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Digital agriculture, sometimes known as smart farming or e-agriculture, [1] are tools that digitally collect, store, analyze, and share electronic data and/or information in agriculture. The Food and Agriculture Organization of the United Nations has described the digitalization process of agriculture as the digital agricultural revolution . [ 2 ]
The E-agriculture in Action series of publications, by FAO-ITU, that provides guidance on emerging technologies and how it could be used to address some of the challenges in agriculture through documenting case studies. E-agriculture in Action: Big Data for Agriculture [22] E-agriculture in Action: Blockchain for Agriculture [23]
Precision agriculture (PA) is a management strategy that gathers, processes and analyzes temporal, spatial and individual plant and animal data and combines it with other information to support management decisions according to estimated variability for improved resource use efficiency, productivity, quality, profitability and sustainability of ...
The IxTheo lists monographs, collected works, journals, essays, encyclopaedia articles, reviews as well as databases, archive materials, literary remains, blogs, podcasts, research data and other electronically available content from all fields of theology. The analysis is carried out across languages, media and denominations.
The institute is the coordinating unit for the All India Coordinated Project for Research on Management of Salt Affected Soils and Use of Saline Water in Agriculture. The research is conducted at nine centres viz. Agra, Bapatla, Gangawati, Kanpur, Indore, Bikaner, Pali, Hisar and Tiruchirapalli. [2] The project is mandated with the ...
In 1930 the, then, Imperial Council of Agricultural Research, started a statistical unit to assist the State Departments of Agriculture and Animal Husbandry in planning their experiments, analysis of experimental data, interpretation of results and rendering advice on the formulation of the technical programmes of the Council.
Data farming is the process of using designed computational experiments to “grow” data, which can then be analyzed using statistical and visualization techniques to obtain insight into complex systems. These methods can be applied to any computational model. Data farming differs from Data mining, as the following metaphors indicate:
additional short-term or yearly data on numbers of scientists by degree status and gender, support-staff numbers, funding sources, categories of spending (salaries, operating costs, and capital investments), and research focus by agricultural subsector and theme, as well as by crop and livestock item.