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温室标准切花菊叶面积预测模型研究 被引量:12

Predicting the Leaf Area of Standard Cut Chrysanthemum in Greenhouse
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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。 [Objective] To develop a model for predicting the leaf area of standard cut chrysanthemum in greenhouses. [Method] Based on the effects of temperature and radiation on chrysanthemum leaf emerging and elongation, an integrated photo-thermal index, the physiological product of thermal effectiveness and PAR was used to predict the leaf area of standard cut chrysanthemum in greenhouse. Experiments with different varieties and planting dates were carried out in greenhouses to collect data to develop and validate the model. [Result] The predicted results agreed well with the observed ones. The determination coefficient (R^2) and the root mean squared error (RMSE) between the predicted and the measured leaf area index (LAI) based on the 1:1 line were 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 was 48.2% and 84.6% higher, respectively. [Conclusion] From the results obtained in this study, it can be concluded that the model can give satisfactory prediction of the leaf area index of standard cut chrysanthemum. It can be used for the prediction of growth and quality as well as for the decision making for the production management of standard cut chrysanthemum in greenhouses.
出处 《中国农业科学》 CAS CSCD 北大核心 2007年第11期2569-2574,共6页 Scientia Agricultura Sinica
基金 国家自然科学基金项目(30771262) 国家"863"计划项目(2006AA10Z218) 上海市科技兴农四新项目(沪农科推字2003第1-2-1号)
关键词 菊花 生理辐热积 叶面积指数 预测模型 Chrysanthemum morifolium Physiological product of thermal effectiveness and PAR Leaf area index Prediction model
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参考文献16

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