For many years, farmers made their decisions based primarily on observation. Despite this, we can evaluate that the data were already part of rural daily life: first, those collected by the farmers' own experience and, later, those acquired with the arrival of basic tools, such as rain gauges.
Now, however, the scenario has changed dramatically. With the digital transformation advance, technologies such as sensors and onboard controllers allow recording hundreds of information every second — from the simplest, about machine speed or its fuel level, to the most complex, such as hydraulic flows and controls activation. Not to mention external references, such as weather forecasts and market indicators, which are increasingly available.
When organised, crossed and associated, these thousands of metrics are capable of presenting to producers and entrepreneurs highly relevant elements, allowing consumption analysis of inputs and fuels, efficiency of machines, productivity of operations, business evolution and so on. The problem is that, currently, we still face a great challenge in agribusiness: how to effectively analyse these numbers and results?
The data amount generated daily by a company in the sector is immeasurable, which often means that they are poorly or underused. Hence, the need to work with intelligent data processing tools. This means transforming them into relevant information that will provide a basis for more agile and efficient strategy formulation and decision making.
Of course, with all the advances, we already have several technologies that work to assist in this process. One example is the software capable of connecting different agricultural sources, uniting elements from different tools to facilitate crossing references. Other Business Intelligence (BI) and management tools are capable to extract data in a personalized way and generate panels and reports to facilitate the information analysis, which appear graphically and with filtering and grouping capabilities.
With this operation monitoring, it is possible carrying out predictive, preventive and diagnostic evaluations. That is, understanding processes and, from that, identifying future probabilities and acting more assertively.
Constant analysis of data related to machine status, for example, can result in a prediction about maintenance need. So, it is possible scheduling activity in advance, preventing equipment from stopping in the middle of the operation due to a problem that could have been avoided. On the other hand, soil conditions analysis during planting, when crossed with harvest results, can indicate the best strategy to adopt in a next operation. At the same time, an evaluation of application speed and amount of inputs destined for a given area is capable to indicate the best way to apply them to ensure savings and productivity. Finally, growth possibilities as of collecting and analysing data are infinite.
It is not by chance that, in the study “Digital Agriculture in Brazil”, launched by the Brazilian Agricultural Research Corporation (Embrapa), most farmers and agricultural companies interviewed highlighted the increasing need to use digital technologies to, mainly, obtain information and plan activities on the property (67.1%) and manage the rural area (59.7%)
Now, with more and more connectivity and technologies available to facilitate data collection and analysis, the intensive use of information being generated by equipment in the field is a trend that is expected to grow more and more in the coming years How to perform this management effectively is still a challenge that is being studied and, little by little, overcome; but this is, without a question, the best way to understand the present and prepare to continue growing in the future – which will indisputably be watered with data.