Spectral Reflectance Indices’ Performance to Identify Seawater Salinity Tolerance in Bread Wheat Genotypes Using Genotype by Yield*Trait Biplot Approach

Elfanah, Ahmed M. S.; Darwish, M.A.; Selim, Adel E.; Shabana, M.; Elmoselhy, Omnya M. A.; Khedr, Rania A.; Ali, Abdelraouf M.; Abdelhamid, Magdi T.

doi:10.3390/agronomy13020353

Cited by 7 publications

(14 citation statements)

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“…Therefore, a composition between seed yield and each constituted trait is desirable concerning high yield. Thus, the desired genotypes were superior, and the breeder used selection methods based on their seed yield and other trait combinations, as recorded by Elfanah et al [34]. The result shows that the Yunwan 52 is the best genotype for seed yield and associated traits, while Yunwan 50 is located on another vertex sector winner for SY*PH according to the biplot "Which-won-where."…”

Section: Discussionmentioning

confidence: 93%

“…The genotype by yield*trait biplot (GYT) was designed by Yan and Frégeau-Reid [33] to supply valuable information for evaluating genotypes on several characters. It is determined by the breeder's use of a graphical method to select and rank genotypes, which displays character forms based on general gains over yield by trait combinations, based on findings by Elfanah et al [34] and Mohammadi [35]. This model of GYT calculates by multiplication of the mean genotype of the seed yield by other traits if the highest value is desired and divided if the lowest value is desired.…”

Section: Introductionmentioning

confidence: 99%

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Yield Adaptability and Stability in Field Pea Genotypes Using AMMI, GGE, and GYT Biplot Analyses

Yang,

Soliman,

et al. 2023

Agriculture

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Pea (Pisum sativum L.) is a vital leguminous crop farmed worldwide. Pea plays an essential role in China’s crop rotation system, but the major restrictions to its cultivation are stability and low yield. Breeding for promising cultivars with a significantly high yield will impact the sustainability of pea production. Additionally, diverse environment trials are crucial in determining the best genotype. The new cultivar “Yunwan 52” was developed by hybridization and subsequently evaluated through yield trials among six pea genotypes across 14 environments during the 2016–2018 growing seasons. The results showed that the average yield of “Yunwan 52” for all tested environments was 2.64 t ha−1 compared to the control cultivar (Yunwan 18, 1.83 t ha−1). Analysis of AMMI variance showed significant differences (p < 0.01) between genotypes, environments, and their interaction. Based on the GGE biplot, some genotypes possessed wide and narrow adaptability to environments, such as Yunwan 52 was considered the most stable and ideal gen-otype across all tested environments. GYT biplot analysis also revealed that this realized cultivar was a superior and stable genotype that can be identified visually by combining all characters in breeding programs. Yunwan 52 distinguishes with purple blossoms and seed coat peas. It is possible to infer that the newly released cultivar “Yunwan 52” has outstanding yield performance and wide adaptability to multiple environmental conditions (resilience to abiotic stress). It will contribute to developing nutritional pea genotypes and increase pea production in irrigated areas.

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Section: Discussionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Yield Adaptability and Stability in Field Pea Genotypes Using AMMI, GGE, and GYT Biplot Analyses

Yang,

Soliman,

et al. 2023

Agriculture

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“…At the same time, other researchers ( Santana et al., 2021 ) used spectral reflectance indices and GT biplots to identify high-yielding corn genotypes evaluated under low- and high-nitrogen applications. Researchers ( Elfanah et al., 2023 ) pointed out the selection of salt-tolerant wheat genotypes based on pots and lysimeter systems (sandy soil) identified employing STI and SRI parameters. In the current study, genotype 1 is the best one from the GYT view with SRI ( Figures 5A, B ) in saline soil (clay field).…”

Section: Discussionmentioning

confidence: 99%

“…The beneficial use of SRIs in wheat breeding programs is essential to recognize their relationship with agronomic traits ( Santana et al., 2021 ). The GT and GYT biplot approaches ( Yan et al., 2007 ; Yan and Frégeau-Reid, 2018 ; Darwish et al., 2023 ; Elfanah et al., 2023 ) allow breeders and statisticians to distinguish the correlation of traits and combinations, as well as the genotype rank and stability of these traits or GYT combinations. Furthermore, these biometrical techniques for identifying stress-tolerant genotypes rely on STIs.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral reflectance and agro-physiological traits for field identification of salt-tolerant wheat genotypes using the genotype by yield*trait biplot technique

et al. 2023

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IntroductionSalinity is the abiotic obstacle that diminishes food production globally. Salinization causes by natural conditions, such as climate change, or human activities, e.g., irrigation and derange misuse. To cope with the salinity problem, improve the crop environment or utilize crop/wheat breeding (by phenotyping), specifically in spread field conditions. For example, about 33 % of the cropping area in Egypt is affected by salinity.MethodsTherefore, this study evaluated forty bread wheat genotypes under contrasting salinity field conditions across seasons 2019/20 and 2020/21 at Sakha research station in the north of Egypt. To identify the tolerance genotypes, performing physiological parameters, e.g., Fv/Fm, CCI, Na+, and K+, spectral reflectance indices (SRIs), such as NDVI, MCARI, and SR, and estimated salinity tolerance indices based on grain yield in non-saline soil and saline soil sites over the tested years. These traits (parameters) and grain yield are simultaneously performed for generating GYT biplots.ResultsThe results presented significant differences (P≤0.01) among the environments, genotypes, and their interaction for grain yield (GY) evaluated in the four environments. And the first season for traits, grain yield (GY), plant height (PH), harvest index (HI), chlorophyll content index (CCI), chlorophyll fluorescence parameter Fv/Fm, normalized difference vegetation index (NDVI) in contrasting salinity environments. Additionally, significant differences were detected among environments, genotypes, and their interaction for grain yield along with spectral reflectance indices (SRIs), e.g., Blue/Green index (BIG2), curvature index (CI), normalized difference vegetation index (NDVI), Modified simple ratio (MSR). Relying on the genotype plus genotype by environment (GGE) approach, genotypes 34 and 1 are the best for salinity sites. Genotypes 1 and 29 are the best from the genotype by stress tolerance indices (GSTI) biplot and genotype 34. Genotype 1 is the best from the genotype by yield*trait (GYT) method with spectral reflectance indices.DiscussionTherefore, we can identify genotype 1 as salinity tolerant based on the results of GSTI and GYT of SRIs and recommend involvement in the salinity breeding program in salt-affected soils. In conclusion, spectral reflectance indices were efficiently identifying genotypic variance.

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“…The increased sensitivity of the index to leaf chlorophyll concentration is due to the effective normalization of different LAI values obtained by applying red and green colors. MCARI measures the depth of chlorophyll absorption and is very sensitive to changes in leaf area index and chlorophyll concentration [50][51]. MCARI values are not affected by lighting conditions, background reflection from soil and other observed non-photosynthetic materials.…”

Section: Vegetation Indicesmentioning

confidence: 99%

Cotton yield estimation using several vegetation indices

BABASHLİ,

BADALOVA,

SHUKUROV

et al. 2024

Turkish Journal of Engineering

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Accurate yield estimation before harvest is important for farmers and researchers to optimize field management and increase productivity. The purpose of this study is to develop efficient cotton plant productivity using field studies and satellite imagery. Nitrogen (N) fertilizer is an important nutrient in plant development, and when suboptimal amounts are applied, it can cause yield reductions. In our study, various indices of plant origin were created for the productivity of the cotton plant, and the productivity was calculated using the indexes with the most influencing factors. Cotton yield monitoring required the extraction of visible vegetation indices from satellite images. Following data extraction, multiple strategies were used to choose feature parameters. Cotton yield monitoring models were built using several vegetation indices based on observable vegetation indicators and their combinations. The results show that vegetation indices derived from satellite images and field survey data were substantially connected with cotton yield. The optimal index was chosen using Chlorophyll Index RedEdge in conjunction with vegetation indices and fielda data. Finally, the study's findings show that satellite imaging has some promise for cotton yield monitoring, which is critical for providing theoretical and technological support for cotton production estimation in the field.

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Spectral Reflectance Indices’ Performance to Identify Seawater Salinity Tolerance in Bread Wheat Genotypes Using Genotype by Yield*Trait Biplot Approach

Cited by 7 publications

References 63 publications

Yield Adaptability and Stability in Field Pea Genotypes Using AMMI, GGE, and GYT Biplot Analyses

Yield Adaptability and Stability in Field Pea Genotypes Using AMMI, GGE, and GYT Biplot Analyses

Hyperspectral reflectance and agro-physiological traits for field identification of salt-tolerant wheat genotypes using the genotype by yield*trait biplot technique

Cotton yield estimation using several vegetation indices

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