Development of a Cucumber Transpiration Model Based on a Simplified Penman-Monteith Model in a Semi-closed Greenhouse

Authors:
Hyungmin Rho Department of Horticulture and Landscape Architecture, National Taiwan University, Taipei 10617, Taiwan

Search for other papers by Hyungmin Rho in
This Site
Google Scholar
Close
,
Jung Su Department of Horticultural Science, College of Agricultural & Life Science, Kyungpook National University, Daegu 41566, South Korea; and Institute of Agricultural Science and Technology, Kyungpook National University, Daegu 41566, Korea

Search for other papers by Jung Su in
This Site
Google Scholar
Close
 Jo
,
Ha Seon Sim Department of Horticultural Science, College of Agricultural & Life Science, Kyungpook National University, Daegu 41566, South Korea

Search for other papers by Ha Seon Sim in
This Site
Google Scholar
Close
,
Yu Hyun Moon Department of Horticultural Science, College of Agricultural & Life Science, Kyungpook National University, Daegu 41566, South Korea

Search for other papers by Yu Hyun Moon in
This Site
Google Scholar
Close
,
Ui Jeong Woo Department of Horticultural Science, College of Agricultural & Life Science, Kyungpook National University, Daegu 41566, South Korea

Search for other papers by Ui Jeong Woo in
This Site
Google Scholar
Close
, and
Sung Kyeom Kim Department of Horticultural Science, College of Agricultural & Life Science, Kyungpook National University, Daegu 41566, South Korea; and Institute of Agricultural Science and Technology, Kyungpook National University, Daegu 41566, Korea

Search for other papers by Sung Kyeom Kim in
This Site
Google Scholar
Close

Click on author name to view affiliation information

Abstract

We aimed to develop a more accurate transpiration model for cucumber (Cucumis sativus L.) plants to optimize irrigation and nutrient usage in soilless greenhouse cultivation. Accurate modeling of transpiration in greenhouse-grown cucumbers is crucial for effective cultivation practices. Existing models have limitations that hinder their applicability. Therefore, this research focused on refining the modeling approach to address these limitations. To achieve this, a comprehensive methodology was employed. The actual transpiration rates of three cucumber plants were measured using a load cell, enabling crop fresh weight changes to be calculated. The transpiration model was developed by making specific corrections to the formula derived from the Penman-Monteith equation. In addition, the study investigated the relationship between transpiration rate and solar radiation (Rad) and vapor pressure deficit (VPD), identifying a nonlinear association between these variables. The transpiration model was adjusted to account for these nonlinear relationships and compensate for Rad and VPD. Comparative analysis between the actual and estimated transpiration rates demonstrated that the developed cucumber transpiration model reduced overestimation by 23.69%. Furthermore, the model exhibited higher coefficients of determination and root mean square error (RMSE) values than existing models, suggesting its superior accuracy in predicting transpiration rates. Implementing the transpiration model-based irrigation method demonstrated the potential for ∼21% nutrient savings compared with conventional irrigation practices. This finding highlights the practical applications of the developed model—accounting for a nonlinearity of Rad and VPD—in optimizing irrigation practices for greenhouse cucumber cultivation.

Water, a valuable yet limited resource, is crucial in supporting agricultural practices. As the water demand continues to rise, there is an increasing need for crop producers to explore efficient irrigation management strategies. These strategies aim to provide crops with an optimal water supply that minimizes water consumption within cultivation systems. Protected cultivation can decrease crop water requirements by as much as 20% to 40% because it allows for better control of the environmental conditions for crop production than open-field production (Saadon et al. 2021). However, greenhouse growers typically supply more irrigation than the crop actually requires (Levidow et al. 2014). Irrigation management practices in greenhouses are usually based on either the readings from a soil moisture sensor or a grower’s personal experience or determined by the levels of accumulated radiation. Irrigation based on accumulated radiation is applied without considering the root zone environment, and the physiological response of crops. Therefore, it is necessary to establish irrigation strategies using an evapotranspiration model to tailor the water supply according to the development and growth stage of crops (Jo and Shin 2021).

A comprehensive understanding of crop evapotranspiration (ETc) is required to precisely estimate water requirements (Paredes et al. 2020). ETc is an important indicator that can be used to determine the frequency and amount of irrigation (Gong et al. 2019), reducing water wastage and improving yields for greenhouse crops (Shin and Son 2016). Several methods have been developed to estimate ETc: indirect (ET-based model) or direct methods (weighing lysimeters, balances, sap-flow sensors, and other devices) (De Pascale et al. 2019). ETc modeling is used to manage greenhouse microclimates and precise irrigation to crops at different growth stages (Katsoulas and Stanghellini 2019). ETc is typically calculated or modeled using climate data and algorithms considering factors that influence evapotranspiration using the FAO 56 method. The FAO 56 Penman-Monteith (P-M) equation has become the global standard for estimating ETc with various applications (Allen et al. 1998). The P-M equation was primarily developed for open-field cultivation conditions, and its practical application requires the use of various input parameters such as soil heat flux density and wind speed. The existing P-M equation tends to overestimate the ETc under soilless culture (Incrocci et al. 2020). This discrepancy arises from the equation’s inclusion of both crop transpiration and soil evaporation, with the latter often being negligible in this cultivation method. In addition, the accuracy of the P-M equation diminishes due to incorrect assumptions regarding factors such as fetch, radiation, relative humidity, and airflow, which deviate from the open-field conditions. Thus, a simplified model consisting of parameters suitable for the environment and growth conditions in the greenhouse should be considered.

Baille’s equation is the most commonly used ETc estimation approach in soilless fruit vegetables grown in greenhouse environments (Baille et al. 1994). This model is a simplified version of the P-M equation, which assumes constant stomatal resistance in the greenhouse (Carmassi et al. 2013). The input parameters of this model are the incident solar radiation (Rad), vapor pressure deficit (VPD), light extinction coefficient (k, a parameter describing the attenuation of light within a plant canopy), and leaf area index (LAI). Among these input parameters, the LAI serves as key parameter of the transpiration model, as the amount of light intercepted by the canopy directly affects the transpiration rate of the crop. This becomes even more important for indeterminate crops, such as cucumber (Cucumis sativus L.) (Qian et al. 2019).

Several researchers used the P-M equation and its simplified derivatives for predicting the transpiration rate of greenhouse-grown crops, including cucumber (Medrano et al. 2005), bell pepper (Capsicum annuum L.) (Nam et al. 2019; Shin et al. 2014), tomato (Solanum lycopersicum L.) (Choi and Shin 2020; Jo and Shin 2021), and zucchini (Cucurbita pepo L.) (Rouphael and Colla 2004). Nevertheless, the existing simplified models have limitations when estimating the transpiration rate under high Rad or low VPD environments, which is common in the summer season in greenhouse cultivation. Because soilless cultivation requires numerous small-scale irrigation events each day, shorter measurement intervals of transpiration for ambient environmental conditions can improve the accuracy of the model (Shin et al. 2014). For precision irrigation management, it is necessary to compensate for the relationship between environmental variables and transpiration rates that affect the crop’s transpiration rate.

In this study, the transpiration rate of cucumber was estimated based on a simplified P-M model in a semiclosed-type greenhouse. We developed and validated a transpiration model by correcting the relationship between the transpiration rate and environmental parameters, using a system capable of measuring real-time transpiration rates.

Materials and Methods

Plant materials and cultivation condition.

The experiment was carried out between 23 Feb and 31 Jul 2021, in a semiclosed greenhouse at Kyungpook National University located in Daegu, Republic of Korea (35.5°N, 128.8°E, elevation 30 m). This greenhouse has a northwest orientation and faces south to ensure uniform solar energy distribution. The greenhouse is 20 m in length × 8 m in width, with a height of 7.35 m per district. The top of the greenhouse was covered with an antireflective glass with direct solar radiation transmittance greater than 90%, and the sidewalls have 16-mm polycarbonate panels with no side ventilator. The greenhouse was ventilated by opening the roof vents to release hot air accumulated inside the greenhouse with 25 °C as the set temperature. Also, for the internal temperature control, the shade screen was set to operate when the light intensity reached 650 W⋅m−2. Heating and cooling inside the greenhouse were controlled using an air source heat pump integrated with a thermal storage tank. During the experiment, the average daytime/nighttime temperature inside the greenhouse was 25.1/19.6 °C, ranging from 13.2 °C to 32.4 °C, and the average daytime/nighttime relative humidity (RH) was 72.2/81.4%, ranging from 52% to 98%. Microclimate data were measured using a dry/wet bulb sensor (MTV Active, Ridder, The Netherlands) and collected into a data logger (MultiMa, Ridder, The Netherlands) located inside the greenhouse. The environment in the greenhouse was automatically controlled according to the values set through the operating software (Synopta, Ridder, The Netherlands). Inside and outside environmental data were collected every minute and stored on a computer with Synopta–HortiMax software installed. The environmental data of the greenhouse are visualized in Fig. 1 and RH and VPD ranges over the course of the study are presented in Table 1, respectively.

Fig. 1.
Fig. 1.

Air temperature and relative humidity (A), root zone environment (B), and relationship between radiation intensity and vapor pressure deficit (C and D) during the experiment.

Citation: HortScience 58, 10; 10.21273/HORTSCI17218-23

Table 1.

Proportion of relative humidity and vapor pressure deficit during the experiment.

Table 1.

The cucumber cultivar used in the experiment was Goodmorning (Nong Woo Bio Co., Ltd., Suwon, Korea), which were grafted transplants [scions: cucumber, rootstock: Fig-leaf gourd (Cucurbita ficifolia Bouche)]. The grafted transplants were obtained at a local nursery and grown until four to five nodes were expanded. On 23 Feb 2021, at the four- to five-node stage, the seedlings were transferred to rock wool cubes (length 100 mm × width 100 mm × height 65 mm; Cultilene, Rijen, The Netherlands). After 3 d of acclimatization in the greenhouse, they were transplanted into rock wool slabs (length 1000 mm × width 150 mm × height 75 mm; Cultilene). The three plants per rock wool slab were planted at a density of 1.5 plants/m2 (Fig. 2). Ten days after transplanting the plants into the slabs, root growth was observed at the bottom. Except for one main stem, the other branches and shoots were pruned. Tendrils and lateral branches were also pruned daily, following the conventional cucumber management protocol that is commonly practiced at commercial farms. The cucumber plants were maintained with 15 leaves through defoliation to retain the balance of the plant sink and source. When two inflorescences were developed on neighboring nodes, the inflorescence that emerged from the node above was removed. All these conventional management practices were to maximize marketable fruit yields by modifying the sink-source balance.

Fig. 2.
Fig. 2.

The transpiration measurement device (A) and a schematic diagram of the device (B) used in this experiment.

Citation: HortScience 58, 10; 10.21273/HORTSCI17218-23

Irrigation event and nutrient management.

A nutrient solution was supplied using a substrate moisture content monitoring system. Irrigation management was practiced daily to maintain the drainage ratio within a 20% to 30% level (Shin and Son 2015b). The nutrient solution was also supplied at night, to maintain a substrate water content of 70% to 80%. The irrigation amount and frequency were adjusted according to the stage of cucumber growth (Table 2). Timer-controlled irrigation was supplied five times a day. The accumulative radiation irrigation control was applied using a dripper based on the accumulative radiation as measured using a pyranometer (SR05-D1A3; Hukseflux, Delft, The Netherlands) installed in the upper part of the greenhouse. The set point of accumulative radiation for triggering a 5-minute irrigation event of 350 mL water per dripper was 100 J⋅cm−2. At the early stages of growth, a standard cucumber nutrient solution at 0.6 dS⋅m−1 of electrical conductivity (EC) was provided (Yamazaki 1982; Table 3). As the plants grew, the EC was adjusted by a 0.2 dS⋅m−1 increment at each stage. When the cucumber plants moved to the fruiting stage phase from the flowering stage, the EC and the pH of the nutrient solution were maintained at 2.4 dS⋅m−1 and in the range of 5.5 to 6.5, respectively.

Table 2.

Irrigation events by days after transplantation (DAT) of cucumbers during the experiment.

Table 2.
Table 3.

Cucumber nutrient solution used in this experiment.

Table 3.

Measurement of transpiration.

Cucumber plant transpiration was measured using a transpiration monitoring system (Fig. 2). The transpiration measurement device (length 1100 mm × width 258 mm × height 156 mm; RMFarm, IReis CO., Ltd., Gangneung, Korea) provided information on the weight change of the crop, irrigation amount, drainage amount, drainage EC and pH, and substrate temperature. This device consisted of two load cells (10 g–40 kg ± 0.5 g, beam-type), and the amounts of irrigation and drainage at each irrigation event were monitored by a flow sensor (tipping bucket, 5 mL/bucket). The EC and pH were calibrated using a calibration solution before starting the experiment, and the sensor electrode and drainage container were cleaned regularly. During the experiment, data about the root zone environmental conditions (moisture content, EC, and pH of the substrate) were integrated to establish an efficient irrigation strategy. The transpiration amount of three cucumber plants was monitored in real time, and the crop weight changes were collected at 1-minute intervals from the webserver. The weight change of the crops on the measurement device at 10-minute intervals was continuously calculated for precise calculation of the amount of transpiration. Crop management practices, such as defoliation and lateral branch removal and harvesting may cause errors; thus, these data were excluded from the actual transpiration calculation. The following transpiration measurement equation was used during the growth of the cucumber plants:
Tr=[ΔGrΔ(Ir + Dr) Δ(Cm + Harv)],
where Tr is the transpiration in mm/10 min; Gr is the gross weight (g); Ir is the irrigation amount in mL; Dr is the drainage amount in mL; Cm is the crop management (g); and Harv is weight reduction due to harvest (g).

Transpiration model and parameters.

The transpiration rate of the cucumber crop was estimated by a formula modified from the existing P-M equation (Baille et al. 1994; Medrano et al. 2005):
Tr = a*(1exp(k*LAI))*Rad + b*LAI*VPD,
where Tr is the transpiration rate in mm/10 min; k is the light extinction coefficient; Rad is the solar radiation in W⋅m−2; LAI is the leaf area index (m2⋅m−2); VPD is the vapor pressure deficit in kPa; and a and b are regression coefficients.
Eq. [2] is similar to the P-M evapotranspiration equation. The bulk surface resistance and aerodynamic resistance of the existing P-M model were modified to LAI and VPD. Rad was the instant light intensity. The VPD was calculated based on air temperature and relative humidity (Eq. [3]). Based on previous studies, the extinction coefficient in the canopy of the cucumber plant was estimated as 0.86 in the equation. The extinction coefficient of the cucumber crop used the results of Medrano et al. (2005). Evaporation on the surface of the substrate consists of a negligible portion of the total weight change in soilless cultivation conditions; thus, it was removed in the calculation.
VPD=0.6107*107.5*t237.3 + t*[1(RH100)],
where VPD is the vapor pressure deficit (kPa); t is the dry-bulb temperature (°C); and RH is the relative humidity (%).
The LAI of the cucumber plants was computed manually by dividing the measured total leaf area by the planting density of 1.5 plants/m2. After transplanting, at 3-week intervals, destructive measurement of the LAI of the three experimental plants was conducted using a leaf area meter (LI-3000C Area Meter; LI-COR Inc., Lincoln, NE, USA). The average value was used to estimate leaf area changes as a function of days after transplanting (DAT). During the experiment period, transpiration and environmental data (Rad and VPD) were collected at 1-minute intervals, but the development of the LAI was not monitored continuously. The time series of the LAI was fitted using growth functions and estimated for continuous LAI development using the Boltzman sigmoid (Motulsky and Christopoulos 2003) regression equation:
LAI'=a1 + exp(c  DATb),
where LAI′ is the estimated leaf area index (m2⋅m−2); DAT is day after transplanting (day); and a, b, and c are regression coefficients.
The transpiration rate under various radiation conditions in the greenhouse was analyzed, and a compensated equation that accounts for the radiation and crop transpiration was determined using the exponential rise to maximum regression equation:
Rad'=c + a*(1exp(b*Rad)),

where is Rad′ is the calibrated solar radiation intensity (W⋅m−2); Rad is the solar radiation intensity (W⋅m−2); and a, b, and c are regression coefficients.

It was also necessary to account for the nonlinear relationship between various VPD conditions and the transpiration rate. As for the regression analysis, the following Gaussian fitting peak regression equation was to correct the VPD and crop transpiration relation:
VPD'=a*exp(0.5*(VPDc)2) b,
where VPD′ is the calibrated vapor pressure deficit (kPa); VPD is the vapor pressure deficit (kPa); and a, b, and c are regression coefficients.
The transpiration model based on Eq. [2] and the other compensated environmental and estimated LAI data calculated from Eqs. [3], [4], [5], and [6] were compiled and integrated to yield the final corrected transpiration model as Eq. [7].
Tr=a*(1exp(k*LAI'))*Rad'+b*LAI'*VPD',
where Tr is the transpiration rate (mm/10 min); K is the light extinction coefficient; Rad′ is the calibrated solar radiation (W⋅m−2); LAI′ is the estimated leaf area index (m2⋅m−2); VPD′ is the calibrated vapor pressure deficit (kPa); and a and b are regression coefficients.

Data analysis and model evaluation.

The data were analyzed using SPSS (Statistical Package for Social Sciences, SPSS Statistics Version 26; IBM, Armonk, NY, USA) statistical program. The nonlinear regression function was employed to calculate the regression coefficients of the transpiration model. SigmaPlot (SigmaPlot 12.5; Systat Software Inc, San Jose, CA, USA) software was used to conduct the curve fitting and to plot graphs for the post hoc graphical analysis of the regression. The performance of the transpiration models for estimating the accumulated transpiration amount was evaluated by the coefficient of determination (R2) and RMSE.

Results

Irrigation analysis.

The total amount of irrigation during the experiment was 1143.38 mm of water and the drainage was 368.56 mm of water, resulting in an average drainage rate of 32.23% (Fig. 3). The total number of irrigation events over the course of the experiment period was 1655 times. During the initial growth stage, timer-controlled irrigation was supplied five times a day, and irrigation based on the accumulated radiation was supplied 12 times a day on average. Because the timer-controlled irrigation treatment caused an average drainage rate of 19.02% at DAT 25 to 26, it was changed to irrigation based on accumulated radiation in DAT 27, considering the crop development stage and the amount of water demanded by the plants. The average number of irrigations in cloudy weather (accumulated radiation under 5 MJ⋅m−2) significantly differed from the target drainage rate range of 20% to 30% by t test (P < 0.05). The average number of irrigations in clear weather was 12, and the average number of irrigations in cloudy weather was 5.5. The average number of irrigation events during the spring cultivation season was 13 times a day, the total irrigation amount was 662.19. mm, and the drainage was 223.01 mm, whereas the average number of irrigation events during the summer cultivation season was 10 per day, the total irrigation amount was 481.19 mm, and drainage was 145.55 mm. Excluding the drainage amount from the total irrigation amount, 71.15% in the spring cultivation season and 66.51% in the summer cultivation season were used for the transpiration of plants.

Fig. 3.
Fig. 3.

Irrigation amount, drainage amount, and the number of irrigations during the experiment.

Citation: HortScience 58, 10; 10.21273/HORTSCI17218-23

Transpiration rates under various environmental conditions.

The total daily accumulated radiation on 9 May and 1 Mar were ∼29.0 and 1.8 MJ⋅m−2 (Fig. 4). The maximum light intensities were 991.1 on 9 May and 136.8 W⋅m−2 on 1 Mar. The daily accumulated transpiration amount was influenced by variations in accumulated radiation, with 9 May recording ∼16.72 times higher transpiration (3.01 mm) compared with 1 Mar (0.18 mm) (Fig. 4A and B). Similarly, differences in VPD conditions affected accumulated transpiration amounts (Fig. 4C and D). The daily VPD ranges on 15 Apr and 16 May were ∼0.64 to 2.96 kPa and 0.03 to 0.4 kPa, respectively. The average VPD at the time when the plants were actively transpiring during the daytime was 2.03 on 15 Apr and 0.15 kPa on 16 May. The average RH on 16 May was 98%, but the RH on 15 Apr was maintained at 60%. Daily accumulated transpiration on 15 Apr (3.28 mm) was ∼3.95 times higher than that on 16 May (0.83 mm). The difference in daily accumulated transpiration under various accumulated radiation and VPD conditions was more affected by radiation fluctuations than by VPD.

Fig. 4.
Fig. 4.

Comparison of accumulated transpiration amounts according to various environmental conditions. Rad, Acc-rad, VPD, and Acc-Tr indicate radiation intensity, accumulated radiation, vapor pressure deficit, and accumulated transpiration, respectively.

Citation: HortScience 58, 10; 10.21273/HORTSCI17218-23

Estimation of LAI.

The LAI, the total leaf surface area per unit ground area, is a critical parameter in assessing plant canopy structure and productivity, and in understanding the processes of light interception linked to photosynthesis and transpiration. The development in LAI at DAT 1 to 159 increased from LAI 0.10 to 4.44 (Fig. 5). LAI increased rapidly in the early stages of growth, reaching a maximum of 4.44 m2⋅m−2 in mid-May (DAT 84). The rate of increase tended to decrease after DAT 84 (4.44 m2⋅m−2). The regression coefficients (a, b, and c) in the sigmoid regression equation (Eq. [4]) were found to be a = 3.9410, b = 8.8508, c = 26.0961, and the coefficient of determination (R2) was 0.98. These coefficients accurately estimated the continuous development of LAI during the experiment.

Fig. 5.
Fig. 5.

Regression curve of leaf area index development according to the number of days after transplanting.

Citation: HortScience 58, 10; 10.21273/HORTSCI17218-23

Relationship between the transpiration rate and the radiation or VPD.

We found the relationship between transpiration rate and neither radiation nor VPD linear (Fig. 6), which could be specific to a greenhouse environment, possibly as a source of overestimating the transpiration rates using the conventional P-M model (Eq. [2]).

Fig. 6.
Fig. 6.

Fitting of nonlinear regression curves between transpiration and environmental factors.

Citation: HortScience 58, 10; 10.21273/HORTSCI17218-23

The change in transpiration rate over the course of the light intensity showed a tendency to increase as the light intensity increased, but there was no linear relationship (Fig. 6A). The accumulated transpiration amount estimated using Eq. [2] was 6.50 mm/d at 18.5 MJ/d/m2 accumulated radiation, while the actual transpiration rate at this time was 3.01 mm/d. The daily accumulated transpiration amount calculated using Eq. [2] was ∼2.16 times greater than the actual transpiration. The transpiration rate estimated using Eq. [2] had a 1.28% higher error rate at above 500 W⋅m−2 than at under 500 W⋅m−2.

Under the low VPD conditions, the transpiration rate gradually increased, peaked at 2.0 kPa, and then declined; thus, the relationship was observed to be not linear (Fig. 6B). In the momentary transpiration rate data collected at 10-minute intervals, the transpiration rate rapidly increased in the range of 0.4 to 2.03 kPa with the maximum rate of 0.0197 mm/10 min at 0.87 kPa. The regression coefficients a, b, and c, and R2 for the radiation and VPD compensation equations showed good fits to the Gaussian peak fitting (Table 4). The relationship of transpiration rate to radiation and VPD showed that the compensated Rad had a higher R2 value (Rad′ = 0.72, and VPD′ = 0.58) than the compensated VPD.

Table 4.

Regression coefficients and coefficients of determination (R2) for radiation and VPD compensation equations.

Table 4.

Development and verification of the cucumber transpiration model.

The performance of the transpiration models was compared and evaluated through the existing transpiration model, the radiation compensation model, the VPD compensation model, and the developed model by determining R2 and RMSE of each model. The developed transpiration model, integrated with the calibrated Rad and VPD, produced the most accurate transpiration rate estimates, with an R2 of 0.76 and an RMSE of 9.69 mm (Table 5).

Table 5.

Comparison of regression coefficients a and b, coefficients of determination (R2), and root mean square error (RMSE) of the cucumber transpiration model calibration of each model.

Table 5.

The estimated accumulated transpiration amounts for each model were as follows: Tr (actual): 535.73 mm, Tr (non cal′): 687.26 mm, Tr (Rad′ cal′): 583.49 mm, Tr (VPD′ cal′): 611.24 mm, and Tr (Rad′ and VPD′ cal′): 560.36 mm (Fig. 7). The estimated accumulated amount of transpiration in each model was slightly higher than the actual transpiration. The calculation of actual transpiration amounts by the models that did not compensate for both Rad and VPD significantly overestimated the accumulated transpiration rate. Also, the relationship between VPD and transpiration rate was not accurately estimated. As a result of estimating the actual accumulated transpiration amount for each transpiration model, R2 was 0.99, accurately predicting the actually accumulated transpiration of cucumbers. The RMSE between the actual and the estimated accumulated transpiration amounts were Tr (non cal′): 85.09 mm, Tr (Rad′ cal′): 21.00 mm, Tr (VPD′ cal′): 40.35 mm, and Tr (Rad′ and VPD′ cal′): 9.69 mm (Table 5). The amount of irrigation according to the accumulated radiation was 1143.38 mm, but irrigation based on transpiration amount while maintaining daily drainage at the level of 20% to 30% requires 904.29 mm. The developed transpiration model–based irrigation method can save ∼21% of the nutrient (i.e., fertigation solution) compared with the conventional irrigation method.

Fig. 7.
Fig. 7.

Comparisons of the measured and estimated accumulated transpiration by each transpiration model during the experiment periods.

Citation: HortScience 58, 10; 10.21273/HORTSCI17218-23

Discussion

To estimate the actual transpiration rate of crops, it is important to understand the parameters affecting transpiration and their relationship to transpiration. Yang et al. (1990) reported that air temperature and RH were the main factors that significantly affected crop transpiration in the greenhouse. In greenhouse conditions, diurnal fluctuations in air temperature and RH are commonly observed. As the air temperature increases during the daytime, the RH tends to decrease, resulting in an elevation of vapor pressure deficit (VPD). These dynamic changes in the microclimate ultimately lead to an increase in cucumber transpiration, albeit with varying rates within the canopy (Yan et al. 2020).

The root zone environment also factors in transpiration. If the substrate temperature was higher or lower than the appropriate range, it could inhibit plant growth (Jo and Shin 2022). In soilless cultivation, the optimum air temperature for the development of cucumber crops is 20.5 to 25.1 °C, the RH range is 60% to 85%, and the substrate temperature should be 16.0 to 22.9 °C (Singh et al. 2017a, 108). Regardless of the change of season, the average air temperature, RH, and substrate temperature during our experiment provided an optimal environment for the transpiration of crops by using ventilation and an air source heat pump (Fig. 1A and B). Because the transpiration rate of crops depends on irrigation management and root zone environment, these also should be considered as factors affecting crop transpiration (Shin and Son 2015b). In soilless cultivation, an increase in substrate EC due to insufficient moisture content is one of the main factors that interferes with transpiration, so a drainage rate of 30% per day was recommended to prevent the accumulation of salt in the substrate (Hellemans 2006). The salt accumulation causes less transpiration due to salinity/drought stress (Shin and Son 2015a). Salinity rinsing of drainage water would help maintain an adequate balance of substrate EC; thus, maintaining a drainage rate at ∼30% in bell peppers was suggested in soilless bell pepper (Capsicum annuum L.) cultivation (Shin and Son 2015b). In a previous study, the transpiration rate of crops was saturated when the moisture content in the substrate was above 60%, and the EC of the substrate was significantly decreased when above 4.0 dS⋅m−1 (Shin and Son 2015b). A real-time transpiration monitoring system was used to maintain the appropriate substrate moisture content and EC through regular irrigation (Figs. 1B and 3). To increase the crop transpiration efficiency, maintaining an appropriate range of osmotic potential with substrate moisture content control is critical for soilless culture.

Momentary fluctuations in light intensity during the day directly affect transpiration rates and can produce variations in the microclimate in the greenhouse (Ali et al. 2009). The VPD is affected by a number of microclimate parameters, such as temperature, RH, and light intensity (Singh et al. 2017b). A previous study reported a significant correlation between light intensity and VPD, but in greenhouses using shading screens or automatic ventilation systems, such a correlation has not been observed (Choi and Shin 2020; Jo and Shin 2021). In the present study, the change in VPD in response to light intensity was similar to those previously reported (Fig. 1C and D). In a modern greenhouse system, when high light comes down into a greenhouse, a greenhouse environment control system triggers the operation of shading curtains, reduces incoming radiation, temperature, and VPD momentarily, lowering the transpiration rate (Jo and Shin 2021). The gap between the accumulative radiation and VPD in the summer season (DAT 140–170) in Fig. 1C demonstrates this point. This dynamic response of light intensity and VPD is a feature of greenhouse microclimate, especially in sunny summer days, that needs attention in developing the irrigation model based on plant transpiration. For this reason, accurate measurements of light intensity and VPD are required to precisely estimate the actual transpiration rates, and it is important to analyze the relationship between transpiration rates and environmental parameters, and to further build an integrative transpiration model.

As for the actual crop transpiration rate, the shorter the measurement interval, the higher the measurement accuracy was achieved by reflecting the response of real plants to factors affecting transpiration (Shin et al. 2014). Jolliet and Bailey (1992) reported that transpiration mainly depends on the light intensity, VPD, and leaf area. The daily accumulated transpiration number of crops calculated at 10-minute intervals differed under various light intensity and VPD conditions (Fig. 4). Accumulated transpiration amounts were significantly greater in both high light intensity and in the optimal VPD range than in low light and VPD conditions. We increased the accuracy of the model by collecting as much data as possible to analyze the relationship between momentary transpiration rate and the environmental parameters: Rad and VPD. A sigmoidal pattern in leaf growth well captured the competitive relationship between vegetative and reproductive organs for nutrients (Fig. 5). Boulard and Baille (1993) reported that transpiration amount increased linearly when LAI increased, but we did not observe a linear relationship between transpiration and LAI. Given the structure of cucumber crops, it is thought that there is no linear relationship between leaf area and transpiration rate because the light intensities at the bottom leaf of crops are greatly reduced by shading effects among adjacent plants, which also reduces transpiration along with photosynthesis of the shaded leaves (Jo and Shin 2020); thus, the optimal LAI range of 3.0 to 3.5 m2⋅m−2 was suggested to maximize the productivity of greenhouse grown cucumber (Xiaolei and Zhifeng 2004).

The transpiration rate had no linear relationship with the Rad or VPD (Fig. 6). Most irrigation methods of soilless cultivation in greenhouses use linear equations to determine levels of accumulated radiation. However, the accumulated radiation used for irrigation control should be lower given that the actual transpiration rate was lower than estimated under high light conditions. In addition, the cucumber exposed to high VPD conditions inhibits transpiration by reducing stomatal density, length, width, and area (Song et al. 2021), which also lowers the actual transpiration rate. Therefore, the relationship between Rad and VPD with transpiration rate was compensated by both nonlinear regression equations. This observation indicates that transpiration was suppressed because the stomata of leaves were closed under high light intensity and unsuitable VPD conditions (Shimomoto et al. 2020). Finally, based on the existing P-M equation, the cucumber transpiration model was developed by compensating for the Rad and VPD (Eq. [7]). There was a better agreement between the estimated accumulated transpiration amount and the measured accumulated transpiration amount using the developed cucumber transpiration model than using the existing model (Fig. 7). The Tr (non cal′) was overestimated by ∼23.69% compared with the Tr (actual) and Tr (Rad′ and VPD′ cal′) model. Although other plastic greenhouse ETc models do not consider Rad and VPD like our study, they do retain a high accuracy, not necessitating the Rad and VPD calibrations (Gallardo et al. 2016; Gallardo et al. 2014), an automatically controlled glass greenhouse requires a high precision of ETc estimates. Thus, our finding reiterates the importance of capturing the relationship between transpiration rate and environmental parameters to accurately estimate transpiration rates.

The transpiration model developed could reflect the transpiration of greenhouse-grown cucumbers reasonably accurately, and can be used as a tool to optimize irrigation management and further to optimize greenhouse RH control, as the water input to the air from the overestimated transpiration and irrigation can be reduced. To integrate this model seamlessly into the algorithm for controlling precision irrigation in soilless cucumber cultivation, the model coefficients need to be adjusted for specific climatic and crop conditions through continuous verification. Future studies should focus on verification of the developed model performance in different settings of growing environments, such as model coefficient calibrations under winter greenhouse microclimate and under deficit irrigation management in a semiarid region. Also, testing the developed model in semiclosed protective plastic houses (e.g., high-tunnels) and comparing the model performance with other ETc models would ensure the model’s verification and application in a broader context.

References Cited

  • Ali MH, Adham AKM, Rahman MM, Islam AKMR. 2009. Sensitivity of Penman-Monteith estimates of reference evapotranspiration to errors in input climatic data. J Agrometerol. 11(1):18. https://doi.org/10.54386/jam.v11i1.1214.

    • Search Google Scholar
    • Export Citation
  • Allen RG, Pereira LS, Raes D, and Smith M. 1998. Crop evapotranspiration—Guidelines for computing crop water requirements-FAO irrigation and drainage paper 56. FAO, Rome 300(9):D05109.

  • Baille M, Baille A, Laury JC. 1994. A simplified model for predicting evapotranspiration rate of 9 ornamental species vs climate factors and leaf-area. Scientia Hortic. 59(3–4):217232. https://doi.org/10.1016/0304-4238(94)90015-9.

    • Search Google Scholar
    • Export Citation
  • Boulard T, Baille A. 1993. A simple greenhouse climate control model incorporating effects of ventilation and evaporative cooling. Agric Meteorol. 65(3–4):145157. https://doi.org/10.1016/0168-1923(93)90001-X.

    • Search Google Scholar
    • Export Citation
  • Carmassi G, Bacci L, Bronzini M, Incrocci L, Maggini R, Bellocchi G, Massa D, Pardossi A. 2013. Modelling transpiration of greenhouse gerbera (Gerbera jamesonii H. Bolus) grown in substrate with saline water in a Mediterranean climate. Scientia Hortic. 156:918. https://doi.org/10.1016/j.scienta.2013.03.023.

    • Search Google Scholar
    • Export Citation
  • Choi YB, Shin JH. 2020. Development of a transpiration model for precise irrigation control in tomato cultivation. Scientia Hortic. 267:109358. https://doi.org/10.1016/j.scienta.2020.109358.

    • Search Google Scholar
    • Export Citation
  • De Pascale S, Incrocci L, Massa D, Rouphael Y, Pardossi A. 2019. Advances in irrigation management in greenhouse cultivation, p 1–44. In: Marcelis LF, Heuvelink E (eds). Achieving sustainable greenhouse cultivation. Burleigh Dodds Science Publishing Limited, Cambridge, UK. https://doi.org/10.19103/as.2019.0052.12.

  • Gallardo M, Fernández MD, Giménez C, Padilla FM, Thompson RB. 2016. Revised VegSyst model to calculate dry matter production, critical N uptake and ETc of several vegetable species grown in Mediterranean greenhouses. Agric Syst. 146:3043. https://doi.org/10.1016/j.agsy.2016.03.014.

    • Search Google Scholar
    • Export Citation
  • Gallardo M, Thompson RB, Giménez C, Padilla FM, Stöckle CO. 2014. Prototype decision support system based on the VegSyst simulation model to calculate crop N and water requirements for tomato under plastic cover. Irrig Sci. 32(3):237253. https://doi.org/10.1007/s00271-014-0427-3.

    • Search Google Scholar
    • Export Citation
  • Gong XW, Liu H, Sun JS, Gao Y, Zhang H. 2019. Comparison of Shuttleworth-Wallace model and dual crop coefficient method for estimating evapotranspiration of tomato cultivated in a solar greenhouse. Agric Water Manage. 217:141153. https://doi.org/10.1016/j.agwat.2019.02.012.

    • Search Google Scholar
    • Export Citation
  • Hellemans B. 2006. Environmental control and paprika growing technique. Substratus Res. Center, Netherlands.

  • Incrocci L, Thompson RB, Fernandez-Fernandez MD, De Pascale S, Pardossi A, Stanghellini C, Rouphael Y, Gallardo M. 2020. Irrigation management of European greenhouse vegetable crops. Agric Water Manage. 242:106393. https://doi.org/10.1016/j.agwat.2020.106393.

    • Search Google Scholar
    • Export Citation
  • Jo WJ, Shin JH. 2020. Effect of leaf-area management on tomato plant growth in greenhouses. Hortic Environ Biotechnol. 61(6):981988. https://doi.org/10.1007/s13580-020-00283-1.

    • Search Google Scholar
    • Export Citation
  • Jo WJ, Shin JH. 2021. Development of a transpiration model for precise tomato (Solanum lycopersicum L.) irrigation control under various environmental conditions in greenhouse. Plant Physiol Biochem. 162:388394. https://doi.org/10.1016/j.plaphy.2021.03.005.

    • Search Google Scholar
    • Export Citation
  • Jo WJ, Shin JH. 2022. Effect of root-zone heating using positive temperature coefficient film on growth and quality of strawberry (Fragaria x ananassa) in greenhouses. Hortic Environ Biotechnol. 63(1):89100. https://doi.org/10.1007/s13580-021-00384-5.

    • Search Google Scholar
    • Export Citation
  • Jolliet O, Bailey B. 1992. The effect of climate on tomato transpiration in greenhouses: measurements and models comparison. Agric For Meteorol 58(1–2):4362. https://doi.org/10.1016/0168-1923(92)90110-P.

    • Search Google Scholar
    • Export Citation
  • Katsoulas N, Stanghellini C. 2019. Modelling crop transpiration in greenhouses: Different models for different applications. Agronomy (Basel). 9(7):392. https://doi.org/10.3390/agronomy9070392.

    • Search Google Scholar
    • Export Citation
  • Levidow L, Zaccaria D, Maia R, Vivas E, Todorovic M, Scardigno A. 2014. Improving water-efficient irrigation: Prospects and difficulties of innovative practices. Agric Water Manage. 146:8494. https://doi.org/10.1016/j.agwat.2014. 07.012.

    • Search Google Scholar
    • Export Citation
  • Medrano E, Lorenzo P, Sánchez-Guerrero MC, Montero JI. 2005. Evaluation and modelling of greenhouse cucumber-crop transpiration under high and low radiation conditions. Scientia Hortic. 105(2):163175. https://doi.org/10.1016/j.scienta.2005.01.024.

    • Search Google Scholar
    • Export Citation
  • Motulsky H, Christopoulos A. 2003. Fitting models to biological data using linear and nonlinear regression: A practical guide to curve fitting. GraphPad Software. Inc., San Diego, CA.

  • Nam DS, Moon T, Lee JW, Son JE. 2019. Estimating transpiration rates of hydroponically-grown paprika via an artificial neural network using aerial and root-zone environments and growth factors in greenhouses. Hortic Environ Biotechnol. 60(6):913923. https://doi.org/10.1007/s13580-019-00183-z.

    • Search Google Scholar
    • Export Citation
  • Paredes P, Pereira LS, Almorox J, Darouich H. 2020. Reference grass evapotranspiration with reduced data sets: Parameterization of the FAO Penman-Monteith temperature approach and the Hargeaves-Samani equation using local climatic variables. Agric Water Manage. 240:106210. https://doi.org/10.1016/j.agwat.2020.106210.

    • Search Google Scholar
    • Export Citation
  • Qian T, Zheng X, Guo X, Wen W, Yang J, Lu S. 2019. Influence of temperature and light gradient on leaf arrangement and geometry in cucumber canopies: Structural phenotyping analysis and modelling. Inf Process Agric. 6(2):224232. https://doi.org/10.1016/j.inpa.2018. 11.002.

    • Search Google Scholar
    • Export Citation
  • Rouphael Y, Colla G. 2004. Modelling the transpiration of a greenhouse zucchini crop grown under a Mediterranean climate using the Penman-Monteith equation and its simplified version. Aust J Agric Res. 55(9):931937. https://doi.org/10.1071/Ar03247.

    • Search Google Scholar
    • Export Citation
  • Saadon T, Lazarovitch N, Jerszurki D, Tas E. 2021. Predicting net radiation in naturally ventilated greenhouses based on outside global solar radiation for reference evapotranspiration estimation. Agric Water Manage. 257:107102. https://doi.org/10.1016/j.agwat.2021.107102.

    • Search Google Scholar
    • Export Citation
  • Shimomoto K, Takayama K, Takahashi N, Nishina H, Inaba K, Isoyama Y, Oh S-C. 2020. Real-time monitoring of photosynthesis and transpiration of a fully-grown tomato plant in greenhouse. Environ Control Biol. 58(3):6570. https://doi.org/10.2525/ecb.58.65.

    • Search Google Scholar
    • Export Citation
  • Shin JH, Park JS, Son JE. 2014. Estimating the actual transpiration rate with compensated levels of accumulated radiation for the efficient irrigation of soilless cultures of paprika plants. Agric Water Manage. 135:918. https://doi.org/10.1016/j.agwat.2013.12.009.

    • Search Google Scholar
    • Export Citation
  • Shin JH, Son JE. 2015a. Changes in electrical conductivity and moisture content of substrate and their subsequent effects on transpiration rate, water use efficiency, and plant growth in the soilless culture of paprika (Capsicum annuum L.). Hortic Environ Biotechnol. 56(2):178185. https://doi.org/10.1007/s13580-015-0154-6.

    • Search Google Scholar
    • Export Citation
  • Shin JH, Son JE. 2015b. Development of a real-time irrigation control system considering transpiration, substrate electrical conductivity, and drainage rate of nutrient solutions in soilless culture of paprika (Capsicum annuum L.). Eur J Hortic Sci. 80(6):271279. https://doi.org/10.17660/eJHS.2015/80.6.2.

    • Search Google Scholar
    • Export Citation
  • Shin JH, Son JE. 2016. Application of a modified irrigation method using compensated radiation integral, substrate moisture content, and electrical conductivity for soilless cultures of paprika. Scientia Hortic. 198:170175. https://doi.org/10.1016/j.scienta.2015.11.015.

    • Search Google Scholar
    • Export Citation
  • Singh MC, Kachwaya DS, Kalsi K. 2018. Soilless cucumber cultivation under protective structures in relation to irrigation coupled fertigation management, economic viability and potential benefits—a review. Int J Curr Microbiol Appl Sci. 7(3):24512468. https://doi.org/10.20546/ijcmas.2018.703.286.

    • Search Google Scholar
    • Export Citation
  • Singh MC, Singh J, Pandey S, Mahay D, Srivastava V. 2017a. Factors affecting the performance of greenhouse cucumber cultivation-a review. Int J Curr Microbiol Appl Sci. 6(10):23042323. https://doi.org/10.20546/ijcmas.2017.610.273.

    • Search Google Scholar
    • Export Citation
  • Singh MC, Singh J, Singh K. 2017b. Optimal operating microclimatic conditions for drip fertigated cucumbers in soilless media under a naturally ventilated greenhouse. Indian J Ecol. 44(4):821826.

    • Search Google Scholar
    • Export Citation
  • Song XM, Bai P, Ding JP, Li JM. 2021. Effect of vapor pressure deficit on growth and water status in muskmelon and cucumber. Plant Sci. 303:110755. https://doi.org/10.1016/j.plantsci.2020.110755.

    • Search Google Scholar
    • Export Citation
  • Xiaolei S, Zhifeng W. 2004. The optimal leaf area index for cucumber photosynthesis and production in plastic greenhouse. Acta Hortic. 633:161165. https://doi.org/10.17660/ActaHortic.2004.633.19.

    • Search Google Scholar
    • Export Citation
  • Yamazaki K. 1982. Nutrient solution culture. Pak-kyo Co., Tokyo, Japan.

  • Yan H, Huang S, Zhang C, Gerrits MC, Wang G, Zhang J, Zhao B, Acquah SJ, Wu H, Fu H. 2020. Parameterization and application of Stanghellini model for estimating greenhouse cucumber transpiration. Water. 12(2):517. https://doi.org/10.3390/w12020517.

    • Search Google Scholar
    • Export Citation
  • Yang XS, Short TH, Fox RD, Bauerle WL. 1990. Transpiration, leaf temperature and stomatal-resistance of a greenhouse cucumber crop. Agric Meteorol. 51(3–4):197209. https://doi.org/10.1016/0168-1923(90)90108-I.

    • Search Google Scholar
    • Export Citation
  • Fig. 1.

    Air temperature and relative humidity (A), root zone environment (B), and relationship between radiation intensity and vapor pressure deficit (C and D) during the experiment.

  • Fig. 2.

    The transpiration measurement device (A) and a schematic diagram of the device (B) used in this experiment.

  • Fig. 3.

    Irrigation amount, drainage amount, and the number of irrigations during the experiment.

  • Fig. 4.

    Comparison of accumulated transpiration amounts according to various environmental conditions. Rad, Acc-rad, VPD, and Acc-Tr indicate radiation intensity, accumulated radiation, vapor pressure deficit, and accumulated transpiration, respectively.

  • Fig. 5.

    Regression curve of leaf area index development according to the number of days after transplanting.

  • Fig. 6.

    Fitting of nonlinear regression curves between transpiration and environmental factors.

  • Fig. 7.

    Comparisons of the measured and estimated accumulated transpiration by each transpiration model during the experiment periods.

  • Ali MH, Adham AKM, Rahman MM, Islam AKMR. 2009. Sensitivity of Penman-Monteith estimates of reference evapotranspiration to errors in input climatic data. J Agrometerol. 11(1):18. https://doi.org/10.54386/jam.v11i1.1214.

    • Search Google Scholar
    • Export Citation
  • Allen RG, Pereira LS, Raes D, and Smith M. 1998. Crop evapotranspiration—Guidelines for computing crop water requirements-FAO irrigation and drainage paper 56. FAO, Rome 300(9):D05109.

  • Baille M, Baille A, Laury JC. 1994. A simplified model for predicting evapotranspiration rate of 9 ornamental species vs climate factors and leaf-area. Scientia Hortic. 59(3–4):217232. https://doi.org/10.1016/0304-4238(94)90015-9.

    • Search Google Scholar
    • Export Citation
  • Boulard T, Baille A. 1993. A simple greenhouse climate control model incorporating effects of ventilation and evaporative cooling. Agric Meteorol. 65(3–4):145157. https://doi.org/10.1016/0168-1923(93)90001-X.

    • Search Google Scholar
    • Export Citation
  • Carmassi G, Bacci L, Bronzini M, Incrocci L, Maggini R, Bellocchi G, Massa D, Pardossi A. 2013. Modelling transpiration of greenhouse gerbera (Gerbera jamesonii H. Bolus) grown in substrate with saline water in a Mediterranean climate. Scientia Hortic. 156:918. https://doi.org/10.1016/j.scienta.2013.03.023.

    • Search Google Scholar
    • Export Citation
  • Choi YB, Shin JH. 2020. Development of a transpiration model for precise irrigation control in tomato cultivation. Scientia Hortic. 267:109358. https://doi.org/10.1016/j.scienta.2020.109358.

    • Search Google Scholar
    • Export Citation
  • De Pascale S, Incrocci L, Massa D, Rouphael Y, Pardossi A. 2019. Advances in irrigation management in greenhouse cultivation, p 1–44. In: Marcelis LF, Heuvelink E (eds). Achieving sustainable greenhouse cultivation. Burleigh Dodds Science Publishing Limited, Cambridge, UK. https://doi.org/10.19103/as.2019.0052.12.

  • Gallardo M, Fernández MD, Giménez C, Padilla FM, Thompson RB. 2016. Revised VegSyst model to calculate dry matter production, critical N uptake and ETc of several vegetable species grown in Mediterranean greenhouses. Agric Syst. 146:3043. https://doi.org/10.1016/j.agsy.2016.03.014.

    • Search Google Scholar
    • Export Citation
  • Gallardo M, Thompson RB, Giménez C, Padilla FM, Stöckle CO. 2014. Prototype decision support system based on the VegSyst simulation model to calculate crop N and water requirements for tomato under plastic cover. Irrig Sci. 32(3):237253. https://doi.org/10.1007/s00271-014-0427-3.

    • Search Google Scholar
    • Export Citation
  • Gong XW, Liu H, Sun JS, Gao Y, Zhang H. 2019. Comparison of Shuttleworth-Wallace model and dual crop coefficient method for estimating evapotranspiration of tomato cultivated in a solar greenhouse. Agric Water Manage. 217:141153. https://doi.org/10.1016/j.agwat.2019.02.012.

    • Search Google Scholar
    • Export Citation
  • Hellemans B. 2006. Environmental control and paprika growing technique. Substratus Res. Center, Netherlands.

  • Incrocci L, Thompson RB, Fernandez-Fernandez MD, De Pascale S, Pardossi A, Stanghellini C, Rouphael Y, Gallardo M. 2020. Irrigation management of European greenhouse vegetable crops. Agric Water Manage. 242:106393. https://doi.org/10.1016/j.agwat.2020.106393.

    • Search Google Scholar
    • Export Citation
  • Jo WJ, Shin JH. 2020. Effect of leaf-area management on tomato plant growth in greenhouses. Hortic Environ Biotechnol. 61(6):981988. https://doi.org/10.1007/s13580-020-00283-1.

    • Search Google Scholar
    • Export Citation
  • Jo WJ, Shin JH. 2021. Development of a transpiration model for precise tomato (Solanum lycopersicum L.) irrigation control under various environmental conditions in greenhouse. Plant Physiol Biochem. 162:388394. https://doi.org/10.1016/j.plaphy.2021.03.005.

    • Search Google Scholar
    • Export Citation
  • Jo WJ, Shin JH. 2022. Effect of root-zone heating using positive temperature coefficient film on growth and quality of strawberry (Fragaria x ananassa) in greenhouses. Hortic Environ Biotechnol. 63(1):89100. https://doi.org/10.1007/s13580-021-00384-5.

    • Search Google Scholar
    • Export Citation
  • Jolliet O, Bailey B. 1992. The effect of climate on tomato transpiration in greenhouses: measurements and models comparison. Agric For Meteorol 58(1–2):4362. https://doi.org/10.1016/0168-1923(92)90110-P.

    • Search Google Scholar
    • Export Citation
  • Katsoulas N, Stanghellini C. 2019. Modelling crop transpiration in greenhouses: Different models for different applications. Agronomy (Basel). 9(7):392. https://doi.org/10.3390/agronomy9070392.

    • Search Google Scholar
    • Export Citation
  • Levidow L, Zaccaria D, Maia R, Vivas E, Todorovic M, Scardigno A. 2014. Improving water-efficient irrigation: Prospects and difficulties of innovative practices. Agric Water Manage. 146:8494. https://doi.org/10.1016/j.agwat.2014. 07.012.

    • Search Google Scholar
    • Export Citation
  • Medrano E, Lorenzo P, Sánchez-Guerrero MC, Montero JI. 2005. Evaluation and modelling of greenhouse cucumber-crop transpiration under high and low radiation conditions. Scientia Hortic. 105(2):163175. https://doi.org/10.1016/j.scienta.2005.01.024.

    • Search Google Scholar
    • Export Citation
  • Motulsky H, Christopoulos A. 2003. Fitting models to biological data using linear and nonlinear regression: A practical guide to curve fitting. GraphPad Software. Inc., San Diego, CA.

  • Nam DS, Moon T, Lee JW, Son JE. 2019. Estimating transpiration rates of hydroponically-grown paprika via an artificial neural network using aerial and root-zone environments and growth factors in greenhouses. Hortic Environ Biotechnol. 60(6):913923. https://doi.org/10.1007/s13580-019-00183-z.

    • Search Google Scholar
    • Export Citation
  • Paredes P, Pereira LS, Almorox J, Darouich H. 2020. Reference grass evapotranspiration with reduced data sets: Parameterization of the FAO Penman-Monteith temperature approach and the Hargeaves-Samani equation using local climatic variables. Agric Water Manage. 240:106210. https://doi.org/10.1016/j.agwat.2020.106210.

    • Search Google Scholar
    • Export Citation
  • Qian T, Zheng X, Guo X, Wen W, Yang J, Lu S. 2019. Influence of temperature and light gradient on leaf arrangement and geometry in cucumber canopies: Structural phenotyping analysis and modelling. Inf Process Agric. 6(2):224232. https://doi.org/10.1016/j.inpa.2018. 11.002.

    • Search Google Scholar
    • Export Citation
  • Rouphael Y, Colla G. 2004. Modelling the transpiration of a greenhouse zucchini crop grown under a Mediterranean climate using the Penman-Monteith equation and its simplified version. Aust J Agric Res. 55(9):931937. https://doi.org/10.1071/Ar03247.

    • Search Google Scholar
    • Export Citation
  • Saadon T, Lazarovitch N, Jerszurki D, Tas E. 2021. Predicting net radiation in naturally ventilated greenhouses based on outside global solar radiation for reference evapotranspiration estimation. Agric Water Manage. 257:107102. https://doi.org/10.1016/j.agwat.2021.107102.

    • Search Google Scholar
    • Export Citation
  • Shimomoto K, Takayama K, Takahashi N, Nishina H, Inaba K, Isoyama Y, Oh S-C. 2020. Real-time monitoring of photosynthesis and transpiration of a fully-grown tomato plant in greenhouse. Environ Control Biol. 58(3):6570. https://doi.org/10.2525/ecb.58.65.

    • Search Google Scholar
    • Export Citation
  • Shin JH, Park JS, Son JE. 2014. Estimating the actual transpiration rate with compensated levels of accumulated radiation for the efficient irrigation of soilless cultures of paprika plants. Agric Water Manage. 135:918. https://doi.org/10.1016/j.agwat.2013.12.009.

    • Search Google Scholar
    • Export Citation
  • Shin JH, Son JE. 2015a. Changes in electrical conductivity and moisture content of substrate and their subsequent effects on transpiration rate, water use efficiency, and plant growth in the soilless culture of paprika (Capsicum annuum L.). Hortic Environ Biotechnol. 56(2):178185. https://doi.org/10.1007/s13580-015-0154-6.

    • Search Google Scholar
    • Export Citation
  • Shin JH, Son JE. 2015b. Development of a real-time irrigation control system considering transpiration, substrate electrical conductivity, and drainage rate of nutrient solutions in soilless culture of paprika (Capsicum annuum L.). Eur J Hortic Sci. 80(6):271279. https://doi.org/10.17660/eJHS.2015/80.6.2.

    • Search Google Scholar
    • Export Citation
  • Shin JH, Son JE. 2016. Application of a modified irrigation method using compensated radiation integral, substrate moisture content, and electrical conductivity for soilless cultures of paprika. Scientia Hortic. 198:170175. https://doi.org/10.1016/j.scienta.2015.11.015.

    • Search Google Scholar
    • Export Citation
  • Singh MC, Kachwaya DS, Kalsi K. 2018. Soilless cucumber cultivation under protective structures in relation to irrigation coupled fertigation management, economic viability and potential benefits—a review. Int J Curr Microbiol Appl Sci. 7(3):24512468. https://doi.org/10.20546/ijcmas.2018.703.286.

    • Search Google Scholar
    • Export Citation
  • Singh MC, Singh J, Pandey S, Mahay D, Srivastava V. 2017a. Factors affecting the performance of greenhouse cucumber cultivation-a review. Int J Curr Microbiol Appl Sci. 6(10):23042323. https://doi.org/10.20546/ijcmas.2017.610.273.

    • Search Google Scholar
    • Export Citation
  • Singh MC, Singh J, Singh K. 2017b. Optimal operating microclimatic conditions for drip fertigated cucumbers in soilless media under a naturally ventilated greenhouse. Indian J Ecol. 44(4):821826.

    • Search Google Scholar
    • Export Citation
  • Song XM, Bai P, Ding JP, Li JM. 2021. Effect of vapor pressure deficit on growth and water status in muskmelon and cucumber. Plant Sci. 303:110755. https://doi.org/10.1016/j.plantsci.2020.110755.

    • Search Google Scholar
    • Export Citation
  • Xiaolei S, Zhifeng W. 2004. The optimal leaf area index for cucumber photosynthesis and production in plastic greenhouse. Acta Hortic. 633:161165. https://doi.org/10.17660/ActaHortic.2004.633.19.

    • Search Google Scholar
    • Export Citation
  • Yamazaki K. 1982. Nutrient solution culture. Pak-kyo Co., Tokyo, Japan.

  • Yan H, Huang S, Zhang C, Gerrits MC, Wang G, Zhang J, Zhao B, Acquah SJ, Wu H, Fu H. 2020. Parameterization and application of Stanghellini model for estimating greenhouse cucumber transpiration. Water. 12(2):517. https://doi.org/10.3390/w12020517.

    • Search Google Scholar
    • Export Citation
  • Yang XS, Short TH, Fox RD, Bauerle WL. 1990. Transpiration, leaf temperature and stomatal-resistance of a greenhouse cucumber crop. Agric Meteorol. 51(3–4):197209. https://doi.org/10.1016/0168-1923(90)90108-I.

    • Search Google Scholar
    • Export Citation
Hyungmin Rho Department of Horticulture and Landscape Architecture, National Taiwan University, Taipei 10617, Taiwan

Search for other papers by Hyungmin Rho in
Google Scholar
Close
,
Jung Su Department of Horticultural Science, College of Agricultural & Life Science, Kyungpook National University, Daegu 41566, South Korea; and Institute of Agricultural Science and Technology, Kyungpook National University, Daegu 41566, Korea

Search for other papers by Jung Su in
Google Scholar
Close
 Jo
,
Ha Seon Sim Department of Horticultural Science, College of Agricultural & Life Science, Kyungpook National University, Daegu 41566, South Korea

Search for other papers by Ha Seon Sim in
Google Scholar
Close
,
Yu Hyun Moon Department of Horticultural Science, College of Agricultural & Life Science, Kyungpook National University, Daegu 41566, South Korea

Search for other papers by Yu Hyun Moon in
Google Scholar
Close
,
Ui Jeong Woo Department of Horticultural Science, College of Agricultural & Life Science, Kyungpook National University, Daegu 41566, South Korea

Search for other papers by Ui Jeong Woo in
Google Scholar
Close
, and
Sung Kyeom Kim Department of Horticultural Science, College of Agricultural & Life Science, Kyungpook National University, Daegu 41566, South Korea; and Institute of Agricultural Science and Technology, Kyungpook National University, Daegu 41566, Korea

Search for other papers by Sung Kyeom Kim in
Google Scholar
Close

Contributor Notes

This study was carried out with financial assistance from the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, and Forestry (IPET) through the Technology Development Program for the Ministry of Agriculture, Food and Rural Affairs (MAFRA), South Korea (Project No. 421001-03).

S.K.K. is the corresponding author. E-mail: skkim76@knu.acr.kr.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 740 740 43
PDF Downloads 560 560 41
  • Fig. 1.

    Air temperature and relative humidity (A), root zone environment (B), and relationship between radiation intensity and vapor pressure deficit (C and D) during the experiment.

  • Fig. 2.

    The transpiration measurement device (A) and a schematic diagram of the device (B) used in this experiment.

  • Fig. 3.

    Irrigation amount, drainage amount, and the number of irrigations during the experiment.

  • Fig. 4.

    Comparison of accumulated transpiration amounts according to various environmental conditions. Rad, Acc-rad, VPD, and Acc-Tr indicate radiation intensity, accumulated radiation, vapor pressure deficit, and accumulated transpiration, respectively.

  • Fig. 5.

    Regression curve of leaf area index development according to the number of days after transplanting.

  • Fig. 6.

    Fitting of nonlinear regression curves between transpiration and environmental factors.

  • Fig. 7.

    Comparisons of the measured and estimated accumulated transpiration by each transpiration model during the experiment periods.

 

Advertisement
Longwood Gardens Fellows Program 2024

 

Advertisement
Save