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中国农学通报 ›› 2019, Vol. 35 ›› Issue (31): 134-138.doi: 10.11924/j.issn.1000-6850.casb20190600315

所属专题: 油料作物 农业气象

• 食品 营养 检测 安全 • 上一篇    下一篇

大豆气候品质评价技术模型研究

姚俊英1, 于宏敏1, 阙粼婧1, 李玉春2   

  1. 1.黑龙江省气象服务中心;2.呼玛县气象局
  • 收稿日期:2019-06-21 修回日期:2019-09-28 接受日期:2019-08-23 出版日期:2019-11-04 发布日期:2019-11-04
  • 通讯作者: 姚俊英
  • 基金资助:
    黑龙江省气象局科学技术研究项目“黑龙江省主要农产品气候品质认证评价方法研究”(HQZD2016004)、“黑龙江省农产品气候品质认证 服务系统”(HQZC2017022)、“黑龙江省大豆气候品质认证技术方法研究”(HQZC2018022);中国气象局软科学研究项目“黑龙江省农业气象服务需求 研究”;黑龙江省气象局软科学研究项目“黑龙江省农产品气候品质认证服务研究”。

Soybean Climatic Quality Assessment: Technical Models

  • Received:2019-06-21 Revised:2019-09-28 Accepted:2019-08-23 Online:2019-11-04 Published:2019-11-04

摘要: 开展大豆气候品质评价技术方法研究既是开展农产品气候品质认证服务工作的必然要求,也是黑龙江省种植结构调整的现实需求。本文采用呼玛县1981-2018年大豆生育期、产量、气象灾情以及气温、降水、日照等气象资料,研究确定了大豆气候品质评价技术指标,并建立了评价模型。确定了大豆气候品质评价6个关键影响因子(年≥10℃积温、开花期低温、开花期日照、开花期降水、结荚鼓粒期降水、初霜冻)和分级指标。通过对专家打分法、相关系数法和通径系数法进行对比分析,最终采用相关系数法确定权重系数建立了大豆气候品质评价模型,历史拟合正确率达到了88%,说明该方法具有较高的实用性。

关键词: 糯玉米, 糯玉米, 苏科糯6号, 育种, 栽培技术

Abstract: The research on the technical methods of soybean climate quality assessment is not only an inevitable requirement for the development of agricultural product climate quality certification services, but also a realistic requirement for planting structure adjustment in Heilongjiang Province. In this paper, the soybean growth period, yield, meteorological disasters, and meteorological data such as temperature, precipitation and sunshine in Huma County from 1981 to 2018 were used to study and determine the technical indicators for soybean climate quality evaluation, and an evaluation model was established. Six key impact factors for soybean climate quality assessment (annual≥10℃ accumulated temperature, flowering period low temperature, flowering period sunshine, flowering period precipitation, pod blast stage precipitation, initial frost) and grading indicators were determined. By comparing and analyzing the expert scoring method, correlation coefficient method and path coefficient method, the correlation coefficient method was used to determine the weight coefficient to establish the soybean climate quality evaluation model. The historical fitting accuracy rate reached 88%, indicating that the method has High practicality.

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