دورية أكاديمية
Satellite-enabled enviromics to enhance crop improvement.
| العنوان: | Satellite-enabled enviromics to enhance crop improvement. |
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| المؤلفون: | Resende RT; Universidade Federal de Goiás (UFG), Agronomy Department, Plant Breeding Sector, Goiânia (GO) 74690-900, Brazil; TheCROP, a Precision-Breeding Startup: Enviromics, Phenomics, and Genomics, No Zip-code, Operating Virtually, Goiânia (GO) and Sete Lagoas (MG), Brazil. Electronic address: rafael.tassinari@ufg.br., Hickey L; Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia., Amaral CH; Earth Lab, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80303, USA; Environmental Data Science Innovation & Inclusion Lab, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80303, USA., Peixoto LL; Universidade Federal de Goiás (UFG), Agronomy Department, Plant Breeding Sector, Goiânia (GO) 74690-900, Brazil., Marcatti GE; TheCROP, a Precision-Breeding Startup: Enviromics, Phenomics, and Genomics, No Zip-code, Operating Virtually, Goiânia (GO) and Sete Lagoas (MG), Brazil; Universidade Federal de São João del-Rei, Forest Engineering Department, Campus Sete Lagoas, Sete Lagoas (MG) 35701-970, Brazil., Xu Y; Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China; BGI Bioverse, Shenzhen 518083, China. Electronic address: xuyunbi@caas.cn. |
| المصدر: | Molecular plant [Mol Plant] 2024 Jun 03; Vol. 17 (6), pp. 848-866. Date of Electronic Publication: 2024 Apr 17. |
| نوع المنشور: | Journal Article; Review |
| اللغة: | English |
| بيانات الدورية: | Publisher: Cell Press Country of Publication: England NLM ID: 101465514 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1752-9867 (Electronic) Linking ISSN: 16742052 NLM ISO Abbreviation: Mol Plant Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: 2015- : Cambridge, MA Cell Press Original Publication: Oxford, England : Oxford University Press, 2008- |
| مواضيع طبية MeSH: | Crops, Agricultural*/genetics, Plant Breeding/methods ; Remote Sensing Technology |
| مستخلص: | Enviromics refers to the characterization of micro- and macroenvironments based on large-scale environmental datasets. By providing genotypic recommendations with predictive extrapolation at a site-specific level, enviromics could inform plant breeding decisions across varying conditions and anticipate productivity in a changing climate. Enviromics-based integration of statistics, envirotyping (i.e., determining environmental factors), and remote sensing could help unravel the complex interplay of genetics, environment, and management. To support this goal, exhaustive envirotyping to generate precise environmental profiles would significantly improve predictions of genotype performance and genetic gain in crops. Already, informatics management platforms aggregate diverse environmental datasets obtained using optical, thermal, radar, and light detection and ranging (LiDAR)sensors that capture detailed information about vegetation, surface structure, and terrain. This wealth of information, coupled with freely available climate data, fuels innovative enviromics research. While enviromics holds immense potential for breeding, a few obstacles remain, such as the need for (1) integrative methodologies to systematically collect field data to scale and expand observations across the landscape with satellite data; (2) state-of-the-art AI models for data integration, simulation, and prediction; (3) cyberinfrastructure for processing big data across scales and providing seamless interfaces to deliver forecasts to stakeholders; and (4) collaboration and data sharing among farmers, breeders, physiologists, geoinformatics experts, and programmers across research institutions. Overcoming these challenges is essential for leveraging the full potential of big data captured by satellites to transform 21st century agriculture and crop improvement through enviromics. (Copyright © 2024 The Author. Published by Elsevier Inc. All rights reserved.) |
| فهرسة مساهمة: | Keywords: enviromic information; envirotyping; genotype–environment interactions; precision breeding; predictive models; remote sensing |
| تواريخ الأحداث: | Date Created: 20240419 Date Completed: 20240604 Latest Revision: 20240628 |
| رمز التحديث: | 20240629 |
| DOI: | 10.1016/j.molp.2024.04.005 |
| PMID: | 38637991 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1752-9867 |
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| DOI: | 10.1016/j.molp.2024.04.005 |