FAIL (the browser should render some flash content, not this).

The Future of Wheat Production on the North China Plain

Reference
Tao, F. and Zhang, Z. 2013. Climate change, wheat productivity and water use in the North China Plain: A new super-ensemble-based probabilistic projection. Agricultural and Forest Meteorology 170: 146-165.
In the words of Tao and Zhang (2013), "future climate change is projected to be one of the major challenges for regional agricultural production in broad regions of the world," and in this regard they note that Tao et al. (2009a,b) and Tao and Zhang (2010) have developed what they call "a new process-based Model to capture the Crop-Weather relationship over a Large Area (MCWLA) and a new super-ensemble-based probabilistic projection (Super EPPS) to account for the uncertainties not only from greenhouse gas emission scenarios and climate change scenarios but also from biophysical processes in crop models, and to assess the impacts of climate change (variability) on regional crop productivity and water use in a probabilistic framework," both of which model-projections "have been demonstrated in addressing the probabilistic responses and adaptations of maize production to climate change in the North China Plain (NCP)."

Tao and Zhang state that the crop model MCWLA-Wheat was first developed "by adapting the process-based general crop model, MCWLA, to winter wheat," after which they indicate that "Bayesian probability inversion and a Markov chain Monte Carlo (MCMC) technique were applied to the MCWLA-Wheat to analyze uncertainties in parameter estimations, and to optimize parameters." Ensemble hindcasts then showed that "the MCWLA-Wheat could capture the interannual variability of detrended historical yield series fairly well, especially over a large area." And finally, based on the MCWLA-Wheat, a Super EPPS was developed and applied to project the probabilistic responses of wheat productivity and water use in the NCP to future climate change. This was done using ten climate scenarios "consisting of the combinations of five global climate models and two greenhouse gases emission scenarios (A1F1 and B1), the corresponding atmospheric CO2 concentration range, and multiple sets of crop model parameters representing the biophysical uncertainties from crop models."

The major finding of the Chinese researchers was that winter wheat yields in the NCP could increase with high probability in the future due to climate change, such that during the 2020s, 2050s and 2080s, with (and without) CO2 fertilization effects and relative to 1961-1990 levels, simulated wheat yields would increase, on average, by 37.3% (18.6%), 67.8% (23.1%) and 87.2% (34.4%), respectively, over 80% of the study area.

These findings of Tao and Zhang are encouraging, especially in light of the analysis of Schmidhuber and Tubiello (2007), which suggests that global food production may need to rise by as much as 70% by the year 2050 in order to adequately feed the nine billion people (compared to today's seven billion) that they project to be inhabiting the planet at that mid-century point in time.

Additional References
Schmidhuber, J. and Tubiello, F.N. 2007. Global food security under climate change. Proceedings of the National Academy of Sciences USA 104: 19,703-19,708.

Tao, F., Yokozawa, M. and Zhang, Z. 2009a. Modeling the impacts of weather and climate variability on crop productivity over a large area: a new process-based model development, optimization, and uncertainties analysis. Agricultural and Forest Meteorology 149: 831-850.

Tao, F. and Zhang, Z. 2010. Adaptation of maize production to climate change in North China Plain: quantify the relative contributions of adaptation options. European Journal of Agronomy 33: 103-116.

Tao, F., Zhang, Z., Liu, J. and Yokozawa, M. 2009b. Modeling the impacts of weather and climate variability on crop productivity over a large area: a new super-ensemble-based probabilistic projection. Agricultural and Forest Meteorology 149: 1266-1278.

Archived 11 September 2013