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Updated: Sep 5, 2026

An Optimized Rhizobox Protocol to Visualize Root Growth and Responsiveness to Localized Nutrients
Published on: October 22, 2018
Root functional traits dominate maize seedling growth responses to different humic acids
Zhaolong Ding1, Ruijia Guo1, Aimaieraili Tuheti2
1Institute of Agricultural Resources and Environment, Xinjiang Academy of Agricultural Sciences, Urumqi, Xinjiang, China.
Introduction:
Humic acids (HAs) are natural organic macromolecules that play a critical role in sustainable agricultural production.
Methods:
This study compared the effects of five HA productstwo lignin-based biological HAs (acid-modified, LHAac; alkali-modified, LHAal), one mineral-derived HA (MHA), one sludge-derived HA (SHA), and one commercial HA (CHA)on maize seedling growth.
Results:
All HA treatments significantly improved root growth, photosynthetic performance, nutrient accumulation, and biomass production. Among them, MHA exhibited the strongest efficacy in promoting root proliferation and increasing specific root length, whereas SHA significantly enhanced root diameter, tissue density, and root activity. HA treatments elevated the net photosynthetic rate (Pn), relative chlorophyll content (SPAD value), and stomatal conductance (Gs) of maize seedlings, while reducing intercellular CO₂ concentration (Ci) in leaves. Mantel correlation analysis and variance partitioning analysis (VPA) revealed that root functional traits accounted for the largest proportion (26.7%) of the variation in seedling biomass, followed by root nutrient status (12.3%). Significant synergistic effects were also observed between photosynthetic parameters and shoot nutrient status, as well as between root-related traits and aboveground physiological indicators.
Discussion:
Collectively, HAs from diverse sources improved maize seedling growth through coordinated changes in root morphology, photosynthetic activity, and nutrient utilization; root functional traits made the largest independent contribution to explaining seedling biomass variation among all measured indicators.
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