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In agriculture, data mining is the use of data science techniques to analyze large volumes of agricultural data. Recent advancements in technology, such as sensors, drones, and satellite imagery, have enabled the collection of large amounts of data on soil health, weather patterns, crop growth, and pest activity.
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]
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.
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 ...
Agricultural data are indispensable for planning and administering related federal and state programs in such areas as consumer protection, conservation and environmental quality, trade, education and recreation. NASS data helps to ensure an orderly flow of goods and services among agriculture's producing, processing and marketing sectors.
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.
The most important distinction between the initial data analysis phase and the main analysis phase, is that during initial data analysis one refrains from any analysis that is aimed at answering the original research question. [109] The initial data analysis phase is guided by the following four questions: [110]
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 ]