scholarly journals INFLUENCE OF SEED SIZE, PROTEIN CONTENT AND CULTIVAR ON EARLY SEEDLING VIGOR EM WHEAT

1977 ◽  
Vol 57 (3) ◽  
pp. 929-935 ◽  
Author(s):  
L. E. EVANS ◽  
G. M. BHATT
Keyword(s):  
Protein Content ◽  
Seed Size ◽  
Selection Criterion ◽  
Correlation Coefficients ◽  
Dry Weight ◽  
Wheat Breeding ◽  
Genotypic Differences ◽  
Seedling Vigor ◽  
Breeding Programs ◽  
Early Seedling

Four greenhouse experiments were conducted to study the influence of seed size, protein content and cultivar on seedling vigor of wheat measured as seedling dry weight at 20 days. The simple and partial correlation coefficients among the variables were all positive and significant. No significant interaction occurred between seed size and genotype. The genotypic differences in seedling vigor may lead to its use as a selection criterion in wheat breeding programs.

Influence of Soybean Seed Size on Response to Atrazine

Weed Science ◽  
1970 ◽  
Vol 18 (1) ◽  
pp. 162-164 ◽  
Author(s):  
Robert N. Andersen
Keyword(s):  
Regression Analysis ◽  
Glycine Max ◽  
Seed Size ◽  
Selection Criterion ◽  
Soybean Seed ◽  
Dry Weight ◽  
Large Seed ◽  
Untreated Check ◽  
Initial Selection

In the greenhouse, we examined approximately 2,700 strains of soybeans [Glycine max (L.) Merr.] for response to 2-chloro-4-(ethylamino)-6-(isopropylamino)-s-triazine (atrazine), a herbicide which inhibits photosynthesis. Duration of survival when grown in soil containing 0.84 kg/ha was the initial selection criterion. Strains thus selected as most tolerant and most susceptible were grown then for 3 to 3 ½ weeks in soil containing 0.45 kg/ha of atrazine. The dry weight of shoots expressed as a percentage of each strain's own untreated check was used to measure the strains' tolerance of atrazine. Tolerance, thus measured, generally increased as seed size increased. Regression analysis indicated that 80% of the variation in response was attributable to variation in seed size. We suggest the possibility of minimizing soybean injury from atrazine (and perhaps other herbicides) by planting large seed.

scholarly journals Impact of seed size on seeds viability, vigor and storability of Hordeum vulgare (L.)

2018 ◽  
Author(s):  
M. Massimi
Keyword(s):  
Seed Germination ◽  
Seed Size ◽  
Seed Quality ◽  
Accelerated Aging ◽  
Dry Weight ◽  
Germination Percentage ◽  
Seedling Vigor ◽  
Hordeum Vulgare L ◽  
Vigor Index ◽  
The Impact

Seed size may influence seed germination, and seedling vigor. Few investigations are available about the effect of seed size on barley seedling vigor in Jordan. The present study was designed to investigate the impact of seed size on germination percentage, seedling dry weight, seedling vigor index, and germination percentage after accelerated aging in barley. Three seed sizes i.e. large, medium, and small (having diameter of > 2.75 millimeter, 2.5 - 2.75 millimeter, and less than 2.5 millimeter, respectively) were tested in the experiment. Seed quality for different seed size categories was evaluated in the laboratory by measuring seed germination, germination after accelerated aging, as well seedling dry weight and vigor index. Results showed significant differences for large seeds in germination percentages, seedling dry weight and vigor index. It may be concluded that large seed size of barley showed best quality.

scholarly journals New Parameters for Seedling Vigor Developed via Phenomics

2019 ◽  
Vol 9 (9) ◽  
pp. 1752 ◽  
Author(s):  
Song Lim Kim ◽  
Yong Suk Chung ◽  
Hyeonso Ji ◽  
Hongseok Lee ◽  
Inchan Choi ◽  
...  
Keyword(s):  
Oryza Sativa L ◽  
Shoot Length ◽  
Seedling Vigor ◽  
Breeding Programs ◽  
Area Index ◽  
Green Area ◽  
Data Points ◽  
Early Seedling ◽  
Important Trait ◽  
Growth Differences

Early seedling establishment in rice (Oryza sativa L.), which is measured by primary/secondary tiller, shoot length, biomass, root-related traits, and leaf area index, is an important trait because it helps to compete for light, air, and water for better tolerating various abiotic stresses. Consequently, it can affect the yield. However, there are not many research studies on this subject. Furthermore, previous studies have only measured the target traits once. However, this does not reflect the variation of growth rate during the seedling stage. Thus, two data points, two weeks and four weeks after planting, were used in the current study. As a result, two QTL regions were detected for the growth differences via plant height and green area (reflecting tillering). We expect that these results can be utilized by breeders to evaluate and select vigorous seedlings for their breeding programs.

scholarly journals Seed Priming Significantly Affects Germination and Post Germination Early Seedling Growth in Okra

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Rima Rima
Keyword(s):  
Root Length ◽  
Dry Weight ◽  
Seed Priming ◽  
Germination Percentage ◽  
Shoot Length ◽  
Seedling Height ◽  
Seedling Vigor ◽  
Commercial Cultivation ◽  
Early Seedling Growth ◽  
Early Seedling

AbstractSeed priming significantly affects germination and post germination emergence in seedling of plants. Okra is an important vegetable plays significant role in human nutrition. However, little information is available regarding effect of seed priming on germination and post germination early vegetative growth. Therefore, a pot experiment was conducted to find out the effect of seed priming on the emergence and seedling vigor in different okra varieties. Six local Bangladeshi okra varieties were primed in water for 24 hours and evaluated their germination and post germination emergence. The varieties were named as V1-V6 (V1 = Flash power, V2 = Durga, V3 = BARI dheros-2, V4 =Okra pornota, V5 =Dandy all green, V6 = Boishakhi). Post priming evaluation was done based on germination percentage, root-shoot length of seedlings, seedling height, and dry weight of seedlings, germination index and speed of germination at different days after sowing (5-15 DAS). Highest germination was observed in V3 (88.00%) and lowest was in V5 (25.33%). Root length was highest in V3 and V4 (8.77cm) and lowest was observed in V2 (5.15cm). Shoot length of the seedlings was highest in V3 (7.94cm) followed by V1 (7.33). Shoot length of the seedlings was lowest in V5 (5.12cm). Seedling height was highest in V3 (16.71cm) which was followed by V6 (16.26cm). The lowest seedling height was observed in V1 (12.50cm). Dry weight of the seedlings was highest in V3 (2.32g) which was followed in V1 (2.26g). The lowest dry weight was observed in V5 (1.99g). The variety V3 performed better in most of the evaluated parameters and suggested for commercial cultivation.

scholarly journals Multi-Trait Machine and Deep Learning Models for Genomic Selection using Spectral Information in a Wheat Breeding Program

2021 ◽  
Author(s):  
Karansher S. Sandhu ◽  
Shruti S. Patil ◽  
Michael O. Pumphrey ◽  
Arron H. Carter
Keyword(s):  
Deep Learning ◽  
Protein Content ◽  
Genomic Selection ◽  
Grain Protein Content ◽  
Wheat Breeding ◽  
Spectral Information ◽  
Grain Protein ◽  
Learning Models ◽  
Breeding Programs ◽  
Wheat Breeding Program

AbstractPrediction of breeding values and phenotypes is central to plant breeding and has been revolutionized by the adoption of genomic selection (GS). Use of machine and deep learning algorithms applied to complex traits in plants can improve prediction accuracies in the context of GS. Spectral reflectance indices further provide information about various physiological parameters previously undetectable in plants. This research explores the potential of multi-trait (MT) machine and deep learning models for predicting grain yield and grain protein content in wheat using spectral information in GS models. This study compares the performance of four machine and deep learning-based uni-trait (UT) and MT models with traditional GBLUP and Bayesian models. The dataset consisted of 650 recombinant inbred lines from a spring wheat breeding program, grown for three years (2014-2016), and spectral data were collected at heading and grain filling stages. MT-GS models performed 0-28.5% and −0.04-15% superior to the UT-GS models for predicting grain yield and grain protein content. Random forest and multilayer perceptron were the best performing machine and deep learning models to predict both traits. These two models performed similarly under UT and MT-GS models. Four explored Bayesian models gave similar accuracies, which were less than machine and deep learning-based models, and required increased computational time. Green normalized difference vegetation index best predicted grain protein content in seven out of the nine MT-GS models. Overall, this study concluded that machine and deep learning-based MT-GS models increased prediction accuracy and should be employed in large-scale breeding programs.Core IdeasPotential for combining high throughput phenotyping, machine and deep learning in breeding.Multi-trait models exploit information from secondary correlated traits efficiently.Spectral information improves genomic selection models.Deep learning can aid plant breeders owing to increased data generated in breeding programs

Genetic variation for wheat spike fertility in cultivars and early breeding materials

2015 ◽  
Vol 154 (1) ◽  
pp. 13-22 ◽  
Author(s):  
N. E. MIRABELLA ◽  
P. E. ABBATE ◽  
I. A. RAMIREZ ◽  
A. C. PONTAROLI
Keyword(s):  
Grain Yield ◽  
Genotypic Variation ◽  
Selection Criterion ◽  
Dry Weight ◽  
Wheat Breeding ◽  
Environment Interaction ◽  
Grain Number ◽  
Narrow Sense Heritability ◽  
Frequency Distributions ◽  
Commercial Cultivars

SUMMARYGrain yield in bread wheat is often tightly associated with grain number/m2. In turn, spike fertility (SF), i.e., the quotient between grain number and spike chaff dry weight, accounts for a great proportion of the variation in grain number among cultivars. In order to examine the potential use of SF as a breeding target, (1) variation for the trait was assessed in six datasets combining commercial cultivars under different environmental conditions, (2) trait heritability was estimated in a set of F1 hybrids derived from controlled crosses between cultivars with contrasting SF and (3) SF distribution pattern was analysed in two F2 segregating populations. Analysis of commercial cultivars revealed considerable variation for SF, under both optimal and sub-optimal conditions. In addition, genotypic variation was consistently larger than genotype × environment interaction variation in all datasets. Narrow sense heritability, estimated by the mid-parent-offspring regression of 20 F1 hybrids and their respective parents, was 0·63. Data from two F2 populations exhibited bell-shaped and symmetric frequency distributions of SF, with a SF mean intermediate between the parental values. Substantial transgressive segregation was detected in both F2 populations. In conclusion, SF appears to be a heritable trait with predominantly additive effects. This warrants further investigation on the feasibility of using SF as an early selection criterion in wheat breeding programs aimed at increasing grain yield.

scholarly journals Strategies Using Genomic Selection to Increase Genetic Gain in Breeding Programs for Wheat

2020 ◽  
Vol 11 ◽  
Author(s):  
Biructawit Bekele Tessema ◽  
Huiming Liu ◽  
Anders Christian Sørensen ◽  
Jeppe Reitan Andersen ◽  
Just Jensen
Keyword(s):  
Genomic Selection ◽  
Genetic Gain ◽  
Selection Criterion ◽  
Phenotypic Selection ◽  
Wheat Breeding ◽  
Breeding Program ◽  
Breeding Programs ◽  
Wheat Breeding Program ◽  
Estimated Breeding Values ◽  
The One

Conventional wheat-breeding programs involve crossing parental lines and subsequent selfing of the offspring for several generations to obtain inbred lines. Such a breeding program takes more than 8 years to develop a variety. Although wheat-breeding programs have been running for many years, genetic gain has been limited. However, the use of genomic information as selection criterion can increase selection accuracy and that would contribute to increased genetic gain. The main objective of this study was to quantify the increase in genetic gain by implementing genomic selection in traditional wheat-breeding programs. In addition, we investigated the effect of genetic correlation between different traits on genetic gain. A stochastic simulation was used to evaluate wheat-breeding programs that run simultaneously for 25 years with phenotypic or genomic selection. Genetic gain and genetic variance of wheat-breeding program based on phenotypes was compared to the one with genomic selection. Genetic gain from the wheat-breeding program based on genomic estimated breeding values (GEBVs) has tripled compared to phenotypic selection. Genomic selection is a promising strategy for improving genetic gain in wheat-breeding programs.

scholarly journals Graphic analysis of trait relations of Iranian bread wheat germplasm under non-saline and saline conditions using the biplot method

Genetika ◽  
2016 ◽  
Vol 48 (2) ◽  
pp. 473-486
Author(s):  
Amir Gholizadeh ◽  
Hamid Dehghani
Keyword(s):  
Seed Yield ◽  
Crop Production ◽  
Block Design ◽  
Selection Criterion ◽  
Positive Association ◽  
Wheat Breeding ◽  
World Knowledge ◽  
Visual Evaluation ◽  
Breeding Programs ◽  
Graphic Analysis

Salinity is one of the most important factors that limit crop production in some regions of the world. Knowledge of the interrelationships between yield and its components will improve the efficiency of breeding programs especially under saline conditions through appropriate selection criteria. This study demonstrated that GT biplot was an excellent tool for visual evaluation of superior genotypes, traits and grouping of them with other statistical techniques. The study was conducted under both saline and non-saline conditions in field based on randomized complete block design with three replications. Electrical conductivity of irrigation water were 2 and 10 dS.m-1 in non-saline and saline conditions, respectively. The obtained data were analyzed using a genotype trait (GT) biplot method based on site regression model. The biplot vector view indicate that there was a strong positive association between PH and BY with seed yield in both non-saline and saline conditions. It seems that PH and BY traits can be used as selection criterion for improving of seed yield in wheat breeding programs, especially under stress conditions in the field. Also among 41 studied genotypes, genotype 32 had good characteristics regarding high seed yield and salt tolerance.

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