ORYZA (v3) rice crop growth modeling for MR269 under nitrogen treatments: Assessment of cross-validation on parameter variability

Khairudin, Nurkhairizan; Muharam, Farrah Melissa; Shahar, Nurul Aina Najwa; Hashim, Muhamad Faiz Che; Ismail, Mohd Razi; Keesman, Karel J.; Zulkafli, Zed

doi:10.1016/j.compag.2022.106809

Cited by 4 publications

(4 citation statements)

References 39 publications

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“…However, Yu et al (2021) found that the ORYZA_V3 model could generate accurate predictions of AGB and yield at harvest, but poor prediction accuracy of rice LAI during the whole growing season. Nurulhuda et al (2022) also found that this crop model overestimated the rice LAI and biomass of dead leaves, and the Nash-Sutcliffe efficiency value was smaller than 0.06. The poor predictive performance may be caused by measurement errors, rice genotypic variability, and predictive uncertainties associated with model structure, parameters, and input data (Rötter et al, 2012;Yu et al, 2021).…”

Section: Discussionmentioning

confidence: 71%

“…Nurulhuda et al. (2022) also found that this crop model overestimated the rice LAI and biomass of dead leaves, and the Nash‐Sutcliffe efficiency value was smaller than 0.06. The poor predictive performance may be caused by measurement errors, rice genotypic variability, and predictive uncertainties associated with model structure, parameters, and input data (Rötter et al., 2012; Yu et al., 2021).…”

Section: Discussionmentioning

confidence: 91%

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Performance evaluation of the ORYZA_V3 model for rice growth and organ nitrogen content

Yu,

Ren,

et al. 2023

Agronomy Journal

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The ORYZA version 3 (ORYZA_V3) model is commonly used for simulating paddy rice (Oryza sativa L.) growth. However, few studies have investigated the performance of the ORYZA_V3 model for predicting in‐season crop growth variables, especially for the nitrogen translocation process among paddy rice organs in the whole growing season, i.e., from sowing to harvest stages. This study examines the prediction accuracy of seven paddy rice growth variables based on one cropping season of field experiment. The results show that: (a) The ORYZA_V3 model generates accurate prediction of paddy rice leaf area index (LAI), above‐ground biomass (AGB), and yield, where root mean square errors (RMSEs) are 0.98‐1.11 ha/ha, 2360.5‐3028.7 kg/ha, and 492.9‐515.2 kg/ha, respectively, coefficient of determination (R2) ranges from 0.76 to 1.00, and index of agreement (D‐index) ranges from 0.50 to 0.98. (b) The prediction accuracy of paddy rice leaf and stem nitrogen contents is acceptable, where RMSE ranges from 0.0017 to 0.0041 kg/kg, R2 ranges from 0.82 to 0.96, and D‐index ranges from 0.93 to 0.98. (c) The ORYZA_V3 model cannot generate accurate prediction of rice root and panicle nitrogen contents, where RMSE ranges from 0.0007 to 0.0022 kg/kg, R2 ranges from 0.01 to 0.20, and D‐index ranges from 0.17 to 0.65. The structural uncertainty in the nitrogen translocation module may be a major reason for the unacceptable crop model prediction. This study demonstrated the substantial potential of the ORYZA_V3 model for simulating and understanding paddy rice growth and organ nitrogen translocation.This article is protected by copyright. All rights reserved

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Section: Discussionmentioning

confidence: 71%

Section: Discussionmentioning

confidence: 91%

Performance evaluation of the ORYZA_V3 model for rice growth and organ nitrogen content

Yu,

Ren,

et al. 2023

Agronomy Journal

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“…Decision-making and planning in agriculture have increasingly made the application of crop model-based decision support tools popular (Amiri et al, 2014;Li et al, 2013;Nurulhuda et al, 2022;Toumi et al, 2016;W. Wang et al, 2017;Xu et al, 2013;Yuan et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Decision‐making and planning in agriculture have increasingly made the application of crop model–based decision support tools popular (Amiri et al., 2014; Li et al., 2013; Nurulhuda et al., 2022; Toumi et al., 2016; W. Wang et al., 2017; Xu et al., 2013; Yuan et al., 2017). It is necessary to ensure the prediction accuracy of crop models to serve as a guide for agricultural managers and policy‐makers.…”

Section: Introductionmentioning

confidence: 99%

Multiple model averaging methods for predicting regional rice yield

Shi²,

Zhan

2023

Agronomy Journal

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Using multiple crop models in an ensemble can generate more accurate crop growth predictions than only using an individual crop model. However, few studies have investigated the performances of individual crop models and multiple model averaging (MMA) methods for predicting regional rice (Oryza sativa L.) yield with limited inputs and observations. This study assessed the performances of three individual crop models, that is, AquaCrop, WOFOST, and Oryza version 3 (OryzaV3), and five MMA methods for predicting regional rice yield. Results showed that the AquaCrop model achieved better performances than the WOFOST and OryzaV3 models for predicting regional rice yield, especially for the early‐season rice yield, which implied that a crop model with a simple structure was powerful for making accurate regional rice yield predictions. The MMA methods achieved better performances than the AquaCrop model for predicting the late‐season rice yield; however, the AquaCrop model and three information criterion (IC) methods, that is, Akaike IC (AIC), Bayesian IC (BIC), and AICc (bias‐corrected), achieved more acceptable prediction accuracies of the early‐season rice yield than the other MMA methods. Overall, not all the MMA methods could necessarily produce more accurate regional rice yield prediction than individual crop models. This study suggested using the simple‐structure crop models and the AIC, AICc, and BIC methods for making accurate regional crop yield prediction when limited data are available for model parameters calibration.

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