Determining optimum plant density and nitrogen rate using field experiment and model simulation

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The aim of this study was to determine optimum plant density and nitrogen rate that increase durum wheat productivity while reducing environmental impacts. A combination of data from field experiments conducted from 2017 to 2020 under rainfed conditions and simulation data of CERES-Wheat model were used for this study. The CERES-Wheat model was calibrated for Utuba cultivar from three-years (2017 to 2019) field experiment data. The model was further verified with the experimental data conducted during the 2020 cropping season under four plant densities and four nitrogen fertilizer rates. Differences in temperature and rainfall patterns during the potential growing season, seasonal analysis was used to determine the optimum plant density and N rate using 37 years (1985–2022) of historical weather data. The simulation results suggested that 275 plants m − 2 with an application of 250 kg ha − 1 N increased grain yield, improved nitrogen use, and produced the highest economic return while minimizing environmental risk under rainfed conditions. Compared with the current plant density (175 plants m − 2 ) and N fertilizer (100 kg ha − 1 ), plant density (275 plants m − 2 with 250 kg ha − 1 N) rate increased grain yield by about 49%, N use efficiency by 23% with the highest net return (2114 US$ ha − 1 ). In general, this study showed that the CERES-Wheat model can be a promising tool for providing crop management recommendations under rainfed durum wheat farming. durum wheat nitrogen fertilizer plant density seasonal analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction The expansion of agricultural land to the marginal area was the primary factor to increase food production, which resulted in to GDP growth in the central highlands of Ethiopia since the early 2005 ( Biazin and Sterk 2013; Fantu et al. 2018 ). However, this horizontal increase in farm land is facing various constraints that hamper crop productivity. Severe land degradation, poor inherent fertility, high dependence on rainfall, and low uses of plant density and fertilizers by smallholder farmers were among the major constraints that could be mentioned (Zerssa et al. 2021; Mezgebu et al. 2022) . In addition, owing to the rapidly growing population in the rural parts of Ethiopia, particularly in the central highlands of the country, the size of cropland per capita has been decreasing drastically. For instance, the average farm size of less populated areas such as Oromia, an average farm size of 1.2 ha, and the densely populated South Nation, Nationality, and People’s regions is 0.5 ha (Holden and Tilahun 2020 ) . This indicates that smallholder farming systems will continue to dominate the agriculture sector and the average farm sizes will continue to decline because the further expansion of cropland will become more difficult, while the population will continue to increase (CSA 2023) . Therefore, future agricultural development should focus on raising productivity per unit area instead of expanding the cultivated area. Sustainable intensification is understood to encompass a wide range of practices, including appropriate but not limited to conservation agriculture, with the potential to produce more food from the existing agricultural land in a valuable and changed environment while maintaining resources ( Wilkus et al. 2022 ). Agronomic intensification practices, including plant density and nutrient supply, have the potential to enhance agricultural production outputs (Li et al. 2023 ). Integrated management strategies, such as optimum plant density and soil nutrient application, achieve the highest yield, adulate grain quality, and long-term sustainability based on environmental circumstances ( Cakmak et al. 2010 ; Niyigaba et al. 2019) . For instance, the supply of optimum nitrogen fertilizer and plant density have been proven to be key ways to increase crop yields in dryland farming (Hochman and Horan 2018; Gastaldi et al. 2020 ; Lollato et al. 2019 ; Zhang et al. 2021 ). In the central highlands of Ethiopia, an increase in nitrogen fertilizer from 100 to 150 kg ha − 1 in combination with the optimum plant density (approximately 200 plants m − 2 ) increased bread wheat yield from 2534 kg ha − 1 to 3567 kg ha − 1 in Eshetu ( 2017 ) and Lakew (2019) showed a 29% increase in wheat yield. In another study conducted in the northern part of Ethiopia, durum wheat grain yield showed a quadratic and linear response to nitrogen fertilizer, and the maximum grain yield (5182 kg ha − 1 ) was obtained at 270 kg ha − 1 N, with the highest net benefit (Alemayehu et al. 2023). These findings confirm that the use of the optimum nitrogen fertilizer rate significantly increased durum wheat yield. Zhang et al. ( 2019 ) also suggested a possible approach to increasing wheat grain yield per unit of land by adjusting nitrogen management along with other crop practices. Plant density is an important agronomic intensification parameter. Plant density has a significant positive effect on wheat yield ( Zheng et al. 2021 ). Beres et al. (2012) reported a positive linear improvement in grain yield in durum wheat at rates as high as 450 plants m − 2 , which was more than twice the rate of standard practices (210 plants m − 2 ) at that time. This positive association between plant density and durum wheat yields was also reported by ( Geleta et al. 2002 ; Zhang et al. 2019 ). In addition to increasing grain yield, increasing plant density, improving nitrogen utilization and uptake efficiency, and increasing wheat root length and density, overall increases below and above ground plant demand ( Dai et al. 2013 ). In Ethiopia, most farmers cultivate durum wheat under low plant density and nitrogen fertilizer. In practice, a low plant density of 175 plants m − 2 and blanket application of nitrogen fertilizer (approximately 100 kg ha − 1 ) restrict yield and nitrogen use efficiency ( Agbahey et al. 2015 ; Bizuwork et al. 2020). In contrast, durum wheat grain yields as high as 5670 kg ha − 1 have been achieved under high plant densities (225 plants m − 2 ) and nitrogen fertilizer rates (150 to 200 kg ha − 1 ) in Ethiopia ( Araya et al. 2019 ). Appropriate plant densities, combined with optimum nitrogen management, are likely to increase grain yield and nitrogen use efficiency ( Yan et al. 2016 ). Thus, knowledge of the interaction between nitrogen nutrients and plant density is important for increasing and maintaining crop yields in the central highlands of Ethiopia. Although field-based experiments are usually effective, they are time-consuming, require expensive resources, and take a longer time to draw valid recommendations. Thus, process-based models have proven to be useful tools for investigating the impact of climate variability and change on crop productivity, resource use efficiency, and environmental impacts on the agricultural system (Batchelor et al. 2002). Moreover, the application of a process-based model also provides an option for designing climate-resilient management strategies to tackle the enormous challenges facing agricultural productivity and to generate knowledge for aiding agricultural developments that would otherwise be impossible through field experimentation ( Hernández-Ochoa et al. 2022 ). The CERES-Wheat model, which is embedded in DSSAT, is widely used to determine the best strategic plant density, irrigation management, and nutrient dynamics ( Li et al. 2018 ; Plaza-Bonilla et al. 2021 ; Araya et al. 2019 ; Liang et al. 2019 ; Shaque et al. 2020) . However, no study has used combined experimental and model simulation approaches of plant density and nitrogen fertilizer rate interaction on the long-term impact of water limitation on durum wheat yield and economic values. Moreover, research on plant density and nitrogen fertilizer for durum wheat in Ethiopia began in the late 1960s in the central highlands ( Hailu 1991 ) . Typical recommended practices for durum wheat in the central highlands of Ethiopia are 175 plants m − 2 and 150 kg ha − 1 N (Amsal et al. 2000; Amsal and Tanner 2001; Nigussie et al. 2013; Abera et al. 2015). Despite previous studies, there is still a lack of systematic investigation on how plant density and nitrogen fertilizer rate interact with the long-term impact of water-limited conditions on durum wheat yield and economic value. In addition, most of the plant density and nitrogen fertilizer recommendations for durum wheat are outdated, and no longer reflect the current agro-climatic variability and change. To fill this gap, we carried out a study that combines experiments and modeling at a field level for durum wheat in central highlands and covers a wider range of weather conditions. The following objectives were addressed: (i) to calibrate the CERES-Wheat model for durum wheat cultivar, (ii) to evaluate the performance of the CERES-Wheat model to simulate the growth, development, and yield of durum wheat under rain-fed cultivation, and (iii) to apply the CERES-Wheat model to determine the optimum plant density and nitrogen fertilizer supply using long-term historical daily weather data for durum wheat rain-fed cultivation. Materials and Methods Description of the study area The experiments were conducted over four consecutive years (2017, 2018, 2019, and 2020) and the main wheat cropping season (June to November) at Memir Hager (8 o 46’33.5″ N and 39 o 16’40.7’’ E). It is found in the Minjar Shenkora district in the Amhara region of Ethiopia. The area has a typical wet-dry climate, with a monsoon season (rain) and summers typically running from June to August. Spring is from September to November, which is the harvest season in the area. Cold dry (December to February) and cooler than the other seasons, and this conceded the dry season in the area. In the area, autumn occurs from March to May, and these months experience an agricultural land preparation period. Soil characteristics and weather conditions of the experimental area The soil's physical, chemical, and hydraulic properties (field capacity, permeant wilting point, bulk density, saturated water), texture, pH, cation exchange capacity (CEC), ammonium and nitrate concentrations, and soil organic matter were obtained from the soil profile description from the Global High-Resolution Soil Profile Database for crop modeling application ( Harvest Choice 2015 ) , based on the latitude and longitude of its geographic grid (Table 1 ). Table 1 Description of soil profile properties (lower soil water limit (LSL), drained soil water upper limit (DUL), saturated upper limit (SAL), bulk density (BD), organic carbon (OC), clay content (SCL), silt content of the soil (SSI), total N (TN), soil phosphorous (P), soil pH (pH) and soil cation exchange capacity (CEC) of the soil used in CERES-wheat model simulations. LSL, DUL, and SAL were calculated using DSSAT 4.8.2. Depth (cm) LSL SDUL SSAT BD OC SLCL SLSI TN pH CEC (Cmol-kg − 1 ) --------(cm 3 cm − 3 )------- g cm − 3 ----------(g kg − 1 )--------- 5 0.213 0.34 0.430 1.18 2.87 35.6 27.2 0.12 6.4 35.7 15 0.224 0.35 0.436 1.20 2.43 37.5 26.5 0.09 6.5 31.3 30 0.239 0.37 0.444 1.23 1.85 40.1 25.3 0.07 6.6 30.4 60 0.253 0.38 0.452 1.28 1.18 42.5 24.2 0.06 6.7 31.7 100 0.253 0.38 0.451 1.34 0.69 42.4 23.7 0.05 6.8 31.9 200 0.244 0.37 0.444 1.40 0.39 41.0 23.2 0.05 7.0 31.8 Historical weather data for daily maximum and minimum air temperature, solar radiation, relative humidity, rainfall, and wind speed from 1985 to 2022 were obtained from the Ethiopian National Meteorology Agency (NMA) and used for long-term simulation analysis. From 2017 to 2020, daily weather data, including maximum and minimum air temperature, rainfall, relative humidity, solar radiation, and wind speeds, were obtained from a local weather station (Minjar Shenkora District Meteorological Station), which was near the experimental sites and used for model calibration and evaluation. Thirty-one years (1985–2016) of average monthly and 2017, 2018, 2019, and 2020 year monthly mean rainfall of the experimental site in durum wheat cropping months are presented in Fig. 1 . The daily maximum air temperature, daily minimum air temperature, and relative humidity during the experimental periods (2017, 2018, 2019, and 2020) are illustrated in Figs. 2 and 3, respectively. Experimental set-up and field management Experiments conducted from 2017 to 2019 were used for model calibration. The durum wheat cultivar ‘Utuba’ at an optimum planting density (225 plants m − 2 ), nitrogen (urea at a rate of 200 kg ha − 1 ), and phosphorus (di-ammonium phosphate) 18% N and 46% P 2 O 5 ) at a rate of 100 kg ha − 1 was planted on July 27, 2017, July 26, 2018, and July 28, 2019. The plot size was 5m x 5m (25 m 2 ) and was replicated three times. The experiment undertaken during the 2020 cropping season was used for the model evaluation. The experiment included the following treatment combinations: four planting densities: 175 plants m − 2 (PD 1 ), 225 plants m − 2 (PD 2 ), 275 plants m − 2 (PD 3 ), and 325 plants m − 2 (PD 4 ) of the Utuba cultivar, and four N fertilizer rates: 0 (control) N 0 , 100 (N 1 ), 150 (N 2 ), and 200 (N 3 ) kg ha − 1 urea fertilizer. The treatments were laid down in a randomized complete block design (RCBD) in a factorial arrangement, and each treatment was replicated three times. The plot size was 3m x 3m (9 m 2 ) and consisted of 16 rows, spaced 20 cm apart. The net central areas of each plot, consisting of 12 central rows of 2.80 m length were used for data collection. The seeds were sown with a row spacing of 20 cm using a hand drill. For nitrogen fertilizer, two split applications, the first one-third N fertilizer was applied at planting, and the remaining (two-thirds N) dose was applied 21 days after crop planting. A full dose of phosphorus fertilizer was applied uniformly at planting. All other crop management practices, such as weeding, diseases, and insect pest protection, were uniformly implemented for all treatments. Trait measurements Wheat phenology traits, such as anthesis and maturity date, were defined by the plant and showed visual signs of the stage being considered. The anthesis date was recorded when approximately 50% of the plants in a plot produced spikes. The maturity date was obtained when the leaves and vegetative parts of the crop were light yellow in color. In the season, the top weight of the plant was determined based on a randomly selected area of 0.25 m 2 sample harvested at four Zadoks growth (GS24 (tillering), GS40 (booting), GS60 (flowering), and GS80 (grain filling) stages of each plot and dried in an oven at 70°C for 48 h, and their weights were determined using an electronic balance. The leaf area index was measured using a plant canopy imager (Model CI-110), taking readings from the middle eight of the 16 rows per plot in all replications by passing the instrument on the top of the canopy at a height of 60 cm. Readings were taken at four growth stages, (GS24 (tillering), GS40 (booting), GS60 (flowering), and GS80 (grain filling). The crop was harvested manually from four central rows 2 m in length in each row to determine the final top weight, grain yield (GY), and harvest index. After harvesting, the threshed grains were separated, cleaned, and weighed using an electronic balance. GY was adjusted to a moisture content of 12.5% wet bases, and a moisture tester was used to test the moisture content of the grain. The Harvest index was computed using the formula of Nichiporovich ( 1967 ). HI = GY/top weight. Model calibration and evaluation The CERES-Wheat model embedded in Decision Support Systems for Agrotechnology Transfer-DSSAT v4.8.2 ( Hoogenboom et al. 2023 ) was used in this study. The cultivar Utuba coefficients of the model were calibrated using data collected from the optimum inputs treated experiments conducted during three growing seasons from 2017, 2018, and 2019. Genetic coefficients were calibrated using the procedure described by Li et al. ( 2018 ). Adjustments were made sequentially, starting with phenological (anthesis and maturity date) traits and crop growth (leaf area index (LAI), top weight, and GY) parameters. The GLUE coefficient estimator was used for parameter estimation. In total, 10,000 random parameter sets were generated using an independent dataset for the crop-growing seasons. After calibrating the cultivar coefficients, the accuracy of the model was evaluated using experimental data collected in 2020. The observed data, such as in-season growth, LAI, and top weight, at final harvest biomass, GY, and harvest index, were collected and compared with simulated values of across each plant densities and nitrogen levels. Statistical analysis For both calibration and evaluation, the results were checked using different statistical indices, including the normalized root-mean-square error (RMSE), normalized RMSE (nRMSE), index of agreement (d), and model efficiency (E). The statistical indices are described as follows. RMSE = \(\sqrt{\frac{{\sum }_{i=1}^{n}{\left({s}_{i}-{M}_{i}\right)}^{2}}{n}}\) (1) nRMSE = \(\frac{ RMSE}{\stackrel{-}{M}}\) (2) E = \(1-\frac{{\sum }_{i-1}^{n}{\left({S}_{i}-{M}_{i}\right)}^{2}}{{\sum }_{i=1}^{n}{\left({M}_{i}-\stackrel{-}{M}\right)}^{2}}\) (3) d = \(1-\frac{{\sum }_{i=1}^{n}{\left(S-{M}_{i}\right)}^{2}}{{\sum }_{i=1}^{n}{\left(\left|{S}_{i}-{M}_{i}\right|+\left|{M}_{i}-{\stackrel{-}{M}}_{i}\right|\right)}^{2}}\) (4) where n is the total number of datasets measured and simulation values and M is the average of the measured values. The normalized RMSE (nRMSE) provides a measure (%) of the relative difference between the measured and simulated data. It is generally agreed that the model performance is excellent when the nRMSE value is less than 10%, good when the nRMSE is between 10 and 20%, fair when the nRMSE is between 20 and 30%, and poor when the nRMSE is greater than 30% ( Loague and Green 1991 ; Jamieson et al. 1991 ). Model application After the calibration and evaluation procedures were completed, the model was used to simulate grain yield, agronomic efficiency, and economic value of treatments using seasonal analysis programs (DSSAT v4.8.2 (Hoogenoboom 2023). The simulation used a 37 years daily weather data from 1985 to 2022. A total of 48 combination comprised of six plant density and eight nitrogen fertilizer rates, were employed for the simulation. The plant densities ranged from 175 to 425 plants m − 2 at an interval of 50 plants m − 2 and N rate treatments ranged from 0 to 350 kg nitrogen ha − 1 in an interval of 50 kg ha − 1 were used for simulation. Following the simulation, the outputs were processed using the biophysical and economic analysis options of the program. These analyses and comparisons can identify and quantify the variability in crop performance associated with the interaction between weather and soil factors in the physical environment. Agronomic efficiency (AE) calculated as the ratio of the difference in grain yield with and without nitrogen application divided by the total applied ( Duan et al. 2014 ). Agronomic efficiency (AE, kg kg-1) = \(\frac{{Y}_{N }-{Y}_{O}}{{A}_{N}}\) 5 Where Y N is the grain yield from treatments with nitrogen fertilizer, Y 0 is the grain yield without N fertilizer treatment, A N is the amount of N fertilizer applied, and UN is the total N uptake by the plant. Economic analysis The results of each combination of plant density (175 (PD 1 ), 225 (PD 2 ), 275 (PD 3 ), 325 (PD 4 ), 375 (PD 5 ), and 425 (PD 6 ) plants m − 2 ) and nitrogen fertilizer rates of 0 (control) (N 0 ), 50 (N 1 ), 100 (N 2 ), 150 (N 3 ), 200 (N 4 ), 250 (N 5 ), 300 (N 6 ), and 350 (N 7 ) kg ha − 1 were also evaluated for economic feasibility using the mean–Gini dominance analysis ( Buccola and Subaei 1984 ). The evaluation procedure of the seasonal analysis program calculates the monetary return for each treatment based on the highest economic return Gini coefficient (GC). The gross margin (US $ t − 1 ) for each combination of treatments was determined using the following equation GM = Y x P-N x C-V Where GM is the gross margin, Y is the simulated durum wheat grain yield (kg ha − 1 ), P is the price of durum wheat (545.23 US $ kg − 1 ) average of the last four years, 2017, 2018, 2019, and 2020), N = nitrogen application rate (kg ha − 1 ) per treatment, C = the cost of nitrogen fertilizer (0.82US $ kg − 1 ), and V is the base production cost (887.00US $ ha − 1 ). The base production cost, durum wheat grain, and nitrogen fertilizer prices were obtained from the Economic Survey of the Debre Zeit Agriculture Research Center of Ethiopia (unpublished data). Result and discussion Model calibration The genetic coefficients of Utuba were calibrated using the GLUE method. The values of the seven genetic coefficients that determined the vegetative and growth stages (P1D, P1D, P5, and PHINT), and grain characteristics (G1, G2, and G3) are presented in Table (2). The genetic coefficients were estimated sequentially: first vegetative, followed by grain characteristics, using 10,000 rounds of the GLUE method. Li et al. ( 2018 ) and He et al. ( 2010 ) used the GLUE method to accurately estimate the genetic coefficients of the DSSAT-CERES-Wheat and Maize models for winter wheat production in Beijing, China, and for sweet corn production in northern Florida, USA, respectively. Table 2 Calibrated genetic coefficient of Utuba used with CERES-Wheat model Cultivar traits Genetic coefficient Unit Value Days, optimum vernalization temperature, required for completed vernalization P1V d 0.02 Photoperiod response (% reduction in rate/ 10 h drop in photoperiod P1D % 70.7 Grain filling (excluding lag) phase duration P5 O C. d 577 Kernel number per unit canopy weight at anthesis G1 no. g − 1 32 Standard kernel size under optimum condition G2 mg d − 1 33 Standard, non-stressed mature tiller weight (incl grain) G3 g 4.1 Thermal time between successive leaf tip appearances PHINT O C d 115 Durum wheat phenology The calibrated CERES-Wheat model accurately simulated durum wheat phenology (anthesis and maturity date), and the values are presented in Table 3 . The model predicted the dates from sowing to anthesis with a 0 (zero) difference and dates from sowing to maturity with a difference of 2 days (107 and 109 dates) between the simulated and measured values (Table 3 ). The four statistical indicators via root mean error square (RMSE), normalized RMSE (nRMSE), model efficiency (E), and index of agreement (d) were used to evaluate the simulated and measured values of the anthesis and maturity dates. The statistics were indicated in an excellent agreement, with RMSE, nRMSE, E, and d values of 1.00 days, 1.61%, 0.92, 0.85 and 1.41 days, 1.31%, 0.99 and 0.91, respectively (Table 3 ). Similarly, a close agreement was observed between the simulated and observed anthesis and maturity dates ( Araya et al. 2019 ; Rettie et al. 2022 ). The model simulated anthesis and maturity dates with RMSE and nRMSE lower than 2 and d values greater than 0.85, respectively, for wheat in Ethiopia. In general, their results confirmed that the calibrated DSSAT-CERES-wheat model is appropriate and convenient for the anthesis and maturity dates of durum wheat in the central highlands of Ethiopia. Leaf area index and top weight The simulated and measured values of LAI and top weight at four Zadoks growth stages (GS24 (tillering), GS40 (booting), GS60 (flowering) and GS80 (grain filling) during the experimental years of 2017, 2018 and 2019 were considered satisfactory because all results were within the acceptable range of statistics with the value of RMSE ranged from 0.48 to 0.66 m 2 m − 2 , nRMSE from 19 to 27%, d value ranged from 0.93 to 97 for LAI (Fig. 3A, B, C). The value of RMSE for top weight ranged from 582 to 1228 kg ha − 1 , the value of nRMSE ranged from 9.4 to 21.2%, the E value ranged from 0.96 to 0.98 and the d value ranged from 0.98 to 0.99 (Fig. 4 D, E, F). The simulation quality of LAI for the durum wheat growth stages was well matched for 2019 and 2017 with an nRMSE of 16 and 19% and d value of 0.95 and 0.97, respectively, whereas in 2018, the nRMSE and d values were 36% and 0.93, respectively (Fig. 3). The top weight simulation resulted in nRMSE was 9% and d value was 0.99 in the year 2019. The nRMSE (21%) and d value (0.97) were for 2017, and the nRMSE (17%) and d values (0.98) were for 2018. The lower values for nRMSE and higher d-values close to one revealed that the model simulated the LAI and top weight quite well. The evaluation statistics of the simulated and measured leaf area index (LAI) at the grain filling stage and the top weight at the final harvest are presented in Table 3 . The simulated LAI and top weight values were in very good agreement with the measured LAI and top weight under optimum plant density (225 plants m − 2 ) and sufficient nitrogen fertilizer (200 kg ha − 1 N). The RMSE, nRMSE, E, and d values of the simulated and measured LAI (0.57 m 2 m − 2 , 11.18, 0.86, and 0.95, respectively) and top weight (1.5 10 3 kg ha − 1 , 12.31%, 0.92, and 0.96, respectively) (Table 3 ). This result indicates that the calibration of the CERES-Wheat model exhibited acceptable levels. Table 3 Statistical evaluation of simulated (S) and observed (O), root-mean-square error (RMSE), normalized RMSE (NRMSE), model efficiency (E) and index of agreement (d) value for phenology (anthesis and maturity date) and leaf area index at (grain filling stage) and top weight, harvest index, and grain yield at final harvest for model calibration of durum wheat cultivar, Utuba (n = 3) Variable S O E RMSE NRMSE d Anthesis date 62 62 0.92 1.00 1.61 0.85 Maturity date 109 107 0.99 1.41 1.31 0.91 Grain yield (10 3 kg ha − 1 ) 4346 4373 0.98 0.50 11.50 0.89 Harvest index (%) 0.38 0.37 0.94 0.02 5.40 0.98 LAI (m 2 m − 2 ) 4.8 5.1 0.86 0.57 11.18 0.95 Top weight (10 3 kg ha − 1 ) 12296 12695 0.92 1.56 12.31 0.96 Wheat grain yield and harvest index The values of the evaluation statistics of simulated and measured grain yield (GY) and harvest index (HI) at the final harvest are presented in Table 3 . The model accurately simulated GY and HI, with a simulated/measured GY of 4346/4373 kg ha − 1 and HI of 0.38/0.37% under normal plant density (225 kg ha − 1 ) and nitrogen fertilizer (200 kg ha − 1 ) supply. The CERES-Wheat model slightly underestimated the grain yield and over-predicted the harvest index compared with the measured grain yield and harvest index (Table 3 ). The statistical indicators also indicated good agreement between the simulated and measured values for both GY and HI, with RMSE, nRMSE, E, and d values of 500 kg ha − 1 , 11.50%, 0.98, 0.89, and 0.02%, 5.40%, 0.94, and 0.98, respectively (Table 3 ). The normalized RMSE and d index between the simulated and measured values were in “good agreement” when ≤ 15% and “excellent” d > 0.9 ( Liu Hai-long et al. 2017 ). Similarly, the statistical indicators confirmed that the model predicted wheat grain yield and harvest index reasonably well, with respective nRMSE and d-index of 7% and 0.82 (Ishaqque et al. 2020; Rettie et al. 2022 ). Overall, the comparison between the measured and simulated data shows a reasonably good calibration of the CERES-Wheat model for anthesis date, maturity date, in-season LAI and top weight, grain yield, top weight, and harvest index at the final harvest. Thus, these results confirmed that the calibration CERES-Wheat model was suitable for simulating phenology, LAI, top weight, grain yield, and harvest index for long-term prediction. Model verification The model was further verified with the experimental data collected during 2020, under four plant densities (PD 1 , PD 2 , PD 3 and PD 4 plants m − 2 ) and four nitrogen (N 0, N 1 , N 2 and N 3 kg ha − 1 N) rates. The CERES-Wheat model slightly overpredicted the LAI (5.1 m 2 m − 2 ) compared to the measured LAI (3.4 m 2 m − 2 ). The simulated LAI at the grain filling stage under rain-fed condition was comparatively less satisfactory, with an nRMSE (21%) and d index (0.82) (Fig. 5 a). A possible reason might be that low soil moisture availability at the grain-filling stage resulted in a higher difference between the simulated and measured LAI values. The simulated and measured top weights at the final harvest matched well, with nRMSE and d values of 9% and 0.99, respectively (Fig. 5 b). This result suggests that the top weight was better than the LAI variable, which is consistent with the results of the previous studies by Wu and Ma (2013) and Ishaque et al. ( 2020 ). Time-serious simulated and measured LAI and top weight of durum wheat are set out in Table 4 . The simulated trends of LAI and tops weight at growth stages (GS24 (tillering), GS40 (booting), GS60 (flowering), and GS80 (grain filling)) for different plant density (PD 1 , PD 2 , PD 3, and PD 4 ) plants m − 2 and nitrogen rates (N 0, N 1 , N 2, and N 3 kg ha − 1 ) are presented in Fig. LAI (a-p) (Fig. 6 ) and top weight (i-xv) (Fig. 7 ). The trend observed between the simulated and measured of LAI showed a good agreement with those of measured LAI. The statistical values of nRMSE and d-index between simulated and measured LAI for different treatment combinations ranged from 10 to 31 and 0.80 to 0.99 in respective order (Table 4 ). Similarly, the trend between simulated and measured top weight was also closely linked with nRMSE and d-value which ranges from 7 to 37% and 0.93 to 0.98, respectively (Table 4 ). Most treatment combinations except PD 1 + N 2 , PD 3 + N 2, and PD 4 + N 2 showed low values for nRMSE and high d-values compared to the nRMSE value of 25, 31.6 and 29.6% for LAI and 30.3, 30.5, and 37.6% for tops weight, respectively. Higher nRMSE indicated that LAI and top weight was comparatively low to predict the model. While the other treatment combinations indicated low nRMSE and high d-values was close related and precisely predict LAI and top weight. Overall, simulated and measured LAI and top weight in good agreement which showed that the calibrated DSSAT-CERES-wheat model could simulate the LAI and top weight of durum wheat in Ethiopian conditions. Table 4 The root means square error (RMSE), normalized RMSE (NRMSE), model efficiency (E), and d-value leaf area index (LAI), and top weight of durum wheat as affected by plant density and N fertilizer rates in 2020 cropping season Leaf area index (LAI) Tops weight Treatment RMSE (kg/ha) nRMSE (%) E d-index RMSE (kg/ha) nRMSE (%) E d-index 175 plant m − 2 0 kg ha − 1 N 0.02 13.5 0.86 0.80 98.2 19.6 0.97 0.97 100 kg ha − 1 N 0.41 14.4 0.94 0.98 1237.4 28.1 0.98 0.96 150 kg ha − 1 N 0.88 25.5 0.90 0.95 1575.9 30.3 0.98 0.95 200 kg ha − 1 N 0.69 14.9 0.94 0.98 494.5 7.1 0.97 0.98 225 plant m − 2 0 kg ha N 0.02 19.4 0.76 0.91 173.1 26.9 0.97 0.95 100 kg ha − 1 N 0.62 21.9 0.87 0.95 1203.9 26.1 0.97 0.96 150 kg ha − 1 N 0.55 15.0 0.96 0.98 602.6 10.3 0.98 0.98 200 kg ha − 1 N 0.75 15.7 0.92 0.98 960.4 15.1 0.98 0.97 275 plant m − 2 0 kg ha − 1 N 0.03 10.9 0.99 0.88 129.6 19.6 0.98 0.97 100 kg ha − 1 N 0.55 17.9 0.88 0.97 496.2 10.0 0.98 0.98 150 kg ha − 1 N 1.09 31.6 0.81 0.92 1577.2 30.5 0.98 0.95 200 kg ha − 1 N 0.50 10.9 0.97 0.99 1127.0 15.9 0.96 0.97 325 plant m − 2 0 kg ha − 1 N 0.03 11.3 0.65 0.86 130.7 18.2 0.97 0.97 100 kg ha − 1 N 0.31 10.0 0.97 0.99 812.8 15.9 0.97 0.97 150 kg ha − 1 N 0.98 29.6 0.90 0.94 1948.6 37.6 0.93 0.93 200 kg ha − 1 N 0.84 18.1 0.90 0.97 768.5 10.1 0.98 0.98 Grain yield The simulated grain yields (GY) are similar to the measured grain yields (Fig. 8 a) with RMSE, nRMSE, and d values of 0.614 t ha − 1 , 18%, and 0.96 for model evaluation (Fig. 8 a). The simulated trends in grain yield under different planting densities and N fertilizer rates were in good agreement with those of the measured GY. However, the model slightly overestimated the grain yield compared to the measured yield in all treatment combinations, except for all plant densities under the no N fertilizer treatment (Fig. 8 b). The accumulation of high biomass growth observed at early growth in good rain followed by a dry period after termination of rain probably enhanced rapid, quick senescence, reduced sink source relation, grain formation, and all contributed to the final yield reduction ( Giuntaeal 1993; Araya et al. 2019 ). In contrast, the model predicted grain yield slightly lower than the measured grain yield in treatment combinations for no N fertilizer across all plant populations (Fig. 8 b). Similarly, in the present study the grain yield predication obtained compared to previous the CERES-Wheat model showed poor performance using nitrogen than no N ( Li et al. 2016 ; Liu Hai-long et al. 2017 ). In Durum wheat, the combination of PD 4 + N 3 had greater grain yield than the no N treatment an across all plant densities (Fig. 8 b). However, no significant difference was observed between PD 2 + N 3 and PD 4 + N 3 treatment combinations (Fig. 8 b). The GY of durum wheat under different plant densities and N fertilizers varied compared to different treatment combinations for simulation/measured GY which ranged from 1260/1380 under treatment combinations of PD 4 + N 0 to 6304/5790 kg ha − 1 in PD 4 + N 4 . In general, good agreement between simulated and measured GY values showed that the calibrated CERES-Wheat model could simulate GY of durum wheat very well in the study. Model applications The analysis to determine the optimum plant density and nitrogen fertilizer rate for durum wheat cultivation in the central highlands of Ethiopia was conducted using the CRESE-wheat model. The CERES-Wheat model was used to simulate grain yield for durum wheat in 48 different treatment combinations, with six plant densities ranged from 175 to 425 plants m − 2 and eight nitrogen fertilizer rates ranging from 0 (control) to 350 kg ha − 1 using 37 (1985 to 2022) years of historical daily weather data under the rain-fed farming system. The 37-year average grain yield increased with increasing nitrogen fertilizer rates from 100 to 250 kg ha − 1 N as the plant density increased from 175 to 275 plants m − 2 (Fig. 9 ). Under 0 (control) kg ha − 1 N fertilization, the 37-year average grain yield decreased from 1632 kg ha − 1 to 765 kg ha − 1 as plant density increased from 175 to 425 plants m − 2 (Fig. 9 ). These results indicate that, under no N fertilization, plant density had a negative effect on grain yield in populations above 175 plants m − 2 . Under high plant density, soil nutrient depilation was high and exacerbated when the soil was incapable of supplying nutrients during crop growth, resulting in decreased grain yield. From the simulated results, the highest (6328 kg ha − 1 ) grain yield was predicted at a nitrogen rate of 250 kg ha − 1 interaction with 275 plants m − 2 , compared to all combinations of nitrogen rate and plant density (Fig. 9 ). No further increases in the 37-year averaged grain yield were observed when the plant density exceeded 275 plants m − 2 under all nitrogen fertilizer rates. The 37-year simulated average yield increased with increasing plant density and nitrogen fertilizer until a critical point (250 kg ha − 1 N and 275 plants m − 2 ), and then declined with further increases in both nitrogen and plant density. Thus, a nitrogen rate of 250 kg ha − 1 and a plant density of 275 plants m − 2 maximized the grain yield for the study site under rain-fed conditions. Durum wheat yield increased when plant density and nitrogen fertilizer increased until a critical point, mainly due to better uptake of resources, especially nitrogen and soil water, while maintaining an optimum spike density. However, as nitrogen nutrient and plant density increased beyond these critical points (250 kg ha − 1 and 275 plants m − 2 ), the yield declined mainly because of increased competition for resources, especially soil water and solar radiation. Under rainfed conditions (water-limited), crop yield is also limited by soil water and solar radiation. Nitrogen nutrients and plant density increased beyond the critical point, and the photosynthetic characteristics of the plant declined, resulting in lower crop photosynthetic assimilation and yield productivity per plant, which might explain the decrease in durum wheat yield observed in this simulated scenario at high nitrogen and plant density ( Zhang et al. 2021 ). Zhang et al. ( 2019 ) reported that plant density exceeded the critical point, and solar radiation became the yield-limiting factor, especially under high-nitrogen conditions. The simulated results indicated that pursuing high nitrogen fertilizer and plant density are not desirable strategies in the rainfed farming system, whereas the relatively optimum nitrogen and plant density may be more conducive to the effective use of resources in the rainfed farming system for yield sustainability. Agronomic use efficiency The agronomic use efficiency (AUE) and partial factor productivity (PFP) in response to plant density and nitrogen fertilizer rate are shown in Fig. 10 . The simulation results showed that the response of AE to plant density and nitrogen rate was linear. For all nitrogen fertilizer rates except the control (zero), the AUE of durum wheat increased as plant density increased. However, when the plant density exceeded 275 plants m − 2 along with the nitrogen rate, the AUE tended to decrease. The rate of 350 kg N ha − 1 at all plant densities resulted in the lowest AUE; however, the dominant AUE was 200 kg ha − 1 N at all plant densities (Fig. 10 ). The treatment combination of 200 kg ha − 1 and 275 plants m − 2 followed by 250 kg ha − 1 and 275 plants m − 2 was given the highest AUE among the 48 simulated scenarios. The rate of 200 kg ha − 1 and under all plant densities produced AUE ranging from 24.7 to 28.2% (average 27%) and 250 kg ha − 1 and under all plant densities, AUE ranged from 24.5 to 27.7 (average 26.4). Owing to this finding, increased two-level plant density from a lower level (175 plants m − 2 ) under a 200 kg ha − 1 N rate can enhance nitrogen use efficiency. This is because increasing plant density enhances nitrogen and nutrient uptake and utilization efficiency by the roots of durum wheat and accelerates the transfer of nitrogen nutrients from the roots to stems and leaves ( Liu et al. 2022 ), cumulatively increasing nitrogen use efficiency ( Dai et al. 2014 ; Zhang et al. 2019 ). Hence, increased durum wheat plant density, along with nitrogen fertilizers up to a certain level, is important for reducing nitrogen fertilizer losses and environmental risks while increasing grain yield ( Tian et al. 2022 ). Once the optimum plant density and nitrogen fertilizer rates were exceeded, both grain yield and AUE decreased (Fig. 10 ). Economic analysis The monetary return of US $ ha − 1 for all 48 scenarios is presented in Fig. 11 . Strategic analysis was used to identify the best strategic treatment combinations for sustainable durum wheat production. In the simulation scenarios, at all plant densities with no nitrogen fertilizer application, the 37-year monetary returns showed negative profit (Fig. 11 ). However, all other nitrogen rates monetary return increased and positive with increasing plant density from 175 up to 275 plants m − 2 , then decline with further increases in plant density beyond 275 plants m − 2 (Fig. 11 ). The result of the strategic analysis of monetary returns $ ha − 1 (Fig. 11 ) showed that the treatment combination of 250 kg ha − 1 and 275 plants m − 2 was the highest among the 48 simulated scenarios. These results are consistent with those of another study that applied the CERES-wheat model ( Araya et al. 2019 ). This study, which was conducted in the northern part of Ethiopia, showed that the treatment in which 160 kg ha − 1 was applied was dominant compared to other rates. However, their study was conducted under irrigation conditions using bread wheat cultivars and environments than in our study, and the lowest N application rate was 160 kg ha − 1 . Conclusion The CERES-Wheat model was calibrated, evaluated, and used to determine the optimum plant density and nitrogen fertilizer rate for durum wheat cultivation under rainfed condition. The results for model calibration under optimum input treatment experiments and evaluation under variable plant density and N rates showed that the model between simulated and measured values for phenology, leaf area index, top weight, grain yield, and harvest index were in good agreement. However, the model slightly overestimated the predicted grain yield compared to the measured yield in the N treatment compared to the unfertilized treatment at all plant densities. Simulation scenarios were tested using 37 years of historical weather data, and seasonal analysis showed how to better optimize plant density and N fertilizer rate to optimize yield, nitrogen use, and economic return. Based on the seasonal analysis of a 37-year simulation, the optimal combination of planting density and nitrogen management was found to be 275 plants m − 2 with 250 kg ha − 1 N. The simulation results suggested that a plant density of 275 plants m − 2 with an N application of 250 kg ha − 1 increased grain yield, improved nitrogen use, and produced the highest economic return while minimizing environmental risk under rainfed conditions. The approach used and the results generated in this study can help develop the best management strategies for advancing crop yield and nitrogen use with the highest economic return while reducing the negative effect on the environment. However, the model was tested and simulated under rainfed conditions. It is advisable to undertake future research to test and simulate the model under irrigation conditions for further application of the model. Declarations Ethics approval and consent to participate No applicable Consent for publication Not applicable Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The we declare that they have no competing interests" in this section. Funding This work was supported by Ethiopian Institute of Agriculture Research (EIAR) and partly by Agriculture Growth Program Phase two (AGPII). Author contributions Bizuwork Tafes Desta conceived the project, set scientific objectives. Bizuwork Tafes and Sisay Eshetu contributed to preparing the field experiment and data a question. Bizuwork Tafes wrote the manuscript. Dr. Alemayehu Zemede and Almaz Mesert manuscript review and editing. Bizuwork Tafes was a major contributor in writing the manuscript. All authors contributed to the article and approved the submitted version. Acknowledgments We would like to acknowledge Debre Zeit Agricultural Research Center for allowing the study some vehicle service and financial process. We would like to thank the Ethiopian National Meteorology Agency (NMA), Agricultural office in Minjar Shenkora district and Natural Resources Research program at Debre Zeit Research Center laboratory for lab analysis. 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Zheng B, Zhang X, Wang Q, Li W, Huang M, Zhou Q, Cai J, Wang X, Cao W, Dai T, Jiang D (2021) Increasing plant density improves grain yield, protein quality and nitrogen agronomic efficiency of soft wheat cultivars with reduced nitrogen rate. Field Crops Research 267:108145. https://doi.org/10.1016/j.fcr. 2021.108145. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4411924","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":303535572,"identity":"06a9d35e-f89e-4619-915c-ecf884784025","order_by":0,"name":"Bizuwork Tafes Desta","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBACAxBRwcAgB6IPPCBOCzMDwxkGBmOwlgRStCQ2gHhEaTFn4D/44UBFXfr8sMMPgbbYyek2ENBi2cDMLHHgzOHcjbfTDIBako3NDhBy2AFmBumPbQdyN85OAGk5kLiNCC3MPw7+q0s3nJ3+gWgtbBIHG5gT5KVziLTFspnZzOLAscOGG6RzCg4kGBDhF3P2xsc3DtTUycvPTt/84UOFnRxBLaBogboQTBJSjgzkG0hRPQpGwSgYBSMKAADhiUbgBGhC6gAAAABJRU5ErkJggg==","orcid":"","institution":"Ethiopian Institute of Agricultural Research","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Bizuwork","middleName":"Tafes","lastName":"Desta","suffix":""},{"id":303535574,"identity":"084b0893-9996-40b8-b3b9-3896b82f3c0a","order_by":1,"name":"Sisay Eshetu Tesema","email":"","orcid":"","institution":"Ethiopian Institute of Agricultural Research","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sisay","middleName":"Eshetu","lastName":"Tesema","suffix":""},{"id":303535576,"identity":"06961430-0e69-4c6a-ae08-b8b6dcb5b79f","order_by":2,"name":"Almaz Meseret Gezahegn","email":"","orcid":"","institution":"Ethiopian Institute of Agricultural Research","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Almaz","middleName":"Meseret","lastName":"Gezahegn","suffix":""},{"id":303535577,"identity":"d73a6ca5-1be8-4edf-aee8-4bc6825aa3d7","order_by":3,"name":"Almayehu Zemede","email":"","orcid":"","institution":"Ethiopian Institute of Agricultural Research","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Almayehu","middleName":"","lastName":"Zemede","suffix":""}],"badges":[],"createdAt":"2024-05-13 08:52:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4411924/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4411924/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57010868,"identity":"b144e583-fdb5-4618-9f9f-7502b80afc84","added_by":"auto","created_at":"2024-05-23 11:16:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":15487,"visible":true,"origin":"","legend":"\u003cp\u003eAverage monthly rainfall of thirty-three years (1985 to 2016) and monthly means rainfall of 2017, 2018, 2019, and 2020 of wheat cropping months (June to November) of the experimental area\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4411924/v1/71bbbbfea0523573b2b4fee9.png"},{"id":57010390,"identity":"194b68f3-5389-469f-920c-5b622c0794fc","added_by":"auto","created_at":"2024-05-23 11:08:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":81967,"visible":true,"origin":"","legend":"\u003cp\u003eDaily maximum temperature (top graph), daily minimum temperature (center graph), and daily relative humidity (bottom graph) during the study period between (2017 and 2020) of the experimental area.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4411924/v1/8e27282c26927a18b50992c6.png"},{"id":57010392,"identity":"99cab3de-dea5-4301-bfde-9eddf82d4944","added_by":"auto","created_at":"2024-05-23 11:08:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":26459,"visible":true,"origin":"","legend":"\u003cp\u003eSimulated (continuous lines) and measured (circular with error bar symbols) leaf area index (a 2017, b 2018, c 2019) and top weight (d 2017, e 2018, f 2019) for durum wheat variety Utuba at optimum plant density (225 plants m\u003csup\u003e-2\u003c/sup\u003e and N fertilizer (200 kg ha\u003csup\u003e-1\u003c/sup\u003e) at Memir Hager, used for model calibration.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4411924/v1/08f562702ca2bf4909fad3a9.png"},{"id":57010396,"identity":"1a5c380b-65c7-4850-b4d5-0e585a46404e","added_by":"auto","created_at":"2024-05-23 11:08:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":12970,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of simulated and measured grain yield (square symbol) and harvest index (circle symbol). Data from 2017, 2018, and 2019 were used to calibrate the model.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4411924/v1/ee24dc8bf94cec701c20cdf8.png"},{"id":57010394,"identity":"a66397cf-9781-4d7d-a052-cdc819b21071","added_by":"auto","created_at":"2024-05-23 11:08:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":23697,"visible":true,"origin":"","legend":"\u003cp\u003eComparison between simulated and measured leaf area index (a) at grain filling stage and top weight (b) at final harvest for model evaluation data obtained from the 2020 cropping season.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4411924/v1/5f260c61d03bbb1434e7318a.png"},{"id":57010869,"identity":"4a885dac-deda-44f6-8154-b267690582e7","added_by":"auto","created_at":"2024-05-23 11:16:49","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":36959,"visible":true,"origin":"","legend":"\u003cp\u003eSimulated (continuous line) measured (circular with error bar symbol) leaf area index of durum wheat variety Utuba under four plant density and N fertilizer application rates, i.e., PD\u003csub\u003e1\u003c/sub\u003e + N\u003csub\u003e0\u003c/sub\u003e (a), PD\u003csub\u003e1\u003c/sub\u003e + N\u003csub\u003e100\u003c/sub\u003e (b), PD\u003csub\u003e1\u003c/sub\u003e + N\u003csub\u003e150\u003c/sub\u003e (c), PD\u003csub\u003e1\u003c/sub\u003e + N\u003csub\u003e200\u003c/sub\u003e (d), PD\u003csub\u003e2\u003c/sub\u003e + N\u003csub\u003e0\u003c/sub\u003e (e), PD\u003csub\u003e2\u003c/sub\u003e + N\u003csub\u003e100\u003c/sub\u003e (f), PD\u003csub\u003e2\u003c/sub\u003e + N\u003csub\u003e150\u003c/sub\u003e (g), PD\u003csub\u003e2 \u003c/sub\u003e+ N\u003csub\u003e200\u003c/sub\u003e (h), PD\u003csub\u003e3 \u003c/sub\u003e+ N\u003csub\u003e0\u003c/sub\u003e (i), PD\u003csub\u003e3\u003c/sub\u003e + N\u003csub\u003e100\u003c/sub\u003e (j), PD\u003csub\u003e3\u003c/sub\u003e + N\u003csub\u003e150 \u003c/sub\u003e(k), PD\u003csub\u003e3\u003c/sub\u003e + N\u003csub\u003e200\u003c/sub\u003e (l),\u0026nbsp; PD\u003csub\u003e4\u003c/sub\u003e + N\u003csub\u003e0\u003c/sub\u003e (m), PD\u003csub\u003e4\u003c/sub\u003e + N\u003csub\u003e100\u003c/sub\u003e (n), PD\u003csub\u003e4\u003c/sub\u003e + N\u003csub\u003e150\u003c/sub\u003e (o),\u0026nbsp; and PD\u003csub\u003e4\u003c/sub\u003e + N\u003csub\u003e200\u003c/sub\u003e (p) under rain-feed condition at Memir Hager 2020 for model evaluation.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4411924/v1/be3063a26063ea665751f1d0.png"},{"id":57010395,"identity":"f3c81792-1374-4b0f-b012-2bd1917d177b","added_by":"auto","created_at":"2024-05-23 11:08:49","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":47984,"visible":true,"origin":"","legend":"\u003cp\u003eSimulated (continuous line) measured (circular with error bar symbol) top weight of durum wheat variety Utuba under four plant density and N fertilizer application rates, i.e., PD\u003csub\u003e1\u003c/sub\u003e + N\u003csub\u003e0\u003c/sub\u003e (i), PD\u003csub\u003e1\u003c/sub\u003e + N\u003csub\u003e100\u003c/sub\u003e (ii), PD\u003csub\u003e1\u003c/sub\u003e + N\u003csub\u003e150\u003c/sub\u003e (iii), PD\u003csub\u003e1\u003c/sub\u003e + N\u003csub\u003e200\u003c/sub\u003e (iv), PD\u003csub\u003e2\u003c/sub\u003e + N\u003csub\u003e0\u003c/sub\u003e (v), PD\u003csub\u003e2\u003c/sub\u003e + N\u003csub\u003e100\u003c/sub\u003e (vi), PD\u003csub\u003e2\u003c/sub\u003e + N\u003csub\u003e150\u003c/sub\u003e (vii), PD\u003csub\u003e2 \u003c/sub\u003e+ N\u003csub\u003e200\u003c/sub\u003e (viii), PD\u003csub\u003e3 \u003c/sub\u003e+ N\u003csub\u003e0\u003c/sub\u003e (ix), PD\u003csub\u003e3\u003c/sub\u003e + N\u003csub\u003e100\u003c/sub\u003e (x), PD\u003csub\u003e3\u003c/sub\u003e + N\u003csub\u003e150 \u003c/sub\u003e(xi), PD\u003csub\u003e3\u003c/sub\u003e + N\u003csub\u003e200\u003c/sub\u003e (xii),\u0026nbsp; PD\u003csub\u003e4\u003c/sub\u003e + N\u003csub\u003e0\u003c/sub\u003e (xiii), PD\u003csub\u003e4\u003c/sub\u003e + N\u003csub\u003e100\u003c/sub\u003e (xiv), PD\u003csub\u003e4\u003c/sub\u003e + N\u003csub\u003e150\u003c/sub\u003e (xv),\u0026nbsp; and PD\u003csub\u003e4\u003c/sub\u003e + N\u003csub\u003e200\u003c/sub\u003e (xvi) under rain-feed condition at Memir Hager 2020 for model evaluation.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4411924/v1/85f725482804c9c004ddf18b.png"},{"id":57010398,"identity":"eb8341cf-0e6a-4a8f-a1d1-067978fbe3a9","added_by":"auto","created_at":"2024-05-23 11:08:49","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":29467,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between the simulated and measured grain yield (a). Comparison of simulated and measured grain yield of durum wheat under four planting density and four N fertilizer rates (b). Note: PD\u003csub\u003e1\u003c/sub\u003e + N\u003csub\u003e0\u003c/sub\u003e (A), PD\u003csub\u003e1\u003c/sub\u003e + N\u003csub\u003e100\u003c/sub\u003e (B), PD\u003csub\u003e1\u003c/sub\u003e + N\u003csub\u003e150\u003c/sub\u003e (C), PD\u003csub\u003e1\u003c/sub\u003e + N\u003csub\u003e200\u003c/sub\u003e (D), PD\u003csub\u003e2\u003c/sub\u003e + N\u003csub\u003e0\u003c/sub\u003e (E), PD\u003csub\u003e2\u003c/sub\u003e + N\u003csub\u003e100\u003c/sub\u003e (F), PD\u003csub\u003e2\u003c/sub\u003e + N\u003csub\u003e150\u003c/sub\u003e (G), PD\u003csub\u003e2 \u003c/sub\u003e+ N\u003csub\u003e200\u003c/sub\u003e (H), PD\u003csub\u003e3 \u003c/sub\u003e+ N\u003csub\u003e0\u003c/sub\u003e (I), PD\u003csub\u003e3\u003c/sub\u003e + N\u003csub\u003e100\u003c/sub\u003e (J), PD\u003csub\u003e3\u003c/sub\u003e + N\u003csub\u003e150 \u003c/sub\u003e(K), PD\u003csub\u003e3\u003c/sub\u003e + N\u003csub\u003e200\u003c/sub\u003e (L), PD\u003csub\u003e4\u003c/sub\u003e + N\u003csub\u003e0\u003c/sub\u003e (M), PD\u003csub\u003e4\u003c/sub\u003e + N\u003csub\u003e100\u003c/sub\u003e (N), PD\u003csub\u003e4\u003c/sub\u003e + N\u003csub\u003e150\u003c/sub\u003e (O), and PD\u003csub\u003e4\u003c/sub\u003e + N\u003csub\u003e200\u003c/sub\u003e (P). Vertical bars are standard deviations of measurements and simulations for model evaluation.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4411924/v1/a779f91036d1ac0cd5eff864.png"},{"id":57010397,"identity":"29830b91-a477-454d-a9e1-84b88bb613b4","added_by":"auto","created_at":"2024-05-23 11:08:49","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":16522,"visible":true,"origin":"","legend":"\u003cp\u003e37-year average simulated grain yield response to plant density and nitrogen fertilizer rates under rainfed condition\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4411924/v1/bc41319cac9c92a481bdea75.png"},{"id":57010391,"identity":"c81ec38f-396c-4d6e-a0f6-d04ed607c536","added_by":"auto","created_at":"2024-05-23 11:08:48","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":21556,"visible":true,"origin":"","legend":"\u003cp\u003e37-year average simulated agronomic use efficiency response to plant density and nitrogen fertilizer rates under rainfed condition\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4411924/v1/a89e96315a353b5bbd3405c6.png"},{"id":57010871,"identity":"710ad98a-ffbc-4e7d-8ee9-07739f3d8c05","added_by":"auto","created_at":"2024-05-23 11:16:49","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":25470,"visible":true,"origin":"","legend":"\u003cp\u003eThe 37-year average simulated monetary return US$ ha\u003csup\u003e-1 \u003c/sup\u003evariable plant density\u003csup\u003e \u003c/sup\u003eand N application rates.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-4411924/v1/7faadb40adb4457017d245b9.png"},{"id":57011191,"identity":"93a59e2a-dd8a-4aa8-997d-74f0b30a912f","added_by":"auto","created_at":"2024-05-23 11:24:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1383792,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4411924/v1/9c6e000c-fa9b-498d-8d17-3a89e84d16a7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Determining optimum plant density and nitrogen rate using field experiment and model simulation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe expansion of agricultural land to the marginal area was the primary factor to increase food production, which resulted in to GDP growth in the central highlands of Ethiopia since the early 2005 (\u003cb\u003eBiazin and Sterk 2013;\u003c/b\u003e Fantu et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, this horizontal increase in farm land is facing various constraints that hamper crop productivity. Severe land degradation, poor inherent fertility, high dependence on rainfall, and low uses of plant density and fertilizers by smallholder farmers were among the major constraints that could be mentioned \u003cb\u003e(Zerssa et al. 2021; Mezgebu et al. 2022)\u003c/b\u003e. In addition, owing to the rapidly growing population in the rural parts of Ethiopia, particularly in the central highlands of the country, the size of cropland per capita has been decreasing drastically. For instance, the average farm size of less populated areas such as Oromia, an average farm size of 1.2 ha, and the densely populated South Nation, Nationality, and People\u0026rsquo;s regions is 0.5 ha (Holden and Tilahun \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. This indicates that smallholder farming systems will continue to dominate the agriculture sector and the average farm sizes will continue to decline because the further expansion of cropland will become more difficult, while the population will continue to increase \u003cb\u003e(CSA 2023)\u003c/b\u003e. Therefore, future agricultural development should focus on raising productivity per unit area instead of expanding the cultivated area.\u003c/p\u003e \u003cp\u003eSustainable intensification is understood to encompass a wide range of practices, including appropriate but not limited to conservation agriculture, with the potential to produce more food from the existing agricultural land in a valuable and changed environment while maintaining resources \u003cb\u003e(\u003c/b\u003eWilkus et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Agronomic intensification practices, including plant density and nutrient supply, have the potential to enhance agricultural production outputs (Li et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Integrated management strategies, such as optimum plant density and soil nutrient application, achieve the highest yield, adulate grain quality, and long-term sustainability based on environmental circumstances \u003cb\u003e(\u003c/b\u003eCakmak et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; \u003cb\u003eNiyigaba et al. 2019)\u003c/b\u003e. For instance, the supply of optimum nitrogen fertilizer and plant density have been proven to be key ways to increase crop yields in dryland farming \u003cb\u003e(Hochman and Horan 2018;\u003c/b\u003e Gastaldi et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lollato et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the central highlands of Ethiopia, an increase in nitrogen fertilizer from 100 to 150 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in combination with the optimum plant density (approximately 200 plants m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) increased bread wheat yield from 2534 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e to 3567 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in Eshetu (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e\u003cb\u003e) and Lakew (2019)\u003c/b\u003e showed a 29% increase in wheat yield. In another study conducted in the northern part of Ethiopia, durum wheat grain yield showed a quadratic and linear response to nitrogen fertilizer, and the maximum grain yield (5182 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) was obtained at 270 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e N, with the highest net benefit \u003cb\u003e(Alemayehu et al. 2023).\u003c/b\u003e These findings confirm that the use of the optimum nitrogen fertilizer rate significantly increased durum wheat yield. Zhang et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) also suggested a possible approach to increasing wheat grain yield per unit of land by adjusting nitrogen management along with other crop practices.\u003c/p\u003e \u003cp\u003ePlant density is an important agronomic intensification parameter. Plant density has a significant positive effect on wheat yield \u003cb\u003e(\u003c/b\u003eZheng et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). \u003cb\u003eBeres et al. (2012)\u003c/b\u003e reported a positive linear improvement in grain yield in durum wheat at rates as high as 450 plants m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, which was more than twice the rate of standard practices (210 plants m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) at that time. This positive association between plant density and durum wheat yields was also reported by \u003cb\u003e(\u003c/b\u003eGeleta et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition to increasing grain yield, increasing plant density, improving nitrogen utilization and uptake efficiency, and increasing wheat root length and density, overall increases below and above ground plant demand \u003cb\u003e(\u003c/b\u003eDai et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In Ethiopia, most farmers cultivate durum wheat under low plant density and nitrogen fertilizer. In practice, a low plant density of 175 plants m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e and blanket application of nitrogen fertilizer (approximately 100 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) restrict yield and nitrogen use efficiency \u003cb\u003e(\u003c/b\u003eAgbahey et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; \u003cb\u003eBizuwork et al. 2020).\u003c/b\u003e In contrast, durum wheat grain yields as high as 5670 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e have been achieved under high plant densities (225 plants m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) and nitrogen fertilizer rates (150 to 200 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in Ethiopia \u003cb\u003e(\u003c/b\u003eAraya et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Appropriate plant densities, combined with optimum nitrogen management, are likely to increase grain yield and nitrogen use efficiency \u003cb\u003e(\u003c/b\u003eYan et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Thus, knowledge of the interaction between nitrogen nutrients and plant density is important for increasing and maintaining crop yields in the central highlands of Ethiopia.\u003c/p\u003e \u003cp\u003eAlthough field-based experiments are usually effective, they are time-consuming, require expensive resources, and take a longer time to draw valid recommendations. Thus, process-based models have proven to be useful tools for investigating the impact of climate variability and change on crop productivity, resource use efficiency, and environmental impacts on the agricultural system \u003cb\u003e(Batchelor et al. 2002).\u003c/b\u003e Moreover, the application of a process-based model also provides an option for designing climate-resilient management strategies to tackle the enormous challenges facing agricultural productivity and to generate knowledge for aiding agricultural developments that would otherwise be impossible through field experimentation \u003cb\u003e(\u003c/b\u003eHern\u0026aacute;ndez-Ochoa et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe CERES-Wheat model, which is embedded in DSSAT, is widely used to determine the best strategic plant density, irrigation management, and nutrient dynamics \u003cb\u003e(\u003c/b\u003eLi et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Plaza-Bonilla et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Araya et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Liang et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; \u003cb\u003eShaque et al. 2020)\u003c/b\u003e. However, no study has used combined experimental and model simulation approaches of plant density and nitrogen fertilizer rate interaction on the long-term impact of water limitation on durum wheat yield and economic values. Moreover, research on plant density and nitrogen fertilizer for durum wheat in Ethiopia began in the late 1960s in the central highlands \u003cb\u003e(\u003c/b\u003eHailu \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1991\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Typical recommended practices for durum wheat in the central highlands of Ethiopia are 175 plants m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e and 150 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e N \u003cb\u003e(Amsal et al. 2000; Amsal and Tanner 2001; Nigussie et al. 2013; Abera et al. 2015).\u003c/b\u003e Despite previous studies, there is still a lack of systematic investigation on how plant density and nitrogen fertilizer rate interact with the long-term impact of water-limited conditions on durum wheat yield and economic value. In addition, most of the plant density and nitrogen fertilizer recommendations for durum wheat are outdated, and no longer reflect the current agro-climatic variability and change. To fill this gap, we carried out a study that combines experiments and modeling at a field level for durum wheat in central highlands and covers a wider range of weather conditions. The following objectives were addressed: (i) to calibrate the CERES-Wheat model for durum wheat cultivar, (ii) to evaluate the performance of the CERES-Wheat model to simulate the growth, development, and yield of durum wheat under rain-fed cultivation, and (iii) to apply the CERES-Wheat model to determine the optimum plant density and nitrogen fertilizer supply using long-term historical daily weather data for durum wheat rain-fed cultivation.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDescription of the study area\u003c/h2\u003e \u003cp\u003eThe experiments were conducted over four consecutive years (2017, 2018, 2019, and 2020) and the main wheat cropping season (June to November) at Memir Hager (8\u003csup\u003eo\u003c/sup\u003e46’33.5″ N and 39\u003csup\u003eo\u003c/sup\u003e16’40.7’’ E). It is found in the Minjar Shenkora district in the Amhara region of Ethiopia. The area has a typical wet-dry climate, with a monsoon season (rain) and summers typically running from June to August. Spring is from September to November, which is the harvest season in the area. Cold dry (December to February) and cooler than the other seasons, and this conceded the dry season in the area. In the area, autumn occurs from March to May, and these months experience an agricultural land preparation period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSoil characteristics and weather conditions of the experimental area\u003c/h2\u003e \u003cp\u003eThe soil's physical, chemical, and hydraulic properties (field capacity, permeant wilting point, bulk density, saturated water), texture, pH, cation exchange capacity (CEC), ammonium and nitrate concentrations, and soil organic matter were obtained from the soil profile description from the Global High-Resolution Soil Profile Database for crop modeling application \u003cb\u003e(\u003c/b\u003eHarvest Choice \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, based on the latitude and longitude of its geographic grid (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of soil profile properties (lower soil water limit (LSL), drained soil water upper limit (DUL), saturated upper limit (SAL), bulk density (BD), organic carbon (OC), clay content (SCL), silt content of the soil (SSI), total N (TN), soil phosphorous (P), soil pH (pH) and soil cation exchange capacity (CEC) of the soil used in CERES-wheat model simulations. LSL, DUL, and SAL were calculated using DSSAT 4.8.2.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDepth (cm)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLSL\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSDUL\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSSAT\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBD\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOC\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSLCL\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSLSI\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTN\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCEC\u003c/p\u003e \u003cp\u003e(Cmol-kg\u003csup\u003e− 1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e--------(cm\u003csup\u003e3\u003c/sup\u003e cm\u003csup\u003e− 3\u003c/sup\u003e)-------\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eg cm\u003csup\u003e− 3\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003e----------(g kg\u003csup\u003e− 1\u003c/sup\u003e)---------\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e35.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e27.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e35.7\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e37.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e31.3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e25.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e30.4\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e42.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e24.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e31.7\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e42.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e23.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e31.9\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e41.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e23.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e31.8\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eHistorical weather data for daily maximum and minimum air temperature, solar radiation, relative humidity, rainfall, and wind speed from 1985 to 2022 were obtained from the Ethiopian National Meteorology Agency (NMA) and used for long-term simulation analysis. From 2017 to 2020, daily weather data, including maximum and minimum air temperature, rainfall, relative humidity, solar radiation, and wind speeds, were obtained from a local weather station (Minjar Shenkora District Meteorological Station), which was near the experimental sites and used for model calibration and evaluation. Thirty-one years (1985–2016) of average monthly and 2017, 2018, 2019, and 2020 year monthly mean rainfall of the experimental site in durum wheat cropping months are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The daily maximum air temperature, daily minimum air temperature, and relative humidity during the experimental periods (2017, 2018, 2019, and 2020) are illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and 3, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eExperimental set-up and field management\u003c/h2\u003e \u003cp\u003eExperiments conducted from 2017 to 2019 were used for model calibration. The durum wheat cultivar ‘Utuba’ at an optimum planting density (225 plants m\u003csup\u003e− 2\u003c/sup\u003e), nitrogen (urea at a rate of 200 kg ha\u003csup\u003e− 1\u003c/sup\u003e), and phosphorus (di-ammonium phosphate) 18% N and 46% P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e) at a rate of 100 kg ha\u003csup\u003e− 1\u003c/sup\u003e was planted on July 27, 2017, July 26, 2018, and July 28, 2019. The plot size was 5m x 5m (25 m\u003csup\u003e2\u003c/sup\u003e) and was replicated three times. The experiment undertaken during the 2020 cropping season was used for the model evaluation. The experiment included the following treatment combinations: four planting densities: 175 plants m\u003csup\u003e− 2\u003c/sup\u003e (PD\u003csub\u003e1\u003c/sub\u003e), 225 plants m\u003csup\u003e− 2\u003c/sup\u003e (PD\u003csub\u003e2\u003c/sub\u003e), 275 plants m\u003csup\u003e− 2\u003c/sup\u003e (PD\u003csub\u003e3\u003c/sub\u003e), and 325 plants m\u003csup\u003e− 2\u003c/sup\u003e (PD\u003csub\u003e4\u003c/sub\u003e) of the Utuba cultivar, and four N fertilizer rates: 0 (control) N\u003csub\u003e0\u003c/sub\u003e, 100 (N\u003csub\u003e1\u003c/sub\u003e), 150 (N\u003csub\u003e2\u003c/sub\u003e), and 200 (N\u003csub\u003e3\u003c/sub\u003e) kg ha\u003csup\u003e− 1\u003c/sup\u003e urea fertilizer. The treatments were laid down in a randomized complete block design (RCBD) in a factorial arrangement, and each treatment was replicated three times. The plot size was 3m x 3m (9 m\u003csup\u003e2\u003c/sup\u003e) and consisted of 16 rows, spaced 20 cm apart. The net central areas of each plot, consisting of 12 central rows of 2.80 m length were used for data collection. The seeds were sown with a row spacing of 20 cm using a hand drill. For nitrogen fertilizer, two split applications, the first one-third N fertilizer was applied at planting, and the remaining (two-thirds N) dose was applied 21 days after crop planting. A full dose of phosphorus fertilizer was applied uniformly at planting. All other crop management practices, such as weeding, diseases, and insect pest protection, were uniformly implemented for all treatments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eTrait measurements\u003c/h2\u003e \u003cp\u003eWheat phenology traits, such as anthesis and maturity date, were defined by the plant and showed visual signs of the stage being considered. The anthesis date was recorded when approximately 50% of the plants in a plot produced spikes. The maturity date was obtained when the leaves and vegetative parts of the crop were light yellow in color. In the season, the top weight of the plant was determined based on a randomly selected area of 0.25 m\u003csup\u003e2\u003c/sup\u003e sample harvested at four Zadoks growth (GS24 (tillering), GS40 (booting), GS60 (flowering), and GS80 (grain filling) stages of each plot and dried in an oven at 70°C for 48 h, and their weights were determined using an electronic balance. The leaf area index was measured using a plant canopy imager (Model CI-110), taking readings from the middle eight of the 16 rows per plot in all replications by passing the instrument on the top of the canopy at a height of 60 cm. Readings were taken at four growth stages, (GS24 (tillering), GS40 (booting), GS60 (flowering), and GS80 (grain filling). The crop was harvested manually from four central rows 2 m in length in each row to determine the final top weight, grain yield (GY), and harvest index. After harvesting, the threshed grains were separated, cleaned, and weighed using an electronic balance. GY was adjusted to a moisture content of 12.5% wet bases, and a moisture tester was used to test the moisture content of the grain. The Harvest index was computed using the formula of Nichiporovich (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1967\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e HI = GY/top weight.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eModel calibration and evaluation\u003c/h2\u003e \u003cp\u003eThe CERES-Wheat model embedded in Decision Support Systems for Agrotechnology Transfer-DSSAT v4.8.2 \u003cb\u003e(\u003c/b\u003eHoogenboom et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) was used in this study. The cultivar Utuba coefficients of the model were calibrated using data collected from the optimum inputs treated experiments conducted during three growing seasons from 2017, 2018, and 2019. Genetic coefficients were calibrated using the procedure described by Li et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Adjustments were made sequentially, starting with phenological (anthesis and maturity date) traits and crop growth (leaf area index (LAI), top weight, and GY) parameters. The GLUE coefficient estimator was used for parameter estimation. In total, 10,000 random parameter sets were generated using an independent dataset for the crop-growing seasons. After calibrating the cultivar coefficients, the accuracy of the model was evaluated using experimental data collected in 2020. The observed data, such as in-season growth, LAI, and top weight, at final harvest biomass, GY, and harvest index, were collected and compared with simulated values of across each plant densities and nitrogen levels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eFor both calibration and evaluation, the results were checked using different statistical indices, including the normalized root-mean-square error (RMSE), normalized RMSE (nRMSE), index of agreement (d), and model efficiency (E). The statistical indices are described as follows.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eRMSE = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sqrt{\\frac{{\\sum }_{i=1}^{n}{\\left({s}_{i}-{M}_{i}\\right)}^{2}}{n}}\\)\u003c/span\u003e\u003c/span\u003e (1)\u003c/h2\u003e \u003cp\u003enRMSE = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{ RMSE}{\\stackrel{-}{M}}\\)\u003c/span\u003e\u003c/span\u003e (2)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eE =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(1-\\frac{{\\sum }_{i-1}^{n}{\\left({S}_{i}-{M}_{i}\\right)}^{2}}{{\\sum }_{i=1}^{n}{\\left({M}_{i}-\\stackrel{-}{M}\\right)}^{2}}\\)\u003c/span\u003e\u003c/span\u003e (3)\u003c/h2\u003e \u003cp\u003ed = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(1-\\frac{{\\sum }_{i=1}^{n}{\\left(S-{M}_{i}\\right)}^{2}}{{\\sum }_{i=1}^{n}{\\left(\\left|{S}_{i}-{M}_{i}\\right|+\\left|{M}_{i}-{\\stackrel{-}{M}}_{i}\\right|\\right)}^{2}}\\)\u003c/span\u003e\u003c/span\u003e (4)\u003c/p\u003e \u003cp\u003ewhere n is the total number of datasets measured and simulation values and M is the average of the measured values. The normalized RMSE (nRMSE) provides a measure (%) of the relative difference between the measured and simulated data. It is generally agreed that the model performance is excellent when the nRMSE value is less than 10%, good when the nRMSE is between 10 and 20%, fair when the nRMSE is between 20 and 30%, and poor when the nRMSE is greater than 30% \u003cb\u003e(\u003c/b\u003eLoague and Green \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Jamieson et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1991\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eModel application\u003c/h2\u003e \u003cp\u003eAfter the calibration and evaluation procedures were completed, the model was used to simulate grain yield, agronomic efficiency, and economic value of treatments using seasonal analysis programs (DSSAT v4.8.2 \u003cb\u003e(Hoogenoboom 2023).\u003c/b\u003e The simulation used a 37 years daily weather data from 1985 to 2022. A total of 48 combination comprised of six plant density and eight nitrogen fertilizer rates, were employed for the simulation. The plant densities ranged from 175 to 425 plants m\u003csup\u003e− 2\u003c/sup\u003e at an interval of 50 plants m\u003csup\u003e− 2\u003c/sup\u003e and N rate treatments ranged from 0 to 350 kg nitrogen ha\u003csup\u003e− 1\u003c/sup\u003e in an interval of 50 kg ha\u003csup\u003e− 1\u003c/sup\u003e were used for simulation. Following the simulation, the outputs were processed using the biophysical and economic analysis options of the program. These analyses and comparisons can identify and quantify the variability in crop performance associated with the interaction between weather and soil factors in the physical environment.\u003c/p\u003e \u003cp\u003eAgronomic efficiency (AE) calculated as the ratio of the difference in grain yield with and without nitrogen application divided by the total applied \u003cb\u003e(\u003c/b\u003eDuan et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAgronomic efficiency (AE, kg kg-1) =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{{Y}_{N }-{Y}_{O}}{{A}_{N}}\\)\u003c/span\u003e\u003c/span\u003e 5\u003c/p\u003e \u003cp\u003eWhere Y\u003csub\u003eN\u003c/sub\u003e is the grain yield from treatments with nitrogen fertilizer, Y\u003csub\u003e0\u003c/sub\u003e is the grain yield without N fertilizer treatment, A\u003csub\u003eN\u003c/sub\u003e is the amount of N fertilizer applied, and UN is the total N uptake by the plant.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEconomic analysis\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eThe results of each combination of plant density (175 (PD\u003csub\u003e1\u003c/sub\u003e), 225 (PD\u003csub\u003e2\u003c/sub\u003e), 275 (PD\u003csub\u003e3\u003c/sub\u003e), 325 (PD\u003csub\u003e4\u003c/sub\u003e), 375 (PD\u003csub\u003e5\u003c/sub\u003e), and 425 (PD\u003csub\u003e6\u003c/sub\u003e) plants m\u003csup\u003e− 2\u003c/sup\u003e) and nitrogen fertilizer rates of 0 (control) (N\u003csub\u003e0\u003c/sub\u003e), 50 (N\u003csub\u003e1\u003c/sub\u003e), 100 (N\u003csub\u003e2\u003c/sub\u003e), 150 (N\u003csub\u003e3\u003c/sub\u003e), 200 (N\u003csub\u003e4\u003c/sub\u003e), 250 (N\u003csub\u003e5\u003c/sub\u003e), 300 (N\u003csub\u003e6\u003c/sub\u003e), and 350 (N\u003csub\u003e7\u003c/sub\u003e) kg ha\u003csup\u003e− 1\u003c/sup\u003e were also evaluated for economic feasibility using the mean–Gini dominance analysis \u003cb\u003e(\u003c/b\u003eBuccola and Subaei \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1984\u003c/span\u003e \u003cb\u003e).\u003c/b\u003e The evaluation procedure of the seasonal analysis program calculates the monetary return for each treatment based on the highest economic return Gini coefficient (GC). The gross margin (US\u003cspan\u003e$\u003c/span\u003e t\u003csup\u003e− 1\u003c/sup\u003e) for each combination of treatments was determined using the following equation\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eGM = Y x P-N x C-V\u003c/p\u003e \u003cp\u003eWhere GM is the gross margin, Y is the simulated durum wheat grain yield (kg ha\u003csup\u003e− 1\u003c/sup\u003e), P is the price of durum wheat (545.23 US\u003cspan\u003e$\u003c/span\u003e kg\u003csup\u003e− 1\u003c/sup\u003e) average of the last four years, 2017, 2018, 2019, and 2020), N = nitrogen application rate (kg ha\u003csup\u003e− 1\u003c/sup\u003e) per treatment, C = the cost of nitrogen fertilizer (0.82US \u003cspan\u003e$\u003c/span\u003ekg\u003csup\u003e− 1\u003c/sup\u003e), and V is the base production cost (887.00US \u003cspan\u003e$\u003c/span\u003eha\u003csup\u003e− 1\u003c/sup\u003e). The base production cost, durum wheat grain, and nitrogen fertilizer prices were obtained from the Economic Survey of the Debre Zeit Agriculture Research Center of Ethiopia (unpublished data).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Result and discussion","content":"\u003ch2\u003eModel calibration\u003c/h2\u003e\u003cp\u003eThe genetic coefficients of Utuba were calibrated using the GLUE method. The values of the seven genetic coefficients that determined the vegetative and growth stages (P1D, P1D, P5, and PHINT), and grain characteristics (G1, G2, and G3) are presented in Table\u0026nbsp;(2). The genetic coefficients were estimated sequentially: first vegetative, followed by grain characteristics, using 10,000 rounds of the GLUE method. Li et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) \u003cb\u003eand\u003c/b\u003e He et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) used the GLUE method to accurately estimate the genetic coefficients of the DSSAT-CERES-Wheat and Maize models for winter wheat production in Beijing, China, and for sweet corn production in northern Florida, USA, respectively.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCalibrated genetic coefficient of \u003cem\u003eUtuba\u003c/em\u003e used with CERES-Wheat model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCultivar traits\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenetic coefficient\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDays, optimum vernalization temperature, required for completed vernalization\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP1V\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ed\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhotoperiod response (% reduction in rate/ 10 h drop in photoperiod\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP1D\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.7\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrain filling (excluding lag) phase duration\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003eO\u003c/sup\u003eC. d\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e577\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKernel number per unit canopy weight at anthesis\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eno. g\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard kernel size under optimum condition\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg d\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard, non-stressed mature tiller weight (incl grain)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eg\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThermal time between successive leaf tip appearances\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePHINT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003eO\u003c/sup\u003eC d\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eDurum wheat phenology\u003c/h2\u003e\u003cp\u003eThe calibrated CERES-Wheat model accurately simulated durum wheat phenology (anthesis and maturity date), and the values are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The model predicted the dates from sowing to anthesis with a 0 (zero) difference and dates from sowing to maturity with a difference of 2 days (107 and 109 dates) between the simulated and measured values (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The four statistical indicators via root mean error square (RMSE), normalized RMSE (nRMSE), model efficiency (E), and index of agreement (d) were used to evaluate the simulated and measured values of the anthesis and maturity dates. The statistics were indicated in an excellent agreement, with RMSE, nRMSE, E, and d values of 1.00 days, 1.61%, 0.92, 0.85 and 1.41 days, 1.31%, 0.99 and 0.91, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Similarly, a close agreement was observed between the simulated and observed anthesis and maturity dates \u003cb\u003e(\u003c/b\u003eAraya et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rettie et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The model simulated anthesis and maturity dates with RMSE and nRMSE lower than 2 and d values greater than 0.85, respectively, for wheat in Ethiopia. In general, their results confirmed that the calibrated DSSAT-CERES-wheat model is appropriate and convenient for the anthesis and maturity dates of durum wheat in the central highlands of Ethiopia.\u003c/p\u003e\u003ch2\u003eLeaf area index and top weight\u003c/h2\u003e\u003cp\u003eThe simulated and measured values of LAI and top weight at four Zadoks growth stages (GS24 (tillering), GS40 (booting), GS60 (flowering) and GS80 (grain filling) during the experimental years of 2017, 2018 and 2019 were considered satisfactory because all results were within the acceptable range of statistics with the value of RMSE ranged from 0.48 to 0.66 m\u003csup\u003e2\u003c/sup\u003e m\u003csup\u003e− 2\u003c/sup\u003e, nRMSE from 19 to 27%, d value ranged from 0.93 to 97 for LAI (Fig.\u0026nbsp;3A, B, C). The value of RMSE for top weight ranged from 582 to 1228 kg ha\u003csup\u003e− 1\u003c/sup\u003e, the value of nRMSE ranged from 9.4 to 21.2%, the E value ranged from 0.96 to 0.98 and the d value ranged from 0.98 to 0.99 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, E, F). The simulation quality of LAI for the durum wheat growth stages was well matched for 2019 and 2017 with an nRMSE of 16 and 19% and d value of 0.95 and 0.97, respectively, whereas in 2018, the nRMSE and d values were 36% and 0.93, respectively (Fig.\u0026nbsp;3). The top weight simulation resulted in nRMSE was 9% and d value was 0.99 in the year 2019. The nRMSE (21%) and d value (0.97) were for 2017, and the nRMSE (17%) and d values (0.98) were for 2018. The lower values for nRMSE and higher d-values close to one revealed that the model simulated the LAI and top weight quite well.\u003c/p\u003e\u003cp\u003eThe evaluation statistics of the simulated and measured leaf area index (LAI) at the grain filling stage and the top weight at the final harvest are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The simulated LAI and top weight values were in very good agreement with the measured LAI and top weight under optimum plant density (225 plants m\u003csup\u003e− 2\u003c/sup\u003e) and sufficient nitrogen fertilizer (200 kg ha\u003csup\u003e− 1\u003c/sup\u003e N). The RMSE, nRMSE, E, and d values of the simulated and measured LAI (0.57 m\u003csup\u003e2\u003c/sup\u003em\u003csup\u003e− 2\u003c/sup\u003e, 11.18, 0.86, and 0.95, respectively) and top weight (1.5 10\u003csup\u003e3\u003c/sup\u003e kg ha\u003csup\u003e− 1\u003c/sup\u003e, 12.31%, 0.92, and 0.96, respectively) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This result indicates that the calibration of the CERES-Wheat model exhibited acceptable levels.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical evaluation of simulated (S) and observed (O), root-mean-square error (RMSE), normalized RMSE (NRMSE), model efficiency (E) and index of agreement (d) value for phenology (anthesis and maturity date) and leaf area index at (grain filling stage) and top weight, harvest index, and grain yield at final harvest for model calibration of durum wheat cultivar, Utuba (n = 3)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eO\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNRMSE\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ed\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnthesis date\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaturity date\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrain yield (10\u003csup\u003e3\u003c/sup\u003ekg ha\u003csup\u003e− 1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4346\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4373\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHarvest index (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAI (m\u003csup\u003e2\u003c/sup\u003e m\u003csup\u003e− 2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTop weight (10\u003csup\u003e3\u003c/sup\u003e kg ha\u003csup\u003e− 1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12296\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12695\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.31\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eWheat grain yield and harvest index\u003c/h2\u003e\u003cp\u003eThe values of the evaluation statistics of simulated and measured grain yield (GY) and harvest index (HI) at the final harvest are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The model accurately simulated GY and HI, with a simulated/measured GY of 4346/4373 kg ha\u003csup\u003e− 1\u003c/sup\u003e and HI of 0.38/0.37% under normal plant density (225 kg ha\u003csup\u003e− 1\u003c/sup\u003e) and nitrogen fertilizer (200 kg ha\u003csup\u003e− 1\u003c/sup\u003e) supply. The CERES-Wheat model slightly underestimated the grain yield and over-predicted the harvest index compared with the measured grain yield and harvest index (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The statistical indicators also indicated good agreement between the simulated and measured values for both GY and HI, with RMSE, nRMSE, E, and d values of 500 kg ha\u003csup\u003e− 1\u003c/sup\u003e, 11.50%, 0.98, 0.89, and 0.02%, 5.40%, 0.94, and 0.98, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The normalized RMSE and d index between the simulated and measured values were in “good agreement” when ≤ 15% and “excellent” d \u0026gt; 0.9 \u003cb\u003e(\u003c/b\u003eLiu Hai-long et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e Similarly, the statistical indicators confirmed that the model predicted wheat grain yield and harvest index reasonably well, with respective nRMSE and d-index of 7% and 0.82 \u003cb\u003e(Ishaqque et al. 2020;\u003c/b\u003e Rettie et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Overall, the comparison between the measured and simulated data shows a reasonably good calibration of the CERES-Wheat model for anthesis date, maturity date, in-season LAI and top weight, grain yield, top weight, and harvest index at the final harvest. Thus, these results confirmed that the calibration CERES-Wheat model was suitable for simulating phenology, LAI, top weight, grain yield, and harvest index for long-term prediction.\u003c/p\u003e\u003ch2\u003eModel verification\u003c/h2\u003e\u003cp\u003eThe model was further verified with the experimental data collected during 2020, under four plant densities (PD\u003csub\u003e1\u003c/sub\u003e, PD\u003csub\u003e2\u003c/sub\u003e, PD\u003csub\u003e3\u003c/sub\u003e and PD\u003csub\u003e4\u003c/sub\u003e plants m\u003csup\u003e− 2\u003c/sup\u003e) and four nitrogen (N\u003csub\u003e0,\u003c/sub\u003e N\u003csub\u003e1\u003c/sub\u003e, N\u003csub\u003e2\u003c/sub\u003e and N\u003csub\u003e3\u003c/sub\u003e kg ha\u003csup\u003e− 1\u003c/sup\u003eN) rates. The CERES-Wheat model slightly overpredicted the LAI (5.1 m\u003csup\u003e2\u003c/sup\u003e m\u003csup\u003e− 2\u003c/sup\u003e) compared to the measured LAI (3.4 m\u003csup\u003e2\u003c/sup\u003e m\u003csup\u003e− 2\u003c/sup\u003e). The simulated LAI at the grain filling stage under rain-fed condition was comparatively less satisfactory, with an nRMSE (21%) and d index (0.82) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). A possible reason might be that low soil moisture availability at the grain-filling stage resulted in a higher difference between the simulated and measured LAI values. The simulated and measured top weights at the final harvest matched well, with nRMSE and d values of 9% and 0.99, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). This result suggests that the top weight was better than the LAI variable, which is consistent with the results of the previous studies by \u003cb\u003eWu and Ma (2013) and\u003c/b\u003e Ishaque et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTime-serious simulated and measured LAI and top weight of durum wheat are set out in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The simulated trends of LAI and tops weight at growth stages (GS24 (tillering), GS40 (booting), GS60 (flowering), and GS80 (grain filling)) for different plant density (PD\u003csub\u003e1\u003c/sub\u003e, PD\u003csub\u003e2\u003c/sub\u003e, PD\u003csub\u003e3,\u003c/sub\u003e and PD\u003csub\u003e4\u003c/sub\u003e) plants m\u003csup\u003e− 2\u003c/sup\u003e and nitrogen rates (N\u003csub\u003e0,\u003c/sub\u003e N\u003csub\u003e1\u003c/sub\u003e, N\u003csub\u003e2,\u003c/sub\u003e and N\u003csub\u003e3\u003c/sub\u003e kg ha\u003csup\u003e− 1\u003c/sup\u003e) are presented in Fig. LAI (a-p) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e) and top weight (i-xv) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The trend observed between the simulated and measured of LAI showed a good agreement with those of measured LAI. The statistical values of nRMSE and d-index between simulated and measured LAI for different treatment combinations ranged from 10 to 31 and 0.80 to 0.99 in respective order (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Similarly, the trend between simulated and measured top weight was also closely linked with nRMSE and d-value which ranges from 7 to 37% and 0.93 to 0.98, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Most treatment combinations except PD\u003csub\u003e1\u003c/sub\u003e + N\u003csub\u003e2\u003c/sub\u003e, PD\u003csub\u003e3\u003c/sub\u003e + N\u003csub\u003e2,\u003c/sub\u003e and PD\u003csub\u003e4\u003c/sub\u003e + N\u003csub\u003e2\u003c/sub\u003e showed low values for nRMSE and high d-values compared to the nRMSE value of 25, 31.6 and 29.6% for LAI and 30.3, 30.5, and 37.6% for tops weight, respectively. Higher nRMSE indicated that LAI and top weight was comparatively low to predict the model. While the other treatment combinations indicated low nRMSE and high d-values was close related and precisely predict LAI and top weight. Overall, simulated and measured LAI and top weight in good agreement which showed that the calibrated DSSAT-CERES-wheat model could simulate the LAI and top weight of durum wheat in Ethiopian conditions.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe root means square error (RMSE), normalized RMSE (NRMSE), model efficiency (E), and d-value leaf area index (LAI), and top weight of durum wheat as affected by plant density and N fertilizer rates in 2020 cropping season\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eLeaf area index (LAI)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eTops weight\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003cp\u003e(kg/ha)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enRMSE\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ed-index\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003cp\u003e(kg/ha)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003enRMSE\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ed-index\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e175 plant m\u003csup\u003e− 2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0 kg ha\u003csup\u003e− 1\u003c/sup\u003e N\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100 kg ha\u003csup\u003e− 1\u003c/sup\u003e N\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1237.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e150 kg ha\u003csup\u003e− 1\u003c/sup\u003e N\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1575.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e200 kg ha\u003csup\u003e− 1\u003c/sup\u003e N\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e494.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e225 plant m\u003csup\u003e− 2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0 kg ha N\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e173.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100 kg ha\u003csup\u003e− 1\u003c/sup\u003e N\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1203.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e150 kg ha\u003csup\u003e− 1\u003c/sup\u003e N\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e602.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e200 kg ha\u003csup\u003e− 1\u003c/sup\u003e N\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e960.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e275 plant m\u003csup\u003e− 2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0 kg ha\u003csup\u003e− 1\u003c/sup\u003e N\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e129.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100 kg ha\u003csup\u003e− 1\u003c/sup\u003e N\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e496.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e150 kg ha\u003csup\u003e− 1\u003c/sup\u003e N\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1577.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e200 kg ha\u003csup\u003e− 1\u003c/sup\u003e N\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1127.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e325 plant m\u003csup\u003e− 2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0 kg ha\u003csup\u003e− 1\u003c/sup\u003e N\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e130.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100 kg ha\u003csup\u003e− 1\u003c/sup\u003e N\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e812.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e150 kg ha\u003csup\u003e− 1\u003c/sup\u003e N\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1948.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e200 kg ha\u003csup\u003e− 1\u003c/sup\u003e N\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e768.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eGrain yield\u003c/h2\u003e\u003cp\u003eThe simulated grain yields (GY) are similar to the measured grain yields (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003ea) with RMSE, nRMSE, and d values of 0.614 t ha\u003csup\u003e− 1\u003c/sup\u003e, 18%, and 0.96 for model evaluation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003ea). The simulated trends in grain yield under different planting densities and N fertilizer rates were in good agreement with those of the measured GY. However, the model slightly overestimated the grain yield compared to the measured yield in all treatment combinations, except for all plant densities under the no N fertilizer treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eb). The accumulation of high biomass growth observed at early growth in good rain followed by a dry period after termination of rain probably enhanced rapid, quick senescence, reduced sink source relation, grain formation, and all contributed to the final yield reduction (\u003cb\u003eGiuntaeal 1993;\u003c/b\u003e Araya et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In contrast, the model predicted grain yield slightly lower than the measured grain yield in treatment combinations for no N fertilizer across all plant populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eb). Similarly, in the present study the grain yield predication obtained compared to previous the CERES-Wheat model showed poor performance using nitrogen than no N \u003cb\u003e(\u003c/b\u003eLi et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Liu Hai-long et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e In Durum wheat, the combination of PD\u003csub\u003e4\u003c/sub\u003e + N\u003csub\u003e3\u003c/sub\u003e had greater grain yield than the no N treatment an across all plant densities (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eb). However, no significant difference was observed between PD\u003csub\u003e2\u003c/sub\u003e + N\u003csub\u003e3\u003c/sub\u003e and PD\u003csub\u003e4\u003c/sub\u003e + N\u003csub\u003e3\u003c/sub\u003e treatment combinations (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eb). The GY of durum wheat under different plant densities and N fertilizers varied compared to different treatment combinations for simulation/measured GY which ranged from 1260/1380 under treatment combinations of PD\u003csub\u003e4\u003c/sub\u003e + N\u003csub\u003e0\u003c/sub\u003e to 6304/5790 kg ha\u003csup\u003e− 1\u003c/sup\u003e in PD\u003csub\u003e4\u003c/sub\u003e + N\u003csub\u003e4\u003c/sub\u003e. In general, good agreement between simulated and measured GY values showed that the calibrated CERES-Wheat model could simulate GY of durum wheat very well in the study.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003ch2\u003eModel applications\u003c/h2\u003e\u003cp\u003eThe analysis to determine the optimum plant density and nitrogen fertilizer rate for durum wheat cultivation in the central highlands of Ethiopia was conducted using the CRESE-wheat model. The CERES-Wheat model was used to simulate grain yield for durum wheat in 48 different treatment combinations, with six plant densities ranged from 175 to 425 plants m\u003csup\u003e− 2\u003c/sup\u003e and eight nitrogen fertilizer rates ranging from 0 (control) to 350 kg ha\u003csup\u003e− 1\u003c/sup\u003e using 37 (1985 to 2022) years of historical daily weather data under the rain-fed farming system. The 37-year average grain yield increased with increasing nitrogen fertilizer rates from 100 to 250 kg ha\u003csup\u003e− 1\u003c/sup\u003e N as the plant density increased from 175 to 275 plants m\u003csup\u003e− 2\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Under 0 (control) kg ha\u003csup\u003e− 1\u003c/sup\u003e N fertilization, the 37-year average grain yield decreased from 1632 kg ha\u003csup\u003e− 1\u003c/sup\u003e to 765 kg ha\u003csup\u003e− 1\u003c/sup\u003e as plant density increased from 175 to 425 plants m\u003csup\u003e− 2\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e). These results indicate that, under no N fertilization, plant density had a negative effect on grain yield in populations above 175 plants m\u003csup\u003e− 2\u003c/sup\u003e. Under high plant density, soil nutrient depilation was high and exacerbated when the soil was incapable of supplying nutrients during crop growth, resulting in decreased grain yield.\u003c/p\u003e\u003cp\u003eFrom the simulated results, the highest (6328 kg ha\u003csup\u003e− 1\u003c/sup\u003e) grain yield was predicted at a nitrogen rate of 250 kg ha\u003csup\u003e− 1\u003c/sup\u003e interaction with 275 plants m\u003csup\u003e− 2\u003c/sup\u003e, compared to all combinations of nitrogen rate and plant density (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e). No further increases in the 37-year averaged grain yield were observed when the plant density exceeded 275 plants m\u003csup\u003e− 2\u003c/sup\u003e under all nitrogen fertilizer rates. The 37-year simulated average yield increased with increasing plant density and nitrogen fertilizer until a critical point (250 kg ha\u003csup\u003e− 1\u003c/sup\u003e N and 275 plants m\u003csup\u003e− 2\u003c/sup\u003e), and then declined with further increases in both nitrogen and plant density. Thus, a nitrogen rate of 250 kg ha\u003csup\u003e− 1\u003c/sup\u003e and a plant density of 275 plants m\u003csup\u003e− 2\u003c/sup\u003e maximized the grain yield for the study site under rain-fed conditions. Durum wheat yield increased when plant density and nitrogen fertilizer increased until a critical point, mainly due to better uptake of resources, especially nitrogen and soil water, while maintaining an optimum spike density. However, as nitrogen nutrient and plant density increased beyond these critical points (250 kg ha\u003csup\u003e− 1\u003c/sup\u003e and 275 plants m\u003csup\u003e− 2\u003c/sup\u003e), the yield declined mainly because of increased competition for resources, especially soil water and solar radiation. Under rainfed conditions (water-limited), crop yield is also limited by soil water and solar radiation. Nitrogen nutrients and plant density increased beyond the critical point, and the photosynthetic characteristics of the plant declined, resulting in lower crop photosynthetic assimilation and yield productivity per plant, which might explain the decrease in durum wheat yield observed in this simulated scenario at high nitrogen and plant density \u003cb\u003e(\u003c/b\u003eZhang et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Zhang et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported that plant density exceeded the critical point, and solar radiation became the yield-limiting factor, especially under high-nitrogen conditions. The simulated results indicated that pursuing high nitrogen fertilizer and plant density are not desirable strategies in the rainfed farming system, whereas the relatively optimum nitrogen and plant density may be more conducive to the effective use of resources in the rainfed farming system for yield sustainability.\u003c/p\u003e\u003ch2\u003eAgronomic use efficiency\u003c/h2\u003e\u003cp\u003eThe agronomic use efficiency (AUE) and partial factor productivity (PFP) in response to plant density and nitrogen fertilizer rate are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003e. The simulation results showed that the response of AE to plant density and nitrogen rate was linear. For all nitrogen fertilizer rates except the control (zero), the AUE of durum wheat increased as plant density increased. However, when the plant density exceeded 275 plants m\u003csup\u003e− 2\u003c/sup\u003e along with the nitrogen rate, the AUE tended to decrease. The rate of 350 kg N ha\u003csup\u003e− 1\u003c/sup\u003e at all plant densities resulted in the lowest AUE; however, the dominant AUE was 200 kg ha\u003csup\u003e− 1\u003c/sup\u003e N at all plant densities (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003e). The treatment combination of 200 kg ha\u003csup\u003e− 1\u003c/sup\u003e and 275 plants m\u003csup\u003e− 2\u003c/sup\u003e followed by 250 kg ha\u003csup\u003e− 1\u003c/sup\u003e and 275 plants m\u003csup\u003e− 2\u003c/sup\u003e was given the highest AUE among the 48 simulated scenarios. The rate of 200 kg ha\u003csup\u003e− 1\u003c/sup\u003e and under all plant densities produced AUE ranging from 24.7 to 28.2% (average 27%) and 250 kg ha\u003csup\u003e− 1\u003c/sup\u003e and under all plant densities, AUE ranged from 24.5 to 27.7 (average 26.4). Owing to this finding, increased two-level plant density from a lower level (175 plants m\u003csup\u003e− 2\u003c/sup\u003e) under a 200 kg ha\u003csup\u003e− 1\u003c/sup\u003e N rate can enhance nitrogen use efficiency. This is because increasing plant density enhances nitrogen and nutrient uptake and utilization efficiency by the roots of durum wheat and accelerates the transfer of nitrogen nutrients from the roots to stems and leaves \u003cb\u003e(\u003c/b\u003eLiu et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), cumulatively increasing nitrogen use efficiency \u003cb\u003e(\u003c/b\u003eDai et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Hence, increased durum wheat plant density, along with nitrogen fertilizers up to a certain level, is important for reducing nitrogen fertilizer losses and environmental risks while increasing grain yield \u003cb\u003e(\u003c/b\u003eTian et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Once the optimum plant density and nitrogen fertilizer rates were exceeded, both grain yield and AUE decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eEconomic analysis\u003c/h2\u003e\u003cp\u003eThe monetary return of US\u003cspan\u003e$\u003c/span\u003e ha\u003csup\u003e− 1\u003c/sup\u003e for all 48 scenarios is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e11\u003c/span\u003e. Strategic analysis was used to identify the best strategic treatment combinations for sustainable durum wheat production. In the simulation scenarios, at all plant densities with no nitrogen fertilizer application, the 37-year monetary returns showed negative profit (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e11\u003c/span\u003e). However, all other nitrogen rates monetary return increased and positive with increasing plant density from 175 up to 275 plants m\u003csup\u003e− 2\u003c/sup\u003e, then decline with further increases in plant density beyond 275 plants m\u003csup\u003e− 2\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e11\u003c/span\u003e). The result of the strategic analysis of monetary returns \u003cspan\u003e$\u003c/span\u003eha\u003csup\u003e− 1\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e11\u003c/span\u003e) showed that the treatment combination of 250 kg ha\u003csup\u003e− 1\u003c/sup\u003e and 275 plants m\u003csup\u003e− 2\u003c/sup\u003e was the highest among the 48 simulated scenarios. These results are consistent with those of another study that applied the CERES-wheat model \u003cb\u003e(\u003c/b\u003eAraya et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This study, which was conducted in the northern part of Ethiopia, showed that the treatment in which 160 kg ha\u003csup\u003e− 1\u003c/sup\u003e was applied was dominant compared to other rates. However, their study was conducted under irrigation conditions using bread wheat cultivars and environments than in our study, and the lowest N application rate was 160 kg ha\u003csup\u003e− 1\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe CERES-Wheat model was calibrated, evaluated, and used to determine the optimum plant density and nitrogen fertilizer rate for durum wheat cultivation under rainfed condition. The results for model calibration under optimum input treatment experiments and evaluation under variable plant density and N rates showed that the model between simulated and measured values for phenology, leaf area index, top weight, grain yield, and harvest index were in good agreement. However, the model slightly overestimated the predicted grain yield compared to the measured yield in the N treatment compared to the unfertilized treatment at all plant densities. Simulation scenarios were tested using 37 years of historical weather data, and seasonal analysis showed how to better optimize plant density and N fertilizer rate to optimize yield, nitrogen use, and economic return. Based on the seasonal analysis of a 37-year simulation, the optimal combination of planting density and nitrogen management was found to be 275 plants m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e with 250 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e N. The simulation results suggested that a plant density of 275 plants m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e with an N application of 250 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e increased grain yield, improved nitrogen use, and produced the highest economic return while minimizing environmental risk under rainfed conditions. The approach used and the results generated in this study can help develop the best management strategies for advancing crop yield and nitrogen use with the highest economic return while reducing the negative effect on the environment. However, the model was tested and simulated under rainfed conditions. It is advisable to undertake future research to test and simulate the model under irrigation conditions for further application of the model.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe we declare that they have no competing interests\u0026quot; in this section.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eEthiopian Institute of Agriculture Research (EIAR) and partly by Agriculture Growth Program Phase two (AGPII).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBizuwork Tafes Desta conceived the project, set scientific objectives. Bizuwork Tafes and Sisay Eshetu contributed to preparing the field experiment and data a question. Bizuwork Tafes wrote the manuscript. Dr. Alemayehu Zemede and Almaz Mesert manuscript review and editing. Bizuwork Tafes was a\u0026nbsp;major contributor in writing the manuscript.\u0026nbsp;All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge Debre Zeit Agricultural Research Center for allowing the study some vehicle service and financial process. We would like to thank the Ethiopian National Meteorology Agency (NMA), Agricultural office in Minjar Shenkora district and Natural Resources Research program at Debre Zeit Research Center laboratory for lab analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbera Tolera, Semu Ernest, Tolosa Debele, Dagen Wegary Kim H (2015) Effects of faba bean break crop and N rates on subsequent grain yield and nitrogen use efficiency of highland maize varieties in Toke Kutaye, western Ethiopia. American Journal of Research Communication 3(10): 32-72. \u003c/li\u003e\n\u003cli\u003eAgbahey JUI, Grethe H, Negatu W (2015) Fertilizer supply chain in Ethiopia: structure, performance and policy analysis. 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Field Crops Research 267:108145. https://doi.org/10.1016/j.fcr. 2021.108145.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"durum wheat, nitrogen fertilizer, plant density, seasonal analysis","lastPublishedDoi":"10.21203/rs.3.rs-4411924/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4411924/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eLow crop management practices were the key factors that leads to a significant reduction in durum wheat yield in the central highlands of Ethiopia. The aim of this study was to determine optimum plant density and nitrogen rate that increase durum wheat productivity while reducing environmental impacts. A combination of data from field experiments conducted from 2017 to 2020 under rainfed conditions and simulation data of CERES-Wheat model were used for this study. The CERES-Wheat model was calibrated for Utuba cultivar from three-years (2017 to 2019) field experiment data. The model was further verified with the experimental data conducted during the 2020 cropping season under four plant densities and four nitrogen fertilizer rates. Differences in temperature and rainfall patterns during the potential growing season, seasonal analysis was used to determine the optimum plant density and N rate using 37 years (1985\u0026ndash;2022) of historical weather data. The simulation results suggested that 275 plants m\u003c/em\u003e \u003csup\u003e \u003cem\u003e\u0026minus;\u0026thinsp;2\u003c/em\u003e \u003c/sup\u003e \u003cem\u003ewith an application of 250 kg ha\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u0026thinsp;1\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eN increased grain yield, improved nitrogen use, and produced the highest economic return while minimizing environmental risk under rainfed conditions. Compared with the current plant density (175 plants m\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u0026thinsp;2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e) and N fertilizer (100 kg ha\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u0026thinsp;1\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e), plant density (275 plants m\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u0026thinsp;2\u003c/em\u003e\u003c/sup\u003e \u003cem\u003ewith 250 kg ha\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u0026thinsp;1\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eN) rate increased grain yield by about 49%, N use efficiency by 23% with the highest net return (2114 US$ ha\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u0026thinsp;1\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e). In general, this study showed that the CERES-Wheat model can be a promising tool for providing crop management recommendations under rainfed durum wheat farming.\u003c/em\u003e\u003c/p\u003e","manuscriptTitle":"Determining optimum plant density and nitrogen rate using field experiment and model simulation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-23 11:08:43","doi":"10.21203/rs.3.rs-4411924/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ff48f28b-5a83-420a-b760-bf4894c70225","owner":[],"postedDate":"May 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-23T11:08:43+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-23 11:08:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4411924","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4411924","identity":"rs-4411924","version":["v1"]},"buildId":"pf3fE39SIOqb-0xH_OWvX","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}