Genotype–Trait (GT) Biplot Analysis for Yield and Quality Stability in Some Sweet Corn (Zea mays L. saccharata Sturt.) Genotypes

Stansluos, Atom Atanasio Ladu; Öztürk, Ali; Niedbała, Gniewko; Türkoğlu, Aras; Haliloğlu, Kamil; Szulc, Piotr; Omrani, Ali; Wojciechowski, Tomasz; Piekutowska, Magdalena

doi:10.3390/agronomy13061538

Cited by 4 publications

(2 citation statements)

References 39 publications

(61 reference statements)

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“…( 2020 ) and Stansluos et al. ( 2023 ), who employed the GGE biplot for evaluating and selecting outstanding genotypes based on various agronomic traits. While the outcomes of parametric stability analysis methods largely concurred with the GGE biplot analysis, additional stability approaches were necessary to categorize stable lines and reinforce our results.…”

Section: Discussionmentioning

confidence: 99%

Deciphering genotype‐by‐environment interaction in new soybean lines based on multiple traits using different adaptability and stability methods

Majidian,

Masoudi,

Hezarjaribi

et al. 2024

Food Science & Nutrition

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The multi‐environmental trials aid breeders in selecting the best genotypes for specific or general adaptability to different environments before commercial release. This study aimed to assess the stability of 13 new soybean pure lines, along with two controls, in terms of seed yield and important agronomic traits. The assessment was based on a completely randomized block design with three replications across four areas during 2020–2022. Various adaptability methods, including parametric, AMMI, GGE biplot, PCA, and SIIG were employed. The mixed analysis showed that the effects of environment, genotype, and genotype–environment (GE) interaction were significant for most studied traits. The AMMI showed the highest portion of environment (65.89%) in soybean seed yield. Based on all stability parameters, lines 2 and 5 were selected for their average seed yields of 3349 and 3142 kg ha−1, respectively. Additionally, lines 6 and 5 showed the most stability, yielding higher than the average, which were 2992 and 3142 kg ha−1, respectively, according to GGE biplot results. Furthermore, lines 2, 5, and 8 were identified as the ideal genotypes concerning seed yield and other agronomic traits, with high SIIG parameters and yields exceeding the average. Finally, the soybean line 5 was deemed the most suitable due to its higher yield, stability, and early maturity (128‐day growth period). Therefore, line 5 is considered appropriate for its high stability and earliness in various regions of Iran.

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Section: Discussionmentioning

confidence: 99%

Deciphering genotype‐by‐environment interaction in new soybean lines based on multiple traits using different adaptability and stability methods

Majidian,

Masoudi,

Hezarjaribi

et al. 2024

Food Science & Nutrition

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show abstract

“…Additionally, genotype by traits biplot and heat map analyses were used as a statistical tool for identification. Biplot is an appropriate tool for identifying cultivars and traits interaction, where cultivars are considered as lines and traits as testers [59], while the Heatmap analysis provides the relative performance of the cultivars [20] as well as the ranking of cultivars based on their performance. A potentially important finding of this study was the fact that when the traditional method and VIs were used for the identification of drought-tolerant cultivars, similar results were observed (Figure 8), indicating its potential for a replacement of the traditional method.…”

Section: Identification Of Drought-tolerant Cultivarmentioning

confidence: 99%

Assessing Drought Stress of Sugarcane Cultivars Using Unmanned Vehicle System (UAS)-Based Vegetation Indices and Physiological Parameters

Khuimphukhieo,

Bhandari,

Enciso

et al. 2024

Remote Sensing

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Sugarcane breeding for drought tolerance is a sustainable strategy to cope with drought. In addition to biotechnology, high-throughput phenotyping has become an emerging tool for plant breeders. The objectives of the present study were to (1) identify drought-tolerant cultivars using vegetation indices (VIs), compared to the traditional method and (2) assess the accuracy of VIs-based prediction model estimating stomatal conductance (Gs) and chlorophyll content (Chl). A field trial was arranged in a randomized complete block design, consisting of seven cultivars of sugarcane. At the tillering and elongation stages, irrigation was withheld, and then furrow irrigation was applied to relieve sugarcane from stress. The physiological assessment measuring Gs and Chl using a handheld device and VIs were recorded under stress and recovery periods. The results showed that the same cultivars were identified as drought-tolerant cultivars when VIs and traditional methods were used for identification. Likewise, the results derived from genotype by trait biplot and heatmap were comparable, in which TCP93-4245 and CP72-1210 cultivars were classified as tolerant cultivars, while sensitive cultivars were CP06-2400 and CP89-2143 for both physiological parameters and VIs-based identification. In the prediction model, the random forest outperformed linear models in predicting the performance of cultivars in untested crops/environments for both Gs and Chl. In contrast, it underperformed linear models in the tested crops/environments. The identification of tolerant cultivars through prediction models revealed that at least two out of three cultivars had consistent rankings in both measured and predicted outcomes for both traits. This study shows the possibility of using UAS mounted with sensors to assist plant breeders in their decision-making.

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Genotype by year interaction for selected traits in sweet maize (Zea maize L.) hybrids using AMMI model

Bocianowski,

Waligóra,

Majchrzak

2024

Euphytica

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This study investigated genotype × environment interactions for the stability of expression of four productivity traits (cobs yield, cobs I class trade share, lend of cobs and fulfilment of cobs) of sweet maize hybrids (Zea mays L.). The additive main effects and multiplicative interaction (AMMI) model was employed to assess genotype × environment interaction. AMMI stability value was used to evaluate both stability and genotype. The genotype selection index was calculated for each hybrid, incorporating both the average trait value and the stability index. Ten sweet maize hybrids were evaluated: Golda, GSS 1453, GSS 3071, GSS 5829, GSS 8529, Overland, Noa, Shinerock, Sindon, and Tessa. Trials were ran conducted over four vegetative seasons at a single location in the Wielkopolska region using replicated field experiments. The AMMI model revealed significant genotypic and environmental effects for all analyzed traits. Based on their superior stability and favorable average trait values, both the Golda cultivar and the GSS 3071 hybrid are recommended for further breeding program inclusion.

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Genotype–Trait (GT) Biplot Analysis for Yield and Quality Stability in Some Sweet Corn (Zea mays L. saccharata Sturt.) Genotypes

Cited by 4 publications

References 39 publications

Deciphering genotype‐by‐environment interaction in new soybean lines based on multiple traits using different adaptability and stability methods

Deciphering genotype‐by‐environment interaction in new soybean lines based on multiple traits using different adaptability and stability methods

Assessing Drought Stress of Sugarcane Cultivars Using Unmanned Vehicle System (UAS)-Based Vegetation Indices and Physiological Parameters

Genotype by year interaction for selected traits in sweet maize (Zea maize L.) hybrids using AMMI model

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