3.1.1. Heat and Mass Variation
The descriptive statistics of the distribution of microclimate parameters for all sensors are presented in
Table 1,
Table 2,
Table 3 and
Table 4. The control sensor minimum and maximum values for SLG (
Table 1 and
Table 3) were 4.12 °C and 29.14 °C, 18.95%, and 98.38%, 3.32 °C and 22.19 °C, 0.06 kPa, and 2.96 kPa, and 7.29 W/m
2 and 647.23 W/m
2, for T, RH, Tdp, VPD, and SR, respectively. The control sensor minimum and maximum values for DLG (
Table 2 and
Table 4) were 3.91 °C and 28.99 °C, 15.49%, and 99.18%, 5.18 °C, and 21.72 °C, 0.05 kPa, and 2.91 kPa, and 9.04 W/m
2 and 653.97 W/m
2. The percentage change in the parameter sum between control (C2) and other sensors in SLG and DLG are shown in
Table 5 and
Table 6, respectively. In SLG, T, RH, Tdp, and VPD sum was 10.29%, 4.70%, 15.04%, and 30.70% higher changes between the C2 and E3, A1, A1, and E3, respectively. In DLG, T, RH, Tdp, and VPD sum were 8.09%, 2.7%, 5.90%, and 28.03% higher changes between the C2 and E3, E3, A3, and E3, respectively.
The ANOVA results in
Table 7 and
Table 8 show the significant differences in the distribution of all the microclimate parameters within the greenhouses. The
p-value corresponding to the
F-statistic of one-way ANOVA is less than 0.05, indicating that one or more treatments are significantly different. Despite the installation of circulation fans in the greenhouses, the distribution was found to be heterogeneous. This significant variation supports the findings by [
8], who stated that significant temperature gradients were observed even when experiments were conducted in small experimental greenhouses. Except for the fog cooling condition, the result is similar to the result obtained by [
12], who reported nonuniformity of T and RH distribution within a conventional DLG during the day and nighttime, under heating, no heating, and no fogging conditions. However, [
22] found no differences in T and RH readings between an SLG and DLG.
Figure 4 and
Figure 5 show the Tukey HSD test of data from the control sensor (C2) and other sensors in the SLG and DLG. In SLG, T, T
dp, and the VPD readings in the control sensor were significantly different from the other sensors. The RH in the control sensor was significantly different from all other sensors except for C1, C3, and E1. Similarly, in DLG, the variables T
dp and VPD gathered in the control sensor were significantly different from those obtained in other sensors. The variables T and RH in the control sensor were significantly different from all the other sensors, except A1 and C1, and A2 and E1. Considering the Tukey pairwise comparison of all sensors, the variables T, RH, T
dp, and VPD were 66.6%, 72.2%, 72.2%, and 80.5% higher in SLG. When DLG is considered, the variables T, RH, T
dp, and VPD were 80.5%, 72.2%, 50.0%, and 86.1%, respectively, higher than the control sensors. These values confirm that the distribution of the variables is heterogeneous. Refs. [
14,
23] recommended ±0.75 °C and ± 3% of standard deviation for T and RH, respectively, for a homogeneous distribution. T and RH in SLG and DLG analyzed in this study, were 4.786 °C and 18.84%, respectively, and 4.911 °C and 21.71%. These values were higher than the recommended, confirming the heterogeneity of heat distribution in both greenhouses.
As shown in
Table 7 and
Table 8, the SR distribution within SLG and DLG was also significantly different. The SR variation can be attributed to the interruption by thermal screens inside the covering material and condensation [
24].
The Pareto chart in
Figure 6 shows the distribution of T, RH, T
dp, and VPD data in the control sensors in SLG and DLG, respectively. In
Figure 6a, the data for these variables in the SLG and DLG 8 were 95% and 94.2%, within the range of 8–23 °C; 40.4% and 40.06%, within the range of 7.3–10.8 °C; 17.1% and 20.0%, within the range of 10.9–13 °C; and, 22.6% and 22.7%, within the range of 18–23 °C. The minimum and maximum temperature setpoint in the SLG and DLG was 8–23 °C, respectively. However, for strawberries, refs [
6,
7] recommends optimum daytime and nighttime temperature ranges of 18–23 °C and 10–13 °C, respectively.
Figure 6b shows the RH data within the SLG and DLG were 10.7% and 10.4%, within the range of 60% and 75%; 20.5% and 19.2%, within 18–59% and 15–59% RH; and 68.7% and 70.3 %, within 76–96% and 76–99% RH, respectively. However, for strawberries, ref [
4,
6] recommend an optimum range of 60–75% RH.
Figure 6c shows that the VPD data within the SLG and DLG were 21.2% and 10.2%, within the range 0.25 kPa and 0.5 kPa; 4.4% and 5.3%, within 0.8 kPa and 1.2 kPa, 9.1% and 7.8%, within 1.3 kPa and 1.6 kPa; 65.3% and 76.6%, were outside 0.2 kPa and 1.6 kPa in SLG and DLG, respectively. However, for strawberries, ref [
4,
6] recommended a range of 0.2–1.6 kPa.
Although 95% and 94.2% of the temperature data were within the setpoint range of 8–23 °C, respectively, approximately 68.7% and 70.3% of RH resulted in the VPD being outside the optimum value in SLG and DLG. The higher temperature sum at C2 than the temperature sums from other sensor points invariably means that the RH at these sensor points was higher. Therefore, this result means that the sensor readings will consequently result in lower VPD readings. Therefore, this result indicates worse conditions at other points.
3.1.2. Air-Leaf Temperature Interaction
During the day, there was a significant difference between the leaf and surrounding air temperature in both greenhouses, but only in SLG at night, as the
p-value corresponding to the
F-statistic of one-way ANOVA is less than 0.05, indicating that one or more treatments are significantly different. The ANOVA results for the air-leaf temperature in daytime and nighttime in SLG and DLG are shown in
Table 9, and the air-leaf temperature patterns in the daytime (
Figure 7A) and nighttime (
Figure 7B) are shown in
Figure 7. During the day, as the thermal screen opens, the incoming SR warms up the microclimate environment increasing the air temperature. As the energy increases, so does the air temperature and the leaves, with both maintaining thermal equilibrium. As shown in
Figure 7A, the rate of transpiration increased as crop temperature increased. The moisture evaporated from the leaf surface creates a cooling effect thereby reducing the leaf temperature. The crop behaved as a heat sink in those instances, resulting in energy loss because the air temperature was higher than the leaf temperature and to maintain thermal equilibrium with the surrounding air.
During the night, as the ambient temperature falls, energy is lost to the ambient because of the temperature difference between the greenhouses air temperature and the ambient temperature through the cover and infiltration.
Figure 7B shows that as the air temperature falls, so does the temperature of the leaves. This is because of the exchange between the crop and the surrounding air. To maintain thermal equilibrium, the crop loses energy to the surrounding, as the greenhouse also loses energy to the ambient. This means that the greenhouse microclimate gains energy from the crop that subsequently loses it to the ambient environment. The leaf temperature was found to be higher than the air temperature at the late hours of the night and early hours of the day (
Figure 7). This can be attributed to the energy gained through the crop root zone during the night from radiated heat from the heating pipe located beneath the root zone. When the greenhouse temperature falls below the set temperature of 8 °C, hot water is pumped through the pipes. The increase continued until the crop temperature exceeded the temperature of the surrounding air. During this period, the crop behaves like a heat source, emitting heat into the surrounding air, allowing the greenhouse microclimate to gain heat. This result is similar to that of [
15], who reported similar crop and greenhouse air temperature trends. The implication of this is that, rather than the assumption of absolute heat loss reported by [
3] and absolute gain reported by [
25] to crop effect in energy estimation in a greenhouse, the TRNSYS building energy simulation crop model component that considers the changes in the air and crop temperature should be used.