Correlation and path coefficient analysis for yield, yield components and quality traits in wheat

Devesh, Pavan; Moitra, PK; Shukla, R. S.

doi:10.37992/2021.1202.057

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Classical breeding methods, including the selection and release of elite cultivars, as well as the recognition of the agronomic traits as the suitable criteria to use in breeding programs, are being remembered as fast, easy and reliable methods to introduce superior fennel cultivars. Screening the best indicators to use in essential oil yield (EOY%) improvement plans and finding the correlation between EOY% and its components is the first priority [11][12][13]. Piccaglia and Marotti [14] reported that biomass weight and the number of umbels per plant are positively related to the essential oil content (R = 0.651 and 0.569, respectively) [14].…”

Section: Introductionmentioning

confidence: 99%

Modeling the Essential Oil and Trans-Anethole Yield of Fennel (Foeniculum vulgare Mill. var. vulgare) by Application Artificial Neural Network and Multiple Linear Regression Methods

et al. 2021

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Foeniculum vulgare Mill. (commonly known as fennel) is used in the pharmaceutical, cosmetic, and food industries. Fennel widely used as a digestive, carminative, galactagogue and diuretic and in treating gastrointestinal and respiratory disorders. Improving low heritability traits such as essential oil yield (EOY%) and trans-anethole yield (TAY%) of fennel by direct selection does not result in rapid gains of EOY% and TAY%. Identification of high-heritable traits and using efficient modeling methods can be a beneficial approach to overcome this limitation and help breeders select the most advantageous traits in medicinal plant breeding programs. The present study aims to compare the performance of the artificial neural network (ANN) and multilinear regression (MLR) to predict the EOY% and TAY% of fennel populations. Stepwise regression (SWR) was used to assess the effect of various input variables. Based on SWR, nine traits—number of days to 50% flowering (NDF50%), number of days to maturity (NDM), final plant height (FPH), number of internodes (NI), number of umbels (NU), seed yield per square meter (SY/m2), number of seeds per plant (NS/P), number of seeds per umbel (NS/U) and 1000-seed weight (TSW)—were chosen as input variables. The network with Sigmoid Axon transfer function and two hidden layers was selected as the final ANN model for the prediction of EOY%, and the TanhAxon function with one hidden layer was used for the prediction of TAY%. The results revealed that the ANN method could predict the EOY% and TAY% with more accuracy and efficiency (R2 of EOY% = 0.929, R2 of TAY% = 0.777, RMSE of EOY% = 0.544, RMSE of TAY% = 0.264, MAE of EOY% = 0.385 and MAE of TAY% = 0.352) compared with the MLR model (R2 of EOY% = 0.553, R2 of TAY% = 0.467, RMSE of EOY% = 0.819, RMSE of TAY% = 0.448, MAE of EOY% = 0.624 and MAE of TAY% = 0.452). Based on the sensitivity analysis, SY/m2, NDF50% and NS/P were the most important traits to predict EOY% as well as SY/m2, NS/U and NDM to predict of TAY%. The results demonstrate the potential of ANNs as a promising tool to predict the EOY% and TAY% of fennel, and they can be used in future fennel breeding programs.

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Section: Introductionmentioning

confidence: 99%

Modeling the Essential Oil and Trans-Anethole Yield of Fennel (Foeniculum vulgare Mill. var. vulgare) by Application Artificial Neural Network and Multiple Linear Regression Methods

et al. 2021

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“…In these cases, direct selection on the basis of correlated response may not be fruitful [34]. For critical evaluation, the correlation coefficient needs to be split into direct and indirect effects using path coefficient analysis since, many characters affect a given trait [35]. Thus, the correlation and path coefficients in combination can give a better insight into cause-and-effect relationship between different pairs of characters [36].…”

Section: Path Coefficient Analysismentioning

confidence: 99%

Unveiling the Complexity of Yield Traits through Genetic Variability, Correlation, and Path Analysis across Diverse Bread Wheat [Triticum aestivum (L.) em. Thell] Germplasm

Naveen,

Hathiram,

Supriya

et al. 2024

J. Adv. Biol. Biotechnol.

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Aims: To elucidate the genetic variability, correlation, and path analysis of various agronomic and morphological traits among fifty genotypes of bread wheat (Triticum aestivum (L.) em. Thell) to facilitate targeted breeding efforts. Study Design: The experiment was laid out in Randomized Complete Block Design (RCBD) in 2 replications. Place and Duration of Study: The study was conducted during the Rabi season of 2019 at Banaras Hindu University, India. Methodology: Fifty genotypes of bread wheat were evaluated for thirteen morphological traits. Analysis of variance (ANOVA), correlation analysis, direct and indirect effects on grain yield per plot through path coefficient analysis were calculated. Assessed traits included biomass, harvest index, germination percentage, tillers per meter, days to flowering, plant height, and spike length. Genetic variability and heritability parameters were calculated for these traits. Genetic variability parameters were also calculated to determine the extent of variability and heritability for these traits. Results: The analysis of variance revealed significant genotypic differences for all studied traits. Correlation analysis highlighted intricate interrelationships among traits, with biomass exhibiting the highest positive direct effect on grain yield, followed by harvest index and germination percentage. In contrast, tillers per meter, days to flowering, and plant height had negative direct effects on yield. Genetic variability parameters indicated substantial variability for traits like harvest index and grain yield per plot, with spike length showing the highest heritability. Conclusion: The study highlights the complexity of grain yield determination, which is affected by various genetic and environmental factors. The results offer deeper insights into the genetic basis of yield-related traits in wheat, stressing the importance of selecting genotypes with beneficial trait combinations to boost yield potential.

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Wheat cultivars grain yield and quality traits variability under conditions of southern Pannonian plain

Timić,

Dražić,

Mirosavljević

et al. 2024

Ratar i povrt

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In order to meet milling industry and grain producers' requirements, wheat grain needs to possess good technological quality, and high and stable grain production. This study analyzed 30 varieties of winter wheat over four growing seasons (2015/16, 2016/17, 2017/18, and 2018/19) at the location Rimski Šančevi, Serbia. The studied traits were under the control of genotype, environment, and their interaction. The analysis of variance (ANOVA) revealed that the influence of the factors varied depending on the studied trait. Thousand grain weight showed the highest variability depending on genotype influence, protein content and sedimentation value that were mostly affected by environmental conditions, while the genotype and environment interaction had the greatest impact on hectoliter weight and grain yield. Using principal component analysis significant positive relationship was observed between protein content and sedimentation value, and between thousand grain weight and hectoliter weight. It was also shown that yield and thousand grain weight, sedimentation value and hectoliter weight are positively related. A strong negative association was noted between grain yield and protein content. The protein content and hectoliter weight did not show any dependency. All analyzed traits are significant for breeding programs, emphasizing the importance of developing high-quality varieties with high yield potential and tolerance to adverse environmental conditions.

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Correlation and path coefficient analysis for yield, yield components and quality traits in wheat

Cited by 3 publications

References 8 publications

Modeling the Essential Oil and Trans-Anethole Yield of Fennel (Foeniculum vulgare Mill. var. vulgare) by Application Artificial Neural Network and Multiple Linear Regression Methods

Modeling the Essential Oil and Trans-Anethole Yield of Fennel (Foeniculum vulgare Mill. var. vulgare) by Application Artificial Neural Network and Multiple Linear Regression Methods

Unveiling the Complexity of Yield Traits through Genetic Variability, Correlation, and Path Analysis across Diverse Bread Wheat [Triticum aestivum (L.) em. Thell] Germplasm

Wheat cultivars grain yield and quality traits variability under conditions of southern Pannonian plain

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