Estimation of Photosynthesis Loss Due to Greenhouse Superstructures and Shade Nets: A Case Study with Paprika and Tomato Canopies

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  • 1 IoP Collaborative Creation Center, Kochi University, 200, Otsu, Monobe, Nankoku City, Kochi 783-8502, Japan
  • | 2 Faculty of Agriculture and Marine Sciences, Kochi University, 200, Otsu, Monobe, Nankoku City, Kochi 783-8502, Japan
  • | 3 Future Farms Solutions, Avenida de la Innovación 15, 04131 Almería, Spain
  • | 4 IoP Collaborative Creation Center, Kochi University, 200, Otsu, Monobe, Nankoku City, Kochi 783-8502, Japan

In horticultural greenhouses, the photosynthetic photon flux density (I) is inevitably lower than that outside because of interference from greenhouse superstructures (e.g., reflection and absorption of radiation by greenhouse coverings and superstructures). In addition, during hot seasons in many regions, I can be lowered by shade nets installed to reduce excessive radiation. These reductions in I can cause a decrease in the canopy photosynthetic rate (Ac), potentially leading to crop yield losses. This study investigated to what extent Ac is reduced inside a modern greenhouse and under a shade net in comparison with that outside. A simple Ac model (i.e., canopy-scale photosynthesis-light curves) was parameterized based on the measurements of Ac and I for paprika and tomato canopies using the open-chamber method. In addition, based on the measurements of I, linear regression models were derived that related outside I (Iout) with I inside arch-roofed, single-span greenhouses [enveloped with a diffuse ethylene tetrafluoroethylene (ETFE) film; Iin] and I under shade nets (composed of aluminum and polyester strips; Ish). An Ac simulation using these models indicated that on a typical sunny summer day in Japan, Ac inside the greenhouses and under the shade nets (Ac,in and Ac,sh, respectively) corresponded to 91% and 52% of Ac outside (Ac,out) for the paprika canopy (for the tomato canopy, Ac,in and Ac,sh corresponded to 90% and 48% of Ac,out, respectively). The simulated Ac loss was more serious on a cloudy day because of the linear Ac-I response under low I conditions (Ac,in/Ac,out and Ac,sh/Ac,out were 69% and 13%, respectively, for the paprika canopy). The loss of Ac,sh may be alleviated by limiting the shading period to only midday hours.

Abstract

In horticultural greenhouses, the photosynthetic photon flux density (I) is inevitably lower than that outside because of interference from greenhouse superstructures (e.g., reflection and absorption of radiation by greenhouse coverings and superstructures). In addition, during hot seasons in many regions, I can be lowered by shade nets installed to reduce excessive radiation. These reductions in I can cause a decrease in the canopy photosynthetic rate (Ac), potentially leading to crop yield losses. This study investigated to what extent Ac is reduced inside a modern greenhouse and under a shade net in comparison with that outside. A simple Ac model (i.e., canopy-scale photosynthesis-light curves) was parameterized based on the measurements of Ac and I for paprika and tomato canopies using the open-chamber method. In addition, based on the measurements of I, linear regression models were derived that related outside I (Iout) with I inside arch-roofed, single-span greenhouses [enveloped with a diffuse ethylene tetrafluoroethylene (ETFE) film; Iin] and I under shade nets (composed of aluminum and polyester strips; Ish). An Ac simulation using these models indicated that on a typical sunny summer day in Japan, Ac inside the greenhouses and under the shade nets (Ac,in and Ac,sh, respectively) corresponded to 91% and 52% of Ac outside (Ac,out) for the paprika canopy (for the tomato canopy, Ac,in and Ac,sh corresponded to 90% and 48% of Ac,out, respectively). The simulated Ac loss was more serious on a cloudy day because of the linear Ac-I response under low I conditions (Ac,in/Ac,out and Ac,sh/Ac,out were 69% and 13%, respectively, for the paprika canopy). The loss of Ac,sh may be alleviated by limiting the shading period to only midday hours.

In modern horticultural crop production, wherever root-zone environments are optimally managed, canopy photosynthesis becomes the primary determinant of crop growth and yield, as suggested by numerous CO2 enrichment experiments (Ainsworth and Long, 2005; Nomura et al., 2021b; Prior et al., 2011). Thus, farm managers and growers aim to optimize the canopy photosynthetic rate (Ac) through various means (e.g., by controlling the aboveground greenhouse environment and leaf area). To optimize Ac, it is important to have a reasonable estimate of Ac under a given environmental condition.

Sufficient photosynthetically active radiation (PAR; radiation within a wavelength of 400–700 nm) is indispensable for canopy photosynthesis, as it drives the “light reaction,” the first step in the photosynthetic reaction. However, in a greenhouse, incoming PAR can be quite different from the PAR outside, depending on the type of greenhouse cover (Baeza et al., 2020; Giacomelli and Roberts, 1993; Pearson et al., 1995). Depending on the transmissivity of the cover material, the photosynthetic photon flux density (PPFD; I) in the greenhouse can become considerably lower than that outside. In addition, some cover materials have diffusing properties, making incoming radiation more uniform (Hemming et al., 2008) and improving Ac (Li et al., 2014). Furthermore, greenhouse superstructures (e.g., ridges, trusses, and beams) can block and reduce the incoming PAR (Giacomelli and Roberts, 1993; von Elsner et al., 2000). These complicating factors and their interactions determine I inside a greenhouse.

In temperate and subtropical regions (e.g., many parts of Japan), incoming radiation can bring excessive energy into a greenhouse from late spring to early autumn. This radiation not only warms up the greenhouse but also reaches human workers, raising the apparent temperature (i.e., the temperature equivalent perceived by humans) to an unbearable point. To reduce such excessive radiation, modern greenhouses are often equipped with movable shade nets below the roofs (Castellano et al., 2008). These shade nets can be extended to cover the entire greenhouse area and create a shade, decreasing both the actual and apparent temperatures. Such shade treatment is expected to become more important in greenhouse horticulture, as global warming is rapidly progressing (Bisbis et al., 2019). An obvious drawback of these shade nets is the reduction in PAR and Ac (Hernández et al., 2015, 2019; Kitta and Katsoulas, 2020; López-Marín et al., 2012; Masabni et al., 2016). Because Ac is closely related to crop yield, inappropriate use of shade nets may result in reduced crop yield. In practice, however, shade nets are often used without considering their effects on Ac, and quantitative information on the loss of Ac due to shade-net operations is lacking.

The purpose of the present study was to assess to what extent the Ac of horticultural fruit vegetables is reduced due to greenhouse superstructures and shade nets in comparison with that outside. To achieve this objective, we first investigated the Ac of paprika (Capsicum annuum L.) and tomato (Solanum lycopersicum L.) canopies with the commonly used open-chamber method and then obtained model parameters for a simple empirical canopy photosynthesis model representing the response of Ac to I. Next, we measured I outside and inside greenhouses and under shade nets (Iout, Iin and Ish, respectively) and derived regression models that related Iout with Iin and Ish. The effects of shade nets on greenhouse air temperature were also evaluated. Finally, using the empirical model of the relationship between Ac and I, we evaluated the loss of Ac due to the greenhouse superstructures and shade nets.

Materials and Methods

Greenhouse and experimental setup

All experiments were conducted in the Nankoku Field located at Monobe Campus, Kochi University (33°33′4″N, 133°40′37″E). Two identical, arch-roofed single-span greenhouses were used (Fig. 1). Each of the two greenhouses was 50 m long, 9 m wide, and 5.2 m high (at the center) and north-south oriented (i.e., the ridges of the greenhouses ran north-south). These two greenhouses were constructed side by side and were only 4.25 m away from each other. These greenhouses were enveloped with a diffuse ETFE film (F-CLEAN Diffused; AGC Green-Tech Co., Ltd., Tokyo, Japan). This covering material has high transmissivity for PAR with haze (i.e., a light-diffusing property) to improve canopy photosynthesis (hemispherical transmission = 0.81; Hemming et al., 2008). The covering material was less than 2 years old at the time of the experiment. The two greenhouses were equipped with movable shade nets (TEMPA 6562 D; Ludvig Svensson, Kinna, Sweden) on the inside, ≈3 m above the ground. The shade nets are composed of aluminum and polyester strips (4 mm wide each) woven in a stripe pattern at a ratio of 5:3 (aluminum: polyester). According to the manufacturer's specifications, the transmissivities for direct and diffuse light are 35% and 32%, respectively. These shade nets can be extended/folded similarly to curtains with motors and can cover the entire ground area of each greenhouse. For ventilation and temperature control, each greenhouse was equipped with motor-operated top and side windows along the north-south direction; the top windows were 0.85 m wide and equipped on both sides of the roof ridge, and the side windows were 2.5 m high and equipped on the east and west sides of each greenhouse. During this study, the top and side windows were all fully open, and the window openings were covered with insect screens with the mesh size of 0.4 mm. The air inside each greenhouse was mixed using four 36-cm diameter electric fans (AB363NA; Fulta Electric Machinery Co., Ltd, Aichi, Japan) installed at the beams. These fans were switched on and off at 10-min intervals.

Fig. 1.
Fig. 1.

Experimental greenhouses. (A) Plan view of the two experimental greenhouses. (B) Frontal view of one of the greenhouses. The arrows over the shade nets indicate that these shade nets can be extended (closed) or folded (opened). The dotted lines at the side and top windows represent insect screens with a mesh size of 0.4 mm, whereas the broken lines at the side windows represent a windscreen with a mesh size of 6 mm. All lengths in the figures are in meters. (C) Photograph of the experimental greenhouses taken from the northwest side.

Citation: HortScience 57, 3; 10.21273/HORTSCI16384-21

Plant materials and cultivation conditions

Paprika and tomato canopies were cultivated hydroponically in each of the two greenhouses. In one greenhouse (Greenhouse 1), tomato plants (S. lycopersicum L., variety ‘Momotaro Peace’) were cultivated using the nutrient film technique (NFT) with a planting density of 1.9 plants/m2 (180 cm between rows and 20 cm between plants within a row). These tomato plants were seeded on 28 July 2020, and transplanted to NFT panels on 6 Aug. 2020. The plants were pruned to a single shoot and led with support strings tied to one of two parallel wires 2.0 m above the ground. Once the plants reached the wire height, the plants were leaned forward and lowered by ≈50 cm by releasing the support strings from the wires. These actions of leaning and lowering move plants forward along the wire. Once a plant reached the edge of the row, the plant was turned back using the other wire. The leaves were pruned once all the fruits on the nearest upper truss were harvested. The tomato plants were supplied with the OAT SA nutrient solution (OAT Agrio Co., Ltd., Tokyo, Japan) with an electrical conductivity (EC) of 2.2 dS⋅m−1. The nutrient supply was switched on and off every 5 min to provide the plant roots with sufficient oxygen.

In the other greenhouse (Greenhouse 2), paprika plants (C. annuum L., variety ‘Fairway’) were cultivated on rockwool cubes with a planting density of 1.3 plants/m2 (180 cm between rows and 30 cm between plants within a row). These paprika plants were seeded on 21 Aug. 2020, and transplanted onto the rockwool cubes on 10 Sept. 2020. The plants were pruned to two shoots, which were led with support strings tied to two parallel wires 2.0 m above the ground. Similar to the tomato canopy, a nutrient solution was prepared following the OAT SA prescription with an EC of 1.8 dS⋅m−1. This nutrient solution was supplied to the paprika plants intermittently every hour during the daytime and every 2 hours during the nighttime. Both the tomato and paprika cultivations ended on 8 July 2021.

Measurements

Canopy photosynthesis.

The Ac of paprika and tomato canopies was measured to construct a model of canopy-scale photosynthetic responses to I. The measurements were performed according to the open-chamber method, which was modified from the method described by Nomura et al. (2020) (Fig. 2). Four open chambers, each being 1.2 m long, 1.2 m wide, and 2.4 m high, were constructed; two chambers were used for the paprika canopy, and the other two chambers were used for the tomato canopy. Four individual paprika plants were enclosed per chamber, and seven individual tomato plants were enclosed per chamber. Each chamber was covered with F-CLEAN Clear (AGC Green-Tech Co., Ltd.). The measurements continued for 18 d, from 19 June to 6 July 2021. The leaf area index (LAI) of paprika and tomato canopies was estimated with an LAI-2200C (LI-COR Biosciences, Lincoln, NE) on 14 June 2021. The LAI values were 1.73 and 1.56 for the paprika and tomato canopies, respectively. I at the top of the canopies was measured with a PPFD sensor (PAR-02; PREDE, Tokyo, Japan) installed inside each chamber during the chamber applications. Ambient air was continuously introduced into each chamber with a blower (San Ace 9CRA0912P0G001; Sanyo Denki, Tokyo, Japan), and Ac (μmol⋅m−2⋅s−1 based on ground area) was calculated according to the difference in CO2 concentrations (Ca) at the chamber inlet and outlet (Ca,in and Ca,out, respectively) multiplied by the airflow rate (Q; mol⋅s−1):
Ac=Q(Ca,inCa,out)/S,
where S is the ground area. S was calculated as the plant row interval (1.8 m) multiplied by the chamber length (1.2 m) to include the aisle width in the calculation of S (i.e., S = 2.16 m2). Q was set to 45 m3⋅h−1, which corresponded to ≈5 min for the chamber air to be replaced entirely. In each greenhouse, Ca,in and Ca,out values, along with the water vapor concentrations at the inlet and outlet of each chamber (Wa,in and Wa,out), were measured with an infrared gas analyzer (LI850, LI-COR Biosciences), whose internal pump brought in the air at the two chambers' inlets and outlets sequentially at 1-min intervals. The air temperature (Ta) was also measured at the inlet and outlet of each chamber using T-type thermocouples. The atmospheric vapor pressure deficit (VPD) was computed based on the Wa and Ta values.
Fig. 2.
Fig. 2.

Schematic view of the open-chamber system for measuring the net photosynthetic rates (Ac) of a crop canopy. Ac was estimated by multiplying the airflow rate with the difference in the CO2 concentrations between inlet and outlet air. The inlet and outlet air was sampled sequentially through switchable air sampling paths, and the CO2 concentrations were measured with an infrared gas analyzer. The infrared gas analyzer was connected to two chambers such that the Ac measurements were duplicated (only one chamber is shown in the figure).

Citation: HortScience 57, 3; 10.21273/HORTSCI16384-21

PPFD and other environmental elements.

I was measured outside and inside greenhouses and under shade nets to evaluate radiation losses due to both the greenhouse superstructures and shade nets. A PPFD sensor (PAR-02) was installed in the northwestern part of each of the two greenhouses on top of a horizontal beam ≈2.5 m above the ground (see Fig. 1A and B). This position was chosen to avoid shade created by the folded shade nets during periods of nonuse. The analog outputs from each PPFD sensor were recorded every 30 seconds using a datalogger (LR8431; Hioki E.E. Corporation, Nagano, Japan) and later averaged over 30 min. Another PPFD sensor (PAR-02) was installed in the middle of an open field, ≈100 m away from the greenhouses. The analog outputs from this outside sensor were recorded every 2 min using a datalogger (MCR-4V; T&D, Nagano, Japan) and later averaged over 30 min. The measurements were conducted for 10 d in 2021: 17, 18, 22, 23, 24, 25, and 31 July, and 1, 7, and 8 Aug. During the experiment, one greenhouse was shaded with the shade net, while the other was unshaded, and the shaded and unshaded greenhouses were switched each day to prevent any biases associated with differences in the two greenhouses. This operation of the shade nets was performed after sunset. Linear regression coefficients relating Iout and Iin as well as Iout and Ish were derived based on the I values obtained outside (Iout) and inside the greenhouses (Iin) and under the shade nets in the greenhouses (Ish). In addition, each of the greenhouses was equipped with a set of environmental sensors (i.e., a pair of thermistors measuring dry- and wet-bulb temperatures and a CO2 sensor; EyeFARM-box EB-500, Nippo, Saitama, Japan) in the center of the greenhouse.

Calculations

Canopy photosynthesis model.

Based on the Ac and I measurements using the open chambers, light-photosynthetic response curves (Ac-I curves) at the canopy scale were obtained by fitting a nonrectangular hyperbola [Eq. (2)], which has been used to express light-photosynthetic response curves at the single-leaf scale (e.g., Hikosaka et al., 2016; Thornley, 2002) as
Ac=ϕI+Amax(ϕI+Amax)24ϕIAmaxθ2θR,
where Amax is the light-saturated canopy photosynthetic rate, ϕ is the initial slope of the Ac-I curve, θ is a convexity term, and R is the respiration rate in light. The values of Amax, ϕ, θ, and R were obtained by fitting Eq. [2] to the Ac-I relationships measured using the chamber system. This fitting was performed by minimizing the sum of squared errors between the measured and predicted Ac values using the Python lmfit package (version 1.0.1; Newville et al., 2014) based on the Levenberg-Marquardt method.

It should be noted that the use of the regression model [Eq. (2)] is not intended for constructing a predictive canopy photosynthesis model for use in other situations; we used the regression model to provide estimates of Ac based on our specific observations of Ac-I relationships. Thus, model parameters obtained through curve fitting will not be applicable to other cases if environmental conditions (e.g., temperature, humidity, and CO2 concentration) and the canopy structure (e.g., LAI) are widely different.

Simulation of canopy photosynthesis.

The effects of shade nets on Ac were evaluated quantitatively using Eq. [2] with a set of parameters obtained from the curve fittings. Daytime responses of Ac to Iout, Iin, and Ish were simulated based on Iout values in typical sunny and cloudy days in the summer in Japan (i.e., Iout measured on 18 and 31 July 2021). In this simulation, Iin and Ish were predicted based on the linear relationships between Iout and Iin and between Iout and Ish, respectively.

Results and Discussion

Canopy photosynthetic rates

Figure 3 shows the changes in the Ac of the paprika canopy, along with the environmental elements measured inside the two chambers. During the daytime, I, Ta, and VPD increased, whereas Ca decreased due to photosynthetic CO2 uptake by the canopy inside the greenhouse. The daytime averages of I, Ca, Ta, and VPD in one of the two chambers (Chamber 1 in Fig. 3) were 273 ± 269 (µmol·m−2·s−1), 437 ± 54 (µmol·mol−1), 35 ± 5 (°C), and 2.08 ± 1.0 (kPa), respectively (the values after the plus-minus signs represent the standard deviations). As shown in Fig. 3, there were no considerable differences between the environments in the duplicated chambers. As a result of these diurnal changes in the environmental conditions, Ac changed dynamically during the daytime. The tomato canopy inside the chambers had environmental conditions similar to those of the paprika canopy (Fig. 4); the daytime averages of I, Ca, Ta, and VPD in one of the chambers (Chamber 1 in Fig. 4) were 316 ± 307 (µmol·m−2·s−1), 428 ± 27 (µmol·mol−1), 35 ± 5 (°C), and 2.3 ± 1.1 (kPa), respectively.

Fig. 3.
Fig. 3.

Changes in the (A) photosynthetic photon flux density (PPFD; I), (B) CO2 concentration (Ca), (C) air temperature (Ta), and (D) vapor pressure deficit (VPD) inside the chambers, and (E) canopy photosynthetic rate (Ac) of the paprika canopy. Measurements were duplicated using two open chambers.

Citation: HortScience 57, 3; 10.21273/HORTSCI16384-21

Fig. 4.
Fig. 4.

Changes in the (A) photosynthetic photon flux density (PPFD; I), (B) CO2 concentration (Ca), (C) air temperature (Ta), and (D) vapor pressure deficit (VPD) inside the chambers, and (E) canopy photosynthetic rate (Ac) of the tomato canopy. Measurements were duplicated using two open chambers.

Citation: HortScience 57, 3; 10.21273/HORTSCI16384-21

Figure 5 shows the Ac-I curves for the paprika and tomato canopies. The Ac-I curves for both species showed the typical shape of photosynthetic nonlinear rectangular hyperbolae represented by Eq. [2]. The duplicate measurements in each canopy provided almost identical results (i.e., in Fig. 5A and B, the circle and triangle markers indistinguishably overlap), suggesting homogeneity in the paprika and tomato canopies. Ac increased almost linearly with I up to I = 450 µmol·m−2·s−1 and then showed signs of light saturation. This response of Ac to I can be explained by two distinct limiting processes, namely, the regeneration and carboxylation of ribulose-1,5-bisphosphate (RuBP) (Farquhar et al., 1980). The linear increase in Ac in the low I region indicates that the photosynthetic process was limited by RuBP regeneration, which ultimately depends on incoming I. In contrast, the apparent saturation of Ac in the high-I region indicates that the photosynthetic process was limited by RuBP carboxylation, which depends mainly on the CO2 concentration. During the experiment, the average daytime Ca,in value was ≈430 µmol·mol−1, which is within the normal CO2 concentration range observed in a greenhouse. If the CO2 concentration was artificially elevated, the saturating I would have been higher than 450 µmol·m−2·s−1 because a higher CO2 concentration alleviates the CO2 limitation of Ac that occurs under high I conditions (Stitt, 1991). However, it should be noted that Ac continued to increase slightly even over I = 450 µmol·m−2·s−1. This slight increase in Ac can be attributed to the lower leaves in the canopy, which are not light saturated due to the shading of the upper leaves (Nomura et al., 2021b). With an increase in I at the top of the canopy, these lower leaves could have received stronger I and increased the Ac.

Fig. 5.
Fig. 5.

Relationships between the canopy photosynthetic rate (Ac) and photosynthetic photon flux density (PPFD; I) measured for (A) paprika and (B) tomato canopies. For each fruit vegetable, measurements were duplicated in two different canopies using two open chambers (the circles and triangles indicate the different canopies). The curves (solid lines) were obtained by fitting Eq. [2] to the observations. The measurements were conducted from 19 June to 6 July 2021.

Citation: HortScience 57, 3; 10.21273/HORTSCI16384-21

During the experiment, the average daytime temperature inside the chambers was ≈35 °C and sometimes exceeded 40 °C, with a maximum of ≈45 °C (see Figs. 3 and 4). Such high temperatures, however, did not cause any explicit Ac drops, which occur at the molecular and single-leaf scales (Mathur et al., 2014; Medlyn et al., 2002; Wise et al., 2004).

Table 1 summarizes the parameter values of the nonrectangular hyperbola [Eq. (2)] obtained by least-square fitting to the measured Ac-I curves for the paprika and tomato canopies shown in Fig. 5. The estimated standard errors of the model parameters were small compared with the best-fit parameters (the standard error/best-fit parameter was less than 3.2%), indicating that the model parameters were identified well. The optimized parameters slightly differed between the two species, which can be attributed to differences in 1) the photosynthetic properties of individual leaves in each canopy [e.g., different leaf-scale parameters such as Vcmax and Jmax (Farquhar et al., 1980; von Caemmerer, 2000)]; 2) the distributions of photosynthetic properties within each canopy (Hirose and Werger, 1987); and 3) canopy structure, including the LAI, leaf angle distribution, and leaf clumping (Chen et al., 2012; Lawlor, 1995; Wang et al., 2007).

Table 1.

Parameter values of the nonrectangular hyperbola [Eq. (2)] obtained by least-square fitting to the measured Ac-I curves for the paprika and tomato canopies (Fig. 5). The estimates of the standard errors of the parameters are also shown after the plus-minus signs.

Table 1.

Effects of the greenhouse superstructures and shade nets on incoming PPFD

Figure 6 shows daily changes in Iout, Iin, and Ish. Iout underwent diurnal changes caused by the movement of the sun and clouds. The first 2 d of the experimental period (17 and 18 July) were cloudy and rainy, but the other days were largely sunny. Iout reached a maximum value of 2400 µmol·m−2·s−1 around noon on 7 Aug.

Fig. 6.
Fig. 6.

Daily changes in the photosynthetic photon flux density (PPFD; I) measured outside (Iout; solid line) and inside the greenhouses (Iin; broken line) and under the shade nets in the greenhouses (Ish; dotted line). The measurements were conducted during four periods between 17 July and 8 Aug. 2021; these periods are separated in the figure by the solid vertical lines. The gray-shaded regions indicate nighttime. Thirty-minute averages are shown.

Citation: HortScience 57, 3; 10.21273/HORTSCI16384-21

In comparison with Iout, Iin was much smaller. This reduction in Iin was partly due to the reflection and absorption of the incoming PAR by the cover material. In addition, Iin was reduced because of interceptions by the greenhouse structural elements (e.g., the ridges, trusses, and folded shade nets), which prevented some of the PAR transmitted by the cover from reaching the PPFD sensor (Matsuda et al., 2020). Because the cover material has a diffusing property that scatters photon flux in various directions, any greenhouse structures above the PPFD sensor could have reduced Iin, even if these structures did not occur along the straight line between the sun and the PPFD sensor. Iin showed a strong linear relationship (R2 = 0.95) with Iout (Fig. 7). This strong linear relationship also may be attributed to the diffusing property of the cover material, which caused the incoming photon flux to be largely uniform. The regression coefficient (i.e., slope) between Iin and Iout was 0.61, indicating that I was reduced by almost 40% due to the greenhouse cover and structures. This Iin/Iout value (0.61) is substantially lower than a laboratory-measured transmissivity value of 0.81 (hemispherical transmission; Hemming et al., 2008). These results indicate that a considerable amount of incoming PAR was blocked by the greenhouse superstructures. In addition, it is possible that the transmission of the cover material was reduced by tiny particles (e.g., dust) attached to the cover material. The value of 0.61 was consistent with that found in a previous study (Giacomelli and Roberts, 1993) reporting the average daily transmission of PAR measured inside greenhouses with four different covering materials (i.e., single-layer glass, acrylic, double-layer glass, and double-layer polyethylene, whose transmissions were 0.56, 0.55, 0.56, and 0.45, respectively, near the plant canopy).

Fig. 7.
Fig. 7.

Relationships between the photosynthetic photon flux density (PPFD; I) measured outside the greenhouses (Iout), inside both greenhouses (Iin) and under the shade nets in the greenhouses (Ish). These relationships were obtained based on the 30-min average values. The regression lines were drawn with forced zero intercepts. A 1:1 line is displayed for reference.

Citation: HortScience 57, 3; 10.21273/HORTSCI16384-21

The shade nets further reduced the incoming I; Ish was only 19% of Iout (Fig. 7) and had a strong linear relationship with Iout (R2 = 0.96). This strong linear relationship can be used to predict the amount of Ish based on Iout in this specific setting. Ish was also linearly correlated with Iin; Ish was only 31% of Iin (not shown in the figures). This value was consistent with the manufacturer's specifications, in which the transmissivities for direct light and diffuse light were 35% and 32%, respectively.

Effects of the shade nets on the greenhouse temperature

Figure 8A shows the daily changes in the temperature (T) measured outside the greenhouses (Tout) and inside the greenhouses without (Tin) and with the shade nets (Tsh). A clear diurnal pattern in T was found; upon sunrise, Tout, Tin, and Tsh began to steeply increase from ≈23 °C to more than 30 °C during the midday hours (i.e., between 1000 and 1500 hr) and then gradually decreased until the next sunrise. Throughout the experimental period, T showed an overall increasing trend.

Fig. 8.
Fig. 8.

(A) Daily changes in temperature (T) measured outside (Tout; solid line) and inside the greenhouses (Tin; broken line) and under the shade nets in the greenhouses (Tsh; dotted line). (B) Increases in T inside the greenhouses in comparison with the outside T (i.e., TinTout and TshTout for greenhouses with the shade nets and without the shade nets, respectively). (C) Differences in T with and without the shade nets (TinTsh). The measurements were performed during four periods between 17 July and 8 Aug. 2021; these periods are separated in the figure by the solid vertical lines. The gray-shaded regions indicate nighttime.

Citation: HortScience 57, 3; 10.21273/HORTSCI16384-21

Figure 8B shows the differences in T inside and outside the greenhouses. Both TinTout and TshTout were positive during the daytime, indicating that the air inside the greenhouses was warmer than the outside air. During the midday hours, the average difference in T outside and inside the greenhouses without shade nets (TinTout) was 2.0 °C, with a maximum value of 3.5 °C. With the application of shade nets, the midday average difference in T (TshTout) was reduced to 1.0 °C, with a maximum value of 2.6 °C, indicating an air-cooling effect of the shade nets.

Figure 8C shows the differences in T inside the greenhouses with and without shade nets (i.e., TinTsh). During the midday hours, TinTsh was almost always positive, with an average value of 0.98 °C, indicating that the greenhouse air without shade nets was nearly 1 °C higher than that when shade nets were present. This temperature reduction by the shade nets was caused by the improvement of greenhouse heat balance due to the reduction in incoming solar radiation, as indicated in Fig. 7, which shows that incoming solar radiation energy can be reduced by 81% when the shade nets are used (assuming that I is proportional to the energy flux density in W·m−2). Because incoming solar radiation is the dominant heat source warming the greenhouse air during the summer (Kittas et al., 2005), such a considerable reduction in radiation by shade nets can contribute to a reduction in T. In addition, such a reduction in solar radiation by the shade nets would cause a reduction in the apparent T (i.e., the temperature equivalent perceived by humans) experienced by workers inside the greenhouse. In contrast to the positive daytime TinTsh, the nighttime TinTsh was negative (i.e., the air temperature was higher when the shade nets were present than when they were absent), indicating that the shade nets trap longwave radiation from the ground below. This warming effect of shade nets can help maintain a higher nighttime T in the winter (Ahemd et al., 2016).

Effects of greenhouse superstructures and shade nets on canopy photosynthesis

Using Eq. [2] and the parameters obtained from the least-square curve fitting, we estimated to what extent Ac would be reduced due to the greenhouse superstructures and shade nets on typical sunny and cloudy summer days (Tables 2 and 3, respectively). As shown in Fig. 9 (i.e., a simulation on the sunny day), the effects of the greenhouse superstructures and shade nets on I were remarkable; only 61% and 19% of Iout was measured below the greenhouse superstructures and shade nets, respectively (Table 2). However, as shown in Fig. 9B for the paprika canopy, the simulated effects of the greenhouse superstructures and shade nets on Ac were much milder on a sunny summer day (31 July 2021); Ac in the greenhouse (Ac,in) and under the shade nets (Ac,sh) corresponded to 91% and 52% of the outside Ac (Ac,out), respectively (for the tomato canopy, Ac,in and Ac,sh corresponded to 90% and 48% of Ac,out; see Table 2). These results were attributed to the saturating Ac-I responses (Fig. 5), in which Ac was saturating over I = 450 µmol·m−2·s−1. In comparison with this threshold value, Iout is almost always excessive on a typical sunny summer day in Japan except in the very early morning and evening. Because of this saturating nature of photosynthesis, the 39% reduction in I by the greenhouse superstructures caused only an ≈9% reduction in Ac. However, the reduction in Ac caused by the shade nets was not marginal; for the paprika and tomato canopies, Ac,sh was only ≈57% and 53% of Ac,in, respectively (Table 2).

Fig. 9.
Fig. 9.

Simulation of typical diurnal responses of canopy photosynthetic rates (Ac) to photosynthetic photon flux density (PPFD; I). I inside a greenhouse and under a shade net was estimated from outside I measured on 31 July 2021, using the linear regression equations shown in Fig. 7. With these I values as the inputs, Ac was estimated using Eq. [2] with the parameters obtained for the paprika canopy (Table 1).

Citation: HortScience 57, 3; 10.21273/HORTSCI16384-21

Table 2.

Effects of the greenhouse superstructures and shade nets on the photosynthetic photon flux density (PPFD; I) and canopy photosynthetic rates (Ac) simulated using Eq. [2] and parameters for the paprika and tomato canopies on a sunny day. Cumulative values of I and Ac during the daytime are shown in mol⋅m−2⋅d−1 (middle columns), and these values were compared as proportions (right columns). I values measured outside on 31 July 2021 were used as the inputs.

Table 2.

Compared with the high I conditions simulated in Fig. 9 and Table 2, the loss of Ac due to greenhouse superstructures and shade nets is more serious under low I conditions. Ac,in/Ac,out simulated on a cloudy day (18 July 2021, see Fig. 6) was only 69% for the paprika canopy (Table 3). This value was much lower than that for the sunny day (i.e., 91%). The lower value of Ac,in/Ac,out on a cloudy day (i.e., low I) is not surprising, as Ac is sensitive to I under low I conditions (i.e., Ac responds to I linearly under low I conditions; see Fig. 5). Thus, the negative effect of greenhouse superstructures on Ac would be more considerable in winter, where I tends to be much lower than that in summer (Marcelis et al., 2006). The application of the shade nets can further reduce Ac; on the cloudy day, Ac,sh/Ac,out was simulated to be only 13% for both the paprika and tomato canopies (Table 3).

Table 3.

Effects of the greenhouse superstructures and shade nets on I and Ac simulated for a cloudy day. I values measured outside on 18 July 2021 were used as the inputs. The other simulation conditions were the same as those for Table 2.

Table 3.

A good strategy for using shade nets is to use them only during the high I period (Kläring and Krumbein, 2013). For example, if the usage of the shade nets was limited to 0800 until 1600 hr, Ac,sh could reach 76% and 71% of Ac,in for the paprika and tomato canopies on a sunny day (data not shown). Such a limitation of shade net usage needs to be considered together with the working environment of human workers, because working in a hot greenhouse environment is a demanding and often dangerous task. To optimize the use of shade nets, future research should involve a more sophisticated analysis integrating detailed models that can estimate the temperature effects on Ac (e.g., Nomura et al., 2021a) and physics-based greenhouse climate models (Sethi et al., 2013) that can simulate the dynamic change in temperature in the presence and absence of shade nets.

Literature Cited

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    • Search Google Scholar
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    • Search Google Scholar
    • Export Citation
  • López-Marín, J., Gálvez, A., González, A., Egea-Gilabert, C. & Fernández, J.A. 2012 Effect of shade on yield, quality and photosynthesis-related parameters of sweet pepper plants Acta Hort. 956 545 552 https://doi.org/10.17660/ActaHortic.2012.956.65

    • Search Google Scholar
    • Export Citation
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    • Search Google Scholar
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    • Search Google Scholar
    • Export Citation
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    • Search Google Scholar
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    • Search Google Scholar
    • Export Citation
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    • Search Google Scholar
    • Export Citation
  • Nomura, K., Takada, A., Kunishige, H., Ozaki, Y., Okayasu, T., Yasutake, D. & Kitano, M. 2020 Long-term and continuous measurement of canopy photosynthesis and growth of spinach Environ. Control Biol. 58 21 29 https://doi.org/10.2525/ecb.58.21

    • Search Google Scholar
    • Export Citation
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    • Search Google Scholar
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  • Sethi, V.P., Sumathy, K., Lee, C. & Pal, D.S. 2013 Thermal modeling aspects of solar greenhouse microclimate control: A review on heating technologies Sol. Energy 96 56 82 https://doi.org/10.1016/j.solener.2013.06.034

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Contributor Notes

This study was supported by a Cabinet Office grant-in-aid, the Advanced Next-Generation Greenhouse Horticulture by IoP (Internet of Plants), Japan, and JSPS KAKENHI Grant Number JP21K14946.

K.N. is the corresponding author. E-mail: jm-koichi.nomura@kochi-u.ac.jp.

  • View in gallery

    Experimental greenhouses. (A) Plan view of the two experimental greenhouses. (B) Frontal view of one of the greenhouses. The arrows over the shade nets indicate that these shade nets can be extended (closed) or folded (opened). The dotted lines at the side and top windows represent insect screens with a mesh size of 0.4 mm, whereas the broken lines at the side windows represent a windscreen with a mesh size of 6 mm. All lengths in the figures are in meters. (C) Photograph of the experimental greenhouses taken from the northwest side.

  • View in gallery

    Schematic view of the open-chamber system for measuring the net photosynthetic rates (Ac) of a crop canopy. Ac was estimated by multiplying the airflow rate with the difference in the CO2 concentrations between inlet and outlet air. The inlet and outlet air was sampled sequentially through switchable air sampling paths, and the CO2 concentrations were measured with an infrared gas analyzer. The infrared gas analyzer was connected to two chambers such that the Ac measurements were duplicated (only one chamber is shown in the figure).

  • View in gallery

    Changes in the (A) photosynthetic photon flux density (PPFD; I), (B) CO2 concentration (Ca), (C) air temperature (Ta), and (D) vapor pressure deficit (VPD) inside the chambers, and (E) canopy photosynthetic rate (Ac) of the paprika canopy. Measurements were duplicated using two open chambers.

  • View in gallery

    Changes in the (A) photosynthetic photon flux density (PPFD; I), (B) CO2 concentration (Ca), (C) air temperature (Ta), and (D) vapor pressure deficit (VPD) inside the chambers, and (E) canopy photosynthetic rate (Ac) of the tomato canopy. Measurements were duplicated using two open chambers.

  • View in gallery

    Relationships between the canopy photosynthetic rate (Ac) and photosynthetic photon flux density (PPFD; I) measured for (A) paprika and (B) tomato canopies. For each fruit vegetable, measurements were duplicated in two different canopies using two open chambers (the circles and triangles indicate the different canopies). The curves (solid lines) were obtained by fitting Eq. [2] to the observations. The measurements were conducted from 19 June to 6 July 2021.

  • View in gallery

    Daily changes in the photosynthetic photon flux density (PPFD; I) measured outside (Iout; solid line) and inside the greenhouses (Iin; broken line) and under the shade nets in the greenhouses (Ish; dotted line). The measurements were conducted during four periods between 17 July and 8 Aug. 2021; these periods are separated in the figure by the solid vertical lines. The gray-shaded regions indicate nighttime. Thirty-minute averages are shown.

  • View in gallery

    Relationships between the photosynthetic photon flux density (PPFD; I) measured outside the greenhouses (Iout), inside both greenhouses (Iin) and under the shade nets in the greenhouses (Ish). These relationships were obtained based on the 30-min average values. The regression lines were drawn with forced zero intercepts. A 1:1 line is displayed for reference.

  • View in gallery

    (A) Daily changes in temperature (T) measured outside (Tout; solid line) and inside the greenhouses (Tin; broken line) and under the shade nets in the greenhouses (Tsh; dotted line). (B) Increases in T inside the greenhouses in comparison with the outside T (i.e., TinTout and TshTout for greenhouses with the shade nets and without the shade nets, respectively). (C) Differences in T with and without the shade nets (TinTsh). The measurements were performed during four periods between 17 July and 8 Aug. 2021; these periods are separated in the figure by the solid vertical lines. The gray-shaded regions indicate nighttime.

  • View in gallery

    Simulation of typical diurnal responses of canopy photosynthetic rates (Ac) to photosynthetic photon flux density (PPFD; I). I inside a greenhouse and under a shade net was estimated from outside I measured on 31 July 2021, using the linear regression equations shown in Fig. 7. With these I values as the inputs, Ac was estimated using Eq. [2] with the parameters obtained for the paprika canopy (Table 1).

  • Ahemd, H.A., Al-Faraj, A.A. & Abdel-Ghany, A.M. 2016 Shading greenhouses to improve the microclimate, energy and water saving in hot regions: A review Scientia Hort. 201 36 45 https://doi.org/10.1016/j.scienta.2016. 01.030

    • Search Google Scholar
    • Export Citation
  • Ainsworth, E.A. & Long, S.P. 2005 What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2 New Phytol. 165 351 372 https://doi.org/10.1111/j.1469-8137.2004.01224.x

    • Search Google Scholar
    • Export Citation
  • Baeza, E., Hemming, S. & Stanghellini, C. 2020 Materials with switchable radiometric properties: Could they become the perfect greenhouse cover? Biosyst. Eng. 193 157 173 https://doi.org/10.1016/j.biosystemseng.2020.02.012

    • Search Google Scholar
    • Export Citation
  • Bisbis, M., Gruda, N. & Blanke, M. 2019 Securing horticulture in a changing climate—A mini review Horticulturae 5 56 https://doi.org/10.3390/horticulturae5030056

    • Search Google Scholar
    • Export Citation
  • Castellano, S., Scarascia Mugnozza, G., Russo, G., Briassoulis, D., Mistriotis, A., Hemming, S. & Waaijenberg, D. 2008 Plastic nets in agriculture: A general review of types and applications Appl. Eng. Agr. 24 799 808 https://doi.org/10.13031/2013.25368

    • Search Google Scholar
    • Export Citation
  • Chen, J.M., Mo, G., Pisek, J., Liu, J., Deng, F., Ishizawa, M. & Chan, D. 2012 Effects of foliage clumping on the estimation of global terrestrial gross primary productivity Global Biogeochem. Cycles 26 1 18 https://doi.org/10.1029/2010GB003996

    • Search Google Scholar
    • Export Citation
  • Farquhar, G.D., von Caemmerer, S. & Berry, J.A. 1980 A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species Planta 149 78 90

  • Giacomelli, G.A. & Roberts, W.J. 1993 Greenhouse covering systems HortTechnology 3 50 58 https://doi.org/10.21273/horttech.3.1.50

  • Hemming, S., Mohammadkhani, V. & Dueck, T. 2008 Diffuse greenhouse covering materials - Material technology, measurements and evaluation of optical properties Acta Hort. 797 469 476 https://doi.org/10.17660/actahortic.2008.797.68

    • Search Google Scholar
    • Export Citation
  • Hernández, V., Hellín, P., Fenoll, J. & Flores, P. 2019 Interaction of nitrogen and shading on tomato yield and quality Scientia Hort. 255 255 259 https://doi.org/10.1016/j.scienta.2019.05.040

    • Search Google Scholar
    • Export Citation
  • Hernández, V., Hellín, P., Fenoll, J., Garrido, I., Cava, J. & Flores, P. 2015 Impact of shading on tomato yield and quality cultivated with different n doses under high temperature climate Procedia Environ. Sci. 29 197 198 https://doi.org/10.1016/j.proenv.2015.07.259

    • Search Google Scholar
    • Export Citation
  • Hikosaka, K., Noguchi, K. & Terashima, I. 2016 Modeling leaf gas exchange 61 100 Hikosaka, K., Niinemets, Ü. & Anten, N.P.R. Canopy photosynthesis: From basics to applications. Springer Berlin https://doi.org/10.1007/978-94-017-7291-4_3

    • Search Google Scholar
    • Export Citation
  • Hirose, T. & Werger, M.J.A. 1987 Maximizing daily canopy photosynthesis with respect to the leaf nitrogen allocation pattern in the canopy Oecologia 72 520 526 https://doi.org/10.1007/BF00378977

    • Search Google Scholar
    • Export Citation
  • Kitta, E. & Katsoulas, N. 2020 Effect of shading on photosynthesis of greenhouse hydroponic cucumber crops Ital. J. Agrometeorol. 2020 41 48 https://doi.org/10.13128/ijam-871

    • Search Google Scholar
    • Export Citation
  • Kittas, C., Karamanis, M. & Katsoulas, N. 2005 Air temperature regime in a forced ventilated greenhouse with rose crop Energy Build. 37 807 812 https://doi.org/10.1016/j.enbuild.2004.10.009

    • Search Google Scholar
    • Export Citation
  • Kläring, H.P. & Krumbein, A. 2013 The effect of constraining the intensity of solar radiation on the photosynthesis, growth, yield and product quality of tomato J. Agron. Crop Sci. 199 351 359 https://doi.org/10.1111/jac.12018

    • Search Google Scholar
    • Export Citation
  • Lawlor, D.W 1995 Photosynthesis, productivity and environment J. Expt. Bot. 46 1449 1461 https://doi.org/10.1093/jxb/46.special_issue.1449

  • Li, T., Heuvelink, E., Dueck, T.A., Janse, J., Gort, G. & Marcelis, L.F.M. 2014 Enhancement of crop photosynthesis by diffuse light: Quantifying the contributing factors Ann. Bot. 114 145 156 https://doi.org/10.1093/aob/mcu071

    • Search Google Scholar
    • Export Citation
  • López-Marín, J., Gálvez, A., González, A., Egea-Gilabert, C. & Fernández, J.A. 2012 Effect of shade on yield, quality and photosynthesis-related parameters of sweet pepper plants Acta Hort. 956 545 552 https://doi.org/10.17660/ActaHortic.2012.956.65

    • Search Google Scholar
    • Export Citation
  • Marcelis, L.F.M., Broekhuijsen, A.G.M., Meinen, E., Nijs, E.M.F.M. & Raaphorst, M.G.M. 2006 Quantification of the growth response to light quantity of greenhouse grown crops Acta Hort. 711 97 103 https://doi.org/10.17660/actahortic.2006.711.9

    • Search Google Scholar
    • Export Citation
  • Masabni, J., Sun, Y., Niu, G. & Del Valle, P. 2016 Shade effect on growth and productivity of tomato and chili pepper HortTechnology 26 344 350 https://doi.org/10.21273/horttech.26.3.344

    • Search Google Scholar
    • Export Citation
  • Mathur, S., Agrawal, D. & Jajoo, A. 2014 Photosynthesis: Response to high temperature stress J. Photochem. Photobiol. B. 137 116 126 https://doi.org/10.1016/j.jphotobiol.2014.01.010

    • Search Google Scholar
    • Export Citation
  • Matsuda, S., Yoshikoshi, H., Suzuki, T., Ohta, Y., Chiba, A., Arima, H., Kumagai, H., Yasutake, D. & Kitano, M. 2020 Calculation of the irradiance of solar radiation in a greenhouse with a complex structure using a diagram for sky view factor J. Agr. Meteorol. 76 44 52 https://doi.org/10.2480/agrmet.D-19-00043

    • Search Google Scholar
    • Export Citation
  • Medlyn, B.E., Dreyer, E., Ellsworth, D., Forstreuter, M., Harley, P.C., Kirschbaum, M.U.F., Le Roux, X., Montpied, P., Strassemeyer, J., Walcroft, A., Wang, K. & Loustau, D. 2002 Temperature response of parameters of a biochemically based model of photosynthesis. II. A review of experimental data Plant Cell Environ. 25 1167 1179 https://doi.org/10.1046/j.1365-3040.2002.00891.x

    • Search Google Scholar
    • Export Citation
  • Newville, M., Stensitzki, T., Allen, D.B. & Ingargiola, A. 2014 LMFIT: Non-linear least-square minimization and curve-fitting for python Zenodo https://doi.org/10.5281/ZENODO.11813

    • Search Google Scholar
    • Export Citation
  • Nomura, K., Takada, A., Kunishige, H., Ozaki, Y., Okayasu, T., Yasutake, D. & Kitano, M. 2020 Long-term and continuous measurement of canopy photosynthesis and growth of spinach Environ. Control Biol. 58 21 29 https://doi.org/10.2525/ecb.58.21

    • Search Google Scholar
    • Export Citation
  • Nomura, K., Yasutake, D., Kaneko, T., Iwao, T., Okayasu, T., Ozaki, Y., Mori, M. & Kitano, M. 2021a Long-term estimation of the canopy photosynthesis of a leafy vegetable based on greenhouse climate conditions and nadir photographs Scientia Hort. 289 110433 https://doi.org/10.1016/j.scienta.2021.110433

    • Search Google Scholar
    • Export Citation
  • Nomura, K., Yasutake, D., Kaneko, T., Takada, A., Okayasu, T., Ozaki, Y., Mori, M. & Kitano, M. 2021b Long-term compound interest effect of CO2 enrichment on the carbon balance and growth of a leafy vegetable canopy Scientia Hort. 283 110060 https://doi.org/10.1016/j.scienta.2021.110060

    • Search Google Scholar
    • Export Citation
  • Pearson, S., Wheldon, A.E. & Hadley, P. 1995 Radiation transmission and fluorescence of nine greenhouse cladding materials J. Agr. Eng. Res. 62 61 69 https://doi.org/10.1006/jaer.1995.1063

    • Search Google Scholar
    • Export Citation
  • Prior, S.A., Brett Runion, G., Christopher Marble, S., Rogers, H.H., Gilliam, C.H. & Allen Torbert, H. 2011 A review of elevated atmospheric CO2 effects on plant growth and water relations: Implications for horticulture HortScience 46 158 162 https://doi.org/10.21273/hortsci.46.2.158

    • Search Google Scholar
    • Export Citation
  • Sethi, V.P., Sumathy, K., Lee, C. & Pal, D.S. 2013 Thermal modeling aspects of solar greenhouse microclimate control: A review on heating technologies Sol. Energy 96 56 82 https://doi.org/10.1016/j.solener.2013.06.034

    • Search Google Scholar
    • Export Citation
  • Stitt, M 1991 Rising CO2 levels and their potential significance for carbon flow in photosynthetic cells Plant Cell Environ. 14 741 762 https://doi.org/10.1111/j.1365-3040.1991.tb01440.x

    • Search Google Scholar
    • Export Citation
  • Thornley, J.H.M 2002 Instantaneous canopy photosynthesis: Analytical expressions for sun and shade leaves based on exponential light decay down the canopy and an acclimated non-rectangular hyperbola for leaf photosynthesis Ann. Bot. 89 451 458 https://doi.org/10.1093/aob/mcf071

    • Search Google Scholar
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  • von Caemmerer, S 2000 Biochemical models of leaf photosynthesis CSIRO Publishing, Clayton VIC Australia https://doi.org/10.1071/9780643103405

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