Cyanogenic glycosides are nitrogen containing secondary metabolites which offer plant defense mechanism against herbivores while recently various insects have developed ability to detoxify, sequester and synthesize these cyanogenic compounds. Dhurrin was the first identified and isolated CNGs from young leaves of sorghum, Sorghum vulgare. The presence of CNGs were confirmed in few species of Chilopods, Diplopods, Heteropterans, Coleopterans and Lepidopterans. Linamarin and Lotaustralin were the major CNGs distributed in Lepidopterans. They have developed an ability to de novo synthesize CNGs and detoxify them with the help of β-cyanoalanine synthase and rhodonase. This may be due to the course of insect evolution or the genes responsible for the synthesis of CNGs may get transferred from plants to insects. In future, the responsive genes in these arthropods should be silenced and expansion of transgenic cyanogenic plants may encouraged to ensure plant defense mechanism.
:In recent years, increasing concern about food safety, health hazards and environmental damage both among consumers and producers is leading to increased adoption of organic farming and production of organic foods in the State. But, some farmers are reluctant to convert because of the perceived high costs and risks involved in organic farming. Despite the attention which has been paid to organic farming over the last few years, very little accessible information exists on the impact of State sponsored organic farming scheme on benefits accrued to the farmers and also the extent of continuing these practices by the farmers after the withdrawal of assistances by the State. Hence, the present study is an attempt to analyze the factors influencing adoption of organic farming by the farmers after the withdrawal of project by the State. The study consists of face to face interaction with 120 randomly selected farmer respondents in Southern Karnataka. The important factors influencing the farmers to continue organic farming was analyzed through logistic regression model. The results indicated that number of trainings attended by the sample farmers on organic farming was the important factor that positively influenced farmers to continue as organic farmers while off-farm income and total land holdings were negatively contributed to continuation of organic farming.
The present study aimed at assessing the extent and pattern of genetic diversity within a core set of soybean germplasm comprising of 98 accessions. A total of thirty-one morphometric traits were studied, among them qualitative traits viz., leaf shape, flower color, seed coat color, and hypocotyl colour showed a higher genetic diversity with higher diversity indices. The variability parameters like mean, range of variation, GCV, PCV, heritability and genetic advance were estimated for 18 quantitative traits. The differences between GCV and PCV estimates were narrow for most quantitative traits indicating less contribution of environmental factors in traits expression. High estimates of heritability coupled with high genetic advance were observed in all quantitative traits except for days to maturity. The traits with higher heritability and GA value may indicate their variability and high selective value. Expression of lines in biplots using the first four principal components explains 79.10% of total variation and says black and yellow seeded genotypes have higher and lower variability to exploit, respectively. Hence, selection pressure could profitably be applied to these traits for their improvement.
Background: Identification of suitable factors that influence significantly to the response is crucial for the traits based breeding program to make a better decision about improvement in productivity. Multiple linear regression (MLR) is the benchmark method commonly using to identify suitable factors for crop improvement. It doesn’t work always due to stringent assumption (Multicollinearity, Linearity) behind the MLR model. Here we tried to develop an efficient model for the selection of major traits that contribute to seed yield in soybean by comparing different models.Methods: Field experiment was conducted using 98 soybean core population through augmented design.18 morphometric traits obtain from soybean core population were considered under the study as regressors.Multiple linear regression (MLR), Principle component Regression (PCR), Regression tree and Random Forest models were compared to select traits based on prediction accuracy.Result: All the models identified the number of pods per plant (NPP) has the most influencing variable to the soybean yield. However random forest has a much higher prediction power (RMSE=4.59, MAPE=0.18) compared to other models under study. The results of random forest revealed that the number of pods per plant, number of branches per plant and other associated characters like plant height at harvest as highly influencing traits for seed yield in soybean.Finally, tried to identify genotypesthat possess superiority about most influencing morphological characters on seed yield using cluster analysis.
Globalization has led to increase in the trade between countries but it has resulted in several other new issues, one such issue is dumping of goods. This paper analyses the impact of anti-dumping duty imposed by India on the imports of raw silk from China during 2003 to 2013. There are certain exceptions, despite the fact that free trade is expected from the members of the World Trade Organization (WTO). One concern is that goods are being dumped in the foreign market, i.e. the exporter sells his product at a lower price in the foreign market than that prevailing in the home market. Article VI in GATT regulates this together with the Anti-dumping Agreement. In 2003 and 2013, Indian imposed an anti-dumping duty on raw silk imported from China. This measure was effective until 2014. As a result of this duty, it is expected that the imports of Chinese raw silk by
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