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中国农学通报 ›› 2021, Vol. 37 ›› Issue (35): 43-50.doi: 10.11924/j.issn.1000-6850.casb2021-0161

所属专题: 农业气象

• 林学·园艺·园林 • 上一篇    下一篇

奉贤黄桃产量的气象影响因子分析及预测模型的建立

徐相明1(), 谈建国2, 顾品强1(), 杜纪红3, 王正大1, 汤晨阳1, 姚寅秋1, 尹荔阳1   

  1. 1上海市奉贤区气象局,上海 201416
    2上海市气候中心,上海 200030
    3上海市农业科学院林木果树研究所,上海 201400
  • 收稿日期:2021-02-19 修回日期:2021-05-15 出版日期:2021-12-15 发布日期:2022-01-07
  • 通讯作者: 顾品强
  • 作者简介:徐相明,男,1984年出生,江苏南通人,高级工程师,硕士,主要从事农业气象及预报服务研究。通信地址:201416 上海市奉贤区柘林镇金海公路2225号,Tel:021-33653866,E-mail: 237991915@qq.com
  • 基金资助:
    上海市气象局2018年“青年英才”计划;上海市气象局2020年科技开发项目“奉贤黄桃品质特征及其关键气象影响因子的研究”(MS202018)

Meteorological Influencing Factors and Forecast Model for the Yield of Yellow Peach in Fengxian

Xu Xiangming1(), Tan Jianguo2, Gu Pinqiang1(), Du Jihong3, Wang Zhengda1, Tang Chenyang1, Yao Yinqiu1, Yin Liyang1   

  1. 1Fengxian District Meteorological Office, Shanghai 201416
    2Shanghai Climate Centre, Shanghai 200030
    3Forestry and Pomology Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai 201400
  • Received:2021-02-19 Revised:2021-05-15 Online:2021-12-15 Published:2022-01-07
  • Contact: Gu Pinqiang

摘要:

利用奉贤1981—2019年逐旬气温、降水、湿度、日照等气象观测资料及1982—2019年黄桃产量数据,分析了黄桃产量的变化趋势及对气象因子的响应,并通过采用单要素指标及相关分析法着重分析2003—2019年黄桃全生育期各气象因子对产量的影响,构建了奉贤黄桃产量的气象预测模型。结果表明:黄桃产量以2003年为界,由波动上升转为上下波动,2003—2019年产量变异系数为22.9%。黄桃果实膨大—成熟期气象因子与产量的相关性较大,其次为花芽分化期、萌动—开花坐果期,落叶—休眠期较小。膨大成熟期气温(日照)在4—6月与产量呈正相关,而在7—8月为负相关,降水(湿度)影响与气温基本相反,其中7月下旬的最低气温、降水量、降水日数、日照均与产量呈显著相关;花芽分化期气温除7月下旬—8月上旬、9月上旬外均与产量呈正相关,且上年10℃终日及10、20℃有效积温与产量均呈显著正相关。花芽分化期各旬降水量(降水日数)与产量的相关性波动大;落叶—休眠期的干旱及暖冬带来的蓄冷量不足可能对产量有一定影响。建立的基于气象因子的黄桃产量最小二乘偏回归方程拟合效果较好,可为黄桃生产及管理部门提供决策服务。

关键词: 奉贤黄桃, 产量, 气象因子, 相关分析, 回归模型

Abstract:

Based on the meteorological observation data of temperature, precipitation, humidity and sunshine on a ten-day basis from 1981 to 2019 and the yield data of yellow peach from 1982 to 2019, the variation trend of yellow peach yield and its response to meteorological factors were analyzed, and the meteorological forecasting model of Fengxian yellow peach yield was established using single factor index and correlation analysis, to analyze the effects of meteorological factors on the yield of yellow peach during the whole growth period from 2003 to 2019. The results showed that the yellow peach yield changed from the wavelike rise to fluctuation in 2003, and the yield variation coefficient was 22.9%. The correlation between meteorological factors and yield of yellow peach was higher in the fruit enlargement and maturity stage, followed by the stage of flower bud differentiation, germination to flowering and fruit-setting stage, and the correlation between meteorological factors and the yield of yellow peach fruit was small in the leaf fallen and dormant stage. Temperature (sunshine) in the fruit enlargement and maturity stage was positively correlated with the yield from April to June, but negatively correlated with the yield from July to August. The effect of precipitation (humidity) was basically opposite to that of temperature, and in late July, the minimum temperature, precipitation, precipitation days and sunshine were significantly correlated with the yield. The temperature at the stage of flower bud differentiation was positively correlated with the yield except from late July to early August and early September, and the last day with daily mean temperature no less than 10℃ in the previous year and the effective accumulated temperature at 10 and 20℃ in the previous year was significantly and positively correlated with the yield. The correlation between precipitation (number of precipitation days) and yield fluctuated greatly in each ten-day period at the stage of flower bud differentiation. In the leaf fallen and dormant stage, drought and insufficient cold storage capacity caused by warmer winters might have some influence on the yield. The least square partial regression equation of yellow peach yield based on meteorological factors has good fitting effect and can provide decision-making service for the production and management departments.

Key words: Fengxian yellow peach, yield, meteorological factors, correlation analysis, regression model

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