Relationship between solar radiation and meteorological variables in predictive models for crop yields.
Saved in:
| Title: | Relationship between solar radiation and meteorological variables in predictive models for crop yields. |
|---|---|
| Alternate Title: | Relação entre a radiação solar e variáveis meteorológicas em modelo preditivo de produtividade agrícola. |
| Authors: | Adam, Abdelkarem M.1 abdoadam7878@gmail.com, Yuan Zheng2 |
| Source: | Revista Brasileira de Engenharia Agrícola e Ambiental - Agriambi. 2025, Vol. 29 Issue 4, p1-9. 9p. |
| Subjects: | Sustainable agriculture, Solar radiation, Farm management, Sorghum farming, Agricultural forecasts |
| Geographic Terms: | Sudan |
| Abstract (English): | Knowledge of the complicated correlation between meteorological variables and crop yield is crucial for food security and agricultural sustainability. This study aimed to investigate how incident solar radiation has affected crop production in the Gadarif region of Sudan over the last 41 years. Using a predictive framework, trends in annual incident solar radiation and temporal variations during sorghum and sesame growing seasons were examined and machine learning (ML) with Extreme Gradient Boosting (XGBoost), Boosted Regression Forest (BRF), and K-Nearest Neighbors (K-NN) was used to predict crop yield. Significant relationships between incident solar radiation indicators and crop yields were identified via detrending approaches and correlation analyses. Results indicate a significant inverse correlation between solar radiation and sorghum yield, and a positive correlation between sesame yield and solar radiation. For both sorghum and sesame yield, K-NN was the most accurate model, demonstrating the significance of incident solar radiation and temperature in predicting crop yield. These findings highlight the potential of ML to improve agricultural forecasting models and inform adaptive agricultural practices in the region. In general, this study provides valuable insights into the dynamic relationship between incident solar radiation and crop yield, emphasizing the importance of considering meteorological factors in agricultural planning and management. [ABSTRACT FROM AUTHOR] |
| Abstract (Portuguese): | O conhecimento da complicada correlação entre as variáveis meteorológicas e o rendimento das culturas é crucial para a segurança alimentar e a sustentabilidade agrícola. Este estudo está centrado na investigação de como a radiação solar incidente afetou a produção agrícola na região de Gadarif, no Sudão, nos últimos quarenta anos. Usando uma estrutura preditiva, a pesquisa avalia tendências recentes na radiação solar incidente anual, examina variações temporais durante as estações de cultivo de sorgo e gergelim e utiliza técnicas de aprendizado de máquina para prever o rendimento das culturas. Além disso, ML, incluindo Extreme Gradient Boosting (XGBoost), Boosted Regression Forest (BRF) e K-Nearest Neighbours (K-NN), foram empregados para previsão de rendimento. Através de abordagens de redução de tendências e análises de correlação, foram identificadas relações significativas entre os indicadores de radiação solar incidente e o rendimento das culturas. Os resultados indicam uma correlação inversa substancial entre a radiação solar e a produção de sorgo, enquanto a produção de gergelim demonstra uma correlação positiva com a radiação solar. Tanto para o rendimento do sorgo como do gergelim, o K-NN surge como o modelo mais preciso, mostrando a importância da radiação solar incidente e da temperatura na previsão do rendimento das culturas. Estas descobertas destacam o potencial da aprendizagem de máquina para melhorar os modelos de previsão agrícola e informar as práticas agrícolas adaptativas na região. Em geral, este estudo fornece informações valiosas sobre a relação dinâmica entre a radiação solar incidente e o rendimento das culturas, enfatizando a importância de considerar fatores meteorológicos no planeamento e gestão agrícola. [ABSTRACT FROM AUTHOR] |
| Copyright of Revista Brasileira de Engenharia Agrícola e Ambiental - Agriambi is the property of Revista Brasileira de Engenharia Agricola e Ambiental and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 182552315 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Relationship between solar radiation and meteorological variables in predictive models for crop yields. – Name: TitleAlt Label: Alternate Title Group: TiAlt Data: Relação entre a radiação solar e variáveis meteorológicas em modelo preditivo de produtividade agrícola. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Adam%2C+Abdelkarem+M%2E%22">Adam, Abdelkarem M.</searchLink><relatesTo>1</relatesTo><i> abdoadam7878@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Yuan+Zheng%22">Yuan Zheng</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Revista+Brasileira+de+Engenharia+Agrícola+e+Ambiental+-+Agriambi%22">Revista Brasileira de Engenharia Agrícola e Ambiental - Agriambi</searchLink>. 2025, Vol. 29 Issue 4, p1-9. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Sustainable+agriculture%22">Sustainable agriculture</searchLink><br /><searchLink fieldCode="DE" term="%22Solar+radiation%22">Solar radiation</searchLink><br /><searchLink fieldCode="DE" term="%22Farm+management%22">Farm management</searchLink><br /><searchLink fieldCode="DE" term="%22Sorghum+farming%22">Sorghum farming</searchLink><br /><searchLink fieldCode="DE" term="%22Agricultural+forecasts%22">Agricultural forecasts</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Sudan%22">Sudan</searchLink> – Name: Abstract Label: Abstract (English) Group: Ab Data: Knowledge of the complicated correlation between meteorological variables and crop yield is crucial for food security and agricultural sustainability. This study aimed to investigate how incident solar radiation has affected crop production in the Gadarif region of Sudan over the last 41 years. Using a predictive framework, trends in annual incident solar radiation and temporal variations during sorghum and sesame growing seasons were examined and machine learning (ML) with Extreme Gradient Boosting (XGBoost), Boosted Regression Forest (BRF), and K-Nearest Neighbors (K-NN) was used to predict crop yield. Significant relationships between incident solar radiation indicators and crop yields were identified via detrending approaches and correlation analyses. Results indicate a significant inverse correlation between solar radiation and sorghum yield, and a positive correlation between sesame yield and solar radiation. For both sorghum and sesame yield, K-NN was the most accurate model, demonstrating the significance of incident solar radiation and temperature in predicting crop yield. These findings highlight the potential of ML to improve agricultural forecasting models and inform adaptive agricultural practices in the region. In general, this study provides valuable insights into the dynamic relationship between incident solar radiation and crop yield, emphasizing the importance of considering meteorological factors in agricultural planning and management. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Abstract (Portuguese) Group: Ab Data: O conhecimento da complicada correlação entre as variáveis meteorológicas e o rendimento das culturas é crucial para a segurança alimentar e a sustentabilidade agrícola. Este estudo está centrado na investigação de como a radiação solar incidente afetou a produção agrícola na região de Gadarif, no Sudão, nos últimos quarenta anos. Usando uma estrutura preditiva, a pesquisa avalia tendências recentes na radiação solar incidente anual, examina variações temporais durante as estações de cultivo de sorgo e gergelim e utiliza técnicas de aprendizado de máquina para prever o rendimento das culturas. Além disso, ML, incluindo Extreme Gradient Boosting (XGBoost), Boosted Regression Forest (BRF) e K-Nearest Neighbours (K-NN), foram empregados para previsão de rendimento. Através de abordagens de redução de tendências e análises de correlação, foram identificadas relações significativas entre os indicadores de radiação solar incidente e o rendimento das culturas. Os resultados indicam uma correlação inversa substancial entre a radiação solar e a produção de sorgo, enquanto a produção de gergelim demonstra uma correlação positiva com a radiação solar. Tanto para o rendimento do sorgo como do gergelim, o K-NN surge como o modelo mais preciso, mostrando a importância da radiação solar incidente e da temperatura na previsão do rendimento das culturas. Estas descobertas destacam o potencial da aprendizagem de máquina para melhorar os modelos de previsão agrícola e informar as práticas agrícolas adaptativas na região. Em geral, este estudo fornece informações valiosas sobre a relação dinâmica entre a radiação solar incidente e o rendimento das culturas, enfatizando a importância de considerar fatores meteorológicos no planeamento e gestão agrícola. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Revista Brasileira de Engenharia Agrícola e Ambiental - Agriambi is the property of Revista Brasileira de Engenharia Agricola e Ambiental and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=182552315 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1590/1807-1929/agriambi.v29n4e285794 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 1 Subjects: – SubjectFull: Sustainable agriculture Type: general – SubjectFull: Solar radiation Type: general – SubjectFull: Farm management Type: general – SubjectFull: Sorghum farming Type: general – SubjectFull: Agricultural forecasts Type: general – SubjectFull: Sudan Type: general Titles: – TitleFull: Relationship between solar radiation and meteorological variables in predictive models for crop yields. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Adam, Abdelkarem M. – PersonEntity: Name: NameFull: Yuan Zheng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 18071929 Numbering: – Type: volume Value: 29 – Type: issue Value: 4 Titles: – TitleFull: Revista Brasileira de Engenharia Agrícola e Ambiental - Agriambi Type: main |
| ResultId | 1 |
