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Elsevier, Agriculture, Ecosystems and Environment, (186), p. 170-177, 2014

DOI: 10.1016/j.agee.2014.01.028

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Environmental Control of Daytime Net Ecosystem Exchange of Carbon Dioxide in Switchgrass

Journal article published in 2014 by Pradeep Wagle ORCID, Vijaya Gopal Kakani
This paper is available in a repository.
This paper is available in a repository.

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Abstract

Net ecosystem CO2 exchange (NEE) over a young switchgrass (Panicum virgatum) stand was measured with the eddy covariance technique across two growing seasons in the southern Great Plains of the United States at Chickasha, OK. The objectives of the study were to characterize the effects of environmental factors on daytime NEE and to explore the underlying mechanisms. Photosynthetic photon flux density (PPFD) was the most significant driver of NEE and explained over 90% of the NEE variation during optimum environmental conditions. The light-response curve showed hysteresis as carbon uptake by the ecosystem decreased up to 62% (monthly average) from morning to afternoon at similar light levels because of the stomatal closure control of photosynthesis at high vapor pressure deficit (VPD). This resultant large hysteresis led to the failure of the rectangular hyperbolic light-response function in explaining the NEE–PPFD relationship. The NEE exhibited an optimum temperature range of 28–34 °C and decreased markedly beyond 35 °C. Our results demonstrated that warm temperature and high VPD altered the NEE–PPFD relationship and thereby affected the ecosystem light-response parameters (respiration, quantum yield, and light saturated photosynthetic capacity). Thus, it is essential to incorporate the effects of temperature and VPD on ecosystem light-response into both empirical and mechanistic models. This study also suggests including the VPD effect in the NEE flux partitioning technique can account for the systematic presence of NEE hysteresis during non-optimal environmental conditions. The results of this study are useful for the modeling community to develop, improve, and validate the models for global change studies, and for the eddy covariance community to develop more robust gap filling methods.