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

Murcia, Miguel Angel Lopez; Moreira, Fabiana Freitas; Hearst, Anthony; Cherkauer, Keith A.; Rainey, Katy Martin

doi:10.1007/s00122-022-04048-5

Cited by 3 publications

(3 citation statements)

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“…With several thousand to millions of single nucleotide polymorphisms (SNPs), GWAS captures significant associations between the trait of interest and molecular markers using a broad range of linear or logistic regression models as well as machine and deep learning algorithms. In soybean, GWAS has unveiled the genetic architecture of multiple economicimportant traits, including tolerance to biotic (Vuong et al, 2015;Chang et al, 2016;Canella Vieira et al, 2022c) and abiotic (Valliyodan et al, 2016;Zeng et al, 2017;Wu et al, 2020) stressors, seed composition (Hwang et al, 2014;Bandillo et al, 2015), agronomic (Sonah et al, 2015;Zhang et al, 2015), physiology-efficient (Lopez et al, 2022), as well as yield (Yoosefzadeh-Najafabadi et al, 2021) and domesticationrelated traits (Han et al, 2016;Wang et al, 2016b). To date, GWAS has not been conducted to identify genomic regions associated with soybean tolerance to dicamba or other herbicides.…”

Section: Introductionmentioning

confidence: 99%

Identification of genomic regions associated with soybean responses to off-target dicamba exposure

et al. 2022

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The widespread adoption of genetically modified (GM) dicamba-tolerant (DT) soybean was followed by numerous reports of off-target dicamba damage and yield losses across most soybean-producing states. In this study, a subset of the USDA Soybean Germplasm Collection consisting of 382 genetically diverse soybean accessions originating from 15 countries was used to identify genomic regions associated with soybean response to off-target dicamba exposure. Accessions were genotyped with the SoySNP50K BeadChip and visually screened for damage in environments with prolonged exposure to off-target dicamba. Two models were implemented to detect significant marker-trait associations: the Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) and a model that allows the inclusion of population structure in interaction with the environment (G×E) to account for variable patterns of genotype responses in different environments. Most accessions (84%) showed a moderate response, either moderately tolerant or moderately susceptible, with approximately 8% showing tolerance and susceptibility. No differences in off-target dicamba damage were observed across maturity groups and centers of origin. Both models identified significant associations in regions of chromosomes 10 and 19. The BLINK model identified additional significant marker-trait associations on chromosomes 11, 14, and 18, while the G×E model identified another significant marker-trait association on chromosome 15. The significant SNPs identified by both models are located within candidate genes possessing annotated functions involving different phases of herbicide detoxification in plants. These results entertain the possibility of developing non-GM soybean cultivars with improved tolerance to off-target dicamba exposure and potentially other synthetic auxin herbicides. Identification of genetic sources of tolerance and genomic regions conferring higher tolerance to off-target dicamba may sustain and improve the production of other non-DT herbicide soybean production systems, including the growing niche markets of organic and conventional soybean.

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Section: Introductionmentioning

confidence: 99%

Identification of genomic regions associated with soybean responses to off-target dicamba exposure

et al. 2022

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“…As previously mentioned, the research for higher yields investigates to maximize leaf interception, using the energy absorbed efficiently for photosynthesis (Loomis & Amthor, 1999). The productive yield of agricultural crops is established based on the efficiency of fundamental factors, such as light interception and the use of radiation (Lopes et al, 2022). For irrigated rice genotypes, there is an increase in productivity with a reduction in spacing (40, 30, 20, and 12.5 cm) (Neto et al, 2000).…”

Section: Efficiency Of Photosynthesis As a Function Of Leaf Arrangementmentioning

confidence: 99%

Effects of Minimum and Maximum Limits of Solar Radiation and Its Temporal and Geographic Interactions

Villa¹,

Petry²,

Santos³

et al. 2022

JAS

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In order to achieve high yields, the use of radiation by agricultural crops must be improved in order to maximize and extend the duration of foliar interception of solar radiation. Appropriately, studies aimed at the understanding and behavior of radiation in plants are extremely relevant. Accordingly, the objective of this study was to approach information about the concepts, radiation availability temporal, geographical, photosynthesis efficiency as a function of leaf arrangement and radiation interception efficiency × leaf area index (LAI). Studies aimed at a significant comprehension of radiation in crops demonstrate that the plant growth and development depend on the intensity and duration of solar radiation. More surveys are required on the subject for a better development of the culture, aiming to guarantee that the plants have better conditions to express their potential.

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“…With several thousand to millions of single nucleotide polymorphisms (SNPs), GWAS captures significant associations between the trait of interest and molecular markers using linear or logistic regression analysis. In soybean, GWAS has unveiled the genetic architecture of multiple economic-important traits, including tolerance to biotic (Vuong et al, 2015;Chang et al, 2016;Canella Vieira et al, 2022c) and abiotic (Valliyodan et al, 2016;Zeng et al, 2017;Wu et al, 2020) stressors, seed composition (Hwang et al, 2014;Bandillo et al, 2015), agronomic (Sonah et al, 2015;Zhang et al, 2015), physiology-efficient (Lopez et al, 2022), as well as domesticationrelated traits (Han et al, 2016;Wang et al, 2016b). To date, GWAS has not been conducted to identify genomic regions associated with soybean tolerance to dicamba or other herbicides.…”

Section: Introductionmentioning

confidence: 99%

Soybean tolerance to off-target dicamba damage

Vieira¹

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Since the commercialization and widespread adoption of dicamba-tolerant (DT) soybean cultivars across the United States, numerous cases of off-target damage to non-DT soybean have been reported. Previous studies have focused on understanding the impact of growth stage, dosage, frequency, and duration of dicamba exposure on the severity of symptomology and yield loss. To date, little research has investigated the effect of genetic components on the observed responses. Therefore, this research included three major components including i) the estimation of yield losses caused by prolonged off-target dicamba exposure, ii) the development of a method to differentiate soybean response to dicamba using unmanned-aerial-vehicle-based imagery and machine learning models, and iii) identification of genomic regions associated with soybean response to off-target dicamba exposure. Across 553 soybean genotypes derived from 239 unique bi-parental populations, a yield penalty of 8.8 percent was observed for every increment in damage score on a 1-4 scale with losses as high as 40 percent. Although the interaction between damage and maturity group (MG) significantly affected yield, genotypes showing the most tolerance had similar yields independent of their MG. This indicated that natural tolerance to off-target dicamba may be conferred by physiological mechanisms other than the length of the recovery window. A quadcopter with a built-in RGB camera was used to collect images of field plots at a height of 20 m above ground level. Seven image features were extracted for each plot, including canopy coverage, contrast, entropy, green leaf index, hue, saturation, and triangular greenness index. Classification models based on artificial neural network (ANN) and random forest (RF) algorithms were developed to differentiate the three classes of response to dicamba. Significant differences for each feature were observed among classes and no significant differences across fields were observed. The ANN and RF models were able to precisely distinguish tolerant and susceptible lines with an overall accuracy of 0.74 and 0.75, respectively. Lastly, two models were implemented to detect significant marker-trait associations: the Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) and a model that allows the inclusion of population structure in interaction with the environment (GxE) to account for variable patterns of genotype responses in different environments. Most accessions (84 percent) showed a moderate response, either moderately tolerant or moderately susceptible, with approximately 8 percent showing tolerance and susceptibility. No differences in off-target dicamba damage were observed across maturity groups and centers of origin. Both models identified significant associations in regions of chromosomes 10 and 19. The BLINK model identified additional significant marker-trait associations on chromosomes 11, 14, and 18, while the GxE model identified another significant marker-trait association on chromosome 15. The significant SNPs identified by both models are located within candidate genes possessing annotated functions involving different phases of herbicide detoxification in plants. Given the widespread adoption of DT systems and potential yield losses in non-DT soybean genotypes, identification of non-DT soybean genotypes with higher tolerance to off-target dicamba may sustain and improve the production of other non-DT herbicide soybean production systems, including the niche markets of organic and conventional soybean.

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

Cited by 3 publications

References 123 publications

Identification of genomic regions associated with soybean responses to off-target dicamba exposure

Identification of genomic regions associated with soybean responses to off-target dicamba exposure

Effects of Minimum and Maximum Limits of Solar Radiation and Its Temporal and Geographic Interactions

Soybean tolerance to off-target dicamba damage

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