A time-dependent parameter estimation framework for crop modeling

Abstract The performance of crop models in simulating various aspects of the cropping system is sensitive to parameter calibration. Parameter estimation is challenging, especially for time-dependent parameters such as cultivar parameters with 2–3 years of lifespan. Manual calibration of the paramete...

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Autores principales:	Faezeh Akhavizadegan, Javad Ansarifar, Lizhi Wang, Isaiah Huber, Sotirios V. Archontoulis
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8c3be05b37e54653af416d31c2667d44
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spelling	oai:doaj.org-article:8c3be05b37e54653af416d31c2667d442021-12-02T18:24:53ZA time-dependent parameter estimation framework for crop modeling10.1038/s41598-021-90835-x2045-2322https://doaj.org/article/8c3be05b37e54653af416d31c2667d442021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90835-xhttps://doaj.org/toc/2045-2322Abstract The performance of crop models in simulating various aspects of the cropping system is sensitive to parameter calibration. Parameter estimation is challenging, especially for time-dependent parameters such as cultivar parameters with 2–3 years of lifespan. Manual calibration of the parameters is time-consuming, requires expertise, and is prone to error. This research develops a new automated framework to estimate time-dependent parameters for crop models using a parallel Bayesian optimization algorithm. This approach integrates the power of optimization and machine learning with prior agronomic knowledge. To test the proposed time-dependent parameter estimation method, we simulated historical yield increase (from 1985 to 2018) in 25 environments in the US Corn Belt with APSIM. Then we compared yield simulation results and nine parameter estimates from our proposed parallel Bayesian framework, with Bayesian optimization and manual calibration. Results indicated that parameters calibrated using the proposed framework achieved an 11.6% reduction in the prediction error over Bayesian optimization and a 52.1% reduction over manual calibration. We also trained nine machine learning models for yield prediction and found that none of them was able to outperform the proposed method in terms of root mean square error and R2. The most significant contribution of the new automated framework for time-dependent parameter estimation is its capability to find close-to-optimal parameters for the crop model. The proposed approach also produced explainable insight into cultivar traits’ trends over 34 years (1985–2018).Faezeh AkhavizadeganJavad AnsarifarLizhi WangIsaiah HuberSotirios V. ArchontoulisNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Faezeh Akhavizadegan Javad Ansarifar Lizhi Wang Isaiah Huber Sotirios V. Archontoulis A time-dependent parameter estimation framework for crop modeling
description	Abstract The performance of crop models in simulating various aspects of the cropping system is sensitive to parameter calibration. Parameter estimation is challenging, especially for time-dependent parameters such as cultivar parameters with 2–3 years of lifespan. Manual calibration of the parameters is time-consuming, requires expertise, and is prone to error. This research develops a new automated framework to estimate time-dependent parameters for crop models using a parallel Bayesian optimization algorithm. This approach integrates the power of optimization and machine learning with prior agronomic knowledge. To test the proposed time-dependent parameter estimation method, we simulated historical yield increase (from 1985 to 2018) in 25 environments in the US Corn Belt with APSIM. Then we compared yield simulation results and nine parameter estimates from our proposed parallel Bayesian framework, with Bayesian optimization and manual calibration. Results indicated that parameters calibrated using the proposed framework achieved an 11.6% reduction in the prediction error over Bayesian optimization and a 52.1% reduction over manual calibration. We also trained nine machine learning models for yield prediction and found that none of them was able to outperform the proposed method in terms of root mean square error and R2. The most significant contribution of the new automated framework for time-dependent parameter estimation is its capability to find close-to-optimal parameters for the crop model. The proposed approach also produced explainable insight into cultivar traits’ trends over 34 years (1985–2018).
format	article
author	Faezeh Akhavizadegan Javad Ansarifar Lizhi Wang Isaiah Huber Sotirios V. Archontoulis
author_facet	Faezeh Akhavizadegan Javad Ansarifar Lizhi Wang Isaiah Huber Sotirios V. Archontoulis
author_sort	Faezeh Akhavizadegan
title	A time-dependent parameter estimation framework for crop modeling
title_short	A time-dependent parameter estimation framework for crop modeling
title_full	A time-dependent parameter estimation framework for crop modeling
title_fullStr	A time-dependent parameter estimation framework for crop modeling
title_full_unstemmed	A time-dependent parameter estimation framework for crop modeling
title_sort	time-dependent parameter estimation framework for crop modeling
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/8c3be05b37e54653af416d31c2667d44
work_keys_str_mv	AT faezehakhavizadegan atimedependentparameterestimationframeworkforcropmodeling AT javadansarifar atimedependentparameterestimationframeworkforcropmodeling AT lizhiwang atimedependentparameterestimationframeworkforcropmodeling AT isaiahhuber atimedependentparameterestimationframeworkforcropmodeling AT sotiriosvarchontoulis atimedependentparameterestimationframeworkforcropmodeling AT faezehakhavizadegan timedependentparameterestimationframeworkforcropmodeling AT javadansarifar timedependentparameterestimationframeworkforcropmodeling AT lizhiwang timedependentparameterestimationframeworkforcropmodeling AT isaiahhuber timedependentparameterestimationframeworkforcropmodeling AT sotiriosvarchontoulis timedependentparameterestimationframeworkforcropmodeling
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A time-dependent parameter estimation framework for crop modeling

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