, 213:6 | Cite as

Influences of the combination of high temperature and water deficit on the heritabilities and correlations of agronomic and fiber quality traits in upland cotton

  • Timothy A. Dabbert
  • Duke Pauli
  • Richard Sheetz
  • Michael A. Gore


With climate change manifested in cotton growing regions primarily as a combination of rising temperatures and prolonged periods of low rainfall, it has become critical to improve the resiliency of upland cotton (Gossypium hirsutum L.) to concurrent heat and drought stress. However, few investigations have considered the effect of this combined stress exposure on the phenotypic and genotypic correlations between important cotton traits, or on their respective heritabilities. To that end, we evaluated two upland cotton recombinant inbred line (RIL) populations under managed well-watered (WW) and water-limited (WL) irrigation regimes in the presence of high temperature across multiple environments. In both RIL populations, the broad-sense heritability for lint yield was higher under WW relative to WL conditions. The highest broad-sense heritabilities in both irrigation regimes were observed for lint percentage and fiber quality (micronaire, length, strength, uniformity, and elongation) traits. The genotypic correlations between lint yield and percentage were among the strongest values estimated, followed by a range of non-significant to moderately strong genotypic correlations between lint percentage and the five fiber quality traits in the two RIL populations. Within a RIL population, the strength and direction of between-trait phenotypic and genotypic correlations were similar for WW relative to WL conditions, although there were notable differences for them between RIL populations. Taken together, these results have the potential to benefit climate-oriented breeding programs when developing selection and testing schemes for the genetic improvement of cotton traits with a variable range of environmental stability under heat and drought stress.


Cotton Heat Drought Heritability Correlations 



This research was supported by Monsanto (TAD), Cotton Incorporated Fellowship (DP) and Core Project Funds (MAG), Cornell University startup funds (MAG), and National Science Foundation IOS-1238187 (MAG). We thank Daniel Ilut and Christine Diepenbrock for their expert comments on the manuscript. The authors wish to especially thank Luke Carpenter and Will Lambert, along with their teams, for management of these experiments in Texas and Georgia, respectively.

Supplementary material

10681_2016_1798_MOESM1_ESM.docx (33 kb)
Supplementary material 1 (DOCX 33 kb)


  1. Aboukheir E, Sheshshayee MS, Prasad TG, Udayakumar M (2012) Cotton: Genetic improvement for drought stress tolerance—current status and research needs. In: Tuteja N, Gill SS, Tiburcio AF, Tuteja R (eds) Improving crop resistance to abiotic stress. Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, pp 1369–1400CrossRefGoogle Scholar
  2. Bernardo R (2010) Breeding for quantitative traits in plants. Stemma Press, WoodburyGoogle Scholar
  3. Brown PW, Zeiher CA (1997) Cotton heat stress vol P-108. College of Agriculture, University of Arizona, TucsonGoogle Scholar
  4. Burke JJ, Velten J, Oliver MJ (2004) In vitro analysis of cotton pollen germination. Agron J 96:359–368CrossRefGoogle Scholar
  5. Carmo-Silva AE, Gore MA, Andrade-Sanchez P, French AN, Hunsaker DJ, Salvucci ME (2012) Decreased CO2 availability and inactivation of Rubisco limit photosynthesis in cotton plants under heat and drought stress in the field. Environ Exp Bot 83:1–11CrossRefGoogle Scholar
  6. Chaves MM, Maroco JP, Pereira JS (2003) Understanding plant responses to drought—from genes to the whole plant. Funct Plant Biol 30:239–264CrossRefGoogle Scholar
  7. Chunlei L, Richard PA (2013) Observed and simulated precipitation responses in wet and dry regions 1850–2100. Environ Res Lett 8:034002CrossRefGoogle Scholar
  8. Dabbert TA, Gore MA (2014) Challenges and perspectives on improving heat and drought stress resilience in cotton. J Cotton Sci 18:393–409Google Scholar
  9. Debat V, David P (2001) Mapping phenotypes: canalization, plasticity and developmental stability. Trends Ecol Evol 16:555–561CrossRefGoogle Scholar
  10. Dhindsa RS, Beasley CA, Ting IP (1975) Osmoregulation in cotton fiber: accumulation of potassium and malate during growth. Plant Physiol 56:394–398CrossRefPubMedPubMedCentralGoogle Scholar
  11. Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics, 4th edn. Longman, HarlowGoogle Scholar
  12. Gilmour AR et al. (2009) ASReml user guide release 3.0 VSN International Ltd, Hemel HempsteadGoogle Scholar
  13. Guthrie D, Watson M, Hake K (1993) The 1993 cotton crop—quality trends. Cotton Physiol Today 4:1–4Google Scholar
  14. Hansen J, Sato M, Ruedy R (2012) Perception of climate change. Proc Natl Acad Sci USA 109:E2415–E2423CrossRefPubMedPubMedCentralGoogle Scholar
  15. Hodges HF, Reddy KR, McKinnon JM, Reddy VR (1993) Temperature effects on cotton. Mississippi State University, StarkvilleGoogle Scholar
  16. Holland JB (2006) Estimating genotypic correlations and their standard errors using multivariate restricted maximum likelihood estimation with SAS Proc MIXED. Crop Sci 46:642–654CrossRefGoogle Scholar
  17. Holland JB, Frey KJ, Hammond EG (2001) Correlated responses of fatty acid composition, grain quality, and agronomic traits to nine cycles of recurrent selection for increased oil content in oat. Euphytica 122:69–79CrossRefGoogle Scholar
  18. Holland JB, Nyquist WE, Cervantes-Martinez CT (2003) Estimating and interpreting heritability for plant breeding: an update. Plant Breed Rev 22:9–112Google Scholar
  19. Kenward MG, Roger JH (1997) Small sample inference for fixed effects from restricted maximum likelihood. Biometrics 53:983–997CrossRefPubMedGoogle Scholar
  20. Littell RC, Milliken GA, Stroup WW, Wolfinger R (2006) SAS system for mixed models. SAS Publishing, CaryGoogle Scholar
  21. Lobell DB, Schlenker W, Costa-Roberts J (2011) Climate trends and global crop production since 1980. Science 333:616–620CrossRefPubMedGoogle Scholar
  22. Lush J (1937) Animal breeding plans. Iowa State University Press Ames, IowaGoogle Scholar
  23. Lynch M, Walsh B (1998) Genetics and analysis of quantitative traits. Sinauer Associates, SunderlandGoogle Scholar
  24. Neter J, Kutner MH, Nachtsheim CJ, Wasserman W (1996) Applied linear statistical models, 4th edn. McGraw-Hill, BostonGoogle Scholar
  25. Oosterhuis DM, Snider JL (2011) High temperature stress on floral development and yield of cotton. In: Oosterhuis DM (ed) Stress physiology in cotton, vol 7., The Cotton Foundation Reference Book SeriesThe Cotton Foundation, Cordova, pp 1–24Google Scholar
  26. Pauli D et al (2016) Field-based high-throughput plant phenotyping reveals the temporal patterns of quantitative trait loci associated with stress-responsive traits in cotton. G3: Genes|Genomes|Genetics 6:865–879CrossRefPubMedCentralGoogle Scholar
  27. Percy RG, Cantrell RG, Zhang J (2006) Genetic variation for agronomic and fiber properties in an introgressed recombinant inbred population of cotton. Crop Sci 46:1311–1317CrossRefGoogle Scholar
  28. Pettigrew WT (2004) Moisture deficit effects on cotton lint yield, yield components, and boll distribution. Agron J 96:377–383CrossRefGoogle Scholar
  29. Pettigrew WT (2008) The effect of higher temperatures on cotton lint yield production and fiber quality. Crop Sci 48:278–285CrossRefGoogle Scholar
  30. Rahmstorf S, Coumou D (2011) Increase of extreme events in a warming world. Proc Natl Acad Sci USA 108:17905–17909CrossRefPubMedPubMedCentralGoogle Scholar
  31. Reddy KR, Hodges HF, Reddy VR (1992) Temperature effects on cotton fruit retention. Agron J 84:26–30CrossRefGoogle Scholar
  32. Reddy KR, Davidonis GH, Johnson AS, Vinyard BT (1999) Temperature regime and carbon dioxide enrichment alter cotton boll development and fiber properties contribution. Agron J 91:851–858CrossRefGoogle Scholar
  33. Ruan Y-L, Llewellyn DJ, Furbank RT (2001) The control of single-celled cotton fiber elongation by developmentally reversible gating of plasmodesmata and coordinated expression of sucrose and K(+) transporters and expansin. Plant Cell 13:47–60PubMedPubMedCentralGoogle Scholar
  34. SAS Institute (2013) The SAS system for Windows. Release 9.4. SAS Institute, CaryGoogle Scholar
  35. Schlenker W, Roberts MJ (2009) Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. Proc Natl Acad Sci USA 106:15594–15598CrossRefPubMedPubMedCentralGoogle Scholar
  36. Snider JL, Oosterhuis DM, Skulman BW, Kawakami EM (2009) Heat stress-induced limitations to reproductive success in Gossypium hirsutum. Physiol Plant 137:125–138CrossRefPubMedGoogle Scholar
  37. Snider JL, Oosterhuis DM, Kawakami EM (2011) Diurnal pollen tube growth rate is slowed by high temperature in field-grown Gossypium hirsutum pistils. J Plant Physiol 168:441–448CrossRefPubMedGoogle Scholar
  38. Ulloa M (2006) Heritability and correlations of agronomic and fiber traits in an okra-leaf upland cotton population. Crop Sci 46:1508–1514. doi: 10.2135/cropsci2005.07-0271 CrossRefGoogle Scholar
  39. Wen Y, Piccinni G, Rowland DL, Cothren JT, Leskovar DI, Kemanian AR, Woodard JD (2013) Lint yield, lint quality, and economic returns of cotton production under traditional and regulated deficit irrigation schemes in Southwest Texas. J Cotton Sci 17:10–22Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.School of Plant SciencesUniversity of ArizonaTucsonUSA
  2. 2.Monsanto CompanyChesterfieldUSA
  3. 3.Plant Breeding and Genetics Section, School of Integrative Plant ScienceCornell UniversityIthacaUSA
  4. 4.Monsanto CompanyLubbockUSA

Personalised recommendations