Cargando…

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

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 al...

Descripción completa

Detalles Bibliográficos
Autores principales: Adnan, A. A., Diels, J., Jibrin, J. M., Kamara, A. Y., Craufurd, P., Shaibu, A. S., Mohammed, I. B., Tonnang, Z. E. H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6380597/
https://www.ncbi.nlm.nih.gov/pubmed/30779756
http://dx.doi.org/10.1371/journal.pone.0200118
_version_ 1783396327591247872
author Adnan, A. A.
Diels, J.
Jibrin, J. M.
Kamara, A. Y.
Craufurd, P.
Shaibu, A. S.
Mohammed, I. B.
Tonnang, Z. E. H.
author_facet Adnan, A. A.
Diels, J.
Jibrin, J. M.
Kamara, A. Y.
Craufurd, P.
Shaibu, A. S.
Mohammed, I. B.
Tonnang, Z. E. H.
author_sort Adnan, A. A.
collection PubMed
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.
format Online
Article
Text
id pubmed-6380597
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-63805972019-03-01 Options for calibrating CERES-maize genotype specific parameters under data-scarce environments Adnan, A. A. Diels, J. Jibrin, J. M. Kamara, A. Y. Craufurd, P. Shaibu, A. S. Mohammed, I. B. Tonnang, Z. E. H. PLoS One Research Article 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. Public Library of Science 2019-02-19 /pmc/articles/PMC6380597/ /pubmed/30779756 http://dx.doi.org/10.1371/journal.pone.0200118 Text en © 2019 Adnan et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Adnan, A. A.
Diels, J.
Jibrin, J. M.
Kamara, A. Y.
Craufurd, P.
Shaibu, A. S.
Mohammed, I. B.
Tonnang, Z. E. H.
Options for calibrating CERES-maize genotype specific parameters under data-scarce environments
title Options for calibrating CERES-maize genotype specific parameters under data-scarce environments
title_full Options for calibrating CERES-maize genotype specific parameters under data-scarce environments
title_fullStr Options for calibrating CERES-maize genotype specific parameters under data-scarce environments
title_full_unstemmed Options for calibrating CERES-maize genotype specific parameters under data-scarce environments
title_short Options for calibrating CERES-maize genotype specific parameters under data-scarce environments
title_sort options for calibrating ceres-maize genotype specific parameters under data-scarce environments
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6380597/
https://www.ncbi.nlm.nih.gov/pubmed/30779756
http://dx.doi.org/10.1371/journal.pone.0200118
work_keys_str_mv AT adnanaa optionsforcalibratingceresmaizegenotypespecificparametersunderdatascarceenvironments
AT dielsj optionsforcalibratingceresmaizegenotypespecificparametersunderdatascarceenvironments
AT jibrinjm optionsforcalibratingceresmaizegenotypespecificparametersunderdatascarceenvironments
AT kamaraay optionsforcalibratingceresmaizegenotypespecificparametersunderdatascarceenvironments
AT craufurdp optionsforcalibratingceresmaizegenotypespecificparametersunderdatascarceenvironments
AT shaibuas optionsforcalibratingceresmaizegenotypespecificparametersunderdatascarceenvironments
AT mohammedib optionsforcalibratingceresmaizegenotypespecificparametersunderdatascarceenvironments
AT tonnangzeh optionsforcalibratingceresmaizegenotypespecificparametersunderdatascarceenvironments