|
|
Persson, T., & Kværnø, S. (2014). Impact of soil properties regionalization methods on regional wheat yield in southeastern Norway..
|
|
|
|
Persson, T., Kværnø, S., & Höglind, M. (2014). Impact of soil properties regionalization procedures on regional timothy dry matter yield and variability in southeastern Norway..
|
|
|
|
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.
|
|
|
|
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.
|
|
|
|
Persson, T., Kværnø, S., & Höglind, M. (2015). Impact of soil type extrapolation on timothy grass yield under baseline and future climate conditions in southeastern Norway. Clim. Res., 65, 71–86.
Abstract: Interactions between soil properties and climate affect forage grass productivity. Dynamic models, simulating crop performance as a function of environmental conditions, are valid for a specific location with given soil and weather conditions. Extrapolations of local soil properties to larger regions can help assess the requirement for soil input in regional yield estimations. Using the LINGRA model, we simulated the regional yield level and variability of timothy, a forage grass, in Akershus and Ostfold counties, Norway. Soils were grouped according to physical similarities according to 4 sets of criteria. This resulted in 66, 15, 5 and 1 groups of soils. The properties of the soil with the largest area was extrapolated to the other soils within each group and input to the simulations. All analyses were conducted for 100 yr of generated weather representing the period 1961-1990, and climate projections for the period 2046-2065, the Intergovernmental Panel on Climate Change greenhouse gas emission scenario A1B, and 4 global climate models. The simulated regional seasonal timothy yields were 5-13% lower on average and had higher inter-annual variability for the least detailed soil extrapolation than for the other soil extrapolations, across climates. There were up to 20% spatial intra-regional differences in simulated yield between soil extrapolations. The results indicate that, for conditions similar to these studied here, a few representative profiles are sufficient for simulations of average regional seasonal timothy yield. More spatially detailed yield analyses would benefit from more detailed soil input.
|
|
