Genetic variability and interrelationships of seed yield and yield components in switchgrass

Das, Mita; Taliaferro, C. M.

doi:10.1007/s10681-008-9866-3

Cited by 19 publications

(14 citation statements)

References 35 publications

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“…X→Y→Z→W. This sequential relationship between yield and yield components was developed into a working model for the investigation of crop yields by Tai (1975), and has been shown to provide a powerful tool for studying G × E interactions (Tai, 1979;Lynch and Tai, 1989;Tai et al, 1994;Fagam et al, 2006;Hui et al, 2008;Das and Taliaferro, 2009;Yasin and Singh, 2010).…”

Section: Introductionmentioning

confidence: 99%

Path analysis of genotype × environment interaction in wheat-barley disomic addition lines

Farshadfar

2013

Acta Agronomica Hungarica

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In order to locate QTLs controlling the phenotypic stability and drought tolerance of yield and yield components in barley, seven disomic addition lines were sown together with their parents (donor and recipient) in a randomized complete block design with three replications under four rainfed and irrigated conditions. The descriptive diagram of yield and yield components exhibited a genotype (G) × environment (E) interaction and moderate variability over different environments, indicating the possibility of selection for stable and drought-tolerant entries. The AMMI stability value (ASV) and yield stability index (YSI) discriminated addition lines 2H and 4H as the most stable and droughttolerant.Path analysis revealed that the relative contribution of the number of seeds per plant (NSPP) (0.71) to grain yield (GY) was higher than that of the number of seeds per spike (SPS) (−0.44) and of thousand-seed weight (TSW) (-0.14). Therefore, the contribution of NSPP to the stability of GY over different environments was higher than that of other yield components. In other words, the instability of GY was caused by TSW and SPS in different environments. Path analysis on the drought susceptibility index revealed that most of the QTLs controlling drought tolerance and drought susceptibility in barley are located on chromosomes 3H and 6H, respectively.

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Section: Introductionmentioning

confidence: 99%

Path analysis of genotype × environment interaction in wheat-barley disomic addition lines

Farshadfar

2013

Acta Agronomica Hungarica

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“…Yield components do not only directly affect the yield but also indirectly affect it by affecting other yield components in negative or positive ways (Bidgoli et al 2006). In such situations, knowledge of the nature of genetic variability and interrelationships among seed yield and yield components would facilitate breeding improvement for these traits (Das and Taliaferro 2009). However, information on the genetic variability and relationships between seed yield and yield components in smooth bromegrass is limited.…”

Section: Introductionmentioning

confidence: 99%

“…Path analysis provides a method for separating direct and indirect effects and measuring the relative importance of the causal factors involved. Several researchers have used this method to assess the importance of the components of yield (Ofori 1996;Das and Taliaferro 2009;El-Nakhlawy and Shaheen 2009;Rashidi et al 2009). However, morphological characters influencing yield are often highly inter-correlated, leading to multi-collinearity when the inter-correlated variables are regressed against yield in a multipleregression equation.…”

Section: Introductionmentioning

confidence: 99%

Algorithmic models of seed yield and its components in smooth bromegrass (Bromus inermis L.) via large sample size under field conditions

et al. 2011

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Based on multi-factor orthogonally designed field experimental plots, the correlations among yield components as well as their direct and indirect effects on the seed yield of Bromus inermis L. cv. 'Carlton' were investigated. The seed yield parameters fertile tillers/m 2 (Y 1 ), spikelets/fertile tiller (Y 2 ), florets/spikelet (Y 3 ), seed number/spikelet (Y 4 ), seed weight (Y 5 ), and seed yield (Z) were determined by hand in the field for the years 2003-2005. Via ridge regression analysis, a steady algorithmic model of seed yield with its five components was found that could closely estimate the seed yield. The component Y 1 had the largest correlation coefficient with Z, followed by Y 2 . The contributions of the five components to the seed yield in decreasing order are Y 3 . The inter-correlation among the components Y 1 to Y 5 and Z exhibited significance but Y 1 was not correlated with Y 3 . Therefore, direct selection for large Y 1 , Y 2 , and Y 4 would be an effective means of selection for high seed yield in the grass.

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“…Path coefficient analysis has been useful in determining selection criteria for rice (Samonte et al, 1998). In order to identify a trait as an indirect selection criterion for grain yield through path coefficients, the trait should have a positive direct effect on grain yield as well as significant positive correlation with grain yield (Das and Taliaferro 2009). Path coefficients of agronomic traits on grain yield of rice are shown in Table 4.…”

Section: Resultsmentioning

confidence: 99%

Foliar application of methanol influences on growth and yield of rice (Oryza sativa L.) under different barnyard grass (Echinochloa crus-galli) densities

Abbasian

Mirshekari

Vishekaei

et al. 2015

Idesia

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Recent reports indicate that vegetative growth and yield of C 3 crops are enhanced by foliar methanol application and that overall crop water use is reduced by methanol sprays. In order to evaluate the effects of methanol and barnyard grass density on rice (Oryza sativa cv. Shiroudi) yield and its components, a field experiment was conducted at the Rice Research Station of Tonekabon, Iran in 2012. The experiment was carried out as a randomized complete block design with a factorial arrangement in three replicates. Studied factors were aqueous methanol solutions (0, 6, 12, 18 and 24% (v/v)) and barnyard grass (Echinochloa crus-galli) densities (0, 16, 24 and 32 plants m -2 ). Methanol was sprayed on the foliage of rice three times during its growth period with two-week intervals. Results indicated that effect of methanol was significant for tiller number and grain yield (P < 0.05), while the effect of barnyard grass density was significant for grain and biological yields (P < 0.01). Moreover, the interaction between methanol and barnyard grass density was significant for grain yield (P < 0.05) and 1000-grain weight (P < 0.01). The greatest mean values for grain yield and 1000-grain weights obtained at 18% (v/v) with weed-free condition, with 7440.8 kg ha -1 and 28.07 g, respectively. Under weed-free conditions grain yield increased as methanol dose increased from 0 to 18% (v/v) and then reduced significantly. At the highest weed density, grain yield decreased significantly as the methanol dose increased. It appears that foliar sprays of aqueous methanol may be recommended for rice under weed-free conditions.Key words: rice, barnyard grass, grain yield, methanol, weed density. de 0, 6, 12, 18 y 24% (v/v) RESUMEN El crecimiento vegetativo y la producción de cultivos C3 se estimulan por la aplicación foliar de metanol y mejoran la eficiencia del uso total de agua. El objetivo de este trabajo fue evaluar el efecto de la aplicación foliar con metanol y la densidad de las pasto dentado en el rendimiento de arroz (Oryza sativa cv. Shiroudi). El experimento se realizó en la Estación Experimental del Arroz de Tonekabon, Irán en 2012. El diseño experimental fue de bloques completos al azar con un arreglo factorial 5X4, con tres repeticiones. Los factores estudiados fueron soluciones acuosas de metanol en concentraciones

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Genetic variability and interrelationships of seed yield and yield components in switchgrass

Cited by 19 publications

References 35 publications

Path analysis of genotype × environment interaction in wheat-barley disomic addition lines

Path analysis of genotype × environment interaction in wheat-barley disomic addition lines

Algorithmic models of seed yield and its components in smooth bromegrass (Bromus inermis L.) via large sample size under field conditions

Foliar application of methanol influences on growth and yield of rice (Oryza sativa L.) under different barnyard grass (Echinochloa crus-galli) densities

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