Symbiotech platform for agroecological transition simulation and steering

Overview

The SymbioTech Platform addresses the growing limitations of conventional monoculture agriculture, including soil depletion and limited climate resilience. It provides a digital simulation and decision-support environment for designing and managing the transition from monoculture to agroecological and agroforestry systems. By coupling biophysical growth models with economic and lifecycle analyses, the platform enables stakeholders to evaluate yields, inputs, climate resilience, carbon, water, and biodiversity outcomes before committing resources. The platform is positioned as a steering service for transition projects, helping farmers, cooperatives, and project developers make informed decisions grounded in complex systems modeling.

Technical specifications

• 3D biophysical growth model that simulates competition between trees and annual or perennial crops for light, water, and nitrogen.
• Broad crop coverage, including soft and durum wheat, corn, prairie mixes, soybean, sunflower, rapeseed, barley, alfalfa, pea, tomato, lettuce, carrot, potato, sugar beet, grapevine, rice, banana, and sugar cane.
• Tree species parameterization for hybrid walnut, poplar, and cherry, with extensibility to additional species.
• Economic simulation module that models crop yields, inputs, climate resilience, and full lifecycle indicators such as carbon, water, and biodiversity.
• Real-time data integration with open data sources, including Sentinel-2 satellite imagery, to feed terrain, climate, and vegetation cover analyses.
• Machine learning layer trained on enriched experimental data to support predictive simulations and continuous monitoring of transition projects.
• Steering service interface designed to translate model outputs into actionable guidance for transition project managers.

Technology readiness level

The platform is at an early-to-mid stage of development, with core biophysical and economic simulation modules already built on prior academic research in complex agroecological systems. The current objective is to co-finance and produce a Proof-of-Concept that validates the coupled modeling, real-time data ingestion, and machine learning components. Future validation will expand the number of experimental sites, capitalize on associated datasets, and refine the predictive scope of the simulations. The platform is not yet commercially deployed and is seeking partners to advance the Proof-of-Concept toward operational readiness.