Climate Change and Maize Yield in Iowa.

Climate is changing across the world, including the major maize-growing state of Iowa in the USA. To maintain crop yields, farmers will need a suite of adaptation strategies, and choice of strategy will depend on how the local to regional climate is expected to change. Here we predict how maize yield might change through the 21st century as compared with late 20th century yields across Iowa, USA, a region representing ideal climate and soils for maize production that contributes substantially to the global maize economy. To account for climate model uncertainty, we drive a dynamic ecosystem model with output from six climate models and two future climate forcing scenarios. Despite a wide range in the predicted amount of warming and change to summer precipitation, all simulations predict a decrease in maize yields from late 20th century to middle and late 21st century ranging from 15% to 50%. Linear regression of all models predicts a 6% state-averaged yield decrease for every 1°C increase in warm season average air temperature. When the influence of moisture stress on crop growth is removed from the model, yield decreases either remain the same or are reduced, depending on predicted changes in warm season precipitation. Our results suggest that even if maize were to receive all the water it needed, under the strongest climate forcing scenario yields will decline by 10-20% by the end of the 21st century.

Impacts of elevated CO2 concentration on the productivity and surface energy budget of the soybean and maize agroecosystem in the Midwest USA.

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 USA 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 (ca. 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.

Geographic bias of field observations of soil carbon stocks with tropical land-use changes precludes spatial extrapolation.

Accurately quantifying changes in soil carbon (C) stocks with land-use change is important for estimating the anthropogenic fluxes of greenhouse gases to the atmosphere and for implementing policies such as REDD (Reducing Emissions from Deforestation and Degradation) that provide financial incentives to reduce carbon dioxide fluxes from deforestation and land degradation. Despite hundreds of field studies and at least a dozen literature reviews, there is still considerable disagreement on the direction and magnitude of changes in soil C stocks with land-use change. We conducted a meta-analysis of studies that quantified changes in soil C stocks with land use in the tropics. Conversion from one land use to another caused significant increases or decreases in soil C stocks for 8 of the 14 transitions examined. For the three land-use transitions with sufficient observations, both the direction and magnitude of the change in soil C pools depended strongly on biophysical factors of mean annual precipitation and dominant soil clay mineralogy. When we compared the distribution of biophysical conditions of the field observations to the area-weighted distribution of those factors in the tropics as a whole or the tropical lands that have undergone conversion, we found that field observations are highly unrepresentative of most tropical landscapes. Because of this geographic bias we strongly caution against extrapolating average values of land-cover change effects on soil C stocks, such as those generated through meta-analysis and literature reviews, to regions that differ in biophysical conditions.