Interactions of Crop and Cooling Equipment on Greenhouse Climate

Transcription

1 Interactions of Crop and Cooling Equipment on Greenhouse Climate A. Perdigones and V. Pascual Universidad Católica de Ávila, Dpto. Ingeniería Agroforestal y Cartográfica C/ Canteros, Ávila Spain J.L. García J. Nolasco and D. Pallarés Avda. Complutense, ETSI Agrónomos, ETSI Agrónomos, Dpto. Dpto. Ingeniería Rural, Producción Vegetal: Fitotecnia Madrid Madrid Spain Spain Keywords: climate, ventilation, shading, fog, transpiration Abstract Experiments combining natural ventilation, shading and low-pressure fog system, with and without crop, were carried out with the aim of evaluating the interactions of crop transpiration and cooling equipment on greenhouse temperature. The relationships among the different variables were analysed with a climate model. Crop transpiration was observed to be a significant factor in greenhouse cooling, reducing the temperature of the greenhouse by 0.8 ºC as an average over the different combinations of equipment. However, the effect of the crop was negligible with high doses of fog where the air is nearly saturated with vapour. Fogging above the screen seemed to be an interesting method for greenhouse cooling without vapour saturation of the air. The energy model produced mean absolute errors lower than 1 ºC; it can be used for evaluation of climate control strategies, even without including transpiration. INTRODUCTION In many Mediterranean greenhouses crop production is limited because the cooling method used do not provide the desired conditions, specially during the hot summer months. Various methods for cooling the greenhouse atmosphere may be used to maintain more suitable conditions for plant growth. Natural ventilation is the normal practice, as nowadays all greenhouses include some type of ventilation system (Boulard et al., 1997). However, natural ventilation is generally not sufficient for extracting the excess energy during sunny summer days. Therefore, other cooling methods have to be used in combination with natural ventilation (Bakker et al., 1995): forced ventilation, shading, whitening (Baille et al., 2001) and evaporative systems. Evaporative systems are based on the conversion of sensible heat into latent heat of evaporated water, with the water supplied mechanically. The main evaporative cooling methods used today are sprinkling, pad-and-fan, and fog or mist (Arbel et al., 1999). On one hand, fog and mist systems can produce effects on canopy transpiration (Katsoulas et al., 2001); on the other hand, transpiration is a contribution to the cooling of the greenhouse. The objectives of the experiments were to compare different methods of cooling and to evaluate the interactions of crop transpiration and cooling equipment with a modelling approach. MATERIALS AND METHODS The experimental greenhouse had an arch-shaped roof, a steel structure and a single layer cover of metacrylate. The covered soil surface was 132 m 2. The height from soil to gutter was 3 m, and the area of metacrylate cover exposed to the outside air was 258 m 2. The greenhouse was equipped with side and roof windows, open thermal/shading screen and low-pressure fog system, under and above the screen; all the equipment was controlled with timers. Helianthus annuus L. (sunflower) was grown in pots inside the greenhouse. Leaf area index (LAI) was estimated each week from five representative Proc. IC on Greensys Eds.: G. van Straten et al. Acta Hort. 691, ISHS

2 plants of the greenhouse, using the number of leaves and the surface area of one representative leaf of each plant (Fig. 1). The area of the leaves was determined in laboratory by a computer program of area measuring. Two data acquisition systems (Datataker DT50) were used for recording climate parameters inside and outside the greenhouse; air temperature was measured in the centre of the greenhouse with a PT100 sensor, at a height of 1.5 m. A complete set of the rest of outside climate parameters was supplied by a meteorological station of the University, placed nearby the greenhouse; global radiation was measured with a "Skye Instruments" pyranometer, and outside temperature with a PT100 sensor. Heating, Window and Thermal Screen Control Strategies Six combinations of cooling equipment, including natural ventilation, shading screen and low-pressure fog system were used along 84 days of summer 2003, 42 with crop and 42 without crop. The average difference of inside and outside temperatures and other climate parameters were calculated every day from 14:00 to 17:00 h. A Newman- Keuls test was used to compare the values obtained with the different combinations; a multiple regression analysis was also performed. The combinations of equipment tested were the following: - 1) Natural ventilation: side and roof windows. - 2) Natural ventilation + shading screen (inside aluminised screen, placed at 3 m height; nominal value, 65% shading). - 3) Natural ventilation + fog system (low-pressure fog; 12 seconds of functioning each 4 minutes; 0.6 l/h m 2 ) - 4) Natural ventilation + fog system (low-pressure fog; 8 seconds of functioning each 1 minute; 1.6 l/h m 2 ) - 5) Natural ventilation + shading screen + fog system above the screen (low-pressure fog; 8 seconds of functioning each 1 minute; 1.6 l/h m 2 ) - 6) Natural ventilation + shading screen + fog system under the screen (low-pressure fog; 8 seconds of functioning each 1 minute; 1.3 l/h m 2 ) Each combination of equipment was changed every one or two days during the period to compensate for possible differences between early and late summer. Most of the 84 days employed in the analysis were sunny: 72 days with more than 700 W/m 2 of outside solar radiation (more than 7.56 MJ/m 2, from 14:00 to 17:00 h), and 12 cloudy days, with radiation values from 300 to 700 W/m 2 (between 3.24 and 7.56 MJ/m 2, from 14:00 to 17:00 h), distributed over the six combinations of equipment. Modelling Energy Balance A model based in energy conservation equations was developed to evaluate by modelling the energy fluxes involved; similar models have already been used (Albright et al., 1985). Fluxes considered were the following: - Energy supplied by solar radiation, τ S. τ is the transmissivity of the cover. There were two coefficients τ, with and without shading screen. - Energy losses through the structure, U (Ti-To). U is the overall heat transfer coefficient (with closed windows). There were two coefficients U, 11.8 W/m 2 ºC with screen, and 14.8 W/m 2 ºC without screen. These values of U were determined the previous winter with the same methodology described in "Calibration of the model"; its determination is easier in winter, with heating supply. - Energy losses through the open windows, V (Ti-To). V is the overall heat transfer coefficient (with open windows). Again there were two coefficients V, with and without shading screen. - Conversion of sensible heat into latent heat of evaporated water by the fog system, F. - Conversion of sensible heat into latent heat of evaporated water by crop transpiration, T, considered proportional to inside solar radiation and LAI of the crop (T = W τs LAI). 204

3 - Heat storage of the greenhouse, C (dti / dt), where C is the heat capacity of the greenhouse as a thermal mass. Since the energy balance was dynamic, the sum of the energy fluxes could be different from zero in each period; energy was stored or released by the thermal mass, affecting the value of the inside air temperature in the next period considered. This first balance supplied the simulated inside temperature of each period calculated from the parameters of the previous period, with the following equation: Ti (next period) = Ti + [τ S - U (Ti-To) - V (Ti-To) - F - W τ S LAI] / C Calibration of the Model Coefficients of the models were extracted using the experimental data (84 days). The models were run with iteration employing Microsoft Excel R, until reaching the minimum mean absolute difference between the simulated and real inside air temperatures. The measured outside temperature and solar radiation of each period, the functioning of windows, screen and fog, the value of LAI of the crop, and the initial inside temperature, were used as inputs to the process; the coefficients τ, V, F, W and C were the outputs. All input data were recorded every 5 min. In each iteration, inside air temperatures were calculated from the values of the previous period of 5 min. The global absolute error was registered, and then a new iteration with other values of the mentioned coefficients started until the error could not be reduced. The search procedure was carried out using Microsoft Excel R SOLVER, which allows certain variables to be altered with the aim of minimizing any given error. So the computer tool searched for the best coefficients τ, V, F, W and C with the aim that the simulated inside temperature was as close as possible to the real temperature. Modelling supplied the energy values (W/m 2 ) of each energy flux, for each one of the six combinations of equipment. Values of F (energy withdrawn from the air by the fog system) and W τs LAI (energy withdrawn from the air by transpiration) were compared among these combinations of equipment. Finally, the same procedure was followed without the transpiration (W τs LAI) as energy flux, to compare the coefficients and energy values obtained and the quantitative influence of transpiration in the precision of the model. RESULTS Experimental Results The results of the analysis of variance are shown in Table 1. The effect of crop on the atmosphere temperature was significant, reducing the temperature by 0.8 ºC on average for the different combinations of equipment. However, the effect of the crop was greater in the situations without fog or with fog above the screen. In our batch of data, fog system seemed to compete with the transpiration of the plants, as could be expected. The multiple regression analysis supplied the following equation (data measured from 14:00 to 17:00 h.; solar radiation from 334 to 916 W/m 2, LAI from 0 to 2.2, wind velocity from 0 to 2.2 m/s): T in -T out = Constant (dependent on the cooling equipment) * S * LAI Wv The combination of cooling equipment explained 69% of the total variation; outside solar radiation (S, W/m 2 ) explained 3%, LAI explained 4% and wind velocity (Wv, m/s) 2%. The global model explained 78% of the variation of the temperature difference (T in -T out ). The equation means, i.e., that a difference of 500 W/m 2 gives only a rise in T in -T out of 1.2 ºC. Checking the experimental data, 72 sunny days showed mean values of 817 W/m 2, T in = 34.4 ºC and T out = 33.2 ºC (T in -T out = 1.2 ºC), and 12 cloudy days, values of 554 W/m 2, T in = 30.3 ºC and T out = 30.4 ºC (T in -T out = -0.1 ºC). 205

4 Modelling Part of the results of modelling are shown in table 2. They confirm that high doses of fogging seemed to reduce the rate of transpiration. Without screen, a dose of fog of 0.6 l/h m 2 resulted in the model predicting a maximum transpiration rate of F = 368 W/m 2, but with 1.6 l/h m 2, maximum transpiration rate was predicted to be F = 98 W/m 2. Transpiration rate was higher with fog above the screen (F = 186 W/m 2 ) than with fog under the screen (it was not significant in modelling), probably because of the vapour saturation of the inside air. Fogging above the screen seemed to be an interesting method for greenhouse cooling with lower increases of the relative humidity below the screen, compared with direct fogging over the crop. Quantitatively, transpiration did not improve the mean absolute error of the energy model employed (lower than 1 ºC; Table 2 and Fig. 2). The model used probably should be improved in the simplistic representation of transpiration, since saturation deficit is not included in the equation. Seginer (2002) has calculated evapotranspiration from the values of radiation and saturation humidity ratio with a more complete approach. Probably the simple model described in the present paper could be used for evaluation of control strategies without using the transpiration flux. CONCLUSIONS Crop transpiration was observed to be a significant factor in greenhouse cooling, reducing the temperature of the greenhouse by 0.8 ºC on average for the different combinations of equipment. However, the effect of the crop was negligible with high doses of fog where the air was nearly saturated. Fogging above the screen seemed to be an interesting method for greenhouse cooling with lower increases of the relative humidity below the screen, compared with direct fogging over the crop. The energy model produced mean absolute errors lower than 1 ºC; it could be used for evaluation of climate control strategies, even without including transpiration. ACKNOWLEDGEMENTS Funding for this research was obtained from the MCYT Spanish project AGL C02 "Algoritmos para el control integral del clima del invernadero orientados a una producción de calidad". Literature Cited Albright, L.D., Seginer, I., Marsh, L.S. and Oko, A In situ thermal calibration of unventilated greenhouses. J. Agric. Eng. Res. 31: Arbel, A., Yekutieli, O. and Barak, M Performance of fog system for cooling greenhouses. J. Agric. Eng. Res. 72: Baille, A., Kittas, C. and Katsoulas, N Influence of whitening on greenhouse microclimate and crop energy partitioning. Agric. For. Meteorol. 107: Bakker, J.C., Bot, G.P.A., Challa, H. and Van de Braak, N.J Greenhouse climate control. an integrated approach. Wageningen Pers, Wageningen Boulard, T., Feuilloley, P. and Kittas, C Natural ventilation of six greenhouse and tunnel types. J. Agric. Eng. Res. 67: Katsoulas, N., Baille, A. and Kittas, C Effect of misting on transpiration and conductances of greenhouse rose canopy. Agric. For. Meteorol. 106: Seginer, I The Penman-Monteith evapotranspiration equation as an element in greenhouse ventilation design. Biosystems Engineering 82(4):

5 Tables Table 1. Experimental results for the different combinations of cooling equipment, for 84 days of summer. Each value is the mean of 7 days, with measurement from 14:00 to 17:00 h. Means with the same letter are not significantly different at probability P < 0.05 (Newman-Keuls test). Inside air temperature - outside temperature Cooling equipment (ºC) With crop Without crop Ventilation a + shading screen b + fog system (0.6 l/h m 2 ) c + fog system (1.6 l/h m 2 ) d + shading screen + fog above the screen (1.6 l/h m 2 ) d + shading screen + fog under the screen (1.3 l/h m 2 ) d a b Table 2. Results obtained from the energy model. Mean values of energy converted into latent heat, by the fog system (F), and by crop transpiration (W τs LAI), supplied by the model. Error of the model is the mean absolute difference between the real and simulated temperatures. Modelling performed with four combinations of cooling equipment, and 14 days for each combination: 12 sunny days, with more than 700 W/m 2 of solar radiation; and 2 cloudy days, with radiation values from 300 to 700 W/m 2. Values of LAI from 0 to 2.2, in the four options compared. In brackets, values of the energy flux [W/m 2 ] Cooling equipment Ventilation + fog system (0.6 l/h m 2 ) Mean LAI: fog system (1.6 l/h m 2 ) Mean LAI: shading screen + fog above the screen (1.6 l/h m 2 ) Mean LAI: shading screen + fog under the screen (1.3 l/h m 2 ) Mean LAI: 0.34 Model without including transpiration Cooling energy F Fog system, kwh/ m 2 day 1.35 [270 W/m 2 ] 2.49 [498 W/m 2 ] 1.15 [231 W/m 2 ] 1.41 [283 W/m 2 ] Error of the model, ºC Model including transpiration (W τs LAI) Cooling energy F Fog system, kwh/ m 2 day 1.15 [230 W/m 2 ] 2.44 [488 W/m 2 ] 0.99 [198 W/m 2 ] 1.41 [283 W/m 2 ] Cooling energy Transpiration, kwh/ m 2 day 0.20 [0-368 W/m 2 ] 0.05 [0-98 W/m 2 ] 0.16 [0-186 W/m 2 ] 0.00 [0 W/m 2 ] Error of the model, ºC

6 Figurese 2,5 With crop Without crop With crop 2 LAI 1,5 1 0,5 0 15/05 22/05 29/05 05/06 12/06 19/06 26/06 03/07 10/07 17/07 24/07 31/07 07/08 14/08 21/08 28/08 DATE Fig. 1. Values of LAI along the period of the experiments (summer 2003). The Newman- Keuls test was used to compare the period with crop and without crop (Helianthus annuus L., sunflower). Inside temperature, ºC 40 Fog system Window open Window closed 5 0 0:00 12:00 24:00 Hour Fig. 2. Measured inside temperature (thick line) and simulated inside temperature (narrow line), with natural ventilation and low-pressure fog system (1.6 l/h m 2 ). Simulation performed with the energy model described. Mean values of 14 days. 208