Modeling Of Physiological Characteristics of Leaves Pine Trees for Estimate Current Annual Increment Growth in Northern Iraq - Ahmed, Abubaker Jasim; A.J.M., Al-Yousif (American Journal of Botany and Bioengineering, 2025)
Physiological processes occurring within tree leaves influence key leaf characteristics, which, in turn, impact overall tree growth. Incorporating these physiological traits into mathematical models enhances their predictive accuracy, particularly when estimating vegetation dynamics. In this study, random samples were collected from pine trees at the Dohuk site, comprising 13 samples with five trees per sample. Phenotypic and physiological attributes of the leaves were measured, including diameter at breast height (DBH), total tree height, and the number of branches per tree. Additional estimates included basal area, crown coverage area, crown length ratio, relative crown length, diameter growth, annual height increment, annual volume growth, leaf surface area, leaf thickness, and nutrient composition. The carbon-to-nitrogen ratio and chemical content of the leaves were also analyzed. Statistical analysis was performed to determine the correlation between essential nutrients (nitrogen, phosphorus, potassium, calcium, magnesium), carbohydrates, and carbon with the annual growth parameters of the trees. The results, visualized using a heat map of the correlation matrix, revealed significant color variations corresponding to correlation values. Findings indicated that annual diameter growth in pine trees significantly affects leaf thickness and mass per unit area, with a coefficient of determination (R²) of 84.57% and a standard error of 0.00212. These results suggest the reliability of this model for growth estimation. Moreover, current annual growth rates in height and volume for Pinus brutia exhibited strong correlations with magnesium and carbon levels. Multiple regression analysis yielded determination coefficients of 95.31% for height and 97.85% for volume, with corresponding standard errors of 0.01038 and 0.000914. Residual analysis confirmed the robustness of these models, making them dependable tools for estimating current annual growth in height and volume of naturally regenerating Pinus brutia in northern Iraq.