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Article

Split Nitrogen Application Rates for Wheat (Triticum aestivum L.) Yield and Grain N Using the CSM-CERES-Wheat Model

by
Gul Roz Khan
1,2,3,
Hiba M. Alkharabsheh
4,*,
Mohammad Akmal
1,
Arwa Abdulkreem AL-Huqail
5,
Nawab Ali
1,6,
Bushra A. Alhammad
7,
Muhammad Mehran Anjum
1,3,
Rabia Goher
1,
Fazli Wahid
8,
Mahmoud F. Seleiman
9,10,* and
Gerrit Hoogenboom
2,11
1
Department of Agronomy, University of Agriculture, Peshawar 25130, Pakistan
2
Food Systems Institute, University of Florida, Gainesville, FL 32611, USA
3
Pakistan Council of Scientific and Industrial Research Laboratories Complex, Peshawar 25120, Pakistan
4
Department of Water Resources and Environmental Management, Faculty of Agricultural Technology, Al Balqa Applied University, Salt 19117, Jordan
5
Department of Biology, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
6
Directorate of Livestock Research and Dairy Development, Peshawar 25000, Pakistan
7
Biology Department, College of Science and Humanity Studies, Prince Sattam Bin Abdulaziz University, Al Kharj 292, Riyadh 11942, Saudi Arabia
8
Department of Agriculture, University of Swabi, Swabi 23561, Pakistan
9
Plant Production Department, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, Riyadh 11451, Saudi Arabia
10
Department of Crop Sciences, Faculty of Agriculture, Menoufia University, Shibin El-Kom 32514, Egypt
11
Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611, USA
 
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Agronomy 2022, 12(8), 1766; https://doi.org/10.3390/agronomy12081766
Submission received: 10 June 2022 / Revised: 20 July 2022 / Accepted: 25 July 2022 / Published: 27 July 2022
(This article belongs to the Special Issue Advances in Modelling Cropping Systems to Improve Yield and Quality)
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Versions Notes

Abstract

:
Crop simulation models can be effective tools to assist with optimization of resources for a particular agroecological zone. The goal of this study was to determine the influence of N rates with different timing of application to wheat crop using prominent varieties using the CSM-CERES-Wheat model of the decision support system for agrotechnology transfer (DSSAT). Data were focused for yield traits, i.e., number of tillers, number of grains, grain weight, grain yield, biomass, and grain N content. To test the applicability of the CSM-CERES-Wheat version 4.7.5 model for agroclimatic conditions of Peshawar, Pakistan, experimental data from two years of experiments (2016–17 and 2017–18) were used for model calibration and evaluation. The simulation results of two years agreed well with field measured data for three commercial varieties. The model efficiency (R2) for wheat varieties was above 0.94 for variables tiller number per unit area (m−2), number of grains (m−2) and number of grains (spike−1), 1000 grain weight (mg), biomass weight (kg ha−1), grain yield (kg ha−1), and harvest N content (kg ha−1). Statistics of cultivars indicated that yield traits, yield, and N can be simulated efficiently for agroecological conditions of Peshawar. Moreover, different N rates and application timings suggested that the application of 140 kg N ha−1 with triple splits timings, i.e., 25% at the sowing, 50% at the tillering, and 25% at the booting stage of the crop, resulted in the maximum yield and N recovery for different commercial wheat varieties. Simulated N losses, according to the model, were highly determined by leaching for experimental conditions where a single N application of 100% or existing double splits timing was applied. The study concluded that 140 kg N ha−1 is most appropriate for wheat crop grown on clay loam soils under a flood irrigation system. However, the N fertilizer has to be given in triple splits of a 1:2:1 ratio at the sowing, tillering, and booting stages of the crop growth.

1. Introduction

Wheat (Triticum aestivum L.) is an important cereal grown throughout the globe. However, it is major source of food and feed for both humans and livestock. It is cultivated worldwide on 215 million hectares, producing 761.5 million tons of grain during 2020–21 [1,2,3]. Wheat is an important crop of Pakistan used for staple foods, meeting the nutritional requirement with higher calories and protein [4]. Pakistan grows annually 8.797 million ha to produce 27 million tons of wheat [2,5]. Nonetheless, the average yield is around 2.84 t ha−1, which is very low when compared with other countries in Asia with a similar climate [2,6]. Demand for quality flour is increasing to ensure optimum nutrient supplementation. Breeders are actively working on variety selection that has a high gluten content as well as yield. Currently, the high-yielding varieties have a sub-optimal gluten range [7]. The focus of agronomic practices is concentrated to improve wheat flour with better kneading quality [8]. Nitrogen fertilizer application rate and timing of application coinciding with crop growth stages is the main focus to improve grains with better flour quality.
Nitrogen is one of the most essential nutrients for the growth of a crop and it is considered to be highly mobile in the soil [9,10,11,12]. A sole application of nitrogen showed an increase of 50 to 150 kg ha−1 in wheat grain yield, which is a significant positive change [13]. Right after the N discovery, a single application was recommended. However, later on, double splits of N were recommended due to its mobility in the soil [14,15]. Nitrogen use directly increases production cost, and its mobility in the soil adversely affects soil and the environment [16]. In developing countries, under changing climate scenarios, the cost of wheat production is high, while N is generally subsidized. N was also found to be deficient in the soil based on crop demand. Increasing N use efficiency is, therefore, the focus of many research studies.
Nitrogen plays a fundamental role in yield and quality [17,18,19]. Better N management is undoubtedly associated with application timings and its proper rate [20,21]. Nitrogen loss through leaching, volatilization, and denitrification is common under a flood irrigation system [22,23,24,25]. Optimum N at different stages of the crop until booting showed positive effects on yield and grain quality [26]. Sufficient N in the soil for crop growth showed positive responses on grain weight, grain filling duration, and grain protein [27]. Split application of N has controlled leaching and volatilization [28,29]. The timing of N application with rates has been determined by the rainfall distribution pattern [30,31] for good N use efficiency [32].
Crop growth is the outcome of responses to the different factors; namely, cultivars, weather, water, nutrients, and their management [33]. Computer-based simulation models are a tool for knowledge acquisition, testing hypotheses, prediction, determination of quantitative relationships, and decision support [34,35]. The decision support system for agriculture technology (DSSAT; www.DSSAT.net; accessed on 1 January 2022) is the world-known crop simulation model used by +1800 countries over the last 20 years. It includes +40 different crop growth models [36,37]. The CERES-Wheat model has been evaluated for a wide range of environments around the world [38,39]. The CERES-Wheat model can be used for yield forecast and calculation of N losses for different N rates and application timing.
In this paper, field experiment data were used for evaluation of the CSM-CERES-Wheat model of DSSAT 4.7.5. The model evaluates changing aspects of the N application for the wheat crop growth and simulates nitrification, denitrification, leaching, volatilization, and plant uptake as per the crop demand [37]. DSSAT has different crop models which require the same input format for weather, soil, and management [38,39,40].
The goal of this study was to evaluate the CERES-Wheat model of DSSAT for field measurements made on wheat crop in the agroecological condition of Peshawar, Pakistan treated with different N rates and timings adjusted with the crop growth. To simulate wheat grown with N rates and application timings, the genetic coefficient for different wheat cultivars was used to minimize the N loss for optimized production.

2. Materials and Methods

2.1. Experimental Site

Three high-yielding wheat varieties were planted after maize harvesting at Agronomy Research Farm, University of Agriculture Peshawar, Pakistan during winter of 2016–17 and 2017–18. The experimental farm is located at 34.01° N, 71.35° E and at an altitude of 350 m from sea level. Peshawar is located 1600 km north of the Indian Ocean and the climate of the city is semi-arid, relatively hot in summers with mildly cool and relatively short winters. The mean maximum and minimum temperature of the summers are 40–25 °C and of the winters are 18–4 °C. The soil of the field is relatively silty loam with lower organic matter.

2.2. Weather Data

Daily weather data of the experimental location were collected from a local weather station of the Pakistan Met. Department. Solar radiation data were calculated from the sunshine hours. Maximum and minimum temperature (°C), solar radiation (MJ m−2), and rainfall (mm) are shown in Figure 1.

2.3. Soil Data

Soil samples were collected from the experimental field. The samples were analyzed for soil physico-properties by standard laboratory procedures. Soil was silty loam (Table 1).

2.4. Treatments and Design

The experiment was a randomized complete block (RCB) split plot, replicated three times. The treatment N application rates (NARs) and N application timing (NAT) were in the main plots, and wheat varieties, i.e., cv. Pakhtunkhwa-2015, DN-84, and Pirsabak-2015, were in the sub-plots with a net plot of size 1.4 m × 4.0 m. Pre-basic seeds of the varieties were obtained from the research station and used for each sowing. Sowing was done on 21 November 2016 and 28 November 2017. Fertilizer was applied as recommended for wheat, i.e., p = 90 and K = 60 kg ha−1, at the time of seedbed preparation from single super phosphate (P2O5 = 18%) and muriate of potash (K2O = 60%) sources. In contrast, nitrogen was applied as per treatments, i.e., 100, 120, 140, and 160 kg ha−1, at different crop growth stages from urea (N-46%). Different NATs were the NAT1, i.e., (single application at seedbed preparation), NAT2, i.e., double application (50% at sowing and 50% at tillering), NAT3, i.e., triple application (25% at sowing, 50% at tillering, and 25% at booting), and NAT4, i.e., triple application (25% at sowing, 25% at tillering, and 50% at booting) of the given rates. The field was irrigated uniformly through flood irrigation (56 mm) at tillering, booting, and grain filling stages as per the crop water demand. All other agronomic practices were kept uniform for experimental units. Weedicide (Affinity 50WP, 2.0) was used once for weed control at the tillering stage (30 DAS) of the crop as a post-emergence weedicide.

2.5. Samplings and Measurements

The number of tillers (m−2) was counted manually in a quarter square area at three locations in an experimental unit. The number of grains per tiller was counted manually at harvest on 15 tillers randomly collected from an experimental unit. The grain weight index was recorded by randomly taking samples of grains, counting (n = 1000) on a seed counter, and weighing (mg). For crop growth data, periodic samples were harvested at 15-day intervals from a 900 cm2 area in an experimental unit (n = 9), and samples were oven-dried and weighed. For grain yield and above-ground biomass yield, four central rows in an experimental unit were harvested, bundled, and sun-dried for 10 days. Dry bundles were weighed for the total above-ground biomass and threshed on a mini-lab wheat thresher, and grains were weighed and moisture contents were recorded and adjusted for the grain yield (grain moisture 15%). Grain samples, after threshing, were collected and processed for N content determination with a standard laboratory procedure [21] to convert into N kg ha−1.

2.6. CSM-CERES-Wheat

Model Calibration

Comparing simulated with observed data for parameter adjustment is known as model calibration that requires specific genetic coefficients. These genetic coefficients are cultivar-specific which reflects its growth, developmental phases, and the grain production and allows the model to simulate the cultivars’ performance in different ecologies [41]. For yield and N content simulation of the tested wheat cultivars, the CSM-CERES-Wheat model of DSSAT was used. Cultivar coefficients are the pre-requisites for the simulation that were developed for all the tested cultivars through calibration (Table 2). Different genetic coefficients for calibration were P1V (vernalization sensitivity coefficient), P1D (photoperiod sensitivity coefficient), P5 (grain fill duration), G1 (kernel number at anthesis), G2 (kernel size under normal condition), G3 (non-stressed dry weight of single tiller at maturity), and PHINT (phyllochron interval between successive leaf tip appearance) [35].

2.7. Model Evaluation

The comparison between the observed and simulated data is known as model evaluation [42]. Besides this, different statistical approaches are used to quantify the association between observed and simulated data, i.e., correlation coefficient (r), coefficient of determination (R2), root mean square error (RMSE), and their respective ratios over the average differences. The RMSE is commonly used for model evaluation and calculated by:
R M S E = 1 N ∗ ∑   ( O i − P i ) 2 .
where P, O, and N stand for predicted values, observed values, and number of observations within treatments, respectively. The deviation of the observed and predicted value is measured by RMSE, which is always positive with a lower value corresponding to the high accuracy and best fit when RMSE approaches zero.

3. Results

Three wheat cultivars were tested during the study, and their genetic coefficients were determined. The genetic coefficients P1V 17.60, 29.00, and 16.32 were obtained for the three cultivars, i.e., Pakhtunkhwa-15, DN-84, and Pirsabak-15, respectively. The genetic coefficients 68.6, 55.0, and 72.0 days were obtained for P1D for the three tested cultivars. Among the three tested cultivars, Pirsabak-2015 showed the maximum value (775.9) for the genetic coefficient followed by Pakhtunkhwa-2015 (771) while the minimum value (672) was observed for DN-84. The genetic coefficient G1 for the three tested cultivars was almost the same, i.e., 18.1, 16.4, and 15.6. Similarly, for G2, the coefficient was also almost similar, i.e., 39.2, 39.3, and 39.8 for cultivars Pakhtunkhwa-2015, DN-84, and Pirsabak-2005, respectively. The genetic coefficient G3 was expressed higher for Pirsabak-2015 followed by Pakhtunkhwa-2015, and the lowest was obtained for DN-84. However, PHINT had a similar value of 100 for all the three tested cultivars.
Statistical data showed that the model was a good fit for simulated and observed values using CERES-Wheat model. The model was a good fit for number of tillers (m−2), number of grains per unit area (m−2), unit grain weight (mg), vegetative mass, grain, and biomass yield (kg ha−1), and grain N (kg ha−1). The model results showed underestimation for N application rates and application timings in the case of vegetative weight, grain number, grain yield, and biomass for all the tested cultivars.

3.1. Number of Tillers

Simulated and observed data were very close for the number of tillers in both seasons. The RMSE values 0.518, 0.895, and 0.305 were observed for different wheat varieties Pakhtunkhwa-2015, DN-84, and Pirsabak-2015, respectively (Figure 2). During 2016–17, simulated and observed data for the number of tillers were found to be the maximum compared to 2017–18 for simulated as well as observed data. The model results indicated that the number of tillers per unit area increased with increasing N application from 0 to 160 kg ha−1 in triple splits, and the lowest number of tillers m−2 was found in the control treatment (0 kg N ha−1). The simulated and observed result showed that a single N application (100%) at sowing produced fewer tillers and more tillers than were produced in triple splits N application (25% at sowing, 25% at tillering, and 50% at the booting stage of the wheat crop). Among the varieties, a higher tiller number per unit area was recorded in Pirsabak-2015 followed by Pakhtunkhwa-2015, and the lowest number of tillers was recorded in DN-84. The model closely matched the observed and predicted tiller density, and as for Pakhtunkhwa-2015, DN-84, and Pirsabak-2015, the RMSE values were 12.5, 5.34, and 15.05, R-square values were 0.949, 0.981, and 0.978, and d-stat values were 0.976, 0.995, and 0.979, respectively, which implies that there was good agreement between simulated and observed values of tiller density of wheat crop. This showed that the model was very robust in simulating the tiller density.

3.2. Number of Grains

Number of grains (m−2) of simulated data under the N application rates and N timings for different wheat cultivars in the growing season 2016–17 and 2017–18 are presented in Figure 3. The RMSE values for varieties Pakhtunkhwa-2015, DN-84, and Pirsabak-2015 were 437.2, 266.076, and 258.78, respectively. Figure 3 shows that the number of grains (m−2) of the simulated and observed was recorded at the maximum during 2016–17 over the year 2017–18. Simulated results indicated that increasing N rates from 0 to 160 kg N ha−1 in triple splits increased the number of grains per unit area while a lower number of grains was recorded in the control treatments. The model overpredicted when N was applied 100% at the time of sowing. In the case of different varieties, the number of grains (m−2) for simulated and observed data was highest in Pakhtunkhwa-2015 followed by Pirsabak-2015, and the lowest number of grains was observed in cultivar DN-84. Model statistics indicated reasonably good agreement between observed and simulated values of grain number. The statistic indices for Pakhtunkhwa-2015, DN-84, and Pirsabak-2015 were R-square values of 0.984, 0.994, and 0.993, RMSE values were 437.2, 266.07, and 258.78, and d-statistics were 0.976, 0.989, and 0.992, respectively. This showed a best fit between observed and simulated values and simulated the grain number quite well.

3.3. Grain Kernel Weight

Figure 4 shows the simulated data for the kernel weight of different wheat cultivars treated with different nitrogen rates and its application timing in 2016–17 and 2017–18. During both years, i.e., 2016–17 and 2017–18, the observed and the simulated values were similar for grain weight of wheat varieties. The RMSE value for each genotype of wheat for kernel weight (mg) was recorded similarly, which was very close to 0.001 for both observed and simulated data. Among different nitrogen treatment applications, heavier grains were produced by the fertilized plots where N was applied at different rates, while lighter grains were observed by the control treatment where no N was added to the experimental plots. Pirsabak-2015 produced heavier grain than Pakhtunkhwa-2015, which had similar readings for DN-84 in the simulations and the observations of unit grain weight. The model was very robust and simulated the grain weight quite well for different cultivars with split N rates. Statistical indices indicated the best fit between observed and simulated values for grain weight. The model statistics for Pakhtunkhwa-2015, DN-84, and Pirsabak-2015 were RMSE with 0.001 for all varieties and d-stat of 0.443, 0.469, and 0.383, respectively, for grain weight.

3.4. Biomass

Simulated data for biomass of wheat under different N application rates and N application timings during 2016–17 and 2017–18 for wheat varieties are presented in Figure 5. In both years, the results from simulations and the observations were reasonably close. For the varieties Pakhtunkhwa-2015, DN-84, and Pirsabak-2015, the RMSE values were 419.32, 300.28, and 553.21, respectively. The wheat biomass increased in 2016–17 according to both the observed and simulation data. There was a strong correlation between increasing the N application rate to 160 kg N ha−1 and increasing the biomass. The model overpredicted where N was applied completely at sowing (100%). The cultivar Pirsabak-2015 had the highest biomass according to both simulated and actual data, followed by Pakhtunkhwa-2015, while cultivar DN-84 had the lowest biomass. The model showed the best performance for simulating biomass. The observed and simulated values’ comparison showed a good fit and robustness of the model. The statistical index values for Pakhtunkhwa-2015, DN-84, and Pirsabak-2015 were R-square of 0.996, 0.992, and 0.985, RMSE of 419.3, 300.2, and 553.2, and d-statistics of 0.99, 0.99, and 0.98, respectively, for biomass.

3.5. Grain Yield

For wheat genotypes, simulated grain yield harvested under various N application rates and timing during 2016–17 and 2017–18 is shown in Figure 6. For both years, there was a strong correlation between the observed and simulated yield. For the cultivars Pakhtunkhwa-2015, DN-84, and Pirsabak-2015, the RMSE values were 231.82, 126.48, and 160.33, respectively. The observed and simulated data for grain production showed the highest values in 2016–17. The crop treated with 140 and 160 kg N ha−1 in triple splits produced the highest simulated and observed grain yield. The model overpredicted the yield for treatment where all N was applied 100% at sowing. Simulated and actual yield data indicated that Pakhtunkhwa-2015 had the highest yield, followed by Pirsa-bak-2015, and variety DN-84 had the lowest grain yield. The model simulated grain yield for all the three cultivators quite well with different N application rates and timing. The simulated and observed values for grain yield indicated that the model had the best fit and simulated the grain yield well. Model statistical indices showed that the model simulated the grain yield quite closely with high robustness. The statistical indices for Pakhtunkhwa-2015, DN-84, and Pirsabak-2015 were R-square of 0.983, 0.982, and 0.979, RMSE of 231.8, 126.4, and 160.3, and d-stat of 0.963, 0.985, and 0.98, respectively.

3.6. Grain N Content

Figure 7 presents simulated data for the grain N content (%) under various treatments, N application rates, and N application timing in 2016–17 and 2017–18 for wheat cultivars. Grain N was close for years of both observed and simulated data. The RMSE values for cultivars Pakhtunkhwa-2015, DN-84, and Pirsabak-2015 were 0.11, 0.76, and 0.76, respectively. However, grain N data from the simulations and observations showed that 2016–17 was the year with the highest levels over 2017–18. The crop treated with 160 kg N ha−1 produced the highest grain N in the case of simulated and observed data. The model was also overpredicted when N was applied fully at the rate of 100% at sowing, and the cultivar Pirsabak-2015, which was followed by Pakhtunkhwa-2015, showed quite a bit of deviance. Grain N measurements for the cultivars Pakhtunkhwa-2015, Pirsabak-2015, and DN-84 were the highest for both the simulated and the observed readings. The model showed the best performance for simulating grain N. The observed and simulated values’ comparison showed a good fit and robustness of the model. The statistical index values for Pakhtunkhwa-2015, DN-84, and Pirsabak-2015 were R-square of 0.95, 0.97, and 0.98, RMSE of 0.11, 0.76, and 0.76, and d-statistics of 0.94, 0.98, and 0.96, respectively, for grain N.

4. Discussion

The DSSAT performance after model calibration was evaluated for different tested variables including yield traits (tillers number m−2, unit grain weight, grain number, grain yield, biomass, and grain N content) and grain N content as recommended by [42]. The model gave good prediction of yield traits and grain N content, and statistics of the results are presented for all cultivars in Table 3. Major yield-contributing traits of a crop are on the basis of the net grain yield harvested in an environment. Variety does play a major role [43]. Among the major traits of wheat, the number of tillers, number of grains, and weight per unit are major traits [44]. Among the yield traits, the CERES-Wheat predicted satisfactory number of tillers per unit area with the treatment given as N application rates and N application timings. Simulated and observed values for the number of tillers per unit were close in association and model best fit for the local condition and given treatments for wheat cultivars planted in 2016–17 and 2017–18. The observed and simulated number of tillers were 350 and 358, respectively, for cultivar Pakhtunkhwa-2015 with a mean r2 value 0.949, RMSE value of 12.54, and d-stat of 0.97. Similar fit statistics were reported by [36]. Similarly for variety DN-84, the observed and simulated number of tillers were 326 and 325, respectively, with r2 of 0.98, RMSE of 5.37, and d-stat of 0.995. This showed that the model simulated the observed data well. Similarly, the model best fits the number of tillers for Pirsabak-2015 with observed and simulated readings of 370 and 382, respectively, r2 of 0.978, RMSE of 15.05, and d-stat of 0.979. It is obvious that number of tillers per unit is independent of the N rates; however, triple splits of N to the crop may be associated with relatively low soil concentration, which resulted in healthy growth of plants and/or better development of the auxiliary buds to initiate tiller [45,46]. The low number of tillers of the single or double splits application obviously has higher N concentration in soil and in plants and might have slowed down the tiller initiation process in plants, expressing fewer tillers per unit by adverse mild toxicity effects on the normal plant growth [47]. The range of RMSE for the number of tillers per unit was within the range of published data for wheat. A similar range of RMSE values was reported for the number of tillers by [48]. Ref. [49] expressed that N increments over the optimum rate did not show increases in the number of tillers. Findings of [50] are also in agreement with this experiment finding for the number of tillers per unit.
Statistics of the model result showed that simulated and observed values for the wheat number of grains per spike were found in close association and the model best fit to conditions for given treatments of wheat cultivars planted in 2016–17 and 2017–18. This goodness of fit statistic can be compared to that reported by [51]. Observed and simulated data fit well for the number of grains reported as 8603 and 8994, respectively, for variety Pakhtunkhwa-2015 with mean r2 of 0.984, RMSE of 437.2, and d-stat of 0.976. Likewise, variety DN-84 showed the number of grains of 9422 and 9651, respectively, with r2 of 0.994, RMSE of 266.07, and d-stat of 0.989 with the close association between model-simulated data and the observed data. The model best fit for the number of grains within cultivar Pirsabak-2015 had observed and simulated values of 8022 and 8245 in 2017–18 and 2018–19, respectively, with r2 of 0.993, RMSE of 258.788 and d-stat of 0.992. Similar best fit statistics compare favorably to those reported by [51]. The triple splits N application better coincided with crop growth and met the desired N requirements of the crop as per its volume over the unit area [52]. It also enabled relatively slow release as per crop growth demand and overcame extra N losses [53]. The higher soil N concentration with less demand for early crop and slower growth enabled higher losses due to N mobility [54]. Moreover, multiple splits N application enabled better N use efficiency [55]. The good fit of observed and simulated values for the number of grains per unit of cultivars justifies the model used for wheat in the area. The simulated number of grains for the given N treatments was best fit by the model. A similar range of RMSE value for plant trait number of grains spike−1 was reported by [56,57], which further confirms that the model fit well with observed data for wheat in this area.
Using the DSSAT CERES-Wheat model, the simulated and observed value for single grain weight was close and fit well by the model to the local condition under the given treatments for wheat cultivars planted in 2016–17 and 2017–18. Observed and simulated values of the data recorded for grain weight were 0.039 g and 0.039 g, respectively, in 2017–18 and 2018–19 for wheat variety Pakhtunkhwa-2015, with RMSE of 0.001 and d-stat of 0.441. For wheat variety DN-84, the observed and simulated reading of grain weight was 0.039 g and 0.039 g, respectively, with RMSE of 0.001 and d-stat of 0.469, confirming that the model fits well with the observed data. Likewise, the model fits well for the single grain weight of Pirsabak-2015 with both observed and simulated values of 0.04 g and 0.04 g, respectively, and RMSE of 0.001 and d-stat of 0.383. The model statistic goodness of fit compares favorably to that presented [58]. The single grain weight of the cultivars was the best fit by the model at the given 140 kg N ha−1. Nitrogen application lower than the optimum resulted in less grain weight and hence less final grain yield. Nonetheless, the model prediction for grain weight was uniform for any given N rate, whereas triple splits N application resulted in a good fit with the observed and simulated data for the grain weight of all cultivars. N application all at sowing or in double splits resulted in lower grain weight compared to triple splits in observed data. Simulated data did not show (p < 0.05) any difference for various application timings. It is obvious that grain growth starts after mid-March when the crop completes anthesis [21]. Assimilates are translocated to the source by the sink from already established biomass [59] which may be usually at the same rate as biomass existing per unit area and the number of grains available in spikes [60]. This results in a similar grain weight under the different N application timings. Research findings of [61] concluded that the CERES model best fit for wheat grain weight with RMSE values of a similar range. The good fit RMSE values of this experiment are comparable to those reported by [50,62].
The DSSAT CERES-Wheat model was simulated for the biomass of wheat crop. Crop biomass is growth per unit area over time. Healthy growth of plants guarantees optimum production, which is made possible with good management and optimum nutrients at proper timings [63]. Simulated and observed values of biomass were recorded in close association with the model and therefore fit well for the local condition and given treatments for cultivar data recorded in 2016–17 and 2017–18. Observed and simulated values of the biomass were 10,127 and 10,507 kg ha−1, respectively, for wheat cultivars Pakhtunkhwa-2015 with mean r2 of 0.996, RMSE of 419.3, and d-stat of 0.99. Similarly, for cultivar DN-84, the observed and simulated reading for biomass was 9422 and 9651 kg ha−1, respectively, with r2 of 0.992, RMSE of 300.28, and d-stat of 0.994, showing that the model simulated the observed data well. Likewise, the model also fit well for the biomass of Pirsabak-2015 with observed and simulated readings of 10,465 and 10,910 kg ha−1, respectively, and r2 of 0.985, RMSE of 553.21, and d-stat of 0.983. Similar best fit statistics are favorably compared with the values reported by [64,65]. Biomass for cultivars was best fit by the model at the given 140 kg N ha−1, which is considered the optimum level for wheat crop [66]. Likewise, the triple splits N application showed a good fit for observed and simulated data of crop biomass for all cultivars. Increasing the N rate from optimum also did not show an increase in biomass, and this was also reported by [67]. The RMSE value indicated that biomass was best fit by the model. A similar RMSE value for wheat was observed in the literature [68,69], which indicates the best fit of the CERES wheat model to this experiment’s observed data.
The DSSAT model is more sensitive to nitrogen stress than to real crop growth in the field according to a literature review; wheat yield simulation with nitrogen was better than without nitrogen fertilization [70]. Grain yield of wheat is a product of the number of tillers, number of grains, and weight per unit area, which differed (p < 0.05) under the given treatments and hence showed differences with N application rates and N application timings [21]. Simulated and observed values for grain yield were close and hence the model was best fit for the local condition under given treatments for wheat cultivars in 2016–17 and 2017–18. Observed and simulated yield was 3311 and 3526 kg ha−1, respectively, for wheat cultivar Pakhtunkhwa-2015 with mean r2 of 0.983, RMSE of 231.8, and d-stat of 0.963. For cultivar DN-84, the observed and simulated yield was 3060 and 3166 kg ha−1, respectively, with r2 of 0.982, RMSE of 126.47, and d-stat of 0.985, which showed that the model simulated the data well. Likewise, the model was also the best fit for grain yield of wheat cultivar Pirsabak-2015 with observed and simulated readings of 3147 and 3281 kg ha−1, respectively, and r2 of 0.979, RMSE of 160.33, and d-stat of 0.98. Simulation results of the statistics showed similar RMSE, r2, and d-stat with the results presented in the literature [71]. Grain yield for all cultivars was best fit by the model at 140 kg N ha−1. N application lower than 140 kg ha−1 resulted in lower grain yield. Likewise, triple splits N application showed a good fit for the observed and simulated yield values of cultivars. The close RMSE value for wheat yield was reported in the literature [40]. Application of N at different rates and growth stages resulted in a strong influence on grain productivity [72,73]. It is obvious that optimum N has to be made available in accordance with crop nutrients’ demand [74]. Split N coincided with growth as per crop demand, enabling optimum production and hence ensuring healthy traits with the maximum yield [75]. The model equally supports a strong link between the simulated and observed values and RMSE value reported in the literature for wheat crop [76,77,78].
The mean grain N for wheat cultivars across different N rates and applied timings at different growth stages are shown in Figure 7. The simulated and observed values for grain N were observed to be close, and the model fit well for the local climate under the given treatments for wheat cultivars in 2016–17 and 2017–18. Grain N is essential for staples food as well as for backing quality [79]. Observed and simulated grain N was 65 and 68 kg ha−1, respectively, for Pakhtunkhwa-2015 with mean r2 of 0.989, RMSE of 3.256, and d-stat of 0.991. Similarly, for variety DN-84, observed and simulated grain N was recorded at 61 and 62 kg ha−1, respectively, with r2 of 0.995, RMSE of 1.396, and d-stat of 0.998, showing that the model best simulated the observed data. Likewise, the model fit well for grain N values of Pirsabak-2015 with observed and simulated readings of 62 and 64 kg ha−1, respectively, in 2017–18 and 2018–19, with r2 of 0.984, RMSE of 3.054, and d-stat of 0.98. These experimental data simulated for grain N indicated a best fit for wheat by the model. Another research finding [47] stated that grain yield and grain quality could successfully be accomplished through the CERES model. The observed and simulated grain N contents were in close association with the best fit for the RMSE value. Our results are supported by the finding of [80]. Grain N for cultivars was best fit by the model at 140 kg N ha−1, the optimum rate. Likewise, triple splits N application resulted in a good fit for the observed and simulated grain N for all cultivars.

5. Conclusions

The simulated and observed yield and other contributing traits, as well as grain N contents, were higher with the application of 140 kg N ha−1 when applied in three splits. Furthermore, it was concluded that the DSSAT CERES-Wheat model 4.7.5 performed well in simulating the tiller number, grain number, grain weight, biomass, grain yield, and grain N content in the experimental area. The over and underestimations of the model were found in the acceptable range. Wheat is the major crop of Pakistan which faces challenges of climate with unexpected climate shocks. The valid coefficient of wheat cultivars using 30 years of previous weather data of the area can be used as useful information for yield forecasting of the wheat crop. The results confirmed the possibility of using the CERES-Wheat model to predict wheat yield which is expected to face adverse effects in the field. The model-derived coefficients are good information to show a use of the new version of DSSAT 4.8 for addressing the future food security issue of the growing population.

Author Contributions

Conceptualization, G.R.K., H.M.A., M.A., G.H., A.A.A.-H., N.A., B.A.A., M.M.A., R.G., F.W. and M.F.S.; methodology, G.R.K., M.A., G.H., N.A., M.M.A., R.G. and F.W.; software, G.R.K., H.M.A., A.A.A.-H., N.A., B.A.A. and M.F.S.; formal analysis, G.R.K., M.A., G.H., N.A., M.M.A., R.G. and F.W.; investigation, G.R.K., H.M.A., A.A.A.-H., N.A., B.A.A. and M.F.S.; resources, G.R.K., M.A., G.H., N.A., M.M.A., R.G. and F.W.; data curation, G.R.K., H.M.A., A.A.A.-H., N.A., B.A.A. and M.F.S.; writing—original draft preparation, G.R.K., N.A., M.M.A., R.G. and F.W.; writing—review and editing, G.R.K., H.M.A., M.A., G.H., A.A.A.-H., N.A., B.A.A., M.M.A., R.G., F.W. and M.F.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R93), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data are presented within the article.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Daily total precipitation solar radiation and daily maximum and minimum temperature for experimental location during first (upper graph) and second growing seasons (lower graph).
Figure 1. Daily total precipitation solar radiation and daily maximum and minimum temperature for experimental location during first (upper graph) and second growing seasons (lower graph).
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Figure 2. Simulated number of tillers (m−2) as a function of N application at different stages: full N application (N100%) at sowing (a,e,i), half of recommended N application (N50%) at sowing + N50% at tillering (b,f,j), quarter of recommended N application (N25%) at sowing + N50% at tillering + N25% at booting (c,g,k), and N25% at sowing + N25% at tillering + N50% at booting stage (d,h,l) for different cultivars.
Figure 2. Simulated number of tillers (m−2) as a function of N application at different stages: full N application (N100%) at sowing (a,e,i), half of recommended N application (N50%) at sowing + N50% at tillering (b,f,j), quarter of recommended N application (N25%) at sowing + N50% at tillering + N25% at booting (c,g,k), and N25% at sowing + N25% at tillering + N50% at booting stage (d,h,l) for different cultivars.
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Figure 3. Using SM-CERES-Wheat to predict number of grains (m−2) as a function of N application at different stages: full N application (N100%) at sowing (a,e,i graphs), half of recommended N application (N50%) at sowing + N50% at tillering (b,f,j graphs), quarter of recommended N application (N25%) at sowing + N50% at tillering + N25% at booting (c,g,k graphs), and N25% at sowing + N25% at tillering + N50% at booting stage (d,h,l graphs) for different cultivars.
Figure 3. Using SM-CERES-Wheat to predict number of grains (m−2) as a function of N application at different stages: full N application (N100%) at sowing (a,e,i graphs), half of recommended N application (N50%) at sowing + N50% at tillering (b,f,j graphs), quarter of recommended N application (N25%) at sowing + N50% at tillering + N25% at booting (c,g,k graphs), and N25% at sowing + N25% at tillering + N50% at booting stage (d,h,l graphs) for different cultivars.
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Figure 4. Using CSM-CERES-Wheat to predict single grain weight (mg) as a function of N application at different stages: full N application (N100%) at sowing (a,e,i graphs), half of recommended N application (N50%) at sowing + N50% at tillering (b,f,j graphs), quarter of recommended N application (N25%) at sowing + N50% at tillering + N25% at booting (c,g,k graphs), and N25% at sowing + N25% at tillering + N50% at booting stage (d,h,l graphs) for different cultivars.
Figure 4. Using CSM-CERES-Wheat to predict single grain weight (mg) as a function of N application at different stages: full N application (N100%) at sowing (a,e,i graphs), half of recommended N application (N50%) at sowing + N50% at tillering (b,f,j graphs), quarter of recommended N application (N25%) at sowing + N50% at tillering + N25% at booting (c,g,k graphs), and N25% at sowing + N25% at tillering + N50% at booting stage (d,h,l graphs) for different cultivars.
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Figure 5. Using CSM-CERES-Wheat to predict biomass (kg ha−1) as a function of N application at different stages: full N application (N100%) at sowing (a,e,i graphs), half of recommended N application (N50%) at sowing + N50% at tillering (b,f,j graphs), quarter of recommended N application (N25%) at sowing + N50% at tillering + N25% at booting (c,g,k graphs), and N25% at sowing + N25% at tillering + N50% at booting stage (d,h,l graphs) for different cultivars.
Figure 5. Using CSM-CERES-Wheat to predict biomass (kg ha−1) as a function of N application at different stages: full N application (N100%) at sowing (a,e,i graphs), half of recommended N application (N50%) at sowing + N50% at tillering (b,f,j graphs), quarter of recommended N application (N25%) at sowing + N50% at tillering + N25% at booting (c,g,k graphs), and N25% at sowing + N25% at tillering + N50% at booting stage (d,h,l graphs) for different cultivars.
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Figure 6. Using CSM-CERES-Wheat to predict grain yield (kg ha−1) as a function of N application at different stages: full N application (N100%) at sowing (a,e,i graphs), half of recommended N application (N50%) at sowing + N50% at tillering (b,f,j graphs), quarter of recommended N application (N25%) at sowing + N50% at tillering + N25% at booting (c,g,k graphs), and N25% at sowing + N25% at tillering + N50% at booting stage (d,h,l graphs) for different cultivars.
Figure 6. Using CSM-CERES-Wheat to predict grain yield (kg ha−1) as a function of N application at different stages: full N application (N100%) at sowing (a,e,i graphs), half of recommended N application (N50%) at sowing + N50% at tillering (b,f,j graphs), quarter of recommended N application (N25%) at sowing + N50% at tillering + N25% at booting (c,g,k graphs), and N25% at sowing + N25% at tillering + N50% at booting stage (d,h,l graphs) for different cultivars.
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Figure 7. Using CSM-CERES-Wheat to predict grain nitrogen (%) as a function of N application at different stages: full N application (N100%) at sowing (a,e,i graphs), half of recommended N application (N50%) at sowing + N50% at tillering (b,f,j graphs), quarter of recommended N application (N25%) at sowing + N50% at tillering + N25% at booting (c,g,k graphs), and N25% at sowing + N25% at tillering + N50% at booting stage (d,h,l graphs) for different cultivars.
Figure 7. Using CSM-CERES-Wheat to predict grain nitrogen (%) as a function of N application at different stages: full N application (N100%) at sowing (a,e,i graphs), half of recommended N application (N50%) at sowing + N50% at tillering (b,f,j graphs), quarter of recommended N application (N25%) at sowing + N50% at tillering + N25% at booting (c,g,k graphs), and N25% at sowing + N25% at tillering + N50% at booting stage (d,h,l graphs) for different cultivars.
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Table
Table 1. Soil physical and chemical properties for the experimental site.
Table 1. Soil physical and chemical properties for the experimental site.
Soil Depth (cm)Lower Limit (cm3/cm3)Drain Upper Limit
(cm3/cm3)		Bulk Density g/cm3Organic Carbon %Clay %Silt %Sand%Total N %Soil pH
0–150.0540.1871.320.636.466.027.00.066.9
15–300.040.1731.380.622.868.427.80.067.0
30–450.0340.1271.480.583.253.638.60.057.1
45–600.0250.1031.510.542.852.646.10.057.1
60–750.0430.1311.540.478.556.745.50.047.3
75–900.0380.1461.590.454.662.435.80.046.9
90–1100.0390.1501.650.444.460.332.40.047.1
110–1250.0350.1271.740.384.856.741.50.037.0
Table
Table 2. Genetic coefficient best fit for the three wheat cultivars for seasons 2016–17 and 2017–18.
Table 2. Genetic coefficient best fit for the three wheat cultivars for seasons 2016–17 and 2017–18.
GCDefinitionCalibrated Values
Pak-2015DN-84Pir-2015
P1VDays, optimum vernalizing temperature16.329.017.60
P1DPhotoperiod response (% reduction in rate/10 h drop in pp)68.355.072.00
P5Grain filling phase duration (°C day)771672775.9
G1Kernel number per unit canopy weight at anthesis (#/g)18.116.415.62
G2Standard kernel size under optimum condition (mg)39.239.339.8
G3Standard non-stressed mature tiller weight (g dwt)1.951.892.00
PHINTInterval between successive leaf tip appearances (°C-d)100100100
GC = Genetic coefficient.
Table
Table 3. Performance of the CERES-Wheat model version 4.7.5 for simulating yield traits, yield, and grain nitrogen content of wheat during 2016–17 and 2017–18.
Table 3. Performance of the CERES-Wheat model version 4.7.5 for simulating yield traits, yield, and grain nitrogen content of wheat during 2016–17 and 2017–18.
VarietiesVariable Name2-Year MeanR-SquareRMSED-Stat
ObservedSimulated
Pakhtunkhwa-2015Biomass (kg ha−1) 10,12710,5070.996419.3190.99
Number of grains (m−2) 860389940.984437.20.976
Grain N (%1.91.90.9530.1060.945
Grain yield (kg ha−1)331135260.983231.8190.963
Single grain weight (mg) 0.0390.039 0.0010.441
Number of tiller m−2 3503580.94912.5490.976
DN-84Biomass (kg ha−1) 942296510.992300.2870.994
Number of grains (m−2) 782080560.994266.0760.989
Grain N (%)2.01.90.9710.0760.98
Grain yield (kg ha−1)306031660.982126.4830.985
Single grain weight (mg) 0.0390.039 0.0010.469
Number of tillers m−2 3263250.9815.3740.995
Pirsabak-2015Biomass (kg ha−1) 10,46510,9100.985553.2140.983
Number of grains (m−2) 802282450.993258.7880.992
Grain N (%)2.01.90.9410.0760.961
Grain yield (kg ha−1)314732810.979160.3320.98
Single grain weight (mg) 0.040.04 0.0010.383
Number of tillers m−2 3703820.97815.0510.979
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Khan, G.R.; Alkharabsheh, H.M.; Akmal, M.; AL-Huqail, A.A.; Ali, N.; Alhammad, B.A.; Anjum, M.M.; Goher, R.; Wahid, F.; Seleiman, M.F.; et al. Split Nitrogen Application Rates for Wheat (Triticum aestivum L.) Yield and Grain N Using the CSM-CERES-Wheat Model. Agronomy 2022, 12, 1766. https://doi.org/10.3390/agronomy12081766

AMA Style

Khan GR, Alkharabsheh HM, Akmal M, AL-Huqail AA, Ali N, Alhammad BA, Anjum MM, Goher R, Wahid F, Seleiman MF, et al. Split Nitrogen Application Rates for Wheat (Triticum aestivum L.) Yield and Grain N Using the CSM-CERES-Wheat Model. Agronomy. 2022; 12(8):1766. https://doi.org/10.3390/agronomy12081766

Chicago/Turabian Style

Khan, Gul Roz, Hiba M. Alkharabsheh, Mohammad Akmal, Arwa Abdulkreem AL-Huqail, Nawab Ali, Bushra A. Alhammad, Muhammad Mehran Anjum, Rabia Goher, Fazli Wahid, Mahmoud F. Seleiman, and et al. 2022. "Split Nitrogen Application Rates for Wheat (Triticum aestivum L.) Yield and Grain N Using the CSM-CERES-Wheat Model" Agronomy 12, no. 8: 1766. https://doi.org/10.3390/agronomy12081766

APA Style

Khan, G. R., Alkharabsheh, H. M., Akmal, M., AL-Huqail, A. A., Ali, N., Alhammad, B. A., Anjum, M. M., Goher, R., Wahid, F., Seleiman, M. F., & Hoogenboom, G. (2022). Split Nitrogen Application Rates for Wheat (Triticum aestivum L.) Yield and Grain N Using the CSM-CERES-Wheat Model. Agronomy, 12(8), 1766. https://doi.org/10.3390/agronomy12081766

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