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Höglind, M. (2014). Europeisk landbruk i et klima i endring (MACSUR)..
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Persson, T., Kværnø, S., & Höglind, M. (2014). Determining the impact of soil regionalization and climate change on wheat and timothy grass yield in southeastern Norway. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Southeastern Norway is characterized by variable soils, which affect its agricultural productivity. The region is dominated by cereal production, but livestock farming with forage crops has increased the latest years. Climate and socio-economic changes could entail a shift from the current production areas of cereal and forage crops. In this study we used the mechanistic models CSM-CERES and LINGRA to evaluate impacts of climate change and soil variability on wheat and timothy yields in Akerhus and Østfold Counties in Southeastern Norway. The models were run for historical (1961-90) and projected future (2046-2065) climatic conditions, and for four soil regionalizations of different resolution (1, 5, 16 and 76 representative soil profiles). The extrapolation of soil characteristics was based on similarities in texture, organic matter, layering and water holding capacity. Across the whole region, there were small differences in both spring wheat and timothy yield between the different soil regionalization resolutions. However, within certain districts within the region the differences in wheat grain yield and timothy biomass yield among the soil resolutions were up to 20 percent. These results indicate that a relatively detailed resolution of the soil proporties is preferred to better understand the impact of shifts in production between cereals and forage grasses on yield level if spatial variability within regions is considered. The climate change scenario used indicated increased yields of both crop types in a future climate. Further steps could include a weighting of the wheat and timothy production across soils according to economic analyses.
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Özkan Gülzari, Ş., Åby, B. A., Persson, T., Höglind, M., & Mittenzwei, K. (2017). Combining models to estimate the impacts of future climate scenarios on feed supply, greenhouse gas emissions and economic performance on dairy farms in Norway. Agric. Syst., 157, 157–169.
Abstract: • This study combines crop, livestock and economic models.
• Models interaction is through use of relevant input and output variables. • Future climate change will result in increased grass and wheat dry matter yields. • Changes in grass, wheat and milk yields in future reduce farm emissions intensity. • Changes in future dry matter yields and emissions lead to increased profitability. There is a scientific consensus that the future climate change will affect grass and crop dry matter (DM) yields. Such yield changes may entail alterations to farm management practices to fulfill the feed requirements and reduce the farm greenhouse gas (GHG) emissions from dairy farms. While a large number of studies have focused on the impacts of projected climate change on a single farm output (e.g. GHG emissions or economic performance), several attempts have been made to combine bio-economic systems models with GHG accounting frameworks. In this study, we aimed to determine the physical impacts of future climate scenarios on grass and wheat DM yields, and demonstrate the effects such changes in future feed supply may have on farm GHG emissions and decision-making processes. For this purpose, we combined four models: BASGRA and CSM-CERES-Wheat models for simulating forage grass DM and wheat DM grain yields respectively; HolosNor for estimating the farm GHG emissions; and JORDMOD for calculating the impacts of changes in the climate and management on land use and farm economics. Four locations, with varying climate and soil conditions were included in the study: south-east Norway, south-west Norway, central Norway and northern Norway. Simulations were carried out for baseline (1961–1990) and future (2046–2065) climate conditions (projections based on two global climate models and the Special Report on Emissions Scenarios (SRES) A1B GHG emission scenario), and for production conditions with and without a milk quota. The GHG emissions intensities (kilogram carbon dioxide equivalent: kgCO2e emissions per kg fat and protein corrected milk: FPCM) varied between 0.8 kg and 1.23 kg CO2e (kg FPCM)− 1, with the lowest and highest emissions found in central Norway and south-east Norway, respectively. Emission intensities were generally lower under future compared to baseline conditions due mainly to higher future milk yields and to some extent to higher crop yields. The median seasonal above-ground timothy grass yield varied between 11,000 kg and 16,000 kg DM ha− 1 and was higher in all projected future climate conditions than in the baseline. The spring wheat grain DM yields simulated for the same weather conditions within each climate projection varied between 2200 kg and 6800 kg DM ha− 1. Similarly, the farm profitability as expressed by total national land rents varied between 1900 million Norwegian krone (NOK) for median yields under baseline climate conditions up to 3900 million NOK for median yield under future projected climate conditions. |
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Mittenzwei, K., Persson, T., Höglind, M., & Kværnø, S. (2017). Combined effects of climate change and policy uncertainty on the agricultural sector in Norway. Agric. Syst., 153, 118–126.
Abstract: Highlights • A framework to study climate and policy uncertainty in agriculture is presented. • Combining both sources of uncertainty has ambiguous effects on agriculture. • Uncertainty needs to be highlighted in modelling tools for policy analysis. Abstract Farmers are exposed to climate change and uncertainty about how that change will develop. As farm incomes, in Norway and elsewhere, greatly depend on government subsidies, the risk of a policy change constitutes an additional uncertainty source. Hence, climate and policy uncertainty could substantially impact agricultural production and farm income. However, these sources of uncertainty have, so far, rarely been combined in food production analyses. The aim of this study was to determine the effects of a combination of policy and climate uncertainty on agricultural production, land use, and social welfare in Norway. Output yield distributions of spring wheat and timothy, a major forage grass, from simulations with the weather-driven crop models, CSM-CERES-Wheat and, LINGRA, were processed in the a stochastic version Jordmod, a price-endogenous spatial economic sector model of the Norwegian agriculture. To account for potential effects of climate uncertainty within a given future greenhouse gas emission scenario on farm profitability, effects on conditions that represented the projected climate for 2050 under the emission scenario A1B from the 4th assessment report of the Intergovernmental Panel on Climate Change and four Global Climate Models (GCM) was investigated. The uncertainty about the level of payment rates at the time farmers make their management decisions was handled by varying the distribution of payment rates applied in the Jordmod model. These changes were based on the change in the overall level of agricultural support in the past. Three uncertainty scenarios were developed and tested: one with climate change uncertainty, another with payment rate uncertainty, and a third where both types of uncertainty were combined. The three scenarios were compared with results from a deterministic scenario where crop yields and payment rates were constant. Climate change resulted in on average 9% lower cereal production, unchanged grass production and more volatile crop yield as well as 4% higher farm incomes on average compared to the deterministic scenario. The scenario with a combination of climate change and policy uncertainty increased the mean farm income more than a scenario with only one source of uncertainty. On the other hand, land use and farm labour were negatively affected under these conditions compared to the deterministic case. Highlighting the potential influence of climate change and policy uncertainty on the performance of the farm sector our results underline the potential error in neglecting either of these two uncertainties in studies of agricultural production, land use and welfare.
Keywords: Climate change; Norway; Agriculture; Policy uncertainty; Modelling; LINGRA; CSM-CERES-Wheat; DSSAT
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Höglind, M., & the partners of LiveM task L1.3. (2017). Bringing together grassland and farm scale modelling. Part 1. Characterizing grasslands in farm scale modelling (Vol. 10).
Abstract: This report provides an overview of how grasslands are represented in six different farmscale models represented in MACSUR. A survey was conducted, followed by a workshop in which modellers discussed the results of the survey, and identified research challenges and knowledge gaps. The workshop was attended by grassland as well as livestock specialists. The investigated models differed largely with respect to how grasslands were represented, e.g. as regards weather and management factors accounted for, spatial and temporal resolution, and output variables. All models had grassland modules that simulate DM yield and herbage N content (or crude protein (CP) content = N content x 6.25). Many models also simulate P content, whereas only one simulate K content. About half of the model simulate herbage energy value and/or herbage fibre content and fibre and/or dry matter digestibility. Critical input data required from grassland models to simulate ruminant productivity and GHG emissions at farm scale was identified by the workshop participants. The different types of input data required were ranked in order of importance as regards their influence on important system outputs. For simulation of ruminant productivity and GHG emissions, herbage DM yield was ranked as the most important input variable from grassland models, followed by CP content together with at least one variable describing herbage fibre characteristics. These findings suggest that work on improving the ability of the current grassland models with respect to simulation of fibre/energy should be prioritized in farm-scale modelling aiming at quantifying livestock production and GHG emissions under different management regimes and climate conditions. More work is also needed on model evaluation, a task that has not been prioritized yet for some models.
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