EMERGING TRENDS IN STATISTICS FOR SUSTAINABLE AGRICULTURE
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TypePrint
- CategoryAcademic
- Sub CategoryText Book
- StreamAgriculture & Food Science
Agriculture is considered as a backbone of economy and source of employment for India and other developing countries. It is the main source of livelihood for people around the world and hence there is a dire need for sustainable agriculture in the light of growing population and climate change. The agricultural sector must meet the needs of the present and future generations for sustainability, while ensuring profitability, environmental health, social and economic equity. Sustainable agriculture must nurture healthy ecosystems and support sustainable management of land, water and natural resources while ensuring world food security. The current digital era through artificial intelligent systems has evolved various aspects of agriculture management for making value from the ever-increasing data originated from numerous sources. Data are playing an important role for good planning and policies for agricultural growth and development. The application of statistical principles and methods is necessary for effective practice in resolving different problems that arise in many branches of agricultural scenarios. Statistical techniques are applied for the selection of seed types, fertilizers, water usage, weather predictions and equipment reliability.Population growth and climate change are worldwide trends that are increasing the importance of using data science to transform agriculture sector. Analyzing data on crop development and farming by applying advanced statistical data analytics tools such as regression models, multivariate analysis techniques, machine learning, data mining and remote censoring will give better understanding of the crop for attaining sustainability in agriculture. Research indicates that big data analysis in agriculture has the potential to increase economic returns by enhancing farmers’ decision-making performance, increasing production and reducing input costs. Machine learning and data analytics methods are being used in agriculture for descriptive and predictive purposes to aid decision-making and sustainability efforts.
This book elucidates upon advanced statistical techniques applied to attain agricultural sustainability. It highlights the importance of addressing agricultural sustainability mentioning the quantifiable outcomes using appropriate scientific techniques. It has been planned in a way to keep esearchers abreast with the emerging trends and technologies in the field of the agriculture.
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