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

Kumar, Kamlesh and Parihar, C M and Nayak, H S and Sena, D R and Godara, S and Dhakar, R and Patra, K and Sarkar, A and Bharadwaj, S and Ghasal, P C and Meena, A L and Reddy, K S and Das, T K and Jat, S L and Sharma, D K and Saharawat, Y S and Singh, Upendra and Jat, M L and Gathala, M K (2024) Modeling maize growth and nitrogen dynamics using CERES-Maize (DSSAT) under diverse nitrogen management options in a conservation agriculture-based maize-wheat system. Scientific Reports (TSI), 14. pp. 1-18. ISSN 2045-2322

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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.

Item Type:	Article
Divisions:	Global Research Program - Resilient Farm and Food Systems
CRP:	UNSPECIFIED
Uncontrolled Keywords:	Ammonia volatilization, CERES-Maize, DSSAT, Nitrate leaching, Zero tillage
Subjects:	Others > Wheat Others > Maize
Depositing User:	Mr Nagaraju T
Date Deposited:	23 Jul 2025 10:54
Last Modified:	23 Jul 2025 10:54
URI:	http://oar.icrisat.org/id/eprint/13235
Official URL:	https://www.nature.com/articles/s41598-024-61976-6
Projects:	UNSPECIFIED
Funders:	UNSPECIFIED
Acknowledgement:	We sincerely acknowledge Indian Council of Agricultural Research (ICAR), ICAR-Indian Agricultural Research Institute (IARI), for providing the facilities. The first author also acknowledges ICAR-IIFSR for granting him study leave for Ph.D. The support received from Divisions of Agronomy, Plant Physiology, Agricultural Physics, and Soil Science & Agricultural Chemistry of ICAR-IARI, New Delhi and ICAR-Indian Institute of Maize Research (IIMR) is also acknowledged. The technical inputs of CGIAR Transforming Agrifood Systems in South Asia (TAFSSA) initiative and support received from CIMMYT is also thankfully acknowledged. Special thanks to Dr. Raj Singh, Former Head (Acting), Division of Agronomy, ICAR-IARI, Dr. Renu Pandey, Pr. Scientist, ICAR-IARI, Dr. V.K. Sharma, Pr. Scientist, ICAR-IARI, and Mr. Sanjeev Kumar of ICAR-IIMR for assistance in data management and analysis work.
Links:	Google Scholar

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