For more than two decades, the sugarcane sector of Valle del Cauca, Colombia, has served as a living laboratory for precision agriculture. This is not a story about theoretical pilots or futuristic promises. It is a story about maps, models, machines, data and measurable results. Yet, as of 2024, less than 10% of the sugarcane area applies variable-rate fertilization, despite consistent evidence of yield gains, input optimization, and reduced environmental footprint.   Fertilizer costs typically represent 20–30% of total production costs in sugarcane an economically sensitive component where every recommendation and every kilogram applied matters. The question, therefore, is no longer whether precision agriculture works. The real question is: why are we not adopting it on a scale?   From the assumption of soil homogeneity to the understanding of spatial variability In 2007, the first large-scale geostatistical studies were conducted in the sugarcane region of southwestern Colombia. More than 20,000 hectares were mapped, revealing an uncomfortable but undeniable truth: soil is not homogeneous and it never has been.  Even after years of detailed soil surveys at 1:10,000 scales, the results showed even greater variability when physiographic changes across the landscape were properly accounted for. This represented a fundamental shift. From that moment on, mathematical models were developed to recommend fertilizers and soil amendments based on spatial variability rather than field averages.   Fertilizer spreaders equipped with GPS and electronic controllers were integrated to adjust application rates in real time. Harvesters were instrumented to generate yield maps, allowing agronomists to verify responses, close the feedback loop, and continuously refine management strategies. Agriculture shifted from a reactive mindset to an analytical one.   Results that are no longer debatable. In a pilot program covering approximately 12,353 hectares, with disciplined monitoring over four consecutive years, the results were unequivocal. On an annual basis, the program achieved ...more

Carlos Mosquera, I.A. Sp. GIS, MBA. Country Representative, ISPA. DINOSAR Project Consortium AGROAP, ELEAF, SARVISION, UNIVERSITY OF ALICANTE, HCP International, EURONOVIA. Agricultural Systems, Soil Health, and Sustainability Challenges Agricultural systems worldwide are impacting soil health conditions, ultimately leading to reduced productivity. In some countries, the expansion of the agricultural frontier is affecting forested areas. The improper use of agricultural inputs and inefficient water management necessitate alternative approaches to sustain food production within a more sustainable model. While increasing productivity is imperative, environmental conservation must remain a priority. Ensuring the long-term health and productivity of soils is crucial for future generations. Smart farming applications based on Earth Observation (EO) have demonstrated their potential to reduce water, fertilizer, and pesticide consumption by approximately 20% while maintaining production levels. The challenge of increasing yields in existing agricultural areas, minimizing environmental impact, optimizing input use, and reducing costs are strategic indicators guiding the path toward sustainability. Gaining a deeper understanding of crop behavior and its interaction with climate and soil will enable better decision-making and promote environmentally friendly management practices.   Read the full article here. 

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 ...more