Modelling the impacts of weather and climate variability on crop productivity over a large area: A new super-ensemble-based probabilistic projection

Modelling the impacts of weather and climate variability on crop productivity over a large area:... Estimates of climate change impacts are plague with uncertainties from many physical, biological, and social-economic processes. Among the urgent research priorities, more comprehensive assessments of impacts that better represent the uncertainties are needed. Here, we develop a new super-ensemble-based probabilistic projection approach to account for the uncertainties from CO 2 emission scenarios, climate change scenarios, and biophysical processes in impact assessment model. We demonstrate the approach in addressing the probabilistic changes of maize production in the North China Plain in future. The new process-based general crop model, MCWLA (Tao, F., Yokozawa, M. Zhang, Z., 2009. Modelling the impacts of weather and climate variability on crop productivity over a large area: a new process-based model development, optimization, and uncertainties analysis. Agric. For. Meteorol. 149, 831–850), is used. MCWLA accounts for the key impact mechanisms of climate variability and is accurate over a large area. We use 10 climate scenarios consisting of the combinations of five GCMs and two emission scenarios, the corresponding atmospheric CO 2 concentration range, and 60 sets of crop model parameters representing the biophysical uncertainties from crop models. The planting window is set to allow planting shift within the window, although the crop cultivar and management practice in future were assumed to be same as the level during the baseline period. The resulting probability distributions indicate expected yield changes of −9.7, −15.7, −24.7% across the maize cultivation grids in Henan province during 2020s, 2050s, and 2080s, with 95% probability intervals of (−29.4, +15.8), (−45.7, +24.0), (−92.8, +20.3) in percent of 1961–1990 yields, respectively. The corresponding value in Shandong province is −9.1, −19.0, −25.5%, with 95% probability intervals of (−28.4, +17.4), (−45.4, +15.9), (−60.1, +12.8). We also investigate the temporal and spatial pattern of changes and variability in maize yield across the region. Besides the new findings on the probabilistic changes of maize productivity in the North China Plain, our study demonstrated an advanced super-ensemble-based probabilistic projection approach in addressing the impacts of climate variability (change) on regional agricultural production and the uncertainties. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Agricultural and Forest Meteorology Elsevier

Modelling the impacts of weather and climate variability on crop productivity over a large area: A new super-ensemble-based probabilistic projection

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Publisher
Elsevier
Copyright
Copyright © 2009 Elsevier B.V.
ISSN
0168-1923
DOI
10.1016/j.agrformet.2009.02.015
Publisher site
See Article on Publisher Site

Abstract

Estimates of climate change impacts are plague with uncertainties from many physical, biological, and social-economic processes. Among the urgent research priorities, more comprehensive assessments of impacts that better represent the uncertainties are needed. Here, we develop a new super-ensemble-based probabilistic projection approach to account for the uncertainties from CO 2 emission scenarios, climate change scenarios, and biophysical processes in impact assessment model. We demonstrate the approach in addressing the probabilistic changes of maize production in the North China Plain in future. The new process-based general crop model, MCWLA (Tao, F., Yokozawa, M. Zhang, Z., 2009. Modelling the impacts of weather and climate variability on crop productivity over a large area: a new process-based model development, optimization, and uncertainties analysis. Agric. For. Meteorol. 149, 831–850), is used. MCWLA accounts for the key impact mechanisms of climate variability and is accurate over a large area. We use 10 climate scenarios consisting of the combinations of five GCMs and two emission scenarios, the corresponding atmospheric CO 2 concentration range, and 60 sets of crop model parameters representing the biophysical uncertainties from crop models. The planting window is set to allow planting shift within the window, although the crop cultivar and management practice in future were assumed to be same as the level during the baseline period. The resulting probability distributions indicate expected yield changes of −9.7, −15.7, −24.7% across the maize cultivation grids in Henan province during 2020s, 2050s, and 2080s, with 95% probability intervals of (−29.4, +15.8), (−45.7, +24.0), (−92.8, +20.3) in percent of 1961–1990 yields, respectively. The corresponding value in Shandong province is −9.1, −19.0, −25.5%, with 95% probability intervals of (−28.4, +17.4), (−45.4, +15.9), (−60.1, +12.8). We also investigate the temporal and spatial pattern of changes and variability in maize yield across the region. Besides the new findings on the probabilistic changes of maize productivity in the North China Plain, our study demonstrated an advanced super-ensemble-based probabilistic projection approach in addressing the impacts of climate variability (change) on regional agricultural production and the uncertainties.

Journal

Agricultural and Forest MeteorologyElsevier

Published: Aug 3, 2009

References

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