Yield prediction models for some wheat varieties with satellite-based drought indices and machine learning algorithms
In recent years, frequent drought events in Konya, one of Türkiye's most important cereal production centres, have led to increased pressure on water and soil resources, resulting in yield losses, particularly in wheat production. Alternative yield prediction models, especially those that play a cru...
| Main Authors: | , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
2025
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| Online Access: | https://hdl.handle.net/10568/173507 |
| _version_ | 1855540559621390336 |
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| author | Cem Akcapınar, M. Cakmak, B. |
| author_browse | Cakmak, B. Cem Akcapınar, M. |
| author_facet | Cem Akcapınar, M. Cakmak, B. |
| author_sort | Cem Akcapınar, M. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | In recent years, frequent drought events in Konya, one of Türkiye's most important cereal production centres, have led to increased pressure on water and soil resources, resulting in yield losses, particularly in wheat production. Alternative yield prediction models, especially those that play a crucial role in agricultural import–export planning in the region, are important for economic contributions and the development of early warning systems. In this context, the aim of this study is to develop models that can be used in the yield prediction of wheat varieties widely grown in the Konya Altınova region. Agricultural drought indices obtained from Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) products of the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite were used to obtain model inputs. These indices are the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI) and Vegetation Supply Water Index (VSWI). In obtaining the input parameters for the models, the growth periods of the varieties in the region were also considered. Using various machine learning algorithms, 21 yield prediction models for Bayraktar-2000, 12 for Kızıltan-91 and 8 for Bezostaya-1 were presented as alternatives, with model performances (coefficient of determination, R2) ranging between 0.74 and 0.97, 0.73 and 0.96, and 0.69 and 0.87, respectively. |
| format | Journal Article |
| id | CGSpace173507 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| record_format | dspace |
| spelling | CGSpace1735072025-10-26T13:02:36Z Yield prediction models for some wheat varieties with satellite-based drought indices and machine learning algorithms Cem Akcapınar, M. Cakmak, B. In recent years, frequent drought events in Konya, one of Türkiye's most important cereal production centres, have led to increased pressure on water and soil resources, resulting in yield losses, particularly in wheat production. Alternative yield prediction models, especially those that play a crucial role in agricultural import–export planning in the region, are important for economic contributions and the development of early warning systems. In this context, the aim of this study is to develop models that can be used in the yield prediction of wheat varieties widely grown in the Konya Altınova region. Agricultural drought indices obtained from Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) products of the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite were used to obtain model inputs. These indices are the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI) and Vegetation Supply Water Index (VSWI). In obtaining the input parameters for the models, the growth periods of the varieties in the region were also considered. Using various machine learning algorithms, 21 yield prediction models for Bayraktar-2000, 12 for Kızıltan-91 and 8 for Bezostaya-1 were presented as alternatives, with model performances (coefficient of determination, R2) ranging between 0.74 and 0.97, 0.73 and 0.96, and 0.69 and 0.87, respectively. 2025-02 2025-03-07T07:26:13Z 2025-03-07T07:26:13Z Journal Article https://hdl.handle.net/10568/173507 en Open Access Cem Akcapınar, M.; Cakmak, B. 2025. Yield prediction models for some wheat varieties with satellite-based drought indices and machine learning algorithms. Irrigation and Drainage, 74(1):237-250. [doi:https://doi.org/10.1002/ird.2989] |
| spellingShingle | Cem Akcapınar, M. Cakmak, B. Yield prediction models for some wheat varieties with satellite-based drought indices and machine learning algorithms |
| title | Yield prediction models for some wheat varieties with satellite-based drought indices and machine learning algorithms |
| title_full | Yield prediction models for some wheat varieties with satellite-based drought indices and machine learning algorithms |
| title_fullStr | Yield prediction models for some wheat varieties with satellite-based drought indices and machine learning algorithms |
| title_full_unstemmed | Yield prediction models for some wheat varieties with satellite-based drought indices and machine learning algorithms |
| title_short | Yield prediction models for some wheat varieties with satellite-based drought indices and machine learning algorithms |
| title_sort | yield prediction models for some wheat varieties with satellite based drought indices and machine learning algorithms |
| url | https://hdl.handle.net/10568/173507 |
| work_keys_str_mv | AT cemakcapınarm yieldpredictionmodelsforsomewheatvarietieswithsatellitebaseddroughtindicesandmachinelearningalgorithms AT cakmakb yieldpredictionmodelsforsomewheatvarietieswithsatellitebaseddroughtindicesandmachinelearningalgorithms |