Tracy E. Twine, Jarod J. Bryant, Katherine Richter, Carl J. Bernacchi, Kelly D. McConnaughay, Sherri J. Morris, Andrew D. B. Leakey. Impacts of elevated CO2 concentration on the productivity and surface energy budget of the soybean and maize agroecosystem in the Midwest U.S. Global Change Biology. doi: 10.1111/gcb.12270
The physiological response of vegetation to increasing atmospheric carbon dioxide concentration ([CO2]) modifies productivity and surface energy and water fluxes. Quantifying this response is required for assessments of future climate change. Many global climate models account for this response; however, significant uncertainty remains in model simulations of this vegetation response and its impacts. Data from in situ field experiments provide evidence that previous modeling studies may have overestimated the increase in productivity at elevated [CO2], and the impact on large-scale water cycling is largely unknown. We parameterized the Agro-IBIS dynamic global vegetation model with observations from the SoyFACE experiment to simulate the response of soybean and maize to an increase in [CO2] from 375 ppm to 550 ppm. The two key model parameters that were found to vary with [CO2] were the maximum carboxylation rate of photosynthesis and specific leaf area. Tests of the model that used SoyFACE parameter values showed a good fit to site-level data for all variables except latent heat flux over soybean and sensible heat flux over both crops. Simulations driven with historic climate data over the central U.S. showed that increased [CO2] resulted in decreased latent heat flux and increased sensible heat flux from both crops when averaged over 30 years. Thirty-year average soybean yield increased everywhere (~10%); however, there was no increase in maize yield except during dry years. Without accounting for CO2 effects on the maximum carboxylation rate of photosynthesis and specific leaf area, soybean simulations at 550 ppm overestimated leaf area and yield. Our results highlight important model parameter values that, if not modified in other models, could result in biases when projecting future crop-climate-water relationships.
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