USDA projects soybean growth in Brazil in the 2025/26 harvest
Planted area should reach 49,1 million hectares, with production estimated at 176 million tons
The National Commission for Cereals, Fibers and Oilseeds, of the Brazilian Agriculture and Livestock Confederation (CNA), met this week to discuss advances in testing with automated soybean classification technologies and to raise the demands identified in the field.
According to the Commission's president, André Dobashi, the CNA has been working to remove subjectivity from soybean classification through automated methods and, at the same time, is working on reviewing the quality standard based on perceptions brought by producers.
“Although there is a regulatory instruction in force, many buyers do not apply it correctly, especially with regard to the use of equipment and compliance with the expected classification schedule. “We need to advance in the transparency of marketing to provide greater security to the sector as a whole,” he said.
At the meeting, CNA technical consultant Mauro Cézar Barbosa presented the project stages and the results obtained by the equipment evaluated. “Today we have technologies such as NIR, X-ray and hyperspectral imaging with potential for commercial use, which can complement the work of the classifiers with greater precision. This equipment allows us to identify defects in the grains with greater accuracy, reducing subjectivity in the analyses”, he explained.
According to CNA technical advisor Tiago Pereira, based on contributions from state federations, priority points were highlighted that need to be addressed in a structured manner.
“Issues such as the lack of more transparent and fair discount criteria, the subjectivity still present in visual analysis and the absence of a clear arbitration mechanism in case of disagreement were highlighted. These issues need to be at the center of the discussion on modernization,” he assessed.
The CNA reinforced that it is available to producers and federations to continue the technical and institutional work in favor of a fairer, more transparent and modern classification.
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