中国农业科学 ›› 2007, Vol. 40 ›› Issue (11): 2569-2574 .

• 园艺 • 上一篇    下一篇

温室标准切花菊叶面积预测模型研究

杨再强,罗卫红,陈发棣,顾俊杰,李向茂,丁琪峰,赵才标,陆亚凡   

  1. 南京农业大学农学院
  • 收稿日期:2006-05-25 修回日期:2007-03-13 出版日期:2007-11-10 发布日期:2007-11-10
  • 通讯作者: 罗卫红

Predicting the Leaf Area of Greenhouse Single Flower Cut Chrysanthemum

Yangzai-qiang   

  1. 南京农业大学农学院
  • Received:2006-05-25 Revised:2007-03-13 Online:2007-11-10 Published:2007-11-10

摘要: 【目的】建立一个温室标准切花菊的叶面积指数预测模型。【方法】根据光温对菊花(Chrysanthemum morifolium Ramat.)出叶和展叶速率的影响,通过不同定植期和不同品种的试验,以综合考虑温度、光合有效辐射和日长的生理辐热积为预测指标,建立了温室标准切花菊叶面积预测模型,并用独立的试验数据对模型进行检验。【结果】模型对温室标准切花菊的叶面积指数的预测精度较高,预测值与实测值基于1﹕1线的决定系数(R2)和回归估计标准误差(RMSE)分别为0.94和0.75。基于生理辐热积的预测模型对叶面积指数的预测精度比积温法和比叶面积法分别提高了48.2%和84.6%。【结论】本研究的叶面积指数预测模型预测精度高,模型参数少,机理性强,可以为温室标准切花菊生长和外观品质的预测与管理提供理论依据与决策支持。

关键词: 菊花, 生理辐热积, 叶面积指数, 预测模型

Abstract: 【OBJECTIVE】A model for predicting the leaf area of single flower cut chrysanthemum was developed for optimising greenhouse single flower cut chrysanthemum production management.【METHOD】Based on the effects of temperature and radiation on chrysanthemum leaf emerging and elongation, experiments with different varieties and planting dates were carried out in greenhouses to collect data to develop and validate the model.【RESULTS】The predicted results agreed well with the observed ones. The determination coefficient (R2) and the root mean squared error (RMSE) between the predicted and the measured leaf area index (LAI) based on the 1:1 line are 0.94 and 0.75, respectively. Compared to the models based on growth degree days (GDD) and specific leaf area (SLA), the prediction accuracy of the model developed in this study is 48.2% and 84.6% higher, respectively.【CONCLUSION】From the results obtained in this study, it can be concluded that the model developed in this study can give satisfactory prediction of the leaf area index of greenhouse single flower cut chrysanthemum and can be used for the prediction of biomass production and production management of single flower cut chrysanthemum.

Key words: Cut chrysanthemum, leaf area index, Physiological product of thermal effectiveness and PAR, Prediction model