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Tao, F., Zhang, Z., Zhang, S., & Rötter, R. P. (2015). Heat stress impacts on wheat growth and yield were reduced in the Huang-Huai-Hai Plain of China in the past three decades. European Journal of Agronomy, 71, 44–52.
Abstract: Heat stress impacts on crop growth and yield have been investigated by controlled-environment experiments, however little is known about the impacts under field conditions at large spatial and temporal scales, particularly in a setting with farmers’ autonomous adaptations. Here, using detailed experiment Observations at 34 national agricultural meteorological stations spanning from 1981 to 2009 in the Huang-Huai-Hai Plain (HHHP) of China, we investigated the changes in climate and heat stress during wheat reproductive growing period (from heading to maturity) and the impacts of climate change and heat stress on reproductive growing duration (RGD) and yield in a setting with farmers’ autonomous adaptations. We found that RGD and growing degree days above 0 degrees C (GDD) from heading to maturity increased, which increased yield by similar to 14.85%, although heat stress had negative impacts on RGD and yield. During 1981-2009, high temperature (>34 degrees C) degree days (HDD) increased in the northern part, however decreased in the middle and southern parts of HHHP due to advances in heading and maturity dates. Change in HDD, together with increase in GDD and decrease in solar radiation (SRD), jointly increased wheat yield in the northern and middle parts but reduced it in the southern part of HHHP. During the study period, increase in GDD and decrease in SRD had larger impacts on yield than change in HDD. However, with climate warming of 2 degrees C, damage of heat stress on yield may offset a large portion of the benefits from increases in RGD and GDD, and eventually result in net negative impacts on yield in the northern part of HHHP. Our study showed that shifts in cultivars and wheat production system dynamics in the past three decades reduced heat stress impacts in the HHHP. The insights into crop response and adaptation to climate change and climate extremes provide excellent evidences and basis for improving climate change impact study and designing adaptation measures for the future. (C) 2015 Elsevier B.V. All rights reserved.
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Zhang, S., Tao, F., & Zhang, Z. (2017). Uncertainty from model structure is larger than that from model parameters in simulating rice phenology in China. Europ. J. Agron., 87, 30–39.
Abstract: Rice models have been widely used in simulating and predicting rice phenology in contrasting climate zones, however the uncertainties from model structure (different equations or models) and/or model parameters were rarely investigated. Here, five rice phenological models/modules (Le., CERES-Rice, ORYZA2000, RCM, Beta Model and SIMRIW) were applied to simulate rice phenology at 23 experimental stations from 1992 to 2009 in two major rice cultivation regions of China: the northeastern China and the southwestern China. To investigate the uncertainties from model biophysical parameters, each model was run with randomly perturbed 50 sets of parameters. The results showed that the median of ensemble simulations were better than the simulation by most models. Models couldn’t simulate well in some specific years despite of parameters optimization, suggesting model structure limit model performance in some cases. The models adopting accumulative thermal time function (e.g., CERES-Rice and ORYZA2000) had better performance in the southwestern China, in contrast, those adopting exponential function (e.g., Beta model and RCM model) had better performance in the northeastern China. In northeastern China, the contribution of model structure and model parameters to model total variance was, respectively, about 55.90% and 44.10% in simulating heading date, and about 75.43% and 24.57% in simulating maturity date. In the southwestern China, the contribution of model structure and model parameters to model total variance was, respectively, about 79.97% and 27.03% in simulating heading date, about 92.15% and 7.85% in simulating maturity date. Uncertainty from model structure was the most relevant source. The results highlight that the temperature response functions of rice development rate under extreme climate conditions should be improved based on environment-controlled experimental data.
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Rosenzweig, C., Elliott, J., Deryng, D., Ruane, A. C., Müller, C., Arneth, A., et al. (2014). Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc. Natl. Acad. Sci. U. S. A., 111(9), 3268–3273.
Abstract: Here we present the results from an intercomparison of multiple global gridded crop models (GGCMs) within the framework of the Agricultural Model Intercomparison and Improvement Project and the Inter-Sectoral Impacts Model Intercomparison Project. Results indicate strong negative effects of climate change, especially at higher levels of warming and at low latitudes; models that include explicit nitrogen stress project more severe impacts. Across seven GGCMs, five global climate models, and four representative concentration pathways, model agreement on direction of yield changes is found in many major agricultural regions at both low and high latitudes; however, reducing uncertainty in sign of response in mid-latitude regions remains a challenge. Uncertainties related to the representation of carbon dioxide, nitrogen, and high temperature effects demonstrated here show that further research is urgently needed to better understand effects of climate change on agricultural production and to devise targeted adaptation strategies.
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Gabaldón-Leal, C., Lorite, I. J., Mínguez, M. I., Lizaso, J. I., Dosio, A., Sanchez, E., et al. (2015). Strategies for adapting maize to climate change and extreme temperatures in Andalusia, Spain. Clim. Res., 65, 159–173.
Abstract: Climate projections indicate that rising temperatures will affect summer crops in the southern Iberian Peninsula. The aim of this study was to obtain projections of the impacts of rising temperatures, and of higher frequency of extreme events on irrigated maize, and to evaluate some adaptation strategies. The study was conducted at several locations in Andalusia using the CERES-Maize crop model, previously calibrated/validated with local experimental datasets. The simulated climate consisted of projections from regional climate models from the ENSEMBLES project; these were corrected for daily temperature and precipitation with regard to the E-OBS observational dataset. These bias-corrected projections were used with the CERES-Maize model to generate future impacts. Crop model results showed a decrease in maize yield by the end of the 21st century from 6 to 20%, a decrease of up to 25% in irrigation water requirements, and an increase in irrigation water productivity of up to 22%, due to earlier maturity dates and stomatal closure caused by CO2 increase. When adaptation strategies combining earlier sowing dates and cultivar changes were considered, impacts were compensated, and maize yield increased up to 14%, compared with the baseline period (1981-2010), with similar reductions in crop irrigation water requirements. Effects of extreme maximum temperatures rose to 40% at the end of the 21st century, compared with the baseline. Adaptation resulted in an overall reduction in extreme T-max damages in all locations, with the exception of Granada, where losses were limited to 8%.
Keywords: climate change; impact; adaptation; maize; crop model; regional climate model; extreme temperature; elevated carbon-dioxide; iberian peninsula; future climate; mediterranean environment; crop productivity; model simulations; pollen viability; european climate; bias correction; change impacts
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Semenov, M. A., & Stratonovitch, P. (2015). Adapting wheat ideotypes for climate change: accounting for uncertainties in CMIP5 climate projections. Clim. Res., 65, 123–139.
Abstract: This study describes integration of climate change projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensemble with the LARS-WG weather generator, which delivers an attractive option for the downscaling of large-scale climate projections from global climate models (GCMs) to local-scale climate scenarios for impact assessments. A subset of 18 GCMs from the CMIP5 ensemble and 2 Representative Concentration Pathways (RCPs), RCP4.5 and RCP8.5, were integrated with LARS-WG. For computationally demanding impact assessments, where it is not practical to explore all possible combinations of GCM x RCP, a climate sensitivity index could be used to select a subset of GCMs which preserves the range of uncertainty found in CMIP5. This would allow us to quantify uncertainty in predictions of impacts resulting fromthe CMIP5 ensemble by conducting fewer simulation experiments. In a case study, we describe the use of the Sirius wheat simulation model to design in silico wheat ideotypes that are optimised for future climates in Europe, sampling uncertainty in GCMs, emission scenarios, time periods and European locations with contrasting climates. Two contrasting GCMs were selected for the analysis, ‘hot’ HadGEM2-ES and ‘cool’ GISS-E2-R-CC. Despite large uncertainty in future climate projections, we were able to identify target traits for wheat improvement which may assist breeding for high-yielding wheat cultivars with increased yield stability.
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