Author: Idit Sofer

Abstract

In recent years, the area of cultivation under screen covers or inside screenhouses is ever widening. Apart of the screen cover`s benefit of protection from insects, and reduction of excessive radiation ,wind, hail and cold damages, the screens modify the crop microclimate and therefore reduce crop water requirements and create water saving potential. These days there is a wide knowledge of the irrigation requirements of open field crops to most of the crops in most of the growth areas in the world, but there is almost no information about the water requirements under screens cover or in screenhouses. Assessment of covered screens crops water consumption can be applied by direct evapotranspiration (ET) measurements or by calculations using micro‐meteorological models, such as Penman‐Monteith (PM) models type. These models were adjusted to open field crops, but there are very few reports in the literature on the application and adaptation of such models for crops under screens or in screenhouses. Development, calibration and improvement of the use of these models can provide a tool for growers to optimize the irrigation management in screenhouses and to save water.
The main goal of this study is to examine the effectiveness and ways to improve PM type's models that exist in the literature in order to improve predictions of ET from orchards growing under screens. Secondary goals were: a. Examination of the effectiveness of forecasting ET models applied under screens in different climatic zones. b. Examination of the effectiveness of net radiation models under screens and the effect of integrating these models within PM type's model on the ET prediction efficiency. c. The influence of different expressions characterizing the aerodynamic resistance on the effectiveness of the PM type models prediction. d. Examination of the use of external climate data in the ET assessment under screens covers.
The principle that stood behind choosing the ways to improve ET models prediction in
screenhouses condition was to consider meteorological variables which can be directly
controlled by changing the screen properties. Qualifying criteria variables are the net
radiation and air resistance between the crop height and the screen`s zero‐plane
displacement, which depends on wind speed.
Investigation of different crops and different climate zones allowed examination of the
models coefficients, and more reliable determination of the preferred model. The models examined were: a reference ET model (ET0), a classical Penman‐Monteith model, adjusted to specific crop (PMra) and a PM model modified for screenhouses conditions by the inclusion of an additional boundary layer resistance (PMsc). The first two models are adjusted for crops in open areas, and in this work were applied using the variables measured under the screen. In contrast, most of the variables in the PMsc model are external meteorological variables that are accessible to farmers.
Net radiation is the most influential factor on the ET models predictions. Therefore, given the lack of accessibility of this variable under the screen, it is very important to accurately predict the net radiation under different screen types. Three methods for modeling net radiation under screens were examined in this study: (1) development of a model according to the radiation balance equations, including a model to calculate global radiation transmittance through different screens incorporating the zenith angle of the sun. (2) Calculation based on the model proposed by Möller et al. (2004) with the amendments proposed in this paper. (3) Calculate according to the ratio found between net radiation measured under the screen and the externally measured global radiation .
Aerodynamic resistance appearing in the literature models is based on the logarithmic wind profile which is valid in the open, but under screens the wind profile may not behave that way. Preliminary measurements performed at the Carmel coast screenhouse support this hypothesis. Replacing the aerodynamic resistance (ra) with the boundary layer resistance (rb) in the PM model was examined. Boundary layer resistance on the leaves depends on the nature of the flow and transport of air. Two approaches were considered to determine the expression of boundary layer resistance between the crop and the screen: (1) using a constant expression to describe rb (as usual in the literature), corresponding to forced convection. (2) Using an accurate characterization of the air movement in the boundary layer on the leaves and placing the appropriate rb expression for every half hour.
One of the problems in implementing the models ET0, PMra or PMrb under screens is the use of internal data inaccessible to growers. Possibility of calibrating the reference model ET0 and PMrb model to external variables only available from meteorological stations was examined by building in – out meteorological relations during 14 days of calibration period, and running the models with these equations in another 14 days period of verification, assuming very strong relationships between inside climatic variables and external ones.
This study examined the effectiveness of different PM type ET models compared to direct ET measurements using the Eddy Covariance technique (EC) in two screenhouses at different climatic regions in Israel: in the semi‐arid climate of the Jordan Valley (for vineyard grapes, SBS species) and in the Mediterranean climate of the Carmel coast (for banana plantation, Grand – nain species). Periods of full coverage of vegetation were selected in order to perform comparison between measured and predicted ET: 28 days in the Jordan Valley during May‐June 2010, in which the crop height was 2.4 m and LAI=4.4. In the Carmel coast 17 days were included, in which the crop height was >4 m and LAI>2 .
During the measurement periods, the vineyard at the valley and bananas at Carmel coast lost an average of 5.4 ± 0.5 and 4.1 ± 0.3 mm water per day, respectively. Predicted net radiation was closest to measurement when calculated using the model by external global radiation, with mean deviation of 2% in both climatic zones. The model according to radiation balance equations gave the second best prediction with mean deviations of 2% and 6% in the valley and the Carmel coast, respectively. This model has significant benefits by overcoming the changes in the transmittance and reflectance of the screens with seasonality due to the global transmittance prediction integrated model according to sun zenith angle, and by its applicability to a variety of different screens with common use in agriculture. The model by Möller et al. (2004) produced the least good prediction with a mean deviation of 8% for the two climate zones. The difference between ET0 calculated by Möller et al. (2004) predicted net radiation and ET0 calculated with measured net radiation was greater than 1 mm per day on average . 
In both climate zones the assessment of ET0 and PMsc models was in good agreement with a deviation of up to about 20% from measurements. But on the other hand, PMra model gave significantly overestimate of evapotranspiration. Changing the resistance from ra to rb improved the PMra performance and reduced the average daily evaporation by 21% in the Jordan Valley and by 12% in the Carmel coast. Sensitivity analysis indicated that ET in screenhouses in a Mediterranean climate area like Carmel coast is very strongly affected by the radiation term and has almost no aerodynamic affect. Nevertheless, replacing the resistances improved the models estimations relative to the measurements in both climate zones: from deviation of 13% ± 11% in the valley and 16% ± 6% in the Carmel coast obtained by PMra model to deviation of 8% ± 5% in the valley and 4% ± 3% in Carmel coast obtained by PMrb model.
The leaf boundary layer airflow inside the screenhouses was laminar in the valley and
turbulent, for most of the time, in Carmel coast. In both climate zones convection was mixed (i.e. combined with free and forced convection). Expression of rb by specific characteristics of air flow and transportation for every half hour influenced the high values of resistances which were obtained at night when ET is very low. Therefore, the daily level had no significant improvement in forecasting models with the rbm resistance adjusted. Despite this finding, sensitivity analysis simulating the behavior of the models PMrb and PMsc at different climatic conditions suggested that it is important to choose proper expression of rb, mainly because of possible changes in VPD.
All internal variables needed to calculate ET0 and PMrb models in screenhouse conditions were well correlated with external variables during 14 days of calibration in the Jordan Valley, with r2 ≥ 0.82. In addition, all variables calculated from calibration equations were in a very good agreement with inside measured variables during the 14 days of verification with linear regression slopes ranging from 0.95 ‐ 1.05 and r2 between 0.86 ‐ 0.97. The models calculated with the predicted meteorological variables yielded an average deviation of 10% compared to the models calculated with measured variables.
In summary, both in terms of predictive ability and practical use, the preferred model for predicting evapotranspiration in screenhouses is the PMsc model. This model showed a very good predictability in both climate regions, and its major advantage is the use of external meteorological data that accessible to growers. It seems that the PMra model is not suitable for predicting evapotranspiration under screens. Replacing ra resistance in PMra model with rb resistance creates the PMrb model, which can be used with the method of calibrating meteorological variables under the screen by external variables. For determine the net radiation under the screens it is recommended, according to the results of this study, to use the radiation balance model with determination of the model coefficients according to the solar zenith angle. This paper proposes a combined sensitivity analysis as a tool enables intelligent agro‐technique management in covering crops with screens in different climatic conditions by evaluating the expected change in evapotranspiration due to micro‐climate
changes associated with the screen presence.