Enhancing Registration Offices’ Communication Through Interpretable Machine-Learning Techniques

Enhancing Registration Offices’ Communication Through Interpretable Machine-Learning Techniques

This study presents a protocol for applying Interpretable Machine Learning (IML) to enhance communication within Variety Registration Offices (VROs). Rather than focusing on a model comparison, we illustrate how two IML-compatible models—Random Forests and AMBARTI—can support a clearer interpretatio...

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Bibliographic Details
Main Authors: Danilo Augusto Sarti, Tommaso Bardelli, Pier Giacomo Bianchi, Anna Pia Maria Giulini
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Agronomy
Subjects:
wheat genotypes
protein content
yields
AMBARTI models
Random Forest models
Online Access:https://www.mdpi.com/2073-4395/15/7/1603
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Summary:This study presents a protocol for applying Interpretable Machine Learning (IML) to enhance communication within Variety Registration Offices (VROs). Rather than focusing on a model comparison, we illustrate how two IML-compatible models—Random Forests and AMBARTI—can support a clearer interpretation of genotype-by-environment (G×E) interactions and variable importance. Using multi-environment wheat trial data from CREA-DC-Milano across Italian sites, we predicted the yield and protein content while visualizing the performance patterns. Genotype g25 ranked first in protein across both years, while g20 led in yield in Year 1. Tolentino consistently supported higher protein levels; Torino and Tolentino led in yield, varying by year. These insights, made accessible through intuitive IML visualizations, proved valuable in supporting VRO, reinforcing the role of IML as a practical communication tool in regulatory processes, agricultural innovation, and food security.
ISSN:2073-4395

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