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高寒地区大豆产量动态预报研究
引用本文:陈雪,高梦竹,赵晶,李新华,乔梁.高寒地区大豆产量动态预报研究[J].农学学报,2023,13(6):91-96.
作者姓名:陈雪  高梦竹  赵晶  李新华  乔梁
作者单位:1. 黑龙江省气象服务中心;2. 黑龙江省气象台;3. 内蒙古兴安盟气象局;4. 黑龙江省气象数据中心
基金项目:黑龙江省气象局科学技术研究项目“基于黑龙江省大豆产量预报的保险产品设计”(HQZC2021007)
摘    要:研究旨在实现逐旬省级和市级的大豆产量预报。利用黑龙江省大豆产量资料分析其时空分布特征;结合各时段气象数据构建温度、降水、日照及综合气候适宜度模型,分析与相对气象产量相关性;构建基于气候适宜度指数的逐旬产量动态预报模型,对黑龙江省大豆产量进行动态预报。结果表明:(1)大豆年均单产空间上从南至北逐级递减,时间上呈年代际变化,各市县年均总产量差距显著,嫩江市大豆产量最高;(2)1995—2015年黑龙江省和嫩江市气候适宜度指数与其对应的大豆相对气象产量显著相关,构建的气候适宜度模型可以客观反映大豆各生长时段内气象条件情况;(3)1995—2015年模型回代检验平均准确率在80%以上,各时段趋势准确年份在12年以上,2017—2019年模型外推预报准确性均超过了85%。建立的产量预报模型可为黑龙江省大豆产量预报提供参考依据。

关 键 词:黑龙江省  大豆  气候适宜度模型  产量预报模型  产量动态预报
收稿时间:2022-05-25

Study on Dynamic Forecast of Soybean Yield in Alpine Region
CHEN Xue,GAO Mengzhu,ZHAO Jing,LI Xinhua,QIAO Liang.Study on Dynamic Forecast of Soybean Yield in Alpine Region[J].Journal of Agriculture,2023,13(6):91-96.
Authors:CHEN Xue  GAO Mengzhu  ZHAO Jing  LI Xinhua  QIAO Liang
Abstract:To realize the soybean yield forecast at the provincial and municipal levels by every ten days, this paper analyzed the spatial and temporal distribution characteristics of soybean yield data in Heilongjiang Province. Established a temperature, precipitation, sunshine and comprehensive climate suitability model based on the meteorological data of each period, and analyzed their correlation with the relative meteorological yield. A dynamic yield forecast model based on climate suitability index was constructed to forecast soybean yield in Heilongjiang Province. The results showed that: (1) the annual per unit area yield of soybean decreased from south to north in space and showed interdecadal variation in time. There was a significant difference in annual total yield among cities and counties, and Nenjiang City had the highest soybean yield; (2) the climate suitability index of Heilongjiang Province and Nenjiang City from 1995 to 2015 was significantly correlated with the corresponding relative meteorological yield of soybean, the climate suitability model could objectively reflect the meteorological conditions in each growth period of soybean; (3) from 1995 to 2015, the average accuracy rate of model back substitution test was more than 80%, the trend accuracy year of each period was more than 12 years, and the accuracy of model extrapolation forecast from 2017 to 2019 exceeded 85%. The established yield forecast model can provide a reference basis for soybean yield prediction in Heilongjiang Province.
Keywords:Heilongjiang Province  soybean  climate suitability model  yield forecast model  dynamic yield forecast  
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