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Variation in shoot architecture traits and their relationship to canopy coverage and light interception in soybean (Glycine max)
Saturday, 2024/03/23 | 07:47:42

Suma SreekantaAllison HaaningAustin DobbelsRiley O'NeillAnna HofstadKamaldeep VirdiFumiaki KatagiriRobert M StuparGary J MuehlbauerAaron J Lorenz

BMC Plant Biol.; 2024 Mar 16; 24(1):194. doi: 10.1186/s12870-024-04859-2.

 

Background: In soybeans, faster canopy coverage (CC) is a highly desirable trait but a fully covered canopy is unfavorable to light interception at lower levels in the canopy with most of the incident radiation intercepted at the top of the canopy. Shoot architecture that influences CC is well studied in crops such as maize and wheat, and altering architectural traits has resulted in enhanced yield. However, in soybeans the study of shoot architecture has not been as extensive.

 

Results: This study revealed significant differences in CC among the selected soybean accessions. The rate of CC was found to decrease at the beginning of the reproductive stage (R1) followed by an increase during the R2-R3 stages. Most of the accessions in the study achieved maximum rate of CC between R2-R3 stages. We measured Light interception (LI), defined here as the ratio of Photosynthetically Active Radiation (PAR) transmitted through the canopy to the incoming PAR or the radiation above the canopy. LI was found to be significantly correlated with CC parameters, highlighting the relationship between canopy structure and light interception. The study also explored the impact of plant shape on LI and CO2 assimilation. Plant shape was characterized into distinct quantifiable parameters and by modeling the impact of plant shape on LI and CO2 assimilation, we found that plants with broad and flat shapes at the top maybe more photosynthetically efficient at low light levels, while conical shapes were likely more advantageous when light was abundant. Shoot architecture of plants in this study was described in terms of whole plant, branching and leaf-related traits. There was significant variation for the shoot architecture traits between different accessions, displaying high reliability. We found that that several shoot architecture traits such as plant height, and leaf and internode-related traits strongly influenced CC and LI.

 

Conclusion: In conclusion, this study provides insight into the relationship between soybean shoot architecture, canopy coverage, and light interception. It demonstrates that novel shoot architecture traits we have defined here are genetically variable, impact CC and LI and contribute to our understanding of soybean morphology. Correlations between different architecture traits, CC and LI suggest that it is possible to optimize soybean growth without compromising on light transmission within the soybean canopy. In addition, the study underscores the utility of integrating low-cost 2D phenotyping as a practical and cost-effective alternative to more time-intensive 3D or high-tech low-throughput methods. This approach offers a feasible means of studying basic shoot architecture traits at the field level, facilitating a broader and efficient assessment of plant morphology.

 

See https://pubmed.ncbi.nlm.nih.gov/38493116/

 

Figure 2: Light Interception (LI) within the soybean canopy can be modeled using logistic regression. (a) Photosynthetically active radiation (PAR) measured using a line bar at 10 cm increments from ground up and a logistic model fit to the data. Fit for one accession is shown as an example including two reps each from 2018 and 2019 (different colors) as well as the mean (black solid line) is indicated. 95% confidence interval for the fit is indicated (black dashed line). Light reaching the ground (PARG), height at 50% PAR (H50PAR) and PAR at 50% height (PAR50H) were calculated from the fit. (b) Variation for LI traits is shown in box plots. (c) Best linear unbiased predictions (BLUPs) were calculated from LI and CC data collected in 2018 and 2019 and used for correlation analysis. Pearson correlation (FDR adjusted p value < 0.05) between different traits is shown as a heat map.

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