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Zhao, G., Hoffmann, H., Van Bussel, L. G. J., Enders, A., Specka, X., Sosa, C., et al. (2014). Responses of crop’s water use efficiency to weather data aggregation: a crop model ensemble study. CropM International Symposium and Workshop.
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Rötter, R. P., Asseng, S., Ewert, F., Rosenzweig, C., Jones, J. W., Hatfield, J. L., et al. (2013). Quantifying Uncertainties in Modeling Crop Water Use under Climate Change..
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Pohanková, E., Hlavinka, P., Kersebaum, K. C., Dubrovský, M., Fischer, M., Balek, J., et al. (2015). Pilot study: Field crop rotations modeling under present and future conditions in the Czech Republic using HERMES model (Vol. 5).
Abstract: The aim of this study is to compare the water and organic material balance, yields and other aspects estimated within crop rotations by the Hermes crop model for present and future climatic conditions in the Czech Republic. Moreover, this is a pilot study for the complex and continuous crop rotations modeling (using both single crop models and ensembles) in connection with transient climate change scenarios. For this purpose, three locations representing important agricultural regions of the Czech Republic (with different climatic conditions) were selected. The crop rotation (including spring barley, silage maize, winter wheat, winter rape, and winter wheat in the listed order) was simulated from 1981-2080. The period 1981-2010 was covered by measured meteorological data, and the period 2011-2080 was represented by a transient synthetic weather series from the weather generator M&Rfi. The generated data was based on five circulation models representing an ensemble of 18 CMIP3 global circulation models to preserve to a large degree the uncertainty of the original ensemble. Two types of crop management were compared, and the influences of soil quality, increasing atmospheric CO2 and magnitude of adaptation measure (in the form of sowing date changes) were also considered. According to the results, if a “dry” scenario (such as GFCM21) would occur, then all the C3 crops produced in drier regions would be devastated in a significant number of seasons; for example, by the 2070s, up to 19.5%, 21.5% and 47.0% of seasons with winter rape, spring barley and winter wheat, respectively, would have a yield level below 50% of the present yield. Negative impacts are likely even on premium-quality soils regardless of the use of a flexible sowing date and accounting for increasing CO2 concentrations. Moreover, in some cases, the use of catch crops can have negative impacts, exacerbating the soil water deficit for the subsequent crops. This study (submitted to Climate Research journal) will be used as a pilot for subsequent activities. In this area, following calculations (the same set of stations and updated climate scenarios) using growth models ensemble (currently includes 12 modeling approaches) started to estimate uncertainty aspects. Consequently, the analysis within wider range of conditions (across continents) and farming methods will be conducted. No Label
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Webber, H., White, J. W., Kimball, B. A., Ewert, F., Asseng, S., Rezaei, E. E., et al. (2018). Physical robustness of canopy temperature models for crop heat stress simulation across environments and production conditions. Field Crops Research, 216, 75–88.
Abstract: Despite widespread application in studying climate change impacts, most crop models ignore complex interactions among air temperature, crop and soil water status, CO2 concentration and atmospheric conditions that influence crop canopy temperature. The current study extended previous studies by evaluating Tc simulations from nine crop models at six locations across environmental and production conditions. Each crop model implemented one of an empirical (EMP), an energy balance assuming neutral stability (EBN) or an energy balance correcting for atmospheric stability conditions (EBSC) approach to simulate Tc. Model performance in predicting Tc was evaluated for two experiments in continental North America with various water, nitrogen and CO2 treatments. An empirical model fit to one dataset had the best performance, followed by the EBSC models. Stability conditions explained much of the differences between modeling approaches. More accurate simulation of heat stress will likely require use of energy balance approaches that consider atmospheric stability conditions.
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Yin, X. G., Kersebaum, K. C., Kollas, C., Manevski, K., Baby, S., Beaudoin, N., et al. (2017). Performance of process-based models for simulation of grain N in crop rotations across Europe. Agric. Syst., 154, 63–77.
Abstract: The accurate estimation of crop grain nitrogen (N; N in grain yield) is crucial for optimizing agricultural N management, especially in crop rotations. In the present study, 12 process-based models were applied to simulate the grain N of i) seven crops in rotations, ii) across various pedo-climatic and agro-management conditions in Europe, under both continuous simulation and single year simulation, and for iv) two calibration levels, namely minimal and detailed calibration. Generally, the results showed that the accuracy of the simulations in predicting grain N increased under detailed calibration. The models performed better in predicting the grain N of winter wheat (Triticum aestivum L.), winter barley (Hordewn vulgare L.) and spring barley (Hordeum vulgare L.) compared to spring oat (Avena saliva L.), winter rye (Secale cereale L.), pea (Piswn sativum L.) and winter oilseed rape (Brassica napus L.). These differences are linked to the intensity of parameterization with better parameterized crops showing lower prediction errors. The model performance was influenced by N fertilization and irrigation treatments, and a majority of the predictions were more accurate under low N and rainfed treatments. Moreover, the multi-model mean provided better predictions of grain N compared to any individual model. In regard to the Individual models, DAISY, FASSET, HERMES, MONICA and STICS are suitable for predicting grain N of the main crops in typical European crop rotations, which all performed well in both continuous simulation and single year simulation. Our results show that both the model initialization and the cover crop effects in crop rotations should be considered in order to achieve good performance of continuous simulation. Furthermore, the choice of either continuous simulation or single year simulation should be guided by the simulation objectives (e.g. grain yield, grain N content or N dynamics), the crop sequence (inclusion of legumes) and treatments (rate and type of N fertilizer) included in crop rotations and the model formalism.
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