Efficient sandalwood growth prediction models based on soil, weather and tree parameters under different Agro-Climatic Zones in Karnataka

Efficient prediction of sandalwood ( Santalum album L.) heartwood growth under varying agro-climatic conditions is vital for optimizing plantation management and enhancing yield. This study integrates key soil characteristics, climatic variables, and tree growth parameters to develop region-specific growth prediction models for Santalum album across multiple Agro-Climatic Zones (ACZs) in Karnataka. Based on a study conducted on sandalwood across 73 locations in nine Agro-Climatic Zones (ACZs) of Karnataka, a statistical assessment was carried out, and regression models were developed to predict tree growth using soil parameters. These models were constructed both within individual ACZs and across pooled locations. The regression model for predicting tree height based on soil parameters yielded R² values of: 0.963 (1.471 m) in ACZ-3, 0.856 in ACZ-4, 0.669 in ACZ-5, 0.858 in ACZ-6, 0.997 when pooled over ACZ-2, 7, 8, 9, and 10, and 0.368 when pooled across all ACZs. The analysis indicated a significant negative effect of sulphur (S) and iron (Fe) on tree height. The tree diameter model produced R² values of: 0.823 in ACZ-3, 0.868 in ACZ-4, 0.441 in ACZ-5, 0.980 in ACZ-6, 0.975 when pooled over ACZ-2, 7, 8, 9, and 10, and 0.076 when pooled over all ACZs. For heartwood diameter, the model gave R² values of: 0.851 in ACZ-3, 0.778 in ACZ-4, 0.372 in ACZ-5, a highly significant 0.995 in ACZ-6, 0.946 when pooled over ACZ-2, 7, 8, 9, and 10, and 0.120 when pooled across all ACZs. The heartwood percentage model resulted in R² values of: 0.735 in ACZ-3, 0.826 in ACZ-4, showing a significant positive effect of manganese (Mn), 0.643 in ACZ-5, with a significant negative effect of potassium (K) and iron (Fe), and a positive effect of zinc (Zn), a significant R² of 0.952 in ACZ-6, 0.915 when pooled over ACZ-2, 7, 8, 9, and 10, and 0.272 when pooled over all ACZs, indicating a significant negative effect of pH and Fe, and a significant positive effect of Mn and Zn. This assessment of variability and interrelationships between tree growth and soil parameters highlights the predictive potential of regression models for sandalwood. These findings can be applied to similar agro-climatic and soil conditions in other regions.