Predictive crop modeling for onion yield, quality, and storage duration

Overview

This solution delivers a dynamic, science-based modeling framework for predicting onion yield, quality, and potential storage duration. By translating existing scientific knowledge into mathematical equations, the model estimates storability as a function of the environmental conditions experienced during the growing cycle, field management practices, and genetic characteristics of the crop. The ultimate goal is to integrate these response equations into a comprehensive cropping system model that supports decision-making across the onion value chain, helping growers, packers, and retailers reduce post-harvest losses and improve supply chain planning.

Technical specifications

• Crop simulation foundation: Built on the Decision Support System for Agrotechnology Transfer (DSSAT), a widely used computer model that predicts growth, development, and yield for more than 40 crops as a function of weather, soil, crop management, and genetics.
• Integrated processes: Simulates plant and soil water dynamics, nutrient balances, and crop development alongside onion-specific quality and storability traits.
• Data-driven equation development: Incorporates results from diverse studies into a unified set of response equations that can be evaluated and refined as new experimental data become available.
• Proven modeling track record: The research team has recently completed comparable dynamic models for strawberries (predicting yield and quality) and grapefruit (predicting storage life and quality), demonstrating transferable expertise.
• Decision support integration: Equations are translated into computer code to produce a new onion module that can be linked into DSSAT for end-to-end prediction of yield, quality, and storability.

Technology readiness level

The proposed solution is at an early-to-mid development stage. The underlying DSSAT platform and the team's modeling methodology are mature and validated across multiple crops. The onion-specific module, however, requires a comprehensive literature review, extraction of experimental data, development of scientific response equations, and coding of the new module. The research team proposes close collaboration with agricultural industry partners to define modeling priorities and gain access to proprietary experimental datasets. Once the module is developed and validated, it will be linked into DSSAT, enabling practical decision support for onion production and post-harvest management.