Leaf growth rate per unit thermal time follows QTL-dependent daily patterns in hundreds of maize lines under naturally fluctuating conditions
- PMID: 17238905
- DOI: 10.1111/j.1365-3040.2006.01611.x
Leaf growth rate per unit thermal time follows QTL-dependent daily patterns in hundreds of maize lines under naturally fluctuating conditions
Abstract
We have analysed daily patterns of leaf elongation rate (LER) in large data sets with 318 genotypes placed in naturally fluctuating temperature and evaporative demand, and examined the effect of targeted alleles on these patterns. The method consisted, firstly, in expressing elongation rate per unit thermal time, so it became temperature independent; secondly, in a joint analysis of diurnal fluctuations of elongation rate and of micrometeorological conditions in several experiments, and finally, in a comparison of daily patterns between groups of genotypes possessing targeted alleles. (1) Conditions for using thermal time at a time step of 15 min were first tested successfully in 318 recombinant inbred lines (RILs) of three mapping populations. (2) An analysis of 1989 time courses revealed a robust daily pattern of LER per unit thermal time (LERth) over several experiments. LERth was constant during the night and was reproducible (for a given RIL) over up to 10 consecutive nights in different experiments. It declined rapidly during the early morning, closely following the daily pattern of transpiration rate. (3) Groups of RILs carrying alleles conferring a high response to temperature had markedly higher night-time plateau of LER than those with low responses. Groups of RILs with high response to evaporative demand had rapid decreases in elongation rate at the transition between night and day, while this decrease was slower in groups of RILs with low response. These results open the way for using kinetics of responses to environmental stimuli as a phenotyping tool in genetic analyses.
Similar articles
-
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. 2007;58(2):339-49. doi: 10.1093/jxb/erl227. Epub 2006 Nov 27. J Exp Bot. 2007. PMID: 17130185
-
Dealing with the genotype x environment interaction via a modelling approach: a comparison of QTLs of maize leaf length or width with QTLs of model parameters.J Exp Bot. 2004 Nov;55(407):2461-72. doi: 10.1093/jxb/erh200. Epub 2004 Jul 30. J Exp Bot. 2004. PMID: 15286140
-
Short-term responses of leaf growth rate to water deficit scale up to whole-plant and crop levels: an integrated modelling approach in maize.Plant Cell Environ. 2008 Mar;31(3):378-91. doi: 10.1111/j.1365-3040.2007.01772.x. Epub 2007 Dec 10. Plant Cell Environ. 2008. PMID: 18088328
-
A hydraulic model is compatible with rapid changes in leaf elongation under fluctuating evaporative demand and soil water status.Plant Physiol. 2014 Apr;164(4):1718-30. doi: 10.1104/pp.113.228379. Epub 2014 Jan 13. Plant Physiol. 2014. PMID: 24420931 Free PMC article.
-
Genetic and genomic dissection of maize root development and architecture.Curr Opin Plant Biol. 2009 Apr;12(2):172-7. doi: 10.1016/j.pbi.2008.12.002. Epub 2009 Jan 20. Curr Opin Plant Biol. 2009. PMID: 19157956 Review.
Cited by
-
Drought and abscisic acid effects on aquaporin content translate into changes in hydraulic conductivity and leaf growth rate: a trans-scale approach.Plant Physiol. 2009 Apr;149(4):2000-12. doi: 10.1104/pp.108.130682. Epub 2009 Feb 11. Plant Physiol. 2009. PMID: 19211703 Free PMC article.
-
Quantitative trait loci and crop performance under abiotic stress: where do we stand?Plant Physiol. 2008 Jun;147(2):469-86. doi: 10.1104/pp.108.118117. Plant Physiol. 2008. PMID: 18524878 Free PMC article. No abstract available.
-
Genetic dissection of temperature-dependent sorghum growth during juvenile development.Theor Appl Genet. 2014 Sep;127(9):1935-48. doi: 10.1007/s00122-014-2350-7. Epub 2014 Jul 15. Theor Appl Genet. 2014. PMID: 25023408
-
PhenoTrack3D: an automatic high-throughput phenotyping pipeline to track maize organs over time.Plant Methods. 2022 Dec 8;18(1):130. doi: 10.1186/s13007-022-00961-4. Plant Methods. 2022. PMID: 36482291 Free PMC article.
-
A Comprehensive Review of High Throughput Phenotyping and Machine Learning for Plant Stress Phenotyping.Phenomics. 2022 Apr 4;2(3):156-183. doi: 10.1007/s43657-022-00048-z. eCollection 2022 Jun. Phenomics. 2022. PMID: 36939773 Free PMC article. Review.
MeSH terms
LinkOut - more resources
Full Text Sources
Other Literature Sources