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Nelson, G. C., Valin, H., Sands, R. D., Havlík, P., Ahammad, H., Deryng, D., et al. (2014). Climate change effects on agriculture: economic responses to biophysical shocks. Proc. Natl. Acad. Sci. U. S. A., 111(9), 3274–3279.
Abstract: Agricultural production is sensitive to weather and thus directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments in yields, area, consumption, and international trade. We apply biophysical shocks derived from the Intergovernmental Panel on Climate Change’s representative concentration pathway with end-of-century radiative forcing of 8.5 W/m(2). The mean biophysical yield effect with no incremental CO2 fertilization is a 17% reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11%, increase area of major crops by 11%, and reduce consumption by 3%. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences include model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change.
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Shrestha, S., Abdalla, M., Hennessy, T., Forristal, D., & Jones, M. B. (2015). Irish farms under climate change – is there a regional variation on farm responses? J. Agric. Sci., 153(03), 385–398.
Abstract: The current paper aims to determine regional impacts of climate change on Irish farms examining the variation in farm responses. A set of crop growth models were used to determine crop and grass yields under a baseline scenario and a future climate scenario. These crop and grass yields were used along with farm-level data taken from the Irish National Farm Survey in an optimizing farm-level (farm-level linear programming) model, which maximizes farm profits under limiting resources. A change in farm net margins under the climate change scenario compared to the baseline scenario was taken as a measure to determine the effect of climate change on farms. The growth models suggested a decrease in cereal crop yields (up to 9%) but substantial increase in yields of forage maize (up to 97%) and grass (up to 56%) in all regions. Farms in the border, midlands and south-east regions suffered, whereas farms in all other regions generally fared better under the climate change scenario used in the current study. The results suggest that there is a regional variability between farms in their responses to the climate change scenario. Although substituting concentrate feed with grass feeds is the main adaptation on all livestock farms, the extent of such substitution differs between farms in different regions. For example, large dairy farms in the south-east region adopted total substitution of concentrate feed while similar dairy farms in the south-west region opted to replace only 0.30 of concentrate feed. Farms in most of the regions benefitted from increasing stocking rate, except for sheep farms in the border and dairy farms in the south-east regions. The tillage farms in the mid-east region responded to the climate change scenario by shifting arable production to beef production on farms.
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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. (2013). Simulating the Nutritive Value of Timothy Summer Regrowth. Agronomy Journal, 105(3), 563.
Abstract: The process-based grass model, CATIMO, simulates the spring growth and nutritive value of timothy (Phleum pratense L.), a forage species widely grown in Scandinavia and Canada, but the nutritive value of the summer regrowth has never been simulated. Our objective was to improve CATIMO for simulating the N concentration, neutral detergent fiber (NDF), in vitro digestibility of NDF (dNDF), and in vitro true digestibility of dry matter (IVTD) of summer regrowth. Daily changes in summer regrowth nutritive value were simulated by modifying key crop parameters that differed from spring growth. More specifically, the partitioning fraction to leaf blades was increased to increase the leaf-to-weight ratio, and daily changes in NDF and dNDF of leaf blades and stems were reduced. The modified CATIMO model was evaluated with data from four independent experiments in eastern and western Canada and Finland. The model performed better for eastern Canada than for the other locations, but the nutritive value attributes of the summer regrowth across locations (range of normalized RMSE = 8-25%, slope < 0.17, R-2 < 0.10) were not simulated as well as those of the spring growth (range of normalized RMSE = 4-16%, 0.85 < slope < 1.07, R-2 > 0.61). These modeling results highlight knowledge gaps in timothy summer regrowth and prospective research directions: improved knowledge of factors controlling the nutritive value of the timothy summer regrowth and experimental measurements of leaf-to-weight ratio and of the nutritive value of leaves and stems.
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