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Lehtonen, H. S., Kässi, P., Korhonen, P., Niskanen, O., Rötter, R., Palosuo, T., et al. (2014). Problems and opportunities in climate change adaptation in North Savo region. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Crop production for feed dominates land use in North Savo in eastern Finland. The value of dairy and beef production is appr. 70 % of the total value of agricultural production of the region. In climate change adaptation research we are especially interested in dairy and meat sectors, which are directly dependent on the development of productivity of crop production. Climate change implies changes in cereals and forage crop yields and nutritive quality. There are most likely increasing problems and risks related to overwintering and growing periods. Grass silage is mainly self-produced on farms and most often there is no market for silage. Silage production and use are vulnerable to changes in local climate, because lost yield cannot be easily replaced from market. Risks and costs due to increasing inter-annual yield volatility can be reduced by good management practices, such as crop rotation, plant protection, soil improvements and better crop protection against plant diseases.However the profitability of such measures is dependent on market and policy conditions. Nevertheless new cultivars and species, as well as various options for production and risk management, are most likely needed in future climate. Some adaptations may have multiple benefits which however may realize only in medium or long run. It is important to safeguard the most important and obviously needed adaptations, and identify market and socio-economic conditions which inhibit farmers from necessary adaptations and lead to reduced productivity and increased production costs.
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Rötter, R. P. (2014). Cross-cutting uncertainties in MACSUR impact projections. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Projections into the future, such as climate change impact projections on crop production for a given region, or, on global food prices and trade are inherently uncertain. Uncertainty does not fall within a single discipline but is dealt with by a wide variety of disciplines, themes and problem domains. Model uncertainty pertaining to the impact modelling chain from climate via crop and livestock to economic and trade modelling is only part of the overall uncertainty*. There is also scenario uncertainty and many other known and unknown “unknowns”1 to be considered in efforts such as MACSUR and its themes (CropM, LiveM, TradeM) to advance model-based integrated assessment of climate change risk assessment for agriculture and food security. Propagation of uncertainties along the climate change impact modelling chain has been portrayed as “uncertainty cascade” 2. We will present different basic approaches for evaluating uncertainty in models. So far, studies addressing quantification and reporting of uncertainties in impact projections still largely focus on two major sources, i.e. the shares originating from climate modelling and from crop modelling. However, a more comprehensive treatment of uncertainty and how it is reported is urgently needed.
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Lehtonen, H. S., Kässi, P., Korhonen, P., Niskanen, O., Rötter, R., Palosuo, T., et al. (2014). Problems and opportunities in climate change adaptation in North Savo region. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Crop production for feed dominates land use in North Savo in eastern Finland. The value of dairy and beef production is appr. 70 % of the total value of agricultural production of the region. In climate change adaptation research we are especially interested in dairy and meat sectors, which are directly dependent on the development of productivity of crop production. Climate change implies changes in cereals and forage crop yields and nutritive quality. There are most likely increasing problems and risks related to overwintering and growing periods. Grass silage is mainly self-produced on farms and most often there is no market for silage. Silage production and use are vulnerable to changes in local climate, because lost yield cannot be easily replaced from market. Risks and costs due to increasing inter-annual yield volatility can be reduced by good management practices, such as crop rotation, plant protection, soil improvements and better crop protection against plant diseases.However the profitability of such measures is dependent on market and policy conditions. Nevertheless new cultivars and species, as well as various options for production and risk management, are most likely needed in future climate. Some adaptations may have multiple benefits which however may realize only in medium or long run. It is important to safeguard the most important and obviously needed adaptations, and identify market and socio-economic conditions which inhibit farmers from necessary adaptations and lead to reduced productivity and increased production costs.
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Lehtonen, H. S., Liu, X., Purola, T., Rötter, R., & Palosuo, T. (2014). Farm level dynamic economic modelling of crop rotation with adaptation practices. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Agriculture is facing increasing challenges under volatile commodity markets, on-going climate change with more frequent extreme weather events and tightened environmental constraints. Crop rotation is considered essential and may even gain more importance for sustainable farming in the context of climate change challenges while monocropping is expected to become increasingly problematic. This is, among others, because of increasing plant protection challenges due to warmer climate which is expected to result in severe droughts, heavy rainfall and waterlogging in northern latitudes more frequently. Such changes require improved soil structure and water retention, also aided by crop rotations, to avoid yield losses. Our objective is to build and apply a dynamic optimization model of farm level crop rotation on many field parcels over 30-40 years. The model takes into account various adaptation management methods such as fungicide treatment, soil improvements such as liming, and nitrogen fertilization, simultaneously with dynamic crop rotation choices. However, these management options come along with costs. Using the model, outcomes of crop growth simulation modeling can be included into economic analysis. Simulated new cultivars, suited for a longer growing season, can be defined as alternatives to current cultivars, both having specific nutrient and other input requirements such as water, labor or pesticides. The model is used in evaluating the value of future cultivars and other management practices in climate and socio-economic scenarios. The first results show that expected market prices have major impacts on the management choices, the resulting yield levels, production and income over time.
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Hoffmann, H., Zhao, G., Van Bussel, L., Enders, A., Specka, X., Sosa, C., et al. (2014). Effects of climate input data aggregation on modelling regional crop yields. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Crop models can be sensitive to climate input data aggregation and this response may differ among models. This should be considered when applying field-scale models for assessment of climate change impacts on larger spatial scales or when coupling models across scales. In order to evaluate these effects systematically, an ensemble of ten crop models was run with climate input data on different spatial aggregations ranging from 1, 10, 25, 50 and 100 km horizontal resolution for the state of North Rhine-Westphalia, Germany. Models were minimally calibrated to typical sowing and harvest dates, and crop yields observed in the region, subsequently simulating potential, water-limited and nitrogen-limited production of winter wheat and silage maize for 1982-2011. Outputs were analysed for 19 variables (yield, evapotranspiration, soil organic carbon, etc.). In this study the sensitivity of the individual models and the model ensemble in response to input data aggregation is assessed for crop yield. Results show that the mean yield of the region calculated from climate time series of 1 km horizontal resolution changes only little when using climate input data of higher aggregation levels for most models. However, yield frequency distributions change with aggregation, resembling observed data better with increasing resolution. With few exceptions, these results apply to the two crops and three production situations (potential, water-, nitrogen-limited) and across models including the model ensemble, regardless of differences among models in simulated yield levels and spatial yield patterns. Results of this study improve the confidence of using crop models at varying scales.
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