Unverified paper record
Why bother with controlled-environment phenotyping when field phenomics is already up and running?
Journal of Experimental Botany · 24 Jul 2026 · 10.1093/jxb/erag379
Abstract
Plant phenomics has undergone rapid development over the past two decades, driven by advances in imaging, robotics, artificial intelligence and data analysis. Whilst field phenotyping is increasingly operational and scalable, the relevance of controlled-environment (CE) phenotyping is questioned because of concerns regarding the limited transferability of results to agricultural conditions. This Expert View first addresses the limitations and risks of using CE as surrogate of outdoor conditions. However, we argue that CE enables the disentangling of interacting environmental drivers allowing causal analysis of plant responses to multiple abiotic and biotic stresses. CE platforms also provide access to complex traits that are difficult or impossible to measure in the field whilst providing a robust framework in combination of field approaches to interpret and predict field performance. We further discuss contexts where CE remains indispensable, including quarantine and biosafety regulations together with emerging opportunities for agricultural innovation. Whilst limitations of CE systems are acknowledged, including issues of extrapolation, pot effects, environmental realism, and the indispensable need for rigorous envirotyping, we conclude that CE phenotyping should be regarded as an enabling analytical framework that complements and strengthens field phenomics for crop adaptation research under climate change.
Plant phenotyping relevance
管理環境フェノタイピングとフィールドフェノミクスの役割・限界・分析枠組みを論じる専門的レビューであり、植物表現型計測の方法論が中心です。
titleWhy bother with controlled-environment phenotyping when field phenomics is already up and running?
abstractThis Expert View first addresses the limitations and risks of using CE as surrogate of outdoor conditions.
abstractCE platforms also provide access to complex traits that are difficult or impossible to measure in the field whilst providing a robust framework in combination of field approaches to interpret and predict field performance.
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