• Contributors: Dennis Baldocchi
  • Publication Type: JOUR
  • Authors: Oikawa, P. Y.*, C. Sturtevant*, S. H. Knox#, J. Verfaillie*, Y. W. Huang, and D. D. Baldocchi.
  • Relevant Sites:

  • tThe partitioning of net ecosystem exchange of CO2(NEE) into photosynthesis and respiration can be chal-lenging and is often associated with assumptions that yield unknown amounts of uncertainty, therebyhindering model development. This occurs because we are inferring two pieces of information fromone equation and measurement, NEE. While there are multiple methods for partitioning NEE, each hasunique limitations that are difficult to evaluate as these techniques are often not implemented simulta-neously. Here we present an analysis of multiple partitioning methods (a non-linear regression modelknown as the Reichstein method, artificial neural networks (ANN), stable carbon isotopes, and soil respi-ration (Rsoil) measurements) under ideal field conditions. We measured ecosystem-scale fluxes of stableC isotopes via a new quantum cascade laser (QCL) spectrometer and paired those measurements with abiophysical model CANVEG in order to close a system of equations and solve for gross primary produc-tivity (GPP) and ecosystem respiration (Reco). Isotope-partitioned GPP and Recowere on average 10–13%lower than Reichstein- and ANN-partitioned GPP and Reco. These results suggest that the Reichstein andANN approaches, which use nighttime NEE to infer daytime Reco, may be overestimating Recoand GPPduring the day. This may be attributed to higher plant respiration rates at night compared to the day,otherwise known as the Kok effect. As isotope measurements and theory become more accessible todiverse ecosystems it will grow to be more than a benchmarking technique and into a valuable meansof improving understanding of carbon cycling. Future studies are encouraged to evaluate these tech-niques in increasingly complex ecosystems to determine when significant differences appear betweenpartitioning methods and how those differences influence modeling of terrestrial carbon budgets.


  • Journal: Agricultural and Forest Meteorology
  • Volume: 234-235
  • No:
  • Pages: 149-163.
  • Publication Year: 2017
  • DOI: 10.1620
  • ISBN:
  • http://www.sciencedirect.com/science/article/pii/S0168192316307432