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. 2024 May 23;14(1):11743.
doi: 10.1038/s41598-024-61976-6.

Modeling maize growth and nitrogen dynamics using CERES-Maize (DSSAT) under diverse nitrogen management options in a conservation agriculture-based maize-wheat system

Kamlesh Kumar  1   2 , C M Parihar  3 , H S Nayak  1   4 , D R Sena  1   5 , Samarth Godara  6 , Rajkumar Dhakar  1 , Kiranmoy Patra  1 , Ayan Sarkar  1 , Sneha Bharadwaj  7 , Prakash Chand Ghasal  2 , A L Meena  2 , K Srikanth Reddy  1 , T K Das  1 , S L Jat  8 , D K Sharma  1 , Y S Saharawat  9 , Upendra Singh  10 , M L Jat  11 , M K Gathala  12
Affiliations

Modeling maize growth and nitrogen dynamics using CERES-Maize (DSSAT) under diverse nitrogen management options in a conservation agriculture-based maize-wheat system

Kamlesh Kumar et al. Sci Rep. .

Abstract

Agricultural field experiments are costly and time-consuming, and often struggling to capture spatial and temporal variability. Mechanistic crop growth models offer a solution to understand intricate crop-soil-weather system, aiding farm-level management decisions throughout the growing season. The objective of this study was to calibrate and the Crop Environment Resource Synthesis CERES-Maize (DSSAT v 4.8) model to simulate crop growth, yield, and nitrogen dynamics in a long-term conservation agriculture (CA) based maize system. The model was also used to investigate the relationship between, temperature, nitrate and ammoniacal concentration in soil, and nitrogen uptake by the crop. Additionally, the study explored the impact of contrasting tillage practices and fertilizer nitrogen management options on maize yields. Using field data from 2019 and 2020, the DSSAT-CERES-Maize model was calibrated for plant growth stages, leaf area index-LAI, biomass, and yield. Data from 2021 were used to evaluate the model's performance. The treatments consisted of four nitrogen management options, viz., N0 (without nitrogen), N150 (150 kg N/ha through urea), GS (Green seeker-based urea application) and USG (urea super granules @150kg N/ha) in two contrasting tillage systems, i.e., CA-based zero tillage-ZT and conventional tillage-CT. The model accurately simulated maize cultivar's anthesis and physiological maturity, with observed value falling within 5% of the model's predictions range. LAI predictions by the model aligned well with measured values (RMSE 0.57 and nRMSE 10.33%), with a 14.6% prediction error at 60 days. The simulated grain yields generally matched with measured values (with prediction error ranging from 0 to 3%), except for plots without nitrogen application, where the model overestimated yields by 9-16%. The study also demonstrated the model's ability to accurately capture soil nitrate-N levels (RMSE 12.63 kg/ha and nRMSE 12.84%). The study concludes that the DSSAT-CERES-Maize model accurately assessed the impacts of tillage and nitrogen management practices on maize crop's growth, yield, and soil nitrogen dynamics. By providing reliable simulations during the growing season, this modelling approach can facilitate better planning and more efficient resource management. Future research should focus on expanding the model's capabilities and improving its predictions further.

Keywords: Ammonia volatilization; CERES-Maize; DSSAT; Nitrate leaching; Zero tillage.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Weather conditions during experimentation (2019 to 2021). The abbreviations SARD, PRECIP, MAX TEMP, and MIN TEMP stand for solar radiation, precipitation, maximum temperature, and minimum temperature, respectively.
Figure 2
Figure 2
Observed vs. simulated grain and biomass yield of maize based on parameterization (a) and evaluation dataset (b).
Figure 3
Figure 3
Observed vs. simulated phenological stages, LAI, (a and b), plant N-uptake (c and d) and soil NO3−-N (e and f) during 3 years of experimentation (2019–2021).
Figure 4
Figure 4
Observed vs. simulated phenological stages (a) days to anthesis, (b) days to maturity. Vertical bars for observed column represents standard error.
Figure 5
Figure 5
Comparison of observed versus simulated grain yield during evaluation year 2021. Vertical bars for observed column represents standard error.
Figure 6
Figure 6
Simulated soil nitrate (NO3−-N) as influenced by different nitrogen management options under long-term contrasting tillage practices during 2019 (a), 2020 (b) and 2021 (c).
Figure 7
Figure 7
Simulated nitrogen uptake of maize as influenced by nitrogen management options under long-term contrasting tillage practices in 2019 (a), 2020 (b) and 2021 (c).
Figure 8
Figure 8
Simulated ammonia volatilization as influenced by different nitrogen management options under long-term contrasting tillage practices during 2019 (a), 2020 (b) and 2021 (c).
Figure 9
Figure 9
Simulated nitrate leaching as influenced by various nitrogen management options under long-term contrasting tillage practices during 2019 (a), 2020 (b) and 2021 (c).
Figure 10
Figure 10
Flowchart illustrating the functioning of the DSSAT CERES-Maize model.
See this image and copyright information in PMC

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