Published in

European Geosciences Union, Biogeosciences Discussions, p. 1-32

DOI: 10.5194/bg-2017-302

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Modeling rhizosphere carbon and nitrogen cycling in <i>Eucalyptus</i> plantation soil

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Green circle
Postprint: archiving allowed
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Abstract

Vigorous Eucalyptus plantations produce 10 5 to 10 6 km ha −1 of fine roots that probably increase carbon (C) and nitrogen (N) cycling in rhizosphere soil. However, the quantitative importance of rhizosphere priming is still unknown for most ecosystems, including these plantations. Therefore, the objective of this work was to propose and evaluate a mechanistic model for the prediction of rhizosphere C and N cycling in Eucalyptus plantations. The potential importance of the priming effect was estimated for a typical Eucalyptus plantation in Brazil. The process-based model (ForPRAN – Forest Plantation Rhizosphere Available Nitrogen) predicts the change in rhizosphere C and N cycling resulting from root growth and consists of two modules: (1) fine-root growth and (2) C and N rhizosphere cycling. The model describes a series of soil biological processes: root growth, rhizodeposition, microbial uptake, enzymatic synthesis, depolymerization of soil organic matter, microbial respiration, N mineralization, N immobilization, microbial death, microbial emigration and immigration, and soil organic matter (SOM) formation. Model performance was quantitatively and qualitatively satisfactory when compared to observed data in the literature. Input variables with the most influence on rhizosphere N mineralization were (in order of decreasing importance) root diameter > rhizosphere thickness > soil temperature > clay concentration. The priming effect in a typical Eucalyptus plantation producing 42 m 3 ha −1 yr −1 of shoot biomass, with assumed losses of 40 % of total N mineralized, was estimated to be 24.6 % of plantation N demand (shoot + roots + litter). The rhizosphere cycling model should be considered for adaptation to other forestry and agricultural production models where the inclusion of such processes offers the potential for improved model performance.