Legume Research

  • Chief EditorJ. S. Sandhu

  • Print ISSN 0250-5371

  • Online ISSN 0976-0571

  • NAAS Rating 6.80

  • SJR 0.391

  • Impact Factor 0.8 (2024)

Frequency :
Monthly (January, February, March, April, May, June, July, August, September, October, November and December)
Indexing Services :
BIOSIS Preview, ISI Citation Index, Biological Abstracts, Elsevier (Scopus and Embase), AGRICOLA, Google Scholar, CrossRef, CAB Abstracting Journals, Chemical Abstracts, Indian Science Abstracts, EBSCO Indexing Services, Index Copernicus
Submit

Full Research Article

The Correlation between Maturity, Plant Architecture and Yield Related Traits of Soybean Cultivars in Northern China

Xiaobin Zhu1, Shihao Wu1, Hongyan Wu1, Wenjing Zhang1, Zhengjun Xia1,*
1Key Laboratory of Soybean Molecular Design Breeding, State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin 150081, China.
2Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing, China.

  • Submitted28-06-2024|

  • Accepted16-08-2024|

  • First Online 13-09-2024|

  • doi 10.18805/LRF-822

ABSTRACT

Background: The traits of the time to flowering and maturity and other important agronomic traits, e.g., plant height, the numbers of nodes and branches are key factors for the adaptability of soybean cultivars to a certain photoperiodic length and ecological environment. Correlation analysis of these traits will facilitate fine-tuning of phenotyping procedure for each trait.

Methods: In this field investigation during 2017-2020 at two experimental stations in Heilongjiang Province, northern China, a total of 1133 cultivars or accessions were phenotyped for traits related to maturity, architecture, e.g. plant height, the numbers of nodes and branches and yield. Statistical analysis was performed to reveal the correlation between different traits, years and locations.

Result: R1 (days to the first flowering stage) and R2 (days to the fully flowering stage) are strongly stable traits among different years and different locations in this study. Significant correlations were identified between maturity trait, plant height, the numbers of nodes and branches. However, the traits of the height of the first effective node and the pod number per plant demonstrated some weak correlation among different years or with these maturity traits. Therefore, phenotyping procedures in this study can be directly applicable for accurate evaluation of maturity and plant architecture-related traits e.g. plant height, the numbers of nodes and branches.

INTRODUCTION

Soybean (Glycine max (L.) Merr.] is a typical short-day crop. The time to flowering and maturity are ecologically and agronomically important traits for breeders to judge a soybean cultivar’s adaptability to latitudinal region. Researchers have cloned numerous major flowering time-related genes, e.g., E1 to E4, J and QNE1 through positional cloning and Genome-wide association study (GWAS) (Liu et al., 2008; Watanabe et al., 2009; Watanabe et al., 2011; Xia et al., 2012; Lu et al., 2017; Xia et al., 2022). The stem growth habits, i.e., indeterminate, determinate, or semi-determinate, in soybeans generally shape plant architecture (Liu et al., 2010). These growth habit types are largely controlled by the Dt1 and Dt2 genes (Zhang et al., 2019). However, more minor effect genes might also affect the time to flowering, maturity and other agronomically important traits, e.g., plant height, branch and pod number per plant. As more genes regulating the above traits have been cloned, more accurate phenotypic data are needed for functional studies as well as for the cloning of minor genes by removing the large effects of known master genes.
       
The classification of the different developmental stages was mainly developed by Fehr et al., (1971). Up to date, quantitative trait locus (QTL) or genome-wide association study (GWAS) analyses on natural or various genetic populations have been well documented and many QTLs or candidate loci have been mapped, some of which were cloned (Sayama et al., 2010; Xia et al., 2021). Analyses among maturity, plant architecture and yield-related traits of some cultivars or accessions have been reported to estimate the internal genetic relationship and response to environmental fluctuations (Kumar et al., 2015; Srivastava et al., 1976). Currently, the northern provinces are the main soybean production area in China. Accurate phenotyping of flowering time, maturity, plant architecture and yield-related traits can benefit soybean production through the latitudinal arrangement of soybean cultivars. In this study, we performed correlation analysis among traits of maturity, plant architecture (plant height, the numbers of nodes and branches, the height of the first effective node) and yield-related traits (the pod number per plant) from 2017 to 2020 at two geographic locations in the northern China towards the fine-tuning phenotyping procedure for agronomically important traits.

MATERIALS AND METHODS

The cultivars or accessions were mainly obtained from the Gene Resource Center of Chinese Academy of Agricultural Sciences, China (Qiu et al., 2013). A total of 1133 different cultivars or accessions (Table 1) were phenotyped at Hailun experimental station (47°47'N, 126°97'E) (years of 2017 to 2020) and Harbin experimental station (45°61'N, 126°74'E) (year of 2017) of the Northeast Institute of Geography and Agroecology, Heilongjiang Province, China. The soil was derived from sedimentary materials of loamy loess and classified as Pachic Haploborolls with a texture lass of silty clay (Hao et al., 2023).
       
Plots were 5-m long with 0.6-m row spaces. Plants were generally 20 cm apart. Each cultivar or accession was arranged in a row and at least 15 plants per cultivar or accession were grown. Seeds were generally sown into the field around May 5th-10th. We followed standard local agricultural practices to control insects and weeds. Flowering time (R1) of each plant is defined as days from emergence (DAE) to one open flower at any node on the main stem (Fehr et al., 1971). Also in full bloom (R2) stands DAE to one open flower at one of the two uppermost nodes on the main stem with a fully developed trifoliate leaf node. R6 represents one pod containing a green seed that fills the pod cavity at one of the four uppermost nodes on the main stem with a fully developed trifoliate leaf node. Beginning maturity (R7) stands for DAE to one normal pod on the main stem that has reached its mature pod color (normally brown or tan), while full maturity (R8) is defined as DAE to 95 percent of the pods that have reached their mature pod color.
       
For a given cultivar, each specific maturity stage is defined only when at least 50% of individual plants reach that stage. Generally, 10 plants per cultivar or accession were harvested individually to measure or account for plant height (PH), branch number (BN), node number on the main stem and height from the ground to the first pod located on the main stem (height of the first effective node), as well as pod number per plant (Sayama et al., 2010; Zhai et al., 2014).
       
The correlation analyses were performed using GraphPad Prism Version 5.0 for Windows, GraphPad Software (San Diego California USA, www.graphpad.com). All correlation plots were generated using ‘ggplot2’ package (Ginestet, 2011) in R. The heatmap was generated based on the matrix of correlation coefficients for all traits using TBtools II v2.096 with dist method of Euclidean, Cluster method of Complete and Branch Form of Cladogram (Chen et al., 2023).

RESULTS AND DISCUSSION

The total cultivars or accessions investigated from 2017 to 2020 in Harbin and Hailun locations in this study is shown in Table 1.
 

Table 1: The number of cultivars or accessions were phenotyped in this study.


 
Correlations among R1, R2, R6, R7 and R8 within the same year
 
At first, R1 and R2 were highly correlated in all years and locations at p <0.001(Fig 1, Fig 2A). In Hailun (HL) location, the correlation coefficients (r) were 0.9795***, 0.9867***, 0.9759*** and 0.9869*** for the years of 2017, 2018, 2019 and 2020, respectively (Fig 1). The correlations between R1 and R6 or R7 or R8 were also statistically significant at p<0.001, although not as higher as that between R1 and R2. In 2018, the r values were 0.5815*** between R1 and R6; 0.5630*** between R1 and R7; and 0.5581*** between R1 and R8 (Fig 2 B-D). Similarly, in 2019, the r values were 0.7141*** between R1 and R6; 0.6914*** between R1 and R7; and 0.6649*** between R1 and R8 (Fig 2 F–H). A similar trend was observed in 2020 (Fig 2 J-L). The r values in 2018 between R2 and R6 (Fig 1) or R7 or R8 were 0.6679***, 0.6582*** and 0.6440***, respectively. In 2019, the r values between R2 and R6 (Fig 1) or R7 and R8 were 0.7820***, 0.7587*** and 0.7308***, respectively. Similar correlations were observed in 2020 in Hailun (Fig 1). At the late maturity stage, R6 was highly correlated with R7 or R8 with the r values of 0.8493*** and 0.8150*** in 2018; 0.8875*** and 0.8320*** in 2019; and 0.9883*** and 0.9679*** (Fig 1) in 2020. A very high correlation was observed between R7 and R8 in all years, with the r values of 0.9264*** in 2018, 0.9288*** in 2019 and 0.9840*** in 2020. Also in Harbin, the r values between R1 and R2 were 0.8843*** in 2017; the r values between R1 and R6 or R7 or R8 were 0.7974***, 0.6885*** and 0.5088***, respectively. Furthermore, the r values between R6 and R7 or R8 were 0.8644*** and 0.8141***, respectively.
 

Fig 1: The correlation heatmap of all traits in soybean.


 

Fig 2: Correlation among the times for different reproductive stages within the same year in Hailun from 2018 to 2020.


 
The correlations of the same R1, R2, R6, R7, R8 between different years in Hailun
 
The correlations of DAE to the same maturity stages between 2017 and 2018 or 2019 and 2020 were shown in Hailun (Fig 1). At the early reproductive stage, the r values were 0.9377*** for R1 between 2018 and 2019 and 0.9672***for R1 between 2018 and 2020. The r value of 0.9500*** was for R2 between 2018 and 2019 and 0.9683*** for R2 between 2018 and 2020 (Fig 1). Meanwhile, at the late reproductive stage, the r values of 0.6560*** and 0.5801*** were for R6 between 2018 and 2019 (Fig 1) and 0.5801*** for R6 between 2018 and 2020. Similarly, the r value of 0.6365*** was for R7 between 2018 and 2019 and 0.5902*** for R7 between 2018 and 2020. Furthermore, the r value of 0.5932*** was R8 between 2018 and 2019 and 0.5729*** for R8 between 2018 and 2020 (Fig 1). Apparently, R2 was a consistently stable trait among different years.

The correlations of R1, R2, R6, R7, R8 between two latitudinal locations
 
The phenotypic data of flowering time and maturity between Hailun and Harbin in 2017 were analyzed. The r values of 0.8204***, 0.9327***, 0.7697***, 0.6130*** and 0.6205*** were respectively for R1, R2, R6, R7 and R8 between Hailun and Harbin in 2017 (Fig 1). Apparently, the R2 was the most stable trait between the two locations.
 
Correlation among agronomic traits and maturity traits
Plant height
 
The traits of plant height (PH) were highly correlated between different years. The r values of 0.6981*** were for PH between 2017 and 2018, 0.5994*** between 2018 and 2019 and 0.5875*** between 2018 and 2020. Also, PH was significantly correlated to R1, R2, R6 and R8. The r values between PH and R1, R2, R6, R7, or R8 were 0.5019***, 0.5109***, 0.5017***, 0.4787*** and 0.4956***, respectively (Fig 1; Fig 3A-D) in 2018 at Hailun. Similarly, r values between PH and R1, R2, R6, R7, or R8 were 0.3876***, 0.4066***, 0.3626***, 0.3370*** and 0.3083***, respectively, in 2019 at Hailun (Fig 1; Fig 3E-H). A similar correlation trend was observed in 2020 at Hailun.
 

Fig 3: Correlation between plant height and the time of different reproductive stages in Hailun from 2017 to 2020.


 
Node numbers
 
The trait of node number was correlated with different years. The r value of 0.5825*** was for node number between 2017 and 2019, 0.4630*** between 2017 and 2020 and 0.4418*** between 2019 and 2020. The r between node number and time for different reproductive stages (R1, R2, R6, R7 and R8) was statistically significantly correlated with a range from 0.2333*** to 0.5882*** (Fig 1). The node number showed a higher correlation with plant height at r = 0.8573*** in 2017, 0.6618*** in 2019 and 0.8121*** in 2020 (Fig 1).
 
Branch number (BN)
 
Based on the phenotypic data of BN in 2017, 2018, 2019 and 2020, the r value between branch numbers among different years was statistically significant, with a range from 0.4512*** (2019 vs. 2017) to 0.5727*** (2019 vs. 2020). Also, the trait of branch number was statistically significantly correlated with the R1, R2, R6 and R8. In 2018, the r values between BN and R1, R2, R6, R7, or R8 were 0.4001***, 0.3958***, 0.3875***, 0.3254*** and 0.3159***, respectively (Fig 1). In 2019, the corresponding r values were 0.5862***, 0.5891***, 0.3931***, 0.4023*** and 0.3991***, respectively (Fig 1). Furthermore, a similar correlation trend was observed in 2020 at Hailun (Fig 1). The trait of BN was significantly correlated with plant height, with r = 0.3704*** in 2018, 0.2899*** in 2019 and 0.5244*** in 2020. Also, BN was significantly correlated with the node number, with the r values of 0.3204***in 2019 and 0.4560*** in 2020.
 
Height of the 1st effective node
 
The trait of height of the first effective node was statistically significant between different years, with the r values of 0.2745*** between 2018 and 2019, 0.2959*** between 2018 and 2020 and 0.2109*** between 2019 and 2020. Also, the trait of height of the first effective node was significantly correlated to R1, R2, R6 and R8 (Fig 1). At Hailun in 2018, the r values between the height of the 1st effective node and R1, R2, R6, R7, or R8 were 0.2327***, 0.2831***, 0.4083***, 0.4022*** and 0.3909***, respectively (Fig 1). Correspondingly, the r values were 0.2518***, 0.2610***, 0.2246***, 0.1847*** and 0.1672***, respectively, in 2019 at Hailun. Furthermore, a similar correlation trend was observed in 2020 at Hailun, with a range of r values from 0.3194*** to 0.3664*** (Fig 1). In 2020, the first effective node was significantly correlated with plant height at the value of 0.5196 ***, with the node number at r = 0.41169*** and with branch number at r = 0.2384 *** (Fig 1).
 
Pod numbers per plant
 
This yield-related trait, pod number per plant, in 2017 was positively correlated with R1, R2, R6, R7, or R8 with the r values of 0.3913***, 0.4526***, 0.5075***, 0.5395*** and 0.5179***, respectively. Also, this trait was significantly related to plant height at r = 0.4422***, nod number at r = 0.5615*** and branch number at r = 0.4740*** (Fig 1). Surprisingly, this trait was not or marginally correlated with flowering time and maturity traits in 2018 and 2019, with a range of r values from -0.0970 (R8 in 2018, p = 0.0297) to 0.0436 (R1 in 2019, p = 0.3702***) (Fig 1). In 2018, the pod number per plant was not significantly correlated with plant height (r = 0.0029) but significantly correlated with branch number (r = 0.2905***) and significantly negatively correlated with the height of the first effective node (r = -0.2355***). However, in 2019, the pod number per plant was not significantly correlated with either plant height (r = 0.0410), or branch number (r = 0.0167), or height of the first effective node (r = 0.0497), or node number (r = -0.0069) (Fig 1).
 
The overall correlation among all traits
 
From the heatmap of correlation coefficients matrix, all maturity related traits were highly correlated and classified into a group, especially for the traits of R1 and R2 (Fig 1). The three architecture traits, e.g. branch number, plant height and node, were not only correlated each other, but also significantly related with maturity traits. Furthermore, the height of 1st effective nodes and pod number per plant were least or inconsistently correlated to above maturity and architecture traits (Fig 1).
       
The domestication of soybean occurred in China about 5000 years ago and there are a large number of germplasms, including landrace and modern cultivars, in China (Qiu et al., 2013). The characterization of the effects of different planting dates on flowering time and maturity in soybeans and other plant species triggered the discovery of photoperiodism (Garner and Allard, 1920). These reproductive-related traits are crucial for soybean production, breeding, soybean functional studies on gene regulatory networks controlling maturity and other related agronomic traits, e.g., plant height and node numbers (Goyal et al., 2015).  As progress is made on the various omics and accomplishments of the T2T genomes of many soybean cultivars (Zhang et al., 2023), accurate phenotypic data will greatly facilitate the cloning of new or minor genes as well as functional study. Currently, the main soybean production area is located in the northern China with higher latitude. Accurate phenotyping of flowering time, maturity and important agronomic traits will enable us to judge the ecological or latitudinal adaptation and yield potential of a given cultivar or accession (Ige et al., 2021).
       
In this study, Fehr’s classification of reproductive stage was basically used for the phenotyping of traits of flowering time and maturity (Fehr et al., 1971). Consistent correlation results among different reproductive stages, different years and different locations indicated that the phenotyping of these traits is accurate and repeatable, reflecting the ecological or latitudinal adaptation of these accessions or cultivars. Especially, the R2 was the most stable trait based on the correlation analysis. Higher correlations were observed between R1 and R2, or between R6 and R8, but moderate correlations were revealed between R1 or R2 and R6 or R7 or R8. This result is in coincidence with many reports that, although many maturity genes can control flowering and maturity, some of them may mainly function in the late reproductive stages, e.g. Gmfulb might function most on the maturity time and reproductive length other than flowering time (R1) (Kumar et al., 2015; Escamilla et al., 2024).
       
As to the three architecture traits, branch number, plant height and node, were classified into a group, which showing a significant correlation each other, as well as a higher correlation with maturity traits (Fig 1). This relationship between these traits disclosed in this study is consistent with previous reports (Jain et al., 2018; Sayama et al., 2010). Variations in genes underlying photoperiod sensitivity and growth habit can have pleiotropic effects on other agronomic characteristics other than the major effects on flowering time and maturity (Cober et al., 2000).
       
The trait of the height of the first effective node approximately corresponds to the height of bottom pods (Cober et al., 2010). In this study, the correlation of this trait were significantly correlated with maturity traits, however, some inconsistences were shown with the same trait between different years or the correlation between this trait with maturity traits in some years. This phenomenon might be ascribed to the influence of the environmental changes in temperature, planting density, flooding and agricultural practices, e.g., intertilling. At the molecular level, the interaction between E1, DT1 and other genes, e.g., GmHY2a, can determine the height of bottom pods (Cober and Tanner, 1995; Zhang et al., 2022). In the past, harvesting losses could be greatly resulted from low bottom pods (Curtis et al., 2000). The “high-bottom pods” might not be so demanding in that resent technical advances have been made in the harvesting machine to harvest soybean cultivars having low-bottom pods.  Similarly, the yield-related trait, the pod number per plant, showed some consistent correlation with maturity, plant height and node number in some years, but not all the years. Yield-related traits are quantitatively inherited and easily subject to the influence of environmental changes, e.g. drought, flooding and agricultural practices such as intertilling, irrigation and the application of herbicides and fertilizer.

CONCLUSION

The maturity traits were classified into one group showing consistent correlation between different years and different locations in this study. The architecture traits, plant height, the numbers of nodes and branches displayed significant correlation within traits, also showed significantly correlated with the maturity traits. However, traits of the height of the first effective node and the pod number per plant showed consistent correlation within the same trait in some years, but some inconsistency also occurred. Accurate phenotyping of the yield-related traits or other environmental-sensitive traits, such as the height of the first effective node, needs a relatively large scale to get rid of the marginal effects and/or controlled density or agricultural management, e.g., intertilling. Although the phenomics of soybeans have emerged, accurate traditional phenotyping is still demanding in soybean production, breeding and functional studies since it can be serve as a standard reference for calibrating automation systems.

ACKNOWLEDGEMENT

This work was supported by Natural Science Foundation of Heilongjiang Province of China (TD2023C005), by grants (U21A20215 and 32272094) from the National Natural Science Foundation of China and by the Strategic Priority Research Program (XDA24010105-4 and XDA28070404) from the Chinese Academy of Sciences.

CONFLICT OF INTEREST

All authors declare that they no conflict of interest.

REFERENCES


  1. Chen, C., Wu, Y., Li, J., Wang, X., Zeng, Z., Xu, J., Liu, Y., Feng, J., Chen, H., He, Y. and Xia, R. (2023). TBtools-II: A one for all, all for one bioinformatics platform for biological big- data mining. Molecular Plant. 16: 1733-1742.

  2. Cober, E.R. and Morrison, M.J. (2010). Regulation of seed yield and agronomic characters by photoperiod sensitivity and growth habit genes in soybean. Theoretical and Applied Genetics. 120: 1005-1012. https://doi.org/10.007/s00122-009-1228-6.

  3. Cober, E.R. and Tanner, J.W. (1995). Performance of related indeterminate and tall determinate soybean lines in short- season areas. Crop Science. 35: 361-364. https://doi.org/10.2135/cropsci1995.0011183X003500020011x. 

  4. Cober, E.R., Madill, J. and Voldeng, H.D. (2000). Early tall determinate soybean genotype E1E1e3e3e4e4dt1dt1 sets high bottom pods. Canadian Journal of Plant Science. 80: 527-531. https://doi.org/10.4141/P99-146. 

  5. Curtis, D.F., Tanner, J.W., Luzzi, B.M. and Hume, D.J. (2000). Agronomic and phenological differences of soybean isolines differing in maturity and growth habit. Crop Science. 40: 1624- 1629. https://doi.org/10.2135/cropsci2000.4061624x. 

  6. Escamilla, D.M., Dietz, N., Bilyeu, K., Hudson, K. and Rainey, K.M. (2024). Genome-wide association study reveals gmfulb as candidate gene for maturity time and reproductive length in soybeans (Glycine max). PLoS ONE. 19: e029 4123.

  7. Fehr, W.R., Caviness, C.E., Burmood, D.T. and Pennington, J.S. (1971). Stage of development descriptions for soybeans, Glycine max (L.) Merrill. Crop Science. 11: 929-931. https://doi.org10.2135/cropsci1971.0011183X0011000600 51x. 

  8. Garner, W.W. and Allard, H.A. (1920). Effect of the relative length of day and night and other factors of the environment on growth and reproduction in plants. Journal of Agricultural Research. 18: 553-606

  9. Ginestet, C. (2011). Ggplot2: Elegant graphics for data analysis. Journal of the Royal Statistical Society Series a-Statistics in Society. 174: 245-246.

  10. Goyal, R., Sharma, S. and Gill, B.S. (2015). Effects of location and planting time on physicochemical and nutritional characteristics of soybean seed. Legume Research 38: 810-815. doi: 10.18805/lr.v38i6.6728.

  11. Hao, X.X., Han, X.Z., Wang, C., Yan, J., Lu, X.C., Chen, X. and Zou, W.X. (2023). Temporal dynamics of density separated soil organic carbon pools as revealed by d13C changes under 17 years of straw return. Agriculture Ecosystems and Environment. 356: 108656. doi: Artn 10865610.10 16/J.Agee.2023.108656.

  12. Ige, G.O. Chigeza, G. Chander, S. Abush, A.T. Ojo, D.K. Akoroda, M. (2021). Genetic assessment of combining ability for seed- yield and its related traits in soybean [Glycine max (L.) Merrill]. Legume Research 44: 21-25. doi: 10.18805/LR-542.

  13. Jain, R.K., Joshi, A., Chaudhary, H.R., Dashora, A. and Khatik, C.L. (2018). Study on genetic variability, heritability and genetic advance in soybean [Glycine max (L.) Merrill]. Legume Research. 41: 532-536. doi: 10.18805/LR-3874.

  14. Kumar, A., Pandey, A. and Pattanayak, A. (2015). Assessment of genotypic variation in soybean (Glycine max). Legume Research. 38: 174-177. doi: 10.5958/0976-0571.2015.0 0042.9.

  15. Liu, B., Kanazawa, A., Matsumura, H., Takahashi, R., Harada, K. and Abe, J. (2008). Genetic redundancy in soybean photoresponses associated with duplication of the phytochrome a gene. Genetics. 180: 995-1007. https://doi.org/10.1534/genetics.108.092742.

  16. Liu, B.H., Watanabe, S., Uchiyama, T., Kong, F.J., Kanazawa, A., Xia, Z.J., Nagamatsu, A., Arai, M., Yamada, T., Kitamura, K., Masuta, C., Harada, K. and Abe, J. (2010). The soybean stem growth habit gene Dt1 Is an ortholog of arabidopsis. Plant Physiology. 153: 198-210. https://doi.org/10.1104/pp.109.150607. 

  17. Lu, S.J., Zhao, X.H., Hu, Y.L., Liu, S.L., Nan, H.Y., Li, X.M., et al. (2017). Natural variation at the soybean J locus improves adaptation to the tropics and enhances yield. Nature Genetics. 49: 773-779. https://doi.org/10.1038/ng.381910.1007/s00122-023-04493-w.

  18. Qiu, L.J., Xing, L.L., Guo, Y., Wang, J., Jackson, S.A. and Chang, R.Z. (2013). A platform for soybean molecular breeding: The utilization of core collections for food security. Plant Molecular Biology. 83: 41-50. https://doi.org/10.1007/s11103-013-0076-6. 

  19. Sayama, T., Hwang, T.Y., Yamazaki, H., Yamaguchi, N., Komatsu, K., Takahashi, M., et al. (2010). Mapping and comparison of quantitative trait loci for soybean branching phenotype in two locations. Breeding Science. 60: 380-389. https://doi.org/10.1270/jsbbs.60.380. 

  20. Srivastava, R.L., Sahai, R.N., Saxena, J.K. and Singh, I.P. (1976). Path-analysis of yield components in soybean (Glycine- Max-L-Merr). Indian Journal of Agricultural Research 10: 171-173.

  21. Watanabe, S., Hideshima, R., Xia, Z.J., Tsubokura, Y., Sato, S., (2009). Map-based cloning of the gene associated with the soybean maturity locus. Genetics. 182: 1251-1262. https://doi.org/10.1534/genetics.108.098772. 

  22. Watanabe, S., Xia, Z.J., Hideshima, R., Tsubokura, Y., Sato, S., Yamanaka, N., Takahashi, R., Anai, T., Tabata, S., Kitamura, K. and Harada, K. (2011). A Map-based cloning strategy employing a residual heterozygous line reveals that the gigantea gene is involved in soybean maturity and flowering. Genetics. 188: 395-407. https://doi.org/10.153 4/genetics.110.125062.

  23. Xia, Z.J., Watanabe, S., Yamada, T., Tsubokura, Y., Nakashima, H., Zhai, H., et al. (2012). Positional Cloning and Characterization Reveal the Molecular Basis for Soybean Maturity Locus E1 that Regulates Photoperiodic Flowering. Proceedings of the National Academy of Sciences of the United States of America. 109: E2155-E2164. https://doi.org/10.1073/pnas.1117982109. 

  24. Xia, Z.J., Zhai, H., Wu, H.Y., Xu, K., Watanabe, S. and Harada, K. (2021). The synchronized efforts to decipher?the molecular basis for soybean?maturity loci E1, E2 and E3 that regulate flowering and maturity. Frontiers in Plant Science. 12: 632754. https://doi.org/10.3389/fpls.2021.632754. 

  25. Xia, Z.J., Zhai, H., Zhang, Y.F., Wang, Y.Y., Wang, L., Xu, K., et al. (2022). QNE1 is a key flowering regulator determining the length of the vegetative period in soybean cultivars. Science China-Life Sciences. 65: 2472-2490. https://doi.org/10.1007/s11427-022-2117-x. 

  26. Zhai, H., Lü, S.X., Wang, Y.Q., Chen, X., Ren, H.X., Yang, J.Y., et al. (2014). Allelic variations at four major maturity E genes and transcriptional abundance of the E1 gene are associated with flowering time and maturity of soybean cultivars. PLoS ONE 9: e97636 https://doi.org/10.1371/journal.pone.0097636.

  27. Zhang, C., Xie, L., Yu, H., Wang, J.H., Chen, Q.S. and Wang, H.F. (2023). The T2T genome assembly of soybean cultivar ZH13 and its epigenetic landscapes. Molecular Plant. 16: 1715-1718. https://doi.org/10.1016/j.molp.2023.10.003. 

  28. Zhang, D.J., Wang, X.T., Li, S., Wang, C.F., Gosney, M.J., Mickelbart, M.V. and Ma, J.X. (2019). A Post-domestication mutation, Dt2, triggers systemic modification of divergent and convergent pathways modulating multiple agronomic traits in soybean. Molecular Plant. 12: 1366-1382. https://doi.org/10.1016/j.molp.2019.05.010. 

  29. Zhang, Z.R., Yang, S.X., Wang, Q.S., Yu, H., Zhao, B.F., Wu, T., Tang, K.Q., Ma, J.J., Yang, X.J. and Feng, X.Z. (2022). Soybean GmHY2a encodes a phytochromobilin synthase that regulates internode length and flowering time. Journal of Experimental Botany. 73: 6646-6662. https://doi.org/10.1093/jxb/erac318.
Reviewed By

Download Article
In this Article
    Contact us
    Follow us

    Editorial Board

    View all (0)