Literature DB >> 35275253

Physiological breeding for yield improvement in soybean: solar radiation interception-conversion, and harvest index.

Miguel Angel Lopez1, Fabiana Freitas Moreira1, Anthony Hearst2, Keith Cherkauer2, Katy Martin Rainey3.   

Abstract

KEY MESSAGE: Efficiency of light interception, Radiation use efficiency and harvest index can be used as targets to improve grain yield potential in soybean. Grain yield (GY) production can be expressed as the result of three main efficiencies: light interception (Ei), radiation use (RUE), and harvest index (HI). Although dissecting GY through these three efficiencies is not entirely new, there is a lack of knowledge about the phenotypic variation, the genetic architecture, and the relative contribution of these three efficiencies on GY in soybean. This knowledge gap coupled with laborious phenotyping prevents the active consideration of these efficiencies into breeding programs. This study aims to reveal the phenotypic variation, heritability, genetic relationships, genetic architecture, and genomic prediction for Ei, RUE, and HI in soybean. We evaluated a maturity control panel of 383 Recombinant Inbred Lines (RILs) selected from the soybean nested association mapping (SoyNAM) population. Dry matter ground measured along with canopy coverage (CC) from UAS imagery were collected in three environments. Light interception was modeled through a logistic curve using CC as a proxy. The total above-ground biomass collected during the growing season and its respective cumulative light intercepted were used to derive RUE through linear models fitting. Additive-genetic correlations, genome-wide association (GWA) and whole-genome regressions (WGR) were performed to evaluate the relationship between traits, their association with genomic regions, and the feasibility of predicting these efficiencies with genomic information. Correlation analyses considered three groups: the entire data set, and the high- and low-yielding RILs to determine association as a function of the GY. Our results revealed moderate to high phenotypic variation for Ei, RUE, and HI with ranges of 8.5%, 1.1 g MJ-1, and 0.2, respectively. Additive-genetic correlation revealed a strong relationship of GY with HI and moderate with RUE and Ei when whole data set was considered, but negligible contribution of HI on GY when just the top 100 was analyzed. The GWA analyses showed that Ei is associated with three SNPs; two of them located on chromosome 7 and one on chromosome 11 with no previous quantitative trait loci (QTLs) reported for these regions. RUE is associated with four SNPs on chromosomes 1, 7, 11, and 18. Some of these QTLs are novel, while others are previously documented for plant architecture and chlorophyll content. Two SNPs positioned on chromosome 13 and 15 with previous QTLs reported for plant height and seed set, weight and abortion were associated with HI. WGR showed high predictive ability for Ei, RUE, and HI with maximum correlation ranging between 0.75 and 0.80. Future improvements in GY can be expected through strategies prioritizing Ei for short-term results when using high yielding germplasm and RUE for medium- and long-term outcomes. This work is a pioneer attempt to integrate traditional physiological traits into the breeding process in the context of physiological breeding.
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

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Year:  2022        PMID: 35275253     DOI: 10.1007/s00122-022-04048-5

Source DB:  PubMed          Journal:  Theor Appl Genet        ISSN: 0040-5752            Impact factor:   5.699


  32 in total

1.  A major and stable QTL associated with seed weight in soybean across multiple environments and genetic backgrounds.

Authors:  Shin Kato; Takashi Sayama; Kenichiro Fujii; Setsuzo Yumoto; Yuhi Kono; Tae-Young Hwang; Akio Kikuchi; Yoshitake Takada; Yu Tanaka; Tatsuhiko Shiraiwa; Masao Ishimoto
Journal:  Theor Appl Genet       Date:  2014-04-10       Impact factor: 5.699

Review 2.  Accelerating yield potential in soybean: potential targets for biotechnological improvement.

Authors:  Elizabeth A Ainsworth; Craig R Yendrek; Jeffrey A Skoneczka; Stephen P Long
Journal:  Plant Cell Environ       Date:  2011-07-21       Impact factor: 7.228

3.  Historical gains in soybean (Glycine max Merr.) seed yield are driven by linear increases in light interception, energy conversion, and partitioning efficiencies.

Authors:  Robert P Koester; Jeffrey A Skoneczka; Troy R Cary; Brian W Diers; Elizabeth A Ainsworth
Journal:  J Exp Bot       Date:  2014-04-30       Impact factor: 6.992

4.  Drought adaptation of stay-green sorghum is associated with canopy development, leaf anatomy, root growth, and water uptake.

Authors:  Andrew K Borrell; John E Mullet; Barbara George-Jaeggli; Erik J van Oosterom; Graeme L Hammer; Patricia E Klein; David R Jordan
Journal:  J Exp Bot       Date:  2014-06-13       Impact factor: 6.992

5.  Changes in SBPase activity influence photosynthetic capacity, growth, and tolerance to chilling stress in transgenic tomato plants.

Authors:  Fei Ding; Meiling Wang; Shuoxin Zhang; Xizhen Ai
Journal:  Sci Rep       Date:  2016-09-02       Impact factor: 4.379

6.  HCVerso3: An Open-Label, Phase IIb Study of Faldaprevir and Deleobuvir with Ribavirin in Hepatitis C Virus Genotype-1b-Infected Patients with Cirrhosis and Moderate Hepatic Impairment.

Authors:  Christoph Sarrazin; Michael Manns; Jose Luis Calleja; Javier Garcia-Samaniego; Xavier Forns; Renee Kaste; Xiaofei Bai; Jing Wu; Jerry O Stern
Journal:  PLoS One       Date:  2016-12-28       Impact factor: 3.240

7.  SoyBase, the USDA-ARS soybean genetics and genomics database.

Authors:  David Grant; Rex T Nelson; Steven B Cannon; Randy C Shoemaker
Journal:  Nucleic Acids Res       Date:  2009-12-14       Impact factor: 16.971

8.  Genotyping by sequencing for genomic prediction in a soybean breeding population.

Authors:  Diego Jarquín; Kyle Kocak; Luis Posadas; Katie Hyma; Joseph Jedlicka; George Graef; Aaron Lorenz
Journal:  BMC Genomics       Date:  2014-08-29       Impact factor: 3.969

9.  Stay-green traits to improve wheat adaptation in well-watered and water-limited environments.

Authors:  John T Christopher; Mandy J Christopher; Andrew K Borrell; Susan Fletcher; Karine Chenu
Journal:  J Exp Bot       Date:  2016-07-21       Impact factor: 6.992

10.  Phenotyping of field-grown wheat in the UK highlights contribution of light response of photosynthesis and flag leaf longevity to grain yield.

Authors:  Elizabete Carmo-Silva; P John Andralojc; Joanna C Scales; Steven M Driever; Andrew Mead; Tracy Lawson; Christine A Raines; Martin A J Parry
Journal:  J Exp Bot       Date:  2017-06-15       Impact factor: 6.992

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