A leaf phenomics approach to estimating below-ground traits in North American Licorice
Harris, Z. N.; Tran, V.; Piotter, E.; Hanlon, M. T.; Rubin, M. J.; Miller, A. J.
Show abstract
Premise of the studyThousands of years of selective breeding has prioritized above-ground yield, with little regard for changes happening below-ground. Despite their central role in plant success and resilience, our knowledge of roots lags behind above-ground structures. Accurately phenotyping root traits is often labor-intensive, expensive, and destructive. In order to advance understanding of the fundamental biology underlying root systems, and to integrate hard-to-measure root traits into breeding programs, high-throughput non-destructive methods are required. MethodsThis study uses American licorice (Glycyrrhiza lepidota Pursh.), a perennial legume with a rich ethnobotanical history, as a model to investigate root system phenotypes. We assess root traits across multiple populations, analyze relationships between above- and below-ground phenotypes, and test the use of multidimensional leaf traits, including spectral reflectance, in predicting root traits. Key resultsAmerican licorice displays significant variation in root traits across source populations and strong correlations between above- and below-ground traits. Leaf spectral reflectance and elemental composition show promise in modeling below-ground traits, though the isometric relationship between plant size and root traits complicates model accuracy. ConclusionsThese findings demonstrate the use of high-dimensional leaf traits as a proxy for root traits, with potential applications for understanding foundational questions in plant biology and in breeding programs targeting the below-ground structures of perennial herbaceous species. Further optimization and larger studies are needed to improve predictive models.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Divide and conquer: Using RhizoVision Explorer to aggregate data from multiple root scans using image concatenation and statistical methods 96%
- Predicting leaf traits across functional groups using reflectance spectroscopy 96%
- Functional phenomics and genetics of the root economics space in winter wheat using high-throughput phenotyping of respiration and architecture 95%
Similar papers in this journal
- Nodal root diameter and node number in maize (Zea mays L.) interact to influence plant growth under nitrogen stress 96%
- Non-destructive, whole-plant phenotyping reveals dynamic changes in water use efficiency, photosynthesis efficiency, and rhizosphere acidification of sorghum accessions under osmotic stress 95%
- Between Two Extremes: Tripsacum dactyloides Root Anatomical Responses to Drought and Waterlogging 95%
Similar papers in this journal
- Phenotyping the hidden half: Combining UAV phenotyping and machine learning to predict barley root traits in the field 96%
- Interactions among rooting traits for deep water and nitrogen uptake in upland and lowland ecotypes of switchgrass (Panicum virgatum L.) 95%
- Nitrogen demand, supply, and acquisition strategy control plant responses to elevated CO2 at different scales 94%
Similar papers in this journal
Similar papers in this journal
- The shared genetic basis of leaf morphology and tensile resistance underlies the effect of growing season length in a widespread perennial grass 94%
- Increases in vein length compensate for leaf area lost to lobing in grapevine 93%
- Leaf shape and size variation in bur oaks: An empirical study and simulation of sampling strategies 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.