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In current scenario, people tend to move towards outskirts and like to settle in places that are close to nature. But, due to urban lifestyle and to fulfill the basic needs, demand of electricity remains the same as in urban areas. This demand of electricity can be only fulfilled by using hybrid renewable energy resources, which is easily available in outskirts. Renewable energy resources are unreliable and more expensive. Researchers are working to make, it more reliable and economic in terms of utilization. This article proposes a metaheuristic grasshopper optimization algorithm (GOA) for the optimal sizing of hybrid PV/wind/battery energy system located in remote areas. The proposed algorithm finds the optimal sizing and configuration of remote village load demand that includes house electricity and agriculture. The optimization problem is solved by minimization of total system cost at a desirable level of loss of power supply’s reliability index (LPSRI). The results of GOA are compared with particle swarm optimization (PSO), genetic algorithm (GA) and hybrid optimization of multiple energy resources (HOMER) software. In addition, results are also validated by modeling and simulation of the hybrid energy system and its configurations at different weather conditions-based results. Hybrid PV/wind/battery is found as an optimal system at remote areas and sizing are[Formula: see text] with cost of energy (COE) (0.3473$/kWh) and loss of power supplies reliability index (LPSRI) (0%). It is clear from the results that GOA based methods are more efficient for selection of optimal energy system configuration as compared to others algorithms.

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The dairy industry impacts environmental health in various ways and the extent of the impact depends upon the knowledge and practices of dairy farmers. This research surveyed the knowledge and practices of the dairy farmers (n-300) towards environmental safety from five different agro-climatic zones of Punjab (India). Data were analyzed using SPSS software through descriptive statistics, Chi-square test of independence with Cramer's V value as measures of effect size. Analysis of variances, followed by Games Howell post hoc test was performed to analyze subgroup differences amongst explanatory variables. The majority of farmers (57.66%) had a low knowledge score on environmental safety. Majority of farmers did not know greenhouse gases emission (81%) from dairy animals/their excreta, the impact of dairy farming on climate change (86.67%), and were not treating farm effluents before discharging them into the environment (92.67%). Climate change (I) followed by air pollution (II), human-animal-environment interaction (III), water pollution (IV), and soil pollution(V) were the rank-wise factors reported to affect dairy farming. Further, socio-demographic and farm characteristics have a positive influence on the farmer's knowledge. The study warrants an extensive awareness campaign on scientific cum eco-friendly dairy farming with an emphasis on measures to reduce environmental pollution and an eco-health approach to bridge the knowledge hiatus.