Interannual, seasonal, and diel variability in the carbon isotope composition of respiration in a C 3/C 4 agricultural ecosystem

Joel J. Fassbinder, Timothy J. Griffis, John M. Baker

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20 Scopus citations


The stable carbon isotope ratio, CO132/CO122, is a valuable tracer for studying the processes controlling the autotrophic (F Ra) and heterotrophic (F Rh) contributions to ecosystem respiration (F R) and the influence of photosynthesis on F R. There is increasing interest in quantifying the temporal variability of the carbon isotope composition of ecosystem respiration (δ R) because it contains information about the sources contributing to respiration and is an important parameter used for partitioning net ecosystem CO 2 exchange using stable isotope methods. In this study, eddy covariance, flux gradient, automated chambers, and stable carbon isotope techniques were used to quantify and improve our understanding of the temporal variability in F R and δ R in a C 3/C 4 agricultural ecosystem. Six years (2004-2009) of isotope flux-gradient measurements indicated that δ R had a very consistent annual pattern during both C 3 (soybean) and C 4 (corn) growing seasons due to significant contributions from F Ra, which was strongly influenced by the isotope composition of the recent photosynthate. However, in the spring, δ R exhibited a C 3 signal regardless of the crop grown in the previous season. One hypothesis for this anomaly is that at these low soil temperatures microbial activity relied predominantly on C 3 substrates. Automated chamber measurements of soil respiration (FRs) and its isotope composition (δRs) were initiated in the early corn growing season of 2009 to help interpret the variability in δ R. These measurements showed good agreement with EC measurements of F R (within 0.5μmolm 2s -1) and isotope flux gradient measurements of δ R (within 2‰) at nighttime for near-bare soil conditions (LAI<0.1). At peak growth, nighttime δ R above the corn canopy was consistently 1-6‰ more enriched than δRs. The relatively enriched signal above the canopy indicates that δ R was strongly influenced by aboveground plant respiration (F R,ag), which accounted for about 40% of F R. The automated chamber data and analyses also revealed a strong diel pattern in δRs. In the early growth period, δRs showed a sharp morning enrichment of up to 4‰ followed by a gradual depletion throughout the afternoon and evening. Daytime enrichment in δRs was most pronounced during dry conditions and was not observed when the upper soil was near saturation. We provide anecdotal evidence that the diel variability during early growth may have been influenced by turbulence (advection/non-diffusive transport), which reduced the kinetic fractionation effect. At peak growth, there is evidence that the sheltering effect of the corn plants diminished the influence of turbulence on the chamber measurement of δRs. Further research is needed to evaluate and separate the contributions of biotic and abiotic (advection and non-steady state effects) influences on chamber δRs observations.

Original languageEnglish (US)
Pages (from-to)144-153
Number of pages10
JournalAgricultural and Forest Meteorology
StatePublished - Feb 15 2012

Bibliographical note

Funding Information:
We thank Matt Erickson and Bill Breiter for their technical assistance at the field site. We also thank Zoran Nesic and Dr. T.A. Black, Biometeorology and Soil Physics Group, University of British Columbia, for their help with the implementation of the automated chamber system. We acknowledge the very helpful comments and criticisms of two anonymous reviewers and the guest editor. Funding for this research has been provided by the National Science Foundation, ATM-0546476 (T.G.) and the Office of Science (BER) U.S. Department of Energy, DE-FG02-06ER64316 (T.G. and J.M.B.).


  • Aboveground plant respiration
  • Automated chambers
  • Autotrophic respiration
  • Eddy covariance
  • Flux gradient
  • Flux partitioning
  • Heterotrophic respiration
  • Stable isotopes
  • Tunable diode laser spectroscopy


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