Estimates of genetic parameters and path analysis of crambe: An important oil plant for biofuel production

Vasconcelos, Ubieli Alves Araújo; Dias, Luíz Antônio dos Santos; Corrêa, Thais Roseli; Rosmaninho, Lucas Barbosa de Castro; Silva, Dandara Rêgo Muniz da; Zaidan, Iasmine Ramos

doi:10.4025/actasciagron.v42i1.42490

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…Furthermore, the correlation coefficient only measured the linear relationships, while nonlinear variables could still be valid. A high correlation does not imply a direct cause-and-effect relationship between the analyzed variables [ 35 ]. According to the results obtained in the GGE Biplot analysis the direct effect magnitudes can change based on the growth environment.…”

Section: Resultsmentioning

confidence: 99%

Yield of soybean genotypes identified through GGE biplot and path analysis

et al. 2022

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Genotype × environment (G×E) interaction is an important source of variation in soybean yield, which can significantly influence selection in breeding programs. This study aimed to select superior soybean genotypes for performance and yield stability, from data from multi-environment trials (METs), through GGE biplot analysis that combines the main effects of the genotype (G) plus the genotype-by-environment (G×E) interaction. As well as, through path analysis, determine the direct and indirect influences of yield components on soybean grain yield, as a genotype selection strategy. Eight soybean genotypes from the breeding program of Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) were evaluated in field trials using a randomized block experimental design, in an 8 x 8 factorial scheme with four replications in eight different environments of the Cerrado of Northeastern Brazil during two crop seasons. Phenotypic performance data were measured for the number of days to flowering (NDF), height of first pod insertion (HPI), final plant height (FPH), number of days to maturity (NDM), mass of 100 grains (M100) and grain yield (GY). The results revealed that the variance due to genotype, environment, and G×E interaction was highly significant (P < 0.001) for all traits. The ST820RR, BRS 333RR, BRS SambaíbaRR, M9144RR and M9056RR genotypes exhibited the greatest GY stability in the environments studied. However, only the BRS 333RR genotype, followed by the M9144RR, was able to combine good productive performance with high yield stability. The study also revealed that the HPI and the NDM are traits that should be prioritized in the selection of soybean genotypes due to the direct and indirect effects on the GY.

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Section: Resultsmentioning

confidence: 99%

Yield of soybean genotypes identified through GGE biplot and path analysis

et al. 2022

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“…When assessing quality parameters of eight acerola genotypes, Mariano-Nasser et al (2017) found low CV e values (3.0-7.9%) for skin lightness, soluble solids, titratable acidity and ascorbic acid and medium CV e values (13.8-19.9%) for skin chroma and hue. Furthermore, the relative CV (CV g /CV e ) was higher than 1 for all quality traits, which is recommended for genotype selection (Azevedo et al, 2021;Vasconcelos et al, 2020).…”

Section: Discussionmentioning

confidence: 97%

“…The estimates of genetic parameters found in this study indicate similar results for different approaches (Bayesian × classical methods), which agrees with studies on kale (Brito et al, 2019) and physic nut (Jatropha curcas L.) (Evangelista et al, 2022). Some advantages of Bayesian inference in relation to frequentist determination of variance components (and consequently coefficients of repeatability) include the feasibility of using previous data information (priori), the lower sensitivity to outliers and lower data assumptions, such as experimental balance and high sample size (Singh, L-Yassin, & Omer, 2015), resulting in more accurate estimates of variance, as reported in guava (Silva et al, 2020) and soybean (Volpato et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Bayesian and classical approaches for the estimation of genetic parameters and coefficients of repeatability of acerola quality traits

Vilvert,

Freitas,

Ferreira

et al. 2023

Acta Sci. Agron.

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Although acerola (Malpighia emarginata DC.) is a tropical fruit of high interest due to its high ascorbic acid content and attractive sensory attributes, fruit production is characterized by high genetic variability. Additionally, the use of new biometric tools for acerola breeding is scarce. This study aimed to estimate genetic parameters and the coefficient of repeatability, as well as determine the optimal number of fruits for quality trait analyses in different acerola genotypes, using different approaches. Twenty-three (Experiment I) and thirty-five (Experiment II) genotypes were evaluated in a randomized block design with four replicates and three plants per plot. Twenty fruits per plant were harvested and evaluated for the following quality traits: diameter, mass, skin color (lightness, chroma and hue), firmness, soluble solids (SS), titratable acidity (TA), SS/TA ratio, and ascorbic acid content. The genetic parameters and the coefficient of repeatability were estimated for each experiment using classical and Bayesian methods. Both approaches achieved similar results on estimating variance components, genetic parameters and the coefficient of repeatability. Genetic parameters showed favorable conditions for acerola selection. The coefficient of repeatability was high for all acerola quality traits. A total of 17 fruits are required for the effective selection of acerola genotypes with an accuracy of 95%.

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Soil chemical properties affecting grain yield and oil content of crambe biofuel crop

de Marins,

Secco,

Ruffato

et al. 2024

Soil Science Soc of Amer J

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Crambe (Crambe abyssinica Hochst) is an important grain crop for biodiesel production. The objective of this study was to evaluate how spatial distribution of soil properties affects crambe grain yield and oil content. We analyzed six different sampling densities, with a distance between sampling points ranging from 5.5 to 44 m, to identify correlations among soil and crop variables, and the best spatial distribution. Crambe oil content was not correlated to any of the physicochemical properties, whereas crambe grain yield was correlated with total porosity and exchangeable Al and Ca, effective cation exchange capacity, available P, K, Cu, and Zn, and aluminum and base saturation, with highly significant correlation (p < 0.001) for P and K and Cu and Zn. Most soil properties had spatial dependence degrees from weak to strong, and were modeled by spherical or exponential semi‐variograms. Spatial data showed that low grain yield is associated with high seed oil content, while crambe grain yield was more correlated with phosphorous availability in soil in the first year (2012) and with Ca, Mg, and soil organic matter in the second year (2013). Spatial variability in soil nutrients is attributed to uneven distribution of fertilizers and surface runoff. The results demonstrate sampling density must be increased to generate more accurate maps for soil chemical properties affecting crambe. The use of a grid of 25 points in a 625 m2 of land is suggested, providing more than 60% accuracy for grain yield estimation and more than 40% for most chemical properties’ estimation.

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Estimates of genetic parameters and path analysis of crambe: An important oil plant for biofuel production

Cited by 5 publications

References 14 publications

Yield of soybean genotypes identified through GGE biplot and path analysis

Yield of soybean genotypes identified through GGE biplot and path analysis

Bayesian and classical approaches for the estimation of genetic parameters and coefficients of repeatability of acerola quality traits

Soil chemical properties affecting grain yield and oil content of crambe biofuel crop

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