Seed Germplasm Data: Current Landscape, Challenges, And Pathways Forward
Seed Germplasm Data: Current Landscape, Challenges, And Pathways Forward
Shiyu Xu 1Yichen Huang 1Niying Shen 1Yutong Liu 1Yanxin Cheng 1Xinyan Xu 1Hua Xue 2Wei Meng 3Haiyan Zhang 3Feng Yang 3Guodong Sun4
作者信息
- 1. School of Information and AI, Beijing Forestry University, Beijing, 100083, China
- 2. School of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China
- 3. School of Information and AI, Beijing Forestry University, Beijing, 100083, China;Hebei Key Lab of Smart National Park, Beijing Forestry University, Beijing, 100083, China
- 4. Hebei Key Lab of Herbage Germplasm Digitalization and Intelligence, Beijing Forestry University, Beijing, 100083, China;School of Information and AI, Beijing Forestry University, Beijing, 100083, China;Hebei Key Lab of Smart National Park, Beijing Forestry University, Beijing, 100083, China
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摘要
Seed germplasm collections underpin crop improvement, biodiversity conservation, ecosystem restoration and seed-sector innovation. Their value, however, increasingly depends on the data that make conserved diversity discoverable, interpretable and usable. The rapid expansion of genomic, phenotypic, environmental and operational information has outpaced the capacity of current systems to connect it. Germplasm records remain fragmented across genebanks, national platforms, research databases and biodiversity infrastructures, with uneven coverage, inconsistent identifiers, shallow accession context and poorly encoded conditions of access and use. These limitations constrain both scientific discovery and practical deployment, from prioritizing climate-resilient breeding materials to sourcing native seed for restoration. In this Review, we synthesize the seed germplasm data landscape across collections, data domains, information systems, representation standards and application pathways. We argue that the central challenge is not data scarcity alone, but the loss of continuity around the accession as a biological, evidential and governed object. We examine how advances in computing and artificial intelligence could transform distributed records into connected biological knowledge. We further outline an evolutionary, policy-aware architecture that extends existing systems rather than replacing them. This framework provides a shared agenda for genebanks, researchers, breeders, industry and policy organizations seeking to build trusted germplasm infrastructures for discovery, adaptation and resilience.
Abstract
Seed germplasm collections underpin crop improvement, biodiversity conservation, ecosystem restoration and seed-sector innovation. Their value, however, increasingly depends on the data that make conserved diversity discoverable, interpretable and usable. The rapid expansion of genomic, phenotypic, environmental and operational information has outpaced the capacity of current systems to connect it. Germplasm records remain fragmented across genebanks, national platforms, research databases and biodiversity infrastructures, with uneven coverage, inconsistent identifiers, shallow accession context and poorly encoded conditions of access and use. These limitations constrain both scientific discovery and practical deployment, from prioritizing climate-resilient breeding materials to sourcing native seed for restoration. In this Review, we synthesize the seed germplasm data landscape across collections, data domains, information systems, representation standards and application pathways. We argue that the central challenge is not data scarcity alone, but the loss of continuity around the accession as a biological, evidential and governed object. We examine how advances in computing and artificial intelligence could transform distributed records into connected biological knowledge. We further outline an evolutionary, policy-aware architecture that extends existing systems rather than replacing them. This framework provides a shared agenda for genebanks, researchers, breeders, industry and policy organizations seeking to build trusted germplasm infrastructures for discovery, adaptation and resilience.关键词
seed germplasm/data/information system/computing/artificial intelligenceKey words
seed germplasm/data/information system/computing/artificial intelligence引用本文复制引用
Shiyu Xu,Yichen Huang,Niying Shen,Yutong Liu,Yanxin Cheng,Xinyan Xu,Hua Xue,Wei Meng,Haiyan Zhang,Feng Yang,Guodong Sun.Seed Germplasm Data: Current Landscape, Challenges, And Pathways Forward[EB/OL].(2026-09-24)[2026-09-29].https://chinaxiv.org/abs/202609.00354.学科分类
农业基础科学
