A statistical modeling approach based on the small-scale field trial and meteorological data for preliminary prediction of the impact of low temperature on Eucalyptus globulus trees

Abstract

AbstractEucalyptus trees are important for industrial forestry plantations because of their high potential for biomass production, but their susceptibility to damage at low temperatures restricts their plantation areas. In this study, a 6-year field trial of Eucalyptus globulus was conducted in Tsukuba, Japan, which is the northernmost reach of Eucalyptus plantations, and leaf damage was quantitatively monitored over four of six winters. Leaf photosynthetic quantum yield (QY) levels, an indicator of cold stress-induced damage, fluctuated synchronously with temperature in the winters. We performed a maximum likelihood estimation of the regression model explaining leaf QY using training data subsets for the first 3 years. The resulting model explained QY by the number of days when the daily maximum temperature was below 9.5 °C over approximately the last 7 weeks as an explanatory variable. The correlation coefficient and coefficient of determination of prediction by the model between the predicted and observed values were 0.84 and 0.70, respectively. The model was then used to perform two kinds of simulations. Geographical simulations of potential Eucalyptus plantation areas using global meteorological data from more than 5,000 locations around the world successfully predicted an area that generally agreed with the global Eucalyptus plantation distribution reported previously. Another simulation based on meteorological data of the past 70 years suggested that global warming will increase the potential E. globulus plantation area in Japan approximately 1.5-fold over the next 70 years. These results suggest that the model developed herein would be applicable to preliminary predictions of E. globulus cold damage in the field.