中国农业气象 ›› 2026, Vol. 47 ›› Issue (3): 473-481.doi: 10.3969/j.issn.1000-6362.2026.03.013

• 农业生态数据栏目 • 上一篇    

1981−2022年江西省冬油菜发育期变化特征数据集

孔祥胜,吴东丽,张全军   

  1. 1.九江市气象局,九江 332000;2.中国气象局气象探测中心,北京 100081;3.临近空间环境特性及效应全国重点实验室,北京 100081;4.中国气象局气象探测工程技术研究中心,北京 100081
  • 收稿日期:2025-03-17 出版日期:2026-03-20 发布日期:2026-03-17
  • 作者简介:孔祥胜,E-mail:345232243@qq.com
  • 基金资助:
    国家重点研发计划项目(2024YFD2301301);中国气象局创新发展专项项目(CXFZ2023J069);中国气象局气象探测中心生态与农业观测技术创新团队项目;九江市科技局气象防灾减灾专项项目(JJ202307)

Dataset of Winter Rape Phenology Change Characteristics in Jiangxi Province in 1981−2022

KONG Xiang-sheng, WU Dong-li, ZHANG Quan-jun   

  1. 1. Jiujiang Meteorological Bureau, Jiujiang 332000, China: 2. Meteorological Observation Centre of China Meteorological Administration, Beijing 100081; 3.State Key Laboratory of Environment Characteristics and Effects for Near-space, Beijing 100081; 4.Engineering Technology Research Center for Meteorological Observation of China Meteorological Administration, Beijing 100081
  • Received:2025-03-17 Online:2026-03-20 Published:2026-03-17

摘要:

基于江西省13个国家级农业气象观测站19812022年冬油菜发育期长期观测数据,通过标准化质量控制体系,结合核密度估计和线性倾向估计法,构建了包含冬油菜播种、出苗、五真叶、现蕾、抽薹、开花、绿熟及成熟8个发育期及其长度变化特征的电子数据集,涵盖江西省13国家级农业气象观测站冬油菜8个发育期日序数和4个发育期长度的核密度分布特征、历史变化趋势方程与显著性检验结果。该数据集以256JPG格式的图件形式保存,分别存放于按照省份或农业气象观测站名称命名的14个子文件夹中,大小为345MB。该数据集可为估算江西省冬油菜产量、优化田间管理、优化种植区划、规避农业气象灾害及品种选育提供科学依据,也可为江西省冬油菜生产系统适应气候变化提供重要的数据支撑,对实现区域“扩大油料产能”国家战略目标具有重要实践意义。实体数据集已通过ScienceDB平台开放共享,数据下载网址为https://doi.org/10.57760/sciencedb.22236。

关键词: 冬油菜, 发育期, 核密度估计, 线性倾向估计

Abstract:

This study constructed a dataset characterizing the historical variations in eight phenology stages (sowing, emergence, fifth true leaf, bud emergence, bolting, flowering, green maturity and maturity) and their lengths for winter rape, based on long−term phenological observation data (1981−2022) from 13 national agrometeorological observation stations in Jiangxi province, China. Utilizing standardized quality control protocols, kernel density estimation and linear tendency analysis, the dataset comprehensively documented the kernel density distribution patterns of Ordinal day from Jan.1 (DOY) for 8 phenology stages and its 4 lengths across all 13 stations, along with historical trend equations and statistical significance test results. The dataset comprised 256 JPG figures (345MB) organized into 14 sub−folders. This data resource provided critical scientific support for estimating yield potential, optimizing field management practices, refining regional cultivation zoning, mitigating agrometeorological risks and breeding improved cultivars. It is significant practical value for advancing the national strategic objective of "expanding oilseed production capacity" and offering essential data−driven insights to enhance the climate resilience of winter rape production systems in Jiangxi province. The dataset is publicly accessible via the ScienceDB platform at https://doi.org/10.57760/sciencedb.22236.

Key words: Winter rape, Phenology, Kernel density estimation, Linear propensity estimation