Evaluation of APSIM next generation for simulating winter wheat growth, yield response to nitrogen, and nitrogen dynamics

Pinto, Jose G. C. P.; Balboa, Guillermo R.; Mueller, Nathan D.; Slater, Glen; Frels, Katherine; dos Santos, Caio L.; Miguez, Fernando E.; Lollato, Romulo P.; Puntel, Laila A.

doi:10.3389/fagro.2025.1740421

ORIGINAL RESEARCH article

Front. Agron., 19 January 2026

Sec. Climate-Smart Agronomy

Volume 7 - 2025 | https://doi.org/10.3389/fagro.2025.1740421

Evaluation of APSIM next generation for simulating winter wheat growth, yield response to nitrogen, and nitrogen dynamics

Jose G. C. P. Pinto^1*

Guillermo R. Balboa^1*

Nathan D. Mueller²

Glen Slater³

Katherine Frels¹

Caio L. dos Santos⁴

Fernando E. Miguez⁴

Romulo P. Lollato⁵

Laila A. Puntel^1,6*

¹Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States
²Mueller Ag Consulting, Lincoln, NE, United States
³South Central Agricultural Laboratory, University of Nebraska-Lincoln, Harvard, NE, United States
⁴Department of Agronomy, Iowa State University, Ames, IA, United States
⁵Department of Agronomy, Kansas State University, Manhattan, KS, United States
⁶Syngenta Group, Basel, Switzerland

Introduction: Process-based crop models such as the Agricultural Production Systems sIMulator Next Generation (APSIM-NG) can simulate crop growth, phenology, and yield under diverse environmental and management conditions, supporting climate-smart agriculture strategies aimed at improving productivity and resilience. However, accurate calibration and validation are required to ensure reliable predictions across cultivars and nitrogen (N) management scenarios.

Methods: We evaluated APSIM-NG performance for simulating winter wheat cultivar responses to N rate using field experiments conducted in Nebraska during the 2020/21 and 2021/22 growing seasons. Trials followed a randomized complete block design with two cultivars (LCS and WB), four N rates (0, 56, 112, and 168 kg N ha⁻¹), and three replications. Observations included phenology, grain yield, protein content, shoot biomass, carbon-to-nitrogen ratio, soil nitrate and ammonium, soil moisture, and weather variables. Model calibration targeted cultivar-specific phenology, biomass, yield, and protein content. Validation was conducted using grain yield data from 29 site-year combinations across five Nebraska counties spanning six growing seasons (2017–2022). Model accuracy was evaluated using RMSE, RRMSE, and mean bias error.

Results: Calibration improved model performance, with well to moderate accuracy for phenology (RRMSE = 2.1–2.2%; RMSE = 3–5 days), grain yield (15–24%), protein content (8–11%), and grain N uptake (11–13%). APSIM-NG moderately captured cultivar differences in leaf N uptake, with RRMSE values of 27% for LCS and 33% for WB. Validation results showed good performance for grain yield in both cultivars (RRMSE = 14% for LCS and 19% for WB). Yield response to N was simulated well for LCS (RRMSE = 18% at the economic optimum N rate) and moderately for WB (32%).

Discussion: Overall, APSIM-NG demonstrated well to moderate performance in simulating phenology, yield, and grain N dynamics across winter wheat cultivars. These results highlight the model’s utility for evaluating N management strategies and supporting climate-smart decision-making aimed at improving nitrogen use efficiency and adaptation to climate variability in wheat systems.

1 Introduction

Feeding an estimated 9 billion people by 2050 highlights the need for sustainable agricultural practices to increase yield in current cropland (FAOSTAT, 2021). Wheat is pivotal in global food security, distinguished by its status as the crop with the largest harvested area globally (Fischer et al., 2014) and accounting for ~20% of human consumed calories and protein (Shewry and Hey, 2015). As a nitrogen-intensive crop, wheat relies on adequate nitrogen (N) availability to achieve optimal yields. However, inefficient N use can lead to environmental pollution and increased production costs (Anas et al., 2020), contributing to greenhouse gas emissions. This challenge is amplified by climate change, which threatens wheat yields through increased heat and drought stress (Zampieri et al., 2017). Therefore, there is a pressing need for climate-smart agriculture (CSA) practices that enhance productivity and resilience while reducing the environmental footprint of agriculture (Lipper et al., 2014; Paustian et al., 2016). This is particularly relevant in the United States (US), where in Nebraska (NE), winter wheat stands as the third most cultivated crop, occupying close to 365,000 hectares in 2022 (USDA-NASS, 2022).

One of the key principles of CSA is the optimization of management practices, particularly nutrient management, to improve resource use efficiency and adapt to changing climatic conditions (Shanker et al., 2024). Process-based crop models support this goal by informing management decisions, predicting crop growth, evaluating environmental impacts, testing management strategies, and guiding breeding efforts (Keating et al., 2003). When combined with climate projections, these models can explore strategies to mitigate climate change’s effects and reduce agricultural production’s environmental footprint (Hao et al., 2021). Additionally, crop models can accurately capture the unique growth dynamics of different cultivars across varying environmental conditions (Asseng et al., 2015).

Crop models have the potential to integrate knowledge of soil, weather, and crop physiology to simulate complex interactions, helping researchers and farmers make informed decisions (Jones et al., 2003; Boote et al., 2010). However, current crop modeling efforts often overlook the unique growth dynamics of different wheat cultivars, limiting their effectiveness in predicting yield and growth (Chenu et al., 2017). For instance, different wheat cultivars exhibit varying N requirements, impacting nutrient use efficiency and optimal N management strategies (Hirel et al., 2007). This is particularly evident in traits such as phenology (Mirosavljević et al., 2024), N uptake dynamics (Lollato et al., 2021), phenotypic plasticity (Giordano et al., 2024), and N use efficiency (Hawkesford, 2017; Hawkesford and Riche, 2020). These cultivar-specific traits underscore the importance of tailored N management strategies to optimize yields across diverse environmental conditions. Understanding these cultivar-specific parameters can improve the accuracy in determining winter wheat’s yield potential and resource use, including nutrient and water dynamics (Asseng et al., 2015; Mirosavljević et al., 2024). Such advancements could enhance farmers’ economic returns and mitigate environmental risks associated with soil degradation, nutrient runoff, and leaching (Tilman et al., 2002).

Determining the Economic Optimum Nitrogen Rate (EONR) is an alternative to balance productivity and sustainability in crop production. The EONR is the rate at which crop yield increase is not large enough to justify economic increases in N fertilizer rate (Puntel et al., 2018). Estimating EONR with accuracy and precision (Miguez and Poffenbarger, 2022) is essential to prevent environmental losses, which rise considerably when N applications exceed the crop’s requirements (Pasley et al., 2021; Tamagno et al., 2022). Nitrogen management strategies can be fine-tuned for economic and environmental aspects by estimating EONR from mechanistic crop simulation models like APSIM-NG.

APSIM-NG has been adopted into wheat modeling applications and tested in previous studies for simulating crop growth, phenology, and N dynamics across varying environmental conditions. Winter wheat has been validated using APSIM-NG in Japan and Northern Europe. For instance, De Silva et al. (2021) assessed the functionality of APSIM-NG in simulating wheat yield and grain protein response to N application, whereas Kumar et al. (2023) investigated the model’s performance by simulating winter wheat growth and N uptake in Northern Europe. Other studies have focused on the model’s performance in different climatic conditions. For example, Chen et al. (2010) evaluated the application of APSIM-NG to wheat productivity under climate variation and irrigation supply in Northern China. Jeuffroy et al. (2014) observed a need to model traits specific to a given cultivar in Australia. For instance, Chen et al. (2020) and Bai et al. (2022) evaluated wheat yield responses to climate change and explored adaptation strategies, such as adjusting sowing dates and selecting suitable cultivars. While APSIM-NG has been widely studied, most validations have been limited to field-level assessments. At the same time, APSIM-NG has been applied at regional and national scales, particularly in studies such as those by Hochman et al. (2009) and Hunt et al. (2019) in Australia, its validation across diverse environmental conditions remains relatively limited compared to field-level assessments. Therefore, further research is needed to evaluate APSIM-NG’s potential to accurately simulate winter wheat growth and yield under diverse environmental conditions and management practices.

The US Great Plains is the world’s largest contiguous winter wheat growing region (Fischer et al., 2014). Efforts to calibrate and validate mechanistic crop simulation models in this region exist, but they were historically limited to other models such as AquaCrop (Ajaz et al., 2023; Moghbel et al., 2024), DSSAT (Zhang et al., 2008; Attia et al., 2016), EPIC (Chen et al., 2024), and SSM (Lollato et al., 2017; Sciarresi et al., 2019). To our knowledge, the use of APSIM in this region has been limited to the classic model (i.e., Berhe et al., 2017). Calibration and validation ensure that APSIM-NG can predict crop outcomes of phenology, LAI, biomass, yield, and protein across diverse conditions. Hence, confident applications may be carried out in other cropping systems and environments (Keating et al., 2003; Holzworth et al., 2018). By leveraging APSIM-NG, our research aims to bridge this gap and equip farmers with better tools for informed and precise decision-making. Therefore, our objectives were to (i) evaluate the performance of APSIM-NG in simulating winter wheat phenology, shoot biomass, leaf area index (LAI), grain yield, yield response to N, and grain protein across different cultivars (ii) calibrate the model to account for cultivar-specific growth dynamics and validate APSIM-NG’s accuracy in predicting these key parameters under diverse environmental conditions.

2 Materials and methods

2.1 Field experiment for model calibration and validation

Experimental data for model calibration and evaluation was obtained from winter wheat dryland plot trials established at the South-Central Agricultural Laboratory (SCAL) research station located near Clay Center, NE, USA (40° 34′ 30.92″ N, 98° 8′ 16.1664″ W) during the 2020/21, 2021/22, and 2022/23 growing seasons. The 2022/23 trial was established but discontinued due to severe drought conditions combined with biotic stress (e.g., pest pressure), which resulted in minimal crop establishment and near-complete loss of aboveground biomass. The trial was therefore terminated because meaningful field measurements could not be collected. The climate at the site is humid continental (warm, rainy summers) with annual precipitation of 725 mm and a mean temperature of 12°C. Daily weather data were obtained from the Nebraska Mesonet (2024). During the calibration years, growing-season (planting to harvest) precipitation totaled 526 mm in 2020/21 and 381 mm in 2021/22 (Supplementary Table S1). The soil classification was Crete silt loam (fine, smectitic, mesic Pachic Udertic Argiustolls). More details about soil properties and weather are described in Supplementary Table S1.

Field experiments were set in a randomized complete block design with two cultivars differing in phenology, a medium-early (LCS Valiant; here on LCS) and a medium-late (WB4699; here on WB), four N rates (0, 56, 112, and 168 kg ha^-1), and three replications in a randomized complete block design. Experimental units were 3 m long and 2 m wide (6 m²), and the row spacing was 0.23 m. Cultivars with contrasting phenology were selected since they are typically exposed to different growing conditions during the critical period, potentially impacting response to N (Cossani and Sadras, 2021; Sadras et al., 2022). The N rates were applied during spring as broadcasted ammonium nitrate (34-0-0) at Feekes growth stage (GS) 4 (Large, 1954). Winter wheat was planted at 250 seeds m^-2 on 22 September 2020, 8 October 2021, and 10 October 2022 (soybean-wheat-corn rotation). Moreover, light tillage was conducted shortly before wheat planting.

External validation data were obtained from the University of Nebraska-Lincoln (UNL) Winter Wheat Variety Test Results (University of Nebraska-Lincoln, 2022), which include yield data collected from five Nebraska counties evaluated across six growing seasons (2017-2022), totaling 29 site-year combinations. These trials were used to benchmark the model’s accuracy across diverse growing conditions. To capture variability in seasonal water availability, site-years were categorized into three precipitation environments based on total precipitation during the growing season (October-June): low (< 250 mm), medium (250–550 mm), and high (> 550 mm).

2.2 Crop and soil measurements

Crop phenology was recorded based on the Feekes scale (Large, 1954), and plant stand was counted at early stages. Feekes stages were assessed visually using ten randomly selected plants per plot. A plot was recorded at a given stage when at least half of the sampled plants showed the corresponding morphological trait. Stages were determined independently each year based on observed plant development. These field-sampled Feekes stages correspond to key developmental transitions (stem elongation, jointing, booting, heading/flowering, grain filling) that align with the phenology stages simulated by APSIM-NG. Crop and soil samples were collected in-season on five dates, corresponding to Feekes GS 4, 6, 10, 10.5, and 11.2, to quantify aboveground biomass (by plant fraction: stem, leaves, spikes), N uptake, soil nitrate, and ammonium. Aboveground plant biomass was determined by clipping a 0.4 m² area from the middle of each plot. The biomass was first partitioned into different plant organs (stem, leaves, and spikes) before being oven-dried at 50°C for at least one week to constant weight. Soil moisture was measured during the growing seasons at depths of 30, 60, and 90 cm with tensiometers, with two replicates per depth installed in both the eastern and western sections of the field. These points were selected to represent the treatments of low N rate (0 kg N ha^-1) and high N rate (168 kg N ha^-1). Additionally, soil temperature sensors were placed at a 30 cm depth. All sensors were connected to a datalogger for continuous data collection.

Five soil cores were collected from each plot and homogenized to quantify soil texture, soil organic matter (SOM), soil nitrate (NO₃^-), and ammonium (NH₄⁺) content at 0-20, 20-40, and 40–60 cm depths, each year before sowing (Supplementary Table S1). Other nutrients, including phosphorus, sulfur, zinc, iron, manganese, copper, calcium, magnesium, sodium, and boron, were at sufficient levels across growing seasons (data not shown). Pest, weed, and disease management practices were implemented in alignment with the University of Nebraska’s recommendations for winter wheat, ensuring that these pressures did not limit the study. Detailed pesticide application information is available in Supplementary Table S1.

An AccuPAR LP-80 ceptometer (Decagon Devices, Pullman, WA) was used to measure leaf area index (LAI) non-destructively at Feekes GS 4, 7, 10, and 10.5. For each plot, four measurements were taken at the four corners of the plot to ensure a representative area of the wheat canopy. Grain yield, moisture content, test weight, and protein were determined at harvest with a Hege 140 self-propelled small plot combine (Wintersteiger, Salt Lake City, UT). Grain protein content was measured using near-infrared reflectance spectroscopy with a Perten DA 7250 (Perten Instruments Inc., Springfield, Illinois).

2.3 APSIM-NG wheat model description

The Agricultural Production Systems Simulator Next Generation (APSIM-NG; Holzworth et al., 2018) is a comprehensive software suite that includes a variety of crop modules (Keating et al., 2003), such as the APSIM-Wheat (Zheng et al., 2015). In our study, this model was employed to simulate the growth and development of winter wheat. The APSIM-Wheat model has the potential to provide a detailed simulation of phenology, biomass, and grain yield, capturing the interactions between soil properties, climatic factors, and management (Holzworth et al., 2014). APSIM-Wheat conducts crop growth and development simulations on a daily time-step. It integrates environmental variables such as solar radiation, temperature, soil moisture, and soil N content alongside agronomic practices to predict growth outcomes (Zheng et al., 2015). Simulated soil moisture values at 30, 60, and 90 cm correspond to layer-weighted averages of APSIM-NG soil water predictions across the 0-30, 30-60, and 60–90 cm depth intervals, respectively. The model delineates the wheat life cycle into eleven distinct developmental phases divided by thermal time accumulation and influenced by vernalization, photoperiod, and N availability from emergence to the terminal spikelet phase. These phases are configured within the model configuration, with thermal time (tt) controlling the progression between phases (Holzworth et al., 2014). The phenological transitions simulated by APSIM-Wheat (e.g., stem elongation, head emergence, flowering, and grain filling) correspond to Feekes GS 4, 6, 10, 10.5, and 11.2 used for field sampling in this study, allowing direct alignment between observed Feekes stages and APSIM’s thermal-time-based developmental phases (Holzworth et al., 2014; Zheng et al., 2015).

A vital aspect of the APSIM-Wheat model is its calculation of daily biomass accumulation derived from the plant’s ability to intercept solar radiation. It includes calculations involving intercepted radiation (I), radiation use efficiency (RUE), diffuse factor (fd), stress factors (fs), and a carbon dioxide response factor (fc). The model calculates the amount of radiation intercepted using the leaf area index (LAI) and the extinction coefficient (k), according to principles described by Monsi and Saeki (2005). More descriptions of the wheat model can be found in the technical documentation (Zheng et al., 2015).

In addition, we utilized the following modules in APSIM-NG version 7270: Soil N for soil N and C cycling (Probert et al., 1998), SoilWat for soil water modeling, including fluctuating shallow groundwater tables and tile drainage (Probert et al., 1998; Cichota et al., 2021), SURFACEOM for residue management (Probert et al., 1998; Thorburn et al., 2001), soiltemp2 for simulating soil temperature dynamics (Campbell, 1985) and management rules for rotation, tillage, planting, fertilizer, and harvesting. The Soil N module was not calibrated because no independent measurements of soil C or N turnover were available; therefore, site-specific soil properties were used in conjunction with APSIM-NG’s default decomposition and mineralization parameters. Details about the calibrated soil profile characteristics by depth, including parameters such as water table depth, bare soil runoff curve number, hydraulic conductivity at the drained upper limit (DUL), and matric potential at DUL, are provided in Supplementary Table S2. In addition, soil hydrologic parameters included in the SoilWater module are provided in Supplementary Table S3.

Each simulation was initiated on January 1, 2014, with a spin-up period to ensure the fast-decomposing organic matter pools stabilized and reached equilibrium before the primary analysis began (Zhao et al., 2014). The simulation was conducted continuously to capture year-to-year carry-over effects, with daily time step outputs generated throughout the process. Soil organic matter values were initially derived from baseline measurements and SSURGO (SSURGO, 2024) up to a depth of 2.44 m for each growing season. Saturated hydraulic conductivity for each layer was estimated based on SOM and texture (Saxton and Rawls, 2006).

The Mesonet weather station was 1.8 km from the experimental site. Simulated management practices inputs were adjusted annually to reflect the actual practices within the winter wheat experiment, which was part of a corn-soybean-wheat rotation, with wheat typically following soybean. These adjustments included winter wheat planting dates, N fertilizer application timing and rates, and tillage (Supplementary Table S1). Both the soybean and corn phases of the rotation were also modeled using APSIM’s default cultivar settings for soybean (maturity group 2) and corn (100-day variety), as outlined in Supplementary Table S4.

2.4 Model calibration

The model calibration was carried out in the R environment (version 4.4, R Core Team, 2024) using the R package “apsimx” (version 2.7.72; Miguez, 2024). The calibration approach sought to minimize the objective function representing the deviation of the simulated data from the observed data (Equation 1; Wallach et al., 2001):

\begin{array}{l} f (x) = \sum_{i = 1} \frac{1}{σ_{i}^{2}} \frac{1}{N_{i}} \sum_{j = 1} \frac{1}{N_{i j}} \sum_{k = 1} {(Y_{i j k} - Y_{i j k}^{*})}^{2} & (1) \end{array}

Where “Y_ijk” and “Y^*_ijk” respectively represent the observed and modeled values for the i^t^h data category (e.g., biomass, grain yield) at the j^th measurement and for the k^th replication. More specifically, the categories of observed data available for calibration were biomass, grain yield, leaf area index, N content by plant organ, soil moisture, and date of occurrence of emergence, flowering, maturity, and harvest. These APSIM-simulated stages correspond to the Feekes stages used for field sampling (GS 4 = stem elongation onset, GS 6 = jointing, GS 10-10.5 = boot to heading/flowering, GS 11.2 = grain filling), ensuring alignment between observed and simulated phenology. The inverse of the variance (1/ $σ_{i}^{2}$ ) was used to weigh the errors for different data categories (Wallach et al., 2001).

The model calibration was carried out in a two-step procedure, first calibrating parameters related to phenology and then parameters related to biomass production and yield (Wallach et al., 2018; Archontoulis et al., 2020). The calibration of crop phenology was carried out by running an initial grid search over a wide range of values (Table 1) and evaluating the objective function (Equation 1) concerning the phenology data. The grid search explored a factorial combination of five equally spaced values within the range indicated in Table 1 of the parameters that control phenology, resulting in approximately 900,000 simulations. The parameters in Table 1 correspond to cultivar- and phenology-related settings in the APSIM-NG Wheat module (Holzworth et al., 2014; Zheng et al., 2015). The default parameter values reported in Table 1 are those provided within the APSIM-NG Wheat module used in this study. APSIM-NG undergoes continuous development, and default parameter values and model structure may vary across software versions. The parameter ranges used in the grid search were defined based on APSIM documentation and previously published model calibration studies (Wallach et al., 2001; Archontoulis et al., 2020). Subsequently, using the Nelder-Mead algorithm, the parameter value combination that minimized the objective function was used as starting values for unconstrained optimization (Nelder and Mead, 1965). Likewise, the calibration of the parameters that control growth and yield followed a procedure similar to phenology calibration. A grid search was conducted using five equally spaced values indicated in Table 1, resulting in 125 simulations. This smaller number was due to the calibration involving only three parameters, each with a narrower search range, reducing computational demands while maintaining optimization efficiency. For every simulation, the objective function (Equation 1) was evaluated concerning the biomass and yield data, and the parameter value combination that minimized the objective function was selected as starting values for the Nelder-Mead optimization (Nelder and Mead, 1965).

Table 1

Table 1. Parameters selected for calibration, default values in the APSIM Wheat model, range of parameter values tested during the grid search step of the calibration, and optimized values for WB4699 and LCS Valiant cultivars.

2.5 Data analysis

2.5.1 Statistics for model evaluation

To assess the accuracy of the APSIM-NG model, we compared the estimated phenology, biomass, LAI, grain yield, protein, soil nitrates, and ammonium against the observed and uncalibrated data. The uncalibrated model refers to the default parameter values provided by APSIM-NG for each respective growing season without any adjustments besides the specific local conditions. Thus, we calculated the root mean squared error (RMSE; Equation 2), relative root mean squared error (RRMSE; Equation 3), and mean bias error (MBE; Equation 4):

\begin{array}{l} R M S E = \sqrt{\frac{\sum_{i = 1}^{n} {(S_{i} - O_{i})}^{2}}{n}} & (2) \end{array}

\begin{array}{l} R R M S E = \frac{R M S E}{\bar{O}} \times 100 & (3) \end{array}

\begin{array}{l} M B E = \frac{1}{n} \sum_{i = 1}^{n} (P_{i} - O_{i}) & (4) \end{array}

where Ō denotes the mean observed value, S_i represents the model estimated value, O_i refers to the observed value, and n is the number of data pairs. The RMSE summarizes the average difference between observed and predicted values, while RRMSE depicts the relative difference. MBE quantifies the average bias between predicted and observed values, indicating whether the model systematically over- or underestimates. In all circumstances, a lower value in all metrics implied high accuracy for the APSIM-NG model predictions. In this study, RRMSE thresholds were set as follows: ≤ 20% for “well” agreement, 20-40% for “moderate” agreement, and ≥ 40% for “poor” agreement, based on previous classifications where normalized RMSE (nRMSE) ~ 20% was considered “reasonable”, and values above 40% indicated “high uncertainty” in model performance (Yang et al., 2014).

2.5.2 Model validation

To validate the model’s performance, we calculated the RMSE (Equation 2), RRMSE (Equation 3), and MBE (here on bias; Equation 4) by comparing the simulated outputs with external observed field experimental data. This validation was exclusively performed for grain yield, as winter wheat was the sole crop with external field data. Corn and soybean were simulated only to preserve rotation effects. Consequently, the validation used external grain yield data with N rates ranging from 30 to 180 kg N ha^-1 collected from five different Nebraska experimental sites over five growing seasons spanning from 2017 to 2022. For each validation site, simulations used the calibrated cultivar parameterizations developed from the SCAL trials, applied separately to LCS and WB. Site-specific soil profiles (SSURGO, 2024) were used, and soil water and mineral N were initialized using SSURGO default layer values. Weather data from the nearest Mesonet station (Nebraska Mesonet, 2024) were included for each site. These inputs ensured a consistent model setup across locations. The validation sites spanned eastern and central Nebraska, located approximately 60 to 250 km from the calibration site (SCAL), representing a broad geographic and environmental range for evaluating model performance.

2.5.3 Economic optimal N rates and yield at EONR

Yield response to N rates was analyzed for the 2020/21 and 2021/22 growing seasons. A quadratic-plateau response function was fitted for the grain yield response to the N application (QP, Equation 5; Cerrato and Blackmer, 1990) using R software (R Core Team, 2023):

\begin{array}{l} y = {\begin{matrix} a + b x + c x^{2}, x < x_{0} \\ a + b x + c x_{0}^{2}, x \geq x_{0} \end{matrix} & (5) \end{array}

where y represents wheat yield (observed), x is the fertilizer N rate, a is the intercept, b is the linear coefficient, c is the quadratic coefficient, and x₀ is the N rate at the joining point. The plateau point (x₀) is the N application rate at which the yield no longer increases with additional N input. EONR uncertainty was determined using the profile-likelihood confidence interval (CI) method following Nigon et al. (2019) for quadratic plateau models. The confidence intervals were computed in R by applying the delta method to estimate the variance of EONR based on the variance-covariance matrix of the nonlinear least squares (NLS) model. Standard errors were derived, and 95% confidence intervals were obtained, assuming normality. The EONR, yield at EONR (YEONR), and the yield with zero N (YN0) were calculated using N response equations. This was done by setting the first derivative of the fitted response curve equal to the price ratio 6.9:1 (wheat:N; US$ per kg of grain per US$ per kg of N). This price ratio was derived from the average historical prices of wheat grain and N over the growing seasons from 2019 to 2022.

3 Results

3.1 Observed phenology, shoot biomass, LAI, yield, and yield response to N

The experimental design enabled the evaluation of two winter wheat cultivars, LCS and WB, across two growing seasons (2020/21 and 2021/22) and four N rates. This approach generated a robust dataset to assess cultivar performance across varying environmental and management conditions and served as the basis for subsequent model calibration. Phenological observations indicated that both cultivars reached key growth stages at similar times across seasons, with no consistent differences (Supplementary Table S6).

Observations showed significant variability in shoot biomass, LAI, and grain yield across cultivars, N rates, and years. Grain yield was significantly affected by N rate in both years (p < 0.001), while cultivar effects were not significant in 2020/21 (p = 0.924) but became significant in 2021/22 (p < 0.05). No significant cultivar × N interaction was observed in either year (Supplementary Table S5).

On average, grain yield was 212.5 kg ha^-1 higher for LCS compared to WB, with observed means of 4561.0 and 4348.5 kg ha^-1, respectively (Table 2). Shoot biomass showed a similar pattern, with N rate showing a consistent effect across years (p < 0.01), and cultivar differences becoming significant only in 2021/22 (p < 0.01), as shown in Supplementary Table S5. At the final sampling date, shoot biomass averaged 5821.3 kg ha^-1 for LCS and 6093.8 kg ha^-1 for WB.

Table 2

Table 2. Summary of observed and simulated (APSIM-NG) grain yield, phenology, leaf, stem, shoot biomass, LAI, leaf N, stem N, grain N content, and grain protein by cultivar. Soil nitrates (NO₃^-) and ammonium (NH₄⁺) at two depths (0–20 and 20–40 cm), and soil moisture at three depths (30, 60, and 90 cm). The number of paired data points is “n.” For quantitative model performance evaluation, see Figure 2 and Supplementary Tables S6, S7.

LAI followed a comparable trend, with significant effects of N rate in both years (p < 0.001). Cultivar effects were only significant in 2021/22 (p < 0.001), as shown in Supplementary Table S5. LAI averaged 2.76 for LCS and 2.68 for WB.

Observed winter wheat yield response to N application varied between cultivars (LCS and WB) and growing seasons (2020/21 and 2021/22; Figure 1). In the 2020/21 growing season, the observed EONR was similar between cultivars at approximately 100 kg N ha^-1 (11 kg N ha^-1 difference). However, the observed YEONR was slightly higher for LCS than WB (6565 kg ha^-1 and 6147 kg ha^-1, respectively; Figure 1). In contrast, the YN0 was lower for LCS than WB (3736 kg ha^-1 and 4852 kg ha^-1, respectively; Figure 1). For 2021/22, the observed EONR was lower for LCS compared to WB (126 kg N ha^-1 and 149 kg N ha^-1, respectively; Figure 1), with a 23 kg N ha^-1 difference. However, the YEONR (3893 kg ha^-1 and 3605 kg ha^-1, respectively) and YN0 (2580 kg ha^-1 and 2247 kg ha^-1, respectively) were slightly higher for LCS compared to the WB (Figure 1).

Figure 1

Graphs showing the relationship between nitrogen rate (N rate) and grain yield for two wheat varieties, LCS Valiant and WB4699, over two years. Each graph displays observed and calibrated data, with EONR, YEONR, and YN0 values noted. Graphs illustrate differences in yield based on nitrogen application, highlighting variation in observed and calibrated models across years 2020/21 and 2021/22.

Figure 1. Response of winter wheat yields to nitrogen application and economic optimal nitrogen rates (EONR) at the South-Central Agricultural Laboratory (SCAL), Nebraska, during the 2020/21 and 2021/22 growing seasons. The yields of two wheat cultivars (WB4699 and LCS Valiant) are presented. Data points represent observed yield values, with vertical bars illustrating the standard deviations of the observed yields. The solid lines depict the fit from the quadratic plateau regression model for the simulated data, whereas the dashed lines represent the observed data’s fit. The green shaded area represents the profile likelihood confidence interval for the observed EONR. The Economic Optimal Nitrogen Rate (EONR), the corresponding yield at EONR (YEONR), and the yield at N0 (YN0) are also indicated for both observed and calibrated simulations.

3.2 APSIM-NG performance for crop growth and phenology

Calibration improved the model’s performance across all crop and soil parameters analyzed, reducing prediction errors and improving agreement between simulated and observed values (Figure 2). For phenology, the model reproduced key growth stages with high accuracy across years and cultivars. Average RMSE values were approximately 5 days in 2020/21 and 4 days in 2021/22, with corresponding RRMSE values of about 2.6-2.7% and 1.5-1.7%, respectively (Figure 2A, Supplementary Table S7). This performance reflects the effective parameterization of cultivar-specific thermal time and photoperiod sensitivity.

Figure 2

Five panels show scatter plots comparing simulated and observed data for two cultivars (LCS Valiant and WB4699). Panel A shows day of year (DOY), B shows biomass, C shows leaf area index (LAI), D shows grain yield, E shows grain protein. Each panel compares uncalibrated and calibrated predictions; calibration improves accuracy, reducing RMSE, RRMSE, and MBE. Data points cluster closer to the line of agreement in calibrated plots across all metrics.

Figure 2. Relationship between simulated and observed values for (A) phenology in days of the year (DOY), (B) biomass, (C) leaf area index (LAI), (D) grain yield, and (E) protein for uncalibrated and calibrated APSIM-NG models. For panel A, horizontal and vertical error bars depict variability in simulated and observed dates, respectively. The solid diagonal line represents the 1:1 line, indicating perfect agreement between simulated and observed values. Data points are colored by cultivars LCS Valiant (light green) and WB4699 (dark green). Metrics displayed include root mean square error (RMSE), relative root mean square error (RRMSE), and mean bias error (MBE).

Simulated grain yield showed well to moderate agreement following calibration. The model captured yield trends across years and cultivars, with RRMSE values of 15% for LCS and 24% for WB (Figure 2D), and simulated cultivar differences consistent with observed means (Table 2). Yield predictions under high N application closely matched observations in 2021/22, although slight underestimation occurred for WB in 2020/21.

Shoot biomass predictions improved moderately after calibration, particularly in 2021/22. The model achieved RRMSE values of 38% for LCS and 46% for WB, with corresponding RMSEs of 1487 kg ha^-1 and 1961 kg ha^-1 (Figure 3B). Despite these improvements, agreement remained moderate to poor, indicating persistent challenges in capturing biomass dynamics under varying environmental conditions. Simulated aboveground biomass tended to be slightly overestimated for both cultivars (Table 2), and leaf and stem component predictions exhibited high variability (Supplementary Figure S1).

Figure 3

Line charts depicting Leaf Area Index (LAI) and Shoot Biomass for cultivars LCS Valiant and WB4699 over time. Chart A shows LAI peaking around 3 for both cultivars, with specific RMSE, RRMSE, and MBE values. Chart B displays Shoot Biomass, peaking near 7500 kg/ha, with similar error metrics. Data points are plotted against dates from early 2021 to mid-2022.

Figure 3. Seasonal simulated (lines) and observed (dots) biomass for LCS Valiant and WB4699 winter wheat cultivars across two biomass components: (A) leaf area index (LAI) and (B) shoot biomass. Simulations were conducted over two growing seasons (2020/21 and 2021/22) in Nebraska. Circles represent observed data, while lines indicate simulated data. Each panel compares model performance for the cultivars LCS Valiant (left) and WB4699 (right). The RMSE (Root Mean Square Error), RRMSE (Relative RMSE), and MBE (Mean Bias Error) values for each simulation are displayed in the top left of each panel.

For LAI, calibration led to moderate improvements in model performance, but overall agreement remained poor. In 2020/21, RRMSE values were 63% for LCS and 75% for WB, while in 2021/22, agreement declined further to 75% and 87%, respectively (Figure 2C, Figure 3A). The model tended to underestimate peak canopy development, particularly in the later growing season, suggesting limitations in simulating cultivar-specific canopy dynamics or potential uncertainty in field measurements (Supplementary Figure S2A).

The high RRMSE values for LAI and biomass were primarily due to early-season measurements with very small observed values, which inflated the percent error despite small absolute differences. As shown in Figure 3, APSIM-NG captured canopy development well in 2020/21 but underestimated LAI in 2021/22 following a hail event that reduced leaf area in the field and was not represented in the model. These discrepancies did not affect soil moisture or nitrogen simulations, which are computed mechanistically by SoilWat and Soil N and are not driven by short-term variation in LAI or biomass.

Grain protein content was simulated with well agreement across both growing seasons and cultivars. After calibration, the model achieved RRMSE values below 10% for LCS and between 8% and 13% for WB (Figure 2E), indicating highly accurate predictions of protein concentration. These results reflect the model’s capacity to capture cultivar-specific N dynamics during the reproductive period. Additional details on organ-level N content, including leaves and stems, are provided in Supplementary Figure S3.

3.3 Model performance for crop yield, yield response to N, plant N uptake, and soil N pools

The APSIM-NG demonstrated variability in crop yield response to N, with model performance ranging from poor to well across treatments and showing clear differentiation between zero (N0) and high (N168) N rates for both cultivars (Supplementary Table S8). Under the 168 kg N ha^-1 treatment, the model performed well for LCS (RRMSE = 11%) and WB (RRMSE = 16%; Supplementary Table S8). However, under the 0 kg N ha^-1 treatment, performance was moderate for LCS (RRMSE = 30%) and poor for WB (RRMSE = 46%). This indicates that the model achieved better performance at higher N rates.

On average, the model predicted yield response to N well for LCS with a RRMSE for EONR of 18.9%. In contrast, the EONR agreement for WB was moderate, with an average RRMSE of 32.2% (Table 3), likely due to differences in phenology and N uptake timing not fully captured by the model. The model showed well agreement for YEONR across both cultivars and growing seasons, with RRMSE values below 10% (Table 3). For YN0, the model performed well in both growing seasons (2020/21 and 2021/22) for the LCS (RRMSE = 14.9% and RRMSE = 17.9%, respectively), while a moderate agreement (RRMSE = 38.5% and RRMSE = 31.8%, respectively) in both growing seasons for WB (Table 3).

Table 3

Table 3. Root mean square error (RMSE), relative root mean square error (RRMSE), and mean bias error (MBE) comparing observed and simulated nitrogen (N) metrics for economic optimum N rate (EONR), yield at EONR (YEONR), and yield at zero N (YN0) for two cultivars (LCS Valiant and WB4699) over two growing seasons (2020/21 and 2021/22).

For N uptake, APSIM-NG performance ranked Stem N< Leaves N < N grain, from 54% (stem) to 12% (grain), with model accuracy improving as N moved from structural to reproductive tissues (Supplementary Figure S3). This pattern highlights organ-specific differences in model performance. For instance, N uptake in the leaves showed moderate agreement for LCS (RRMSE = 27%) and WB (RRMSE = 33%; Supplementary Figure S3A). Stem N uptake has shown poor agreement for both cultivars (RRMSE = 51% for LCS and RRMSE = 58% for WB; Supplementary Figure S3B). In contrast, grain N uptake performed well for both cultivars (RRMSE = 11% for LCS and RRMSE = 13% for WB; Supplementary Figure S3C).

Soil NO₃^- and NH₄⁺ measurements were used to establish initial soil N conditions during model calibration in APSIM-NG. At the start of the calibration, measured soil NO₃^- values included 7.13 kg ha^-1 and 3.7 kg ha at 20–40 cm. For NH₄⁺, the model simulated 0.53 kg ha^-1 in the 0–20 cm depth and 0.05 kg ha^-1 in the 20–40 cm depth, providing a baseline for simulating N dynamics. During the first paired observed-simulated comparison, the model closely approximated observed values, with simulated NO₃^- of 2.4 kg ha^-1 in the 0–20 cm depth versus 2.7 kg ha^-1 observed, and simulated NH₄⁺ at 0.9 kg ha^-1 versus 0.85 kg ha^-1 observed (Supplementary Figures S2C, E). Beyond initialization, the model captured general seasonal trends in soil NO₃^- and NH₄⁺, including early-season increases and late-season depletion. However, it underestimated the magnitude of observed peaks, particularly in the 0–20 cm depth, and failed to reproduce full-season dynamics at 20–40 cm (Supplementary Figures S2C, E). However, due to the inherent variability and limited spatial and temporal resolution of soil sampling data, it is challenging to capture the full dynamics of soil N accurately. In contrast, crop performance metrics, such as yield and N uptake, integrate the cumulative effects of soil and environmental conditions over time, making them more reliable and comprehensive indicators of model accuracy.

3.4 Winter wheat APSIM-NG model validation across Nebraska environments

The APSIM-NG winter wheat model captured yield variability across Nebraska environments with reasonable accuracy (Figure 4). For LCS, the model achieved an average error of 14% (RRMSE), with an overall RMSE of 750 kg ha^-1 and minimal bias (MBE = -38 kg ha^-1). The best predictions occurred in high-precipitation environments (> 550 mm cumulative rainfall), particularly in Saunders County in 2022, where error dropped below 1% (RRMSE = 0.9%). Nearly all LCS validation site-years (12 out of 13) were also classified as high precipitation, and accuracy across these cases ranged widely, from well agreement (e.g., Saunders 2022) to moderate agreement (e.g., Lancaster 2021; RRMSE = 25%). The single medium-precipitation case (Saunders 2018) did not yield improved performance (RRMSE = 23.3%; moderate agreement), although overall performance remained acceptable. Notably, model calibration was performed in medium-precipitation seasons (526 mm in 2020/21 and 381 mm in 2021/22; Supplementary Table S1), yet the best validation performance occurred in high-precipitation environments, indicating that accuracy was influenced by factors beyond precipitation regime alone.

Figure 4

Two scatter plots compare observed versus simulated grain yield for LCS Valiant and WB4699. Both charts feature a diagonal line indicating ideal correlation. Symbols represent precipitation environments: circles (low), triangles (medium), and squares (high). Colors denote counties: green (Clay), orange (Jefferson), blue (Lancaster), pink (Saunders), and light green (Washington). LCS Valiant has RMSE 750 kg/ha, RRMSE 14%, MBE -38 kg/ha. WB4699 has RMSE 1302 kg/ha, RRMSE 19%, MBE -1068 kg/ha.

Figure 4. Relationship between simulated and observed grain yield (kg ha⁻¹) for the validation dataset of two wheat cultivars, LCS Valiant (left) and WB4699 (right), using the APSIM-NG model across Nebraska environments. The solid diagonal line represents the 1:1 line, indicating perfect agreement between simulated and observed values. Data points are colored by county and shaped by precipitation environment (Low < 250 mm, Medium 250–550 mm, High > 550 mm). Reported metrics include root mean square error (RMSE), relative root mean square error (RRMSE), and mean bias error (MBE).

For WB, the model showed greater variability, with a mean RRMSE of 19%, an overall RMSE of 1302 kg ha^-1, and a systematic underestimation of yields (MBE = -1068 kg ha^-1). As with LCS, the best-performing WB site-year occurred in a high-precipitation environment (>550 mm), such as Saunders in 2019 (RRMSE = 5%). Moderate errors were observed across medium-precipitation environments (e.g., Jefferson 2020; RRMSE = 22%), while some high-precipitation cases produced the largest errors (e.g., Saunders 2021; RRMSE = 29%).

Across all validation sites, the model performed well across precipitation regimes, with most county-environment combinations falling within the well-agreement range (16 out of 24 site-years; RRMSE < 20%). Moderate errors occurred in only a subset of cases (8 out of 25 site-years; 20-24% RRMSE), and no environment exceeded the poor-agreement threshold (>40% RRMSE). These results demonstrate that APSIM-NG can reliably reproduce winter wheat yield responses across diverse Nebraska environments with acceptable uncertainty, highlighting precipitation and site-specific factors as key drivers of prediction accuracy.

4 Discussion

4.1 Model strengths

The APSIM-NG model effectively simulated key winter wheat processes, including phenology, grain yield, and grain protein content, with high accuracy following calibration. Calibrated RMSE for phenology was reduced to 4–5 days and RRMSE to < 2.2%, demonstrating the model’s strength in capturing cultivar development timing. These improvements are critical, as accurate phenology drives management decisions such as N application timing (Holzworth et al., 2018; Hu et al., 2021).

Trait-based calibration improved predictions across traits, especially grain yield and grain protein. For example, grain yield RRMSE declined from 33% to 15% for LCS and 29% to 24% for WB, while grain protein RRMSE decreased from 9.7% to 8.1% for LCS and 23% to 11% for WB. These gains reflect practical tuning of cultivar-specific parameters such as Phyllochron, GrainNumber, and MaximumNConc, consistent with prior studies on genotype calibration (e.g., Asseng et al., 2002; Kumar et al., 2023).

To enhance model transferability, we calibrated APSIM-NG using all available calibration years simultaneously, rather than treating each year independently. This approach allowed cultivar parameters to reflect stable physiological characteristics, while site-specific conditions were handled through weather and soil inputs. The model maintained reasonable accuracy across diverse conditions by prioritizing system-level fidelity over per-season fit, increasing confidence for broader applications. These results demonstrate the importance of integrating field-level management and environmental data into the model configuration. By using observed planting dates, N application timing, and cultivar-specific parameter sets, APSIM-NG could replicate phenological development and final grain yield across site-years. This alignment between observed management practices and simulation inputs is critical for models to generalize across years, particularly when environmental drivers (e.g., precipitation timing) are highly variable (Holzworth et al., 2014; Keating et al., 2003; Wallach et al., 2001). The ability to simulate year-to-year variation while maintaining cultivar consistency highlights the calibration strategy’s robustness. Yield in APSIM is determined by grain number and grain weight, rather than a fixed harvest index, allowing the model to compensate for variability in biomass allocation (Sadras and Lawson, 2011; Holzworth et al., 2018). This aligns with previous studies showing that water stress affects N dynamics and yield predictions (Brisson et al., 2002; Lobell and Asseng, 2017).

Our calibration of the APSIM-NG model for winter wheat in the Great Plains demonstrates its potential as a decision-support tool for CSA. Rather than indicating uniformly accurate performance, the model reproduced the overall yield response to N rates across cultivars, with higher accuracy at high N rates and reduced accuracy under zero-N conditions. The model still supports EONR estimation under varying environmental conditions, though uncertainty is greater when soil N availability is low. Such capability can reduce over-fertilization and greenhouse gas emissions (e.g., N₂ Wang et al., 2023), supporting the CSA goals of improving productivity and resilience while lowering environmental impact (Lipper et al., 2014). The model’s ability to represent cultivar-specific traits also supports breeding and selection of genotypes better adapted to changing climates (Bai et al., 2022).

4.2 Model limitations

Shoot biomass and LAI predictions remained moderate to poor and were highly sensitive to seasonal variability and cultivar dynamics. In 2020/21, biomass was often underestimated, likely due to early-season N stress and delayed rainfall following spring fertilization (Loecke et al., 2004). Conversely, 2021/22 showed biomass overestimation, potentially related to hail damage and lower total precipitation. These results indicate the model’s sensitivity to environmental drivers, particularly water and N interactions, but highlight its limitations in simulating real-world stress events not represented in the model. This aligns with Zhao et al. (2014), who noted APSIM’s difficulty representing biomass under fluctuating N and water stress.

Biomass simulation challenges were partly due to limited translocation modeling during grain filling. Due to a simplified representation of remobilization processes, the model tends to overestimate vegetative biomass, particularly stems and leaves. This was evident in 2021/22, where overestimated biomass did not translate into overestimated yield. This mismatch supports prior findings that APSIM and other models may misrepresent sink-source dynamics during reproductive stages (e.g., Martre et al., 2003; Dreccer et al., 2009; Sinclair and Jamieson, 2006). Previous studies suggest that biomass is more sensitive to short-term environmental variation, while yield is often buffered by physiological mechanisms that stabilize grain production under moderate stress (Sinclair and Muchow, 1999; Asseng et al., 2002). This distinction reflects the physiological stability of grain sink strength under stress, which buffers yield even when canopy growth is variable (Goudriaan and Van Laar, 1994; Reynolds et al., 2005).

LAI predictions were similarly poor (RRMSE > 60%), with a consistent underestimation of peak canopy development. This could reflect both model structure and field measurement limitations. Ceptometer data may include noise, and APSIM’s treatment of LAI has not been as rigorously validated as its grain yield predictions (Ahmed et al., 2016; Pokovai and Fodor, 2019). LAI showed minimal value for yield prediction in this study, supporting prior work that stresses temporal canopy dynamics over peak values (Waldner et al., 2019; Thompson et al., 2024). Additionally, cultivar × environment interactions and canopy plasticity may contribute to LAI variability, posing challenges for general parameterization. These findings are consistent with prior evaluations of APSIM and similar models, which often perform well for yield but struggle with dynamic traits like LAI and biomass due to limitations in water-N interactions and structural assumptions (Hochman et al., 2009; Archontoulis et al., 2014; Asseng et al., 2015).

Lastly, poor prediction of NO₃^- and NH₄⁺ (Supplementary Figures S2C, E; RRMSE = 160.3% for NO₃^- and RRMSE = 131.21% for NH₄⁺) could affect the EONR uncertainty due to soil and crop dynamics (Archontoulis et al., 2020). These limitations reinforce the need to improve how APSIM-NG simulates yield response to N.

4.3 Sources of error

One possible reason for poor biomass and LAI predictions is the lack of explicit root growth and plasticity simulation. Root architecture, rooting depth, and water/nutrient uptake efficiency vary across cultivars and environments but are simplified in current APSIM configurations. Similarly, traits like tillering capacity and stay-green phenotype, which affect canopy dynamics, are not yet genotype-specific in APSIM-NG. These physiological features influence N uptake and canopy development but are often averaged in model parameterizations. Including cultivar-level physiological plasticity could improve LAI and biomass simulations, especially under variable N or water availability (MansChadi et al., 2006; Chenu et al., 2011; Giordano et al., 2024).

Low accuracy on soil N dynamics (NO₃^- and NH₄⁺) suggests focusing future improvements on refining the simulation of soil N transformations, including mineralization, immobilization, and leaching, particularly under fluctuating moisture conditions.

4.4 Simulating winter wheat yield response to N

After calibration, the model’s performance improved significantly, as did EONR and yield response to N, YEONR, and YN0. In addition, the calibrated model generally underestimated EONR for both cultivars and growing seasons, with variability in performance across years.

While calibration of the APSIM-NG model improved its performance in simulating yield at EONR, some challenges still limit its predictive ability. A key issue is that the EONR estimation relies on small yield increments near the maximum N response, where even minor yield prediction errors can cause large deviations in EONR (Puntel et al., 2016). For example, Baum et al. (2023) found that a 10% error in yield predictions for corn can lead to a 34% error in EONR. Moreover, the dynamic nature of the N cycle, as influenced by management, soil properties, and weather interactions, exacerbates prediction errors, especially at lower N rates (Morris et al., 2018; Puntel et al., 2018; Correndo et al., 2021). Limited field data, particularly for low N, also hinders precise EONR estimation, as evidenced in the confidence intervals in Figure 4 (Miguez and Poffenbarger, 2022). This uncertainty is particularly true when the model fails to accurately capture the quadratic yield response to N, a critical pattern for EONR prediction (Baum et al., 2023). After calibration, the model performed well in simulating yields under zero N (YN0), particularly for WB, which showed consistently better agreement across both seasons. This suggests the model captures cultivar-specific responses under low N conditions, likely reflecting physiological differences between the cultivars. Similar trends have been noted in previous studies, where cultivar-specific traits such as N use efficiency and root architecture influenced yield predictions under limited N availability (Brisson et al., 2002; Hammer et al., 2010). These findings highlight the importance of cultivar-specific calibration (Asseng et al., 2015; Zhao et al., 2014) and the need to tailor N management strategies to differences in growth and uptake (Mirosavljević et al., 2024; MansChadi et al., 2006; Sadras and Lawson, 2011).

While we expected the model to capture N rate × environment × cultivar interactions, accurately simulating soil N remains challenging due to the complexity of N transformations under variable conditions (Butterbach-Bahl et al., 2011; Davidson and Kanter, 2014). This reinforces the need for improved soil inputs and root/N uptake calibration (Gabrielle et al., 2006; Zhang et al., 2015).

For example, real-time nitrate sensing technologies (Jiang et al., 2019; Baumbauer et al., 2022) could enhance model responsiveness to in-season conditions. These sensors allow dynamic soil N adjustment, improving realism over static pre-season inputs, similar to past improvements with soil moisture sensing (Patrignani and Ochsner, 2018). While other factors, such as water or phenology, may be better captured, poor soil N simulation limits APSIM’s utility in nutrient-focused applications.

A promising approach is integrating APSIM-NG with real-time sensors and remote sensing tools (Thompson et al., 2024), allowing dynamic simulation of N availability and crop response. Such tools could adjust N uptake in real time and improve in-season prediction accuracy (Asseng et al., 2002). This would expand APSIM-NG’s utility for precision N management and sustainability. Improving soil N and mineralization predictions could also enhance EONR estimation (Nowatzke et al., 2022; Thompson et al., 2024).

5 Conclusion

This study demonstrated that the APSIM-NG model performed well in simulating key physiological processes, including phenology, grain yield, grain protein, and grain N uptake, for winter wheat under Nebraska’s agricultural conditions. The model effectively captured cultivar-specific growth dynamics, particularly for LCS, and showed potential for supporting genotype-based N management. These findings highlight the model’s strengths in simulating grain yield and grain N content, while revealing weaknesses in dynamic processes such as early-season biomass development and vegetative N partitioning. Further improvements are needed to accurately simulate in-season dynamics such as vegetative growth and N uptake. These results meet our objective of evaluating APSIM-NG’s ability to simulate cultivar-specific winter wheat responses to N and validate its accuracy under contrasting environments.

Despite these limitations, APSIM-NG remains a valuable research and decision-support tool when used cautiously. It can inform phenology-based management (e.g., N timing), evaluate cropping system diversification (e.g., incorporating wheat into corn-soybean rotations), and guide cultivar selection for N efficiency. Beyond these applications, APSIM-NG holds significant potential for the Nebraska wheat industry by enabling the prediction of growth stages under varying management practices (e.g., cultivar maturity, planting date), which can improve the timing of N and fungicide applications, help stagger maturity and harvest, and support the assessment of double-cropping opportunities. The model could also contribute to statewide yield and protein forecasting efforts, benefiting both producers and the milling and baking industry. If results were disseminated through outreach platforms, such as the UNL CropWatch or linked with data services like the Nebraska Mesonet, APSIM-NG could serve as a practical resource for scouting, decision support, and communication with stakeholders. These applications support broader goals of improving nutrient management and expanding wheat integration in Midwest cropping systems.

Future work should leverage the calibrated APSIM-NG model to develop and evaluate climate-smart strategies for winter wheat in the Midwest. This includes testing adaptation options under projected climate scenarios, developing dynamic in-season N management responsive to real-time conditions, integrating remote sensing data to refine biomass and N simulations, and assessing the long-term impacts of practices such as no-till and cover crops on soil health, carbon sequestration, and greenhouse gas emissions. Further refinement of N cycling, water stress, and in-field loss processes will enhance model reliability for practical use.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

JC: Conceptualization, Data curation, Formal Analysis, Methodology, Visualization, Writing – original draft, Investigation. GB: Conceptualization, Methodology, Supervision, Writing – review & editing, Formal Analysis, Funding acquisition. NM: Investigation, Supervision, Writing – review & editing, Resources. GS: Data curation, Investigation, Writing – review & editing. KF: Supervision, Writing – review & editing. CD: Data curation, Formal Analysis, Software, Writing – review & editing, Methodology. FM: Methodology, Software, Writing – review & editing, Formal Analysis. RP: Writing – review & editing, Supervision. LP: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – review & editing, Formal Analysis.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This work was supported in part by an award from the USDA-NRCS Conservation Innovation Grants, On-Farm Conservation Innovation Trials, award number NR203A750013G014.

Acknowledgments

We thank Dr. Cody Creech, Dr. Amanda Easterly, and the Crops Testing Program for supporting and contributing to this research. We also thank the APSIM Initiative for providing public access to their software, which has been invaluable to our study.

Conflict of interest

Author NDM is the owner of Mueller Ag Consulting, an independent agricultural consulting business. Author LAP was employed by company Syngenta.

The remaining author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fagro.2025.1740421/full#supplementary-material

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