What is Precision Agriculture?
Precision agriculture, also known as precision farming, is a broad term commonly used to describe particular farm management concepts, sometimes referred to as satellite farming or site specific crop management (SSCM). The term first came into popular use with the introduction of GPS (global positioning satellites) and GNSS (global navigation satellite systems) as well as other methods of remote sensing which allowed farm operators to create precision maps of their fields that provide detailed information on their exact location while in-field. Advancements in technology have enabled the practice of precision agriculture to expand, providing even greater advantages for farmers and agricultural operations, including yield monitoring and crop scouting.
There are several aspects of precision agriculture that can be applied to various types of farming operations, but they all share a commonality – the use of technology to enhance economic performance, better use of inputs and help to mitigate environmental damage. Let’s look at some of the concepts behind precision agriculture in more detail.
Spatial and Temporal Variability
Spatial and temporal variability are considered the key working aspects of precision agriculture. Spatial variability refers to the identification and measuring of variables, such as land features or general topography, moisture levels present in the soil, soil nutrient levels - including nitrogen, potassium, and magnesium, as well as soil pH levels, crop yields, and more. Temporal variability essentially refers to the aspect of time, by which the information gathered under spatial variability can be plotted to distinguish rates, including the rate of nutrient depletion, soil erosion and soil moisture content levels.
Decision Support Systems
The use of decision support systems (DSS) is often incorporated into precision agriculture as it pertains to managing the information collected through spatial and temporal practices. The essential components of decision support systems generally rely on four main principles, including Intelligence, which refers to the information that requires a decision to be made upon. Design, refers to developing a solution or alternative action that could potentially alleviate a particular problem. Choice, choosing the most appropriate action or solution to the problem as identified under the design process. The last step is Implementation, applying the action or solution that was chosen from the design process to remedy the problem(s).
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