专题——农业基础性长期性科技工作

国家作物种质资源观测鉴定站点体系布局方法研究

展开
  • 1.中国农业科学院作物科学研究所,北京 100081
    2.国家作物种质资源数据中心,北京 100081
陈彦清,女,博士,研究方向:种质资源信息管理;E-mail:chenyanqing@caas.cn

收稿日期: 2020-11-19

  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(Y2017LM01)

A Layout Method for National Crop Germplasm Resource Observation and Identification Station Systems

Expand
  • 1.Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
    2.National Data Center for Crop Germplasm Resources, Beijing 100081, China

Received date: 2020-11-19

  Online published: 2021-03-11

摘要

目的

作物种质资源观测鉴定站点的合理布局是获取科学有效观测鉴定数据的前提。本研究目的是通过建立布局合理、规范科学的种质资源长期观测鉴定体系,对资源的重要性状开展综合鉴定评价,整合观测鉴定数据,为作物种质资源大数据体系建设提供重要内容,为农业科学研究和现代种业发展提供坚实的基础数据支撑。

方法

本文首先根据调研,确定全国有能力进行评价鉴定的站点共379家,然后以气候生产潜力为载体,确定与作物生长密切相关的环境因素并计算各站点的因素值,最后以这些因素作为聚类因子选择空间最邻近聚类方法进行空间聚类,建立每类站点的泰森多边形完成区域的划分。

结果

基于以上方法,确定气温、降水、海拔、纬度和日照时长是与作物生长密切相关的环境因素,将站点聚类成26类,通过建立泰森多边形将全国划分成26个评价鉴定区,并在分区基础上设置6条站点布设原则以指导区域内站点选择。

结论

站点体系布局关系到未来观测评价结果的代表性和科学性,站点的选择是一个非常复杂的过程。本文的站点体系布局方法将环境因素和空间因素有效结合,不仅能够有效指导作物种质资源领域多环境评价鉴定地点的选择,也可为作物相关领域的观测站点布设提供参考,从而获得更具有价值和代表性的鉴定数据。

本文引用格式

陈彦清, 曹永生, 林雨楠, 方沩 . 国家作物种质资源观测鉴定站点体系布局方法研究[J]. 农业大数据学报, 2020 , 2(4) : 20 -28 . DOI: 10.19788/j.issn.2096-6369.200403

Abstract

Objective

Obtaining accurate scientific observation and identification data is crucial to establishing a comprehensive layout of crop germplasm resource observation and identification sites. The purpose of this research was to establish a rationally distributed, normative and scientific long-term observation and identification system for germplasm resources, carry out comprehensive identification and evaluation of the importance of resources, integrate observation and identification data, provide necessary content for the construction of a crop germplasm resources big data system and provide solid basic data support for agricultural scientific research and modern seed industry development.

Methods

First, 379 sites were identified on the basis of the investigation. Then the environmental factors most closely related to crop growth were determined, and the factor values of each site were calculated using the climate production potential. Finally, these factors were used to select the best method for identifying spatial clustering, and generate a Tyson polygon for each type of site to create area divisions.

Results

With consideration of the above methods, it was determined that temperature, precipitation, altitude, latitude and sunshine duration were the environmental factors most closely related to crop growth. The stations were clustered into 26 categories, China was divided into 26 evaluation and identification areas by generating a Tyson polygon, and six site layout principles were set on the basis of zoning to guide site selection in the region.

Conclusion

The layout of the station system affects the representativeness and scientific accuracy of future observation and evaluation results. The selection of stations is a highly complex process; the site system layout method used in this research effectively combined environmental and spatial factors, which not only effectively guided the selection of multiple environmental assessment sites in the crop germplasm resources field, but also provided reference for the layout of observation sites in crop-related fields, so as to obtain more valuable and representative identification data.

参考文献

1 中华人民共和国国务院办公厅.国务院办公厅关于加强农业种质资源保护与利用的意见,国办发〔2019〕56号[Z]. 2020-02-11.
1 General Office of the State Council of the People's Republic of China. The opinions of the General Office of the State Council on strengthening the protection and utilization of agricultural germplasm resources, GBF〔2019〕No. 56[Z]. 2020-02-11.
2 中华人民共和国农业部.农业部关于启动农业基础性长期性科技工作的通知,农科教发[2017]5号[Z].2017-3-25.
2 The ministry of agriculture of the People's Republic of China.issued on starting the basic long-term scientific and technological work in agriculture,NKJF [2017] No. 5[Z]. 2017-3-25.
3 沈文忠,赵伟荣,张绪美,等. 太仓市水稻和小麦气候生产潜力估算[J].中国农学通报,2019,35(35):1-10.
3 Shen W Z, Zhao W R, Zhang X M, et al. Estimation of Climatic Potential Productivity of Rice and Wheat in Taicang[J], Chinese Agricultural Science Bulletin, 2019,35(35):1-10.
4 赵放,李秀芬,林伟楠,等. 气候变化对玉米气候生产潜力的影响[J].农业工程,2019,9(08):132-134.
4 Zhao F,Li XX F,Lin W N, et al. Impact of Climate Change on Climate Productivity Potential of Maize[J], Agricultural Engineering,2019,9(08):132-134.
5 Gryze S D,Wolf A,Kaffka S R, et al.Simulating green-house gas budgets of four California cropping systems under conventional and alternative management[J].Ecological Applications,2010,(20):1805-1819.
6 李秀芬,赵慧颖,朱海霞,等.黑龙江省玉米气候生产力演变及其对气候变化的响应[J].应用生态学报,2016,27(8):2561-2570.
6 Li X F,Zhao H Y,Zhu H X, et al. Evolution of maize climate productivity and its response to climate change in Heilongjiang Province,China[J].Chinese Journal of Applied Ecology, 2016,27(8):2561-2570.
7 Chavas D R,Izaurralde R C,Thomson A M,et al.Longterm climate change impacts on agricultural productivity in eastern China[J].Agricultural and Forest Meteorology,2009(2):1118-1128.
8 Han J, Kamber M. Data Mining: concepts and Technique (second edition)[M]. San Francisco: Morgan Kaufmann, 2005.
9 Halkidi M, Batistakis Y, Vazirgiannis M. On Clustering Validation Techniques[J]. Intelligent Information Systems, 2001,17(223):107-145.
10 Bezdek J, Pal N. Some New Indexes of Cluster Validity[J]. IEEE Transactions on Systems, Man, And Cybernetics-Part B: Cybernetics, 1998,28(3):301-315.
11 刘海燕,顾敏,毛亚萍等. 空间聚类有效性评价方法对比与研究[J].地理信息世界,2020,27(1):72-77, 83.
11 Liu H Y Gu M, Mao Y P, et al. A Comparative Study of Validity Evaluation Approaches of Spatial Clustering[J]. Geomatics World, 2020,27(1):72-77, 83.
12 Vendramin L, Ricardo J, Eduardo C, et al. On the Comparison of Relative Clustering Validity Criteria[C]// In:Proceedings of SIAM SDM'09, Sparks, 2009:733-744.
13 周叶林.科学研究中的信息论及其应用[J].今日南国, 2009,5(124):201-202.
13 Zhou Y L. Information theory and its application in scientific research[J]. The South of Chinatoday, 2009,5(124):201-202.
14 Dunn J. Well Separated Clusters and Optimal Fuzzy Partitions[J], Journal of Cybernetica, 1974,4: 95-104.
15 Sharma S. Applied Multivariate Techniques[M]. State of New Jersey: John Wiley & Sons Inc., 1996.
16 Halkidi M, Vazirgiannis M. Clustering Validity Assessment: finding the Optimal Partitioning of a Data Set[C]//
16 Proceeding of ICDM2001,187-194.
17 刘旭,李立会,黎裕,等. 作物种质资源研究回顾与发展趋势,农学学报,2018,8(1):1-6.
17 Li u X Li L H, Li Y, et al. Crop Germplasm Resources: Advances and Trends, Journal of Agriculture, 2018,8(1):1-6.
18 中华人民共和国农业部.农业部办公厅关于确定第一批国家农业科学观测实验站的通知[Z].2018-01-30.
18 The ministry of agriculture of the People's Republic of China.Notice of the general office of the Ministry of agriculture on determining the first batch of national agricultural scientific observation and experimental stations[Z].2018-01-30.
19 中华人民共和国农业农村部. 农业农村部办公厅关于确定第二批国家农业科学观测实验站的通知[Z].2020-1-2.
19 Ministry of Agriculture and Rural Affairs of the People’s Republic of China. Notice of the general office of the Ministry of agriculture and rural areas on determining the second batch of national agricultural scientific observation and experimental stations[Z].2020-1-2.
20 , 农用地质量分等规程[S].
20 , Regulations for gradation on agricultural land quality[S].
文章导航

/