# Optimizing Nitrogen Fertilization to Enhance Productivity and Profitability of Upland Rice Using CSM–CERES–Rice

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## Abstract

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_{30}, N

_{60}, N

_{90}and a control–N

_{0}) applied under three different sowing windows (SD1, SD2 and SD3). Cultivar coefficients were calibrated with data from N

_{90}under all sowing windows in both seasons and the remaining treatments were used for model validation. Following model validation, simulations were extended up to N

_{240}to identify the sowing date’s specific economic optimum N fertilization rate (EONFR). Results indicated that CSM–CERES–Rice performed well both in calibration and validation, in simulating rice performance under different N fertilization rates. The d-index and nRMSE values for grain yield (0.90 and 16%), aboveground dry matter (0.93 and 13%), harvest index (0.86 and 7%), grain N contents (0.95 and 18%), total crop N uptake (0.97 and 15%) and N use efficiencies (0.94–0.97 and 11–15%) during model validation indicated good agreement between simulated and observed data. Extended simulations indicated that upland rice yield was responsive to N fertilization up to 180 kg N ha

^{−1}(N

_{180}), where the yield plateau was observed. Fertilization rates of 140, 170 and 130 kg N ha

^{−1}were identified as the EONFR for SD1, SD2 and SD3, respectively, based on the computed profitability, marginal net returns and N utilization. The model results suggested that N fertilization rate should be adjusted for different sowing windows rather than recommending a uniform N rate across sowing windows. In summary, CSM–CERES–Rice can be used as a decision support tool for determining EONFR for seasonal sowing windows to maximize the productivity and profitability of upland rice production.

## 1. Introduction

^{−1}was recommended based on various rice cultivars by the Rice Department of Thailand [11]. Another recommendation of 49–82 kg N ha

^{−1}was made based on the status of soil fertility [12]. Inappropriate N fertilization rates, along with wide variation in sowing windows without considering the impact of rainfall in adopted sowing windows, have impacted upland rice productivity. Rice growth, yield and quality is significantly affected by sowing date [13,14] and sowing is susceptible to impacts of climate change [15]. Thus, the main challenge is to optimize the N input to a level where the crop productivity is not affected for each adopted sowing window. An improved N use efficiency is desired in rice cropping systems. A higher N use efficiency would result in an optimal N fertilization rate for upland rice [4]. Nitrogen utilization patterns also differ under different sowing periods based on variations in moisture availability, which is regulated by rainfall events. Therefore, an optimal N fertilizer application rate tailored to each sowing window will help to maximize N utilization and resource use efficiency, which will enhance the productivity and profitability of upland rice.

^{−1}intervals. The DSSAT and APSIM models can be run on desired N rates to observe plateau yield or up to the N rate where the crop no longer responds to N addition, and the economically optimum N fertilization rate can be identified without the use of regression models [44]. This unique characteristic of crop simulation models provides superiority over regression or quadratic equations.

## 2. Results

#### 2.1. Model Calibration and Performance Evaluation

#### 2.1.1. Phenology

_{90}under SD1, SD2 and SD3 for both seasons’ data, whereas the difference in the number of days to anthesis for model evaluation ranged from −1 to 2 days for SD2 and SD3 for both seasons’ data. For SD1, the difference in the number of days to anthesis for model evaluation ranged from −5 to −7 in the first season, and −2 to −3 in the second season. A similar model performance pattern was observed for simulating days to maturity. Differences in the number of days to maturity for model calibration ranged from −3 to −15 for SD1 and −2 to −3 days for N

_{90}under SD2 and SD3 in both seasons, respectively, whereas the difference in the number of days to maturity for model evaluation ranged from −1 to −9 days for SD2 and SD3 in both seasons. For SD1, the difference in the number of days to maturity for model evaluation ranged from −13 to −15 in the first season and from −1 to −4 in the second season.

#### 2.1.2. Grain Yield and Aboveground Dry Matter

_{90}under SD1, SD2 and SD3 for both seasons’ data, and they were 0.90 and 16% for model evaluation, respectively, for N

_{0}, N

_{30}and N

_{60}for both seasons’ data (Figure 2A,B). Generally, the grain yield was underestimated except for N

_{90}under SD1, where it was overestimated by 11%. The underestimation of grain yield for model calibration with N

_{90}under SD1, SD2 and SD3 for both seasons’ data ranged within 3–14%, whereas the underestimation of grain yield for model evaluation with N

_{0}, N

_{30}and N

_{60}for SD1, SD2 and SD3 for both seasons ranged 1–32%. Statistical indicators d-index and nRMSE for aboveground dry matter were 0.98 and 5% for model calibration, respectively, for N

_{90}under SD1, SD2 and SD3 for both seasons’ data, and they were 0.93 and 13% for model evaluation, respectively, for N

_{0}, N

_{30}and N

_{60}for both seasons’ data (Figure 2C,D). Aboveground dry matter was also underestimated in general except for N

_{90}under SD3 in the second season, which was overestimated only by 2%. Underestimation of aboveground dry matter for model calibration with N

_{90}under SD1, SD2 and SD3 for both seasons’ data ranged within 4–7%, whereas the underestimation of aboveground dry matter for model evaluation with N

_{0}, N

_{30}and N

_{60}for SD1, SD2 and SD3 for both seasons ranged within 7–27%. The d-index and nRMSE for aboveground dry matter were 0.85 and 5% for model calibration, respectively, for N

_{90}under SD1, SD2 and SD3 for both seasons’ data, and were 0.86 and 7% for model evaluation, respectively, for N

_{0}, N

_{30}and N

_{60}for both seasons’ data (Figure 2E,F). Simulations for harvest index also followed a similar trend with grain yield and aboveground dry matter and they were generally underestimated except for all nitrogen treatments in SD1 in the second season, where it was overestimated by 1–11% and for N

_{30}in SD2 and SD3 during the first season, where it was overestimated by 5% and 2%, respectively. The underestimation of the harvest index for model calibration with N

_{90}under SD1, SD2 and SD3 for both seasons’ data ranged within 1–9%, except for SD1 in the second season, where it was overestimated by 8%. Similarly, an underestimation of the harvest index for model evaluation with N

_{0}, N

_{30}and N

_{60}for SD1, SD2 and SD3 for both seasons ranged within 1–16%, except for N

_{0}, N

_{30}and N

_{60}in the second season, where it was overestimated by 6%, 1% and 11%, respectively.

#### 2.1.3. Nitrogen Uptake

_{90}under SD1, SD2 and SD3 for both seasons’ data, and they were 0.95 and 18% for model evaluation, respectively, for N

_{0}, N

_{30}and N

_{60}for both seasons’ data (Figure 3A,B). Generally, the grain nitrogen was underestimated, except in the second season, for N

_{60}and N

_{90}under SD1, where it was overestimated by 25% and 19% and for N

_{0}, N

_{30}, N

_{60}and N

_{90}under SD3, where it was overestimated by 20–23%, respectively. The underestimation of grain nitrogen for model calibration with N

_{90}under SD1, SD2 and SD3 for both seasons’ data ranged within 7–21% except for N

_{90}under SD1 and SD3, where it was overestimated by 19% and 23%, respectively. Grain nitrogen was underestimated for SD1, SD2 and SD3 for both seasons by 8–33% except for N

_{60}under SD1, which was overestimated by 25% and for N

_{0}, N

_{30}and N

_{60}under SD3 that were overestimated by 22, 23 and 20%, respectively, in the second season for model evaluation with N

_{0}, N

_{30}and N

_{60}nitrogen fertilization treatments.

_{90}under SD1, SD2 and SD3 for both seasons’ data, and they were 0.97 and 15%, respectively, for model evaluation for N

_{0}, N

_{30}and N

_{60}under SD1, SD2 and SD3 for both seasons’ data (Figure 3C,D). Simulations for total crop nitrogen uptake also followed an identical trend with grain nitrogen and they were generally underestimated, except for all nitrogen treatments in SD3 in the second season, where it was overestimated by 20–29%, and for N

_{60}in SD3 during the first season, where it was overestimated by 4%, and for N

_{90}during the second season, where it was overestimated by 11%. The underestimation of total crop nitrogen uptake for model calibration with N

_{90}under SD1, SD2 and SD3 for both seasons’ data ranged within 3–19%, except for SD1 in the second season, where it was overestimated by 11%, and for SD3 in the second season, where it was overestimated by 21%. Similarly, the underestimation of total crop nitrogen uptake for model evaluation with N

_{0}, N

_{30}and N

_{60}for SD1, SD2 and SD3 for both seasons ranged within 2–18%, except for N

_{30}under SD3 in the first season, where it was overestimated by 4%, and for N

_{60}under SD1 in the second season, where it was overestimated by 24% and for N

_{0}, N

_{30}and N

_{60}in the second season, where it was overestimated by 29%, 21% and 20%, respectively.

#### 2.1.4. Nitrogen Use Efficiencies

_{90}under SD1, SD2 and SD3 for both seasons’ data, and they were 0.94 and 15% for model evaluation, respectively, for N

_{0}, N

_{30}and N

_{60}for both years of data (Figure 4A,B). In general, the N use efficiencies under N fertilization treatments were underestimated except for N

_{90}under SD1 in the second season, where it was overestimated by 11%. The underestimation of N use efficiency for model calibration with N

_{90}under SD1, SD2 and SD3 for both seasons’ data ranged within 3–14% except under SD1, where it was overestimated by 11%. Model evaluation results indicated that N use efficiency was underestimated for SD1, SD2 and SD3 in both seasons, ranging from 1 to 32% (Figure 4A,B).

_{90}under SD1, SD2 and SD3 for both seasons’ data, and they were 0.97 and 11% for model evaluation, respectively, for N

_{0}, N

_{30}and N

_{60}under SD1, SD2 and SD3 for both seasons’ data (Figure 4C,D). Simulations for N utilization efficiency also followed a trend similar to that with N use efficiency, and generally NUtE was underestimated, except for N

_{90}in SD2 and SD3 in the first year, where it was overestimated by 4% and 1%, respectively, and for N

_{30}in SD2 during the second season, where it was overestimated by 1%. There were no differences between simulated and observed values for N

_{90}under SD1 in the second season. Underestimation of N utilization efficiency for model calibration with N

_{90}under SD1, SD2 and SD3 for both seasons’ data ranged within 6–31%, except under SD2 and SD3 in the first season, where it was overestimated by 4% and 1%, respectively, and no differences were observed for SD1 in the second season. Similarly, the underestimation of N utilization efficiency for model evaluation with N

_{0}, N

_{30}and N

_{60}for SD1, SD2 and SD3 ranged from 5 to 17% in the first season and 5 to 68% in the second season, except for N

_{30}under SD2, where it was overestimated by 5% and 1%, respectively, in the first and second seasons.

#### 2.2. Simulations for Optimum Nitrogen Fertilization Rate

_{0}, N

_{30}, N

_{60}and N

_{90}in all sowing windows indicated a linear trend for grain yield and aboveground dry matter (Figure 5) and grain N contents and total crop N uptake (Figure 6). The calibrated CSM–CERES–Rice was then used to simulate the yields by extending the N fertilization rate beyond observed N fertilizer rates at increments of 30 kg up to 240 kg N ha

^{−1}(N

_{120}, N

_{150}, N

_{180}, N

_{210}and N

_{240}) until the grain yield no longer responded to N fertilization. By increasing the range of N fertilizer rates, simulated grain yield, dry matter, grain N content and crop N uptake response curves were observed to obey quadratic regression curves with a distinct plateau (Figure 5 and Figure 6). Simulations indicated that the grain yield of upland rice was responsive to N fertilization up to N

_{180}under SD1 and SD2, and up to N

_{150}under SD3 during the first season; and up to N

_{150}under SD1, up to N

_{180}under SD2 and up to N

_{150}under SD3 during the second season—whereas no further increase in grain yield was observed beyond these N fertilization rates. Grain yield only declined under SD1 in the second season at N

_{180}and was constant up to N

_{240.}Aboveground dry matter indicated a slightly different trend in comparison to the grain yields. It increased up to N

_{210}under SD1, SD2 and SD3 during the first season, and up to N

_{110}under SD1 and SD2, and up to N

_{150}under SD3 during the second season. Aboveground dry matter slightly declined at N

_{240}under all sowing windows in both seasons, except for SD3 in the second season, where it was slightly variable between N

_{150}and N

_{240}.

_{150}under SD1 and SD2, and up to N

_{150}under SD3 during the first season; and up to N

_{150}under SD1, up to N

_{180}under SD2 and up to N

_{120}under SD3 during the second season. No further increase in grain N contents was observed beyond these N fertilization rates, and they were similar up to N

_{210}, except under SD1 in the second season, where they decreased by only 1 kg ha

^{−1}. Total crop N uptake increased with N fertilization rate up to N

_{180}under SD1, up to N

_{210}under SD2 and SD3 during the first season; and up to N

_{210}under SD1, SD2 and SD3 during the second season. No further increase in total crop N uptake was observed beyond N

_{210}, and total crop N uptake was similar at N

_{210}and N

_{240}.

#### 2.3. Optimization of Nitrogen Fertilization Rate for Different Sowing Windows

_{0}). Gross return was increased with N fertilization. Extended simulations to determine the EONFR indicated that the highest profit with N fertilization over N

_{0}was observed at N

_{140}for SD1, at N

_{170}for SD2 and at N

_{130}for SD3 during the first growing season; and at N

_{140}for SD1, at N

_{180}for SD2 and at N

_{150}for SD3 during the second growing season. A narrow margin of profit over control, MNR, allowed for the precise estimation of EONFR. The highest MNR was observed at N

_{140}for SD1, at N

_{170}for SD2 and at N

_{130}for SD3 for both seasons (Table 1). The highest profit and MNR were observed at SD2 in both seasons. Simulated grain N contents, total crop N uptake and N use efficiencies were also at their highest under SD2 (Table 2). Based on grain yield performance, the highest MNR and promising levels of grain N contents, total crop N uptake and N use efficiencies, N

_{140}for SD1, N

_{170}for SD2 and N

_{130}for SD3, respectively, were identified as EONFR.

## 3. Discussion

## 4. Materials and Methods

#### 4.1. Study Area

#### 4.2. Field Experiments and Management

^{−1}(N

_{30}), 60 kg N ha

^{−1}(N

_{60}) and 90 kg N ha

^{−1}(N

_{90}), as well as a control with no N fertilization (N

_{0}), and planting was performed at three sowing dates (SD): SD1 (August/September), SD2 (September/October) and SD3 (October/November). Prior to planting in both seasons, all experimental plots were equally fertilized with phosphorus (19 kg P

_{2}O

_{5}ha

^{−1}) and potassium (13 kg K

_{2}O ha

^{−1}) [48]. Nitrogen fertilization was performed in two splits at tillering and panicle initiation stages using urea (46% N) as a N fertilizer source. Supplementary irrigation was also applied in experimental plots at planting and during the hot and dry intervals of each sowing window. All the treatments were repeated three times. Detailed information on field preparation, experimental design and plot management, basal fertilization, N fertilizer treatments application, supplementary irrigation and insect pest and weed management can be accessed in our previous study [48].

#### 4.3. Data Collection and Computations

^{−1}kg

^{−1}N) (1) and N use efficiency (NUE: kg ha

^{−1}kg

^{−1}N) (2) [34] were computed using the following equations:

#### 4.4. Model Configuration and Simulations

_{90}) at three sowing windows in both seasons. Cultivar coefficients for plant growth and development were further adjusted in a sequence using trial and error method until a best fit was achieved between the simulated and observed [34].

_{0}, N

_{30}, N

_{60}and N

_{90}, simulations to observe upland rice response to N additions were performed by increasing the N fertilization rate at regular increments of 30 kg up to 240 kg N ha

^{−1}(N

_{120}, N

_{150}, N

_{180}, N

_{210}and N

_{240}) until the grain yield no longer responded to N fertilization. To determine the optimal N fertilization rate, the model was run with 10 kg N ha

^{−1}increments for each sowing window with the N fertilization rates below and above the points where the highest yield was observed, following the technique mentioned by Puntel et al. [44], by which the economic optimum N fertilization rate (EONFR) can be determined.

#### 4.5. Statistics and Economics

^{2}) was used in regression analysis. A high R

^{2}value is indicative of the accuracy of the results.

_{i}refers to the model predicted value, O

_{i}refers to the observed field value, n refers to the number of observations and Ō refers to the overall mean of the observed field values.

_{i}refers to the model predicted value, O

_{i}refers to the observed field value, ${P}_{i}^{\prime}$ is P

_{i}− Ō and ${O}_{i}^{\prime}$ is O

_{i}− Ō.

^{−1}) in the year 2018 [48]. The EONFR was estimated based on computed marginal net return (MNR) (6) [35]. The EONFR for a treatment was considered where the maximum MNR was observed:

^{−1}), NFR is the N fertilization rate (kg ha

^{−1}) and Nc is the cost of N (USD 0.48 kg

^{−1}).

## 5. Conclusions

^{−1}(N

_{240}) indicated that upland rice yield was responsive to N fertilization up to 180 kg N ha

^{−1}(N

_{180}), at which the yield plateau was observed, and no increase in grain yield was observed beyond N

_{180}. Considering the impact of sowing windows, maximum crop performance for productivity, N uptake, N utilization, profitability and MNR were observed at SD2 in both seasons. Fertilization rates of 140, 170 and 130 kg N ha

^{−1}were identified as the economic optimum N fertilization rates (EONFR) for SD1, SD2 and SD3, respectively, based on the computed profitability, marginal net returns and N utilization. Simulation results suggest that the optimum N fertilizer application is a practical management strategy that would enhance the crop productivity, NUE and profitability of upland rice systems. However, it should be noted that climatic conditions, particularly rainfall patterns, are variable over different sowing windows and seasons. In addition, model results also indicated variability in optimum N fertilization rates for different sowing windows. Therefore, N fertilization rates should be determined and practiced according to the prevailing or predicted climatic conditions for different sowing windows and seasons. In this regard, based on the performance of CSM–CERES–Rice, it is recommended that the model be used to determine N fertilization rates for adopted seasonal sowing windows, which will lead to a more efficient N utilization and improved profitability of upland rice production.

## Supplementary Materials

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**Results of model calibration (

**left**) and performance evaluation (

**right**) for simulated versus observed days to anthesis (

**A**,

**B**) and days to maturity (

**C**,

**D**) after the planting of upland rice planted under four nitrogen fertilization treatments, three sowing dates and two growing seasons. Data points are the average, and vertical error bars represent ± standard errors for observed data obtained from three experimental replicates.

**Figure 2.**Results of model calibration (

**left**) and performance evaluation (

**right**) for simulated versus observed grain yield (

**A**,

**B**), aboveground dry matter (

**C**,

**D**) and harvest index (

**E**,

**F**) of upland rice planted under four nitrogen fertilization treatments, three sowing dates and two growing seasons. Data points are the average, and vertical error bars represent ± standard errors for observed data obtained from three experimental replicates.

**Figure 3.**Results of model calibration (

**left**) and performance evaluation (

**right**) for simulated versus observed grain nitrogen uptake (

**A**,

**B**), and total crop nitrogen uptake (

**C**,

**D**) of upland rice planted under four nitrogen fertilization treatments, three sowing dates and two growing seasons. Data points are the average, and vertical error bars represent ± standard errors for observed data obtained from three experimental replicates.

**Figure 4.**Results of model calibration (

**left**) and performance evaluation (

**right**) for simulated versus observed nitrogen use efficiency (

**A**,

**B**), and nitrogen utilization efficiency (

**C**,

**D**) of upland rice planted under four nitrogen fertilization treatments, three sowing dates and two growing seasons. Data points are the average, and vertical error bars represent ± standard errors for observed data obtained from three experimental replicates.

**Figure 5.**Regression relationships between nitrogen (N) fertilization rates and observed (linear—O) and simulated (quadratic—S) grain yield (

**A**,

**B**) and between N fertilization rates and observed (linear) and simulated (quadratic) aboveground dry matter (

**C**,

**D**) for upland rice planted at three sowing dates during the first growing season 2018–2019 (

**A**,

**C**) and the second growing season 2019–2020 (

**B**,

**D**).

**Figure 6.**Regression relationships between nitrogen (N) fertilization rates and observed (linear—O) and simulated (quadratic—S) grain N contents (

**A**,

**B**) and between N fertilization rates and observed (linear) and simulated (quadratic) total crop N uptake (

**C**,

**D**) for upland rice planted at three sowing dates during the first growing season 2018–2019 (

**A**,

**C**) and the second growing season 2019–2020 (

**B**,

**D**).

**Table 1.**Simulated economic optimum nitrogen fertilization rate (EONFR), grain yields, gross returns, profits over control and marginal net return (MNR) for upland rice planted at three sowing windows (SD1–SD3) during the first growing season 2018–2019 and the second growing season 2019–2020.

Growing Season | Sowing Window | EONFR | Grain Yield | Gross Return | Profit | MNR |
---|---|---|---|---|---|---|

kg ha^{−1} | kg ha^{−1} | USD ha^{−1} | USD ha^{−1} | USD ha^{−1} | ||

2018–2019 | SD1 | 140 | 3935.0 | 7004.0 | 3859.0 | 6937.1 |

SD2 | 170 | 5226.0 | 9302.3 | 5437.9 | 9220.7 | |

SD3 | 130 | 3580.0 | 6372.4 | 3602.1 | 6310.0 | |

2019–2020 | SD1 | 140 | 3391.0 | 6036.0 | 2662.9 | 5968.8 |

SD2 | 170 | 4378.0 | 7792.8 | 4252.4 | 7711.2 | |

SD3 | 130 | 2840.0 | 5055.2 | 2438.6 | 4992.8 |

**Table 2.**Simulated economic optimum nitrogen fertilization rate (EONFR), grain nitrogen (N) contents, total crop N uptake, N use efficiency (NUE) and N utilization efficiency (NUtE) for upland rice planted at three sowing windows (SD1–SD3) during the first growing season 2018–2019 and the second growing season: 2019–2020.

Growing Season | Sowing Window | EONFR | Grain N Contents | Total Crop N Uptake | NUE | NUtE |
---|---|---|---|---|---|---|

kg ha^{−1} | kg ha^{−1} | kg ha^{−1} | kg ha^{−1} kg^{−1} N | kg ha^{−1} kg^{−1} N | ||

2018–2019 | SD1 | 140 | 36.0 | 75.0 | 28.1 | 52.5 |

SD2 | 170 | 63.0 | 114.0 | 30.7 | 45.8 | |

SD3 | 130 | 32.0 | 63.0 | 27.5 | 56.8 | |

2019–2020 | SD1 | 140 | 33.0 | 64.0 | 24.2 | 53.0 |

SD2 | 170 | 51.0 | 91.0 | 25.8 | 48.1 | |

SD3 | 130 | 30.0 | 57.0 | 21.9 | 49.8 |

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## Share and Cite

**MDPI and ACS Style**

Hussain, T.; Mulla, D.J.; Hussain, N.; Qin, R.; Tahir, M.; Liu, K.; Harrison, M.T.; Sinutok, S.; Duangpan, S.
Optimizing Nitrogen Fertilization to Enhance Productivity and Profitability of Upland Rice Using CSM–CERES–Rice. *Plants* **2023**, *12*, 3685.
https://doi.org/10.3390/plants12213685

**AMA Style**

Hussain T, Mulla DJ, Hussain N, Qin R, Tahir M, Liu K, Harrison MT, Sinutok S, Duangpan S.
Optimizing Nitrogen Fertilization to Enhance Productivity and Profitability of Upland Rice Using CSM–CERES–Rice. *Plants*. 2023; 12(21):3685.
https://doi.org/10.3390/plants12213685

**Chicago/Turabian Style**

Hussain, Tajamul, David J. Mulla, Nurda Hussain, Ruijun Qin, Muhammad Tahir, Ke Liu, Matthew T. Harrison, Sutinee Sinutok, and Saowapa Duangpan.
2023. "Optimizing Nitrogen Fertilization to Enhance Productivity and Profitability of Upland Rice Using CSM–CERES–Rice" *Plants* 12, no. 21: 3685.
https://doi.org/10.3390/plants12213685