Evaluation of sunnhemp (Crotalaria juncea) genotypes for high fibre yield

Four genetically different genotypes of sunnhemp (Crotalaria juncea L.) were evaluated for fibre yield and its yield component traits for three years 2008-2009 to 2010-2011 at Sunnhemp Research Station, Pratapgarh, Uttar Pradesh. Significant differences among experimental genotypes were recorded for fibre yield and its attributes. High fibre yield/ha was recorded for SUIN-029 (9.06 q/ha) followed by SUIN-80 (8.94 q/ha). The highest green biomass yield (337.30 q/ha) and stick yield (52.41 q/ha) were recorded for SUIN-029. The analysis of the data for all years revealed superiority of genotype SUIN-029 for most of the fibre yield traits. This genotype can be used as donor for future breeding programme

Characterization of the Rust Fungus, \u3ci\u3ePuccinia emaculata\u3c/i\u3e, and Evaluation of Genetic Variability for Rust Resistance in Switchgrass Populations

Several fungal pathogens have been identified on ornamental and native stands of switchgrass (Panicum virgatum L.). Diseases of switchgrass, particularly rust, have been largely neglected and are likely to become the major limiting factor to biomass yield and quality, especially when monocultured over a large acreage. Based on teliospore morphology and internal transcribed spacer-based diagnostic primers, the rust pathogen collected from switchgrass research fields in Oklahoma was identified as Puccinia emaculata. Furthermore, to identify genetically diverse source(s) of rust resistance, several switchgrass genotypes from both upland (cv. ‘Summer’ and ‘Cave-in-Rock’) and lowland (cv. ‘Alamo’ and ‘Kanlow’) ecotypes were evaluated in Ardmore, Oklahoma during 2008 and 2009 and in growth chamber assays. Field and growth chamber evaluations revealed a high degree of genetic variation within and among switchgrass cultivars. In general, Alamo and Kanlow showed moderate resistance to P. emaculata, while Summer was highly susceptible. Distinct ecotypic variations for reactions to rust were also prevalent with the lowlands maintaining a high level of resistance. These results suggest the potential for improvement of rust resistance via the selection of resistant individuals from currently available cultivars. Further, the selection pressure on the pathogen would also be reduced by employing several rust resistant cultivars in production-scale situations

Context-Specific Genomic Selection Strategies Outperform Phenotypic Selection for Soybean Quantitative Traits in the Progeny Row Stage

Evaluating different breeding selection strategies for relative utility is necessary to choose those that maximize efficiency. Soybean [Glycine max (L.) Merr.] seed yield and fatty acid, protein, and oil contents are all commercially important traits that display complex quantitative inheritance. A soybean population consisting of 860 F5–derived recombinant inbred lines (RILs), genotyped with 4867 polymorphic single nucleotide polymorphism (SNPs) was used to compare phenotypic and context specific genomic selection (GS) strategies. To simulate progeny rows, each RIL was grown in a single plot in 2010 in Knoxville, TN, and phenotype was recorded. A subset of 276 RILs with similar maturity was then grown in multilocation, replicated field trials in 2013 to compare the performance of each selection method in field conditions. Notably, the preferred method for each trait was GS. Of the GS approaches evaluated, Epistacy performed best for yield, and BayesB and/or genomic best linear unbiased prediction (G-BLUP) were preferred for each of the other traits. Yield was the only trait for which the predictions had a large change when the number of SNPs and the number of RILs were randomly reduced for the G-BLUP model, with the best predictions occurring when RILs with different maturity that were not grown in 2013 were removed from the training set. These findings provide important information on how soybean breeders can maximize selections from the progeny row stage for yield and fatty acid, protein, and oil contents by using appropriate selection strategies

Additional file 1 of Ethnomedicinal landscape: distribution of used medicinal plant species in Nepal

Additional file 1. Data table of geography, demography, ethnicity, and used medicinal plants by distric