Optimizing Sustainable Tillage Practices

Advancing environmental impact through precise tillage research and implementation.

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The Research

Our research focuses on optimizing tillage practices through machine learning, aiming to reduce soil erosion, enhance carbon retention, and improve crop yield. Utilizing CNN-based models, we assess tillage intensity, timing, and fertilizer quantities.

The Environmental Impact Potential

  • Reduces soil erosion and nutrient loss across diverse agricultural landscapes.
  • Improves carbon retention, aligning with global sustainability goals.
  • Decreases fertilizer usage by 43%, aiding in runoff reduction.
  • Simulations predict a 57% reduction in emissions from optimized tillage practices.
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The Implementation

Implemented across 200+ farms in the USA, Netherlands, Belgium, France, and India, our optimized tillage model has demonstrated measurable environmental benefits, reducing carbon emissions and resource usage. This global reach enables us to refine practices suitable for various climates and soil conditions.

Future Plans

We are actively exploring partnerships with organizations like Precision Sustainable Agriculture. Future deployments are planned for regions in Africa and the Philippines, expanding our model’s impact to address diverse agricultural needs worldwide.

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Awards

  • Publishing in Nature Sustainable Agriculture.
  • 1st Place at ISTRO 2024.
  • 1st Place in Environmental Engineering at the California Science and Engineering Fair.
  • Presented at the United Nations ICSD Conference.