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Unveiling Drought-Resilient Pathways: Integrating High Throughput Phenotyping and Multivariate Modeling to Enhance Barley Adaptation to Climate Change

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Title Unveiling Drought-Resilient Pathways: Integrating High Throughput Phenotyping and Multivariate Modeling to Enhance Barley Adaptation to Climate Change
 
Creator Ouahid, safae
 
Contributor Backhaus, Anna Elisabeth
Jimenez, José-Antonio
Visioni, Andrea
Sanchez-Garcia, Miguel
 
Subject phenotypic traits
high throughput phenotyping
multivariate modeling
remote-sensing
 
Description The increasing threat of climate change makes developing drought-resilient crops ever more important.
Barley (Hordeum vulgare), is a highly drought-tolerant cereal and a key player in the future of farming.
Moreover, the pivotal role of plant architecture, development patterns and roots in conferring drought
tolerance to plants has been understudied, despite their potential importance for drought tolerance. In this
context, we delve into the intricate interplay between barley plants and the environment – specially drought -
with a distinct focus on leveraging multi-data integration and machine learning techniques to analyse high throughput phenotyping data from the field. By employing automated ground-based platforms, such as the
Phenomobile equipped with multi-spectral, RGB cameras, LiDAR and the Physiotron – a lysimeter with a
multi-sensor bridge – that provides controlled environmental conditions for in-depth study of roots, for
monitoring responses to stress with unmatched precision, we can capture large data encompassing many
critical phenotypic indicators at plot and field level. This large dataset is subjected to multivariate modeling
to discover complex relationships between multiple traits and environmental factors. We concentrate on
predicting complex traits such as root traits, biomass accumulation, yield, stress responses that are
fundamental to barley's resilience under stress. Leveraging the power of machine learning with phenomics
and genotypic data holds the promise of unraveling the complex relationships between genetic makeup and
observable traits enabling us to understand the fundamental genetic drivers of various phenotypic
characteristics By identifying hidden correlations and interdependencies, our models will enable the
prediction of phenotypic traits of interest under different stress conditions, offering invaluable insights into
barley’s drought resistance potential and performance. Our work will highlight the importance of data
integration and machine learning to unlock the potential of agricultural research.
 
Date 2024-01-16T18:03:52Z
2024-01-16T18:03:52Z
 
Type Poster
 
Identifier https://apbaconf2023.um6p.ma/conference-book.pdf
https://mel.cgiar.org/reporting/download/hash/6e3e10d9b6765eb786a5229b0407e977
Safae Ouahid, Anna Elisabeth Backhaus, José-Antonio Jimenez, Andrea Visioni, Miguel Sanchez-Garcia. (12/10/2023). Unveiling Drought-Resilient Pathways: Integrating High Throughput Phenotyping and Multivariate Modeling to Enhance Barley Adaptation to Climate Change. Beirut, Lebanon: International Center for Agricultural Research in the Dry Areas (ICARDA).
https://hdl.handle.net/20.500.11766/69045
Open access
 
Language en
 
Rights Copyrighted; Non-commercial educational use only
 
Format PDF
 
Publisher International Center for Agricultural Research in the Dry Areas (ICARDA)