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Höglind, M., Thorsen, S. M., & Semenov, M. A. (2013). Assessing uncertainties in impact of climate change on grass production in Northern Europe using ensembles of global climate models. Agricultural and Forest Meteorology, 170, 103–113.
Abstract: Forage-based dairy and livestock production is the backbone of agriculture in Northern Europe in economic terms. Changes in growing conditions that affect forage grass yield may have great economic consequences. This study assessed the impact of climate change on two grass species, timothy and ryegrass, at 14 locations in Northern Europe (Iceland, Scandinavia, Baltic countries) in a near-future scenario (2040-2065) compared with the baseline period 1960-1990. Local-scale climate scenarios were based on the CMIP3 multi-model ensembles of 15 global climate models in order to quantify the uncertainty in the impacts relating to highly uncertain projections of future climate. Potential yield of timothy, the most important perennial forage grass in Northern Europe, was simulated under the assumption of optimal overwintering conditions and current CO2 level, in order to obtain an estimate of the effect of changes in summer climate per se. The risk of frost and ice damage during winter was also assessed. The simulation results demonstrated that potential grass yield will increase throughout the study area, mainly as a result of increased growing temperatures. The yield response to climate change was slightly larger in irrigated than non-irrigated conditions (14% and 11%, respectively), due to larger water deficit for the 2050 scenario. However, a geo-climatic gradient was evident, with the largest predicted yield response at western locations. A geo-climatic gradient was also revealed with respect to potential frost damage, which was predicted to increase during winter in some areas east of the Baltic Sea for timothy, and for a larger number of locations both east and west of the Baltic Sea for perennial ryegrass. The risk of frost damage in spring was predicted to increase mainly in western parts of the study area. If frost damage to perennial ryegrass increases during winter, the expected increase in winter temperature due to global warming may not necessarily improve overwintering conditions, so the growing zone may not necessarily expand to the north and east of the study area by 2050. The uncertainty in impacts was frequently, but not consistently, greater in western than eastern locations. (C) 2012 Elsevier B.V. All rights reserved.
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Höglind, M., Van Oijen, M., Cameron, D., & Persson, T. (2016). Process-based simulation of growth and overwintering of grassland using the BASGRA model. Ecol. Model., 335, 1–15.
Abstract: Process-based models (PBM) for simulation of weather dependent grass growth can assist farmers and plant breeders in addressing the challenges of climate change by simulating alternative roads of adaptation. They can also provide management decision support under current conditions. A drawback of existing grass models is that they do not take into account the effect of winter stresses, limiting their use for full-year simulations in areas where winter survival is a key factor for yield security. Here, we present a novel full-year PBM for grassland named BASGRA. It was developed by combining the LINGRA grassland model (Van Oijen et al., 2005a) with models for cold hardening and soil physical winter processes. We present the model and show how it was parameterized for timothy (Phleum pratense L.), the most important forage grass in Scandinavia and parts of North America and Asia. Uniquely, BASGRA simulates the processes taking place in the sward during the transition from summer to winter, including growth cessation and gradual cold hardening, and functions for simulating plant injury due to low temperatures, snow and ice affecting regrowth in spring. For the calibration, we used detailed data from five different locations in Norway, covering a wide range of agroclimatic regions, day lengths (latitudes from 59 degrees to 70 degrees N) and soil conditions. The total dataset included 11 variables, notably above-ground dry matter, leaf area index, tiller density, content of C reserves, and frost tolerance. All data were used in the calibration. When BASGRA was run with the maximum a-posteriori (MAP) parameter vector from the single, Bayesian calibration, nearly all measured variables were simulated to an overall normalized root mean squared error (NRMSE) <0.5. For many site x experiment combinations, NRMSE was <0.3. The temporal dynamics were captured well for most variables, as evaluated by comparing simulated time courses versus data for the individual sites. The results may suggest that BASGRA is a reasonably robust model, allowing for simulation of growth and several important underlying processes with acceptable accuracy for a range of agroclimatic conditions. However, the robustness of the model needs to be tested further using independent data from a wide range of growing conditions. Finally we show an example of application of the model, comparing overwintering risks in two climatically different sites, and discuss future model applications. Further development work should include improved simulation of the dynamics of C reserves, and validation of winter tiller dynamics against independent data. (C) 2016 Elsevier B.V. All rights reserved.
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Jing, Q., Bélanger, G., Baron, V., Bonesmo, H., Virkajärvi, P., & Young, D. (2012). Regrowth simulation of the perennial grass timothy. Ecol. Model., 232, 64–77.
Abstract: Several process-based models for simulating the growth of perennial grasses have been developed but few include the simulation of regrowth. The model CATIMO simulates the primary growth of timothy (Phleum pratense L), an important perennial forage grass species in northern regions of Europe and North America. Our objective was to further develop the model CATIMO to simulate timothy regrowth using the concept of reserve-dependent growth. The performance of this modified CATIMO model in simulating leaf area index (LAI), biomass dry matter (DM) yield, and N uptake of regrowth was assessed with data from four independent field experiments in Norway, Finland, and western and eastern Canada using an approach that combines graphical comparison and statistical analysis. Biomass DM yield and N uptake of regrowth were predicted at the same accuracy as primary growth with linear regression coefficients of determination between measured and simulated values greater than 0.79, model simulation efficiencies greater than 0.78, and normalized root mean square errors (14-30% for biomass and 24-34% for N uptake) comparable with the coefficients of variation of measured data (1-21% for biomass and 1-25% for N uptake). The model satisfactorily simulated the regrowth LAI but only up to a value of about 4.0. The modified CATIMO model with its capacity to simulate regrowth provides a framework to simulate perennial grasses with multiple harvests, and to explore management options for sustainable grass production under different environmental conditions. Crown Copyright (C) 2012 Published by Elsevier B.V. All rights reserved.
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