防虫网覆盖塑料大棚小气候模拟模型研究
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摘要
单栋塑料大棚是我国南方地区蔬菜生产的主要设施类型,近年来用防虫网覆盖塑料大棚进行绿色农产品生产的面积不断扩大。防虫网覆盖塑料大棚内的小气候状况直接关系到棚内作物的生长、上市期和产量。由于防虫网覆盖塑料大棚属于简易设施,棚内一般没有环境监测仪器。防虫网覆盖塑料大棚小气候预测模型可以利用棚外气象要素预测棚内小气候状况,因此,是获取防虫网覆盖塑料大棚小气候信息的重要手段。本研究以防虫网覆盖塑料大棚小白菜生产为研究对象,将大棚内空气热量与质量平衡的理论分析与不同定植期的小白菜生产试验研究相结合,定量分析棚外气象条件—大棚结构——棚内小气候——棚内作物之间的关系。在此基础上,建立了塑料大棚内小气候模拟模型,并进一步用独立的试验数据对模型进行了检验。本文的主要研究结果如下：
1.建立了小白菜叶面积指数和叶宽的模拟模型
根据小白菜生长对光温的反应,综合光温指标——辐热积为预测指标,构建了小白菜叶面积指数(LAI)和叶宽(d)的模拟模型：式中,RTE(tin)为棚内气温tin的相对热效应,PTEPPT和TEP分别为日辐热积和累计辐热积(MJ·m-2)；Tb、Tm、T(?)和Tou别为小白菜生长下限温度、上限温度、最适温度下限和最适温度上限(℃)。C为小白菜叶面积指数或叶宽的最大值,a、b为与小白菜有关的参数。小白菜叶面积指数模拟值与实测值之间1：1线的r2>0.97,RMSE<0.300m2·m-2,RE<18.0%。小白菜叶宽模拟值与实测值之间1：1线的R2>0.94,RMSE<0.27cm,RE<11.0%.
2.建立了塑料大棚塑料薄膜透光率的计算模型
根据田间试验资料,应用回归统计分析方法,建立了大棚塑料薄膜透光率与太阳高度角(τ)的统计模型：式中,β为太阳高度角,(?)为地理纬度,δ为太阳赤纬,ω为时角,Dy为日序,以1月1日为1,依次排列。塑料薄膜透光率的模拟值与实际值之间1：1线的R2、RMSE和RE分别为0.91、1.45%和1.85%。
3.建立了大棚内净辐射统计模型
根据试验观测资料,用回归统计分析方法建立了棚内净辐射与棚外总辐射之间的统计模型：
R。=-5.5143+0.6967·τ·S0式中,Rn为大棚内净辐射(w·m-2),τ为大棚薄膜透光率(%),S0为棚外太阳总辐射(w·m-2)。大棚内净辐射模拟值与实测值之间1：1线的R2、RMSE和RE分别为0.96、22.4w·m-2和19.6%。
4.建立了小白菜叶温的统计模型
根据大棚内田间试验观测数据,应用多元回归分析方法,建立了小白叶温的统计模型：
T1=20.38476+0.14778tin+1.67901×10-2Rn式中,T1为小白菜叶温,℃；Rn为棚内净辐射,J·s-1·m-2；tin为棚内气温,℃。小白菜叶温模拟值与实测值之间1：1线的R2、RMS和RE分别为0.63、1.5℃和5.6%。
5.确定了不同目数防虫网的流量系数和综合风压系数
根据不同防虫网覆盖塑料大棚内小白菜蒸腾实测数据和棚内外的气象数据,应用Penmam-Monteith蒸腾模型,确定了20目、25目和28目防虫网覆盖塑料大棚自然通风下的流量系数和综合风压系数。20目、25目和28目防虫网覆盖塑料大棚的平均流量系数分别为0.771、0.758和0.736,平均综合风压系数分别为0.33、0.37和0.39。
6.构建了防虫网覆盖塑料大棚内小气候模拟模型
充分考虑作物蒸腾对棚内小气候的反馈作用,根据能量平衡和质量平衡原理,建立了以塑料大棚外气象要素(太阳辐射、温度、相对湿度、风速、气压)为驱动变量,以塑料大棚结构(容积、表面积、通风窗面积、棚内地表面积)、覆盖材料(塑料薄膜、防虫网)、小白菜(叶宽、叶面积指数)等为参数的塑料大棚内小气候模拟模型：式中,q8为大棚内温度变化导致的空气能量变化,J/s；qv为自然通风导致大棚内空气能量变化,J/s；qc为通过大棚塑料薄膜的热交换量,J/s；qrad为到达大棚内太阳辐射能导致大棚内的空气能量变化,J/s；qrran为小白菜蒸腾消耗的潜热,J/s；qs为大棚内空气与小白菜之间的显热交换,J/s；Xin为大棚内空气绝对湿度,kg·m-3；Xout为大棚外空气绝对湿度,kg·m-3,t为时间,s；Gv为大棚自然通风率,m3·s-1；V为大棚容积,m3；A为大棚内地表面积,m2；E为小白菜蒸腾速率,g·m-2·s-1。
用试验观测数据(独立样本)对模型进行了检验。结果表明,模型对夏季晴天、多云天、阴天蒸腾速率预测值与实际观测值之间1：1线的决定系数(R2)分别为0.95、0.91、0.94,回归估计标准误差(RMSE)分别为0.018g·m-2·s-1、0.014g·m-2·s-1和0.015g·m-2·s-1,相对误差(RE)分别为14.27%、18.05%和15.80%；对夏季晴天、多云天、阴天棚内空气温度预测值与实测值之间1：1线的决定系数(R2)分别为0.96、0.93和0.92,RMSE分别为1.6、1.5和1.2℃,RE分别为5.6%、5.5%和4.5%；对夏季晴天、多云天、阴天棚内空气相对湿度预测值与实测值之间1：1线的R2分别为0.89、0.88和0.80,RMSE分别为4.4%、4.6%和4.0%,RE分别为5.4%、5.5%和4.4%。
本研究建立的防虫网覆盖塑料大棚内小气候模拟模型,以大棚外气象因子为驱动变量,以大棚结构、地理位置和作物等为参数,预测防虫网覆盖塑料大棚内空气温度和相对湿度。模型预测精度高,机理性强,实用性强,为我国该类塑料大棚内温湿度预测、大棚结构优化和农业气象服务提供了理论依据和决策支持。
Plastic greenhouse tunnel is the major type of greenhouses used for the growth of vegetables in Southern China. The area of plastic greenhouse tunnels covered with insect-proof nets has been increasing in recent years due to the considerations of product safety of vegetable productions. Crops grown in plastic greenhouse tunnels covered with insect-proof nets are strongly affected by the inside microclimate conditions. Models for predicting the microclimate inside the greenhoused tunnels are useful tool to obtain the information of the inside microclimate due to the lack of instruments for monitoring the inside microclimate. The objective of this study was to develop a model for predicting the microclimate inside the plastic greenhouse tunnels covered with insect-proof nets using outside weather data. For this purpose, experiments with different sowing dates for Brassica chinensis L. were conducted in plastic greenhouse tunnels covered with insect-proof nets located at Shanghai (latitude30°56'42"N, Longitude121°19'1" E) in2006. Based on the analysis of energy and mass balances of air inside the plastic greenhouse tunnels, a model for predicting the microclimate inside the plastic greenhouse tunnels covered with insect-proof was developed. Independent experimental data were used to validate the model. The main results are presented as follows:
1. Predicting the leaf area and leaf width of Brassica chinensis L.
The integrated photo-thermal index, the product of thermal effectiveness and PAR (TEP) was used to predict the leaf area and leaf width of Brassica chinensis L. The product of thermal effectiveness and PAR can be calculated as follows: where RTE is the relative thermal effectiveness, PTEP and TEP are the daily total product of thermal effectiveness and PAR and the accumulated of PTEP, respectively; Tb, Tm, Tab Tou are the base temperature, the maximum temperature, the base optimum temperature, and the upper optimum temperature, respectively, for Brassica chinensis L. growth; y stands for the leaf width or LAI of Brassica chinensis L.; C is the maximum leaf width or LAI of Brassica chinensis L.; a and b are the curve fitting coefficients. The determination coefficient (R2), the root mean squared errors (RMSE) and the relative prediction error between the predicted and measured values based on the1:1line are0.97,0.300m2·m-2and18.0%for leaf area index, respectively; and0.94,0.27cm and11.0%for leaf width, respectively.
2. Calculation of the transmittance of plastic tunnels
According to the experimental data, the transmittance of plastic tunnels is dependent on the solar elevation angle and can calculated as follows:
τ=0.649+0.254·10-3.esinβ0127
sin β=sinφ·sin δ+cosφ·cos δ·cos[2π(ω-1)/24]
sin δ=-sin(π·23.45/180)·cos[2π(Dy+10)/365]
cos δ=(1-sin δ·cos δ)0.5where β is the solar elevation angle,φ is the latitude,8is the solar declination, ω is the hour angle, Dy is day of year. Based on the1:1line, the determination coefficient (R2), the root mean squared errors(RMSE) and the relative prediction error (RE) between the calculated and the observed values of transmittance is respectively0.91,1.45%and1.85%, respectively.
3. Statistical model of net radiation inside of the plastic tunnels
According to experiment data, the net radiation inside of the plastic tunnels is dependent on the total radiation outside of the tunnesl and can calculated as follows:
Rn=-5.5143+0.6967·r·S0
Where Rn is net radiation inside of the plastic tunnels (w-m"2), τ is the transmittance of plastic tunnels (%), So is the total radiation outside of the plastic tunnels (w·m-2). Based on the1:1line, the determination coefficient (R2), the root mean squared errors (RMSE) and the relative prediction error between the calculated and observed values of based on the1:1line are0.96,22.4w·m-2and19.6%, respectively, for net radiation inside of the plastic tunnels.
4. Determination of the leaf temperature of Brassica Chinese L.
According to the experimental data, a statistical model for calculating leaf temperature of Brassica Chinese L. was developed.
Tl=20.38476+0.14778tin+1.67901×10-2Rn where T1is the leaf temperature of Brassica Chinese L.,℃; Rn is the net radiation inside the plastic tunnels, J·s-1·m-2;tm is temperature inside the plastic tunnels,℃. Based on the1:1line, the determination coefficient (R2), the root mean squared errors (RMSE) and the relative prediction error (RE) between the simulated and observed values are, respectively,0.63、1.5℃and5.6%for leaf temperature.
5. Determination of the dilution coefficient of different insect-proof nets and the coefficient of synthetic wind pressure
According to the measured transpiration data of Brassica chinensis L. and the microclimate data inside and outside of the plastic tunnels, the coefficients of dilution and synthetic wind pressure of plastic tunnels, respectively, covered with20numbers,25numbers and28numbers insect-proof nets were determined by using the Penmam-Monteith equations. The dilution coefficient and the coefficient of synthetic wind pressure, are, respectively,0.7710.33for the insect-proof net of20numbers,0.758and0.37for the insect-proof net of25numbers, and0.736and0.39for the insect-proof net of28numbers.
6. The microclimate simulation model of the plastic greenhouse tunnel covered with the insect-proof nets
Taken account into the interaction between crop transpiration and the microclimate inside the plastic tunnel, a microclimate simulation model of the plastic greenhouse tunnel covered with the insect-proof nets was developed based on the principle of energy and mass balances.
qa＝qv+qc+qrad=qtran+qs
dχin/dt=[(A/U)·E·10-3]+(Gv/V)(χout-χin)
where qa (J·s-1) stands for air energy change resulted from the temperature change inside the plastic tunnel; qv (J·s-1) stands for air energy change resulted from natural ventilation of the plastic tunnel; qc (J·s-1) stands for the amount of heat exchange of the plastic tunnel; qrad (J·s-1) stands for air energy change resulted from radiation; qtran(J·S-1) stands for latent heat consumed by transpiration of Brassica chinensis L.; qs (J·s-1) stands for sensible heat exchange between air inside the plastic tunnel and Brassica chinensis L.; Xin (kg·m-3) stands for air relative humidity inside the plastic tunnel; Xout (kg·m-3) stands for air relative humidity outside of the plastic tunnel; t stands for time; Gv (m3·s-1) stands for natural ventilation efficiency of the plastic tunnel; V(m) stands for volume of the plastic tunnel; A (m2) stands for superficial area of the plastic tunnel; E (g·m-2·s-1) stands for transpiration rates of Brassica chinensis L..
Independent experimental data were used to validate the model. The results show that based on the1:1line, the determination coefficient (R2) between the observed and the predicted transpiration rate under sunny, cloudy and overcast conditions in summer were0.95,0.91,0.94, respectively; the root mean squared errors(RMSE) were0.018g·m-2·s-1,0.014g·m-2·s-1,0.015g·m-2·s-1, respectively, and the relative prediction error (RE) were14.27%,18.05%,15.80%, respectively. The determination coefficient (R2) were0.96,0.93,0.92, respectively; the root mean squared errors (RMSE) were1.6℃,1.5℃,1.2℃and the relative prediction error (RE) were5.6%5.5%and4.5%, respectively, for air temperature inside the tunnel. The determination coefficient (R2) were0.89,0.88,0.80, respectively; the root mean squared errors (RMSE) were4.4%、4.6%and4.0%; the relative prediction error (RE) were5.4%、5.5%and4.4%, respectively, for relative humidity inside the tunnel.
The microclimate simulation model developed in this study can predict the air temperature, the relative humidity and crop canopy transpiration inside the plastic tunnels covered with insect-proof nets by using the weather data outside the tunnel and information about the structure of the tunnels and crop as inputs. From the results obtained in this study, it can be concluded that the model developed in this study can give a satisfactory predictions and can be used for decision making for predicting the humidity-temperature inside the tunnel, optimum the structure of tunnel and the service of agricultural meteorology.
1.建立了小白菜叶面积指数和叶宽的模拟模型
根据小白菜生长对光温的反应,综合光温指标——辐热积为预测指标,构建了小白菜叶面积指数(LAI)和叶宽(d)的模拟模型：式中,RTE(tin)为棚内气温tin的相对热效应,PTEPPT和TEP分别为日辐热积和累计辐热积(MJ·m-2)；Tb、Tm、T(?)和Tou别为小白菜生长下限温度、上限温度、最适温度下限和最适温度上限(℃)。C为小白菜叶面积指数或叶宽的最大值,a、b为与小白菜有关的参数。小白菜叶面积指数模拟值与实测值之间1：1线的r2>0.97,RMSE<0.300m2·m-2,RE<18.0%。小白菜叶宽模拟值与实测值之间1：1线的R2>0.94,RMSE<0.27cm,RE<11.0%.
2.建立了塑料大棚塑料薄膜透光率的计算模型
根据田间试验资料,应用回归统计分析方法,建立了大棚塑料薄膜透光率与太阳高度角(τ)的统计模型：式中,β为太阳高度角,(?)为地理纬度,δ为太阳赤纬,ω为时角,Dy为日序,以1月1日为1,依次排列。塑料薄膜透光率的模拟值与实际值之间1：1线的R2、RMSE和RE分别为0.91、1.45%和1.85%。
3.建立了大棚内净辐射统计模型
根据试验观测资料,用回归统计分析方法建立了棚内净辐射与棚外总辐射之间的统计模型：
R。=-5.5143+0.6967·τ·S0式中,Rn为大棚内净辐射(w·m-2),τ为大棚薄膜透光率(%),S0为棚外太阳总辐射(w·m-2)。大棚内净辐射模拟值与实测值之间1：1线的R2、RMSE和RE分别为0.96、22.4w·m-2和19.6%。
4.建立了小白菜叶温的统计模型
根据大棚内田间试验观测数据,应用多元回归分析方法,建立了小白叶温的统计模型：
T1=20.38476+0.14778tin+1.67901×10-2Rn式中,T1为小白菜叶温,℃；Rn为棚内净辐射,J·s-1·m-2；tin为棚内气温,℃。小白菜叶温模拟值与实测值之间1：1线的R2、RMS和RE分别为0.63、1.5℃和5.6%。
5.确定了不同目数防虫网的流量系数和综合风压系数
根据不同防虫网覆盖塑料大棚内小白菜蒸腾实测数据和棚内外的气象数据,应用Penmam-Monteith蒸腾模型,确定了20目、25目和28目防虫网覆盖塑料大棚自然通风下的流量系数和综合风压系数。20目、25目和28目防虫网覆盖塑料大棚的平均流量系数分别为0.771、0.758和0.736,平均综合风压系数分别为0.33、0.37和0.39。
6.构建了防虫网覆盖塑料大棚内小气候模拟模型
充分考虑作物蒸腾对棚内小气候的反馈作用,根据能量平衡和质量平衡原理,建立了以塑料大棚外气象要素(太阳辐射、温度、相对湿度、风速、气压)为驱动变量,以塑料大棚结构(容积、表面积、通风窗面积、棚内地表面积)、覆盖材料(塑料薄膜、防虫网)、小白菜(叶宽、叶面积指数)等为参数的塑料大棚内小气候模拟模型：式中,q8为大棚内温度变化导致的空气能量变化,J/s；qv为自然通风导致大棚内空气能量变化,J/s；qc为通过大棚塑料薄膜的热交换量,J/s；qrad为到达大棚内太阳辐射能导致大棚内的空气能量变化,J/s；qrran为小白菜蒸腾消耗的潜热,J/s；qs为大棚内空气与小白菜之间的显热交换,J/s；Xin为大棚内空气绝对湿度,kg·m-3；Xout为大棚外空气绝对湿度,kg·m-3,t为时间,s；Gv为大棚自然通风率,m3·s-1；V为大棚容积,m3；A为大棚内地表面积,m2；E为小白菜蒸腾速率,g·m-2·s-1。
用试验观测数据(独立样本)对模型进行了检验。结果表明,模型对夏季晴天、多云天、阴天蒸腾速率预测值与实际观测值之间1：1线的决定系数(R2)分别为0.95、0.91、0.94,回归估计标准误差(RMSE)分别为0.018g·m-2·s-1、0.014g·m-2·s-1和0.015g·m-2·s-1,相对误差(RE)分别为14.27%、18.05%和15.80%；对夏季晴天、多云天、阴天棚内空气温度预测值与实测值之间1：1线的决定系数(R2)分别为0.96、0.93和0.92,RMSE分别为1.6、1.5和1.2℃,RE分别为5.6%、5.5%和4.5%；对夏季晴天、多云天、阴天棚内空气相对湿度预测值与实测值之间1：1线的R2分别为0.89、0.88和0.80,RMSE分别为4.4%、4.6%和4.0%,RE分别为5.4%、5.5%和4.4%。
本研究建立的防虫网覆盖塑料大棚内小气候模拟模型,以大棚外气象因子为驱动变量,以大棚结构、地理位置和作物等为参数,预测防虫网覆盖塑料大棚内空气温度和相对湿度。模型预测精度高,机理性强,实用性强,为我国该类塑料大棚内温湿度预测、大棚结构优化和农业气象服务提供了理论依据和决策支持。
Plastic greenhouse tunnel is the major type of greenhouses used for the growth of vegetables in Southern China. The area of plastic greenhouse tunnels covered with insect-proof nets has been increasing in recent years due to the considerations of product safety of vegetable productions. Crops grown in plastic greenhouse tunnels covered with insect-proof nets are strongly affected by the inside microclimate conditions. Models for predicting the microclimate inside the greenhoused tunnels are useful tool to obtain the information of the inside microclimate due to the lack of instruments for monitoring the inside microclimate. The objective of this study was to develop a model for predicting the microclimate inside the plastic greenhouse tunnels covered with insect-proof nets using outside weather data. For this purpose, experiments with different sowing dates for Brassica chinensis L. were conducted in plastic greenhouse tunnels covered with insect-proof nets located at Shanghai (latitude30°56'42"N, Longitude121°19'1" E) in2006. Based on the analysis of energy and mass balances of air inside the plastic greenhouse tunnels, a model for predicting the microclimate inside the plastic greenhouse tunnels covered with insect-proof was developed. Independent experimental data were used to validate the model. The main results are presented as follows:
1. Predicting the leaf area and leaf width of Brassica chinensis L.
The integrated photo-thermal index, the product of thermal effectiveness and PAR (TEP) was used to predict the leaf area and leaf width of Brassica chinensis L. The product of thermal effectiveness and PAR can be calculated as follows: where RTE is the relative thermal effectiveness, PTEP and TEP are the daily total product of thermal effectiveness and PAR and the accumulated of PTEP, respectively; Tb, Tm, Tab Tou are the base temperature, the maximum temperature, the base optimum temperature, and the upper optimum temperature, respectively, for Brassica chinensis L. growth; y stands for the leaf width or LAI of Brassica chinensis L.; C is the maximum leaf width or LAI of Brassica chinensis L.; a and b are the curve fitting coefficients. The determination coefficient (R2), the root mean squared errors (RMSE) and the relative prediction error between the predicted and measured values based on the1:1line are0.97,0.300m2·m-2and18.0%for leaf area index, respectively; and0.94,0.27cm and11.0%for leaf width, respectively.
2. Calculation of the transmittance of plastic tunnels
According to the experimental data, the transmittance of plastic tunnels is dependent on the solar elevation angle and can calculated as follows:
τ=0.649+0.254·10-3.esinβ0127
sin β=sinφ·sin δ+cosφ·cos δ·cos[2π(ω-1)/24]
sin δ=-sin(π·23.45/180)·cos[2π(Dy+10)/365]
cos δ=(1-sin δ·cos δ)0.5where β is the solar elevation angle,φ is the latitude,8is the solar declination, ω is the hour angle, Dy is day of year. Based on the1:1line, the determination coefficient (R2), the root mean squared errors(RMSE) and the relative prediction error (RE) between the calculated and the observed values of transmittance is respectively0.91,1.45%and1.85%, respectively.
3. Statistical model of net radiation inside of the plastic tunnels
According to experiment data, the net radiation inside of the plastic tunnels is dependent on the total radiation outside of the tunnesl and can calculated as follows:
Rn=-5.5143+0.6967·r·S0
Where Rn is net radiation inside of the plastic tunnels (w-m"2), τ is the transmittance of plastic tunnels (%), So is the total radiation outside of the plastic tunnels (w·m-2). Based on the1:1line, the determination coefficient (R2), the root mean squared errors (RMSE) and the relative prediction error between the calculated and observed values of based on the1:1line are0.96,22.4w·m-2and19.6%, respectively, for net radiation inside of the plastic tunnels.
4. Determination of the leaf temperature of Brassica Chinese L.
According to the experimental data, a statistical model for calculating leaf temperature of Brassica Chinese L. was developed.
Tl=20.38476+0.14778tin+1.67901×10-2Rn where T1is the leaf temperature of Brassica Chinese L.,℃; Rn is the net radiation inside the plastic tunnels, J·s-1·m-2;tm is temperature inside the plastic tunnels,℃. Based on the1:1line, the determination coefficient (R2), the root mean squared errors (RMSE) and the relative prediction error (RE) between the simulated and observed values are, respectively,0.63、1.5℃and5.6%for leaf temperature.
5. Determination of the dilution coefficient of different insect-proof nets and the coefficient of synthetic wind pressure
According to the measured transpiration data of Brassica chinensis L. and the microclimate data inside and outside of the plastic tunnels, the coefficients of dilution and synthetic wind pressure of plastic tunnels, respectively, covered with20numbers,25numbers and28numbers insect-proof nets were determined by using the Penmam-Monteith equations. The dilution coefficient and the coefficient of synthetic wind pressure, are, respectively,0.7710.33for the insect-proof net of20numbers,0.758and0.37for the insect-proof net of25numbers, and0.736and0.39for the insect-proof net of28numbers.
6. The microclimate simulation model of the plastic greenhouse tunnel covered with the insect-proof nets
Taken account into the interaction between crop transpiration and the microclimate inside the plastic tunnel, a microclimate simulation model of the plastic greenhouse tunnel covered with the insect-proof nets was developed based on the principle of energy and mass balances.
qa＝qv+qc+qrad=qtran+qs
dχin/dt=[(A/U)·E·10-3]+(Gv/V)(χout-χin)
where qa (J·s-1) stands for air energy change resulted from the temperature change inside the plastic tunnel; qv (J·s-1) stands for air energy change resulted from natural ventilation of the plastic tunnel; qc (J·s-1) stands for the amount of heat exchange of the plastic tunnel; qrad (J·s-1) stands for air energy change resulted from radiation; qtran(J·S-1) stands for latent heat consumed by transpiration of Brassica chinensis L.; qs (J·s-1) stands for sensible heat exchange between air inside the plastic tunnel and Brassica chinensis L.; Xin (kg·m-3) stands for air relative humidity inside the plastic tunnel; Xout (kg·m-3) stands for air relative humidity outside of the plastic tunnel; t stands for time; Gv (m3·s-1) stands for natural ventilation efficiency of the plastic tunnel; V(m) stands for volume of the plastic tunnel; A (m2) stands for superficial area of the plastic tunnel; E (g·m-2·s-1) stands for transpiration rates of Brassica chinensis L..
Independent experimental data were used to validate the model. The results show that based on the1:1line, the determination coefficient (R2) between the observed and the predicted transpiration rate under sunny, cloudy and overcast conditions in summer were0.95,0.91,0.94, respectively; the root mean squared errors(RMSE) were0.018g·m-2·s-1,0.014g·m-2·s-1,0.015g·m-2·s-1, respectively, and the relative prediction error (RE) were14.27%,18.05%,15.80%, respectively. The determination coefficient (R2) were0.96,0.93,0.92, respectively; the root mean squared errors (RMSE) were1.6℃,1.5℃,1.2℃and the relative prediction error (RE) were5.6%5.5%and4.5%, respectively, for air temperature inside the tunnel. The determination coefficient (R2) were0.89,0.88,0.80, respectively; the root mean squared errors (RMSE) were4.4%、4.6%and4.0%; the relative prediction error (RE) were5.4%、5.5%and4.4%, respectively, for relative humidity inside the tunnel.
The microclimate simulation model developed in this study can predict the air temperature, the relative humidity and crop canopy transpiration inside the plastic tunnels covered with insect-proof nets by using the weather data outside the tunnel and information about the structure of the tunnels and crop as inputs. From the results obtained in this study, it can be concluded that the model developed in this study can give a satisfactory predictions and can be used for decision making for predicting the humidity-temperature inside the tunnel, optimum the structure of tunnel and the service of agricultural meteorology.
引文
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