A semi-physiological model of cold hardening and dehardening in walnut stem

It has been hypothesized that the increase in temperature in this century could lead to an increase in frost damage to plant tissues. Several models have been proposed to describe the development of cold hardiness, but never taking into account extreme climatic and/or physiological events. Our results on walnut tree (Juglans regia L.) show that cold hardiness was best correlated with average daily temperatures minimal temperatures over the last 15 days before sampling (), indicating that the freezing tolerance depended on the tree's climatic history. Moreover, this study also shows that the accumulation of sucrose and the water content (WC) decrease are an essential step towards cold hardiness. Thus, a simple linear model based on climatic () and physiological (soluble sugars, WC) explanatory variables was developed to predict the cold hardiness level in walnut stem at any time during the leafless period. Each of the three input variables can be assigned a specific role contributing to the simulated function, cold hardiness. The extent and robustness of this relation was assessed on extreme physiological events on walnut trees bearing three main branches. On each tree, one branch was defoliated to limit the local carbohydrate and transpiration, one was girdled to increase local carbohydrate and prevent carbohydrate export and the third one was kept untreated as control. As expected, these treatments impacted both local carbon reserves and WC in the stems born by each main branch in comparison with the control on the same tree. The impact of these treatments on stem's freezing tolerance, as evaluated by an electrolyte leakage method (LT50), confirmed the direct impact of soluble sugar and WC on cold hardiness over a wide range of carbohydrate and WC. This is discussed in relation to the branch autonomy theory for carbon but also for water during summer growth and winter periods. The present study demonstrates the importance of physiological parameters in the prediction of cold hardiness and proposes a way to model cold hardiness with extreme climatic and/or physiological events.