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Article

A Model for the Effect of Low Temperature and Poor Light on the Growth of Cucumbers in a Greenhouse

School of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China
*
Author to whom correspondence should be addressed.
Agronomy 2022, 12(12), 2992; https://doi.org/10.3390/agronomy12122992
Submission received: 5 October 2022 / Revised: 24 November 2022 / Accepted: 25 November 2022 / Published: 28 November 2022
(This article belongs to the Section Horticultural and Floricultural Crops)

Abstract

:
With the expansion of cucumber cultivation, many growers continue to experience extreme weather and environmental issues. This study aimed to examine and model the effects of low temperature (LT) and poor light (PL) stresses on cucumber growth. The experiment was designed as an orthogonal experiment that analyzed temperature, light, and duration. The daily maximum/minimum temperatures of the experiment were set as per the following four levels: 13 °C/3 °C, 16 °C/6 °C, 19 °C/9 °C, 22 °C/12 °C, and the control at 28 °C/18 °C. The light was divided into two levels: 200 μmol∙m−2∙s−1 and 400 μmol∙m−2∙s−1, with 800 μmol∙m−2∙s−1 as the control. Treatment duration was set at 2, 5, 8, and 11 days. Stress with different LT, PL, and duration was expressed using the stress effect (0–1), which decreased with an increase in stress level. Meanwhile, treatment with a temperature of 3 °C and light of 400 μmol∙m−2∙s−1 for 11 days had the smallest effect on stress, which was only 67% of that of the control following 50 days of recovery, and had the most severe effect on cucumber growth. The proportion of dry weight allocated to leaves and stems decreased with increasing low temperatures and poor light stress, but the proportion allocated to fruit increased. The highest percentage of fruit distribution was found in the treatment with temperature of 9 °C, light of 200μmol∙m−2∙s−1, and 11 days duration, being 3.57 times higher than the control. In order to better investigate the effects of LT and PL stress on cucumber growth, light and temperature effect (LTE), growing degree days (GDD), and product of thermal effectiveness and PAR (TEP) models were developed based on temperature and light. The root mean square error (RMSE) of the LTE model was found to be 4.214 g∙plant−1, 36.3% of that of the GDD model and 78.8% of that of the TEP model, better simulating the above-ground dry weight of cucumber plants.

1. Introduction

Cucumbers (Cucumis sativus L.) are known to be creeping or climbing herbs, widely planted in temperate and tropical areas [1]. Cucumber is suitable for both fresh and processed food and represents an important share in the annual production of vegetables [2]. It is one of the major greenhouse-cultivated crops in China and possesses considerable economic benefits. In recent years, although the planting area of greenhouse cucumbers in China has increased, the production environment and environmental control system have remained relatively rudimentary [3]. Therefore, it is vital to study and address the effects of unseasonal weather on cucumber growth and production.
Temperature is one of the key factors affecting cucumber growth and development [4]. Different temperature conditions can have different effects on cucumber growth, development, and yield [5]. Miao found that the suitable temperature for cucumber cultivation is 25–30 °C [6]. Zhou et al. showed that cucumber leaves will bear damage when the temperature exceeds 35 °C [7]. Moreover, MINCHIN and SIMON found that cucumber leaves also become damaged when the temperature falls below 12 °C [8]. Light conditions also play a key role in the growth and development of cucumbers [9]. Specifically, light intensity between 600–800 μmol·m–2·s–1 is suitable for cucumber growth [10]. Janina et al. found that chlorophyll fluorescence parameters and the fruit yield of cucumber increase when grown under appropriate light conditions [11]. Yan et al. found that poor light inhibits plant growth, and as light intensity decreases, the growth of morphological indicators, such as plant height, stem thickness, leaf area, number of leaves, and plant width, decreases [12]. Górnik studied the physiological effects of different light conditions on cucumber seedlings and found that the activities of various enzymes were enhanced under poor light [13]. Zhao et al. found that both low temperature and poor light stress reduced the dry weight of cucumbers [14]. With the continuous development of China’s greenhouse cultivation and the rapid increase in greenhouse planting areas, low temperature and poor light are becoming increasingly prominent. Zhang put forward that most greenhouses have poor environmental regulation and low resistance to crop weather hazards [15]. Moreover, Hui found that low winter temperatures in northern China make it difficult to grow crops in solariums even after heating [16]. Overall, low temperatures and poor light serve as the main meteorological disasters for greenhouse cucumber cultivation in China [17].
Both temperature and light are known to affect the growth and development of cucumber, while changes in cucumber leaf area and organ dry weight mass more directly reflect the growth of cucumber. Currently, many models exist for simulating leaf area and dry weight during normal plant growth. In a study on the leaf area simulation of greenhouse crops, Fan et al. used a particle swarm–random forest algorithm and meteorological data in order to construct a leaf area growth prediction model [18]. Huang et al. established the leaf area simulation model of lettuce under different photoperiods using the product of thermal effectiveness and PAR (TEP) model [19]. In a study on dry weight simulation in greenhouse crops, Liu et al. employed the TEP model to establish the product of dry weight accumulation and found that organ dry weight mass is exponentially related to the organ partition coefficient and decreased with increasing irradiation heat product [20]. Chen et al. used the growing degree days (GDD) model to establish a maize dry weight simulation model [21]. Yang et al. used the light and temperature effect (LTE) model to construct a simulation model for dry weight production and distribution in greenhouse chrysanthemums [22]. Notably, few studies on dry weight simulation under LT and PL stress currently exist [23]. Sionit et al. found that LT affects the dry weight distribution of soybean and reduces the dry weight of all parts of soybean [24]. Liu et al. studied the effects of different sowing periods on the dry weight of winter wheat and found that the dry weight of all organs of the latter [25].
LT and PL stress remain a challenge for China’s agricultural development. Currently, there are more ongoing studies on dry weight partitioning and yield of crops under single-factor stresses of LT and PL. However, less research has been conducted on dry weight production and distribution under LT or PL and LT–PL combined stress. Accordingly, this study seeks to: (1) explore the actual amount of change in the dry weight partition index under combined low temperature and oligohaline hazards; (2) conduct an analysis to compare the effects of LTE, TEP, and GDD simulations and select the most accurate model for LT and PL stress; (3) observe the dry weight ratios of various organs of cucumber under LT and PL stress; (4) simulate the leaf area of individual plants under different treatments and observe its accuracy using the specific leaf area (SLA) method according to the selected dry weight model.

2. Materials and Methods

2.1. Experimental Materials

China’s representative cucumber variety, “Jin You”, was selected as the experiment material. “Jin You” is a hybrid variety with great pest and disease resistance and high yield, prevalent in China’s northwest and southwest [26]. Seedling cucumber plants were purchased from a greenhouse grower in Nanjing, China, and planted in the greenhouse at Nanjing University of Information Science and Technology (NUIST) with a density of 4 plants/m2. According to the environmental conditions suitable for the growth of cucumbers [27], the control greenhouse environment was set to 28 (maximum temperature)/18 (minimum temperature); humidity was 70% ± 5; light condition was 800 μmol∙m−2∙s−1; photoperiod was 12/12 h (6:00–18:00 during the day). The plants needed to be fertilized once 7 days after planting with a compound of N, P, and K fertilizer (N-P-K: 20%-20%-20%). An average of 2000 g of diluted fertilizer were used per square meter (fertilizer/water: 1/1000). Plants with four leaves were transplanted into nutrient pots (21 cm height, 24 upper diameter, and 19 cm lower diameter) and placed in each of eight artificial climate chambers (TPG1260, Australia) for environmental control trials. The substrate in the pot was peat soil perlite vermiculate = 2:1:1(v/v/v). Fertilizer was combinedwith water, watered every three days, and a dilute fertilizer was applied every 18 days (No fertilizer treatment during the 11 days of stress). The fertilizer was a N, P, and K compound fertilizer (N-P-K: 15%-4%-26%), with an average of approximately 400 g of diluted fertilizer per pot (fertilizer/water: 1/1000), controlling soil humidity to maintain 70 ± 5 until the end of the 50 days of recovery.

2.2. Experimental Design

An artificial environment control experiment was conducted in 2021 in the artificial climate chambers of Nanjing University of Information Science and Technology (NUIST) (PGC-FLEX, Conviron, Canada), using the cucumber variety “Jin You 101”. The experiment was designed as a mixed orthogonal test (Table 1) with four levels of temperature settings (maximum daily temperature/minimum daily temperature): 13 °C/3 °C, 16 °C/6 °C, 19 °C/9 °C, 22 °C/12 °C, and the control was set at 28 °C/18 °C. The light settings (day/night light) were divided into two levels: 200 μmol∙m−2∙s−1 and 400 μmol∙m−2∙s−1, with 800 μmol∙m−2∙s−1 as the control. The experiment’s duration was divided into 2, 5, 8, and 11 days. The relative air humidity was set at 70% ± 5%, and the photoperiod was set at 12/12 h (6:00–18:00 during the day) during the experiment. In the experiment, cucumbers of the same variety with relatively uniform growth trends were placed in eight climatic chambers at the same time. The minimum temperature–light settings for the eight climatic chambers were 3 (°C)−200 (μmol∙m−2∙s−1), 3-400, 6-200, 6-400, 9-200, 9-400, 12-200, and 12-400. Three plants were used as the experiment unit. All cucumber plants were returned to the greenhouse at the end of the stress days for recovery observations.

2.3. The Methods of Measurement

2.3.1. Determination of Leaf Area

To determine the whole leaf area of cucumber plants, the method used for a single leaf was the paper sample weighing method. Cucumber leaves were laid flat on top of A4 paper and traced along the edges of the cucumber leaves with a marker. The A4 paper was then cut and weighed according to the shape of the traced leaf. The leaf area was calculated according to the following formula [21].
Leaf   area = Area   of   1   sheet   of   A 4   paper ×   leaf     shapedpaper   weight 1   sheet   of   A 4   paper   weight ,
where the unit of area is cm2, and the unit of weight is g.

2.3.2. Determination of Dry Weight

Three uniformly growing cucumber plants were selected at 10-day intervals for destructive sampling. The different organs of the plants were pretreated at 105 °C for 15 min, then dried at 85 °C to a constant weight. Their dry weight was measured separately using an electronic balance with an accuracy of 0.0001g.

2.4. Introduction to the Three Growth Models

2.4.1. Light and Temperature Effect Model

The thermal effect model was used to represent the effect of temperature on cucumber. Its growth and quality accumulation were mainly determined by the temperature thermal effect (fT) and the light effect (fL). The product of the temperature thermal effect and the light effect was defined as LTE.
The temperature thermal effect (fT) was expressed in terms of the relative thermal effect (RTE), which was then related to the mean temperature (T) as:
f T = RTE(T) = {         0                                                 ( T < T b ) ( T T b ) / ( T o b T b ) ( T b T < T o b )         1                                                 ( T o b T T o u ) ( T m T ) / ( T b T o u ) ( T o u < T T m )         0                                                 ( T > T m ) ,
where RTE (T) is the relative thermal effect, Tb is the lower temperature limit (°C), Tm is the upper temperature limit (°C), Tob is the lower optimum temperature for growth (°C), and Tou is the upper optimum temperature for growth (°C).
L is the daily photosynthetically active radiation (μmol∙m−2∙d−1) calculated as follows.
  L = i = 1 24 ( PAR ( i ) × 3600 ) ,  
where PAR(i) is the mean photosynthetically active radiation at hour i in 1 day (μmol∙m−2∙d−1); 3600 is the factor that accumulates the PAR for that hour.
The light effect (fL) is:
f L = ( 1 e α L ) ,    
where α is the curvature of the function. Liu et al. found that the curvature of greenhouse cucumbers was 0.0005 [28].
The LTE was then calculated as:
LTE = ( f T ( j ) × f L ( j ) ) ,
where LTE is the cumulative light and temperature effect during cucumber growth, and fT(j) and fL(j) are the temperature effect and light effect on day j of the cucumber growth period, respectively.

2.4.2. Growing Degree Days Model

The growing degree days model (GDD) considers mainly temperature conditions and is the cumulative value of the difference between the average daily temperature and the lower temperature limit of crop development. The equation is as follows.
  GDD = ( T avg T min ) ,    
T a v g {               T x + T n 2                               T m i n < T a v g < T m a x T min                                                                                           T a v g T m i n             T m a x                                                           T a v g T m a x     ,                                                                      
where Tavg represents the average daily temperature, Tx represents the maximum daily temperature, Tn represents the minimum daily temperature, and Tmin and Tmax are the lower and upper limit temperatures for growth and development, respectively.

2.4.3. Product of Thermal Effectiveness and PAR Model

The leaf area of cucumber plants is mainly determined by the thermal effect (TE) and photosynthetic radiation (PAR). The product of thermal effectiveness and PAR is defined as TEP, which can establish a dynamic relationship with the leaf area of a single cucumber plant. The thermal effect TE is derived cumulatively from the relative thermal effectiveness (RTE) and defined as the ratio of one day of crop growth at actual temperature conditions to one day of growth at optimum temperature conditions. The value of RTE is always between 0 and 1. The relationship between it and temperature (T) can be expressed by Equation (8).
RTE(T) = {         0                                                 ( T < T b ) ( T T b ) / ( T o b T b )                     ( T b T < T o b )         1                                                                                 ( T o b T T o u ) ( T m T ) / ( T b T o u )                     ( T o u < T T m )         0                                                             ( T > T m ) ,
where RTE(T) is the relative thermal effect at T, Tb is the lower growth limit temperature (°C), Tm is the upper growth limit temperature (°C), and Tob is the lower optimum temperature for growth (°C).
The cumulative heat product is obtained by accumulating the daily relative heat product RTEP (relative product of thermal effectiveness and PAR). The daily RTEP is obtained by multiplying the average relative thermal effect for each hour of the day by the total photosynthetically active radiation for the corresponding hour and then adding up the total, calculated by the formula
RTEP = i = 1 24 ( RTE ( i ) × PAR ( i ) × 3600 / 10 6 ) ,
where RTEP is the daily relative radiation heat product, J∙m−2∙s−1, RTE(i) is the average relative heat effect at hour i in 1 day, PAR(i) is the average photosynthetically active radiation at hour i in 1 day, (J∙m−2∙s−1), 3600 is the unit conversion factor for converting the average photosynthetically active radiation at hour i, (J∙m−2∙s−1), to the total photosynthetically active radiation at that hour, (J∙m−2∙h−1), and 106 is the unit conversion factor for converting J∙m−2∙h−1 to MJ∙m−2∙h−1.
TEP = ( RTEP ) ,  
where TEP is the cumulative heat product of irradiation during the growth of cucumber, (MJ∙m−2).

2.5. Simulation of the Stress Effect

The effect of LT and PL stress on plant dry weight production can be better studied by defining the stress effect. The equation was calculated as follows.
p = W SS W SCK ,
where P is the stress effect, W SS is the dry weight of the above-ground part of the plant (g∙plant−1) at a certain time after stress, and W SCK is the dry weight of the normal growing plant (g∙plant−1).
The P (stress effect) under each LT and PL treatment was calculated using Equation (11). To better model the relationship between temperature, light, duration, and stress effects, P was fitted to the equation as follows,
P = a × D + b × T + c × PAR + d ,
where P is the stress effect, D is the days of stress (d), T is the daily minimum temperature (°C), PAR is photosynthetically active radiation (μmol-m−2∙s−1), and a, b, c, and d are the fitted parameters.
Using experimental data, the relationship among the stress effect (P), the daily minimum temperature (T), the photo-synthetically active radiation (PAR), and the duration (D) of the treatment can be obtained as follows
P = 0.787 + 0.0178 T + 0.0000083 PAR 0.01580 D ; R 2 = 0.888 ; RMSE = 0.012 ; N = 104 .  
Figure 1a,b shows the relationship between stress effect and LT–PL stresses. With the long-term LT and PL stresses, the trend of stress effect decreased.

2.6. The Dry Weight Simulation of Three Models

2.6.1. Light and Temperature Effect Model

The logistic equation was used to simulate the relationship between dry weight production and LTE.
W s h = 264.7382 1 + e 2.4067 0.063522 × L T E ,
where W s h is the dry weight production, and LTE is the temperature and light effect.

2.6.2. Growing Degree Days Model

The logistic equation was used to simulate the relationship between dry weight production and GDD.
W s h = 180.0527 1 + e 2.0918 0.002946 × G D D ,
where W s h is the dry weight production, and GDD is the growing degree days.

2.6.3. Product of Thermal Effectiveness and PAR Model

The logistic equation was used to simulate the relationship between dry weight production and TEP.
W s h = 172.1216 1 + e 2.0068 0.013269 × T E P ,
where W s h is the dry weight production, and TEP is the product of thermal effectiveness and PAR.

2.7. Simulation Equations for the Dry Weight and LTE of Various Organs of Cucumber

2.7.1. Simulation Equations for Leaf Dry Weight and LTE

The negative exponential equation (Equation (16)) was used to fit the relationship between the proportion of leaf dry weight allocation and LTE.
P I = 0.551 e 0.0105 L T E ,
where P I is the proportion of leaf dry weight allocated, and LTE is the temperature and light effect during the recovery period. Multiplying the stress effect by the simulated above-ground dry weight under control treatment provides the simulated above-ground dry weight under LT and PL stresses. Multiplying the above-ground dry weight simulations under each stress by the fitted leaf dry weight allocation ratio provides the simulated dry weight of the leaves under each stress.

2.7.2. Simulation Equations for Steam Dry Weight and LTE

The negative exponential equation (Equation (17)) was used to fit the relationship between the proportion of dry weight allocated to cucumber stems and LTE.
                    P S = 0.287 e 0.0114 L T E
where P S is the proportion of stem dry weight allocated, and LTE is the temperature and light effect. Simulated values of the total dry weight of cucumber under different LT and PL treatments can be obtained by multiplying the stress effect (Equation (11)) with the simulated amount of dry weight of cucumber under control treatment (Equation (10)). The resulting dry weight of the cucumber under LT and PL stresses was multiplied by the fitted value of the stem dry weight allocation ratio to obtain the simulated value of the stem dry weight of the cucumber under LT and PL stresses.

2.7.3. Simulation Equations for Fruit Dry Weight and LTE

The negative exponential equation (Equation (18)) was used to fit the relationship between the proportion of dry weight allocated to cucumber fruits and LTE.
P f = 0.5038 1 + e 0.777 0.0729 L T E ,
where P f is the fruit allocation index, and LTE is the temperature and light effect. The simulated values of above-ground dry weight simulated by the LT and PL were multiplied by the simulated values of the fruit allocation ratio to obtain the simulated amount of dry weight in fruits under LT and PL stress. Similarly, the simulated above-ground dry weight of different treatments under LT and PL stress was multiplied by the fair value of the fruit dry weight distribution ratio to obtain the simulated dry weight of fruit under LT and PL stress.

2.8. Simulation Equations for Cucumber Leaf Area

The specific leaf area (SLA) method was used to model the leaf area of a plant.
S L A = A W i ,  
where S L A is the specific leaf area, A is the leaf area of a plant, and W i is the dry weight of a plant.
The equation for the simulated value of leaf area is as follows:
A s = W l s × S L A ,  
where A s is the specific leaf area, W l s is the simulated dry weight mass of the leaves under different treatments, and SLA is the specific leaf area.

2.9. Method to Model Texting

The root mean square error (RMSE) was used to test the conformity of all simulated and observed values of the model with the following formula. The lower the value of RMSE, the higher the accuracy of the model and the more stable it is.
RMSE = i = 1 n ( OBS i SIM i ) 2 n ,
where OBS i is the actual observed value, SIM i is the value modeled by the model, and n is the sample size.

3. Results

3.1. Effect of Low Temperature and Poor Light on the Degree of Stress

The effect of each LT–PL composite treatment on the stress effect can be clearly seen in Figure 2. The stress effect fluctuated to varying degrees as the number of recovery days increased, but there was an overall upward trend. Overall, 3-400-11d, 6-400-2d, 6-400-5d, 6-200-8d, 9-400-2d, 9-400-5d, 9-200-8d, 12-200-2d, and 12-400-8d treatments showed an increasing trend in stress effect from 0-10 days of early recovery, while all other treatments showed slowly decreasing trend. The trend of the stress effect for each treatment varied but was less volatile at the middle and late stages of recovery (10-50d).
The 3-400-11d treatment showed a significantly lower stress effect than the control, only 63% of the control at 50 days of recovery. This indicates that the 3-400-11d treatment caused the most damage to the cucumber and was the most difficult to recover from; the stress effect under the 6-400-2d showed the smallest variation and was closer to control, reaching 84% of control at 50 days of recovery. This indicates that the 6-400-2d treatment caused relatively little damage to cucumber growth.

3.2. Comparison of Each Model

A better view of the model simulation can be obtained by looking at the relationship between the simulated values and the 1:1 line. Figure 3 shows the results of the LTE, GDD, and TEP models for simulating the above-ground dry weight of greenhouse cucumber at flowering and fruiting. The LTE model is closer to the 1:1 line than the other two models. This means the simulation of the LTE model is better. As can be seen from Table 2, the RMSE of the LTE model is 4.21 g·plant−1, which is only 36.3% of the GDD model; the R2 of LTE is 0.9487, which is 1.5% higher than the TEP model. This indicates that the LTE model has a smaller margin of error and a higher accuracy rate. Therefore, the LTE model was used to simulate the leaf area and dry weight content of cucumbers at the flowering and fruiting stages under different disaster stresses.

3.3. Changes of Dry Weight in Cucumber under Low Temperature and Poor Light Stresses

3.3.1. Patterns of Change in Leaf Dry Weight

The changes in dry weight per leaf of cucumber plants under LT and PL stresses are shown in Figure 4a–d. The trend of change in dry weight per leaf with LTE was basically the same for all treated cucumber plants., i.e., leaf dry weight increased with the increase in LTE. The slowest increase occurred when LTE was between 0 and 10, with progressively more pronounced differences between treatments thereafter. Different LT–PL stresses and different temperature–light effects had a significant effect on the leaf dry weight of cucumber plants, but the trend of change in leaf dry weight was basically the same., i.e., the leaf dry weight decreased significantly with increasing levels of LT–PL stress. Compared to the control, the decreasing trend of leaf dry weight under each treatment at 3 °C and 6 °C was significantly greater than that at 9 °C and 12 °C. The 3-400-11d treatment had the greatest effect on the dry weight of cucumber leaves, which was only 76.80% of that of control at an LTE of 44.2. The 12-200-2d treatment had the least effect on the dry weight of cucumber leaves, which was 95.96% of that of the control at an LTE of 44.2.

3.3.2. Patterns of Change in Steam Dry Weight

The changes in stem dry weight of single cucumber plants under LT and PL stresses are shown in Figure 5a–d. The trend in stem dry weight with LTE was essentially the same as leaf dry weight and steam dry weight. The dry weight of stems under each treatment increased slowly when the LTE was between 0 and 10. The 3-400-11d treatment had the greatest effect on the dry weight of cucumber leaves, which was only 66.36% of that of the control at an LTE of 44.2. The 12-200-2d treatment had the least effect on the dry weight of cucumber leaves, 94.75% of that of control at an LTE of 44.2. This indicates that the 3-400-11d treatment caused the most damage to steam and the 12-200-2d treatment had less effect on the dry weight quality of cucumber stems.

3.3.3. Patterns of Change in Fruit Dry Weight

The changes in fruit dry weight of single cucumber plants under LT and PL stresses are shown in Figure 6a–d. The trend in stem dry weight with LTE was essentially the same as leaf dry weight and steam dry weight. The dry weight of stems under each treatment increased slowly when the LTE was between 0 and 20. Compared to the 3 °C, 6 °C, and 9 °C treatments, the changes in fruit dry weight with an LTE increase at 12 °C were observed to not change much and were close to the control. This indicates that the LT of 12 °C has less effect on the dry weight of cucumber fruit. The 3-400-11d treatment had the greatest effect on the dry weight of cucumber leaves, which was only 65.25% of that of the control at an LTE of 44.2. This indicates that the 3-400-11d treatment caused the most damage to fruit dry weight.

3.4. Simulation of Dry Weight Distribution Ratio in Different Organs under Low Temperature and Poor Light Stress

3.4.1. Leaf Dry Weight Distribution Ratio Simulation

The relationship between the proportion of dry weight distribution in cucumber leaves and LTE under each stress treatment is shown in Figure 7a–d. It can be seen from the figure that the proportion of dry weight in cucumber leaves decreased as LTE increased; they were negatively correlated.

3.4.2. Steam Dry Weight Distribution Ratio Simulation

The relationship between the proportion of dry weight allocated to cucumber stems and LTE under each stress treatment is shown in Figure 8a–d. It can be seen from the figure that the proportion of dry weight in cucumber leaves decreased as LTE increased; they were negatively correlated.

3.4.3. Fruit Dry Weight Distribution Ratio Simulation

The relationship between the proportion of dry weight allocated to cucumber fruits and LTE under each stress treatment is shown in Figure 9a–d. The proportion of fruit dry weight distribution under LT and PL stress increased with the temperature and light effect. The proportion of fruit dry weight distribution satisfied the exponential function relationship with the temperature and light effect.

3.4.4. Comparison of Simulated and Measured Values

(1) Testing of leaf dry weight models
A comparison of the simulated and measured dry weight values for cucumber leaves is shown in Figure 10. The predicted values mainly lie on the line when the dry weight lies between 0–45 g∙plant−1, but when the dry weight lies between 45–60 g∙plant−1, the predicted values deviate slightly from the actual values.
(2) Testing of the stem dry weight models
A comparison of the simulated and measured values of cucumber stem dry weight is shown in Figure 11. The predicted values mainly lie on the line when the dry weight lies between 0–20 g∙plant−1, but when the dry weight lies between 20–30 g∙plant−1, the predicted values deviate slightly from the actual values.
(3) Testing of the fruit dry weight model
A comparison of the simulated and measured dry weight values for cucumber fruit is shown in Figure 12. The predicted values mainly lie on the 1:1 line when the dry weight lies between 0–60 g∙plant−1, but the predicted values deviate slightly from the actual values when the dry weight lies between 60–75 g∙plant−1.

3.5. Leaf Area Model for Facility Cucumber under Low Temperature and Poor Light Stress

3.5.1. Leaf Area Variation Patterns

The trends of leaf area of cucumber plants per plant under LT and PL stresses are shown in Figure 13a–d. The trend in leaf area per plant of cucumber plants with LTE was the same under all treatments, increasing with LTE. The differences in leaf area under each treatment were not significant when LTE was between 0 and 10 and significant when LTE was between 40 and 50. The leaf area size of cucumber monocots decreased with increasing LT and PL stress under the same LTE and duration of days and decreased with increasing duration of days under the same conditions of LTE and LT and PL stress. Notably, the change in the cucumber leaf area with LTE was closest to that of control under each treatment at 12 °C.

3.5.2. Testing of the Leaf Area Model

The simulated single-leaf area of the cucumber compared to the measured single-leaf area is shown in Figure 14. Under both LT and PL stress, the simulated values of leaf area were concentrated on the 1:1 line in the early stages (0–11,000 cm2·plant−1) and slightly deviated in the later stages (11,000–15,000 cm2·plant−1). The overall R2 of the model was 0.950, and the RMSE was 160.3 cm2·plant−1.

4. Discussion

In order to better investigate the effects of low temperature (LT) and poor light (PL) on the growth of cucumber, this paper proposed the stress effect and constructed a relationship model between LT, PL, and duration. Zhou et al. found that the dry weight and leaf area of each organ of cucumber decreased with a rise in LT stress [29]. Xue et al. found that PL stress caused irreversible damage to cucumbers, in which the leaf area of the cucumbers became significantly smaller with a rise in PL stress [30], all of which were found to be consistent with the results of this study. According to the two single-factor stresses, this study further investigated the changes in cucumber organs and leaf area under compound disasters. Moreover, it simulated the dry weight and leaf area of cucumber organs under each stress-by-stress effect as well as the dry weight of normal growth. The corresponding findings may hold important implications for agrometeorological disaster prevention and mitigation as well as facility environmental regulation.
The establishment of a suitable model for cucumber growth prediction can provide a scientific basis for greenhouse environmental control and quantitative management of crop growth and development [31]. By comparing the RMSE and R2 of the three models, the LTE model was found to have the best simulation results, which may be because this model considered the temperature and light conditions that had the greatest influence on plant growth, while the GDD model only considered temperature conditions unilaterally. Although the TEP model also considered temperature and light conditions, the difference between the magnitude of light units and temperature units was too large, leading to a larger simulation [32]. This may have been due to the simulated values being related to the stress coefficient, which was shown to rise significantly during the later stages of cucumber growth. Moreover, the error in the stress coefficient was found to increase as the dry weight grew larger. In later experiments, the model error could be reduced by dividing the cucumber stress coefficients into fertility stages while increasing the sample size.
Despite the satisfactory results from the dry weight and leaf area models for cucumbers, this study had certain limitations. Specifically, only a single variety of ‘Jin You 101’ was considered in this experiment, and more cucumber varieties could be added at a later stage. In addition, the experiment was conducted in the south; hence, environmental differences between different regions could also be considered in further studies.

5. Conclusions

LT and PL complex stresses have a significant impact on the growth of greenhouse cucumbers. This study conducted an analysis of the effects of LT and PL stresses in order to observe the growth of dry weight in the above-ground plant organs of cucumber. Accordingly, the findings showed that the proportion of dry weight allocated to leaves and stems decreased as the LT and PL stresses increased (the 3-400-11d treatment had the greatest effect). Meanwhile, the proportion of dry weight allocated to fruits was shown to rise gradually. With the same level of LT and PL stress and recovery time, the stress effect tended to decrease as the number of days of stress increased, which was found to be more detrimental to the growth and development of the cucumber. Three models were used to simulate the dry weight of cucumber under the combined hazards according to temperature and light conditions, which had an R2 of 0.9487 and RMSE of 4.21 g∙plant−1 for the LTE model, R2 of 0.9391 and RMSE of 11.61 g∙plant−1 for the GDD model, and R2 of 0.9350 and RMSE of 5.35 g∙plant−1 for the TEP model. In terms of the LTE model, the leaf area of cucumber under stress was simulated by SLA, which had an R2 of 0.95 and RMSE of 160.3 cm2·plant−1. Accordingly, the model simulation was determined to be effective and may provide decisional support for the layout and cultivation management of cucumber growing areas in facilities. Furthermore, it may provide a scientific basis for meteorological disaster prevention in different regions.

Author Contributions

Conceptualization, F.Z. and Z.Y.; methodology, Z.Y.; software, F.Z.; validation, Z.Y. and C.Y.; formal analysis, F.Z.; investigation, F.Z.; resources, C.Y.; data curation, Z.Y.; writing—original draft preparation, F.Z.; writing—review and editing, F.Z.; visualization, J.L.; supervision, C.L.; project administration, Z.Y.; funding acquisition, Z.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the Key R&D Programme 2019YFD100220 and National Natural Science Foundation Item (Grant No. 42275200).

Data Availability Statement

Not applicable.

Acknowledgments

I would like to thank Supervisor Z.Y. for his guidance when I wrote this article, as well as Luo Jing, Wu Xiangyi, Zhang Yuanda, and other colleagues for their help in revising the paper.

Conflicts of Interest

The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.

References

  1. Gou, C.X.; Zhu, P.Y.; Meng, Y.J.; Yang, F.; Xu, Y.; Xia, P.F.; Chen, J.F.; Li, J. Evaluation and genetic analysis of parthenocarpic germplasms in cucumber. Genes 2022, 13, 225. [Google Scholar] [CrossRef] [PubMed]
  2. Yu, B.W.; Ming, F.Y.; Liang, Y.G.; Wang, Y.X.; Gan, Y.W.; Qiu, Z.K.; Yan, S.S.; Cao, B.H. Heat stress resistance mechanisms of two cucumber varieties from different regions. Int. J. Mol. Sci. 2022, 23, 1817. [Google Scholar] [CrossRef]
  3. Qu, H. Exploration of the effect of environmental temperature on the growth and development of cucumber. Mod. Agric. Sci. Technol. 2015, 23, 104–108. [Google Scholar] [CrossRef]
  4. Farazmand, A. Effect of the temperature on development ofTetranychus urticae (Acari: Tetranychidae) feeding on cucumber leaves. Int. J. Acarol. 2020, 46, 381–386. [Google Scholar] [CrossRef]
  5. Zhou, X.; Feng, G.L.; Li, Z.H.; Liu, S.X.; Zhao, S.; Li, Y.; Wei, M. Effects of environmental conditions on absorption and distribution of silicon and formation of bloom on fruit surface of cucumber. J. Appl. Ecol. 2020, 31, 124–126. [Google Scholar] [CrossRef]
  6. Mou, M.M. Studies on the Mechanism of Heat Injury and Heat Adaptation and Summer Cultivation Techniques of Cucumber(Cucumis sativus L.). Master’s Thesis, Nanjing Agricultural University, Nanjing, China, 2000. [Google Scholar]
  7. Nikolaou, G.; Neocleous, D.; Katsoulas, N.; Kittas, C. Effects of cooling systems on greenhouse microclimate and cucumber growth under mediterranean climatic conditions. Agronomy 2019, 9, 300. [Google Scholar] [CrossRef] [Green Version]
  8. Hamedalla, A.M.; Ali, M.M.; Ali, W.M.; Ahmed, M.A.A.; Kaseb, M.O.; Kalaji, H.M.; Gajc-Wolska, J.; Yousef, A.F. Increasing the performance of cucumber (Cucumis sativus L.) seedlings by LED illumination. Sci. Rep. 2022, 12, 12–17. [Google Scholar] [CrossRef]
  9. Minchin, A.; Simon, E.W. Chilling injury in cucumber leaves in relation to temperature. J. Exp. Bot. 1973, 24, 1231–1235. [Google Scholar] [CrossRef]
  10. Zhao, L.J.; Sun, Y.P.; Hernandez-Viezcas, J.A.; Servin, A.D.; Hong, J.; Niu, G.H.; Peralta-Videa, J.R.; Duarte-Gardea, M.; Gardea-Torresdey, J.L. Influence of CeO2 and ZnO nanoparticles on cucumber physiological markers and bioaccumulation of Ce and Zn: A life cycle study. J. Agric. Food Chem. 2013, 61, 11945–11951. [Google Scholar] [CrossRef]
  11. Gajc-Wolska, J.; Kowalczyk, K.; Przybysz, A.; Mirgos, M.; Orliński, P. Photosynthetic Efficiency and Yield of Cucumber (Cucumis sativus L.) Grown under HPS and LED Lighting in Autumn–Winter Cultivation. Plants 2021, 10, 2042. [Google Scholar] [CrossRef]
  12. Yan, Z.N.; Wang, L.; Wang, Y.F.; Chu, Y.Y.; Lin, D.; Yang, Y.J. Morphological and physiological properties of Greenhouse-Grown cucumber seedlings as influenced by supplementary Light-Emitting diodes with same daily light integral. Horticulturae 2021, 7, 361. [Google Scholar] [CrossRef]
  13. Górnik, K. Sensitivity of ‘Monika’ Cucumis sativus seedlings to low temperature and induction of chilling tolerance. Plant Breed. Seed Sci. 2015, 71, 3–11. [Google Scholar] [CrossRef]
  14. Zhao, H.L.; Yang, Z.Q. Effect of low temperature and weak light single factor stress on photosynthesis characteristics, dry weight distribution and fruit quality of greenhouse cucumber leaves. North. Hortic. 2018, 2, 1–8. [Google Scholar] [CrossRef]
  15. Zhang, Y.; Henke, M.; Li, Y.M.; Xu, D.M.; Liu, A.H.; Liu, X.G.; Li, T.L. Towards the maximization of energy performance of an energy-saving Chinese solar greenhouse: A systematic analysis of common greenhouse shapes. Sol. Energy 2022, 236, 320–334. [Google Scholar] [CrossRef]
  16. Hui, A.B. Temperature and Light Characteristics of Assembly Sunlight Greenhouse and Its Effect on Tomato Production. Master’s Thesis, Northeast Agricultural University, Haerbin, China, 2019. [Google Scholar]
  17. Zhang, Z.; Wang, P.; Chen, Y.; Zhang, S.; Tao, F.L.; Liu, X.F. Spatial pattern and decadal change of agro-meteorological disasters in the main wheat production area of China during 1991–2009. J. Geogr. Sci. 2014, 24, 387–396. [Google Scholar] [CrossRef]
  18. Fan, S.X.; Yang, C.X.; Yang, Q.L.; Han, S.C. Prediction model of Panax notoginseng leaf area growth based on particle swarm-optimization random forest algorithm and meteorological data. Chin. Tradit. Herb. Drugs 2022, 53, 3103–3110. [Google Scholar] [CrossRef]
  19. Hang, T.; Lu, N.; Takagaki, M.; Mao, H.P. Leaf area model based on thermal effectiveness and photosynthetically active radiation in lettuce grown in mini-plant factories under different light cycles. Sci. Hortic. 2019, 252, 113–120. [Google Scholar] [CrossRef]
  20. Liu, Y.; Yun, X.F.; Wang, Y. Analysis of model between dry weight accumulation and thermal radiation accumulation for greenhouse tomato. J. Agric. Mech. Res. 2020, 42, 29–33. [Google Scholar] [CrossRef]
  21. Chen, Y.; Xu, M.Z.; Wang, Y.H.; Bai, Y.L.; Lu, Y.L.; Wang, L. Quantitative study on effective accumulated temperature and dry weight and nitrogen accumulation of summer maize under different nitrogen supply levels. Sci. Agric. Sin. 2022, 55, 2973–2987. [Google Scholar] [CrossRef]
  22. Yang, Z.Q.; Luo, W.H.; Chen, F.D.; Xie, Y.P.; Gu, J.J. A photo-thermal based model for predicting the dry weight production and partitioning of multi-stem cut Chrysanthemum in greenhouse. Acta Ecol. Sin. 2009, 29, 1478–1485. [Google Scholar] [CrossRef]
  23. Ding, B.; Liang, H.H.; Zhao, X.; Li, S.L.; Sun, R.D.; Guo, S.N.; Chen, B.Y.; Wang, H.J.; Wang, N.; Sun, M.Q.; et al. Effect of different coverage methods on dry weight accumulation and yield of maize in low temperature cold zone. Mol. Plant Breed. 2022, 20, 1358–1362. [Google Scholar] [CrossRef]
  24. Sionit, N.; Strain, B.R.; Flint, E.P. Interaction of temperature and co2 enrichment on soybean: Growth and dry weight partitioning. Can. J. Plant Sci. 1987, 67, 59–67. [Google Scholar] [CrossRef]
  25. Liu, K.Z.; Zhang, C.X.; Guan, B.B.; Yang, R.; Liu, K.; Wang, Z.Z.; Li, X.; Xue, K.Y.; Yin, L.J.; Wang, X.Y. The effect of different sowing dates on dry weight and nitrogen dynamics for winter wheat: An experimental simulation study. PeerJ 2021, 9, e11700. [Google Scholar] [CrossRef]
  26. Chen, Z.W.; Wang, Y.F.; Zhang, X.; Yu, H.S. Progress on Study of production of Cucumber Hybrid Seeds. Chin. Agric. Sci. Bull. 2005, 32, 245–248. [Google Scholar] [CrossRef]
  27. Odhiambo, J.A.; Aguyoh, J.N. Soil moisture levels affect growth, flower production and yield of cucumber. Agric. Trop. Subtrop. 2022, 55, 1–8. [Google Scholar] [CrossRef]
  28. Liu, R.; Wang, H.; Guzmán, J.L.; Li, M. A model-based methodology for the early warning detection of cucumber downy mildew in greenhouses. Comput. Electron. Agric. 2022, 194, 106751. [Google Scholar] [CrossRef]
  29. Zhou, Y.H.; Yu, J.Q.; Qian, Q.Q.; Huang, L.F. Effects of chilling and low light on cucumber seedlings growth and their antioxidative enzyme activities. Chin. J. Appl. Ecol. 2003, 49, 921–924. [Google Scholar]
  30. Xue, X.P.; Li, N.; Zhang, J.B.; Xiong, Y. Effects of sparse sunlight on the growth in the flowering and fruit set stage and the fruit quality of cucumber in solar greenhouse. J. Mar. Meteorol. 2020, 40, 77–83. [Google Scholar] [CrossRef]
  31. Laktionov, I.; Vovna, O.; Kabanets, M.; Derzhevetska, M.; Zori, A. Mathematical model of measuring monitoring and temperature control of growing vegetables in greenhouses. Int. J. Des. Nat. Ecodyn. 2020, 15, 325–336. [Google Scholar] [CrossRef]
  32. Lee, S.S.; Yang, S.K.; Hong, S.B. A GDD model for super sweet corn grown under black PE film mulch. Korean J. Crop Sci. 2008, 53, 42–49. [Google Scholar]
Figure 1. Simulation model of LT and PL stress effect. (a) Three-dimensional figure of stress effects with temperature and time under the 400 μmol∙m−2∙s−1 poor light; (b) 3D figure of stress effects with temperature and time under the 200 μmol∙m−2∙s−1 poor light.
Figure 1. Simulation model of LT and PL stress effect. (a) Three-dimensional figure of stress effects with temperature and time under the 400 μmol∙m−2∙s−1 poor light; (b) 3D figure of stress effects with temperature and time under the 200 μmol∙m−2∙s−1 poor light.
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Figure 2. Stress effect under different LT and PL stresses. (a) Changes in stress effect with recovery days for each treatment at 3 °C; (b) changes in stress effect with recovery days for each treatment at 6 °C; (c) changes in stress effect with recovery days for each treatment at 9 °C; (d) changes in stress effect with recovery days for each treatment at 12 °C. Note: The stress effect represents the level of stress, the lower the stress effect, the more severe the stress on cucumber. Control represents the stress effect on cucumbers under normal growth conditions (here 1).
Figure 2. Stress effect under different LT and PL stresses. (a) Changes in stress effect with recovery days for each treatment at 3 °C; (b) changes in stress effect with recovery days for each treatment at 6 °C; (c) changes in stress effect with recovery days for each treatment at 9 °C; (d) changes in stress effect with recovery days for each treatment at 12 °C. Note: The stress effect represents the level of stress, the lower the stress effect, the more severe the stress on cucumber. Control represents the stress effect on cucumbers under normal growth conditions (here 1).
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Figure 3. Comparison of observed and simulated values of shoot dry weight. Note: This line represents the situation when the observed and simulated values coincide exactly. Comparing the simulated values with the line provides a better view of the simulation.
Figure 3. Comparison of observed and simulated values of shoot dry weight. Note: This line represents the situation when the observed and simulated values coincide exactly. Comparing the simulated values with the line provides a better view of the simulation.
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Figure 4. Variation trend of leaf dry weight of cucumber under LT and PL stresses. (a) Changes in dry weight of single cucumber leaves with LTE under 3 °C treatments. (b) Changes in dry weight of single cucumber leaves with LTE under 6 °C treatments. (c) Changes in dry weight of single cucumber leaves with LTE under 9 °C treatments. (d) Changes in dry weight of single cucumber leaves with LTE under 12 °C treatments. Note: Control represents the variation in leaf dry weight per cucumber plant with the increase in LTE under normal growth conditions.
Figure 4. Variation trend of leaf dry weight of cucumber under LT and PL stresses. (a) Changes in dry weight of single cucumber leaves with LTE under 3 °C treatments. (b) Changes in dry weight of single cucumber leaves with LTE under 6 °C treatments. (c) Changes in dry weight of single cucumber leaves with LTE under 9 °C treatments. (d) Changes in dry weight of single cucumber leaves with LTE under 12 °C treatments. Note: Control represents the variation in leaf dry weight per cucumber plant with the increase in LTE under normal growth conditions.
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Figure 5. Variation trend of stem dry weight per plant of cucumber under LT and PL stresses. (a) Changes in dry weight of single cucumber stems with LTE under 3 °C treatments. (b) Changes in dry weight of single cucumber stems with LTE under 6 °C treatments. (c) Changes in dry weight of single cucumber stems with LTE under 9 °C treatments. (d) Changes in dry weight of single cucumber stems with LTE under 12 °C treatments. Note: Control represents the variation in stem dry weight per cucumber plant with the increase in LTE under normal growth conditions.
Figure 5. Variation trend of stem dry weight per plant of cucumber under LT and PL stresses. (a) Changes in dry weight of single cucumber stems with LTE under 3 °C treatments. (b) Changes in dry weight of single cucumber stems with LTE under 6 °C treatments. (c) Changes in dry weight of single cucumber stems with LTE under 9 °C treatments. (d) Changes in dry weight of single cucumber stems with LTE under 12 °C treatments. Note: Control represents the variation in stem dry weight per cucumber plant with the increase in LTE under normal growth conditions.
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Figure 6. Variation trend of fruit dry weight per plant of cucumber under LT and PL stresses. (a) Changes in dry weight of single cucumber fruits with LTE under 3 °C treatments. (b) Changes in dry weight of single cucumber fruits with LTE under 6 °C treatments. (c) Changes in dry weight of single cucumber fruits with LTE under 9 °C treatments. (d) Changes in dry weight of single cucumber fruits with LTE under 12 °C treatments. Note: Control represents the variation in fruit dry weight per cucumber plant with the increase in LTE under normal growth conditions.
Figure 6. Variation trend of fruit dry weight per plant of cucumber under LT and PL stresses. (a) Changes in dry weight of single cucumber fruits with LTE under 3 °C treatments. (b) Changes in dry weight of single cucumber fruits with LTE under 6 °C treatments. (c) Changes in dry weight of single cucumber fruits with LTE under 9 °C treatments. (d) Changes in dry weight of single cucumber fruits with LTE under 12 °C treatments. Note: Control represents the variation in fruit dry weight per cucumber plant with the increase in LTE under normal growth conditions.
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Figure 7. The relationship between the leaf dry weight distribution ratio of cucumbers and LTE under LT and PL stresses. (a) Changes in leaf dry weight distribution ratio with LTE under 3 °C treatments. (b) Changes in leaf dry weight distribution ratio with LTE under 6 °C treatments. (c) Changes in leaf dry weight distribution ratio with LTE under 9 °C treatments. (d) Changes in leaf dry weight distribution ratio with LTE under 12 °C treatments. Note: Control represents the variation in leaf dry weight fit (proportion) per cucumber plant with the increase in LTE under normal growth conditions.
Figure 7. The relationship between the leaf dry weight distribution ratio of cucumbers and LTE under LT and PL stresses. (a) Changes in leaf dry weight distribution ratio with LTE under 3 °C treatments. (b) Changes in leaf dry weight distribution ratio with LTE under 6 °C treatments. (c) Changes in leaf dry weight distribution ratio with LTE under 9 °C treatments. (d) Changes in leaf dry weight distribution ratio with LTE under 12 °C treatments. Note: Control represents the variation in leaf dry weight fit (proportion) per cucumber plant with the increase in LTE under normal growth conditions.
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Figure 8. The relationship between the steam dry weight distribution ratio of cucumbers and LTE under LT and PL stresses. (a) Changes in steam dry weight distribution ratio with LTE under 3 °C treatments. (b) Changes in steam dry weight distribution ratio with LTE under 6 °C treatments. (c) Changes in steam dry weight distribution ratio with LTE under 9 °C treatments. (d) Changes in steam dry weight distribution ratio with LTE under 12 °C treatments. Note: Control represents the variation in stem dry weight fit (proportion) per cucumber plant with the increase in LTE under normal growth conditions.
Figure 8. The relationship between the steam dry weight distribution ratio of cucumbers and LTE under LT and PL stresses. (a) Changes in steam dry weight distribution ratio with LTE under 3 °C treatments. (b) Changes in steam dry weight distribution ratio with LTE under 6 °C treatments. (c) Changes in steam dry weight distribution ratio with LTE under 9 °C treatments. (d) Changes in steam dry weight distribution ratio with LTE under 12 °C treatments. Note: Control represents the variation in stem dry weight fit (proportion) per cucumber plant with the increase in LTE under normal growth conditions.
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Figure 9. The relationship between the fruit dry weight distribution ratio of cucumbers and LTE under LT and PL stresses. (a) Changes in fruit dry weight distribution ratio with LTE under 3 °C treatments. (b) Changes in fruit dry weight distribution ratio with LTE under 6 °C treatments. (c) Changes in fruit dry weight distribution ratio with LTE under 9 °C treatments. (d) Changes in fruit dry weight distribution ratio with LTE under 12 °C treatments. Note: Control represents the variation in fruit dry weight fit (proportion) per cucumber plant with the increase in LTE under normal growth conditions.
Figure 9. The relationship between the fruit dry weight distribution ratio of cucumbers and LTE under LT and PL stresses. (a) Changes in fruit dry weight distribution ratio with LTE under 3 °C treatments. (b) Changes in fruit dry weight distribution ratio with LTE under 6 °C treatments. (c) Changes in fruit dry weight distribution ratio with LTE under 9 °C treatments. (d) Changes in fruit dry weight distribution ratio with LTE under 12 °C treatments. Note: Control represents the variation in fruit dry weight fit (proportion) per cucumber plant with the increase in LTE under normal growth conditions.
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Figure 10. Comparison of observed and simulated values of leaf dry weight fit under LT and PL stresses. Note: The line represents the situation when the observed and simulated values coincide exactly. Comparing the simulated values with the line provides a better view of the simulation.
Figure 10. Comparison of observed and simulated values of leaf dry weight fit under LT and PL stresses. Note: The line represents the situation when the observed and simulated values coincide exactly. Comparing the simulated values with the line provides a better view of the simulation.
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Figure 11. Comparison of observed and simulated values of stem dry weight fit under LT and PL stresses. Note: The line represents the situation when the observed and simulated values coincide exactly. Comparing the simulated values with the line provides a better view of the simulation.
Figure 11. Comparison of observed and simulated values of stem dry weight fit under LT and PL stresses. Note: The line represents the situation when the observed and simulated values coincide exactly. Comparing the simulated values with the line provides a better view of the simulation.
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Figure 12. Comparison of observed and simulated values of fruit dry weight fit under LT and PL stresses. Note: The line represents the situation when the observed and simulated values coincide exactly. Comparing the simulated values with the line provides a better view of the simulation.
Figure 12. Comparison of observed and simulated values of fruit dry weight fit under LT and PL stresses. Note: The line represents the situation when the observed and simulated values coincide exactly. Comparing the simulated values with the line provides a better view of the simulation.
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Figure 13. Variation trend of leaf area per plant of cucumber under LT and PL stresses. (a) Changes in leaf area of a single cucumber with LTE under 3 °C treatments. (b) Changes in leaf area of a single cucumber with LTE under 6 °C treatments. (c) Changes in leaf area of a single cucumber with LTE under 9 °C treatments. (d) Changes in leaf area of a single cucumber with LTE under 12 °C treatments. Note: Control represents the variation in leaf area per cucumber plant with the increase in LTE under normal growth conditions.
Figure 13. Variation trend of leaf area per plant of cucumber under LT and PL stresses. (a) Changes in leaf area of a single cucumber with LTE under 3 °C treatments. (b) Changes in leaf area of a single cucumber with LTE under 6 °C treatments. (c) Changes in leaf area of a single cucumber with LTE under 9 °C treatments. (d) Changes in leaf area of a single cucumber with LTE under 12 °C treatments. Note: Control represents the variation in leaf area per cucumber plant with the increase in LTE under normal growth conditions.
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Figure 14. Comparison of observed and simulated values of leaf area fit under LT and PL stresses. Note: The line represents the situation when the observed and simulated values coincide exactly. Comparing the simulated values with the line provides a better view of the simulation.
Figure 14. Comparison of observed and simulated values of leaf area fit under LT and PL stresses. Note: The line represents the situation when the observed and simulated values coincide exactly. Comparing the simulated values with the line provides a better view of the simulation.
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Table 1. Orthogonal experimental design.
Table 1. Orthogonal experimental design.
TreatmentsTemperature [°C]Light [μmol·m−2·s−1]Duration [d]
3-200-2d13/32002
3-200-5d13/32005
3-400-8d13/34008
3-400-11d13/340011
6-400-2d16/64002
6-400-5d16/64005
6-200-8d16/62008
6-200-11d16/620011
9-400-2d19/94002
9-400-5d19/94005
9-200-8d19/92008
9-200-11d19/920011
12-200-2d22/122002
12-200-5d22/122005
12-400-8d22/124008
12-400-11d
Control
22/12
28/18
400
800
11
Table 2. The RMSE in each model.
Table 2. The RMSE in each model.
MODELRMSE [g·Plant−1]R2
LTE4.210.9487
GDD11.610.9391
TEP5.350.9350
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Zhang, F.; Luo, J.; Yuan, C.; Li, C.; Yang, Z. A Model for the Effect of Low Temperature and Poor Light on the Growth of Cucumbers in a Greenhouse. Agronomy 2022, 12, 2992. https://doi.org/10.3390/agronomy12122992

AMA Style

Zhang F, Luo J, Yuan C, Li C, Yang Z. A Model for the Effect of Low Temperature and Poor Light on the Growth of Cucumbers in a Greenhouse. Agronomy. 2022; 12(12):2992. https://doi.org/10.3390/agronomy12122992

Chicago/Turabian Style

Zhang, Fengyin, Jing Luo, Changhong Yuan, Chunying Li, and Zaiqiang Yang. 2022. "A Model for the Effect of Low Temperature and Poor Light on the Growth of Cucumbers in a Greenhouse" Agronomy 12, no. 12: 2992. https://doi.org/10.3390/agronomy12122992

APA Style

Zhang, F., Luo, J., Yuan, C., Li, C., & Yang, Z. (2022). A Model for the Effect of Low Temperature and Poor Light on the Growth of Cucumbers in a Greenhouse. Agronomy, 12(12), 2992. https://doi.org/10.3390/agronomy12122992

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