Agmatix, a data-driven agronomy company, has announced a strategic collaboration with BASF to develop a digital solution aimed at detecting and predicting the presence of soybean cyst nematode (SCN). SCN is one of the most damaging yet often invisible threats to soybean crops, causing substantial yield losses. This collaboration emerged through BASF’s AgroStart, an open innovation and partnership platform that fosters the development of advanced agricultural solutions. The initiative is designed to provide soybean growers with real-time, scalable insights to mitigate the impact of SCN infestations.
Leveraging AI for Advanced Detection
At the core of this initiative is Agmatix’s AI-powered technology engine, Axiom, which processes vast amounts of raw agronomic data into standardized and enriched datasets. This approach enables the development of a machine-learning model capable of detecting and predicting SCN infestations with a high degree of accuracy.
Dr. Shai Sela, Chief Scientist at Agmatix, emphasized the importance of data quality in building an effective AI model.
“By harmonizing and enriching SCN field trial data through our platform, we can ensure consistent, reliable outputs, regardless of region, soil type, or cropping conditions. This approach significantly enhances the model’s predictive capabilities, providing growers with actionable insights.”
Combining Agricultural Expertise with AI
Developed through AgroStart, this initiative merges Agmatix’s AI-driven agronomic capabilities with BASF’s expertise in seed and crop protection solutions. The result is a scalable, user-friendly digital model that integrates seamlessly with existing farm management practices. The tool will enable users to access near real-time SCN risk assessments, helping them tailor pest management strategies more effectively.
Mika Eberl, Head of AgroStart and Digital Officer at BASF Agricultural Solutions, highlighted the value of digital technologies in modern agriculture. “Collaborating with companies like Agmatix allows us to integrate artificial intelligence with our existing seed and trait, seed treatment, and crop protection solutions. This collaboration strengthens our current offerings and complements future innovations like Nemasphere, ensuring growers have a comprehensive approach to safeguarding soybean yields,” he stated.
Addressing the Challenges of SCN Detection
SCN remains the most destructive pest in soybean farming, often going undetected until it has already caused substantial yield losses. Traditional detection methods, such as soil sampling and mid-season root digs, are labor-intensive and not widely adopted. A digital tool capable of providing early detection could help raise awareness and complement existing detection practices.
Michael McCarville, BASF Trait Development Manager, underscored the economic impact of SCN on soybean growers.
“Nematodes are the leading cause of soybean yield loss in the United States, costing farmers an estimated US$ 1.5 billion in yield annually. BASF remains committed to developing solutions through AgroStart and future product portfolio innovations to help farmers combat this devastating pest and protect their yields.”
Future Outlook
With planting-time decisions playing a crucial role in managing SCN infestations, growers require an efficient and scalable solution for detection and forecasting. Ron Baruch, President & CEO at Agmatix, explained the driving force behind this collaboration.
“Our partnership with BASF is focused on delivering a practical, data-driven tool to help growers minimize SCN-related yield losses. By digitizing large volumes of field trial data within Axiom, we believe our machine-learning model will empower growers to take action before SCN causes irreversible damage.”
The collaboration between Agmatix and BASF represents a step forward in integrating digital agriculture with pest management strategies. By providing accurate predictive data on soybean cyst nematode (SCN) infestations, this initiative lays the groundwork for future advancements in crop protection and agronomic decision-making.
