, Landi P, Pea G, Gianfranceschi L, Villa M, Morgante M, Pè ME (2007) Classical genetic and quantitative trait loci analyses of heterosis in a maize hybrid between two elite inbred lines. Genetics 176(1):625–644

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Garcia AAF, Wang S, Melchinger AE, Zeng ZB (2008) Quantitative trait loci mapping and the genetic basis of heterosis in maize and rice. Genetics 180(3):1707–1724

    Article  PubMed Central  PubMed  Google Scholar 

  • Guo TT, Yang N, Tong H, Pan QC, Yang XH, Tang JH, Wang JK, Li JS, Yan JB (2014) Genetic basis of grain yield heterosis in an “immortalized F2” maize population. Theor Appl Genet 127(10):2149–2158

    Article  PubMed  Google Scholar 

  • Hallauer AR, Carena MJ (2009) Maize breeding. In: Carena MJ (ed) Cereals. Springer, New York, pp 3–98

    Chapter  Google Scholar 

  • Hallauer AR, Carena MJ, Filho JBM (2010) Means and variances. In: Bradshaw JE (ed) Quantitative genetics in maize breeding. Springer Science Business Media, New York, pp 33–68

    Chapter  Google Scholar 

  • Hammer GL, Dong Z, McLean G, Doherty A, Messina C, Schussler J, Zinselmeier C, Paszkiewicz S, Cooper M (2009) Can changes in canopy and/or root system architecture explain historical maize yield trends in the US corn belt? Crop Sci 49(1):299–312

    Article  Google Scholar 

  • Hunter RB (1980) Increased leaf area (source) and yield of maize in short-season areas. Crop Sci 20(5):571–574

    Article  Google Scholar 

  • Jiao YP, Zhao HN, Ren LH, Song WB, Zeng B, Guo JJ, Wang BB, Liu ZP, Chen J, Li W, Zhang M, Xie SJ, Lai JS (2012) Genome-wide genetic changes during modern breeding of maize. Nat Genet 44:812–815

    Article  CAS  PubMed  Google Scholar 

  • Khavkin E, Coe EH (1997) Mapped genomic locations for developmental functions and QTLs reflect concerted groups in maize (Zea mays L.). Theor Appl Genet 95(3):343–352

    Article  CAS  Google Scholar 

  • Knapp SJ, Stroup WW, Ross WM (1985) Exact confidence intervals for heritability on a progeny mean basis. Crop Sci 25:192–194

    Article  Google Scholar 

  • Kosambi DD (1943) The estimation of map distances from recombination values. Ann Eugen 12:172–175

    Article  Google Scholar 

  • 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

    Article  CAS  PubMed  Google Scholar 

  • Ku LX, Sun ZH, Wang CL, Zhang J, Zhao RF, Liu HY, Tai GQ, Chen YH (2012) QTL mapping and epistasis analysis of brace root traits in maize. Mol Breed 30(2):697–708

    Article  Google Scholar 

  • Lander ES, Green P, Abrahamson J, Barlow A, Daly MJ, Lincoln SE, Etoh T (1987) MAPMAKER: an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics 1:174–181

    Article  CAS  PubMed  Google Scholar 

  • Li Y, Wang TY (2010) Germplasm base of maize breeding in China and formation of foundation parents. J Maize Sci 18(5):1–8

    Google Scholar 

  • Li YL, Niu SZ, Dong YB, Cui DQ, Wang YZ, Liu YY, Wei MG (2007) Identification of trait-improving quantitative trait loci for grain yield components from a dent corn inbred line in an advanced backcross BC2F2 population and comparison with its F2: 3 population in popcorn. Theor Appl Genet 115(1):129–140

    Article  CAS  PubMed  Google Scholar 

  • Lima MDA, 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 17:227–239

    Article  Google Scholar 

  • Liu TD, Song FB (2012) Maize photosynthesis and microclimate within the canopies at grain-filling stage in response to narrow-wide row planting patterns. Photosynthetica 50(2):215–222

    Article  CAS  Google Scholar 

  • Liu XH, Tan ZB, Rong TZ (2009) Molecular mapping of a major QTL conferring resistance to SCMV based on immortal RIL population in maize. Euphytica 167(2):229–235

    Article  CAS  Google Scholar 

  • Liu Y, Wang LW, Sun CL, Zhang ZX, Zheng YL, Qiu FZ (2014a) Genetic analysis and major QTL detection for maize kernel size and weight in multi-environments. Theor Appl Genet 127:1019–1037

    Article  CAS  PubMed  Google Scholar 

  • Liu Z, Yu TT, Mei XP, Chen XN, Wang GQ, Wang JG, Liu CX, Wang X, Cai YL (2014b) QTL mapping for leaf angle and leaf space above ear position in maize(Zea mays L.). J Agric Biotech 22(2):177–187

    Google Scholar 

  • Ma DL, Xie RZ, Niu XK, Li SK, Long HL, Liu YE (2014) Changes in the morphological traits of maize genotypes in China between the 1950s and 2000s. Eur J Agron 58:1–10

    Article  Google Scholar 

  • Maddonni GA, Otegui ME (1996) Leaf area, light interception, and crop development in maize. Field Crop Res 48(1):81–87

    Article  Google Scholar 

  • Maddonni GA, Otegui ME, Cirilo AG (2001) Plant population density, row spacing and hybrid effects on maize canopy architecture and light attenuation. Field Crop Res 71(3):183–193

    Article  Google Scholar 

  • Melchinger AE, Utz HF, Schön CC (1998) Quantitative trait locus (QTL) mapping using different testers and independent population samples in maize reveals low power of QTL detection and large bias in estimates of QTL effects. Genetics 149(1):383–403

    CAS  PubMed Central  PubMed  Google Scholar 

  • Mickelson SM, Stuber CS, Senior L, Kaeppler SM (2002) Quantitative trait loci controlling leaf and tassel traits in a B73 × Mo17 population of maize. Crop Sci 42(6):1902–1909

    Article  CAS  Google Scholar 

  • Mock JJ, Pearce RB (1975) An ideotype of maize. Euphytica 24:613–623

    Article  Google Scholar 

  • Papst C, Bohn M, Utz HF, Melchinger AE, Klein D, Eder J (2004) QTL mapping for European corn borer resistance (Ostrinia nubilalis Hb.), agronomic and forage quality traits of testcross progenies in early-maturing European maize (Zea mays L.) germplasm. Theor Appl Genet 108(8):1545–1554

    Article  CAS  PubMed  Google Scholar 

  • Peng B, Li YX, Wang Y, Liu C, Liu ZZ, Tan WW, Zhang Y, Wang D, Shi YS, Sun BC, Song YC, Wang TY, Li Y (2011) QTL analysis for yield components and kernel-related traits in maize across multi-environments. Theor Appl Genet 122:1305–1320

    Article  PubMed  Google Scholar 

  • Pepper GE, Pearce RB, Mock JJ (1977) Leaf orientation and yield of maize. Crop Sci 17:883–886

    Article  Google Scholar 

  • Phillips PC (2008) Epistasis—the essential role of gene interactions in the structure and evolution of genetic systems. Nat Rev Genet 9:855–867

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Qiao SB, Wang YH, Yang KC, Rong TZ, Pan GT, Gao SB (2009) Effects contributed by different donor parents and backcross times on R08 improvement. Acta Agron 35(12):2187–2196

    Article  CAS  Google Scholar 

  • Qin H, Cai Y, Liu Z, Wang G, Wang J, Guo Y, Wang H (2012) Identification of QTL for zinc and iron concentration in maize kernel and cob. Euphytica 187(3):345–358

    Article  CAS  Google Scholar 

  • Rice WR (1989) Analyzing tables of statistical tests. Evolution 43(1):223–225

    Article  Google Scholar 

  • Rossini MA, Maddonni GA, Otegui ME (2011) Inter-plant competition for resources in maize crops grown under contrasting nitrogen supply and density: variability in plant and ear growth. Field Crop Res 121(3):373–380

    Article  Google Scholar 

  • Russell WA (1985) Evaluations for plant, ear, and grain traits of maize cdtivars representing seven eras of breeding. Maydica 30:85–96

    Google Scholar 

  • Russell WA (1991) Genetic improvement of maize yields. Adv Agron 46:245–298

    Article  Google Scholar 

  • Sangoi L, Gracietti MA, Rampazzo C, Bianchetti P (2002) Response of Brazilian maize hybrids from different eras to changes in plant density. Field Crop Res 79(1):39–51

    Article  Google Scholar 

  • Springer NM, Stupar RM (2007) Allelic variation and heterosis in maize: how do two halves make more than a whole? Genome Res 17(3):264–275

    Article  CAS  PubMed  Google Scholar 

  • Steduto P, Hsiao TC (1998) Maize canopies under two soil water regimes: II. Seasonal trends of evapotranspiration, carbon dioxide assimilation and canopy conductance, and as related to leaf area index. Agric Forest Meteorol 89(3):185–200

    Article  Google Scholar 

  • Steinhoff J, Liu W, Reif JC, Della Porta G, Ranc N, Würschum T (2012) Detection of QTL for flowering time in multiple families of elite maize. Theor Appl Genet 125(7):1539–1551

    Article  PubMed  Google Scholar 

  • Stewart DW, Costa C, Dwyer LM, Smith DL, Hamilton RI, Ma BL (2003) Canopy structure, light interception, and photosynthesis in maize. Agron J 95(6):1465–1474

    Article  Google Scholar 

  • Stich B, Yu J, Melchinger AE, Piepho HP, Utz HF, Maurer HP, Buckler ES (2007) Power to detect higher-order epistatic interactions in a metabolic pathway using a new mapping strategy. Genetics 176(1):563–570

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Stöckle CO, Kemanian AR (2009) Crop radiation capture and use efficiency: a framework for crop growth analysis. In: Calderini DF, Sadras VO (eds) Crop physiology: applications for genetic improvement and agronomy. Elsevier, Amsterdam, pp 145–170

    Chapter  Google Scholar 

  • Stuber CW, Edwards MD, Wendel JF (1987) Molecular marker-facilitated investigations of in maize. II. Factors influencing yield and its component traits. Crop Sci 27:639–648

    Article  Google Scholar 

  • Su SW, Gao HM, Gou XL (1990) A study on the yield potential in maize hybrids with different leaf angles. Acta Agron Sin 16(4):364–372

    Google Scholar 

  • Tang H, Yan JB, Huang YQ, Zheng YL, Li JS (2005) QTL mapping of five agronomic traits in maize. Acta Genet Sin 32:203–209

    PubMed  Google Scholar 

  • Tanksley SD (1993) Mapping polygenes. Ann Rev Genet 27:205–233

    Article  CAS  PubMed  Google Scholar 

  • Tetio-Kagho F, Gardner FP (1988) Responses of maize to plant population density. I. Canopy development, light relationships, and vegetative growth. Agron J 80(6):930–935

    Article  Google Scholar 

  • 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):159–162

    Article  CAS  PubMed  Google Scholar 

  • Tollenaar M (1989) Genetic improvement in grain yield of commercial maize hybrids grown in Ontario from 1959 to 1988. Crop Sci 29:1365–1371

    Article  Google Scholar 

  • Tollenaar M (1991) Physiological basis of genetic improvement of maize hybrids in Ontario from 1959 to 1988. Crop Sci 31:119–124

    Article  Google Scholar 

  • Troyer AF (1996) Breeding widely adapted, popular maize hybrids. Euphytica 92(1–2):163–174

    Article  Google Scholar 

  • Tuberosa R, Sanguineti MC, Landi P, Salvi S, Casarini E, Conti S (1998) RFLP mapping of quantitative trait loci controlling abscisic acid concentration in leaves of drought-stressed maize (Zea mays L.). Theor Appl Genet 97(5–6):744–755

    Article  CAS  Google Scholar 

  • Veldboom LR, Lee M, Woodman WL (1994) Molecular marker-facilitated studies in an elite maize population: I. Linkage analysis and determination of QTL for morphological traits. Theor Appl Genet 88(1):7–16

    Article  CAS  PubMed  Google Scholar 

  • Voorrips RE (2002) MapChart: software for the graphical presentation of linkage maps and QTLs. J Hered 93(1):77–78

  • Wang QC, Niu YZ, Xu QZ, Wang ZX, Zhang J (1996) Effect of plant-type on rate of canopy apparent photosynthesis and yield in maize (Zea mays L.). Acta Agron Sin 22(2):223–227

    CAS  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 

  • Welcker C, Boussuge B, Bencivenni C, Ribaut JM, Tardieu F (2007) Are source and sink strengths genetically linked in maize plants subjected to water deficit? A QTL study of the responses of leaf growth and of anthesis-silking interval to water deficit. J Exp Bot 58(2):339–349

    Article  CAS  PubMed  Google Scholar 

  • Wright SI, Bi IV, Schroeder SG, Yamasaki M, Doebley JF, McMullen MD, Gaut BS (2005) The effects of artificial selection on the maize genome. Science 308(5726):1310–1314

    Article  CAS  PubMed  Google Scholar 

  • Würschum T (2012) Mapping QTl for agronomic traits in breeding populations. Theor Appl Genet 125:201–210

    Article  PubMed  Google Scholar 

  • Xu YB (2010) Molecular plant breeding. CAB International, Cambridge

    Book  Google Scholar 

  • Xu QW, Wang QC, Niu YZ, Wang ZX, Zhang J (1995) Studies on relationship between plant type and canopy photosynthesis in maize. Acta Agronomica Sin 21(4):492–496

    Google Scholar 

  • Yamasaki M, Tenaillon MI, Bi IV, Schroeder SG, Sanchez-Villeda H, Doebley JF, Gaut BS, McMullen MD (2005) A Large-Scale Screen for artificial selection in maize identifies candidate agronomic loci for domestication and crop improvement. Plant Cell 17:2859–2872

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Yang J, Zhu J, Williams RW (2007) Mapping the genetic architecture of complex traits in experimental populations. Bioinformatics 23:1527–1536

    Article  CAS  PubMed  Google Scholar 

  • Yang J, Hu CC, Hu H, Yu RD, Xia Z, Ye XZ, Zhu J (2008) QTLNetwork: mapping and visualizing genetic architecture of complex traits in experimental populations. Bioinformatics 24:721–723

    Article  PubMed  Google Scholar 

  • Yang N, Lu YL, Yang XH, Huang J, Zhou Y, Ali F, Wen WW, Liu J, Li JS, Yan J (2014) Genome wide association studies using a new nonparametric model reveal the genetic architecture of 17 agronomic traits in an enlarged Maize Association Panel. PLoS Genet 10(9):e1004573

    Article  PubMed Central  PubMed  Google Scholar 

  • Yu YT, Zhang JM, Shi YS, Song YC, Wang TY, Li Y (2006) QTL analysis for plant height and leaf angle by using different populations of maize. J Maize Sci 14(2):88–92

    CAS  Google Scholar 

  • Yuan LP (2001) Breeding of super hybrid rice. In: Peng S, Hardy B (eds) Rice research for food security and poverty alleviation. International Rice Research Institute, Los Banos, pp 143–149

    Google Scholar 

  • Zhang SH, Bonjean APA (2010) Maize breeding and production in China. In: He ZH, Bonjean APA (eds) Cereals in China. CIMMYT, El Batan, pp 35–49

    Google Scholar 

  • Zhang J, Ku LX, Zhang WQ, Yang S, Liu HY, Zhao RF, Chen YH (2010) QTL mapping of internodes length above upmost ear in maize. J Maize Sci 18(4):45–48

    Google Scholar 

  • Zhang HW, Ma P, Zhao ZN, Zhao GW, Tian BH, Wang JH, Wang GY (2012) Mapping QTL controlling maize deep-seeding tolerance-related traits and confirmation of a major QTL for mesocotyl length. Theor Appl Genet 124(1):223–232

    Article  PubMed  Google Scholar 

  • Zhu LY (2012) QTL Mapping for plant type, ear traits and genetic analysis of a male sterile line in maize (Zea mays L.). Doctoral dissertation, Hebei Agricultural University

Download references

Acknowledgments

This work was supported by Grants from the Ministry of Science and Technology of China (2011CB100106). We thank Dr. Richard G. F. Visser and two anonymous reviewers for valuable suggestions and careful corrections. We also thank the help of Drs. Z. H. Zhang, K. Zhou and C. Xia for revision.

Conflict of interest

The authors declare that they have no conflict of interest.

Author information

Author notes

    Authors and Affiliations

    1. College of Agronomy, Sichuan Agricultural University, Chengdu, 611130, China

      Xianbin Hou, Qianlin Xiao, Bin Wei, Xiangge Zhang, Yong Gu, Yongbin Wang, Jiang Chen, Yufeng Hu & Yubi Huang

    2. Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China

      Yinghong Liu

    3. College of Life Science, Sichuan Agricultural Universitiy, Ya’an, 625014, China

      Hanmei Liu & Junjie Zhang

    Authors
    1. Xianbin Hou
      View author publications

      You can also search for this author in PubMed Google Scholar

    2. Yinghong Liu
      View author publications

      You can also search for this author in PubMed Google Scholar

    3. Qianlin Xiao
      View author publications

      You can also search for this author in PubMed Google Scholar

    4. Bin Wei
      View author publications

      You can also search for this author in PubMed Google Scholar

    5. Xiangge Zhang
      View author publications

      You can also search for this author in PubMed Google Scholar

    6. Yong Gu
      View author publications

      You can also search for this author in PubMed Google Scholar

    7. Yongbin Wang
      View author publications

      You can also search for this author in PubMed Google Scholar

    8. Jiang Chen
      View author publications

      You can also search for this author in PubMed Google Scholar

    9. Yufeng Hu
      View author publications

      You can also search for this author in PubMed Google Scholar

    10. Hanmei Liu
      View author publications

      You can also search for this author in PubMed Google Scholar

    11. Junjie Zhang
      View author publications

      You can also search for this author in PubMed Google Scholar

    12. Yubi Huang
      View author publications

      You can also search for this author in PubMed Google Scholar

    Corresponding author

    Correspondence to Yubi Huang.

    Additional information

    X. Hou and Y. Liu have contributed equally to this work.

    Electronic supplementary material

    Below is the link to the electronic supplementary material.

    Supplementary material 1 (DOCX 1103 kb)

    Rights and permissions

    Reprints and permissions

    About this article

    Check for updates. Verify currency and authenticity via CrossMark

    Cite this article

    Hou, X., Liu, Y., Xiao, Q. et al. Genetic analysis for canopy architecture in an F2:3 population derived from two-type foundation parents across multi-environments. Euphytica 205, 421–440 (2015). https://doi.org/10.1007/s10681-015-1401-8

    Download citation

    • Received:

    • Accepted:

    • Published:

    • Issue Date:

    • DOI: https://doi.org/10.1007/s10681-015-1401-8

    Keywords

    Search

    Navigation