Quantitative trait loci for large sink capacity enhance rice grain yield under free-air CO2 enrichment conditions View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-12

AUTHORS

Hiroshi Nakano, Satoshi Yoshinaga, Toshiyuki Takai, Yumiko Arai-Sanoh, Katsuhiko Kondo, Toshio Yamamoto, Hidemitsu Sakai, Takeshi Tokida, Yasuhiro Usui, Hirofumi Nakamura, Toshihiro Hasegawa, Motohiko Kondo

ABSTRACT

The global atmospheric CO2 concentration has been increasing annually. To determine the trait that effectively increases rice (Oryza sativa L.) grain yield under increased atmospheric CO2 concentrations, as predicted in the near future, we grew a chromosome segment substitution line (CSSL) and a near-isogenic line (NIL) producing high spikelet numbers per panicle (CSSL-GN1 and NIL-APO1, respectively) under free-air CO2 enrichment (FACE) conditions and examined the effects of a large sink capacity on grain yield, its components, and growth-related traits under increased atmospheric CO2 concentrations. Under ambient conditions, CSSL-GN1 and NIL-APO1 exhibited a similar grain yield to Koshihikari, as a result of the trade-off between increased spikelet number and reduced grain filling. However, under FACE conditions, CSSL-GN1 and NIL-APO1 had an equal or a higher grain yield than Koshihikari because of the higher number of spikelets and lower reduction in grain filling. Thus, the improvement of source activity by increased atmospheric CO2 concentrations can lead to enhanced grain yield in rice lines that have a large sink capacity. Therefore, introducing alleles that increase sink capacity into conventional varieties represents a strategy that can be used to develop high-yielding varieties under increased atmospheric CO2 concentrations, such as those predicted in the near future. More... »

PAGES

1827

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-017-01690-8

DOI

http://dx.doi.org/10.1038/s41598-017-01690-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1085371175

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/28500344


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curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41598-017-01690-8'

Turtle is a human-readable linked data format.

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RDF/XML is a standard XML format for linked data.

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