| Titre : | Yield prediction models for some wheat varieties with satellite-based drought indices and machine learning algorithms (2025) |
| Auteurs : | M.C. Akcapinar ; B. Çakmak |
| Type de document : | Article |
| Dans : | Irrigation and Drainage (vol. 74, n. 1, February 2025) |
| Article en page(s) : | p. 237-250 |
| Langues : | Anglais |
| Langues du résumé : | Anglais |
| Catégories : |
Catégories principales 06 - AGRICULTURE. FORÊTS. PÊCHES ; 6.5 - Gestion des ExploitationsThésaurus IAMM MODELE ; PREVISION ; RENDEMENT ; RENDEMENT DES CULTURES ; BLE ; CEREALICULTURE ; TURQUIE |
| Résumé : | 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 Altinova 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 Kiziltan-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. |
| Cote : | En ligne |
| URL / DOI : | https://doi.org/10.1002/ird.2989 |
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