基于农业环境的农业科学观测数据融合研究
收稿日期: 2020-12-01
网络出版日期: 2021-03-11
基金资助
农业科技创新联盟建设-农业基础性长期性科研工作(Y2017LM03);中央级公益性科研院所基本科研业务费所级统筹项目(BSRF201701)
Research on Data Fusion in Agricultural Scientific Observation Based on of Agricultural Environment
Received date: 2020-12-01
Online published: 2021-03-11
长期定位观测是揭示全球环境结构与功能演变规律的重要研究手段，我国农业科学观测数据积累具有良好的实验站基础，已形成由土壤肥力网和种质资源圃网为代表的长期定位观测网络，但信息化程度较低，农业科学长期定位观测工作在系统化、标准化、网络化方面仍有较长的路要走。本文以农业环境科学观测数据为出发点，重点关注农业科学观测数据的跨领域融合问题，探讨农业环境与作物种质资源、土壤质量、病虫害、农业微生物、畜禽养殖、渔业资源环境、农产品质量安全的交叉与联系，研究农业环境对农业科学观测大数据融合的纽带作用。进一步论述了以农业环境为纽带的农业科学观测数据融合，建立了农业科学观测数据融合框架，讨论了农业科学观测大数据融合在联网观测、数据标准化、数据采集和数据融合等方面的条件。提出了在联网观测的基础上，着重发展学科交叉，推动数据标准化，稳定经费支持，保障数据采集，加强交叉学科研究，探索数据融合方法等建议，以期促进农业科学观测大数据融合发展，挖掘农业科学观测数据的价值，指导农业生产，为农业政策制定提供基础支撑。
胡国铮, 干珠扎布, 余沛东, 杨振琳, 高清竹 . 基于农业环境的农业科学观测数据融合研究[J]. 农业大数据学报, 2020 , 2(4) : 86 -94 . DOI: 10.19788/j.issn.2096-6369.200411
Long term positioning observation is an important research method to reveal the evolution of global environmental structure and function. The scientific observation data accumulation in agriculture has a good foundation of experimental stations in China, leading by Soil Fertility Network and Germplasm Resource Nursery Network. However, there is still a long way to go in the systematization, standardization and networking of long-term positioning observation of agricultural science. Based on the scientific observation data of agricultural environmental, this paper focuses on the cross domain fusion of agricultural scientific observation data. The basic role of agricultural environment on scientific observed agricultural big data fusion was discussed on the intersection and connection of agricultural environment with crop germplasm resource, soil quality, crop pests and diseases, agricultural microorganisms, livestock and poultry breeding, fishery resources and environment, quality and safety of agricultural product. After discussing the link function of agricultural environment to the big data fusion of agricultural scientific observation, we established a framework of agricultural scientific observation data fusion and addressed the requirements of big data fusion in agricultural scientific observation on the network observation, data standardization, data collection and data fusion. We suggested emphasized development on interdisciplinary based on network observation, promoting data standardization, and sustaining financial support to ensure data collection, enhancing interdisciplinary research to explore data fusion methods, in order to promote the fusion of big data in agricultural scientific observation, explore the value of agricultural scientific observation data, guide agricultural production, and providing basic support for agricultural policy-making.
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