国家作物种质资源观测鉴定站点体系布局方法研究

doi:10.19788/j.issn.2096-6369.200403

摘要/Abstract

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。

关键词: 作物种质资源, 环境因子, 空间聚类, 区域划分, 农业大数据, 观测站点布局, 科学数据, 观测数据

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.

Key words: crop germplasm resources, environmental factor, spatial clustering, regional division, agricultural big data, observation station distribution, scientific data, observation data

中图分类号:

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.

Yanqing Chen, Yongsheng Cao, Yunan Lin, Wei Fang. A Layout Method for National Crop Germplasm Resource Observation and Identification Station Systems[J]. Journal of Agricultural Big Data, 2020, 2(4): 20-28.

图/表 7

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表1

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表2

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图5

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分组数	一级分组伪F值	二级分组第一小组伪F值	二级分组第二小组伪F值
2	291.0432	85.2622	94.1622
3	243.5635	83.8287	97.9573
4	233.3227	101.7008	162.0380
5	269.1534	107.4381	146.5259
6	249.2426	115.5728	140.8070
7	242.0424	118.5736	138.9963
8	235.9106	126.9021	137.5531
9	234.9926	135.7732	138.5778
10	265.2248	151.0375	142.9855
11	265.3803	152.3274	153.2233
12	268.7586	154.8697	166.4119
13	269.0556	155.2184	177.2834
14	274.8999	153.5072	176.4605
15	279.1272	153.3548	177.1460

因素	一级最优分组后各因素R²值	二级第一小组最优分组后各因素R²值	二级第二小组最优分组后各因素R²值
纬度	0.6424	0.9502	0.9715
海拔	0.0010	0.9655	0.9296
降水	0.6113	0.8987	0.9140
气温	0.4645	0.9190	0.9474
日照时长	0.4558	0.9058	0.8939

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