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农学学报 ›› 2025, Vol. 15 ›› Issue (4): 83-91.doi: 10.11923/j.issn.2095-4050.cjas2024-0035

• 农业信息 农业气象 • 上一篇    下一篇

鱼台水稻光温模型研究与产量预报

朱雨晴(), 李华昭()   

  1. 山东省济宁市气象局,山东济宁 272113
  • 收稿日期:2024-02-29 修回日期:2024-11-20 出版日期:2025-04-20 发布日期:2025-04-17
  • 通讯作者:
    李华昭,女,1972年出生,山东嘉祥人,高级工程师,本科,研究方向:农业气象。通信地址:272100 山东省济宁市任城区科苑路 济宁市气象局,Tel:0537-2235819,E-mail:
  • 作者简介:

    朱雨晴,女,1993年出生,工程师,硕士研究生,研究方向:设施农业气象。通信地址:272100 山东省济宁市任城区科苑路 济宁市气象局,E-mail:

  • 基金资助:
    不同模型构建方法对设施作物生育期模拟的精确度比较(2020sdqxm15); 气象精准服务特色农产品基地建设助力乡村产业振兴研究(2019JNZL02)

Study on Light-temperature Model of Rice in Yutai and Yield Forecast

ZHU Yuqing(), LI Huazhao()   

  1. Jining Meteorological Bureau, Jining Shandong 272113
  • Received:2024-02-29 Revised:2024-11-20 Online:2025-04-20 Published:2025-04-17

摘要: 为了探究鱼台水稻在不同积温条件下的生长发育规律,并分析不同生育期日照时数、温度对鱼台水稻产量因素的影响,为鱼台地区的水稻的优化种植提供农业气象服务依据。本研究以2017—2022年鱼台水稻的生长指标数据与生育期间积温等气象要素,构建了Logistic生长模型。并采用相关分析、回归分析等统计方法,分析出不同生育期光温对水稻产量因素的影响,据此建立了产量因素预报模型。结果表明:Logistic模型对鱼台水稻生长发育的模拟,整体精确度较高,其模拟值与实测值的标准误差(RMSE)在0.591~5.100之间、标准均方根误差(nRMSE)在0.087~0.107之间、与1:1直线间的R2在0.970~0.996之间。水稻返青分蘖、拔节、孕穗和灌浆成熟期的日照时数与产量显著相关,抽穗和灌浆成熟期的积温与产量显著相关,利用多元线性回归法建立了水稻产量和水稻穗粒数的预报模型,通过历史回带与直方图等方法验证得到,预报模型具有较高的准确性。

关键词: 鱼台水稻, 积温, 生长模型, 相关分析, 回归分析, 历史回代, 模型验证, 产量预报模型, 产量因素

Abstract:

The aims were to study the growth and development law of Yutai rice under different accumulated temperature conditions, and to explore the influence of sunshine hours and temperature in different growth stages on the yield factors of Yutai rice, and to provide agricultural meteorological service basis for optimal planting of rice in Yutai area. A Logistic growth model was constructed based on the growth index data of Yutai rice from 2017 to 2022 and meteorological factors such as accumulated temperature during growth period. By using statistical methods such as correlation analysis and regression analysis, the influence of light and temperature in different growth stages on rice yield factors was analyzed, and the prediction model of yield factors was established accordingly. The results showed that the overall accuracy of Logistic model was high in the simulation of rice growth and development in Yutai, and the Root Mean Square Error (RMSE) between the simulated value and the measured value was between 0.591 and 5.100, the Normalized Root Mean Squared Error (nRMSE) was between 0.087 and 0.107, and the R2 between the simulated value and the measured value was between 0.970 and 0.996. The number of sunshine hours in tillering, jointing, booting and grain filling maturity of rice was significantly correlated with yield, and the accumulated temperature in heading and grain filling maturity was significantly correlated with yield. The prediction model of rice yield and grain number per ear was established by multiple linear regression method, which was verified by historical band and histogram. The prediction model has high accuracy.

Key words: Yutai rice, accumulated temperature, growth model, correlation analysis, regression analysis, historical generation, modelling verification, yield forecasting model, yield factor