Dear Dr. Drlatief Ahmad , you have come up with really an interesting issue.
Precision agriculture (PA) is also known as precision farming. Perhaps the easiest way to understand PA is to think of it as everything that makes the practice of farming more accurate and controlled when it comes to the growing of crops and raising livestock. A key component of this farm management approach is the use of information technology and a wide array of items such as GPS guidance, control systems, sensors, robotics, drones, autonomous vehicles, variable rate technology, GPS-based soil sampling, automated hardware, telematics, and software.
Precision agriculture was born with the introduction of GPS guidance for tractors in the early 1990s, and the adoption of this technology is now so widespread globally that it’s probably the most commonly-used example of precision ag today. John Deere was the first to introduce this technology using GPS location data from satellites. A GPS-connected controller in a farmer’s tractor automatically steers the equipment based on the coordinates of a field. This reduces steering errors by drivers and therefore any overlap passes on the field. In turn, this results in less wasted seed, fertilizer, fuel, and time.
Another important term related to the combining of methodology with technology is Precision Agronomics. it’s about providing more accurate farming techniques for planting and growing crops. Precision agronomics can involve any of the following elements:
Variable rate technology (VRT) – VRT refers to any technology that enables the variable application of inputs and allows farmers to control the amount of inputs they apply in a specific location. The basic components of this technology include a computer, software, a controller and a differential global positioning system (DGPS). There are three basic approaches to using VRT – map-based, sensor-based and manual. The adoption of variable rate technology is currently estimated at 15% in North America and is expected to continue to grow rapidly over the next five years.
GPS soil sampling – Testing a field’s soil reveals available nutrients, pH level, and a range of other data that is important for making informed and profitable decisions. In essence, soil sampling allows growers to consider productivity differences within a field and formulate a plan that takes these differences into account. Collection and sampling services that are worth the effort will allow the data to be used for input for variable rate applications for optimizing seeding and fertilizer.
Computer-based applications – Computer applications can be used to create precise farm plans, field maps, crop scouting and yield maps. This, in turn, allows for the more precise application of inputs such as pesticides, herbicides, and fertilizers, thus helping to reduce expenses, produce higher yields and create a more environmentally-friendly operation. The challenge with these software systems is they sometimes deliver a narrow value that doesn’t allow data to be used for making bigger farm decisions, especially with the support of an expert. Another concern with many software applications is poor user interfaces, and the inability to integrate the information they provide with other data sources to enrich and show significant value to farmers.
Remote sensing technology – Remote sensing technology has been in use in agriculture since the late 1960s. It can be an invaluable tool when it comes to monitoring and managing land, water, and other resources. It can help determine everything from what factors may be stressing a crop at a specific point in time to estimating the amount of moisture in the soil. This data enriches decision-making on the farm and can come from several sources including drones and satellites.
At its most basic level, precision agronomics takes the role of an agronomist and helps make the methods they use more accurate and scalable.
Summary
The primary aim of precision agriculture and precision agronomics is to ensure profitability, efficiency, and sustainability while protecting the environment. This is achieved by using the big data gathered by this technology to guide both immediate and future decisions on everything from where in the field to apply a particular rate, to when it’s best to apply chemical, fertilizer or seed.
"Time requirement and high cost of data handling were cited as the main problems in PA."
In coming times, we expect industry and technology companies to continue to explore the possibilities posed by the marriage of technology with the needs of the agricultural producers to produce enough food to feed the world’s projected 9 billion people by 2050.