scholarly journals Trait-QTL-heritability of grain yield and other agronomic traits under low nitrogen conditions in bi-parental maize populations

2021 ◽  
Vol 2 (1) ◽  
pp. 096-116
Author(s):  
Collins Kimutai ◽  
Manje Gowda ◽  
Oliver Kiplagat
Keyword(s):  
Grain Yield ◽  
Complex Traits ◽  
Agronomic Traits ◽  
Complex Nature ◽  
Maize Crop ◽  
Maize Populations ◽  
Solution Mapping ◽  
Genomic Regions ◽  
Low Nitrogen ◽  
Nitrogen Conditions

Limited or low Nitrogen is a wanting abiotic stress in maize mainly in Sub-Sahara Africa, affecting yields and quality development of maize crop. As an approach to getting a breeding solution; mapping of QTLs and understanding the heritability factor can provide useful information and guide for breeders in developing low nitrogen resilient maize. QTL mapping which is a molecular breeding component forms an actual basis in estimation of genomic regions associated to the expression of quantitative traits, and how heritable are such traits. Conducting a selection for Low N-tolerance is challenging due to its complex nature with strong interaction between genotypes and environments; therefore, marker assisted breeding is key to improving such complex traits, but at the same time requires markers associated with the trait of interest. In this study, three bi-parental populations were subjected to either or both low and optimum N conditions to detect and determine the QTLs heritability for grain yield and other agronomic traits. Essential to the study; genotype by environmental interaction, significance and heritability was examined for each population with most traits expressing low (<0.2) and moderate to high heritabilities (0.3>). These QTLs with high heritabilities across environments will be of great value for rapid introgression into maize populations using marker assisted selection approach. The study was a preliminary and therefore require further validation on heritability and fine mapping for them to be useful in MAS.

scholarly journals Genetic Dissection of Grain Yield and Agronomic Traits in Maize under Optimum and Low-Nitrogen Stressed Environments

2020 ◽  
Vol 21 (2) ◽  
pp. 543 ◽  
Author(s):  
Berhanu Tadesse Ertiro ◽  
Michael Olsen ◽  
Biswanath Das ◽  
Manje Gowda ◽  
Maryke Labuschagne
Keyword(s):  
Grain Yield ◽  
Agronomic Traits ◽  
Genotyping By Sequencing ◽  
Genetic Dissection ◽  
Common Qtl ◽  
Chromosome Position ◽  
Maize Varieties ◽  
Dh Lines ◽  
Genomic Regions ◽  
Low Nitrogen

Understanding the genetic basis of maize grain yield and other traits under low-nitrogen (N) stressed environments could improve selection efficiency. In this study, five doubled haploid (DH) populations were evaluated under optimum and N-stressed conditions, during the main rainy season and off-season in Kenya and Rwanda, from 2014 to 2015. Identifying the genomic regions associated with grain yield (GY), anthesis date (AD), anthesis-silking interval (ASI), plant height (PH), ear height (EH), ear position (EPO), and leaf senescence (SEN) under optimum and N-stressed environments could facilitate the use of marker-assisted selection to develop N-use-efficient maize varieties. DH lines were genotyped with genotyping by sequencing. A total of 13, 43, 13, 25, 30, 21, and 10 QTL were identified for GY, AD ASI, PH, EH, EPO, and SEN, respectively. For GY, PH, EH, and SEN, the highest number of QTL was found under low-N environments. No common QTL between optimum and low-N stressed conditions were identified for GY and ASI. For secondary traits, there were some common QTL for optimum and low-N conditions. Most QTL conferring tolerance to N stress was on a different chromosome position under optimum conditions.

scholarly journals Heterosis in crosses among white grain maize populations with high quality protein

2006 ◽  
Vol 41 (1) ◽  
pp. 59-66 ◽  
Author(s):  
Márcio Costa Rodrigues ◽  
Lázaro José Chaves ◽  
Cleso Antônio Patto Pacheco
Keyword(s):  
Grain Yield ◽  
Agronomic Traits ◽  
Least Square ◽  
Superior Performance ◽  
Normal Equation ◽  
High Quality ◽  
Maize Populations ◽  
High Quality Protein ◽  
Female Flowering ◽  
White Grain

The objective of this work was to investigate heterosis and its components in 16 white grain maize populations presenting high quality protein. These populations were divided according to grain type in order to establish different heterosis groups. The crosses were carried out according to a partial diallel cross design among flint and dent populations. Seven agronomic traits were evaluated in three environments while four leaf diseases and incidence of corn stunt were evaluated in one. Least square procedure was applied to the normal equation X'Xbeta = X'Y, to estimate the model effects and their respective sum of squares. Among the heterosis components, in diallel analysis, significance for average heterosis in grain yield, number of days to female flowering and to all evaluated diseases was detected. Specific heterosis was significant for days to female flowering and resistance to Puccinia polysora. Results concerned to grain yield trait indicate that populations with superior performance in dent group, no matter what flint population group is used in crosses, tend to generate superior intervarietal hybrids. In decreasing order of preference, the dent type populations CMS 476, ZQP/B 103 and ZQP/B 101 and the flint type CMS 461, CMS 460, ZQP/B 104 and ZQP/B 102 are recommended to form composites.

scholarly journals Genome-wide association study for yield and yield related traits under reproductive stage drought in a diverse indica-aus rice panel

2020 ◽  
Author(s):  
Aditi Bhandari ◽  
Nitika Sandhu ◽  
Jérôme Bartholome ◽  
Tuong-Vi Cao-Hamadoun ◽  
Nourollah Ahmadi ◽  
...  
Keyword(s):  
Grain Yield ◽  
Field Experiments ◽  
Genome Wide Association Study ◽  
Agronomic Traits ◽  
Reproductive Stage ◽  
Phenotypic Variance ◽  
Rice Varieties ◽  
Drought Tolerant ◽  
Genome Wide ◽  
Genomic Regions

Abstract Background Reproductive-stage drought stress is a major impediment to rice production in rainfed areas. Conventional and marker-assisted breeding strategies for developing drought-tolerant rice varieties are being optimized by mining and exploiting adaptive traits, genetic diversity; identifying the alleles, and understanding their interactions with genetic backgrounds for their increased contribution to drought tolerance. Field experiments were conducted in this study to identify marker-trait associations (MTAs) involved in response to yield under reproductive-stage (RS) drought. A diverse set of 280 indica-aus accessions was phenotyped for ten agronomic traits including yield and yield-related traits under normal irrigated condition and under two managed reproductive-stage drought environments. The accessions were genotyped with 215,250 single nucleotide polymorphism markers. Results The study identified a total of 219 significant MTAs for 10 traits and candidate gene analysis within a 200kb window centred from GWAS identified SNP peaks detected these MTAs within/ in close proximity to 38 genes, 4 earlier reported major grain yield QTLs and 6 novel QTLs for 7 traits out of the 10. The significant MTAs were mainly located on chromosomes 1, 2, 5, 6, 9, 11 and 12 and the percent phenotypic variance captured for these traits ranged from 5 to 88%. The significant positive correlation of grain yield with yield-related and other agronomic traits except for flowering time, observed under different environments point towards their contribution in improving rice yield under drought. Seven promising accessions were identified for use in future genomics-assisted breeding programs targeting grain yield improvement under drought. Conclusion These results provide a promising insight into the complex genetic architecture of grain yield under reproductive-stage drought in different environments. Validation of major genomic regions reported in the study will enable their effectiveness to develop drought-tolerant varieties following marker-assisted selection as well as to identify genes and understanding the associated physiological mechanisms.

scholarly journals Evaluating Maize Genotype Performance under Low Nitrogen Conditions Using RGB UAV Phenotyping Techniques

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1815 ◽  
Author(s):  
Ma. Luisa Buchaillot ◽  
Adrian Gracia-Romero ◽  
Omar Vergara-Diaz ◽  
Mainassara A. Zaman-Allah ◽  
Amsal Tarekegne ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Grain Yield ◽  
Vegetation Index ◽  
Yield Potential ◽  
Plant Phenotyping ◽  
Phenotypic Traits ◽  
Optimal Conditions ◽  
Maize Varieties ◽  
Low Nitrogen ◽  
Nitrogen Conditions

Maize is the most cultivated cereal in Africa in terms of land area and production, but low soil nitrogen availability often constrains yields. Developing new maize varieties with high and reliable yields using traditional crop breeding techniques in field conditions can be slow and costly. Remote sensing has become an important tool in the modernization of field-based high-throughput plant phenotyping (HTPP), providing faster gains towards the improvement of yield potential and adaptation to abiotic and biotic limiting conditions. We evaluated the performance of a set of remote sensing indices derived from red–green–blue (RGB) images along with field-based multispectral normalized difference vegetation index (NDVI) and leaf chlorophyll content (SPAD values) as phenotypic traits for assessing maize performance under managed low-nitrogen conditions. HTPP measurements were conducted from the ground and from an unmanned aerial vehicle (UAV). For the ground-level RGB indices, the strongest correlations to yield were observed with hue, greener green area (GGA), and a newly developed RGB HTPP index, NDLab (normalized difference Commission Internationale de I´Edairage (CIE)Lab index), while GGA and crop senescence index (CSI) correlated better with grain yield from the UAV. Regarding ground sensors, SPAD exhibited the closest correlation with grain yield, notably increasing in its correlation when measured in the vegetative stage. Additionally, we evaluated how different HTPP indices contributed to the explanation of yield in combination with agronomic data, such as anthesis silking interval (ASI), anthesis date (AD), and plant height (PH). Multivariate regression models, including RGB indices (R2 > 0.60), outperformed other models using only agronomic parameters or field sensors (R2 > 0.50), reinforcing RGB HTPP’s potential to improve yield assessments. Finally, we compared the low-N results to the same panel of 64 maize genotypes grown under optimal conditions, noting that only 11% of the total genotypes appeared in the highest yield producing quartile for both trials. Furthermore, we calculated the grain yield loss index (GYLI) for each genotype, which showed a large range of variability, suggesting that low-N performance is not necessarily exclusive of high productivity in optimal conditions.

scholarly journals Multi-Trait Multi-Environment Genomic Prediction of Agronomic Traits in Advanced Breeding Lines of Winter Wheat

2021 ◽  
Vol 12 ◽  
Author(s):  
Harsimardeep S. Gill ◽  
Jyotirmoy Halder ◽  
Jinfeng Zhang ◽  
Navreet K. Brar ◽  
Teerath S. Rai ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Genomic Prediction ◽  
Complex Traits ◽  
Agronomic Traits ◽  
Wheat Breeding ◽  
Successful Implementation ◽  
Limited Information ◽  
Multiple Traits ◽  
Breeding Lines

Genomic prediction is a promising approach for accelerating the genetic gain of complex traits in wheat breeding. However, increasing the prediction accuracy (PA) of genomic prediction (GP) models remains a challenge in the successful implementation of this approach. Multivariate models have shown promise when evaluated using diverse panels of unrelated accessions; however, limited information is available on their performance in advanced breeding trials. Here, we used multivariate GP models to predict multiple agronomic traits using 314 advanced and elite breeding lines of winter wheat evaluated in 10 site-year environments. We evaluated a multi-trait (MT) model with two cross-validation schemes representing different breeding scenarios (CV1, prediction of completely unphenotyped lines; and CV2, prediction of partially phenotyped lines for correlated traits). Moreover, extensive data from multi-environment trials (METs) were used to cross-validate a Bayesian multi-trait multi-environment (MTME) model that integrates the analysis of multiple-traits, such as G × E interaction. The MT-CV2 model outperformed all the other models for predicting grain yield with significant improvement in PA over the single-trait (ST-CV1) model. The MTME model performed better for all traits, with average improvement over the ST-CV1 reaching up to 19, 71, 17, 48, and 51% for grain yield, grain protein content, test weight, plant height, and days to heading, respectively. Overall, the empirical analyses elucidate the potential of both the MT-CV2 and MTME models when advanced breeding lines are used as a training population to predict related preliminary breeding lines. Further, we evaluated the practical application of the MTME model in the breeding program to reduce phenotyping cost using a sparse testing design. This showed that complementing METs with GP can substantially enhance resource efficiency. Our results demonstrate that multivariate GS models have a great potential in implementing GS in breeding programs.

scholarly journals Agronomic performance of maize populations divergently selected for diferulate cross-linkage

2016 ◽  
Vol 154 (7) ◽  
pp. 1270-1279 ◽  
Author(s):  
R. SANTIAGO ◽  
J. BARROS-RIOS ◽  
A. ALVAREZ ◽  
R. A. MALVAR
Keyword(s):  
Cell Wall ◽  
Grain Yield ◽  
Plant Height ◽  
Agronomic Traits ◽  
Divergent Selection ◽  
Flowering Plant ◽  
Corn Borer ◽  
Corn Borers ◽  
Maize Populations ◽  
Grain Moisture

SUMMARYThe direct response of a divergent selection programme for total cell wall ester-linked diferulate concentration in maize pith stalk tissues and its indirect effect on cell wall degradability and corn borer resistance have been previously evaluated. Since increased total diferulate concentration is expected to improve crop performance in response to corn borers, the objective of the present research was to evaluate the indirect response of the divergent selection for diferulates on agronomic traits under corn borer infestation. For this purpose, five maize populations with contrasting total diferulate concentrations were evaluated four environments for performance under protected and infested conditions. Measured traits were: days to anthesis, days to silking, plant height, stalk lodging, grain moisture at harvest and grain yield. High diferulate populations showed a significant reduction in anthesis (precocity), and were 11 cm taller than the starting population, while low diferulate populations were 9 cm shorter, and showed nearly 1 t/ha lower grain yield than the original and high diferulate populations. The analysis showed that cycles of selection were positively correlated with flowering, plant height and grain yield. The infestations with borers produced >1 t/ha of reduction in grain yield; although the higher diferulate populations showed a better performance under infestation than the low diferulate populations. This positive effect on the grain yield by increasing diferulate content can be considered an extra in order to breed for resistance to corn borers.

scholarly journals Identification of Quantitative Trait Loci Hotspots Affecting Agronomic Traits and High-Throughput Vegetation Indices in Rainfed Wheat

2021 ◽  
Vol 12 ◽  
Author(s):  
Rubén Rufo ◽  
Andrea López ◽  
Marta S. Lopes ◽  
Joaquim Bellvert ◽  
Jose M. Soriano
Keyword(s):  
Abiotic Stress ◽  
Quantitative Trait Loci ◽  
Grain Yield ◽  
High Throughput ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Vegetation Index ◽  
Agronomic Traits ◽  
A Genome ◽  
Trait Loci

Understanding the genetic basis of agronomic traits is essential for wheat breeding programs to develop new cultivars with enhanced grain yield under climate change conditions. The use of high-throughput phenotyping (HTP) technologies for the assessment of agronomic performance through drought-adaptive traits opens new possibilities in plant breeding. HTP together with a genome-wide association study (GWAS) mapping approach can be a useful method to dissect the genetic control of complex traits in wheat to enhance grain yield under drought stress. This study aimed to identify molecular markers associated with agronomic and remotely sensed vegetation index (VI)-related traits under rainfed conditions in bread wheat and to use an in silico candidate gene (CG) approach to search for upregulated CGs under abiotic stress. The plant material consisted of 170 landraces and 184 modern cultivars from the Mediterranean basin. The collection was phenotyped for agronomic and VI traits derived from multispectral images over 3 and 2 years, respectively. The GWAS identified 2,579 marker-trait associations (MTAs). The quantitative trait loci (QTL) overview index statistic detected 11 QTL hotspots involving more than one trait in at least 2 years. A CG analysis detected 12 CGs upregulated under abiotic stress in six QTL hotspots and 46 downregulated CGs in 10 QTL hotspots. The current study highlights the utility of VI to identify chromosome regions that contribute to yield and drought tolerance under rainfed Mediterranean conditions.

scholarly journals Identification of Wheat Cultivars for Low Nitrogen Tolerance Using Multivariable Screening Approaches

Agronomy ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 417
Author(s):  
Bhudeva S. Tyagi ◽  
John Foulkes ◽  
Gyanendra Singh ◽  
Sindhu Sareen ◽  
Pradeep Kumar ◽  
...  
Keyword(s):  
Grain Yield ◽  
Geometric Mean ◽  
Stability Index ◽  
Tolerance Index ◽  
Wheat Cultivars ◽  
Wheat Varieties ◽  
Physiological Basis ◽  
Selection Indices ◽  
Low Nitrogen ◽  
Nitrogen Conditions

A set of thirty-six wheat cultivars were grown for two consecutive years under low and high nitrogen conditions. The interactions of cultivars with different environmental factors were shown to be highly significant for most of the studied traits, suggesting the presence of wider genetic variability which may be utilized for the genetic improvement of desired trait(s). Three cultivars, i.e., RAJ 4037, DBW 39 and GW 322, were selected based on three selection indices, i.e., tolerance index (TOL), stress susceptibility index (SSI), and yield stability index (YSI), while two cultivars, HD 2967 and MACS 6478, were selected based on all four selection indices which were common in both of the study years. According to Kendall’s concordance coefficient, the consistency of geometric mean productivity (GMP) was found to be highest (0.778), followed by YSI (0.556), SSI (0.472), and TOL (0.200). Due to the high consistency of GMP followed by YSI and SSI, the three selection indices could be utilized as a selection tool in the identification of high-yielding genotypes under low nitrogen conditions. The GMP and YSI selection indices had a positive and significant correlation with grain yield, whereas TOL and SSI exhibited a significant but negative correlation with grain yield under both high and low nitrogen conditions in both years. The common tolerant genotypes identified through different selection indices could be utilized as potential donors in active breeding programs to incorporate the low nitrogen tolerant genes to develop high-yielding wheat varieties for low nitrogen conditions. The study also helps in understanding the physiological basis of tolerance in high-yielding wheat genotypes under low nitrogen conditions.

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