L. M. York

This paper surveys the current state of root phenotyping, identifying key trends, challenges, and needs in the field.

The paper screens synthetic peptides for enhancing nutrient uptake in plants, finding that CEP1 increases nitrate, phosphate, and sulfate uptake in Medicago and Arabidopsis.

RhizoVision Explorer is an open-source software for root image analysis that provides accurate measurements and batch processing, validated against ground truth data and compared to existing tools.

This study examines root trait interactions and deep nitrate capture in switchgrass ecotypes under water and nitrogen stress, finding shared and distinct responses and a link between deep rooting and aboveground growth.

The paper introduces a high-throughput phenotyping platform to measure multiple ion uptake rates in maize roots, revealing shared genetic mechanisms and heritable variation in nutrient uptake.

The paper combines functional phenomics and root economics theory to analyze root traits in winter wheat, identifying genetic regions and traits linked to root efficiency for sustainable breeding.

The paper creates the Global Root Trait (GRooT) Database to integrate root trait data into large-scale analyses by standardizing and curating root trait records from diverse studies.

The paper identifies a collaboration gradient in root economics that expands the traditional fast-slow resource acquisition spectrum by incorporating mycorrhizal partnerships.

RhizoVision Crown is an open-source platform for high-throughput root crown phenotyping in crops, validated with simulated images and used on soybean and wheat, showing species-specific heritability.

This study shows that maize with reduced nodal roots reallocates biomass to lateral and early-emerging axial roots, enhancing nitrate foraging efficiency under low nitrogen conditions.

The paper uses digital imaging to analyze root system architecture changes in alfalfa affected by Phymatotrichopsis Root Rot disease, identifying compensatory root growth patterns in survivor plants.

The paper introduces functional phenomics as a new field integrating high-throughput phenotyping, big data analytics, and simulation modeling to better understand plant physiology and its impact on ecosystem services.