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农学学报

农学学报 ›› 2024, Vol. 14 ›› Issue (9): 46-53.doi: 10.11923/j.issn.2095-4050.cjas2023-0189

• 农业信息 农业气象 • 上一篇    下一篇

寒地水稻开花期多雨寡照复合逆境灾损评估指标

吕佳佳1,2(), 初征1,2, 郭立峰1,2, 李宇光3, 刘旭4, 丁海玖5, 王秋京1,2(), 周宝才1,2   

  1. 1 中国气象局东北地区生态气象创新开放实验室，哈尔滨 150030
    2 黑龙江省气象科学研究所，哈尔滨 150030
    3 黑龙江省生态气象中心，哈尔滨 150030
    4 黑龙江省气象数据中心，哈尔滨 150030
    5 克山县气象局，黑龙江齐齐哈尔 161600
  • 收稿日期:2023-08-22 修回日期:2024-03-21 出版日期:2024-09-18 发布日期:2024-09-18
  • 通讯作者:
    王秋京，女，1979年出生，黑龙江哈尔滨人，高级工程师，硕士，研究方向：生态与农业气象。通信地址：150030 黑龙江省哈尔滨市香坊区电碳路71号 黑龙江省气象科学研究所，Tel：0451-55101013，E-mail：。
  • 作者简介:

    吕佳佳，女，1983年出生，黑龙江哈尔滨人，高级工程师，硕士，研究方向：生态与农业气象。通信地址：150030 黑龙江省哈尔滨市香坊区电碳路71号黑龙江省气象科学研究所，Tel：0451-55101013，E-mail：。

  • 基金资助:
    黑龙江省自然科学基金项目“气候变化背景下黑龙江玉米生育期气候资源利用率评估研究”(LH2021D020); 黑龙江省气象局科技创新发展项目“寒稻生殖生长期冷涡型复合灾害产量损失评估指标”(HQ2023016); 中国气象局沈阳大气环境研究所联合开放基金课题资助“东北冷涡对农业生产影响预报和风险预警研究”(2022SYIAEKFZD04-02)

Evaluation Index of Composite Adversity Disaster Damage of Rainy and Low Light at Rice Flowering Period in Cold Region

LV Jiajia1,2(), CHU Zheng1,2, GUO Lifeng1,2, LI Yuguang3, LIU Xu4, DING Haijiu5, WANG Qiujing1,2(), ZHOU Baocai1,2   

  1. 1 Innovation and Opening Laboratory of Eco-Meteorology in Northeast China, CMA, Harbin 150030, Heilongjiang, China
    2 Meteorological Academician Workstation of Heilongjiang Province, Harbin 150030, Heilongjiang, China
    3 Heilongjiang Ecometeorological Center, Harbin 150030, Heilongjiang, China
    4 Meteorological Data Center of Heilongjiang Province, Harbin 150030, Heilongjiang, China
    5 Keshan Meteorological Station, Qiqihaer 161600, Heilongjiang, China
  • Received:2023-08-22 Revised:2024-03-21 Online:2024-09-18 Published:2024-09-18

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摘要：

黑龙江省是中国优质粳稻生产核心种植区，开花期多雨寡照复合发生，严重影响寒地水稻结实率和产量。为了保障国内优质稻米的供应及国家粮食安全，本研究旨在构建一个针对多雨寡照复合发生的判识指标，并定量评估其对产量的损失。通过整合气象数据、水稻生育期信息、产量资料以及历史灾情记录，运用多层灰色关联分析法探究致灾因子、作物产量结构与相对气象产量之间的灰色映射关系。据此，建立了寒地水稻多雨寡照复合指数(RSCI)和一个描述复合逆境与产量损失率关联度的模型。利用K-均值聚类分析方法和历史典型灾害年份数据确定了灾害的临界值和等级，进而形成了评估多雨寡照复合发生导致产量损失的评估指标体系。本研究明确了寒地水稻在不同等级（轻度、中度、重度）多雨寡照条件下的临界阈值和相应的产量损失率，历史灾情验证显示多雨寡照判识率达到100%，产量损失率判识准确率超过80%。在1958—2021年间，全省水稻不同程度多雨寡照的发生频率呈轻度高于中度高于重度的趋势，且北部农区的发生频率高于南部农区。该研究成功构建了一套多雨寡照复合发生的判识指标，为定量化评估产量损失提供了重要的技术支撑。

关键词: 黑龙江省, 寒地水稻, 优质粳稻, 开花期, 多雨寡照, 复合指数, 产量损失, 灰色关联分析, K-均值聚类分析

Abstract:

Heilongjiang Province serves as the primary cultivation region for high-quality japonica rice production in China. The occurrence of heavy rainfall during the flowering period significantly impacts the seed setting rate of rice in cold regions, resulting in a decrease in yield. It is important to establish the identification index of the occurrence of rainy and low light conditions and quantitatively assess the yield loss for ensuring the domestic supply of high-quality rice and national food security. In this study, a multi-layer grey correlation analysis method was adopted to investigate the grey relationship among disaster factors, crop yield structure and final yield by combining meteorological, growth period, yield data and disaster historical data, and to construct the rice rainy and low light composite index (RSCI) in cold region, and establish the correlation degree model between complex adversity and yield loss rate. Based on K-mean clustering analysis method and historical typical disaster years, the critical value and grade of disaster were determined, and the evaluation index of yield loss caused by rainy and low light weather was established. The critical threshold and yield loss rate of rice with mild, moderate and severe rainfall were studied. The results of historical disaster verification showed that the identification rate of rainy and low light was 100%, and the identification accuracy rate of yield loss rate was higher than 80%. From 1958 to 2021, the frequency of rice rainy and low-light disasters in different degrees in the province was as follows: mild was higher than moderate and severe, and the frequency of rice rainy and low-light disasters in the northern agricultural area was higher than that in the southern agricultural area. In this study, the identification index of the occurrence of rain and light combination was constructed to provide technical support for quantitative evaluation of yield loss.

Key words: Heilongjiang Province, japonica rice, high-quality japonica rice, flowering period, rainy and low light combined damage, composite index, yield loss, grey relational analysis, K-means clustering analysis