Ideotype optimization with Crop Modelling: CERES-Rice

Published: 6 May 2025| Version 1 | DOI: 10.17632/msg7bfg5t6.1
Contributor:
Edgar S Correa

Description

This dataset supports the study on rice ideotype optimization using the CERES-Rice model integrated with AI-driven sensitivity analysis and genetic algorithm optimization. It includes the input files, environmental scenarios (weather and soil data), and the output results from 1,884 virtual rice cultivars. Each cultivar is evaluated across four contrasting environments to analyze performance based on grain yield and water use efficiency. The dataset is structured to allow reproduction of the full analysis pipeline, including outputs and the final ideotype comparison with field cultivars.

Files

Steps to reproduce

Download the Dataset and Code Code repository: https://github.com/EdgarStevenC/Crop-Growth-Modelling Set Up the Environment: Ensure MATLAB (R2020b or later recommended) is installed. Add all code folders (e.g., CODE_CORE, CODE_ANALYSIS, CODE_OPTIMIZATION) to your MATLAB path.

Institutions

  • Universite Montpellier Faculte des Sciences de Montpellier
  • Pontificia Universidad Javeriana Facultad de Ingenieria
  • Amelioration Genetique et Adaptation des Plantes Mediterraneennes et Tropicales
  • CIRAD Montpellier-Occitanie

Categories

Cereal Crop, Agricultural Plant, Crop Genetics, Crop Simulation Model, Water Use Efficiency, Precision Agriculture, Computational Biology

Licence