Path analysis based on genetic association of yield components and insects pest in upland cotton varieties

Shaheen, Mussarat; Rauf, Hafiz Abdul; Taj, Muhammad Ahmed; Ali, Muhammad Zeeshan; Bashir, Muhammad Amjad; Atta, Sagheer; Farooq, Hasnain; Alajmi, Reem A.; Hashem, Mohamed; Alamri, Saad

doi:10.1371/journal.pone.0260971

Cited by 7 publications

(5 citation statements)

References 12 publications

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“…Same result for number of bolls per plant was observed by Dahiphale et al (2018) and Nikhil et al (2018). The trait boll weight (0.601) was observed to have a high direct effect on seed cotton yield per plant same as observed by Shaheen et al (2021) and Nikhil et al (2018), followed by the trait number of locules per boll (0.3535).…”

Section: Direct Effectsupporting

confidence: 79%

Genetic studies of correlation and path coefficient analysis for yield and fibre quality traits in cotton (Gossypium hirsutum L.)

Keerthivarman¹

2022

YMER

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Sixteen Line × Tester hybrids were studied to better understand yield component relationships. The number of bolls per plant and boll weight had a substantial positive correlation and a high direct influence on seed cotton yield per plant, according to both association and route analysis analyses. Finally, selecting some features, such as number of bolls per plant and boll weight, directly impacts seed cotton yield per plant. Furthermore, number of bolls per plant had a substantially favourable indirect influence on number of sympodial branches per plant, micronaire value, and lint index through number of sympodial branches per plant, micronaire value, and lint index. The lint index had a significant indirect impact on the seed index. Selection based on these qualities indirectly increases seed cotton output per plant by a large margin. As a result, the yield and yield potential have improved. Key words: Path analysis, Cotton, Correlation co-efficient and Gossypium hirsutum L

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Section: Direct Effectsupporting

confidence: 79%

Genetic studies of correlation and path coefficient analysis for yield and fibre quality traits in cotton (Gossypium hirsutum L.)

Keerthivarman¹

2022

YMER

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“…Path analysis provides a reliable basis for statistical decision making. Since it was first proposed by American scholar Sewall Wright [10], it has been widely used in many fields [11,12].…”

Section: Path Analysismentioning

confidence: 99%

Establishment of a Reference Evapotranspiration Forecasting Model Based on Machine Learning

Guo,

Cao,

Lin

2024

Agronomy

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Water scarcity is a global problem. Deficit irrigation (DI) reduces evapotranspiration, improving water efficiency in agriculture. Reference evapotranspiration (ET0) is an important factor in determining DI. ET0 forecasting predicts field water consumption and enables proactive irrigation decisions, offering guidance for water resource management. However, implementation of ET0 forecasting faces challenges due to complex calculations and extensive meteorological data requirements. This project aims to develop a machine learning system for ET0 forecasting. The project involves studying ET0 methods and identifying required meteorological parameters. Historical meteorological data and weather forecasts were obtained from meteorological websites and analyzed for accuracy after preprocessing. A machine learning-based model was created to forecast reference crop evapotranspiration. The model’s input parameters were selected through path analysis before it was optimized using Bayesian optimization to reduce overfitting and improve accuracy. Three forecasting models were developed: one based on historical meteorological data, one based on weather forecasts, and one that corrects the weather forecasts. All three models achieved good accuracy, with root mean square errors ranging from 0.52 to 0.81 mm/day. Among them, the model based on weather forecast had the highest accuracy; the RMSE six days before the forecast period was between 0.52 and 0.75 mm/day, and the RMSE on the seventh day of the forecast period was 1.12 mm/day. In summary, this project has established a mathematical model of ET0 prediction based on machine learning, which can achieve more accurate predictions for within a few days.

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“…Path analysis was carried out using the genotypic correlation coefficients to know the direct and indirect effects of the components on yield as suggested and illustrated [26] [27].…”

Section: Estimation Of Path Coefficient Analysismentioning

confidence: 99%

Relationship of Quantitative Traits in Different Morphological Characters of Pea (Pisum Sativum L.)

Tasnim

Poly

Jahan

et al. 2022

J. Multidiscip. Appl. Nat. Sci.

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An experiment was undertaken to elucidate the genetic relationship between different quantitative traits for commercial cultivation and to evaluate selection criteria in pea breeding programs in five inbred parents. Their 17 F4’s derivatives in pea (Pisum sativum L.) evaluated ten characters during the winter season (November to February) of 2017-18 at the research farm, BSMRAU, Gazipur, Bangladesh. Analysis of variance explored significant differences among the genotypes for all the characters. Phenotypic coefficients of variation (PCV) were close to genotypic coefficients of variation (GCV) for all the characters indicating less influence on the environment and potentiality of selection. A high heritability relationship with high genetic advance was observed for plant height, pod per plant, hundred seed weight, and seed yield per plot. Pod length showed a highly significant positive correlation with pod width and hundred seeds weight. Only days to first flowering showed a highly negative correlation with pod length and hundred seed weight. Path coefficient analysis revealed that plant height, pod per plant, and seeds per pod had a highly positive effect on yield per plant. Therefore, associating and selecting those traits, yield improvement must be possible in pea, and the days to maturity, plant height, pods per plant, pod length, and seed showed a considerable positive and highly significant correlation with plant height, pod per plant, seed per pod, and yield per plant at both genotypic and phenotypic levels indicating yield could be increased with the increase of days to maturity, plant height, pods per plant, pod length, and seed.

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Path analysis based on genetic association of yield components and insects pest in upland cotton varieties

Cited by 7 publications

References 12 publications

Genetic studies of correlation and path coefficient analysis for yield and fibre quality traits in cotton (Gossypium hirsutum L.)

Genetic studies of correlation and path coefficient analysis for yield and fibre quality traits in cotton (Gossypium hirsutum L.)

Establishment of a Reference Evapotranspiration Forecasting Model Based on Machine Learning

Relationship of Quantitative Traits in Different Morphological Characters of Pea (Pisum Sativum L.)

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