As we move more and more into the adoption of precision ag technologies we can easily become over-whelmed with the amount of data that is generated. Reading an article recently posted on PrecisonAg.com explaining the generation of enormous amounts of data as “big data”. The phenomenon of big data can be good or bad depending on what side of the fence you are on. From a scientist point of view this big data is great but from a practical standpoint in real-world farming operations it can be a burden and easy for producers to look the other way. The questions that need to be asked are what do we need to collect, how do we store the data, and then finally how do we utilize the data to increase profits. A producer ultimately only wants to collect data that can help him make money in the future.
No doubt increasing profits is the most important but in order to get there you first have to have a plan for the what data to collect and how to store the data. If your goal is to manage on a 5 acre scale then some of the data and quality of data is not as important but if you want to manage every 60 ft2 of a field or less then quality and quantity of data is important. What is your goal for scale of management?
The bottom line is that you need to develop a goal for what you want to use precision ag technologies and its generated data for and go from there. If you want to vary seeding rate, collect a year or two of yield data and begin to develop seeding zones. The one thing that I would suggest is start collecting quality yield data ASAP. Often time’s producers do not have enough previous collected data such as yield data and this is the limiting factor in what you can and cannot do once you have all of the equipment in place.
Transforming the data into management decisions is not rocket science. On the surface I’m sure that it may seem overwhelming and I’m sure we as scientists have made it appear that you need an advanced degree to utilize the information but this is not the case. You simply need a goal(s) and someone to help you through the thinking process and perhaps in implementing the practices. Keep it simple for the first few years and only change one thing at a time. Go with the item that you feel will bring the biggest return (i.e. grid sampling, variable rate seeding, etc) and start there. This will also help in determining what data is needed to achieve this goal.