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Related Experiment Video

Updated: Dec 7, 2025

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

Published on: August 5, 2020

A data-driven simulation platform to predict cultivars' performances under uncertain weather conditions.

Gustavo de Los Campos¹, Paulino Pérez-Rodríguez², Matthieu Bogard³

¹Departments of Epidemiology & Biostatistics and Statistics & Probability, IQ - Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA. gustavoc@msu.edu.

Nature Communications

|September 26, 2020

Summary

Predicting crop performance is challenging due to genotype-by-environment interactions (G×E). This study introduces a simulation platform using field data and weather records to forecast cultivar yields under uncertain future conditions.

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Area of Science:

Agricultural Science
Computational Biology
Genetics

Background:

Genotype-by-environment interactions (G×E) significantly impact crop performance.
Predicting cultivar yields under variable weather conditions remains a challenge.

Purpose of the Study:

To develop and validate a computer simulation platform for predicting cultivar performance.
To leverage field trial data, DNA sequences, and historical weather records for G×E analysis.

Main Methods:

Utilized Monte Carlo methods to integrate uncertainty in weather and model parameters.
Trained a model on extensive experimental wheat yield data (n=25,841) to learn G×E patterns.
Validated predictive performance using left-trial-out cross-validation.

Main Results:

Generated approximately 143 million simulated grain yield data points for 28 wheat genotypes across 16 French locations over 16 years.
Demonstrated the platform's utility in predicting yield distributions and performing stability analyses.
The simulation platform accurately predicted cultivar performance based on G×E patterns.

Conclusions:

Computer simulations integrating diverse data sources offer a powerful approach to predict crop yields.
The developed platform provides valuable insights for agricultural decision-making under climate uncertainty.
This method enhances our ability to forecast genotype-by-environment interactions for improved crop breeding.