Registration of ‘S16‐3747GT’: A high‐yielding determinate maturity group V soybean cultivar with broad biotic and abiotic stressors tolerance

Chen, Pengyin; Shannon, J. Grover; Vieira, Caio Canella; Nascimento, Emanuel Ferrari do; Ali, Muhammad Mooneeb; Lee, Dongho; Scaboo, Andrew; Crisel, M.; Smothers, Scotty; Clubb, Michael; Selves, Stewart; Nguyen, Henry T.; Li, Zenglu; Mitchum, Melissa G.; Bond, Jason P.; Meinhardt, Clinton G.; Usovsky, Mariola; Li, Shuxian; Gillen, Anne M.; Smith, James R.; Mengistu, Alemu; Zhang, B.; Mozzoni, Leandro; Robbins, R. T.; Moseley, David

doi:10.1002/plr2.20222

Cited by 5 publications

(3 citation statements)

References 29 publications

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“…Each plot consisted of a single 2.13 m long row spaced 0.76 m apart. The Lee Farm and Rhodes Farm have been exposed to prolonged and homogeneously distributed off-target dicamba damage since 2017, where significant yield losses due to off-target dicamba exposure have been reported between non-DT and DT soybean genotypes (Canella Vieira et al, 2022a;Chen et al, 2022;Canella Vieira et al, 2022b). Off-target dicamba exposure was a result of dicamba volatilization from nearby cropping systems consisting of DT soybean and cotton.…”

Section: Plant Materials and Data Collectionmentioning

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: Plant Materials and Data Collectionmentioning

confidence: 99%

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

et al. 2022

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“…S11‐16653C was evaluated in a total of 78 environments across nine states (Alabama, Arkansas, Kansas, Louisiana, Tennessee, Mississippi, Missouri, North Carolina, Tennessee, and Virginia) from 2012 to 2016 and showed competitive yield and broad range of adaptation across multiple states. Overall, S11‐16653C is an excellent non‐GM soybean cultivar for commercial production and has been used as a parental line of several MU‐FDREEC released cultivars (S16‐5540GT [P. Chen, Ali, et al., 2022]; S16‐15170C [P. Chen, Shannon, Vieira, et al., 2022], S16‐7922C [P. Chen, Shannon, Vieira, Ali, et al., 2022], and S16‐3747GT [Chen, Shannon, Lee, Vieira, Nascimiento, et al., 2022]).…”

Section: Introductionmentioning

confidence: 99%

Registration of ‘S11‐16653C’ soybean: A high‐yielding conventional cultivar with broad resistance to diseases and nematodes

Shannon

Chen

Lee

et al. 2022

J of Plant Registrations

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The soybean [Glycine max (L.) Merr] line S11‐16653C (Reg. no. CV‐554, PI 700000) was developed and released as a cultivar by the University of Missouri, Fisher Delta Research, Extension, and Education Center (MU‐FDREEC). S11‐16653C is an early maturity group V (relative maturity 5.3), high‐yielding conventional (non‐genetically modified) cultivar with determinate growth habit. It is a chloride excluder and has a broad disease resistance package, including soybean cyst nematode (Heterodera glycines Ichinohe) races 1 (HG type 2.5.7), 2 (HG type 1.2.5.7), 3 (HG type 5.7), 5 (HG type 2.5.7), southern root‐knot nematode [Meloidogyne incognita (Kofold & White) Chitwood], peanut root‐knot nematode [Meloidogyne arenaria (Neal) Chitwood], reniform nematode (Rotylenchulus reniformis Linford & Oliveira), stem canker (Diaporthe aspalathi Jansen, Castlebury & Crous), Phytophthora root rot (caused by Phytophthora sojae M.J.Kaufmann & J.W.Gerdemann), and sudden death syndrome (Fusarium virguliforme O'Donell & T. Aoki). S11‐16653C was tested in 78 locations across nine states (Alabama, Arkansas, Kansas, Louisiana, Missouri, Mississippi, North Carolina, Tennessee, and Virginia) from 2012 to 2016. It was entered in internal yield tests at the MU‐FDREEC (2012–2014), USDA Uniform Soybean Tests (2014–2016), and soybean state variety tests (2015). It showed consistent high‐yielding performance and broad adaptability throughout the trials and locations. Overall, S11‐16653C is a high‐yielding conventional soybean cultivar that has potential for commercial production and serves as an exceptional germplasm for soybean breeding.

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Section: Plant Materials and Data Collectionmentioning

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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Registration of ‘S16‐3747GT’: A high‐yielding determinate maturity group V soybean cultivar with broad biotic and abiotic stressors tolerance

Cited by 5 publications

References 29 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

Registration of ‘S11‐16653C’ soybean: A high‐yielding conventional cultivar with broad resistance to diseases and nematodes

Soybean tolerance to off-target dicamba damage

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