Role of Agricultural Engineering and Statistics for Sustainable Natural Resource Management Text

Towards achieving the goals of livelihood security, it is important to conserve the natural resources and improve economic viability of farming. Natural resource management issues are inherently complex as they involve the ecological cycles, hydrological cycles, climate, animals, plants and geography etc. All these are dynamic and inter-related. In addition to the natural systems, natural resource management also has to manage various stakeholders and their interests, policies, politics, geographical boundaries, economic implications and so on. It is very difficult to deal with all aspects at the same time. The emphasis on sustainability attempts to understand the ecological nature of the agriculture and allied areas.An integrated approach is needed for recognizing and implementing the intertwined social, cultural, economic and political aspects of resource management. A more holistic global perspective evolved from the Brundtland Commission and the advocacy of sustainable development. Developing location specific, cost effective, eco-friendly conservation and management technologies for higher input use efficiency, agricultural productivity & profitability without deteriorating natural resource base is vital for sustainable management of natural resources to ensure agricultural development. In this context, two days’ national seminar has been organized by Department of Farm Engineering- BHU (with the support of SRICC-BHU, NABARD and CSIR)on the topic “Role of Agricultural Engineering and Statistics for Sustainable Natural Resource Management” to address the issues and challenges ahead during the 21st century. Emphasis has been given on novel strategies for forecasting climate change and their effect on natural resources, eco-responsive agriculture allied enterprises, role of indigenous technologies, interactions of water with environment, land and water resources management, sustainable management of natural resources by application of machine learning methods.