2: Advanced Phenotyping and Crop Modelling for Adaptation to Drylands
- Page ID
- 134467
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)- 2.1: UAV Method Based on Multispectral Imaging for Field Phenotyping
- This page explores the use of UAVs with multispectral imaging for cereal crop phenotyping, focusing on sorghum in West Africa to enhance agricultural productivity. It outlines an automated image processing pipeline for analyzing crop data, which includes creating orthomosaics and extracting vegetation indices for yield monitoring.
- 2.2: Agro-physiological Responses of 10 West Africa Sorghum Varieties to Early Water Deficit Assessed by UAV and Ground Phenotyping
- This page contains studies assessing drought effects on ten sorghum varieties over two years at Cheikh Anta Diop University, Senegal. Key findings include significant impacts on growth traits and recovery capabilities, with genotypic variations noted under drought conditions. UAV technology was utilized for data collection, enhancing phenotyping methods.
- 2.3: Toward a Regional Field Phenotyping Network in West Africa
- This page discusses the necessity of doubling West African crop production by 2050 to meet global demand and emphasizes the role of high-throughput phenotyping through UAVs in enhancing crop yields. It outlines the development of a structured field phenotyping network that integrates various research centers to improve varietal development and farmer assessment.
- 2.4: High-throughput Root Phenotyping- Opportunities and Challenges for the Adaptation of Arid and Semi-arid Crops to Future Climates
- This page discusses the impact of climate change on crop yields, particularly in arid areas of Africa, and emphasizes the importance of root traits in improving drought resilience. It reviews advances in phenotyping techniques, including high-throughput methods and imaging technologies, for assessing root architecture in crops like pearl millet and maize.
- 2.5: Using Root-Soil Interactions in the Rhizosphere as Valuable Traits for Selection Against Drought
- This page discusses the rhizosphere's crucial role in enhancing crop drought resistance through root-soil-microbiota interactions and rhizosheath formation, particularly in crops like pearl millet and maize in drought-affected areas of West Africa. Research highlights the importance of mycorrhizal inoculation, genetic control of rhizosheath size, and the influence of root traits on microbial diversity.
- 2.6: Designing Dual-purpose Sorghum Ideotypes for High Grain and Biomass Yields Suitable for Various Target Environments in Senegal
- This page presents a comprehensive study on optimizing dual-purpose sorghum ideotypes using the SAMARA model to boost yields in semiarid Senegal. It identifies superior traits like deep roots and stay-green leaves, demonstrating significant improvement in grain and biomass production. The research spans multiple years of trials across various conditions, focusing on calibration and evaluation to refine genotypic parameters.


