2020 ESA Annual Meeting (August 3 - 6)

COS 48 Abstract - The determinants of optimal leaf area in eucalypt plantations

Rose Brinkhoff1, Mark J Hovenden2,3 and Mark A Hunt1,4, (1)School of Natural Sciences, University of Tasmania, Sandy Bay, TAS, Australia, (2)School of Natural Sciences, ARC Industrial Transformation Training Centre for Forest Value, Sandy Bay, TAS, Australia, (3)Biological Sciences, School of Natural Sciences, University of Tasmania, Hobart, TAS, Australia, (4)ARC Industrial Transformation Training Centre for Forest Value, Sandy Bay, TAS, Australia
Background/Question/Methods

Leaf area index (LAI) is an important driver of primary productivity, and affects water and nutrient cycling. Extra leaves have both a cost and a benefit to a plant in terms of carbon and water balance and nutrient economics. Greater leaf area increases photosynthetic area, but also incurs a respiratory cost to the plant in terms of leaf construction and maintenance. Optimal leaf area is therefore influenced by the trade-off between carbon gains through photosynthesis and carbon loss through respiration, but is also influenced by transpirational demands. Furthermore, optimal leaf area responds to environmental factors such as nutrition, temperature and water supply. Using three field experiments across a rainfall and temperature gradient in Tasmania, I investigated the way in which nutrient supply influences the optimal leaf area of the globally-important plantation tree, Eucalyptus nitens.

Results/Conclusions

Results show that the costs and benefits of extra leaf area depend on nutrient supply as well as site characteristics related to temperature and water. Specifically, LAI was highest at intermediate nitrogen levels over the first growing season, with associated changes to leaf water potential and maximum leaf photosynthetic rate. Thus, leaf area response to nutrition is decidedly non-linear in this system with corresponding influences on plant water use and physiology. These results will contribute to the development of efficient nutrition management of production forests through an improved ability to predict and model the impact of fertiliser on productivity.