|
|
Trnka, M., Hlavinka, P., Wimmerová, M., Pohanková, E., Rötter, R., Olesen, J. E., et al. (2017). Paper on model responses to selected adverse weather conditions (Vol. 10).
Abstract: Based on the Trnka et al. (2015) study that indicated that heat and drought will be the most important stress factors for most of the European what area the further effort focused on these two extremes. The crop model HERMES has been tested for its ability to replicate correctly drought stress, heat stress and combination of both stresses. While data on the drought stress were available for both field and growth chambers, heat stress and its combination with heat stress was available only for the growth chambers. The modified version of the HERMES crop model was developed by Dr. Kersebaum and is being currently prepared for the journal paper publication.
|
|
|
|
van Middelkoop, J. C., & Kipling, R. P. (2017). Modelling the impact of climate change on livestock productivity at the farm-scale: An inventory of LiveM outcomes (Vol. 10).
Abstract: The report presented here provides an inventory of reports and conference papers produced by the partners of the livestock and grassland modelling theme (LiveM) of the Modelling European Agriculture with Climate Change for Food Security (MACSUR) knowledge hub. The findings presented illustrate the diverse nature of the multidisciplinary LiveM research community, and provide a reference source for those seeking to identify and pull out farm-level modelling outputs from the work of MACSUR and its partners. The survey of farm-scale outputs from LiveM revealed the interdependent, dual role of a knowledge hub: to increase the capacity of modelling to meet stakeholder and societal needs under climate change, and to apply that increased capacity to provide new understanding and solutions at the policy and (the focus here) farm scale. While capacity building work across disciplines is time-consuming, difficult, and to a large extent invisible to stakeholders, such work is vital to ensuring that subsequent scientific outcomes reflect best practice, and integrated expertise. Long term, sustained funding of network-based capacity building activities is highlighted as essential to ensuring that the farm-scale modelling work highlighted here can continue to build on ongoing improvements in model quality, flexibility and stakeholder relevance.
|
|
|
|
Van Oijen, M., Cameron, D., Levy, P. E., & Preston, R. (2017). Correcting errors from spatial upscaling of nonlinear greenhouse gas flux models. Environmental Modelling & Software, 94, 157–165.
|
|
|
|
Wang, E., Martre, P., Zhao, Z., Ewert, F., Maiorano, A., Rötter, R. P., et al. (2017). The uncertainty of crop yield projections is reduced by improved temperature response functions. Nature Plants, 3, 17102.
Abstract: Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections. Erratum: doi: 10.1038/nplants.2017.125
|
|
|
|
Webber, H., Martre, P., Asseng, S., Kimball, B., White, J., Ottman, M., et al. (2017). Canopy temperature for simulation of heat stress in irrigated wheat in a semi-arid environment: A multi-model comparison. Field Crops Research, 202, 21–35.
Abstract: Even brief periods of high temperatures occurring around flowering and during grain filling can severely reduce grain yield in cereals. Recently, ecophysiological and crop models have begun to represent such phenomena. Most models use air temperature (Tair) in their heat stress responses despite evidence that crop canopy temperature (Tc) better explains grain yield losses. Tc can deviate significantly from Tair based on climatic factors and the crop water status. The broad objective of this study was to evaluate whether simulation of Tc improves the ability of crop models to simulate heat stress impacts on wheat under irrigated conditions. Nine process-based models, each using one of three broad approaches (empirical, EMP; energy balance assuming neutral atmospheric stability, EBN; and energy balance correcting for the atmospheric stability conditions, EBSC) to simulate Tc, simulated grain yield under a range of temperature conditions. The models varied widely in their ability to reproduce the measured Tc with the commonly used EBN models performing much worse than either EMP or EBSC. Use of Tc to account for heat stress effects did improve simulations compared to using only Tair to a relatively minor extent, but the models that additionally use Tc on various other processes as well did not have better yield simulations. Models that simulated yield well under heat stress had varying skill in simulating Tc. For example, the EBN models had very poor simulations of Tc but performed very well in simulating grain yield. These results highlight the need to more systematically understand and model heat stress events in wheat.
|
|
