Relationship analysis between Ganoderma boninense-derived Basal Stem Rot disease severity with multiple leaf physiology parameters in mature oil palm tree responses toward water use efficiency (WUE)

Basal Stem Rot (BSR) disease caused by Ganoderma boninense pathogen, is a significant disease in oil palm-producing countries. In Malaysia and Indonesia, the disease attacks on a massive scale in oil palm plantations. Various methods of assessment have been explored on the disease in oil palm plantations. However, none of the measures have yielded an absolute solution. Therefore, this research analysed BSR disease and the resulting plant physiology reaction through water use efficiency (WUE) in oil palm trees to provide a comprehensive understanding of both conditions. The study sought to evaluate WUE performance for mature oil palm trees in varying BSR disease severity and suggested a novel model assessment from WUE for BSR disease severity. Based on the overall result, the WUE parameters were affected by BSR severity in different levels, especially when being compared between healthy (T0) and affected (T1, T2, T3) categories. Secondly, the correlation between WUE with different variables, Photosynthetic Rate (Pr), Transpiration Rate (Tr), Temperature Leaf (TempL), Intercellular CO2 Concentration (Ci), Stomata Conductance (gs), and Chlorophyll Content (F), for frond 9 and frond 17 corresponded to the BSR disease severity. By performing a correlation analysis, a high correlation (0.5 and -0.5) was observed between these variables in both frond levels except in the Chlorophyll Content (F) variables (positive low correlation). Thirdly, the variables that contributed significantly to the WUE performance in mature oil palm included Pr, Tr, and Ci. Specifically, Pr was the highest value in both frond levels while Tr and Ci were inconsistent and ranked second and third place in different frond levels, respectively. Finally, the WUE model developed provides a significant result in BSR disease severity with RMSE (0.54-0.19, 0.48-0.32) for training and validation models respectively. The model order is sorted into Model 1, Model 2, Model 3, and Model 4 in ascending order based on the RMSE accuracy assessment.