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

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cg.contributor.affiliationICAR-Indian Agricultural Research Institute (IARI), New Delhi, Indiaen
cg.contributor.affiliationICAR-Indian Institute of Farming System Research, Modipuram, Indiaen
cg.contributor.affiliationCornell Universityen
cg.contributor.affiliationInternational Water Management Instituteen
cg.contributor.affiliationICAR-Indian Agricultural Statistical Research Institute (IASRI), New Delhi, Indiaen
cg.contributor.affiliationICAR-Indian Agricultural Research Institute (IARI), Gogamukh, Assam, Indiaen
cg.contributor.affiliationICAR-Indian Institute of Maize Researchen
cg.contributor.affiliationInternational Fertilizer Development Centreen
cg.contributor.affiliationInternational Crops Research Institute for the Semi-Arid Tropicsen
cg.contributor.affiliationInternational Maize and Wheat Improvement Centeren
cg.contributor.donorCGIAR Trust Funden
cg.contributor.initiativeTransforming Agrifood Systems in South Asia
cg.coverage.countryIndia
cg.coverage.iso3166-alpha2IN
cg.coverage.regionSouthern Asia
cg.coverage.subregionNew Delhi
cg.identifier.doihttps://doi.org/10.1038/s41598-024-61976-6en
cg.identifier.urlhttps://www.nature.com/articles/s41598-024-61976-6.pdfen
cg.isijournalISI Journalen
cg.issn2045-2322en
cg.issue1en
cg.journalScientific Reportsen
cg.reviewStatusPeer Reviewen
cg.subject.actionAreaResilient Agrifood Systems
cg.subject.impactAreaClimate adaptation & mitigation
cg.volume14en
dc.contributor.authorKumar, K.en
dc.contributor.authorParihar, C. M.en
dc.contributor.authorNayak, H. S.en
dc.contributor.authorSena, Dipaka R.en
dc.contributor.authorGodara, S.en
dc.contributor.authorDhakar, R.en
dc.contributor.authorPatra, K.en
dc.contributor.authorSarkar, A.en
dc.contributor.authorBharadwaj, S.en
dc.contributor.authorGhasal, P. C.en
dc.contributor.authorMeena, A. L.en
dc.contributor.authorReddy, K. S.en
dc.contributor.authorDas, T. K.en
dc.contributor.authorJat, S. L.en
dc.contributor.authorSharma, D. K.en
dc.contributor.authorSaharawat, Y. S.en
dc.contributor.authorSingh, U.en
dc.contributor.authorJat, M. L.en
dc.contributor.authorGathala, M. K.en
dc.date.accessioned2024-05-31T23:56:31Zen
dc.date.available2024-05-31T23:56:31Zen
dc.identifier.urihttps://hdl.handle.net/10568/144223
dc.titleModeling maize growth and nitrogen dynamics using CERES-Maize (DSSAT) under diverse nitrogen management options in a conservation agriculture-based maize-wheat systemen
dcterms.abstractAgricultural 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.en
dcterms.accessRightsOpen Access
dcterms.available2024-05-23en
dcterms.bibliographicCitationKumar, K.; Parihar, C. M.; Nayak, H. S.; Sena, Dipaka R.; Godara, S.; Dhakar, R.; Patra, K.; Sarkar, A.; Bharadwaj, S.; Ghasal, P. C.; Meena, A. L.; Reddy, K. S.; Das, T. K.; Jat, S. L.; Sharma, D. K.; Saharawat, Y. S.; Singh, U.; Jat, M. L.; 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, 14:11743. [doi: https://doi.org/10.1038/s41598-024-61976-6]en
dcterms.extent14:11743.en
dcterms.issued2024-05-23en
dcterms.languageen
dcterms.licenseCC-BY-4.0
dcterms.publisherSpringer Science and Business Media LLCen
dcterms.subjectmaizeen
dcterms.subjectplant growthen
dcterms.subjectmodellingen
dcterms.subjectnitrogenen
dcterms.subjectammoniaen
dcterms.subjectvolatilizationen
dcterms.subjectconservation agricultureen
dcterms.subjectwheaten
dcterms.subjectzero tillageen
dcterms.subjectleaf area indexen
dcterms.subjectbiomassen
dcterms.subjectgrainen
dcterms.subjectcrop yielden
dcterms.subjectforecastingen
dcterms.typeJournal Article

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