Genetic Variability and Interrelationship in Maize (Zea mays L.) Genotypes for Grain Yield  and Yield Component Characters

Rani, M. Prasanna; Anusha, C. R.; Lavanya, G. Roopa; Kiran, M. Sai

doi:10.9734/ijecc/2022/v12i111375

Cited by 3 publications

(11 citation statements)

References 4 publications

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“…High genetic advance for grain yield was also found by Sesay et al [14], Sravanti et al [22] and Shrestha et al [19]. Oruganti et al [18] observed "high genetic advance for grain yield and plant height. Moderate genetic advance was recorded by number of kernels per row (11.92%), days to maturity (11.16%) and 100-kernel weight (10.62%)".…”

Section: Estimation Of Heritability and Genetic Advancesupporting

confidence: 55%

“…High estimates of PCV and GCV was also reported for grain yield and ear height by Shrestha et al [19]. Similarly, Jilo et al [15], Mager et al [17], Rai et al [16], Oruganti et al [18], Yadav et al [20] and Thakur et al [21] were reported for grain yield. The other characters shelling percentage, days to 50%tasseling, days to 50%silking, days to maturity and number of kernel rows per ear were recorded low PCV and GCV.…”

Section: Phenotypic and Genotypic Coefficients Of Variationmentioning

confidence: 72%

“…These results were also confirmed by results of Jilo et al [15] and Rai et al [16] who observed low PCV and GCV for days to 50%tasseling, days to 50 % silking and number of kernel rows per ear. The results of Sravanti et al [22] and Oruganti et al [18] also shown low PCV and GCV for days to 50% tasseling, Days to 50% silking and Days to maturity and Thakur et al [21] for days to maturity, days to 50% tasseling, days to 50% silking and shelling percentage.…”

Section: Phenotypic and Genotypic Coefficients Of Variationmentioning

confidence: 93%

“…[14] for plant height, ear height, and100-kernel weight. Mager et al [17] for plant height, ear height test weight and Oruganti et al [18] for grain yield and plant height.…”

Section: Analysis Of Variancementioning

confidence: 99%

“…Sesay et al [14] and Shrestha et al [19] were observed for grain yield. Oruganti et al [18] for plant height and grain yield per cob. The characters, number of kernels/row, days to maturity and 100-kernel weight recorded high heritability with moderate estimates of genetic advance.…”

Section: Estimation Of Heritability and Genetic Advancementioning

confidence: 99%

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Assessment of Genetic Variability, Heritability and Genetic Advance in Maize Genotypes (Zea mays L.)

Kumar,

Sunil,

Sekhar

et al. 2024

JEAI

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Aim: The aim of the present research work was to estimate the genetic variability, broad sense heritability and genetic advance for various yield and its contributing characters of 39 maize genotypes. Methodology: 39 maize genotypes were sown in randomized block design and replicated thrice to evaluate various genetic parameters. The data was recorded on twelve quantitative traits including days to fifty percent tasseling, days to fifty percent silking, days to maturity, plant height, ear height, ear length, ear diameter, number of kernel rows/ear, number of kernels/row, 100kernels weight, shelling % and grain yield/plant. Results: The mean sum of squares due to genotypes for each of the 12 traits under study indicated significant differences in analysis of variance. High to moderate genotypic and phenotypic coefficients of variation as well as genetic advance were observed in the traits yield per plant, plant height, ear height, number of kernels per row, and 100-kernel weight. These findings suggest that the heritability of these traits is most likely the result of additive gene effects and that selection in early generations may be effective for these traits. On the other hand, the remaining characters days to 50% tasseling, days to 50% silking, number of kernel rows per ear, and shelling percentage were recorded with high heritability with low genotypic and phenotypic coefficient of variation and genetic advance suggesting non-additive gene action for control of these traits which provides limited scope for improvement by selection. Conclusion: Among the characters studied, grain yield, plant height, and ear height had high heritability and high values of genetic advance indicating that these characters were controlled by additive gene action and so phenotypic selection is effective for improvement of these traits.

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Section: Estimation Of Heritability and Genetic Advancesupporting

confidence: 55%

Section: Phenotypic and Genotypic Coefficients Of Variationmentioning

confidence: 72%

Section: Phenotypic and Genotypic Coefficients Of Variationmentioning

confidence: 93%

“…[14] for plant height, ear height, and100-kernel weight. Mager et al [17] for plant height, ear height test weight and Oruganti et al [18] for grain yield and plant height.…”

Section: Analysis Of Variancementioning

confidence: 99%

Section: Estimation Of Heritability and Genetic Advancementioning

confidence: 99%

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Assessment of Genetic Variability, Heritability and Genetic Advance in Maize Genotypes (Zea mays L.)

Kumar,

Sunil,

Sekhar

et al. 2024

JEAI

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Enhancing Crop Yield Predictions with PEnsemble 4: IoT and ML-Driven for Precision Agriculture

Pukrongta,

Taparugssanagorn,

Sangpradit

2024

Applied Sciences

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This research introduces the PEnsemble 4 model, a weighted ensemble prediction model that integrates multiple individual machine learning models to achieve accurate maize yield forecasting. The model incorporates unmanned aerial vehicle (UAV) imagery and Internet of Things (IoT)-based environmental data, providing a comprehensive and data-driven approach to yield prediction in maize cultivation. Considering the projected growth in global maize demand and the vulnerability of maize crops to weather conditions, improved prediction capabilities are of paramount importance. The PEnsemble 4 model addresses this need by leveraging comprehensive datasets encompassing soil attributes, nutrient composition, weather conditions, and UAV-captured vegetation imagery. By employing a combination of Huber and M estimates, the model effectively analyzes temporal patterns in vegetation indices, in particular CIre and NDRE, which serve as reliable indicators of canopy density and plant height. Notably, the PEnsemble 4 model demonstrates a remarkable accuracy rate of 91%. It advances the timeline for yield prediction from the conventional reproductive stage (R6) to the blister stage (R2), enabling earlier estimation and enhancing decision-making processes in farming operations. Moreover, the model extends its benefits beyond yield prediction, facilitating the detection of water and crop stress, as well as disease monitoring in broader agricultural contexts. By synergistically integrating IoT and machine learning technologies, the PEnsemble 4 model presents a novel and promising solution for maize yield prediction. Its application holds the potential to revolutionize crop management and protection, contributing to efficient and sustainable farming practices.

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Association Analysis over Seasons among Morphological, Physiological and Yield Components with Kernel Yield in Maize (Zea mays L.)

Sabitha,

Reddy,

Reddy

et al. 2024

JABB

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Forty five single cross hybrids made from 10 inbred lines of maize through diallel mating design were evaluated for three seasons viz. rabi, summer and kharif from 2016-17 to 2017-18.Kernel yield had consistent significant and positive associations with SPAD meter readings, specific leaf area, cob length, cob girth, number of kernel rows cob-1,number of kernels row-1,100 kernel weight and harvest index in rabi, summer and kharif seasons. Similar trend of positive and significant association of kernel yield with all the above characters were recorded at genotypic level indicating existence a close relationship among these characters. Days to 50% tasseling, days to 50% silking, days to maturity and specific leaf weight showed consistent negative and significant correlations with kernel yield both at phenotypic and genotypic level indicating yield penalty with increase in days to 50% tasselling, days to 50% silking and specific leaf weight because more of vegetative growth and less time for reproductive growth which consequently lead to less kernel yield. The associations of anthesis-silking interval with kernel yield were consistently negative and non-significant in all the three seasons at phenotypic level revealing that narrow interval of anthesis silking interval facilates good seed setting. Plant height showed either negative and significant or negative but non-significant association with kernel yield across seasons suggesting that any increase in plant height may lead to reduction in kernel yield and thus medium plant height is desirable for recording higher kernel yields in Maize. Based on the results of character association analysis it was concluded that SPAD chlorophyll meter readings, specific leaf area, cob length, cob girth, number of kernel rows cob-1, number of kernels row-1, 100 kernel weight and harvest index might be given due importance, while formulating selection indices aimed at kernel yield improvement as these characters had showed consistently positive and significant associations with kernel yield. A plant with medium plant height and duration coupled with higher SPAD meter readings, specific leaf area, cob length, cob girth, number of kernel rows cob-1,number of kernels row-1,100 kernel weight and harvest index is desirable for getting higher kernel yield in maize.

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Genetic Variability and Interrelationship in Maize (Zea mays L.) Genotypes for Grain Yield and Yield Component Characters

Cited by 3 publications

References 4 publications

Assessment of Genetic Variability, Heritability and Genetic Advance in Maize Genotypes (Zea mays L.)

Assessment of Genetic Variability, Heritability and Genetic Advance in Maize Genotypes (Zea mays L.)

Enhancing Crop Yield Predictions with PEnsemble 4: IoT and ML-Driven for Precision Agriculture

Association Analysis over Seasons among Morphological, Physiological and Yield Components with Kernel Yield in Maize (Zea mays L.)

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