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Article

Simulation of Soil Water and Nitrogen Dynamics for Tomato Crop Using EU-Rotate_N Model at Different Nitrogen Levels in the Greenhouse

by
Ikram Ullah
1,
Zhuangzhuang Cao
1,
Hua Bing
1,
Xiangying Xu
2 and
Minmin Miao
1,*
1
College of Horticulture and Landscape Architecture, Yangzhou University, Yangzhou 225012, China
2
College of Information Engineering, Yangzhou University, Yangzhou 225012, China
*
Author to whom correspondence should be addressed.
Agronomy 2023, 13(8), 2006; https://doi.org/10.3390/agronomy13082006
Submission received: 22 June 2023 / Revised: 14 July 2023 / Accepted: 24 July 2023 / Published: 28 July 2023
(This article belongs to the Special Issue Efficient Management of Water, Energy, Fertilizer, and Rhizosphere Microbiome for Facility Crops)
Browse Figures
Versions Notes

Abstract

:
To pursue high yields, the excessive application of nitrogen (N) fertilizer has been reported in high-residual soil nitrate levels, excessive nitrate leaching, and nitrate contamination of groundwater. In this study, tomato crops (Lycopersicon esculentum Mill.) were subjected to various nitrogen treatments, and the nitrate nitrogen content, soil water content at different soil layers, dry matter, and yield were measured. A mechanistic model, EU-Rotate_N, was used to simulate the aforementioned indexes in a region of Jiangsu province with a relatively higher water table. The predicted values of soil moisture and soil nitrate content at various soil depths agree well with the measured values during tomato growth. The statistical index of soil water content ranged from 0.367 to 0.749, 0.856 to 0.947, and the statistical index of soil nitrate nitrogen content ranged from 0.365 to 0.698, and 0.869 to 0.932, for Autumn-Winter (AW) and Spring-Summer (SS) crops, respectively. Moreover, the dry weight and yield simulation effects of the tomato are also in good agreement with the actual measured values. The results show that the EU-Rotate_N model is effective in simulating soil water content, nitrate nitrogen content, dry matter quality, and yield in Jiangsu province, with little underestimation in soil water content at a soil depth of 20–30 cm during SS, which might be improved further considering the high water table of the region.

1. Introduction

China is a major producer of greenhouse vegetable production (GVP), with a greenhouse area accounting for over 85% of the total area of the world’s greenhouse agriculture [1]. More than 60% of China’s total greenhouse vegetable production area is located in eastern provinces, with Liaoning, Hebei, Shandong, and Jiangsu provinces accounting for 22.6, 10.4, 14.2, and 10.9%, respectively [2]. Jiangsu Province is one of the provinces with the fastest development of protected vegetables in China, accounting for about one-sixth of the national protected vegetable planting area [3,4]. Among GVP, tomato is one of the most common and important vegetables in people’s lives, not only ensuring the annual supply of our people’s “vegetable basket”, but also one of the effective ways to increase farmers’ income [5]. With the aim to increase the yield, excessive N application is very common practice while ignoring the negative impacts on the environment, resulting in excessive accumulation of nutrients due to water and nitrogen leaching in the greenhouse soil, which not only leads to the decline of the yield and quality, but also leads to problems such as excessive accumulation of soil nutrients and groundwater pollution [6,7].
Along with N concentration in soils, it may also change soil characteristics of moisture retention when N inputs exceed crop N demands [8], with increased N leaching loss [9]. The changes in irrigation management greatly affect nutrient management [10]. Nitrogen changes the way that water moves through the vadose zone [11]. High-application rates of irrigation, combined with high application of N fertilization, increases nitrate leaching [12]. As a consequence, the risk of nitrate (NO3−) leaching into groundwater increases and, consequently, increases the cost of production as well [13]. On the other hand, irrigation as a water saving strategy has the potential of significantly reducing fertilizer use [14], inhibiting nutrient and fertilizer leaching below the root-zone, increasing nitrogen use efficiency and crop yield, and minimizing the impact of fertilizer on the environment [15,16]. According to studies, the trade-off between the decrease in N loss and increase in yield is based on determining the exact plant’s N needs [17,18]. In order to better implement the input-saving strategy, it is extremely important to determine the different nutrient requirements of greenhouse vegetable crops at each growth stage [19]. However, it is regrettable that, at present, there is very little research on the fertilizer requirements of important protected vegetables in our province at various growth stages, and there is still a lack of scientific data to support the rational allocation of total fertilizer amount in protected vegetables at various growth stages. For this reason, It is helpful to establish and use models quantifying the N fate that can predict crop soil moisture dynamics, nitrogen movement direction, and the whole process of crop development [20].
Determining the consequences of management practices has relied heavily on field experiments. Experiments on field plots have been conducted using a variety of watering and nitrogen fertilizer techniques in order to investigate the impact of these practices on nitrogen use efficiency and identify the most effective watering and nitrogen fertilizer practice [21,22,23]. However, these experimental approaches are time-consuming and costly, and there are some processes that are hard to quantify because of the restricted treatments applied in experiments [24]. The optimal N fertilizer application rates were also suggested by certain research conducted in experiments [25,26].
In contrast, simulation models offer a promising alternative for developing management strategies by highlighting the most effective management practices. These can be used to investigate various management scenarios after being calibrated and validated for a specific agricultural system [27]. Moreover, these constructed models serve as useful tools of good management practices to producers, technical advisers, and policymakers. Many recent mechanistic models including WHCNS [28,29], RZWQM2 [30], Hortsyst [31] CropSyst [32], VegSyst [33,34], HYDRUS [35] have successfully simulated soil moisture, nitrate leaching, and nitrate uptake for different vegetable crops, soil types, and management conditions [5,36,37]. Among these, the N_ABLE-based developed EU-Rotate_N model is effective for simulating the interactions between water and nitrogen in soil-plant systems [38,39]. Some research used EU-Rotate_N and utilized it to predict water and nitrogen behavior [20,40].
N leaching was predicted to be the main source of N losses according to studies conducted in North China by testing the European conditions-based EU-Rotate_N model. However, the current study has been conducted in Jiangsu Province with a difference in environmental conditions of Jiangsu, compared to Europe and northern China, with the purpose to test the adaptation of the EU-based model for better results in Jiangsu province for the management of water and nitrogen application to greenhouse vegetable crops. In this experiment, the greenhouse tomato was taken as the research object in Jiangsu province, a subtropical region with humid climate, where scarce studies were found using EU-Rotate_N for vegetable production in greenhouses. Therefore, as a result, the primary purpose of this research is to assess the EU-Rotate_N model’s ability to predict the water and N movement in soil under different N doses in Jiangsu province.

2. Materials and Methods

2.1. Study Site and Conditions

This experiment was carried out in the plastic greenhouse of Yangzhou Lehuo Agricultural Sightseeing Park (East Longitude 119°48′, North Latitude 32°29′). This area is a typical vegetable production area in Yangzhou City, Jiangsu Province. It has a humid subtropical climate with all four seasons. The annual atmospheric nitrogen subsidence of the test site is 22.7 kg ha−1, and the average air temperature inside the greenhouse was 24.2 °C in 2018 and 23.6 °C in 2019. The dimension of the plastic greenhouse was 50 m in length and 6 m in width. The sunshade net is used when the temperature is high in summer. A tomato crop was used in this experiment as a test material.
A soil depth of 0–30 cm was sampled to determine the granulometric composition of soil where the total depth was divided into three depths with a distance of 10 cm for each position. The composition and basic physical properties of the selected soil depth (0–30 cm) in the greenhouse are shown in Table 1.

2.2. Experiment in Greenhouse

The experiment was designed as a random block design consisting of 4 nitrogen doses (N1, N2, N3, N4) with 3 replicates. The greenhouse, with an area of 50 m × 6 m, was organized into four plots and each plot had a specific N fertilizer dosage treatment. Each plot was separated from the other using 40 cm deep plastic boards to prevent any sideways movement of nutrients. The tomato plant-to-plant spacing was set at 35 cm. The rest of the routine management like plastic film mulching, timely control of diseases and insect pests, pruning, etc., was conducted accordingly. Each treatment was replicated 3 times.
The tomato experiment was carried out in two different growing seasons. Seedlings were transplanted on 16 September 2018, for the Autumn–Winter (AW) growing season, and for the Spring-Summer (SS) season, seedlings were transplanted on 10 March 2019. After planting, soil samples, and plant samples were taken every 10 days. Data collection ended on 9 December and 10 June for the autumn–winter and spring-summer growing seasons, respectively. A self-compensating drip irrigation system was used as a mode of irrigation for the whole experiment. The specific details of irrigation for tomatoes are shown in Table 2.
According to previous research [41], and local practical experience, the recommended value of chemical fertilizer dosage per hectare for the first crop in AW is: N 375.72 kg, P2O5 176.77 kg, K2O 467.88 kg; the recommended amount of fertilizer per hectare for the second crop in SS: N 361.22 kg, P2O5 149.47 kg, K2O 480.02 kg. The amount of nitrogen fertilizer in the crop of AW is based on the recommended value of 375.72 kg ha−1, and there are 4 levels of 0.5, 0.75, 1, and 1.25. The amount of nitrogen fertilizer in the second crop is based on the recommended value of 361.22 kg ha−1, with 4 levels of 0.5, 0.75, 1, and 1.25 (Table 3).
Nitrogen fertilizers are supplied in the form of urea, phosphorus fertilizers are supplied in the form of superphosphate, and potassium fertilizers are supplied in the form of potassium sulfate. Phosphorus fertilizer is applied at one time in the form of basal fertilizer when preparing the soil. Since tomatoes have distinct fertilizer needs at each stage of development, the growing process was divided into three distinct phases: the first was 20% of the total N applied between days 0 and 20, the next was 40% between days 20 and 60, and the final 40% was between days 60 and maturity. The fertilizer sausages were weighed and applied with the drip water by using a Venturi applicator at each watering.

2.3. Data Collection and Measurements

2.3.1. Collection of Soil Samples, Plant Samples and Determination of Nutrient Content

Soil samples were collected from each of the three replicates of all treatments to determine the physical and hydraulic properties of the soil. Soil nitrate contents of the collected soil samples were determined using a continuous flow analyzer (Auto Analyzer− III, Germany) for analyzing soil nitrate-nitrogen content with 2 mol L−1 KCl extracting solution [42,43]. Similarly, tomato plant samples were also collected, and destructive sampling was carried out in sub-plots after planting, and 3 plants were randomly sampled in each plot. The above-ground part of the plant was separated by stem, leaf and fruit, and its fresh weight was weighed. After oven drying at 105 °C for 15 min, it was dried at 72 °C to constant weight, and the dry matter was calculated. After the fruits were ripened, they were picked according to the plots. Each treatment was repeated 3 times, and the fresh weights of the fruits were weighed, respectively, to calculate the yield.

2.3.2. Determination of Yield and Dry Weight

When the tomato grew to the commodity stage, it was manually harvested and weighed according to the plot, and the total yield was finally calculated. The dry weight of each treatment was measured and recorded after drying each time the tomato plants were sampled.

2.3.3. Meteorological Data

A weather station (NHQXZ60l) was installed to record meteorological data in the greenhouse, including a cup anemometer (NHFS45BP, 0.5 m) and psychrometer (NH121WS-R) for measuring air temperature and relative humidity. All were purchased from Wuhan Zhongke Nenghui Technology Development Co., Ltd. (Wuhan, China), and the data were recorded every ten minutes. Since the experiment was carried out in a greenhouse, the rainfall was 0. The data in the weather station can be exported by computer, and Excel is used to process the data to calculate the daily minimum and maximum of sunshine hours, humidity, temperature, and wind speed for the corresponding year to use in the EU-Rotate_N model.

2.4. Description of EU-Rotate_N Model

The EU-Rotate_N model was constructed to optimize nitrogen fertilizer use in European field crop rotations, mainly coordinated by Rahn and Zhang [39], based on the N_ABLE model of Greenwood et al. [44]. The model is applicable to a variety of vegetables, and is mainly used to simulate the impact of different farmland water and fertilizer management measures on soil water and nitrogen transport and vegetable yield, in order to optimize nitrogen and water inputs under the rotation mode of vegetables and crops. The model includes a series of sub-modules, which can simulate the growth of aboveground and underground parts of plants, nitrogen mineralization in soil and straw, nitrogen uptake by plants, etc. These simulations are all affected by meteorological factors such as rainfall, temperature, and radiation. Modules for soil organic matter, root growth, water and soil movement, and freezing effects were developed to simulate crop development, and nitrogen and water changes on a daily basis, as described in detail by [39]. Previously, several trials using organic and conventional vegetable crops in Europe have validated the EU-Rotate_N model usage. The model version number used in this study was 1.8. The flow chart of the model is shown in Figure 1.
The model used the Penman–Monteith method of FAO to calculate reference crop evapotranspiration [45], and also used the soil water balance proposed by Ritchie to simulate soil water movement [46]. Moreover, the model used the U.S. National Resource Conservation Service developed methodology to determine runoff volume (NRCS, 2004). The nitrogen mineralization section is based on DAISY model procedures [47]. The ALFAM model [48] was used as an empirical equation to predict ammonia volatilization from manure. The AMOVOL model was used to create simulated urea hydrolysis and gaseous N emissions [49].
In addition to the existing organic/inorganic fertilizers, nitrogen mineralization parameters, and planting crops/returning crops parameter files in the model, the model input also needs information shown in Table 4. Based on the entered input information data, the model provides the output data (Table 4) upon a successful run.

2.5. Model Verification Effect Evaluation Index

In this study, three statistical evaluation indices were used to assess the agreement between the simulated and measured data:
(1)
Root mean square error (RMSE) [50];
R M S E = ∑ i = 1 n ( P i − O i ) 2 n
(2)
Nash–Sutcliffe modeling efficiency (NSE) [51];
N S E = 1 − ∑ i = 1 n O i − P i 2 ∑ i = 1 n O i − O 2
(3)
Agreement Index (d) [52];
d = 1 − ∑ i = 1 n O i − P i 2 ∑ i = 1 n P i − O + O i − O 2
Among them: n depicts the number of samples, O is the mean value of the observed data, and Pi and Oi are the predicted and observed values, respectively. The lower the value of root mean square error (RMSE), the better the model’s accuracy. The modeling efficiency (NSE) is between −∞ and 1, and the closer to 1, the better the consistency between the simulated value and the measured value. The mean square error to potential error ratio is represented by the agreement index (d), and the closer d is to 1, the better the match between the simulated value and the measured value is. When NSE > 0.36 and d > 0.7, it shows that the simulated value agrees well with the measured value [53]. When simulating crop growth indicators, d ≥ 0.75 and NSE ≥ 0 are usually required; for soil indicator simulations, d ≥ 0.6 and NSE ≥ −1.0 are usually required [54].

2.6. Calculations and Statistical Analysis

Data entry and later on performed calculations were performed using Microsoft Excel software (Excel 2016) to evaluate the model performance based on statistical indices of RMSE, NSE and d values, whereas yield data was compared based the analysis of variance, which was conducted using SPSS 23.0, and the least significant difference test (LSD, p < 0.05) was used to establish differences between treatments.

3. Results

3.1. Meteorological Data in the Greenhouse

The tomato crop was planted from 16 September to 9 December for Autumn–Winter and March 24 to June for Spring–Summer. During the crop growth period, average air temperatures recorded in the plastic greenhouse were 20.2 °C and 24.3 °C in AW (2018) and SS (2019), whereas the highest temperature in the shed reached up to 48.2 °C, the lowest temperature was 14.9 °C, and the average sunshine hours were 9.28 h (Figure 2).

3.2. Model Parameters

The tomato experiment was carried out for two crops from September 2018 to July 2019. In this experiment, some parameters of the model refer to (Sun et al., 2013 [40]) using the EU-Rotate_N model to simulate the parameters, and then use it according to the measured data of N1, and finally the measured data of other treatments (N2, N3, N4) was used to verify the model. The value and parameters used in greenhouse tomato with EU-Rotate_N model is shown in Table 5.

3.3. Simulation of Soil Moisture Content in Protected Tomato Fields and Model Evaluation Index

In the tomato greenhouse experiment with two different cropping seasons, Figure 3 presents a comparison of the simulated value to the measured soil moisture. The Spring–Summer and Autumn–Winter crops were irrigated 10 times and 11 times, respectively. In the simulation results, the water content of the 0–10 cm and 10–20 cm soil layers of each treatment peaked on the corresponding date, indicating that the simulation results were more in line with the actual situation. It can also be seen intuitively from the figure that the moisture content of the soil changes sharply from 0–10 cm, the upper layer of soil, followed by soil moisture content changes at the 10–20 cm soil layer and the 20–30 cm soil layer, which are relatively flat. This is mainly because the surface soil is affected by the external climate and irrigation has a greater impact on the upper surface.
There was no obvious change in soil moisture dynamics under different nitrogen treatments, mainly because the amount of watering was the same in all treatments, and the water content in the soil was mainly affected by the crop water uptake in terms of evapotranspiration. Figure 3 also shows that the water content in the soil of both crops is relatively high in the initial stage of planting, and the variations in the water content in the soil begins to decrease in the later stage of growth and development. This is mainly related to watering and plant absorption, which is more in line with reality.
The statistical indices of soil water content predicted by the model for greenhouse tomato are shown in Table 6. The RMSE value of the soil water content in each layer of different nitrogen treatments ranges from 0.024–0.042, which is close to 0; the range of NSE value is 0.36–0.749, and the d values are all greater than 0.850, enough to show that the model can simulate the water dynamics for greenhouse tomatoes.

3.4. Simulation of Soil Nitrate Nitrogen Content in Protected Tomato Fields and Model Evaluation Index

Nitrogen concentration in the soil was measured and compared to the predicted values in two greenhouse tomato crops subjected to varying nitrogen levels (Figure 4). In the 0–10 cm soil layer, the Autumn–Winter and Spring–Summer crop planting stages had a large decrease in nitrate nitrogen. At this time, there was a small increase in the 10–20 cm and 20–30 cm soil layers, which was mainly due to the colonization water used for irrigation during planting, resulting in nitrogen seeping down with the water.
Over the course of a greenhouse tomato’s whole growth cycle, the EU-Rotate_N model results were less matched with the actual measured values in layer 10–20 cm during the middle and late stages of crop, compared to the initial stages (Figure 4). However, variation between the simulated and actual measured values follows the same trend. The decrease in nitrate content in the first and second crop growth period is relatively large, which is mainly related to the need for more nutrients for tomato plant growth and fruit expansion, which is more in line with the actual situation.
Table 7 is the evaluation index of the simulated effect of different nitrogen treatments for two crops of greenhouse tomatoes. It can be seen from the table that the RMSE, NSE and d value ranges are 16.528–27.523, 0.365–0.698, 0.869–0.932, respectively; the data results can show that the simulated values agree well with the measured value, and the model can be used to simulate the nitrate nitrogen content of greenhouse tomatoes in Jiangsu province of China.

3.5. Yield and Dry Weight Simulation Effect of Tomato in Greenhouse

In Figure 5, with the increase of nitrogen concentration, the yield also increases, and the yields of N3 and N4 in the two crops are not significant, which can indicate that the N3 treatment can already supply the nitrogen absorbed by the plant growth and development. The comparison between the measured value and the simulated value of tomato yield in greenhouses show that the EU-Rotate_N model can predict the tomato yield very well with an average error of only 1%. The simulation results are acceptable when NSE > 0.36 and d > 0.7. According to Table 8, the EU-Rotate_N model is capable of making accurate predictions regarding tomatoes yield in greenhouses.
Figure 6 shows the linear regression relationship between the simulated and the measured value of dry weight of greenhouse tomato. The values of linear correlation coefficients between the measured and the simulated dry weight are all greater than 0.97, close to 1, which shows that the dry weight simulation effect of greenhouse tomato is relatively good.

4. Discussion

In the work reported here, the EU-Rotate_N model has been evaluated for tomato crop grown in greenhouses in the environmental conditions of Jiangsu province. Environmental conditions of Jiangsu province slightly differ than the work reported earlier using the EU–Rotate_N model. The model was initially tested in Mediterranean conditions to simulate N uptake and dry matter accumulation of tomato crop in open field conditions [55], followed by the study conducted in greenhouse conditions for cucumber crop in northern China by calibrating some of the model parameters due to differences in management practices and crop growth conditions [24]. Similarly, some crop parameters of the model were adjusted as used by some domestic scholars to evaluate the EU–Rotate_N model for greenhouse tomato crop in subtropical conditions of Jiangsu province of China [20,40], and then were provided with the input of the measured soil properties of tested soil. The soil properties measured at different soil depths (Table 1) were used in the input file of the EU–Rotate_N model for the calibration of soil moisture and nitrate nitrogen content. The “trial and error method” was used to adjust the model parameters with the measured data of non-limiting N treatment (N1) until the degree of agreement between the simulated value and the measured value reached a level of high agreement, and then used the actual measured data of other N treatments (N2, N3, N4) to verify the model in simulating the soil water content, soil nitrogen content, dry matter mass, and yield.
This study suggests that overall performance of the EU–Rotate_N model was satisfactory, indicating that the calibration of the model for tomato crop in the conditions of Jiangsu province could have the ability to simulate the fate of water contents in soil appropriately. However, the model overestimated the soil water content after the initial crop growth stage during SS, at the soil depth of 10–20 cm. These high values of simulated water content in soil, over the actual observed values, could be due to a lesser contribution of soil water in crop water uptake because of more soil evaporation during summer, which results solely from the contribution of soil moisture available in the soil layer below 10 cm and, ultimately, the actual residual soil water content is lower than the simulated values. Soil evaporation in the autumn–winter season was lower than that in the spring–summer season. This is partly due to the fact that the air temperature in the autumn–winter season was relatively low. Therefore, the model could be further improved by making the model adapt to inter-seasonal variation in soil evaporation [56]. Similarly, from Figure 3, it can also be found that the measured value is relatively lower than the simulated value. This may be because the simulated value output in the model is a time-dependent variable, and the soil water content is measured by field sampling in this study, and then dried. The dry method will take a certain amount of time during the sampling and determination, so most of the measured soil moisture content is always slightly smaller than the simulated value. This is in agreement with the research results of [57]. Moreover, the sharp changes in soil water content with irrigation events were due to more water leaching down than the water used by the crop at the initial growth stage when the crop has a small leaf area and small root depth, which showed that our study showed a similar trend in sharp changes of soil moisture variation with the study conducted by Sun et al. [58], where frequent excessive water moved away from the rootzone. The values of performance indices RMSE, NSE and d of the used model were found to be in acceptable range for simulation of soil water content. The performance of the model was confirmed by the performance indices observed in the study conducted by Sun et al. [40], who used the EU-Rotate_N model to simulate the tomato in the Shouguang greenhouse facility. Hence, the performance of the EU-Rotate_N model was confirmed suitable to utilize by growers in greenhouse vegetable production in Jiangsu province when compared to different regions [59,60] in simulating soil moisture calibration.
According to our study, the simulated values of nitrate content in soil under N treatment of N3 and N2 were matched well with observed values along the crop growth during both seasons, with slight underestimation by the model in soil depth of 10–20 cm in AW. These high values in the soil nitrate content of measured values, over those simulated at a soil depth of 10–20 cm, was mainly due to the colonization water used for irrigation during planting, resulting in nitrogen seeping down with the water. Moreover, transpiration being the limiting factor for the modeled nitrate content in soil while not considering the decreased N uptake extracted by roots maintains its significance for the winter crops, when evapotranspiration demand is lower [61]. Therefore, the higher level of complexity of nitrogen transformation and transport in soil makes it challenging for the majority of computer models to accurately capture soil nitrogen dynamics, including nitrogen mineralization and nitrogen in soil solution. In another study [28], the WHCNS_veg model was used to simulate facilities of tomato and the results depicted that the NRMSE value of soil nitrate nitrogen ranged from 39.6% to 48.8%, and the NSE range was from −0.31 to 0.26. The simulation results of this model for the soil nitrate nitrogen content of facility tomato were poor, which was inconsistent with the results of this study providing evidence of the EU-Rotate_N model’s increased performance. The ranges of RMSE and NSE values of soil nitrate nitrogen content in this study were 16.528–27.523, 0.365–0.698, respectively, and the water and nitrate nitrogen contents were well predicted [62]. The general similarities between simulated and measured values showed the capacity of both EU-Rotate_N to simulate the effects of N fertilization management on soil mineral N dynamics.
Vegetable yield and dry weight is closely related to water and fertilizer management [14], as the nitrogen amount showed a significant impact on yield (Figure 5). As the observed tomato yield resulted no significant increase in yield above the N3 treatment, the simulated yield with insignificantly higher values to nitrogen fertilization amount also showed that the model provided a yield at N3 against nitrogen fertilizer application, with no more increase in yield by increasing the nitrogen amount. Similarly, the simulated and measured value of dry weight of greenhouse tomato showed a linear relationship, indicating the feasibility of the EU-Rotate_N model.

5. Conclusions

The EU-Rotate N model was calibrated and validated for greenhouse tomato in Jiangsu province China in two seasons with differences in N fertilization. The targeted parameters to evaluate the model performance included the soil water content, soil nitrate content, fresh yield and dry weight of tomato. Although the model overestimated the soil water content after the initial crop growth stage during SS at the soil depth of 10–20 cm, the RMSE value range of the soil moisture simulation effect evaluation index of different nitrogen treatments for each soil layer was 0.024–0.042, close to 0; the minimum NSE value was 0.367–0.749, and the d value was found basically greater than 0.850. The values of performance indices RMSE, NSE and d of the used model were found to be in acceptable range for simulation of soil water content. Similarly, for the simulation performance indices of soil nitrate nitrogen content, the RMSE, NSE and d value ranges were 16.528–27.523, 0.365–0.698, 0.869–0.932, respectively. Compared with measured values, the model underestimated the nitrate contents in the soil layer of 10–20 cm with negligible effect on dry weight and yield. The correlation coefficients between predicted and measured values of dry weight were greater than 0.97 and close to 1. The EU-Rotate_N model can predict the tomato yield of greenhouses very well, with an average error of only 1%. Therefore, the used EU-Rotate_N model can be used to provide a theoretical basis for managing the water and nitrogen behavior in greenhouse tomatoes in Jiangsu Province

Author Contributions

Conceptualization, M.M. and H.B.; methodology, Z.C.; software, X.X.; validation, M.M.; formal analysis, I.U.; data curation, Z.C.; writing—original draft preparation, I.U.; writing—review and editing, I.U.; supervision, M.M.; project administration, M.M.; funding acquisition, M.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by “Special Funds for Scientific and Technological lnnovation of Jiangsu province, China, grant number BE2022425” and “Jiangsu Modern Agricultural (Vegetable) Industrial Technology System, grant number (JATS [2023])”.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Qasim, W.; Xia, L.; Lin, S.; Wan, L.; Zhao, Y.; Butterbach-Bahl, K. Global greenhouse vegetable production systems are hotspots of soil N2O emissions and nitrogen leaching: A meta-analysis. Environ. Pollut. 2021, 272, 116372. [Google Scholar] [PubMed]
  2. Hu, W.; Zhang, Y.; Huang, B.; Teng, Y. Soil environmental quality in greenhouse vegetable production systems in eastern China: Current status and management strategies. Chemosphere 2017, 170, 183–195. [Google Scholar] [CrossRef]
  3. Guo, Y.; Wang, H.; Zhang, W.; Chen, B.; Song, D. Sustainability evaluation of protected vegetables production in China based on emergy analysis. J. Clean. Prod. 2023, 388, 135928. [Google Scholar]
  4. Kalkhajeh, Y.K.; Huang, B.; Hu, W.; Ma, C.; Gao, H.; Thompson, M.L.; Hansen, H.C.B. Environmental soil quality and vegetable safety under current greenhouse vegetable production management in China. Agric. Ecosyst. Environ. 2021, 307, 107230. [Google Scholar] [CrossRef]
  5. Liang, H.; Lv, H.; Batchelor, W.D.; Lian, X.; Wang, Z.; Lin, S.; Hu, K. Simulating nitrate and DON leaching to optimize water and N management practices for greenhouse vegetable production systems. Agric. Water Manag. 2020, 241, 106377. [Google Scholar]
  6. He, F. Studies on Optimizing Nitrogen Management and Environmental Implication in Greenhouse Tomato Cropping System; China Agricultural University: Beijing, China, 2006; (In Chinese with English Abstract). [Google Scholar]
  7. Wu, Y.; Yan, S.; Fan, J.; Zhang, F.; Zhao, W.; Zheng, J.; Guo, J.; Xiang, Y.; Wu, L. Combined effects of irrigation level and fertilization practice on yield, economic benefit and water-nitrogen use efficiency of drip-irrigated greenhouse tomato. Agric. Water Manag. 2022, 262, 107401. [Google Scholar]
  8. Hati, K.M.; Swarup, A.; Mishra, B.; Manna, M.; Wanjari, R.; Mandal, K.; Misra, A. Impact of long-term application of fertilizer, manure and lime under intensive cropping on physical properties and organic carbon content of an Alfisol. Geoderma 2008, 148, 173–179. [Google Scholar] [CrossRef]
  9. Manevski, K.; Børgesen, C.D.; Li, X.; Andersen, M.N.; Zhang, X.; Abrahamsen, P.; Hu, C.; Hansen, S. Optimising crop production and nitrate leaching in China: Measured and simulated effects of straw incorporation and nitrogen fertilisation. Eur. J. Agron. 2016, 80, 32–44. [Google Scholar]
  10. Zhang, J.; Cao, Z.; Dai, H.; Zhang, Z.; Miao, M. Yield, nitrogen uptake and nitrogen leaching of sensor-based fertigation-cultured tomato in a shallow groundwater region: Effect of shallow groundwater on tomato irrigation. J. Agric. Sci. 2019, 12, 10. [Google Scholar]
  11. Hong, E.-M.; Choi, J.-Y.; Nam, W.-H.; Kang, M.-S.; Jang, J.-R. Monitoring nutrient accumulation and leaching in plastic greenhouse cultivation. Agric. Water Manag. 2014, 146, 11–23. [Google Scholar] [CrossRef]
  12. Lv, H.; Zhao, Y.; Wang, Y.; Wan, L.; Wang, J.; Butterbach-Bahl, K.; Lin, S. Conventional flooding irrigation and over fertilization drives soil pH decrease not only in the top-but also in subsoil layers in solar greenhouse vegetable production systems. Geoderma 2020, 363, 114156. [Google Scholar] [CrossRef]
  13. Li, Y.; Sun, Y.; Liao, S.; Zou, G.; Zhao, T.; Chen, Y.; Yang, J.; Zhang, L. Effects of two slow-release nitrogen fertilizers and irrigation on yield, quality, and water-fertilizer productivity of greenhouse tomato. Agric. Water Manag. 2017, 186, 139–146. [Google Scholar] [CrossRef]
  14. 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]
  15. Liu, C.; Wang, R.; Wang, W.; Hu, X.; Cheng, Y.; Liu, F. Effect of fertilizer solution concentrations on filter clogging in drip fertigation systems. Agric. Water Manag. 2021, 250, 106829. [Google Scholar] [CrossRef]
  16. Wu, Y.; Si, W.; Yan, S.; Wu, L.; Zhao, W.; Zhang, J.; Zhang, F.; Fan, J. Water consumption, soil nitrate-nitrogen residue and fruit yield of drip-irrigated greenhouse tomato under various irrigation levels and fertilization practices. Agric. Water Manag. 2023, 277, 108092. [Google Scholar] [CrossRef]
  17. Zhou, W.; Lv, H.; Chen, F.; Wang, Q.; Li, J.; Chen, Q.; Liang, B. Optimizing nitrogen management reduces mineral nitrogen leaching loss mainly by decreasing water leakage in vegetable fields under plastic-shed greenhouse. Environ. Pollut. 2022, 308, 119616. [Google Scholar]
  18. Ullah, I.; Hanping, M.; Javed, Q.; Rasool, G.; Ali, M.; Azeem, A.; Saif, M. Nitrogen Fertilization Effects on Growth, Leaf Gas Exchange and Chlorophyll Fluorescence of Brassica juncea. Int. J. Agric. Biol. 2020, 24, 1070–1076. [Google Scholar]
  19. Ren, K.; Xu, M.; Li, R.; Zheng, L.; Liu, S.; Reis, S.; Wang, H.; Lu, C.; Zhang, W.; Gao, H. Optimizing nitrogen fertilizer use for more grain and less pollution. J. Clean. Prod. 2022, 360, 132180. [Google Scholar] [CrossRef]
  20. Liang, H.; Chen, Q.; Liang, B.; Hu, K. Modeling the effects of long-term reduced N application on soil N losses and yield in a greenhouse tomato production system. Agric. Syst. 2020, 185, 102951. [Google Scholar]
  21. Ullah, I.; Mao, H.; Rasool, G.; Gao, H.; Javed, Q.; Sarwar, A.; Khan, M.I. 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]
  22. 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 2020, 11, 3. [Google Scholar] [CrossRef]
  23. Shabbir, A.; Mao, H.; Ullah, I.; Buttar, N.A.; Ajmal, M.; Lakhiar, I.A. Effects of drip irrigation emitter density with various irrigation levels on physiological parameters, root, yield, and quality of cherry tomato. Agronomy 2020, 10, 1685. [Google Scholar] [CrossRef]
  24. Sun, Y.; Zhang, J.; Wang, H.; Wang, L.; Li, H. Identifying optimal water and nitrogen inputs for high efficiency and low environment impacts of a greenhouse summer cucumber with a model method. Agric. Water Manag. 2019, 212, 23–34. [Google Scholar] [CrossRef]
  25. Ullah, I.; Hanping, M.; Chuan, Z.; Javed, Q.; Azeem, A. Optimization of irrigation and nutrient concentration based on economic returns, substrate salt accumulation and water use efficiency for tomato in greenhouse. Arch. Agron. Soil Sci. 2017, 63, 1748–1762. [Google Scholar] [CrossRef]
  26. Zhou, H.; Kang, S.; Li, F.; Du, T.; Shukla, M.K.; Li, X. Nitrogen application modified the effect of deficit irrigation on tomato transpiration, and water use efficiency in different growth stages. Sci. Hortic. 2020, 263, 109112. [Google Scholar] [CrossRef]
  27. Liang, H.; Hu, K.; Batchelor, W.D.; Qin, W.; Li, B. Developing a water and nitrogen management model for greenhouse vegetable production in China: Sensitivity analysis and evaluation. Ecol. Model. 2018, 367, 24–33. [Google Scholar]
  28. Liang, H.; Hu, K.; Batchelor, W.D.; Chen, Q.; Liang, B.; Li, B. Modeling dissolved organic nitrogen dynamics under different N management practices for intensive greenhouse production using an improved WHCNS_veg model. Geoderma 2019, 337, 1039–1050. [Google Scholar] [CrossRef]
  29. Li, G.; Xie, H.; Zhang, J.; Li, H. WHCNS-Veg Modelling of N2O, NH3 and NO3− Dynamics in a Vegetable Production System under Different Fertilization and Irrigation Regimes. Atmosphere 2022, 13, 1289. [Google Scholar]
  30. Cheng, H.; Yu, Q.; Abdalhi, M.A.; Li, F.; Qi, Z.; Zhu, T.; Cai, W.; Chen, X.; Feng, S. RZWQM2 simulated drip fertigation management to improve water and nitrogen use efficiency of maize in a solar greenhouse. Agriculture 2022, 12, 672. [Google Scholar] [CrossRef]
  31. Martínez-Ruiz, A.; López-Cruz, I.L.; Ruiz-García, A.; Pineda-Pineda, J.; Prado-Hernández, J.V. HortSyst: A dynamic model to predict growth, nitrogen uptake, and transpiration of greenhouse tomatoes. Chil. J. Agric. Res. 2019, 79, 89–102. [Google Scholar] [CrossRef] [Green Version]
  32. Suárez-Rey, E.; Romero-Gámez, M.; Giménez, C.; Thompson, R.; Gallardo, M. Use of EU-Rotate_N and CropSyst models to predict yield, growth and water and N dynamics of fertigated leafy vegetables in a Mediterranean climate and to determine N fertilizer requirements. Agric. Syst. 2016, 149, 150–164. [Google Scholar] [CrossRef]
  33. Gallardo, M.; Thompson, R.; Giménez, C.; Padilla, F.; Stöckle, C. Prototype decision support system based on the VegSyst simulation model to calculate crop N and water requirements for tomato under plastic cover. Irrig. Sci. 2014, 32, 237–253. [Google Scholar] [CrossRef]
  34. Giménez, C.; Gallardo, M.; Martínez-Gaitán, C.; Stöckle, C.; Thompson, R.; Granados, M. VegSyst, a simulation model of daily crop growth, nitrogen uptake and evapotranspiration for pepper crops for use in an on-farm decision support system. Irrig. Sci. 2013, 31, 465–477. [Google Scholar] [CrossRef]
  35. Doltra, J.; Muñoz, P. Simulation of nitrogen leaching from a fertigated crop rotation in a Mediterranean climate using the EU-Rotate_N and Hydrus-2D models. Agric. Water Manag. 2010, 97, 277–285. [Google Scholar] [CrossRef]
  36. Gallardo, M.; Thompson, R.; Rodríguez, J.; Rodríguez, F.; Fernández, M.; Sánchez, J.; Magán, J. Simulation of transpiration, drainage, N uptake, nitrate leaching, and N uptake concentration in tomato grown in open substrate. Agric. Water Manag. 2009, 96, 1773–1784. [Google Scholar] [CrossRef]
  37. Xu, Q.; Hu, K.; Liang, H.; Leghari, S.J.; Knudsen, M.T. Incorporating the WHCNS model to assess water and nitrogen footprint of alternative cropping systems for grain production in the North China Plain. J. Clean. Prod. 2020, 263, 121548. [Google Scholar] [CrossRef]
  38. Nendel, C. Evaluation of Best Management Practices for N fertilisation in regional field vegetable production with a small-scale simulation model. Eur. J. Agron. 2009, 30, 110–118. [Google Scholar] [CrossRef]
  39. Rahn, C.; Zhang, K.; Ramos, C.; Doltra, J.; De-Paz, J.M.; Riley, H.; Fink, M.; Nendel, C.; Thorup-Kristensen, K.; Pedersen, A. EU-Rotate_N—A decision support system—To predict environmental and economic consequences of the management of nitrogen fertiliser in crop rotations. Eur. J. Hortic. Sci. 2010, 75, 20–32. [Google Scholar]
  40. Sun, Y.; Hu, K.; Fan, Z.; Wei, Y.; Lin, S.; Wang, J. Simulating the fate of nitrogen and optimizing water and nitrogen management of greenhouse tomato in North China using the EU-Rotate_N model. Agric. Water Manag. 2013, 128, 72–84. [Google Scholar] [CrossRef]
  41. Wang, A.; Gallardo, M.; Zhao, W.; Zhang, Z.; Miao, M. Yield, nitrogen uptake and nitrogen leaching of tunnel greenhouse grown cucumber in a shallow groundwater region. Agric. Water Manag. 2019, 217, 73–80. [Google Scholar] [CrossRef]
  42. Bremner, J. Determination of nitrogen in soil by the Kjeldahl method. J. Agric. Sci. 1960, 55, 11–33. [Google Scholar] [CrossRef]
  43. Li, K.-Y.; Zhao, Y.-Y.; Yuan, X.-L.; Zhao, H.-B.; Wang, Z.-H.; Li, S.-X.; Malhi, S.S. Comparison of factors affecting soil nitrate nitrogen and ammonium nitrogen extraction. Commun. Soil Sci. Plant Anal. 2012, 43, 571–588. [Google Scholar] [CrossRef]
  44. Greenwood, D.; Rahn, C.; Draycott, A.; Vaidyanathan, L.; Paterson, C. Modelling and measurement of the effects of fertilizer-N and crop residue incorporation on N-dynamics in vegetable cropping. Soil Use Manag. 1996, 12, 13–24. [Google Scholar] [CrossRef]
  45. Allen, R.G.; Pereira, L.S.; Raes, D.; Smith, M. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao Rome 1998, 300, D05109. [Google Scholar]
  46. Ritchie, J.T. Soil water balance and plant water stress. In Understanding Options for Agricultural Production; Tsuji, G.Y., Hoogenboom, G., Thornton, P.K., Eds.; Kluwer Academic Press: Dordrecht, The Netherlands, 1998; Volume 7, pp. 41–54. [Google Scholar]
  47. Hansen, S.; Jensen, H.; Nielsen, N.; Svendsen, H. DAISY: Soil Plant Atmosphere System Model; NPO Report No. A; Miljoeministeriet: Copenhagen, Denmark, 1990; Volume 10, pp. 1–276.
  48. Søgaard, H.T.; Sommer, S.G.; Hutchings, N.; Huijsmans, J.; Bussink, D.; Nicholson, F. Ammonia volatilization from field-applied animal slurry—The ALFAM model. Atmos. Environ. 2002, 36, 3309–3319. [Google Scholar] [CrossRef]
  49. Sadeghi, A.; McInnes, K.; Kissel, D.; Cabrera, M.; Koelliker, J.; Kanemasu, E. Mechanistic model for predicting ammonia volatilization from urea. TVA Bull. Tenn. Val. Auth. 1988, Y-206, 67–92. [Google Scholar]
  50. Willmott, C.J. Some comments on the evaluation of model performance. Bull. Am. Meteorol. Soc. 1982, 63, 1309–1313. [Google Scholar] [CrossRef]
  51. Nash, J.E.; Sutcliffe, J.V. River flow forecasting through conceptual models part I—A discussion of principles. J. Hydrol. 1970, 10, 282–290. [Google Scholar] [CrossRef]
  52. Willmott, C.J. On the validation of models. Phys. Geogr. 1981, 2, 184–194. [Google Scholar] [CrossRef]
  53. Van Liew, M.W.; Garbrecht, J. Hydrologic simulation of the little Washita river experimental watershed using SWAT 1. JAWRA J. Am. Water Resour. Assoc. 2003, 39, 413–426. [Google Scholar] [CrossRef]
  54. Yang, J.; Yang, J.-Y.; Liu, S.; Hoogenboom, G. An evaluation of the statistical methods for testing the performance of crop models with observed data. Agric. Syst. 2014, 127, 81–89. [Google Scholar] [CrossRef]
  55. Soto, F.; Gallardo, M.; Giménez, C.; Peña-Fleitas, T.; Thompson, R. Simulation of tomato growth, water and N dynamics using the EU-Rotate_N model in Mediterranean greenhouses with drip irrigation and fertigation. Agric. Water Manag. 2014, 132, 46–59. [Google Scholar] [CrossRef]
  56. Peñarrocha, D.; Estrela, M.J.; Millán, M. Classification of daily rainfall patterns in a Mediterranean area with extreme intensity levels: The Valencia region. Int. J. Climatol. J. R. Meteorol. Soc. 2002, 22, 677–695. [Google Scholar] [CrossRef]
  57. Guo, R.; Nendel, C.; Rahn, C.; Jiang, C.; Chen, Q. Tracking nitrogen losses in a greenhouse crop rotation experiment in North China using the EU-Rotate_N simulation model. Environ. Pollut. 2010, 158, 2218–2229. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  58. Sun, L.; Li, B.; Yao, M.; Mao, L.; Zhao, M.; Niu, H.; Xu, Z.; Wang, T.; Wang, J. Simulation of Soil Water Movement and Root Uptake under Mulched Drip Irrigation of Greenhouse Tomatoes. Water 2023, 15, 1282. [Google Scholar] [CrossRef]
  59. Kröbel, R.; Sun, Q.; Ingwersen, J.; Chen, X.; Zhang, F.; Müller, T.; Römheld, V. Modelling water dynamics with DNDC and DAISY in a soil of the North China Plain: A comparative study. Environ. Model. Softw. 2010, 25, 583–601. [Google Scholar] [CrossRef]
  60. Ramos, T.; Simionesei, L.; Jauch, E.; Almeida, C.; Neves, R. Modelling soil water and maize growth dynamics influenced by shallow groundwater conditions in the Sorraia Valley region, Portugal. Agric. Water Manag. 2017, 185, 27–42. [Google Scholar] [CrossRef]
  61. Šimůnek, J.; Van Genuchten, M.T.; Šejna, M. The HYDRUS software package for simulating two-and three-dimensional movement of water, heat, and multiple solutes in variably-saturated media. Tech. Man. Version 2006, 1, 241. [Google Scholar]
  62. Thorp, K.R.; Malone, R.W.; Jaynes, D.B. Simulating long-term effects of nitrogen fertilizer application rates on corn yield and nitrogen dynamics. Trans. ASABE 2007, 50, 1287–1303. [Google Scholar] [CrossRef] [Green Version]
Agronomy 13 02006 g001 550
Figure 1. Schematic diagram of EU-Rotate_N model [40].
Figure 1. Schematic diagram of EU-Rotate_N model [40].
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Figure 2. Climatic variation in greenhouse during 2018–2019. Tmin is the minimum air temperature, Tmax is the maximum air temperature, and Tsun is the time duration of sunshine inside greenhouse.
Figure 2. Climatic variation in greenhouse during 2018–2019. Tmin is the minimum air temperature, Tmax is the maximum air temperature, and Tsun is the time duration of sunshine inside greenhouse.
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Figure 3. Simulated and measured water content of different soil layers in different treatments of protected tomato.
Figure 3. Simulated and measured water content of different soil layers in different treatments of protected tomato.
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Figure 4. Simulated and measured values of nitrate nitrogen content in different soil layers of protected tomato in different treatments.
Figure 4. Simulated and measured values of nitrate nitrogen content in different soil layers of protected tomato in different treatments.
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Figure 5. Simulated and measured values of tomato yield. Vertical bars with different letters represent values that are statistically significant by LSD test at p < 0.05.
Figure 5. Simulated and measured values of tomato yield. Vertical bars with different letters represent values that are statistically significant by LSD test at p < 0.05.
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Figure 6. Simulated and measured values of tomato dry weight.
Figure 6. Simulated and measured values of tomato dry weight.
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Table
Table 1. Physical and hydraulic characteristics of selected soil depth.
Table 1. Physical and hydraulic characteristics of selected soil depth.
Soil Layer
(cm)		Clay
(%)		Sand
(%)		Bulk Density
(g·cm−3)		FC
(cm3·cm−3)		θs
(cm3·cm−3)		pH
0–1034.21.501.350.300.466.70
10–2044.89.161.400.270.397.05
20–3037.411.071.410.290.367.16
FC: Field water holding capacity; θs; Saturated water content.
Table
Table 2. Specific irrigation management measures for protected tomato.
Table 2. Specific irrigation management measures for protected tomato.
Autumn-WinterSpring-Summer
Date
(d/m/y)		Irrigation Quantity
(mm)		Date
d/m/y		Irrigation Quantity
(mm)
12 September 201837.9524 March 201956.43
15 September 201816.8927 March 201934.93
20 September 201825.1312 April 201945.43
10 October 201842.3325 April 201955.54
18 October 201836.525 May 201959.87
22 October 201814.1711 May 201926.74
20 October 201835.1916 May 201947.70
5 November 201838.3421 May 201950.91
15 November 201835.3330 May 201958.68
22 November 201842.055 June 201950.59
11 June 201954.62
16 June 201942.88
Table
Table 3. Setting of nitrogen levels in greenhouse experiment.
Table 3. Setting of nitrogen levels in greenhouse experiment.
CropNitrogen Application Levels
(kg ha−1)
N1N2N3N4
AW187.86281.79375.72469.65
SS180.61270.92361.22451.53
AW: Autumn–Winter, SS: Spring-Summer.
Table
Table 4. Input and output of EU-Rotate_N model.
Table 4. Input and output of EU-Rotate_N model.
Category
InputLocation information:Latitude, altitude, atmospheric N deposition
Simulation time:Simulation start date, Simulation end date
Soil properties:Organic matter content, clay content, pH, unit weight, field water capacity, saturated water content, carbon-nitrogen ratio
Climatic data:Solar radiation, wind speed, precipitation, Humidity, Temperature
Initial conditions:Soil water content, soil mineral nitrogen content
Irrigation management:Irrigation date, amount, method and N concentration in water
Fertilizer application:Organic and mineral fertilizer types, application methods, application dates
Crop parameters:crop type, plant spacing, row width, planting date, harvest date, harvest times, target yield
Output Soil nitrogen content, soil water content, leakage, crop dry matter quality, daily nitrogen demand, daily water demand
Table
Table 5. Parameters used in greenhouse tomato with EU-Rotate_N model.
Table 5. Parameters used in greenhouse tomato with EU-Rotate_N model.
ParametersDescriptionValue
BaseBase temperature (°C)7.00
B0Crop specific parameters of the critical N-curve equation3.00
PNINFCrop specific parameters of critical N1.60
RLUXLuxury N consumption coefficient1.0
L_iniLength of the initial growth period (d)30.0
L_devLength of the development period (d)60.0
L_midLength of the middle period (d)50.0
L_latLength of the late growth period (d)15.0
DdglagLag period before root growth begins (°C d−1)100
K_inicrop coefficient at initial stage 0.15
K_midCrop coefficient at middle stage0.90
K_endCrop coefficient in end stage0.90
KrzVertical root penetration rate (m d–1 °C–1)0.0014
H_maxMaximum crop height (m)1.60
HIHarvest index0.50
Table
Table 6. Model evaluation index of the soil water content of protected tomato.
Table 6. Model evaluation index of the soil water content of protected tomato.
TreatmentSoil Layer
(cm)		RMSE NSEd
N10–100.0420.6660.935
10–200.0370.4800.856
20–300.0350.5700.895
N20–100.0400.7000.941
10–200.0340.3990.858
20–300.0280.5460.892
N30–100.0390.6360.935
10–200.0300.3670.859
20–300.0270.5490.890
N40–100.0390.7490.947
10–200.0330.5040.870
20–300.0240.5140.878
Table
Table 7. Model evaluation index of soil nitrate nitrogen content in protected tomato.
Table 7. Model evaluation index of soil nitrate nitrogen content in protected tomato.
Treatment(cm)
Soil Layer		RMSE NSEd
N10–1027.5230.4630.901
10–2024.2800.3650.878
20–3020.6600.6540.909
N20–1025.4200.5520.919
10–2024.7550.4030.884
20–3019.3400.5070.885
N30–1026.3710.4880.912
10–2026.7660.3980.869
20–3016.5280.5310.894
N40–1024.8350.6370.932
10–2026.7370.4830.888
20–3013.70305870.924
Table
Table 8. Model evaluation index of protected tomato yield.
Table 8. Model evaluation index of protected tomato yield.
TreatmentRMSENSEd
AW–20181.0220.9770.994
SS–20191.2490.9650.992
AW: Autumn–Winter, SS: Spring–Summer.
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Ullah, I.; Cao, Z.; Bing, H.; Xu, X.; Miao, M. Simulation of Soil Water and Nitrogen Dynamics for Tomato Crop Using EU-Rotate_N Model at Different Nitrogen Levels in the Greenhouse. Agronomy 2023, 13, 2006. https://doi.org/10.3390/agronomy13082006

AMA Style

Ullah I, Cao Z, Bing H, Xu X, Miao M. Simulation of Soil Water and Nitrogen Dynamics for Tomato Crop Using EU-Rotate_N Model at Different Nitrogen Levels in the Greenhouse. Agronomy. 2023; 13(8):2006. https://doi.org/10.3390/agronomy13082006

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Ullah, Ikram, Zhuangzhuang Cao, Hua Bing, Xiangying Xu, and Minmin Miao. 2023. "Simulation of Soil Water and Nitrogen Dynamics for Tomato Crop Using EU-Rotate_N Model at Different Nitrogen Levels in the Greenhouse" Agronomy 13, no. 8: 2006. https://doi.org/10.3390/agronomy13082006

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