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Options for calibrating CERES-maize genotype specific parameters under data-scarce environments

Author: Adnan, A. A.
Author: Diels, J.
Author: Jibrin, J.M.
Author: Kamara, A.Y.
Author: Craufurd, P.
Author: Shaibu, A.S.
Author: Mohammed, I.B.
Author: Tonnang, H.
Year: 2019
ISSN: 1932-6203 (Print)
Format: PDF
Language: English
Publisher: Public Library of Science
Copyright: CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose.
Type: Article
Place of Publication: San Francisco, CA (USA)
Issue: 2
Issue: art. : e0200118
Volume: 14
DOI: 10.1371/journal.pone.0200118
Description: Most crop simulation models require the use of Genotype Specific Parameters (GSPs) which provide the Genotype component of G×E×M interactions. Estimation of GSPs is the most difficult aspect of most modelling exercises because it requires expensive and time-consuming field experiments. GSPs could also be estimated using multi-year and multi locational data from breeder evaluation experiments. This research was set up with the following objectives: i) to determine GSPs of 10 newly released maize varieties for the Nigerian Savannas using data from both calibration experiments and by using existing data from breeder varietal evaluation trials; ii) to compare the accuracy of the GSPs generated using experimental and breeder data; and iii) to evaluate CERES-Maize model to simulate grain and tissue nitrogen contents. For experimental evaluation, 8 different experiments were conducted during the rainy and dry seasons of 2016 across the Nigerian Savanna. Breeder evaluation data were also collected for 2 years and 7 locations. The calibrated GSPs were evaluated using data from a 4-year experiment conducted under varying nitrogen rates (0, 60 and 120kg N ha-1). For the model calibration using experimental data, calculated model efficiency (EF) values ranged between 0.88–0.94 and coefficient of determination (d-index) between 0.93–0.98. Calibration of time-series data produced nRMSE below 7% while all prediction deviations were below 10% of the mean. For breeder experiments, EF (0.58–0.88) and d-index (0.56–0.86) ranges were lower. Prediction deviations were below 17% of the means for all measured variables. Model evaluation using both experimental and breeder trials resulted in good agreement (low RMSE, high EF and d-index values) between observed and simulated grain yields, and tissue and grain nitrogen contents. It is concluded that higher calibration accuracy of CERES-Maize model is achieved from detailed experiments. If unavailable, data from breeder experimental trials collected from many locations and planting dates can be used with lower but acceptable accuracy.
Agrovoc: MAIZE
Agrovoc: DATA
Related Datasets:
Journal: PLoS ONE

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  • Sustainable Intensification
    Sustainable intensification agriculture including topics on cropping systems, agronomy, soil, mechanization, precision agriculture, etc.

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