An historical perspective on elevated CO2 research: Early insights guiding today's research agenda

SPEAKER: Professor Richard J Norby, Oak Ridge National Laboratory, Tennessee

The focus of much of the research on plant responses to elevated CO₂ over the past several decades has been on the role of vegetation in the global carbon cycle and the feedback that plant responses provide to atmospheric CO₂ concentration. This has not always been the case. The initial thrust of vegetation effects research was toward crop plant physiology and agricultural productivity. Although carbon cycle researchers had recognized as early as 1970 that CO₂ fertilization of the biosphere could slow the increase of atmospheric CO₂, the potential effect was often dismissed as insignificant. In a seminal paper in 1981, Paul Kramer challenged whether the extensive literature on photosynthesis and dry matter production of crop plants was relevant to plants in nature, and he concluded that reliable predictions of the global effects of increasing CO₂ concentration required long-term measurements of plant growth from experiments in which elevated CO₂ is combined with water and nitrogen stress. With the research imperative refocused on global carbon cycle feedbacks, workshops and reports at that time identified many critical questions and research challenges for understanding CO₂ fertilization in that context. A fundamental issue about CO₂ fertilization was framed in 1983: will increased productivity in elevated CO₂ lead to greater plant biomass or faster turnover of leaves and roots. Resolving this dichotomy has required a strong focus on ecosystem-scale responses: NPP, carbon allocation, turnover, and nutrient interactions. Hypotheses in the 1980’s considered soil microbial interactions (mycorrhizae, nitrogen fixation, and exudation-stimulated nutrient dynamics), and these remain prominent research priorities today. While many of these early-identified questions have been tackled, those questions are still relevant today and are being addressed now with more sophisticated approaches in more complex ecosystems and with a close integration of experiments and models. The more we learn, the more complicated the analysis becomes…but that’s the real world!