Variation of agronomic traits among introduced winter bread wheat cultivars

Desheva, Gergana; Sabeva, M.; Zacharieva, M.

doi:10.15547/tjs.2016.02.009

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…Moreover, Liu et al (2019) successfully predicted the number of stem tillers in wheat using wheat mulch, while a reasonable number of tillers is the basis of the formation of yields. Wheat coverage at the flowering stage is mainly made up of the number of wheat spikes, which is a prerequisite for evaluating wheat yield (Desheva et al, 2018). Nerson (1980) found a highly positive correlation between the spike number and yield.…”

Section: Rationality Of Canopy Volumetric Characteristics For Optimiz...mentioning

confidence: 99%

The estimation of wheat yield combined with UAV canopy spectral and volumetric data

Liu,

Wu,

Mou

et al. 2024

Food and Energy Security

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Estimating wheat yield accurately is crucial for efficient agricultural management. While canopy spectral information is widely used for this purpose, the incorporation of canopy volumetric features (CVFs) remains underexplored. This study bridges this gap by utilizing unmanned aerial vehicle (UAV) multispectral imaging to capture images and elevation data of wheat at key developmental stages—gestation and flowering stages. We innovatively leveraged the elevation differences between these stages to calculate canopy height, develop a novel CVF, and refine the wheat yield prediction model across various wheat varieties, nitrogen fertilizer levels, and planting densities. The integration of canopy volume information significantly enhanced the accuracy of our yield prediction model, as evidenced by an R2 of 0.8380, an RMSE of 313.3 kg/ha, and an nRMSE of 11.33%. This approach not only yielded more precise estimates than models relying solely on spectral data but also introduced a novel dimension to wheat yield estimation methodologies. Our findings suggest that incorporating canopy volume characteristics can substantially optimize wheat yield prediction models, presenting a groundbreaking perspective for agricultural yield estimation.

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Section: Rationality Of Canopy Volumetric Characteristics For Optimiz...mentioning

confidence: 99%

The estimation of wheat yield combined with UAV canopy spectral and volumetric data

Liu,

Wu,

Mou

et al. 2024

Food and Energy Security

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“…Varieties differ in genetic potential, which is realized to varying degrees under the direct influence of environmental changes (Morgounov et al, 2014;Tsenov et al, 2021). Therefore, the assessment of the performance (stability) of the variety is directly related to the specific information about the relationship between individual indexes, their genetic conditioning and the peculiarities of their change due to environments (Desheva, 2016;Taneva et al, 2019;Kyratzis et al, 2022). What part of the grain quality the variety will realize depends on its genetic stability, which is different for individual indexes?…”

Section: Introductionmentioning

confidence: 99%

Environment- and Genotype-Dependent Stability in the Common Wheat Grain Quality (Triticum aestivum L.)

Tsenov¹,

Gubatov²,

Yanchev³

2023

IJIAAR

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The study was conducted to evaluate the stability of common wheat varieties in four locations with proven different environmental conditions. Three indexes of grain quality were studied: wet gluten content, (WGC); gluten index of grain (GI) and grain sedimentation value (Zeleny). The stability of varieties has been evaluated by many parameters that reflect different aspects of the complete picture for it. The indexes studied are strongly influenced by environmental conditions. The most genetically stable among them is the gluten index (GI), where the genotype has a decisive role of about 70% of the variation, and the most unstable is the wet gluten content (WGC), with only 17% of the effect. As a result of reliable GE, the ranking of the varieties according to the performance of each of the indexes is different in the individual locations. The ranking of varieties in terms of stability according to the ranks of each of the parameters is very different. Even a visual representation of the results, which clears the picture to the maximum extent, shows a different set of stable varieties in each of the quality indexes. Only a few of the varieties (G2, G6, G9, G13, G18, G20, G22) have a good balance between the size and stability of all quality parameters, with a moderate compromise with the grain yield level. The assessment of the stability of the variety in terms of quality can be made according to any of the indexes used. The stability of the variety depends to a large extent on the effect of the environment, which must be considered when selecting a specific index for assessment. The most suitable for this purpose is the gluten index (GI), where the influence of genotype is the strongest, with a significant GE interaction accounting for 25% of all variation. The stability of the variety does not depend on the magnitude of the quality indexes. Stable can be both quality (G2, G6) and varieties with very low grain quality (G18, G20, G22). Stability of quality, at high levels of indexes, is associated with low grain yield and vice versa. From this point of view, combining high yield stability and grain quality at the highest possible levels is a very rare exception (G2, G9).

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“…Multivariate analyses are routinely used for the assessment of the genetic divergence in germplasm collections. Cluster and principal component analyses have been widely used in studies for delineating with the variability among the genotypes (5)(6)(7)(8).…”

Section: Introductionmentioning

confidence: 99%

Genetic Diversity in Sodium Azide-Induced Barley Mutant Lines

Dyulgerova¹,

Dyulgerov²

2019

TJS

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The aim of this study was to assess the diversity among sodium azide-induced mutants of winter 2-rowed barley for 8 quantitative traits of by multivariate analyses. Forty-seven mutant lines and their parental varieties Kuber, Zagorets, Asparuh, and Imeon were tested in complete block design with four replications. The studied traits included spike length, awn length, peduncle length, spikelet and grain number per spike, grain weight per spike, 1000 grain weight and grain yield. Principal component and cluster analyses were used for the assessment of mutants. Traits spike length, spikelet number per a spike, grain number per spike and grain weight per spike had the largest contribution to the divergence between mutants. The cluster and principal component analyses employed in the present study divided the mutant lines into different groups, which were deviated significantly from the respective parents. Diverse mutant lines with improved yield-related traits suitable for including in winter barley breeding program were identified.

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Variation of agronomic traits among introduced winter bread wheat cultivars

Cited by 3 publications

References 2 publications

The estimation of wheat yield combined with UAV canopy spectral and volumetric data

The estimation of wheat yield combined with UAV canopy spectral and volumetric data

Environment- and Genotype-Dependent Stability in the Common Wheat Grain Quality (Triticum aestivum L.)

Genetic Diversity in Sodium Azide-Induced Barley Mutant Lines

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