Satellite-enabled enviromics to enhance crop improvement

扫码查看

原文链接

万方数据
维普

外文摘要：Enviromics refers to the characterization of micro-and macroenvironments based on large-scale environ-mental datasets.By providing genotypic recommendations with predictive extrapolation at a site-specific level,enviromics could inform plant breeding decisions across varying conditions and anticipate produc-tivity in a changing climate.Enviromics-based integration of statistics,envirotyping(i.e.,determining envi-ronmental 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,infor-matics 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 Al 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 insti-tutions.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.

外文关键词：

envirotypingprecision breedinggenotype-environment interactionsremote sensingpredictive modelsenviromic information

作者：

Rafael T.Resende、Lee Hickey、Cibele H.Amaral、Lucas L.Peixoto、Gustavo E.Marcatti、Yunbi Xu

展开 >

作者单位：

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

Queensland Alliance for Agriculture and Food Innovation,The University of Queensland,Brisbane,QLD,Australia

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

Universidade Federal de São João del-Rei,Forest Engineering Department,Campus Sete Lagoas,Sete Lagoas(MG)35701-970,Brazil

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

展开 >

基金：

Brazilian agencies Coordena?ao de Aperfei?oamento de Pessoal de Nivel Superior(CAPES)Conselho Nacional de Desenvolvimento Cientifico eTecnológico(CNPq)ARC Future Fellowship from the Australian Research CouncilUniversity of Colorado Boulder Grand Challenge,CIRES Earth LabAgricultural Science and Technology Innovation Program(ASTIP)of the Chinese Academy of Agricultural Sciences,Shenzhen SciencHebei Science and Technology ProgramProvincial Technology Innovation Program of Shandong,China

项目编号：

FT220100350KQTD202303010928390070215A7612D

出版年：

2024

DOI：

10.1016/j.molp.2024.04.005

分子植物(英文版)

中科院上海生命科学研究院植物生理生态所　中国植物生理学会

分子植物(英文版)

CSTPCD

影响因子：0.659

ISSN：1674-2052

年,卷(期)：2024.17(6)