Lehtonen, H. S., Kässi, P., Korhonen, P., Niskanen, O., Rötter, R., Palosuo, T., et al. (2014). Specific problems and solutions 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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Schönhart, M. (2014). Spillovers between MACSUR and Austrian climate change research projects. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: The Austrian regional case study in MACSUR extends the methods and builds upon the results of the CC-ILA project. CC-ILA enables cooperation between landscape planners and landscape ecologists to analyse mitigation and adaptation strategies for sustainable rural land use and landscape developments in a case study landscape. Subsequent research in MACSUR includes analysis towards rural development and the improvement of the climate impact data base for grasslands. The latter is achieved by collaborating with Crop-M partner LFZ Raumberg-Gumpenstein, who is able to utilize spill-overs within the Agromet-Monitor project.
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Van den Pol-van Dasselaar, A. (2014). Stakeholder consultation on functions of grasslands in Europe. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Active participation of stakeholders was one of the key objectives of the FP7-funded project MultiSward (Grant Agreement n° FP7-244983). MultiSward aimed to increase the reliance of farmers on grasslands and on multi-species swards for competitive and sustainable ruminant production systems. Stakeholders were consulted via international and national meetings. Furthermore, an on-line questionnaire on the functions of grasslands was developed in eight languages and almost 2000 valid responses were obtained from European stakeholders. All of the stakeholder groups that were identified as being important in the stakeholder analysis responded to the questionnaire: primary producers, policy makers, researchers, advisors, NGO’s (for nature conservation and for protection of the environment), industry (mainly processing and seed industry) and education. This method of stakeholder consultation will be illustrated using the results on appreciation of the following functions of grasslands: adaptation to climate change, mitigating greenhouse gas emissions and carbon sequestration.
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Sharif, B., Olesen, J. E., & Schelde, K. (2014). Statistical learning approach for modelling the effects of climate change on oilseed rape yield. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Statistical learning is a fairly new term referring to a set of supervised and unsupervised modelling and prediction techniques. It is based on traditional statistics but has been highly enhanced inspired by developments in machine learning and data mining. The main focus of statistical learning is to estimate the functions that quantify relations between several parameters and observed responses. These functions are further used for prediction, inference or a combination of both. For a particular case of quantitative responses, regularization techniques in regression are developed to overcome the weaknesses of ordinary least square (OLS) regression in prediction. These new shrinkage methods outperform OLS for prediction, especially in large datasets. In this study, a large dataset of field experiments on winter oilseed rape in Denmark for 22 years (1992 to 2013) was collected. Biweekly climatic data along with sowing date, harvest date, soil type and previous crop are considered as the explanatory variables. Yield of winter oilseed rape is considered as response variable. LASSO and Elastic Nets are the regularization techniques used to estimate the functions. Hold-one-out cross validation method for testing the prediction power reveals that these techniques are much useful in both prediction and inference. Since these techniques are included in recent versions of some software packages (e.g. R), they can be easily implemented by users at any level. The estimated function (model) is further used to predict the oilseed rape yield responses to climate change for several scenarios using representative weather data produced by a weather generator.
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Milford, A. B. (2014). Sustainable food consumption as a mitigation and adaptation strategy. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Studies of GHG emissions from agriculture show that there are large differences in emissions from different products. In addition some foods require more land and water resources than others, which mean that in a future with food scarcity, moving from less to more sustainable food may become a necessity for there to be enough food for everyone. A changing of consumption and production from less to more sustainable food is thus both a mitigation strategy, as well as an adaptation strategy if climate change results in less available agricultural land and water resources, and more food loss due to more extreme weather conditions. Forecasting the economic consequences of climate change on agricultural production cannot be done without taking into account our future consumption patterns. What will be produced will always to a large extent be a result of what is being demanded by consumers. This is a presentation of two ongoing projects related to this theme. One is a newly started project on factors which influence meat consumption. In a cross-country regression analysis we will estimate the importance of different factors such as income, price levels and degree of urbanization. We are particularly interested in the interlinkages between meat production and consumption at national levels. The other project looks at typical diets in England, Spain and Norway, and will estimate through a multi objective optimization process how the diets can be changed, through taxes and subsidies, towards a diet which is both more healthy and climate friendly.
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