Smart Farming: How Data Analysis Can Help Increase Crop Efficiency

Producing more with less. This is the main objective of the agribusiness industry. The scarcity of resources and skilled labor in the field, in addition to the high costs of inputs for production, makes the use of data increasingly relevant. In this context, data can support managers in making precise decisions, to increase efficiency and productivity in the field.

"Investing in the use of technology is one of the main issues for producers, but the fact is that it pays itself off. If we just consider the diesel savings from idle engines, which is just one of a series of productive advantages," said Alexandre Alencar, director of agriculture engineering for AEM member company Hexagon's Autonomy & Positioning division, a global leader in solutions for agribusiness.

According to Alencar, idle engines are extremely common in traditional agricultural operations, since machine use is not optimized as it is in operations that use data to program and control equipment activities. "Every month, an exorbitant amount of fuel is spent unnecessarily when, due to human error or even convenience, machines are left running while waiting for inputs or other equipment to arrive so they can continue their activities," he said.

An example of saving fuel is the synchronization of haul-out allocation with the cutting rhythm of harvesters, offered by Hexagon. The system monitors the positioning of each machine and notifies when a tractor must move to the operating location, considering distances between the equipment, times and routes for movement, and the synchronization of execution of activities between them.

Data analysis can support efficiency in agribusiness both in this case and in countless other ways. To shed some light on the subject, Alencar cited the advantages of its use for the sector in four main areas:

Operational Data

The example of reducing idle machine time given above falls into this group, but it is by far not the only one. According to Alencar, gathering data on machine performance, production time, the positioning and allocation of resources, as well as the health of the equipment itself, can help improve operations.

"Waiting for a machine to break down before carrying out maintenance represents lost time and a productivity that producers feel acutely. Instead, sensors can use data to diagnose and anticipate issues, suggesting preventive maintenance before the problem is critical," he explained.

"Investing in the use of technology is one of the main issues for producers, but the fact is that it pays itself off."-- Hexagon's Alexandre Alencar

Performance Data

This is a set of data directly related to crop productivity. "It's already possible to measure the production of each crop row, or even at a small piece in the crop row, to identify localized variations within each plot. If, for example, the effective weight is lower than expected, it's possible that the section is being affected by some pest or nutrient deficiency," he explained.

Based on this data, producers can act efficiently and identify the problem that needs to be solved locally. On the other hand, without detailed data, corrective actions are applied to the entire field.

A very common example of this is the use of pesticides over the entire length of the crop in a flat-rate application. "This practice is extremely counterproductive. Not only because of the high price of these inputs, but also because of the environmental impact it presents with the excess use of products. Overapplication can lead to the contamination of groundwater or water supplies," he said,

Climate and Weather Data

Weather stations positioned throughout production areas can be used to collect data that tracks rainfall and drought, temperature variation, and excessive heat, among other relevant information.

"The impact of weather on crop growth is obvious. For example, sugarcane's sucrose production is directly associated with the plant's response to weather conditions. Faced with variations in temperature, dry weather and low humidity, it concentrates the amount of sugar, resulting in higher quality production. Using weather data can help farmers plan the best time to harvest," said Alencar.

In addition to weather information, there are many other data sources associated with geography and geology that are extremely useful to the producer, such as those related to soil health, topography (which helps in planning crop layouts and harvest logistics). Alencar also mentions data can help with fire control, such as information on soil and air humidity, wind speed and direction, temperature and precipitation history.

Precision Agriculture Aata

Quality positioning systems in agricultural equipment can allow machines to follow A/B lines across the field with centimeter (sub-inch) level accuracy. "This data is fed to auto steering systems to reduce reliance on operators and mitigate human errors such as running over crops. This can cause future sprouting problems, especially in perennial or semi-perennial crops," said Alencar. The same technology also optimizes the planting area, standardizing the distance between seeds, allowing more to be produced in the same area.

Precision agriculture data is also useful for reducing overlapping and the application of inputs in unwanted areas, as well as reducing the use of pesticides. According to Alencar, once problem areas have been identified, prescription maps can be generated that inform the machine where and how much product to apply to the crop.

"The ability to track each stage of agricultural production in detail is the basis for achieving some ESG parameters and remaining relevant in today's market. It's high time producers embraced ESG (Environment Social and Governance) effectively in their crops, because we can already see important markets closing to producers who don't meet these requirements, mainly in Europe," he said.

Finally, Alencar points out that the use of data in agribusiness is an extremely relevant option for increasing the efficiency of agricultural producers, but that it also requires a specialized team to analyze the information collected, which must be proportional in size and specialization to the volume of data generated by each production. Then, the so-called 'office farmer' is a career in high demand, according to Alencar. "I have seen a growing demand for professionals capable of identifying failures and opportunities remotely to make the best decisions with the data generated by these support systems," he added.

For more AEM member perspectives,subscribe to the AEM Industry Advisor.

Public link

Please use the above public link if you want to share this noodl on another website.