Predictive modeling of arbuscular mycorrhizal fungi benefits for crop yield and soil health

Overview

This research initiative develops predictive mathematical models for arbuscular mycorrhizal fungi (AMF) and their impact on agricultural productivity and soil health. AMF form symbiotic relationships with most crop plants, enhancing nutrient uptake, improving stress resilience, and contributing to soil carbon storage and water retention. The project aims to consolidate existing scientific knowledge into quantitative equations that can forecast AMF benefits across diverse environmental and management scenarios. By integrating these equations into established crop simulation frameworks, growers, agronomists, and input suppliers can predict how AMF influence yield and long-term soil health before deploying products in the field.

Technical specifications

Core approach:

• Literature-based synthesis of AMF research into mathematical response equations
• Modular modeling design enabling flexible integration with existing crop models
• Systems analysis to identify research gaps and prioritize future studies

Modeled AMF functions:

• Nutrient uptake enhancement under varying soil conditions
• Stress resilience responses to weather extremes
• Carbon sequestration contributions to soil organic matter
• Soil water holding characteristics

Integration platform:

• Built upon the Decision Support System for Agrotechnology Transfer (DSSAT), a validated crop simulation model covering more than 40 crops
• Leverages existing modules for soil water dynamics, nutrient cycling, and soil carbon
• Follows a proven framework previously used to model nitrogen fixation in grain legumes as a function of plant carbohydrate supply, soil temperature, and soil moisture

Technology readiness level

The project is in an early-to-mid research and development stage. The research team brings extensive prior experience developing dynamic crop simulation models within DSSAT and has already validated a comparable biological module for nitrogen fixation in legumes. Current validation efforts include a comprehensive literature review to extract AMF response data across weather, soil, crop management, and genetic conditions. The team is seeking close collaboration with industry partners to define modeling priorities and access proprietary experimental datasets. Future work will translate the derived equations into computer code, produce functional AMF modules, and ultimately link them into the Cropping System Model within DSSAT for broader agricultural application.