List of symbols used in this study.
<div><p>Accurate prediction of crop phenological stage is essential for evaluating management strategies and assessing crop responses to environmental changes. In this work, we modified Non-dominated Sorting Genetic Algorithm with the core algorithm of PEST (MNSGA-II) and compared it to...
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2025
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إضافة وسم
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| _version_ | 1852020372304560128 |
|---|---|
| author | Yonghui Zhang (279832) |
| author2 | Yujie Zhang (399434) Haiyan Jiang (39944) Liang Tang (48583) Xiaojun Liu (58952) Weixing Cao (575808) Yan Zhu (112437) |
| author2_role | author author author author author author |
| author_facet | Yonghui Zhang (279832) Yujie Zhang (399434) Haiyan Jiang (39944) Liang Tang (48583) Xiaojun Liu (58952) Weixing Cao (575808) Yan Zhu (112437) |
| author_role | author |
| dc.creator.none.fl_str_mv | Yonghui Zhang (279832) Yujie Zhang (399434) Haiyan Jiang (39944) Liang Tang (48583) Xiaojun Liu (58952) Weixing Cao (575808) Yan Zhu (112437) |
| dc.date.none.fl_str_mv | 2025-05-15T16:14:49Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0323927.t004 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/List_of_symbols_used_in_this_study_/29078464 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Genetics Biotechnology Ecology Plant Biology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified soybean phenology using soybean phenological model mean absolute error little difference among evaluating management strategies crop phenological stage crop model parameter crop model according case study using assessing crop responses simulated data based independent experimental data relatively ideal algorithm div >< p source data observed data suitable algorithm source datasets rmse ), results provide modified non mae ), exactly simulate environmental changes differential evolution core algorithm certain advantage actual requirements accurate prediction |
| dc.title.none.fl_str_mv | List of symbols used in this study. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>Accurate prediction of crop phenological stage is essential for evaluating management strategies and assessing crop responses to environmental changes. In this work, we modified Non-dominated Sorting Genetic Algorithm with the core algorithm of PEST (MNSGA-II) and compared it to two other algorithms of Generalized Likelihood Uncertainty Estimation (GLUE) and Differential Evolution (DE) to calibrate the cultivar-specific parameters (CSPs) of CROPGRO-Soybean phenological model (CSPM) so as to exactly simulate the soybean phenology using the multi-source datasets of multi-site, multi-year, and multi-cultivar. Independent experimental data are used to validate the CSPM with the optimized parameters. The root means square error (RMSE), the mean absolute error (MAE), and coefficient of determination (R<sup>2</sup>) are used to evaluate the effects of different algorithms on calibrating the CSPs. The RMSEs (MAEs, R<sup>2</sup>) between all observed data and simulated data based on MNSGA-II, GLUE and DE are 4.28 (3.53, 0.9445) days, 4.76 (4.05, 0.9438) days and 5.17 (4.85, 0.9336) days, respectively, with little difference among the three algorithms. MNSGA-II has a certain advantage in calibration effect, and GLUE is the most stable during the repetition of each calibration. The MNSGA-II can be considered as a relatively ideal algorithm for estimating the crop model parameter. Which algorithm should be selected to calibrate the parameters of crop model according to the actual requirements. These results provide a reference to choose the suitable algorithm for estimating crop model parameter.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_eba6b356ccdc5bce5d8258d06bf383cd |
| identifier_str_mv | 10.1371/journal.pone.0323927.t004 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29078464 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | List of symbols used in this study.Yonghui Zhang (279832)Yujie Zhang (399434)Haiyan Jiang (39944)Liang Tang (48583)Xiaojun Liu (58952)Weixing Cao (575808)Yan Zhu (112437)GeneticsBiotechnologyEcologyPlant BiologyEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedsoybean phenology usingsoybean phenological modelmean absolute errorlittle difference amongevaluating management strategiescrop phenological stagecrop model parametercrop model accordingcase study usingassessing crop responsessimulated data basedindependent experimental datarelatively ideal algorithmdiv >< psource dataobserved datasuitable algorithmsource datasetsrmse ),results providemodified nonmae ),exactly simulateenvironmental changesdifferential evolutioncore algorithmcertain advantageactual requirementsaccurate prediction<div><p>Accurate prediction of crop phenological stage is essential for evaluating management strategies and assessing crop responses to environmental changes. In this work, we modified Non-dominated Sorting Genetic Algorithm with the core algorithm of PEST (MNSGA-II) and compared it to two other algorithms of Generalized Likelihood Uncertainty Estimation (GLUE) and Differential Evolution (DE) to calibrate the cultivar-specific parameters (CSPs) of CROPGRO-Soybean phenological model (CSPM) so as to exactly simulate the soybean phenology using the multi-source datasets of multi-site, multi-year, and multi-cultivar. Independent experimental data are used to validate the CSPM with the optimized parameters. The root means square error (RMSE), the mean absolute error (MAE), and coefficient of determination (R<sup>2</sup>) are used to evaluate the effects of different algorithms on calibrating the CSPs. The RMSEs (MAEs, R<sup>2</sup>) between all observed data and simulated data based on MNSGA-II, GLUE and DE are 4.28 (3.53, 0.9445) days, 4.76 (4.05, 0.9438) days and 5.17 (4.85, 0.9336) days, respectively, with little difference among the three algorithms. MNSGA-II has a certain advantage in calibration effect, and GLUE is the most stable during the repetition of each calibration. The MNSGA-II can be considered as a relatively ideal algorithm for estimating the crop model parameter. Which algorithm should be selected to calibrate the parameters of crop model according to the actual requirements. These results provide a reference to choose the suitable algorithm for estimating crop model parameter.</p></div>2025-05-15T16:14:49ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0323927.t004https://figshare.com/articles/dataset/List_of_symbols_used_in_this_study_/29078464CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/290784642025-05-15T16:14:49Z |
| spellingShingle | List of symbols used in this study. Yonghui Zhang (279832) Genetics Biotechnology Ecology Plant Biology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified soybean phenology using soybean phenological model mean absolute error little difference among evaluating management strategies crop phenological stage crop model parameter crop model according case study using assessing crop responses simulated data based independent experimental data relatively ideal algorithm div >< p source data observed data suitable algorithm source datasets rmse ), results provide modified non mae ), exactly simulate environmental changes differential evolution core algorithm certain advantage actual requirements accurate prediction |
| status_str | publishedVersion |
| title | List of symbols used in this study. |
| title_full | List of symbols used in this study. |
| title_fullStr | List of symbols used in this study. |
| title_full_unstemmed | List of symbols used in this study. |
| title_short | List of symbols used in this study. |
| title_sort | List of symbols used in this study. |
| topic | Genetics Biotechnology Ecology Plant Biology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified soybean phenology using soybean phenological model mean absolute error little difference among evaluating management strategies crop phenological stage crop model parameter crop model according case study using assessing crop responses simulated data based independent experimental data relatively ideal algorithm div >< p source data observed data suitable algorithm source datasets rmse ), results provide modified non mae ), exactly simulate environmental changes differential evolution core algorithm certain advantage actual requirements accurate prediction |