A new regional cotton growth model based on reference crop evapotranspiration for predicting growth processes
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource, 1 online resource.
- Language
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Englisch
- Bibliographic citation
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A new regional cotton growth model based on reference crop evapotranspiration for predicting growth processes ; volume:13 ; number:1 ; day:5 ; month:5 ; year:2023 ; pages:1-19 ; date:12.2023
Scientific reports ; 13, Heft 1 (5.5.2023), 1-19, 12.2023
- Creator
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Lin, Shudong
Deng, Mingjiang
Wei, Kai
Wang, Quanjiu
Su, Lijun
- Contributor
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SpringerLink (Online service)
- DOI
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10.1038/s41598-023-34552-7
- URN
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urn:nbn:de:101:1-2023090609311636401561
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:51 AM CEST
Data provider
This object is provided by:
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Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Lin, Shudong
- Deng, Mingjiang
- Wei, Kai
- Wang, Quanjiu
- Su, Lijun
- SpringerLink (Online service)
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Calibration of mass transfer-based models to predict reference crop evapotranspiration
Reconstructing climate trends adds skills to seasonal reference crop evapotranspiration forecasting
The best alternative for estimating reference crop evapotranspiration in different sub-regions of mainland China
Evaluation of Brevibacillus brevis as a potential plant growth promoting rhizobacteria for cotton (Gossypium hirsutum) crop
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Exogenously applied growth regulators protect the cotton crop from heat-induced injury by modulating plant defense mechanism
Bias-correcting individual inputs prior to combined calibration leads to more skillful forecasts of reference crop evapotranspiration
Advanced reference crop evapotranspiration prediction: a novel framework combining neural nets, bee optimization algorithm, and mode decomposition
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Reference crop evapotranspiration database in Spain (1961–2014)
Bias-correcting input variables enhances forecasting of reference crop evapotranspiration
Calibration of mass transfer-based models to predict reference crop evapotranspiration
Reconstructing climate trends adds skills to seasonal reference crop evapotranspiration forecasting
The best alternative for estimating reference crop evapotranspiration in different sub-regions of mainland China
Evaluation of Brevibacillus brevis as a potential plant growth promoting rhizobacteria for cotton (Gossypium hirsutum) crop
Estimation reference crop evapotranspiration (ET0) using artificial intelligence model in an arid climate with external data
Exogenously applied growth regulators protect the cotton crop from heat-induced injury by modulating plant defense mechanism
Bias-correcting individual inputs prior to combined calibration leads to more skillful forecasts of reference crop evapotranspiration
Advanced reference crop evapotranspiration prediction: a novel framework combining neural nets, bee optimization algorithm, and mode decomposition
Machine learning techniques in estimation of eggplant crop evapotranspiration
Estimation of irrigated crop artificial irrigation evapotranspiration in China
Reference crop evapotranspiration database in Spain (1961–2014)
Bias-correcting input variables enhances forecasting of reference crop evapotranspiration
Calibration of mass transfer-based models to predict reference crop evapotranspiration
Reconstructing climate trends adds skills to seasonal reference crop evapotranspiration forecasting
The best alternative for estimating reference crop evapotranspiration in different sub-regions of mainland China
Evaluation of Brevibacillus brevis as a potential plant growth promoting rhizobacteria for cotton (Gossypium hirsutum) crop
Estimation reference crop evapotranspiration (ET0) using artificial intelligence model in an arid climate with external data
Exogenously applied growth regulators protect the cotton crop from heat-induced injury by modulating plant defense mechanism
Bias-correcting individual inputs prior to combined calibration leads to more skillful forecasts of reference crop evapotranspiration
Advanced reference crop evapotranspiration prediction: a novel framework combining neural nets, bee optimization algorithm, and mode decomposition
Machine learning techniques in estimation of eggplant crop evapotranspiration