Evaluating Lepidopteran Defoliation Resistance in Soybean Breeding Lines Containing the Stink Bug (Hemiptera: Pentatomidae) Resistance IAC-100 Cultivar in Their Pedigrees

2007 ◽  
Vol 100 (3) ◽  
pp. 962-968 ◽  
Author(s):  
Robert M. McPherson ◽  
Glenn R. Buss
Keyword(s):  
Stink Bug ◽  
Soybean Breeding ◽  
Breeding Lines

Assessing Stink Bug Resistance in Soybean Breeding Lines Containing Genes from Germplasm IAC-100

2007 ◽  
Vol 100 (4) ◽  
pp. 1456-1463 ◽  
Author(s):  
Robert M. McPherson ◽  
Glenn R. Buss ◽  
Phillip M. Roberts
Keyword(s):  
Stink Bug ◽  
Soybean Breeding ◽  
Breeding Lines

scholarly journals Agronomic Traits of Soybean Breeding Lines with Low Stachyose and Raffinose Contents

2017 ◽  
Vol 62 (2) ◽  
pp. 143-148
Author(s):  
Do Su Ha ◽  
◽  
Jin Young Moon ◽  
Sang Woo Choi ◽  
Sang In Shim ◽  
...  
Keyword(s):  
Agronomic Traits ◽  
Soybean Breeding ◽  
Breeding Lines

scholarly journals GENOTYPE BY ENVIRONMENT INTERACTION OF ADVANCED SOYBEAN BREEDING LINES IN LAMPUNG UPLAND SOILS

Zuriat ◽  
2015 ◽  
Vol 19 (1) ◽  
Author(s):  
Darman M. Arsyad ◽  
, Purwantoro
Keyword(s):  
Genotype By Environment Interaction ◽  
Yield Potential ◽  
Environment Interaction ◽  
Breeding Line ◽  
Upland Soil ◽  
Soybean Breeding ◽  
Breeding Lines ◽  
Genotype By Environment ◽  
Upland Soils ◽  
Plot Design

Availability of adapted cultivars is needed for the development of soybean cultivation in upland soil in Lampung, Sumatra. Twelve advanced soybean breeding lines and two check varieties (Tanggamus and Wilis) were tested on upland soils of Lampung Province. The trials were conducted in the District of Tulangbawang, Central Lampung, East Lampung and South Lampung during the late rainy season of 2003/2004. Two sites were selected per district for a total of eight sites for the four districts. A split plot design with three replications was used within each location. Plot size was 2.4 m x 4.5 m, plant spacing of 40 cm between rows and 15 cm within a row, and two plants per hill. The mainplot was fertilizer: A= Low inputs (22.5 kg N, 27 kg P2O5, and 40 kg K2O per ha), and B=Medium inputs (22.5 kg N, 36 kg P2O5, 53 kg K2O and 0.56 t CaO per ha). The fertilizers were applied before planting. The subplots were 12 soybean breeding lines together with two check soybean varieties. Results showed that soybean yields were affected significantly by locations, fertilizers, breeding lines, location x fertilizer and location x breeding line interactions. Plant height was affected siginificantly by locations, fertilizers, breeding lines and location x breeding interaction. Pods per plant  were affected by locations, fertilizers, location x fertilizer and location x breeding line interactions. All breeding lines and varieties were responsive to changing environments. Based on the smallest Pi parameter (the distance mean square between the cultivar’s response and the maximum response averaged over all locations), and the highest frequency of rank, three breeding lines, namely W3465-27-2 (released in 2004 as cv. Ratai), cv. Tanggamus and D3578- 3/K3911-66-3 were identified as wide adaptability genotypes with the yield potential of more than 2.4 t/ha with an average of 1.6 t/ha.

Antibiosis in Soybean Breeding Lines for the Management of Corn Earworm for Sustainable Soybean Production

2007 ◽  
Vol 30 (2) ◽  
pp. 137-146 ◽  
Author(s):  
R. B. Dadson ◽  
I. Javaid ◽  
F. M. Hashem ◽  
J. M. Joshi
Keyword(s):  
Corn Earworm ◽  
Soybean Breeding ◽  
Breeding Lines ◽  
Soybean Production

Variation among Soybean Breeding Lines in Relation to Yield and Seed‐Fill Duration

1988 ◽  
Vol 80 (5) ◽  
pp. 825-829 ◽  
Author(s):  
James R. Smith ◽  
Randall L. Nelson ◽  
Bruce L. Vasilas
Keyword(s):  
Soybean Breeding ◽  
Breeding Lines ◽  
Seed Fill

scholarly journals Establishment of Plot-Yield Prediction Models in Soybean Breeding Programs Using UAV-Based Hyperspectral Remote Sensing

2019 ◽  
Vol 11 (23) ◽  
pp. 2752 ◽  
Author(s):  
Xiaoyan Zhang ◽  
Jinming Zhao ◽  
Guijun Yang ◽  
Jiangang Liu ◽  
Jiqiu Cao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Growth Stage ◽  
Vegetation Index ◽  
Prediction Models ◽  
Hyperspectral Remote Sensing ◽  
Yield Prediction ◽  
Breeding Programs ◽  
Soybean Breeding ◽  
Breeding Lines ◽  
Plot Yield

Yield evaluation of breeding lines is the key to successful release of cultivars, which is becoming a serious issue due to soil heterogeneity in enlarged field tests. This study aimed at establishing plot-yield prediction models using unmanned aerial vehicle (UAV)-based hyperspectral remote sensing for yield-selection in large-scale soybean breeding programs. Three sets of soybean breeding lines (1103 in total) were tested in blocks-in-replication experiments for plot yield and canopy spectral reflectance on 454~950 nm bands at different growth stages using a UAV-based hyperspectral spectrometer (Cubert UHD185 Firefly). The four elements for plot-yield prediction model construction were studied respectively and concluded as: the suitable reflectance-sampling unit-size in a plot was its 20%–80% central part; normalized difference vegetation index (NDVI) and ration vegetation index (RVI) were the best combination of vegetation indices; the initial seed-filling stage (R5) was the best for a single stage prediction, while another was the best combination for a two growth-stage prediction; and multi-variate linear regression was suitable for plot-yield prediction. In model establishment for each material-set, a random half was used for modelling and another half for verification. Twenty-one two growth-stage two vegetation-index prediction models were established and compared for their modelling coefficient of determination (RM2) and root mean square error of the model (RMSEM), verification RV2 and RMSEV, and their sum RS2 and RMSES. Integrated with the coincidence rate between the model predicted and the practical yield-selection results, the models, MA1-2, MA4-2 and MA6-2, with coincidence rates of 56.8%, 58.5% and 52.4%, respectively, were chosen for yield-prediction in yield-test nurseries. The established model construction elements and methods can be used as local models for pre-harvest yield-selection and post-harvest integrated yield-selection in advanced breeding nurseries as well as yield potential prediction in plant-derived-line nurseries. Furthermore, multiple models can be used jointly for plot-yield prediction in soybean breeding programs.

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