基于多源数据的华北平原夏玉米种植区划研究

doi:10.12133/j.smartag.2019.1.2.201901-SA002

Smart Agriculture ›› 2019, Vol. 1 ›› Issue (2): 73-84.doi: 10.12133/j.smartag.2019.1.2.201901-SA002

基于多源数据的华北平原夏玉米种植区划研究

刁兴良^1,², 杨再洁³, 李奇峰¹, 于景鑫¹, 郑文刚¹, 史磊刚^1,^*()

^1. 北京农业信息技术研究中心,北京 100097
^2. 山东农业大学信息科学与工程学院,山东泰安 271018
^3. 中化化肥有限公司,北京 100031

收稿日期:2019-01-30 修回日期:2019-04-10 出版日期:2019-04-30
基金资助:
国家重点研发计划项目(2017YFD0301004);北京市农林科学院科技创新能力建设专项(KJCX20180703)
作者简介:刁兴良（1995-）,男,硕士,研究方向：农业大数据技术与工程,Email：805605322@qq.com。
通信作者:

Regionalization research of summer corn planting in North China Plain based on multi-source data

Diao Xingliang^1,², Yang Zaijie³, Li Qifeng¹, Yu Jingxin¹, Zheng Wengang¹, Shi Leigang^1,^*()

^1. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
^2. College of Information Science and Engineering, Shandong Agricultural University, Tai'an 271018, China
^3. Sinochem Fertilizer Co., Ltd, Beijing 100031, China

Received:2019-01-30 Revised:2019-04-10 Online:2019-04-30

摘要/Abstract

摘要：

精准识别农业生产环境信息和农业生产特征,对气象、土壤和作物等多源数据进行综合分类,是提高农业资源利用效率和优化农业种植结构的基础。本研究基于近20年（1998~2017年）气象数据和华北五省的玉米单产统计数据,首先构建了华北平原气候资源和玉米生产时空分布特征数据库,研究区内的降雨量、活动积温、日照时数、太阳辐射和玉米单产均存在显著的时空变化;利用作物精细种植区划方法,将华北平原夏玉米种植区分为极不适宜区、不适宜区、较适宜区、适宜区、极适宜区五大类,各类面积分别占总体的比例约为10%、11%、25%、30%、24%;进一步通过环境类别归属度分析方法,将每一大类分为5小类,概率大于75%的相对稳定区域约占总面积的63%,小于75%的波动区域约占37%;极不适宜区、不适宜区和较适宜区,三类时空分布比较稳定,隶属度为100%分别占各类面积的87.67%、70.41%和84.28%,波动区主要发生在极适宜区和适宜区,以及适宜区和较适宜区之间。本研究构建的华北平原夏玉米精细区划结果,对提高研究区资源利用效率和优化玉米产业布局具有重要的指导意义。

关键词: 夏玉米, 多源数据, 时空分布, 精细区划

Abstract:

Accurate identification of agricultural production environment information and agricultural production characteristics, comprehensive classification of meteorological, soil and crop multi-source data, are the bases for improving the efficiency of agricultural resource utilization and optimizing the structure of agricultural cultivation. Based on the meteorological data of nearly 20 years and the statistics of com yield, this study first constructed a database of spatial and temporal distribution characteristics of climate resources and com production in North China Plain, and there were significant spatiotemporal changes in rainfall, activity accumulated temperature, sunshine hours, solar radiation and corn yield. By using the method of fine crop planting regionalization, the summer com planting areas in the North China plain were divided into 5 categories: the extremely unsuitable area, the unsuitable area, the less suitable area, the suitable area, and the most suitable area, the proportions of each type of area in the total area is about 10%, 11%, 25%, 30%, 24%, respectively, further through using the Environmental Category attribution analysis method, each large class was divided into 5 subcategories, the probability was greater than 75% the relatively stable region accounts for about 63% of the total area, the fluctuation area of less than 75% is about the stable spatial and temporal distribution of 37%; the extremely unsuitable area, the unsuitable area and the less suitable area, these three kinds of spatial and temporal distributions were relatively stable, the belonging degree was 100%, accounting for 87.67%, 70.41% and 84.28%, respectively, the fluctuation zone mainly occurs between the extremely suitable zone and the suitable zone, and between the suitable zone and the relatively suitable zone. The fine zoning of summer com in North China Plain has important guiding significance for improving the utilization efficiency of local resources and optimizing the layout of com industry.

Key words: summer com, multi-source data, temporal and spatial distribution, fine regional planning

中图分类号:

F304.5

刁兴良, 杨再洁, 李奇峰, 于景鑫, 郑文刚, 史磊刚. 基于多源数据的华北平原夏玉米种植区划研究[J]. 智慧农业, 2019, 1(2): 73-84.

Diao Xingliang, Yang Zaijie, Li Qifeng, Yu Jingxin, Zheng Wengang, Shi Leigang. Regionalization research of summer corn planting in North China Plain based on multi-source data[J]. Smart Agriculture, 2019, 1(2): 73-84.

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基于多源数据的华北平原夏玉米种植区划研究

Regionalization research of summer corn planting in North China Plain based on multi-source data

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