| 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 tunnelsAccording 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.esinβ0127sin 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 tunnelsAccording 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·S0Where 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 pressureAccording 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 netsTaken 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. |
PDF Full Text Request