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Popp, A., Rose, S. K., Calvin, K., Van Vuuren, D. P., Dietrich, J. P., Wise, M., et al. (2014). Land-use transition for bioenergy and climate stabilization: model comparison of drivers, impacts and interactions with other land use based mitigation options. Clim. Change, 123(3-4), 495–509.
Abstract: In this article, we evaluate and compare results from three integrated assessment models (GCAM, IMAGE, and ReMIND/MAgPIE) regarding the drivers and impacts of bioenergy production on the global land system. The considered model frameworks employ linked energy, economy, climate and land use modules. By the help of these linkages the direct competition of bioenergy with other energy technology options for greenhouse gas (GHG) mitigation, based on economic costs and GHG emissions from bioenergy production, has been taken into account. Our results indicate that dedicated bioenergy crops and biomass residues form a potentially important and cost-effective input into the energy system. At the same time, however, the results differ strongly in terms of deployment rates, feedstock composition and land-use and greenhouse gas implications. The current paper adds to earlier work by specific looking into model differences with respect to the land-use component that could contribute to the noted differences in results, including land cover allocation, land use constraints, energy crop yields, and non-bioenergy land mitigation options modeled. In scenarios without climate change mitigation, bioenergy cropland represents 10-18 % of total cropland by 2100 across the different models, and boosts cropland expansion at the expense of carbon richer ecosystems. Therefore, associated emissions from land-use change and agricultural intensification as a result of bio-energy use range from 14 and 113 Gt CO2-eq cumulatively through 2100. Under climate policy, bioenergy cropland increases to 24-36 % of total cropland by 2100.
Keywords: bio-energy; miscanthus; emissions; crop
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Wallach, D., Mearns, L. O., Ruane, A. C., Rötter, R. P., & Asseng, S. (2016). Lessons from climate modeling on the design and use of ensembles for crop modeling. Clim. Change, 139(3-4), 551–564.
Abstract: Working with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor.
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Zhao, G., Hoffmann, H., van Bussel, L. G. J., Enders, A., Specka, X., Sosa, C., et al. (2015). Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables. Clim. Res., 65, 141–157.
Abstract: We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 process-based crop models and the ensemble mean, we simulated 30 yr continuous cropping systems for 2 crops (winter wheat and silage maize) under 3 production conditions for the state of North Rhine-Westphalia, Germany. The DAE was evaluated for 5 weather data resolutions (i.e. 1, 10, 25, 50, and 100 km) for 3 response variables including yield, growing season evapotranspiration, and water use efficiency. Five metrics, viz. the spatial bias (Delta), average absolute deviation (AAD), relative AAD, root mean squared error (RMSE), and relative RMSE, were used to evaluate the DAE on both the input weather data and simulated results. For weather data, we found that data aggregation narrowed the spatial variability but widened the., especially across mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but changed only slightly for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area.
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Palosuo, T., Rotter, R. P., Salo, T., Peltonen-Sainio, P., Tao, F., & Lehtonen, H. (2015). Effects of climate and historical adaptation measures on barley yield trends in Finland. Clim. Res., 65, 221–236.
Abstract: In this study, the WOFOST crop simulation model was used together with comprehensive empirical databases on barley Hordeum vulgare L. to study the contributions of different yield-determining and -limiting factors to observed trends of barley yield in Finland from 1988 to 2008. Simulations were performed at 3 study sites representing different agro-ecological zones, and compared with the data from experimental sites and that reported by local farmers. Yield gaps between simulated potential yields and farmers’ yields and their trends were assessed. Positive observed yield trends of Finnish barley mostly resulted from the development and usage of new, high-yielding cultivars. Simulated trends in climatic potential and water-limited potential yields of individual cultivars showed a slight declining trend. Yield gaps showed an increasing trend in 2 out of 3 study areas. Since the mid-1990s, a major reason for this has been the lack of market and policy incentives favouring crop management decisions, i.e. annual fertilisation, soil maintenance, drainage and crop rotation decisions, aiming for higher yields. The study indicates potential options for increasing or maintaining barley yields in the future. The breeding of new climate-resilient cultivars is the primary option. However, this needs to work alongside overall adjustments to farm management and must be supported by financial incentives for farmers to increase yields.
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Hoffmann, H., Zhao, G., van Bussel, L. G. J., Enders, A., Specka, X., Sosa, C., et al. (2015). Variability of effects of spatial climate data aggregation on regional yield simulation by crop models. Clim. Res., 65, 53–69.
Abstract: Field-scale crop models are often applied at spatial resolutions coarser than that of the arable field. However, little is known about the response of the models to spatially aggregated climate input data and why these responses can differ across models. Depending on the model, regional yield estimates from large-scale simulations may be biased, compared to simulations with high-resolution input data. We evaluated this so-called aggregation effect for 13 crop models for the region of North Rhine-Westphalia in Germany. The models were supplied with climate data of 1 km resolution and spatial aggregates of up to 100 km resolution raster. The models were used with 2 crops (winter wheat and silage maize) and 3 production situations (potential, water-limited and nitrogen-water-limited growth) to improve the understanding of errors in model simulations related to data aggregation and possible interactions with the model structure. The most important climate variables identified in determining the model-specific input data aggregation on simulated yields were mainly related to changes in radiation (wheat) and temperature (maize). Additionally, aggregation effects were systematic, regardless of the extent of the effect. Climate input data aggregation changed the mean simulated regional yield by up to 0.2 t ha(-1), whereas simulated yields from single years and models differed considerably, depending on the data aggregation. This implies that large-scale crop yield simulations are robust against climate data aggregation. However, large-scale simulations can be systematically biased when being evaluated at higher temporal or spatial resolution depending on the model and its parameterization.
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