Correlations and Path Coefficients for Yield Related Traits in Soybean Progenies

Vu, Hang T.; Le, Thi Tuyet Cham; Vu, Dinh Hoa; Nguyen, Thanh Tuan; Ngoc, Thi Bich

doi:10.3923/ajcs.2019.32.39

Cited by 11 publications

(6 citation statements)

References 12 publications

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“…Yield components have been reported as characters that determine the soybean yield (He et al, 2017;Ghanbari et al, 2018;Wang et al, 2010). Vu et al (2019) found a significant positive direct effects of total number of seeds per plant and 100 seed weight on grain yield of soybean progeny. Kuswantoro et al (2018) suggested to use the days to flowering and weight of 100 seeds as selection criteria for high yield in soybean.…”

Section: Introductionmentioning

confidence: 90%

Agronomic Performance of Soybean Genotypes in Lowland Paddy Fields under Zero-tillage Condition

2020

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In Indonesia, soybean is mostly cultivated in lowland following the yearly planting pattern of paddy – paddy – soybean under zero-tillage condition. The research aim was to evaluate the agronomic performance of several soybean genotypes in lowland paddy fields under zero-tillage condition. A total of 12 soybean genotypes, including the check varieties of Wilis and Anjasmoro, were evaluated in lowland after rice planting in three locations (Klaten, Pasuruan, and Tabanan). A randomized block design with four replications was used in each location. The soybean yield is a complex character which determined by interrelated agronomic characters. The averages seed yield in Klaten, Pasuruan, and Tabanan were 2.97 t/ha, 3.02 t/ha, and 2.68 t/ha, respectively. Two genotypes produced equal yield with Anjasmoro, i.e. AT12-1062 (3.01 t/ha) and AT12-1037 (3.0 t/ha). Anjasmoro variety had the highest 100 seed weight (15.40 g), and only AT12-1035 showed the equal seed weight. The average days to maturity of 12 genotypes was 83 days. In addition to Anjasmoro variety, soybean genotypes AT12-1062 and AT12-1037 (medium maturity and medium seed size) as the new findings form this study were potential to be developed at lowland paddy fields under zero-tillage condition. The availability of the soybean genotypes adaptive to lowland paddy field under zero tillage condition is important to optimize the soybean productivity as well as the income of farmers in Indonesia.

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Section: Introductionmentioning

confidence: 90%

Agronomic Performance of Soybean Genotypes in Lowland Paddy Fields under Zero-tillage Condition

2020

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“…Genotypes MR-22A, MR-34A, MR-4A, MR-2A, MR-5A, and MR-8A showed higher plant height compared to others. The number of branches generally have a strong positive correlation with the number of seeds and yield (Vu et al, 2019). It is because the increasing number of branches indicates an increasing number of pods per plant (Egli, 2013).…”

Section: Plant Height and Number Of Branches Per Plantmentioning

confidence: 99%

Morphological characteristics and yield performance of mutant soybean [Glycine max (L.) Merr.] M6 genotypes in Jatinangor, Sumedang, West Java

Yoel

Rachmadi

2020

ipas

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Current breeding efforts in soybean largely focus on identifying genotypes with high seed yield and large seed size. Our study applied a mutation using 250 Gy Gamma-Ray Irradiation to soybean cv. Anjasari. The variability in the M1 generation was identified, and the planting was continued to M2 M3, M4, M5 until M6 generation. The objectives of this research were to evaluate mutant lines exhibiting a good performance in yield and morphological characteristics that can support the yield component in M6 generation. This research was conducted from January 2019 to May 2019 at Ciparanje Experiment Station, Faculty of Agriculture, Universitas Padjadjaran, Jatinangor, West Java. The experiment was arranged in a Randomized Completely Block Design with fifteen mutant soybean genotypes as treatments with two replications within each treatment. The results showed several genotypes produced higher yield and better agro-morphological characteristics. The genotypes MR-33, MR-4A, and MR-2A showed significantly higher number of total pods per plant. Meanwhile, the genotypes MR-35A, MR-37, MR-23, MR-36A, MR-3A, MR-29A MR-34A, MR-4A, MR-17, and MR-5A showed a higher value in weight of 100 seeds. The highest yield was observed in MR-29A and MR-23, which then will be used as the genotypes for multilocation test for stability.

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“…Merrill] Statistical modeling is one of the way to identify significantly associated charcters to yield. Classical variable selection method like Multiple regression approach (Ghanbari et al, 2018;Vu et al, 2019) is the benchmark statistical technique commonly used for analysing the relationship between the triats. But sometimes it misleads the researchers due to its stringent assumptions like Multicollinearity, Linearity etc.…”

Section: Introductionmentioning

confidence: 99%

Trait Based Modelling Approach for Selection of Elite Germplasm Accessions in Soybean [Glycine max (L). Merrill]

Shruthi

Siddaraju

Naveena

et al. 2021

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Background: Identification of suitable factors that influence significantly to the response is crucial for the traits based breeding program to make a better decision about improvement in productivity. Multiple linear regression (MLR) is the benchmark method commonly using to identify suitable factors for crop improvement. It doesn’t work always due to stringent assumption (Multicollinearity, Linearity) behind the MLR model. Here we tried to develop an efficient model for the selection of major traits that contribute to seed yield in soybean by comparing different models.Methods: Field experiment was conducted using 98 soybean core population through augmented design.18 morphometric traits obtain from soybean core population were considered under the study as regressors.Multiple linear regression (MLR), Principle component Regression (PCR), Regression tree and Random Forest models were compared to select traits based on prediction accuracy.Result: All the models identified the number of pods per plant (NPP) has the most influencing variable to the soybean yield. However random forest has a much higher prediction power (RMSE=4.59, MAPE=0.18) compared to other models under study. The results of random forest revealed that the number of pods per plant, number of branches per plant and other associated characters like plant height at harvest as highly influencing traits for seed yield in soybean.Finally, tried to identify genotypesthat possess superiority about most influencing morphological characters on seed yield using cluster analysis.

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Correlations and Path Coefficients for Yield Related Traits in Soybean Progenies

Cited by 11 publications

References 12 publications

Agronomic Performance of Soybean Genotypes in Lowland Paddy Fields under Zero-tillage Condition

Agronomic Performance of Soybean Genotypes in Lowland Paddy Fields under Zero-tillage Condition

Morphological characteristics and yield performance of mutant soybean [Glycine max (L.) Merr.] M6 genotypes in Jatinangor, Sumedang, West Java

Trait Based Modelling Approach for Selection of Elite Germplasm Accessions in Soybean [Glycine max (L). Merrill]

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