Agriculture is the main occupation of the majority of people in India. The majority of the population in India is dependent (directly or indirectly) on agriculture as an occupation. The agriculture sector requires more freshwater and power for better yield in the current scenario. Nevertheless, the ever-increasing rate of energy consumption, limited fossil fuels, and rising pollution have made the expansion of renewable resources essential. Due to the suitable solar potential available in India, the deployment of solar energy has been more as compared to other renewable resources. The current study aims to discuss the various technologies, initiatives and policies of solar energy usage in agriculture. This work delivers an assessment of the advancement of solar energy vis-à-vis agricultural applications through the greenhouse concept and photovoltaic approach in India. Various agricultural applications of solar energy, such as solar water desalination system, solar water pumping system, solar crop dryer system for food safety, etc. are discussed as a means to promote solar-based technology. It also highlights the scenario of solar energy in India with important accomplishments, developmental approaches, and future potential. In-depth studies of various policies and government initiatives including those in research and development are also discussed. The current survey on solar technologies will be an aid to agribusiness frameworks to comprehend the statuses, obstructions, and extent of advancement. Finally, some future recommendations for further developments in this approach are discussed. This work sheds light on varied areas of solar energy-assisted agricultural systems as a potentially sustainable and eco-friendly pathway.
Environmental Science and Pollution Research - Plant species sustaining under a polluted environment for a long time are considered as potentially resistant species. Those plant species can be... 相似文献
The present study has tried to develop ecological insecurity model (EIM) in the growing stone quarrying and crushing dominated areas using robust machine learning techniques and attempted to link it with ecosystem service value (ESV). Satellite image-based landscape metrics have been used for developing machine learning-oriented EIM, and the global coefficient of Costanza et al. (Glob Environ Change 26:152–158, 2014) has been used for computing ESV. Field parameter-based ecological insecurity index (EII) has been developed for validating the EIMs along with the statistical methods. Applied Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN) revealed that 21.88 to 60.79% area has predicted as highly ecologically insecure in all the selected four stone quarrying and crushing dominated clusters and this is has inflated from 2000 to 2020. All the applied models are acceptable in terms of their performances, but the RF model is found to be the best representative in relation to EII. It causes considerable loss of ESV which ranges from 160,845.18 US$ to 757,445.17 US$ in all the clusters from 2000 to 2020. The findings of the study are useful for ecological management in this area. It further recommends applying such an approach in such similar fields to establish the general finding and provides knowledge to the state of arts.
