Precision agriculture began to gain momentum in the early 1990s. The implementation of production monitors initially focused on corn and soybean crops. However, significant advancements were made, such as Graeme Cox’s contributions in 1996, which led to the development of production maps for sugarcane. Later, in Brazil, the concept was refined and validated for use in both manual harvesting with sugarcane loaders and mechanical harvesters.

 

In Colombia, the first equipment (production monitor) for sugarcane harvesting arrived in 2007, along with the necessary concepts for understanding and utilizing this technology. This initiative was driven by one of the oldest sugar mills, which implemented production maps in sugarcane loaders and harvesters across approximately 40,000 hectares.

 

Initially, sensors for rotation, elevation, and hydraulic pressure were installed on sugarcane loaders. By coordinating various actions, these sensors helped define an algorithm for counting cane loads and geolocating them. Additionally, four load cells were incorporated as part of the productivity monitors in harvesters, which later evolved into a single load cell for increased versatility.

 

These systems require continuous monitoring and discipline for proper maintenance and operation. Over time, other sugar mills adopted this technology, and by 2013, it had become widespread. It is estimated that over 80% of sugar mills in Colombia have implemented these tools. However, challenges remain, including the need for training in maintenance and operation, as well as a lack of technical and agronomic knowledge for utilizing the data strategically in crop management.

 

Fortunately, the introduction of optical monitors for production measurement has improved data quality. Nonetheless, filtering and selection of this data are essential to create maps with minimal noise during interpolation. Unfortunately, the cost of this equipment presents a barrier to its extensive adoption among potential users. Other agricultural technology companies have proposed estimating production based on the operational parameters of harvesters; however, some tests have revealed that the algorithms need further adjustment to yield reliable information.

 

Since the introduction of production maps for sugarcane, there has been integration with soil data to recommend fertilizers, evaluate treatment outcomes, and identify areas of low productivity and limiting factors. These production maps have also validated differences and similarities with vegetation indices, showing approximately 34% correlation in evaluations conducted in various environments. In the lowest production areas, these correlations may slightly increase.

 

It is crucial to recognize that while vegetation index maps provide valuable information, they are complementary to, rather than a replacement for, production maps.

 

Undoubtedly, production maps for sugarcane remain and will continue to be a valuable tool for validating limiting factors and assessing treatment outcomes aimed at enhancing productivity. The real impact lies not just in the ability to interpolate and construct maps, but in correlating various factors to achieve a better understanding of crop behavior in its respective environment.