Traditional and Modern Plant Breeding Methods with Examples in Rice (<i>Oryza sativa</i> L.)

Breseghello, F.; Coelho, Alexandre Siqueira Guedes

doi:10.1021/jf305531j

Cited by 245 publications

(139 citation statements)

References 60 publications

(97 reference statements)

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“…Conventional breeding has been used for production of salt tolerant rice varieties through two basic steps [66]. The first is to generate/obtain a breeding population that is highly variable for salt tolerance; and the second involves selection among the segregating progeny for individuals that combine most of the parent's useful traits and high degree of salt tolerance.…”

Section: Conventional Breedingmentioning

confidence: 99%

Improvement of Salinity Stress Tolerance in Rice: Challenges and Opportunities

et al. 2016

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Rice (Oryza sativa L.) is an important staple crop that feeds more than one half of the world's population and is the model system for monocotyledonous plants. However, rice is very sensitive to salinity and is the most salt sensitive cereal crop with a threshold of 3 dSm −1 for most cultivated varieties. Despite many attempts using different strategies to improve salinity tolerance in rice, the achievements so far are quite modest. This review aims to discuss challenges that hinder the improvement of salinity stress tolerance in rice as well as potential opportunities for enhancing salinity stress tolerance in this important crop.

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Section: Conventional Breedingmentioning

confidence: 99%

Improvement of Salinity Stress Tolerance in Rice: Challenges and Opportunities

et al. 2016

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“…According to some authors (BRESEGHELLO; COELHO, 2013), landraces have lower yield, but others have shown that some landraces may be more productive than improved cultivars (MKOI; CHIMPHANGO; DAKORA, 2009;PEKSEN, 2004). Thus, studies of traditional cultivars enable the identification of cultivars that can be used directly by farmers or used in breeding programs (BOUKAR;FATOKUN, 2009).…”

Section: Introductionmentioning

confidence: 99%

Green Bean Yield and Path Analysis in Cowpea Landraces

et al. 2016

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-Cowpea (Vigna unguiculata (L.) Walp.) is widely cultivated in northeast Brazil for production of both green grains and dry grains. Green grains are cowpea grains with a 60 to 70% moisture content that are very appreciated by the local people. The cultivation of cowpea in northeast Brazil is performed mostly using landraces and the low yields achieved in some areas of that region are generally attributed to the cultivation of less productive landraces. The objectives of this research were to identify the best landraces in terms of green bean yield and the characters that hold the most positive direct effects on this yield via path analysis. Twelve landraces of undetermined growth were evaluated in two experiments conducted in Mossoró, Brazil. The first experiment (E1) was sprinkler-irrigated and the second (E2) was conducted under rainfed conditions, but was irrigated whenever necessary. Pod yield and dimensions and green grains were evaluated, in addition to the main grain yield components. A randomized block design with five replications was used. The data presented here identified eight landraces with the highest yields. In addition, cowpea yield was higher in the dry season (E1) than in the rainy season (E2). The number of pods per plant had the largest direct positive effect on green grain yield.Keywords: Vigna unguiculata. Green grain. Landraces. RENDIMENTO DE GRÃOS VERDES E ANÁLISE DE TRILHA EM VARIEDADES TRADICIONAIS DE FEIJÃO-CAUPI

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“…However, the genetic gain for these traits with the conventional breeding process is nearly stagnant (Dal-Bianco et al, 2012). Several limitations inherent in a breeding program may be noted: (i) a lack of knowledge of the genetic material that is present in germplasm banks, which could be used to perform cross-breeding with the greatest potential to generate superior cultivars; (ii) sparse and mismanaged experimental trials; (iii) failures and a lack of standardization in the collection of phenotypic data; (iv) lack of environmental correlation analysis of the phenotypic data; (v) lack of knowledge of the genetic basis of the traits of interest; (vi) neglect of disease and pest occurrence; and (g) low investment in research and biotechnology (Breseghello and Coelho, 2013;Mahon, 1983;Prohens, 2011).…”

Section: Probabilities Segregations and Heritability Of Resistance mentioning

confidence: 99%

Mixed Modeling of Yield Components and Brown Rust Resistance in Sugarcane Families

et al. 2016

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Sugarcane (Saccharum spp.) is a complex autopolyploid with high potential for biomass production that can be converted into sugar and ethanol. Genetic improvement is extremely important to generate more productive and resistant cultivars. Populations of improved sugarcane are generally evaluated for several traits simultaneously and in multi-environment trials. In this study, we evaluated two full-sib families of sugarcane (SR1 and SR2) at two locations and 3 yr for stalk diameter, stalk height, stalk number, stalk weight, soluble solid content (Brix), sucrose content of cane, sucrose content of juice, fi ber, cane yield, sucrose yield, and resistance to brown rust (Puccinia melanocephala). Using a mixed model approach, we included appropriate variance-covariance (VCOV) structures for modeling heterogeneity and correlation of genetic eff ects and nongenetic residual eff ects. Th e genotypic correlations between traits were calculated across the adjusted means as the standard Pearson product-moment coeffi cient. Th rough the VCOV structures estimated for each trait, in general, the heritabilities ranged from 0.78 to 0.94. Additionally, we detected 17 and 12 signifi cant genotypic correlations between the evaluated traits for SR1 and SR2, respectively. Th e analysis of the severity data for brown rust revealed that 66 and 32% of the full-sib genotypes in SR1 and SR2, respectively, had at least 90% probability of being resistant. Abbreviations: AIC, Akaike information criterion; BIC, Bayesian information criterion; BLUP, best linear unbiased prediction; FIB, fi ber; GEI, genotype ´ environment interaction; GLMM, generalized linear mixed model; LMM, linear mixed model; METs, multi-environment trials; POL%C, sucrose content of cane in percentage; POL%J, sucrose content of juice in percentage; REML, restricted maximum likelihood; SD, stalk diameter; SH, stalk height; SN, stalk number; SR1, SP80-3280 ´ RB835486 full-sib family of sugarcane 1; SR2, SP81-3250 ´ RB925345 full-sib family of sugarcane 2; SW, stalk weight; TCH, tonnes of cane per hectare; TPH, tonnes of sucrose per hectare; VCOV, variance-covariance. core ideas• A linear mixed model is efficient in production data analysis of sugarcane.• In general, the broad-sense heritability of the traits were high, ranging from 0.78 to 0.94.• A generalized linear mixed model can be applied in brown rust analysis of sugarcane.• Multi-environment trials were applied to the genetic improvement of sugarcane.

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Traditional and Modern Plant Breeding Methods with Examples in Rice (Oryza sativa L.)

Cited by 245 publications

References 60 publications

Improvement of Salinity Stress Tolerance in Rice: Challenges and Opportunities

Improvement of Salinity Stress Tolerance in Rice: Challenges and Opportunities

Green Bean Yield and Path Analysis in Cowpea Landraces

Mixed Modeling of Yield Components and Brown Rust Resistance in Sugarcane Families

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