Though Rice is cultivated in huge quantities, various disease causing agents will reduce the yield leading to not only losing the economy but also a food crisis. Production of rice is constrained by fungal, bacterial and viral diseases. In the current review, we focused on various pathological symptoms in Oryza species that cause high yield losses. In this context, plant breeders are attempting progressive research activity to achieve more yield and disease-resistant varieties that balance the world’s rice demand and increase the farmers' income. Rice was recognized as a genetic model for research in genetics and molecular biology, for understanding growth, development, tolerance to stress and disease resistance because of its small genome. The present review focuses on the various causative agents of diminishing rice yield along with the strategies to eradicate the pathogen and thereby increasing the yield. Recent research advances at genetic level have paved a way for novel approach to understand the significance between the pheno-genotypic variations with the crop yield of rice. Further, the review also includes the advanced methodologies at molecular level so as to save the rice cultivators from economic crisis. Disease resistant genes are identified and screened using molecular markers like SSR (simple sequence repeats), RAPD (Randomly Amplified Polymorphic DNA), and RFLP (restriction fragment length polymorphisms) analysis. There exist few reports in the literature about rice cultivation, but to the best of our knowledge in a single review both cause and remedy were not discussed in detail. In this context, our review provides an insight into the aspects attributing the crop loss followed by suggesting the suitable alternative method for enhancing crop yield.
Though Rice is cultivated in huge quantities, various disease causing agents will reduce the yield leading to not only losing the economy but also a food crisis. Production of rice is constrained by fungal, bacterial and viral diseases. In the current review, we focused on various pathological symptoms in Oryza species that cause high yield losses. In this context, plant breeders are attempting progressive research activity to achieve more yield and disease-resistant varieties that balance the world’s rice demand and increase the farmers' income. Rice was recognized as a genetic model for research in genetics and molecular biology, for understanding growth, development, tolerance to stress and disease resistance because of its small genome. The present review focuses on the various causative agents of diminishing rice yield along with the strategies to eradicate the pathogen and thereby increasing the yield. Recent research advances at genetic level have paved a way for novel approach to understand the significance between the pheno-genotypic variations with the crop yield of rice. Further, the review also includes the advanced methodologies at molecular level so as to save the rice cultivators from economic crisis. Disease resistant genes are identified and screened using molecular markers like SSR (simple sequence repeats), RAPD (Randomly Amplified Polymorphic DNA), and RFLP (restriction fragment length polymorphisms) analysis. There exist few reports in the literature about rice cultivation, but to the best of our knowledge in a single review both cause and remedy were not discussed in detail. In this context, our review provides an insight into the aspects attributing the crop loss followed by suggesting the suitable alternative method for enhancing crop yield.
Aim -The aim of this work is to efficiently extract the maximum power in low power PV based high voltage gain boost converters for green energy environment by considering two different innovative MPPT algorithms by limiting the oscillation. Materials and Methods -Perturb and Observe(P&O) and Incremental conductance (INC) Maximum power point tracking (MPPT) Algorithms are implemented and investigated so as to identify the point where the maximum power is available with reduced oscillations. Results -Based on the findings obtained it is inferred that the P&O algorithm extracts maximum output power with minimum ripple voltage magnitude (1.5v) while in INC extracts (1.75v). Conclusion -P&O MPPT algorithm gives superior output power in contrast with INC algorithm for the selected data.
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