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

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cg.contributor.affiliationInternational Center for Agricultural Research in the Dry Areasen_US
cg.contributor.affiliationThe Spanish National Research Councilen_US
cg.contributor.donorCGIAR System Organizationen_US
cg.contributor.donorCGIAR Trust Funden_US
cg.contributor.initiativeAccelerated Breedingen_US
cg.coverage.countryMoroccoen_US
cg.coverage.iso3166-alpha2MAen_US
cg.coverage.regionNorthern Africaen_US
cg.creator.identifierBackhaus, Anna Elisabeth: 0000-0001-5202-9372en_US
cg.creator.identifierVisioni, Andrea: 0000-0002-0586-4532en_US
cg.creator.identifierSanchez-Garcia, Miguel: 0000-0002-9257-4583en_US
cg.identifier.urlhttps://apbaconf2023.um6p.ma/conference-book.pdfen_US
cg.subject.actionAreaGenetic Innovationen_US
dc.contributor.authorOuahid, Safaeen_US
dc.contributor.authorBackhaus, Anna Elisabethen_US
dc.contributor.authorJimenez, José-Antonioen_US
dc.contributor.authorVisioni, Andreaen_US
dc.contributor.authorSanchez-Garcia, Miguelen_US
dc.date.accessioned2024-01-16T18:06:18Zen_US
dc.date.available2024-01-16T18:06:18Zen_US
dc.identifier.urihttps://hdl.handle.net/10568/137796en_US
dc.titleUnveiling Drought-Resilient Pathways: Integrating High Throughput Phenotyping and Multivariate Modeling to Enhance Barley Adaptation to Climate Changeen_US
dcterms.abstractThe 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.en_US
dcterms.accessRightsOpen Accessen_US
dcterms.available2023-10-12en_US
dcterms.bibliographicCitationSafae 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).en_US
dcterms.formatPDFen_US
dcterms.languageenen_US
dcterms.licenseCopyrighted; Non-commercial educational use onlyen_US
dcterms.publisherInternational Center for Agricultural Research in the Dry Areasen_US
dcterms.subjectmachine learningen_US
dcterms.subjectbarleyen_US
dcterms.subjectphenotypic traitsen_US
dcterms.subjecthigh throughput phenotypingen_US
dcterms.subjectmultivariate modelingen_US
dcterms.subjectremote-sensingen_US
dcterms.typePosteren_US

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