Key Soybean Maturity Groups to Increase Grain Yield in Brazil

Zdziarski, Andrei Daniel; Todeschini, Matheus Henrique; Milioli, Anderson Simionato; Woyann, Leomar Guilherme; Madureira, Alana; Stoco, Matheus Giovane; Benin, Giovani

doi:10.2135/cropsci2017.09.0581

Cited by 15 publications

(9 citation statements)

References 14 publications

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“…Early cycle genotypes can result in GY damage, and large cycles can increase exposure to insects and diseases. According to Zdziarski et al (2018), genotypes with medium cycles are the most adapted to achieve high GY in the main production regions in Brazil. Thus, in a dataset with different minimum and maximum values for these traits, changes in the ideotype definition may be necessary.…”

Section: Discussionmentioning

confidence: 99%

Selection indexes based on linear‐bilinear models applied to soybean breeding

et al. 2020

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Recently developed selection indexes provide solutions for plant breeding, using linear‐bilinear models that consider factors as fixed or random. This work aimed to compare the multitrait selection indexes based on factor analysis and ideotype‐design (FAI‐BLUP), GGE biplot, and grain yield × trait index (GYT), and proposes the use of predicted genetic values together with the GYT index (best linear unbiased prediction used in grain yield*trait index, GYT‐BLUP). In addition, this work indicates the best index to select superior soybean [Glycine max (L.) Merr.] genotypes, closer to the ideotype. Data from 35 homozygous soybean lines and four checks, were obtained from trials conducted in six locations in the southern region of Brazil in the 2014/2015 crop season. The grain yield, yield components, morphological and grain composition were evaluated. Phenotypic data were used for GGE biplot and GYT analysis, using the software GGE biplot. Genetic values were predicted with mixed models considering genotype and location as random and fixed effects, respectively. Thus, genetic values were used in GYT‐BLUP and FAI‐BLUP indexes. These methods were compared by Spearman's rank correlation. Genetic gains obtained by indexes and traits were estimated. Soybean lines L1 and L22, and cultivars C3 and C4 were selected based on their performance for multiple traits, for indexes used. Thus, we suggest to combined FAI‐BLUP and GYT‐BLUP indexes. The GYT‐BLUP has a high importance for grain yield, which was related to all other traits. FAI‐BLUP gave similar weights for all traits. So, combining different approaches can provide better answers to breeders.

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Section: Discussionmentioning

confidence: 99%

Selection indexes based on linear‐bilinear models applied to soybean breeding

et al. 2020

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“…This result can also be correlated with the MG of those datasets and the relationship between MG and the population structure. The M2 dataset comes from a small breeding zone, in which MGs between V and VI are mainly used; the M4 zone is large and the MGs range from VI to VIII (Zdziarski et al., 2018). In other words, because of its latitude, M4 is a more diverse zone than M2.…”

Section: Discussionmentioning

confidence: 99%

The accuracy of different strategies for building training sets for genomic predictions in segregating soybean populations

Mendonça

Fritsche‐Neto

2020

Crop Science

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The design of the training set is a key factor in the success of the genomic selection approach. The nature of line inclusion in soybean [Sorghum bicolor (L.) Moench.] breeding programs is highly dynamic, so generating a training set that endures across the years and regions is challenging. Therefore, we aimed to define the best strategies for building training sets to apply genomic selection in segregating soybean populations for traits with different genetic architectures. We used two datasets for grain yield (GY) and maturity group (MG) from two different soybean breeding regions in Brazil. Five training set schemes were tested. In addition, we included a training set formed by an optimization algorithm based on the predicted error variance. The predictions achieved good values for both traits, reaching 0.5 in some scenarios. The best scenario changed according to the trait. Although the best performance was achieved with the use of full‐sibs in the MG dataset, for GY, full‐sibs and a set of advanced lines were equivalent. For both traits, no improvement in predictive ability resulted from training set optimization. Furthermore, the use of advanced lines from the same breeding program is recommended as a training set for GY, so the training set is continually renewed and closely related to the breeding populations, and no additional phenotyping is needed. On the other hand, to improve prediction accuracies for MG, it is necessary to use training sets with less genetic variability but with more segregation resolution.

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“…Maturity groups are estimated by comparing experimental lines to well-known cultivars grown in the same conditions. The choice of these references is usually guided by published lists of the most stable cultivars and, consequently, of the most suitable check genotypes for each maturity group [10,12,13].…”

Section: Introductionmentioning

confidence: 99%

High-Throughput Phenotyping of Soybean Maturity Using Time Series UAV Imagery and Convolutional Neural Networks

Trevisan

Pérez

Schmitz³

et al. 2020

Remote Sensing

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Soybean maturity is a trait of critical importance for the development of new soybean cultivars, nevertheless, its characterization based on visual ratings has many challenges. Unmanned aerial vehicles (UAVs) imagery-based high-throughput phenotyping methodologies have been proposed as an alternative to the traditional visual ratings of pod senescence. However, the lack of scalable and accurate methods to extract the desired information from the images remains a significant bottleneck in breeding programs. The objective of this study was to develop an image-based high-throughput phenotyping system for evaluating soybean maturity in breeding programs. Images were acquired twice a week, starting when the earlier lines began maturation until the latest ones were mature. Two complementary convolutional neural networks (CNN) were developed to predict the maturity date. The first using a single date and the second using the five best image dates identified by the first model. The proposed CNN architecture was validated using more than 15,000 ground truth observations from five trials, including data from three growing seasons and two countries. The trained model showed good generalization capability with a root mean squared error lower than two days in four out of five trials. Four methods of estimating prediction uncertainty showed potential at identifying different sources of errors in the maturity date predictions. The architecture developed solves limitations of previous research and can be used at scale in commercial breeding programs.

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Key Soybean Maturity Groups to Increase Grain Yield in Brazil

Cited by 15 publications

References 14 publications

Selection indexes based on linear‐bilinear models applied to soybean breeding

Selection indexes based on linear‐bilinear models applied to soybean breeding

The accuracy of different strategies for building training sets for genomic predictions in segregating soybean populations

High-Throughput Phenotyping of Soybean Maturity Using Time Series UAV Imagery and Convolutional Neural Networks

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