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作 者 ：李仙岳,杨培岭*,任树梅,张少炎

期 刊 ：生态学报 2009年 29卷 5期 页码：2312~2319

关键词：叶面积；辐射；蒸腾；模型；果树；

Keywords:leaf area, radiation, transpiration, model, fruit tree,

摘 要 ：为了揭示特色经济果树叶面积、冠层辐射与其蒸腾的关系，采用液流技术连续监测了北京昌平北流果园试验地15年生杏树（Prunus armenica L.）、16年生樱桃（Prunus avium L.）5~7月份蒸腾动态变化，同步监测了气象与土壤水分数据。引入代表土壤、气象等环境因素的时间特征参数（K），代表不同果树吸收能量差异的叶面积（LA）与表示参照作用的参考蒸腾量（T0），采用双系数方法建立了蒸腾预测模型（T=K·LA·T0）。通过叶面积、净辐射与蒸腾的相关分析，确立了5、6、7月份的时间特征参数分别为0.14、0.09、0.06。通过与仅考虑辐射的一元回归方程比较分析，提出的模型精度显著提高，5、6、7月份的R2（09、085、0.75）提高1倍，RMSE（2.50、1.92、1.57），ARD（12.51％、20.76％、19.62%）则是后者的1/2。进一步采用Priestley Taylor（PT）计算值作为参考蒸腾量，利用本模型计算了 3个月2种果树共177d蒸腾量。将其与液流法实测蒸腾量进行拟合，得到回归方程y=1.01x(R2=0.92)。可见本模型精度高，形式简单，便于应用。

Abstract:In order to reveal inherent relation between leaf area，canopy radiation and transpiration on economical fruit trees, dynamic changes of transpiration of 15 year old apricot trees (Prunus armenica L.) and 16 year old cherry trees (Prunus avium L.) were continuously monitored with sap flow measuring system in Beiliu orchard in Changping, Beijing from May to July,2007. The meteorological data and soil moisture were also measured. Using time character parameter(K) which represents soil, weather and other environmental factors, leaf area(LA) which represents how much energy is absorbed by fruit trees, and reference crop transpiration(T0) which serves as reference, the dual coefficient method was used to establish the model of predicting transpiration (T=K·LA·T0). The correlation analysis between leaf area, net radiation and transpiration was carried out, and K was determined in May, June and July, which is 0.014, 0.09 and 0.06 respectively. By the comparison analysis between the model and linear regression model in that only the radiation was considered, the accuracy was significantly improved. R2 (0.9,0.85,0.75) in May, June and July was twice , the RMSE (2.50,1.92,1.57) and ARD (12.51％,20.76％,19.62%) were half of latter’s. As reference transpiration which got by Priestley Taylor (PT) was used in the model to calculate transpiration of 177 days on two fruit trees in three months. Then regressing with measured data, the equation that y = 1.01x was got(R2 = 0.92). It showed that the model was very simple to predict transpiration with high precision and easy to use.

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