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Resilience Strategies of Carob (Ceratonia siliqua L.) to Salt Stress: Multivariate Approaches and Artificial Intelligence for Sustainable Agriculture
* 1 , 2, 3 , 4 , 4 , 5, 6 , 4
1  Laboratory of Natural Resources and Sustainable Development, Faculty of Sciences, University Ibn TofaIl, Kenitra, Morocco
2  Biology and Health Laboratory, Higher Institute of Nursing and Health Techniques Faculty of Science, Ibn Tofail University, Kenitra 14000, Morocco
3  Natural Resources and Sustainable Development laboratory, Faculty of Sciences, Ibn Tofail University, Ke-nitra, Morocco
4  Laboratory of Natural Resources and Sustainable Development, Faculty of Sciences, Ibn Tofaïl University—KENITRA-University Campus, Kenitra 14000, Morocco
5  LASIRE, Equipe Physico-Chimie de l’Environnement, CNRS UMR 8516, Université Lille, Sciences et Tech-nologies, CEDEX, 59655 Villeneuve d′Ascq, France
6  Department of Chemistry, Université d’Artois, IUT de Béthune, 62400 Béthune, France
Academic Editor: Bin Gao

Abstract:

In arid and semi-arid regions, where water scarcity and soil salinization are major challenges, carob (Ceratonia siliqua L.) has gained attention as a potential drought- and salt-tolerant crop. However, its ability to withstand salt stress is determined by a complex interplay of genetic, physiological, and environmental factors, which requires a comprehensive approach to fully understand. This study aims to explore the resilience of carob to salt stress by integrating advanced multivariate methods, Partial Least Squares Structural Equation Modeling (PLS-SEM), and Artificial Intelligence-assisted Bayesian Inference to identify key determinants of stress tolerance. Through a Multivariate Analysis of Variance (MANOVA), we found significant effects of salt stress (F(5,30) = 7.3637, p < 2.2e-16) and ecotype (F(5,30) = 16.4968, p < 2.2e-16) on physiological and biochemical traits. The Principal Component Analysis (PCA) revealed a distinct separation between stressed and control plants, with the first principal component (PC1) explaining 76.66% of the variance, which was closely related to biomass, water content, and root length. The PLS-SEM model identified root length as the primary factor influencing biomass (coefficient = 0.629, p < 0.05), while water content and chlorophyll had no direct significant effect. Hierarchical Bayesian Inference allowed for the assessment of intra-population variability, showing that the Ouazzane provenance exhibited the highest salt resistance, followed by Safi and Aït Attab, while Khemissat proved the most vulnerable. Additionally, specific environmental effects (E[i]) demonstrated that certain provenances, such as Berkane and Aït Attab, benefitted significantly from co-cultivation with Spergularia salina under 10 g/L NaCl. These findings underline the importance of varietal selection and biotic interactions as effective strategies to mitigate salt stress, highlighting the potential for agroecological adaptation of carob to increasingly saline environments, offering valuable insights for sustainable agricultural practices in areas affected by salinity.

Keywords: Salt stress resilience, multivariate analysis, PLS-SEM modeling, Bayesian inference, agroecological adaptation.

 
 
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