# Root Distribution of Tomato Cultivated in Greenhouse under Different Ventilation and Water Conditions

^{*}

## Abstract

**:**

_{p}) (i.e., K

_{0.9}: 0.9 E

_{p}; K

_{0.5}: 0.5 E

_{p}), and three ventilation modes through opening the greenhouse vents at different locations (T

_{R}: open the roof vents only; T

_{RS}: open both the roof and south vents; T

_{S}: open the south vents only) to reveal the effects of the ventilation mode and irrigation amount on the root distribution of greenhouse tomato. Six treatments were designed in blocks with the ventilation mode as the main treatment and the irrigation amount as the vice treatment. On this basis, the normalized root length density (NRLD) model of six treatments was developed by considering air environment, soil water and temperature conditions, root length density (RLD) and yield. The results showed that air speed of the T

_{RS}was significantly higher than T

_{R}and T

_{S}(p < 0.01), and the air temperature and relative humidity under different ventilation showed the rule: T

_{R}> T

_{S}> T

_{RS}. There was a significant third-order polynomial function relationship between NRLD and soil depth, and the coefficient of the cubic term (R

_{0}) had a bivariate quadratic polynomial function relationship with irrigation amount and air speed (determination coefficient, R

^{2}= 0.86). Root mean square errors of the simulated and measured value of NRLD under T

_{R}, T

_{RS}and T

_{S}were 0.20, 0.23 and 0.27 in 2020, and 0.31, 0.23 and 0.28 in 2021, respectively, normalized root mean squared errors were 15%, 17%, 20% in 2020, and 23%, 18% and 21% in 2021. The RLD distribution ratio from the ground surface to a one-quarter relative root depth was 74.1%, and 88.0% from the surface to a one-half relative root depth. The results of the yield showed that a better combination of ventilation and irrigation was recommended as T

_{RS}combined with K

_{0.9}.

## 1. Introduction

_{2}) is obtained directly through diffusive gas exchange from the atmosphere to the inter-root soil. Ventilation can increase the concentration of oxygen in greenhouse. Ventilation can also reduce the physiological stress of the crop by changing the temperature and humidity in the greenhouse [13]. At the same time, suitable temperature and humidity would reduce the production of crop pests and diseases [14], hence promoting crop growth. In addition, the effect of different drip irrigation amounts on tomato root distribution was also studied [15,16], and the results indicated that mild water stress favored the growth of deep root. Ullah et al. (2021) [17] reported that root length and root surface area can significantly increase by reducing the irrigation amount. Mild water stress could promote root growth and plant development, which is beneficial for improving water use efficiency [18].

## 2. Results

#### 2.1. Meteorological, Soil Water and Temperature Condition

#### 2.1.1. Variations in Meteorological Factors

_{RS}> T

_{S}> T

_{R}, and it varied between 0.06 and 0.27 m s

^{−1}in the T

_{R}treatment, 0.06 and 0.72 m s

^{−1}in the T

_{RS}treatment, and 0.06 and 0.36 m s

^{−1}in the T

_{S}treatment in the two study years. The difference in air speed for the three ventilation treatments began to increase significantly 60 days after transplanting, and also significant differences between T

_{RS}and the other two treatments were found in our study (Table 1), which was entirely due to the different opening states of these vents.

_{a}and RH under the three ventilation treatments are shown in Figure 2; the right and middle panels indicate the average T

_{a}and RH during the whole growth stages, and the left panel indicates the cumulative T

_{a}and RH with the number of days after transplanting, for the whole growth stages, and the opposite trends for daily T

_{a}and RH are obvious [28]. The average daily T

_{a}and RH of the T

_{RS}treatment were highly significantly different from the T

_{R}and T

_{S}treatments (p < 0.01) in the middle of the day during the ventilation period (9:00–17:00) for the two seasons. The daily T

_{a}and RH of the T

_{RS}were 28.06 °C and 59.36%, which were 6.4%, 5.1% and 7.4%, 5.5% lower than the T

_{R}and T

_{S}, respectively. The cumulative temperature was closer between T

_{R}and T

_{S}but ~6.48% and ~5.38% higher than T

_{RS}in 2020 and 2021, respectively. The daily T

_{a}and RH of T

_{RS}were 28.06 °C and 59.36%, which were 6.4% and 5.1% and 7.4% and 5.5% lower than those of T

_{R}and T

_{S}, respectively. The maximum RH (96.61%) during both growth periods occurred in T

_{R}, which was 1.07% and 1.12% higher than that T

_{RS}and T

_{S}, respectively.

#### 2.1.2. Variations in Soil Water and Temperature

_{RS}was 0.87% °C

^{−1}in K

_{0.9}, which was 11.2% and 13.3% lower than T

_{R}and T

_{S}, respectively. However, for T

_{RS}, the ST and SWC of the two irrigation treatments (K

_{0.9}and K

_{0.5}) were significantly different, with an overall higher soil temperature and water content in K

_{0.9}treatment than K

_{0.5}treatment, while the difference of SWC/ST between K

_{0.5}and K

_{0.9}was not significant.

#### 2.2. Distribution of Root Length Density

_{0.9}> K

_{0.5}, and RLD within rows was higher than between rows. K

_{0.9}and K

_{0.5}within rows increased by 19.9% and 18.5% (T

_{R}), 15.0% and 21.3% (T

_{RS}) and 19.0% and 14.9% (T

_{S}) compared to between rows. The RLD under T

_{R}and T

_{S}varied from 10.44 to 15.20 cm cm

^{−3}and 5.00 to 9.94 cm cm

^{−3}, respectively, while T

_{RS}was between 6.55 and 7.58 cm cm

^{−3}under the K

_{0.9}and K

_{0.5}treatments, respectively. There was a larger change between T

_{R}and T

_{S}, while the RLD of T

_{RS}was similar under two different irrigation treatments. A similar phenomena can be found where the RLD under the three ventilation treatments decreased gradually with the deepening of the soil layer below 10 cm soil depth.

#### 2.3. Development of the NRLD Model

^{2}was greater than 0.96 for all treatments, and the F−statistics reached a highly significant level (p < 0.01). There was a highly significant correlation between soil depth and RLD, indicating that the function was good. Correlation coefficients for each treatment (R

_{0}, R

_{1}and R

_{2}) can represent the trend of curve and varied from −97.16 to −79.01, 128.15 to 152.77 and −72.73 to −66.67, respectively. Under the same ventilation mode, R

_{0}and R

_{2}decreased and then increased, while R

_{1}increased then decreased with the increase in irrigation amount. Under the same irrigation amount, R

_{0}and R

_{2}increased then decreased, while R

_{1}decreased then increased with the increase in air speed. R

_{3}represented the relative RLD value at the relative depth Z

_{r}= 0. As no root system existed at soil surface (0 cm), it could be regarded as a theoretical value and did not have practical significance.

_{1}, R

_{2}, R

_{3}and R

_{0}(p < 0.01) (Figure 5). With K and S as independent variables and parameter R

_{0}as the dependent variable over the whole growth stages, the binary quadratic polynomial function could be fitted by using the Levenberg–Marquardt method combined with the general global optimization algorithm by using 1stOpt software (R

^{2}of 0.86), which could be derived under different irrigation amounts and air speeds in the equation for parameter R

_{0}.

^{−1}.

_{1}, R

_{2}, R

_{3}and R

_{0}, and the calculated Equation (1), expressions of the one-dimensional NRLD distribution model for drip-irrigated greenhouse tomatoes were obtained as follows:

#### 2.4. Validation and Application of the NRLD Distribution Model

#### 2.4.1. Validation

_{r}) were obtained by substituting the irrigation amount and average air speed into Equation (2). The measured and simulated values were compared to obtain a 1:1 histogram at three ventilation treatments (data points = 72) (Figure 6) and a plot of NRMSE statistics (data points = 12) (Table 3). Under different irrigation amounts, the simulated values of three ventilation treatments agreed well with the measured values, and the RMSE under T

_{R}, T

_{RS}and T

_{S}was 0.20, 0.23 and 0.27 in 2020, and 0.31, 0.23 and 0.28 in 2021, respectively. Table 3 shows the NRMSE between the simulated and measured values of the relative RLD in the two study years. The NRMSE between the simulated and measured values under T

_{R}, T

_{RS}and T

_{S}was 19%, 18% and 21%, respectively, indicating that the performance of the model was perfect.

#### 2.4.2. Application

_{max}) and RLD are known. A relative RLD distribution model was used to accurately estimate the ratio of root length to total root length (RL/TRL) under different relative sampling depths by using two irrigation amounts and three ventilation treatments in 2020 (irrigation amount: K

_{0.9}= 247.5 mm, K

_{0.5}= 137.5 mm; average air speed: T

_{R}= 0.092 m s

^{−1}, T

_{RS}= 0.152 m s

^{−1}, T

_{S}= 0.115 m s

^{−1}). The relative RLD and RL/TRL were estimated and compared with the measured data (Table 4). The drip-irrigated tomato roots were mainly distributed in the upper soil layer (0–20 cm), which was similar to the research results of Li et al. (2020) [31]. Root length from the surface to a one-quarter depth of relative root system accounted for 74.1% of total root length, while that from the surface to a one-half depth of relative root system accounted for 88.0%. Under the same irrigation amount, RL/TRL increased then decreased with the increase in air speed. Under the same ventilation, RL/TRL increased gradually with the increase in irrigation amount, the difference between the simulated and measured values was greater in the high irrigation amount than in the low irrigation amount, and measured values were lower than the simulated values. Using 2020 as an example, the root distribution under different ventilation and irrigation amount with pan evaporation coefficients of 0.6, 0.8 and 1.0 was predicted (Table 5). The one-dimensional NRLD distribution model can clarify the proportion of root distribution at different soil layers under different ventilation and irrigation amounts, which is beneficial to guide the middle and late ventilation and irrigation management of greenhouse tomatoes.

#### 2.5. Root System and Yield

_{RS}K

_{0.9}treatment has the highest yield (147.6 t ha

^{−1}and 148.1 t ha

^{−1}), followed by the T

_{S}K

_{0.9}treatment (143.1 t ha

^{−1}and 144.5 t ha

^{−1}). The lowest is the T

_{RS}K

_{0.5}treatment (119.4 t ha

^{−1}and 126.3 t ha

^{−1}). At the highest yield, the root length density is not the highest, but it is at the middle level of the six treatments. Considering the tomato yield, root length density, irrigation amount and ventilation, the combination of T

_{RS}and K

_{0.9}is beneficial to increase tomato yield.

## 3. Discussion

#### 3.1. Effect of Ventilation Mode and Irrigation Amount on Greenhouse Environment

_{RS}was significantly higher than T

_{R}and T

_{S}. The magnitude of the indoor air speed was a good measure of the water vapor diffusion capacity of the greenhouse [35]. T

_{a}and RH were also influenced by the ventilation mode, which showed that T

_{R}> T

_{S}> T

_{RS}in our study. The air speed regulated the T

_{a}and RH around the leaves, as well as the rate of water vapor diffusion, which had a significant impact on the internal environment of the greenhouse [36]. For instance, T

_{a}and RH of T

_{RS}were significantly lower than T

_{R}(Table 1). ST and SWC, as important indicators of the soil environment, were influenced by the irrigation amount. The ASWC/ST in the 0–20 cm soil layer of T

_{R}was significantly higher than T

_{RS}, because air speed had a greater effect on crop evapotranspiration and low air speed slowed the soil water depletion process.

#### 3.2. Effect of Ventilation Mode and Irrigation Amount on RLD Distribution

_{0.9}> K

_{0.5}. In this study, root length increased then decreased from the surface to one-half of the relative root depth, with 85.8%, 88.5% and 89.7% for T

_{R}, T

_{RS}and T

_{S}, respectively, indicating that excessive air speed did not increase root amount at one-half, and suitable ventilation is conducive to the growth of tomato roots. Similar findings have been proven by Ge et al. (2019) [41]. The reason was primarily due to excessive air speed causing the crop stomata to close, affecting transpiration and preventing the roots from absorbing water and nutrients properly. A higher proportion of deep soil root distribution in better plants would be able to absorb nutrients from deep soil to improve yield [42,43]. Shu et al. (2020) [44] analyzed the effect of different drip irrigation rates on tomato roots and found that moderate deficit irrigation was beneficial to root “active rooting”, which promoted the development of aboveground parts by absorbing water and nutrients from soil, increasing yields and harvests and improving water use efficiency.

#### 3.3. Performance of NRLD Distribution Models in Different Ventilation Mode and Irrigation Amount

_{0}, R

_{1}and R

_{2}can reflect the simulation accuracy. R

_{3}is a theoretical value of the NRLD at the surface, and other parameters accurately describe the accuracy of root development fit in soil. Wu et al. (1999) [9] used the normalization method to establish a third-order polynomial function for the relative RLD of wheat and maize, and the function was able to accurately simulate root variation in different soil layers, which was similar to our study. Use of normalization had a good applicability for root modeling. Compared with the maize root system model established by Zou et al. (2018) [28], the parameters R

_{0}and R

_{2}fitted in this study were small, while the parameters R

_{1}and R

_{3}were large. The mean value of R

_{3}was 12.19, which was higher than the value of 5.51 fitted by Zou et al. (2018) [28]. The reason for the difference were that the root system data collected by Zou et al. were all roots, while the tomato root system in our study was partial roots, resulting in the differences in model parameters. However, the R

^{2}of the binary quadratic polynomial fit (Equation (1)) under different ventilation modes and irrigation amounts was 0.86, higher than the fitting accuracy of Zou et al. (R

^{2}of 0.84), which may be caused by differences in crop type, root sampling depths and sample numbers.

#### 3.4. Application of the NRLD Distribution Model

_{0.9}and K

_{0.5}averaged 88.9% and 87.1%, respectively, and the simulated values were close to the measured values. Combining the 2020 ventilation data and setting evaporation pan coefficients of 1.0, 0.8 and 0.6 for these three irrigation treatments, the results show that reducing irrigation leads to an increase in the proportion of deep roots, and conversely, an increase in the proportion of shallow roots. Combined with the NRLD distribution model, the proportion of greenhouse drip-irrigated tomato roots distributed in different soil layers could be determined in real time, providing a reference for optimizing water and environmental management strategies for crops in middle and late periods. For instance, when the proportion of shallow soil roots is high, the irrigation amount could be reduced, and ventilation could be controlled in an appropriate amount to save water and increase yields.

#### 3.5. Effect of Ventilation and Irrigation Amount on Tomato Yield

_{0.9}) was significantly higher than the low irrigation treatment (K

_{0.5}), which is the same as the results of Li et al. (2020) [47], in which a water deficit would be caused under low irrigation treatment, leading to difficulties in water uptake by plant roots, limiting nutrient translocation, affecting carbohydrate synthesis and fruit quantity and quality, as well as affecting plant physiology and reducing plant yield. In addition, a low irrigation amount would lead to a reduced stomatal closure and photosynthetic capacity of plants, and then result in a reduced substance synthesis and lower yields [48]. In our study, a combination of T

_{RS}and K

_{0.9}treatments was the most beneficial for greenhouse tomato yield.

## 4. Materials and Methods

#### 4.1. Experimental Site and Design

^{2}. The direction of the greenhouse is east–west, and it sinks 0.5 m. A steel frame construction is used to support the roof of the greenhouse and is covered with a 0.2 mm thick polyethylene drip-free film to maintain the air temperature inside. Meanwhile, 5 cm thick insulation quilts are used to maintain warmth at the seedling stage. There are three vents, one on the roof (60 m × 30 cm) and another on the bottom of the south side (60 m × 1.5 m). The soil in the greenhouse at a depth of 0–100 cm is a silt loam, including 16.3% clay, 77.1% silt and 6.6% sand. Mean field water capacity and wilting water content at a soil depth of 0–100 cm are 0.31 and 0.11 cm

^{3}cm

^{−3}, respectively, with an average bulk density of 1.59 g cm

^{−3}.

^{−2}. Drip irrigation was used, with a drip head flow rate of 1.1 L h

^{−1}. Each plot was replicated three times, and six treatments were designed in blocks with the ventilation mode as the main treatment and the irrigation amount as the vice treatment. Here, three ventilation treatments were set: T

_{R}(open the roof vents only), T

_{RS}(open both the roof and south vents), T

_{S}(open the south vents only), and two irrigation treatments were set according to the cumulative water evaporation (E

_{p}) from a standard 20 cm evaporation pan (20 cm diameter and 11 cm deep): K

_{0.9}(0.9 E

_{p}) and K

_{0.5}(0.5 E

_{p}). The irrigation events were performed based on the average surface evaporation of the three ventilation treatments. According to the results of our previous research [49], soil water content of 0–60 cm accounts for 80–90% and 60–65% of field water capacity, respectively, under an irrigation amount of 0.9 E

_{p}and 0.5 E

_{p}. The evaporation pan was placed 30 cm above the crop canopy and adjusted according to tomato plants’ development. The evaporation water amount was measured every day at 8:00 using a measuring cylinder with an accuracy of 0.1 mm, and 20 mm of distilled water was refilled after the measurement. When the accumulated water evaporation reaches 20 ± 2 mm, irrigation events was conducted [50]. The irrigation amount (I

_{r}) was calculated according to Equation (3).

_{r}is the irrigation amount, mm; E

_{p}is the accumulated evaporation, mm; and φ is the water surface evaporation coefficient.

^{3}was installed at the head of each plot to precisely control the irrigation amount. A supplementary irrigation amount of 20 mm was performed by drip irrigation after transplanting to maintain the seedlings alive. In this study, 112 kg hm

^{−2}urea (containing 46% N), 150 kg hm

^{−2}potassium sulfate (containing 50% K

_{2}O), and 120 kg hm

^{−2}superphosphate (containing 14% P

_{2}O

_{5}) were used as base fertilizers and ploughed to a depth of 16 cm with a rotary spade. Thereafter, differential pressure fertilizer tanks were used for topdressing urea at 18.8 kg hm

^{−2}and potassium sulfate at 25 kg hm

^{−2}. The fertilizer was applied four times when the first, second, third and fourth truss fruits began to expand. The agronomic practices (e.g., topping, spraying, fruit thinning) were the same as those used locally. The irrigation amounts were 247.5 and 245.7 mm (K

_{0.9}) and 137.5 and 136.5 mm (K

_{0.5}) in 2020 and 2021, respectively.

#### 4.2. Measurement

#### 4.2.1. Environmental Factors

^{−1}. Data were collected every 5 s, and the 15 min average was recorded in a CR1000 data logger (Campbell Scientific Inc., Logan, UT, USA). The air temperature and relative humidity were measured by using an automatic climate station (CS215, Campbell Scientific, Inc, Monterrey, CA, USA) with accuracies of 0.02 °C and 0.18 °C, and 15 min averages were calculated and stored.

#### 4.2.2. Soil Water Content

^{−6}% was used to determine the water content of the soil layer at 0, 10, 20, 30, 40 and 60 cm in the middle of two drip heads of the same drip tape [51], with data collected automatically every 15 min.

#### 4.2.3. Soil Temperature

#### 4.2.4. Root Distribution

^{−3}) was calculated according to Equation (4):

^{−3}; RL is the root length in different soil layers, cm and V is the root auger volume (384.85 cm

^{3}).

#### 4.2.5. Yield

#### 4.3. Root Length Density Distribution Model

_{r}is the standardized root depth, between 0 and 1, dimensionless; Z

_{i}is the depth of the rooted soil layer, cm; Z

_{max}is the maximum rooting depth, cm, obtained for the root-free soil layer, and the maximum rooting depth in this experiment is 80 cm; NRLD (Z

_{r}) is the relative root length density value, dimensionless; and RLD (Z

_{r}) is the root length density value at Z

_{r}, cm cm

^{−3}.

_{r}) at each lateral position at the relative sampling depth (Z

_{r}) based on previous studies [9,52]. The equation is as follows:

_{0}, R

_{1}and R

_{2}are polynomial parameters and R

_{3}represents the theoretical value of the NRLD at the surface.

#### 4.4. Model Evaluation

^{2}), root mean square error (RMSE) and normalized root mean squared error (NRMSE).

_{i}is the measured values; ${\widehat{\mathrm{Y}}}_{\mathrm{i}}$ is the simulated values; $\overline{\mathrm{Y}}$ is the measured mean value; and $\overline{\widehat{\mathrm{Y}}}$ is the simulated mean value. R

^{2}is close to 1, the better the correlation is; RMSE can indicate the average difference between the simulated and observed values, and the closer it is to 0, the smaller the deviation is. NRMSE indicates how good the model simulation performance is; when NRMSE < 10%, the model simulation performance is excellent; when 10% ≤ NRMSE < 20%, the model simulation performance is good; when 20% ≤ NRMSE < 30%, the model simulation performance is average; and when NRMSE ≥ 30%, the simulation performance is considered poor.

## 5. Conclusions

_{RS}was more beneficial to greenhouse gas exchange than T

_{R}and T

_{S}. T

_{a}and RH were affected by the ventilation treatments in the following manner: T

_{R}> T

_{S}> T

_{RS}. The ventilation and irrigation amount had an interaction effect on soil water and temperature.

_{0}) had a bivariate quadratic polynomial relationship with the irrigation amount and air speed. The RMSE between the simulated and measured values of NRLD at K

_{0.9}and K

_{0.5}were 0.20, 0.23 and 0.27 in 2020 and 0.31, 0.23 and 0.28 in 2021, respectively, indicating that performance of the model was perfect.

_{RS}and K

_{0.9}was the most beneficial to increase tomato yield.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

- Zhen, H.; Gao, W.; Jia, L.; Qiao, Y.; Ju, X. Environmental and economic life cycle assessment of alternative greenhouse vegetable production farms in peri-urban Beijing, China. J. Clean. Prod.
**2020**, 269, 122380. [Google Scholar] [CrossRef] - Wei, Z.; Du, T.; Li, X.; Fang, L.; Liu, F. Interactive Effects of Elevated CO
_{2}and N Fertilization on Yield and Quality of Tomato Grown under Reduced Irrigation Regimes. Front. Plant Sci.**2018**, 9, 328. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Ghannem, A.; Ben Aissa, I.; Majdoub, R. Effects of regulated deficit irrigation applied at different growth stages of greenhouse grown tomato on substrate moisture, yield, fruit quality, and physiological traits. Environ. Sci. Pollut. Res.
**2021**, 28, 46553–46564. [Google Scholar] [CrossRef] [PubMed] - Zapata-Sierra, A.; Moreno-Pérez, M.; Reyes-Requena, R.; Manzano-Agugliaro, F. Root distribution with the use of drip irrigation on layered soils at greenhouses crops. Sci. Total. Environ.
**2021**, 768, 144944. [Google Scholar] [CrossRef] [PubMed] - Ji, T.; Guo, X.; Wu, F.; Wei, M.; Li, J.; Ji, P.; Wang, N.; Yang, F. Proper irrigation amount for eggplant cultivation in a solar greenhouse improved plant growth, fruit quality and yield by influencing the soil microbial community and rhizosphere environment. Front. Microbiol.
**2022**, 13, 981288. [Google Scholar] [CrossRef] [PubMed] - Gong, X.; Li, X.; Li, Y.; Bo, G.; Qiu, R.; Huang, Z.; Gao, S.; Wang, S. An improved model to simulate soil water and heat: A case study for drip-irrigated tomato grown in a greenhouse. Agric. Water Manag.
**2023**, 277, 108121. [Google Scholar] [CrossRef] - Hu, J.; Gettel, G.; Fan, Z.; Lv, H.; Zhao, Y.; Yu, Y.; Wang, J.; Butterbach-Bahl, K.; Li, G.; Lin, S. Drip fertigation promotes water and nitrogen use efficiency and yield stability through improved root growth for tomatoes in plastic greenhouse production. Agric. Ecosyst. Environ.
**2021**, 313, 107379. [Google Scholar] [CrossRef] - Shabbir, A.; Mao, H.; Ullah, I.; Buttar, N.A.; Ajmal, M.; Solangi, K.A. Improving Water Use Efficiency by Optimizing the Root Distribution Patterns under Varying Drip Emitter Density and Drought Stress for Cherry Tomato. Agronomy
**2021**, 11, 3. [Google Scholar] [CrossRef] - Wu, J.; Zhang, R.; Gui, S. Modeling soil water movement with water uptake by roots. Plant Soil
**1999**, 215, 7–17. [Google Scholar] [CrossRef] - Zuo, Q.; Shi, J.; Li, Y.; Zhang, R. Root length density and water uptake distributions of winter wheat under sub-irrigation. Plant Soil
**2006**, 285, 45–55. [Google Scholar] [CrossRef] - Ning, S.; Chen, C.; Zhou, B.; Wang, Q. Evaluation of normalized root length density distribution models. Field Crops Res.
**2019**, 242, 107604. [Google Scholar] [CrossRef] - Li, Y.; Niu, W.; Dyck, M.; Wang, J.; Zou, X. Yields and Nutritional of Greenhouse Tomato in Response to Different Soil Aeration Volume at two depths of Subsurface drip irrigation. Sci. Rep.
**2016**, 6, 39307. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Gázquez, J.; Lopez, J.; Pérez-Parra, J.; Baeza, E.; Sáez, M.; Parra, A. Greenhouse Cooling Strategies for Mediterranean Climate Areas. Acta Hortic.
**2008**, 45, 425–432. [Google Scholar] [CrossRef] - Tantau, H.-J.; Lange, D. Greenhouse climate control: An approach for integrated pest management. Comput. Electron. Agric.
**2003**, 40, 141–152. [Google Scholar] [CrossRef] - Babalola, O.; Fawusi, M.O.A. Drought susceptibility of two tomato (Lycopersicum esculentum) varieties. Plant Soil
**1980**, 55, 205–214. [Google Scholar] [CrossRef] - Sacco, A.; Greco, B.; Di Matteo, A.; De Stefano, R.; Barone, A. Evaluation of Tomato Genetic Resources for Response to Water Deficit. Am. J. Plant Sci.
**2013**, 04, 131–145. [Google Scholar] [CrossRef] [Green Version] - Ullah, I.; Mao, H.; Rasool, G.; Gao, H.; Javed, Q.; Sarwar, A.; Khan, M. Effect of Deficit Irrigation and Reduced N Fertilization on Plant Growth, Root Morphology, and Water Use Efficiency of Tomato Grown in Soilless Culture. Agronomy
**2021**, 11, 228. [Google Scholar] [CrossRef] - Mardani, S.; Tabatabaei, S.H.; Pessarakli, M.; Zareabyaneh, H. Physiological responses of pepper plant (Capsicum annuum L.) to drought stress. J. Plant Nutr.
**2017**, 40, 1453–1464. [Google Scholar] [CrossRef] [Green Version] - Niu, W.-Q.; Jia, Z.-X.; Zhang, X.; Shao, H.-B. Effects of Soil Rhizosphere Aeration on the Root Growth and Water Absorption of Tomato. CLEAN—Soil Air Water
**2012**, 40, 1364–1371. [Google Scholar] [CrossRef] - Howell, T.; Moriconi, J.I.; Zhao, X.; Hegarty, J.; Fahima, T.; Santa-Maria, G.E.; Dubcovsky, J. A wheat/rye polymorphism affects seminal root length and yield across different irrigation regimes. J. Exp. Bot.
**2019**, 70, 4027–4037. [Google Scholar] [CrossRef] [Green Version] - Zubaidi, A.; McDonald, G.K.; Hollamby, G.J. Shoot growth, root growth and grain yield of bread and durum wheat in South Australia. Aust. J. Exp. Agric.
**1999**, 39, 709–720. [Google Scholar] [CrossRef] - Novák, V. Water uptake of maize roots under conditions of non-limiting soil water content. Soil Technol.
**1994**, 7, 37–45. [Google Scholar] [CrossRef] - Yang, D.; Zhang, T.; Zhang, K.; Greenwood, D.J.; Hammond, J.P.; White, P.J. An easily implemented agro-hydrological procedure with dynamic root simulation for water transfer in the crop–soil system: Validation and application. J. Hydrol.
**2009**, 370, 177–184. [Google Scholar] [CrossRef] [Green Version] - Zuo, Q.; Jie, F.; Zhang, R.; Meng, L. A Generalized Function of Wheat’s Root Length Density Distributions. Vadose Zone J.
**2004**, 3, 271–277. [Google Scholar] [CrossRef] - Yadav, B.K.; Mathur, S. Modeling Soil Water Uptake by Plants Using Nonlinear Dynamic Root Density Distribution Function. J. Irrig. Drain. Eng.
**2008**, 134, 430–436. [Google Scholar] [CrossRef] - Novák, V. Estimation of soil-water extraction patterns by roots. Agric. Water Manag.
**1987**, 12, 271–278. [Google Scholar] [CrossRef] - Metselaar, K.; Pinheiro, E.A.R.; Lier, Q.D.J.V. Mathematical Description of Rooting Profiles of Agricultural Crops and its Effect on Transpiration Prediction by a Hydrological Model. Soil Syst.
**2019**, 3, 44. [Google Scholar] [CrossRef] [Green Version] - Gong, X.W.; Qiu, R.J.; Sun, J.S.; Ge, J.K.; Li, Y.B.; Wang, S.S. Evapotranspiration and crop coefficient of tomato grown in a solar greenhouse under full and deficit irrigation. Agric. Water Manag.
**2020**, 235, 106154. [Google Scholar] [CrossRef] - Chandran, U.S.S.; Tarafdar, A.; Mahesha, H.S.; Sharma, M. Temperature and Soil Moisture Stress Modulate the Host Defense Response in Chickpea during Dry Root Rot Incidence. Front. Plant Sci.
**2021**, 12, 653265. [Google Scholar] [CrossRef] - Lahti, M.; Aphalo, P.J.; Finer, L.; Ryyppo, A.; Lehto, T.; Mannerkoski, H. Effects of soil temperature on shoot and root growth and nutrient uptake of 5-year-old Norway spruce seedlings. Tree Physiol.
**2005**, 25, 115–122. [Google Scholar] [CrossRef] [Green Version] - Li, M.; Schmidt, J.E.; Lahue, D.G.; Lazicki, P.; Kent, A.; Machmuller, M.B.; Scow, K.M.; Gaudin, A.C.M. Impact of Irrigation Strategies on Tomato Root Distribution and Rhizosphere Processes in an Organic System. Front. Plant Sci.
**2020**, 11, 360. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Liu, R.; Wang, Z.; Ye, H.; Li, W.; Zong, R.; Tian, X. Effects of Different Plastic Mulching Film on Soil Hydrothermal Status and Water Utilization by Spring Maize in Northwest China. Front. Mater.
**2021**, 8, 774833. [Google Scholar] [CrossRef] - Boulard, T.; Fatnassi, H.; Roy, J.; Lagier, J.; Fargues, J.; Smits, N.; Rougier, M.; Jeannequin, B. Effect of greenhouse ventilation on humidity of inside air and in leaf boundary-layer. Agric. For. Meteorol.
**2004**, 125, 225–239. [Google Scholar] [CrossRef] - Gong, X.; Li, X.; Qiu, R.; Bo, G.; Ping, Y.; Xin, Q.; Ge, J. Ventilation and irrigation management strategy for tomato cultivated in greenhouses. Agric. Water Manag.
**2022**, 273, 107908. [Google Scholar] [CrossRef] - López, A.; Valera, D.L.; Molina-Aiz, F. Sonic Anemometry to Measure Natural Ventilation in Greenhouses. Sensors
**2011**, 11, 9820–9838. [Google Scholar] [CrossRef] [PubMed] - He, X.; Wang, J.; Guo, S.; Zhang, J.; Wei, B.; Sun, J.; Shu, S. Ventilation optimization of solar greenhouse with removable back walls based on CFD. Comput. Electron. Agric.
**2018**, 149, 16–25. [Google Scholar] [CrossRef] - Liu, A.; Qu, Z.; Nachshon, U. On the potential impact of root system size and density on salt distribution in the root zone. Agric. Water Manag.
**2020**, 234, 106118. [Google Scholar] [CrossRef] - Machado, R.M.A.; Do Rosário, M.; Oliveira, G.; Portas, C.A.M. Tomato root distribution, yield and fruit quality under subsurface drip irrigation. Plant Soil
**2003**, 255, 333–341. [Google Scholar] [CrossRef] [Green Version] - Hou, M.; Jin, Q.; Lu, X.; Li, J.; Zhong, H.; Gao, Y. Growth, Water Use, and Nitrate-15N Uptake of Greenhouse Tomato as Influenced by Different Irrigation Patterns, 15N Labeled Depths, and Transplant Times. Front. Plant Sci.
**2017**, 8, 666. [Google Scholar] [CrossRef] - Wang, L.; Ning, S.; Chen, X.; Li, Y.; Guo, W.; Ben-Gal, A. Modeling tomato root water uptake influenced by soil salinity under drip irrigation with an inverse method. Agric. Water Manag.
**2021**, 255, 106975. [Google Scholar] [CrossRef] - Ge, J.K.; Liu, Y.F.; Gong, X.W.; Liu, Z.J.; Li, Y.B.; Xu, C.D. Response of Greenhouse Crop Ecophysiology, Water Consumption and Yield to Ventilation Environment Regulation. J. Inst. Eng. Ser. A
**2019**, 100, 743–752. [Google Scholar] [CrossRef] - Wu, Y.; Yan, S.; Fan, J.; Zhang, F.; Xiang, Y.; Zheng, J.; Guo, J. Responses of growth, fruit yield, quality and water productivity of greenhouse tomato to deficit drip irrigation. Sci. Hortic.
**2021**, 275, 109710. [Google Scholar] [CrossRef] - Fang, Y.; Du, Y.; Wang, J.; Wu, A.; Qiao, S.; Xu, B.; Zhang, S.; Siddique, K.; Chen, Y. Moderate Drought Stress Affected Root Growth and Grain Yield in Old, Modern and Newly Released Cultivars of Winter Wheat. Front. Plant Sci.
**2017**, 8, 672. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Shu, L.-Z.; Liu, R.; Min, W.; Wang, Y.-S.; Hong-Mei, Y.; Zhu, P.-F.; Zhu, J.-R. Regulation of soil water threshold on tomato plant growth and fruit quality under alternate partial root-zone drip irrigation. Agric. Water Manag.
**2020**, 238, 106200. [Google Scholar] [CrossRef] - Ning, S.; Shi, J.; Zuo, Q.; Wang, S.; Ben-Gal, A. Generalization of the root length density distribution of cotton under film mulched drip irrigation. Field Crops Res.
**2015**, 177, 125–136. [Google Scholar] [CrossRef] - Hernández, V.; Hellín, P.; Fenoll, J.; Garrido, I.; Cava, J.; Flores, P. Increasing Yield and Quality of Tomato Cultivated under High Temperature Conditions through the Use of Elicitors. Procedia Environ. Sci.
**2015**, 29, 184. [Google Scholar] [CrossRef] [Green Version] - Li, H.; Liu, H.; Gong, X.; Li, S.; Pang, J.; Chen, Z.; Sun, J. Optimizing irrigation and nitrogen management strategy to trade off yield, crop water productivity, nitrogen use efficiency and fruit quality of greenhouse grown tomato. Agric. Water Manag.
**2020**, 245, 106570. [Google Scholar] [CrossRef] - Talbi, S.; Romero-Puertas, M.C.; Hernandez, A.; Terron, L.; Ferchichi, A.; Sandalio, L.M. Drought tolerance in a saharian plant oudneya Africana: Role of antioxidant defences. Environ. Exp. Bot.
**2015**, 111, 114–126. [Google Scholar] [CrossRef] - Gong, X.; Qiu, R.; Ge, J.; Bo, G.; Ping, Y.; Xin, Q.; Wang, S. Evapotranspiration partitioning of greenhouse grown tomato using a modified Priestley–Taylor model. Agric. Water Manag.
**2021**, 247, 106709. [Google Scholar] [CrossRef] - Gong, X.; Liu, H.; Sun, J.; Gao, Y.; Zhang, H. Comparison of Shuttleworth-Wallace model and dual crop coefficient method for estimating evapotranspiration of tomato cultivated in a solar greenhouse. Agric. Water Manag.
**2019**, 217, 141–153. [Google Scholar] [CrossRef] - Liu, H.; Duan, A.-W.; Li, F.-S.; Sun, J.-S.; Wang, Y.-C.; Sun, C.-T. Drip Irrigation Scheduling for Tomato Grown in Solar Greenhouse Based on Pan Evaporation in North China Plain. J. Integr. Agric.
**2013**, 12, 520–531. [Google Scholar] [CrossRef] - Zou, H.Y.; Zhang, F.C.; Wu, L.F.; Xiang, Y.Z.; Fan, J.L.; Li, Z.J.; Li, S.E. Normalized root length density distribution model for spring maize under different water and fertilizer combinations. Trans. Chin. Soc. Agric. Eng.
**2018**, 34, 133–142. [Google Scholar] [CrossRef]

**Figure 1.**Variations in air speed inside greenhouses under different ventilation treatments in 2020 and 2021.

**Figure 2.**Characteristics of air temperature (T

_{a}) and relative humidity (RH) under different ventilation treatments in 2020 and 2021.

**Figure 3.**Change curves of soil temperature (ST), soil water content (SWC) and ratio of soil water content to temperature (SWC/ST) in 2021. ASWC is the average soil water content, AST is the average soil temperature in the 0–20 cm soil layer and ASWC/ST is the average soil water-heat ratio.

**Figure 5.**Relationship between parameters R

_{1}, R

_{2}, R

_{3}and R

_{0}. Relationship between parameters R

_{1}and R

_{0}, relationship between parameters R

_{2}and R

_{0}and relationship between parameters R

_{3}and R

_{0}.

**Figure 6.**Comparison of simulated and measured NRLD values for greenhouse tomato under different ventilation and irrigation amounts.

**Table 1.**Air speed (m s

^{−1}), air temperature (T

_{a}, °C) and relative humidity (RH, %) inside the greenhouse under different ventilation treatments in 2020 and 2021.

Year | Treatment | Cropping Seasons | Time (9:00–17:00) | ||||
---|---|---|---|---|---|---|---|

T_{R} | T_{RS} | T_{S} | T_{R} | T_{RS} | T_{S} | ||

2020 | Air speed | 0.085 ± 0.00 c | 0.159 ± 0.01 a | 0.113 ± 0.01 b | 0.086 ± 0.00 c | 0.148 ± 0.01 a | 0.110 ± 0.01 b |

T_{a} | 30.40 ± 0.62 a | 28.55 ± 0.54 b | 30.22 ± 0.62 a | 29.76 ± 0.25 a | 28.03 ± 0.22 b | 29.59 ± 0.24 a | |

RH | 64.31 ± 1.39 a | 61.12 ± 1.42 b | 62.41 ± 1.44 ab | 65.40 ± 0.57 a | 62.44 ± 0.67 b | 64.56 ± 0.69 a | |

2021 | Air speed | 0.121 ± 0.01 b | 0.253 ± 0.02 a | 0.143 ± 0.01 b | 0.091 ± 0.00 c | 0.163 ± 0.01 a | 0.109 ± 0.00 b |

T_{a} | 30.98 ± 0.59 a | 28.61 ± 0.58 b | 30.14 ± 0.52 ab | 30.23 ± 0.32 a | 28.09 ± 0.26 b | 29.52 ± 0.26 a | |

RH | 61.52 ± 1.47 a | 54.90 ± 1.73 b | 61.18 ± 1.52 a | 62.80 ± 0.83 a | 56.28 ± 0.89 b | 62.05 ± 1.00 a |

**Table 2.**Experiential parametric simulation results of the NRLD fit function for greenhouse tomato at the late growth stage under different ventilation and irrigation amounts in 2021.

Treatment | R_{0} | R_{1} | R_{2} | R_{3} | R^{2} | F |
---|---|---|---|---|---|---|

T_{R}K_{0.9} | −82.33 | 134.68 | −70.78 | 12.19 | 0.985 | 19.29 ** |

T_{R}K_{0.5} | −97.16 | 152.77 | −75.68 | 12.15 | 0.9621 | 19.88 ** |

T_{RS}K_{0.9} | −80.79 | 130.72 | −67.75 | 11.57 | 0.9686 | 20.23 ** |

T_{RS}K_{0.5} | −86.21 | 140.18 | −72.73 | 12.28 | 0.9876 | 20.2 ** |

T_{S}K_{0.9} | −79.01 | 128.15 | −66.67 | 11.45 | 0.9775 | 19.94 ** |

T_{S}K_{0.5} | −80.80 | 131.13 | −68.36 | 11.74 | 0.9803 | 20.43 ** |

Treatment | T_{R}K_{0.9} | T_{R}K_{0.5} | T_{RS}K_{0.9} | T_{RS}K_{0.5} | T_{S}K_{0.9} | T_{S}K_{0.5} |
---|---|---|---|---|---|---|

2020 | 0.129 | 0.175 | 0.160 | 0.187 | 0.201 | 0.203 |

2021 | 0.193 | 0.273 | 0.159 | 0.195 | 0.209 | 0.220 |

Location | Irrigation Treatment | Ventilation Treatment | Average Values | ||||||
---|---|---|---|---|---|---|---|---|---|

T_{R} | T_{RS} | T_{S} | |||||||

Simulated | Measured | Simulated | Measured | Simulated | Measured | Simulated | Measured | ||

Z_{r} = 1/4 | K_{0.9} | 73.9% | 71.8% | 74.3% | 68.4% | 74.7% | 74.5% | 74.3% | 71.6% |

K_{0.5} | 73.4% | 73.8% | 74.1% | 71.2% | 74.3% | 75.1% | 73.9% | 73.4% | |

Average | 73.7% | 72.8% | 74.2% | 69.8% | 74.5% | 74.8% | 74.1% | 72.5% | |

Z_{r} = 1/2 | K_{0.9} | 87.0% | 86.4% | 89.1% | 86.1% | 90.6% | 87.8% | 88.9% | 86.8% |

K_{0.5} | 84.6% | 88.4% | 88.0% | 83.9% | 88.7% | 88.3% | 87.1% | 86.9% | |

Average | 85.8% | 87.4% | 88.5% | 85.0% | 89.7% | 88.1% | 88.0% | 86.8% |

Year | Treatment | T_{R}K_{0.9} | T_{R}K_{0.5} | T_{RS}K_{0.9} | T_{RS}K_{0.5} | T_{S}K_{0.9} | T_{S}K_{0.5} |
---|---|---|---|---|---|---|---|

2020 | Yield (t ha^{−1}) | 139.5 ± 10.06 a | 124.2 ± 2.47 b | 147.6 ± 4.39 a | 119.4 ± 7.28 b | 143.1 ± 4.87 a | 125.1 ± 6.53 b |

RLD (cm cm^{−3}) | 4.85 ± 0.72 a | 4.35 ± 0.40 ab | 3.82 ± 0.28 bc | 3.02 ± 0.57 cd | 3.15 ± 0.43 cd | 2.39 ± 0.26 d | |

2021 | Yield (t ha^{−1}) | 143.7 ± 9.23 ab | 128.5 ± 4.65 c | 148.1 ± 7.49 a | 126.3 ± 4.76 c | 144.5 ± 5.58 ab | 130.1 ± 7.24 bc |

RLD (cm cm^{−3}) | 6.58 ± 1.23 a | 5.22 ± 0.67 ab | 3.79 ± 0.81 bc | 3.27 ± 0.79 c | 4.97 ± 0.58 b | 2.50 ± 0.14 c |

**Table 5.**Predicted root distribution in 2020 by setting different evaporation coefficient (1.0, 0.8 and 0.6).

Ventilation Treatment | Irrigation Treatment | Soil Depth (cm) | Zr = 1/4 | Zr = 1/2 | |||||
---|---|---|---|---|---|---|---|---|---|

10 | 20 | 30 | 40 | 50 | 60 | ||||

T_{R} | K_{1.0} | 5.13 | 1.52 | 0.22 | 0.22 | 0.55 | 0.21 | 84.77% | 90.32% |

K_{0.8} | 5.14 | 1.46 | 0.17 | 0.23 | 0.60 | 0.23 | 84.26% | 89.36% | |

K_{0.6} | 5.17 | 1.33 | 0.08 | 0.25 | 0.70 | 0.28 | 83.24% | 87.42% | |

T_{RS} | K_{1.0} | 5.11 | 1.63 | 0.29 | 0.20 | 0.46 | 0.17 | 85.63% | 91.96% |

K_{0.8} | 5.12 | 1.57 | 0.25 | 0.21 | 0.51 | 0.19 | 85.18% | 91.11% | |

K_{0.6} | 5.13 | 1.52 | 0.21 | 0.22 | 0.55 | 0.21 | 84.72% | 90.23% | |

T_{S} | K_{1.0} | 5.10 | 1.72 | 0.36 | 0.19 | 0.39 | 0.14 | 86.33% | 93.29% |

K_{0.8} | 5.11 | 1.66 | 0.31 | 0.20 | 0.44 | 0.16 | 85.85% | 92.38% | |

K_{0.6} | 5.13 | 1.56 | 0.24 | 0.22 | 0.52 | 0.20 | 85.04% | 90.85% |

_{R}, T

_{RS}and T

_{S}in 2020, for model application.

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Ge, J.; Liu, H.; Gong, X.; Yu, Z.; Li, L.; Li, Y.
Root Distribution of Tomato Cultivated in Greenhouse under Different Ventilation and Water Conditions. *Plants* **2023**, *12*, 1625.
https://doi.org/10.3390/plants12081625

**AMA Style**

Ge J, Liu H, Gong X, Yu Z, Li L, Li Y.
Root Distribution of Tomato Cultivated in Greenhouse under Different Ventilation and Water Conditions. *Plants*. 2023; 12(8):1625.
https://doi.org/10.3390/plants12081625

**Chicago/Turabian Style**

Ge, Jiankun, Huanhuan Liu, Xuewen Gong, Zihui Yu, Lusheng Li, and Yanbin Li.
2023. "Root Distribution of Tomato Cultivated in Greenhouse under Different Ventilation and Water Conditions" *Plants* 12, no. 8: 1625.
https://doi.org/10.3390/plants12081625