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Related Experiment Video

Updated: Jun 12, 2026

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

Climate-resilient Durum wheat selection using explainable machine learning and multi-environment trials.

Nadia Nawel Azizi¹, Louiza Smichette¹, Nesrine Hacini¹

¹Laboratory of Functional and Evolutionary Ecology Research, Faculty of Nature and Life Sciences, Chadli Bendjedid University of El Tarf, El-Tarf, Algeria.

Frontiers in Plant Science

|June 11, 2026

Summary

This summary is machine-generated.

Climate change impacts durum wheat yield in Algeria. Researchers identified stable, climate-resilient genotypes by analyzing genotype × environment interactions and using machine learning to pinpoint key environmental drivers like precipitation and water deficit.

Keywords:

SHAP analysis climate change durum wheat genotype × environment interaction machine learning yield stability

Related Experiment Videos

Last Updated: Jun 12, 2026

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

Area of Science:

Agricultural Science
Plant Breeding
Climate Change Adaptation

Background:

Climate change, characterized by rising temperatures and water scarcity, significantly threatens durum wheat production in Mediterranean semi-arid regions.
Genotype × environment interactions are crucial factors influencing crop performance under variable climatic conditions.

Purpose of the Study:

To evaluate durum wheat genotypes for yield stability and grain quality under rainfed Mediterranean conditions.
To identify climate-resilient durum wheat varieties suitable for deployment in challenging environments.
To understand the key environmental drivers affecting durum wheat yield variability.

Main Methods:

Field trials of ten durum wheat genotypes across three Algerian environments over five growing seasons (2021-2025).
Statistical analyses including ANOVA, AMMI, ASI, and GGE biplots for yield stability.
Application of drought tolerance indices (STI, DSI, YSI) and supervised machine learning with SHAP for driver analysis.

Main Results:

Significant effects of environment, genotype, and their interaction on grain yield, with over 50% yield variation between sites.
Identification of 'Waha' and 'Skh/hau.heca-1' as broadly adapted and stable genotypes.
'Ammar-8', 'Da-6blak', and 'Lahaucan' exhibited high yield potential but greater environmental sensitivity.
SHAP analysis revealed precipitation and water deficit as primary yield drivers (50% importance), followed by grain number and soil water capacity.

Conclusions:

Integrating stability analysis, drought tolerance indices, and explainable machine learning offers a robust method for identifying climate-resilient durum wheat.
This framework aids in targeted varietal deployment for improved durum wheat production under Mediterranean rainfed agriculture.
Understanding key environmental drivers enhances breeding strategies for climate adaptation in durum wheat.