Abstract
A generalized simulation model, ROOTSIMU, which utilizes dynamic carbon and water balance algorithms, was modified to simulate loblolly pine (Pinustaeda L.) seedling growth and water uptake for a 100-day transplant period. The modifications included an allowance for time-dependent changes in photosynthesis and carbon allocation. Heat sums were used to control the initiation of growth. Additional compartments were added to separate the physiological functions of suberized and nonsuberized roots and secondary woody tissues. Values used to initialize the model were largely derived from the published literature. The predicted results of a simulation run using 1985 and 1986 weather data are reported. Changes in simulated plant water potential were closely related to periods of rainfall or high evaporative demand. Midday values were occasionally less than −7 MPa when evaporative demand was high. Simulated responses to the 1986 drought indicated that initial soil water potentials at planting affected survival at values of less than −0.064 MPa in a sandy soil. Simulated growth was very sensitive to the photosynthetic rate, less sensitive to initial soluble carbohydrate concentration, and insensitive to instantaneous carbon allocation in relation to drought stress. The predicted increase in total root length for 1985 corresponded to the responses reported in several controlled environmental studies, but these were generally higher than those reported under field conditions. The results suggest that the carbon balance algorithm represents potential root growth within the constraints imposed by the model assumptions. The extreme diurnal fluctuations in plant water potential indicate that one or more important components of the plant system used to regulate short-term drought stress are not represented. Both stem tissue capacitance and the hydraulic conductance of mycorrhizal mycelia at low soil moisture contents may be important in controlling short-term water deficits. Further advances in the application of similar models depend upon an evaluation of these variables and a better theoretical and experimental determination of the effects of the geometry of the transplanted root system.
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