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Functional phenomics for improved climate resilience in Nordic agriculture

Journal of Experimental Botany · 1 Sept 2022 · 10.1093/jxb/erac246

Abstract

The five Nordic countries span the most northern region for field cultivation in the world. This presents challenges per se, with short growing seasons, long days, and a need for frost tolerance. Climate change has additionally increased risks for micro-droughts and water logging, as well as pathogens and pests expanding northwards. Thus, Nordic agriculture demands crops that are adapted to the specific Nordic growth conditions and future climate scenarios. A focus on crop varieties and traits important to Nordic agriculture, including the unique resource of nutritious wild crops, can meet these needs. In fact, with a future longer growing season due to climate change, the region could contribute proportionally more to global agricultural production. This also applies to other northern regions, including the Arctic. To address current growth conditions, mitigate impacts of climate change, and meet market demands, the adaptive capacity of crops that both perform well in northern latitudes and are more climate resilient has to be increased, and better crop management systems need to be built. This requires functional phenomics approaches that integrate versatile high-throughput phenotyping, physiology, and bioinformatics. This review stresses key target traits, the opportunities of latitudinal studies, and infrastructure needs for phenotyping to support Nordic agriculture.

Plant phenotyping relevance

作物の機能的フェノミクス、高スループット表現型解析、標的形質、フェノタイピング基盤を中心に論じるレビューであり、方法論的レビューとして適格。

abstractThis review stresses key target traits, the opportunities of latitudinal studies, and infrastructure needs for phenotyping to support Nordic agriculture.
abstractThis requires functional phenomics approaches that integrate versatile high-throughput phenotyping, physiology, and bioinformatics.

Code and data availability

This is a review article on functional phenomics for Nordic agriculture. It contains no paper-specific phenotype datasets, plant images, sensor data, analysis code, or trained models. All referenced assets are either cited prior studies, generic public resources (FAOSTAT, IPCC report, Wikimedia images), or the article/

No evidence-backed public reproduction asset is currently recorded.

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