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Mittenzwei, K. (2015). The role of uncertainty in assessing agricultural responses to food security and climate change: A Case Study from Norway (Vol. 4).
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Mittenzwei, K. (2015). The importance of climate and policy uncertainty in Norwegian agriculture (Vol. 5).
Abstract: The paper addresses future climate and policy uncertainty for agricultural production and food security in Norway. The two crop simulation models, CSM-CERES-Wheat and, the LINGRA model, were used to determine the impact of climate change on grain yield of spring wheat, and harvest security and biomass yield of timothy, an important forage grass in Northern Europe, respectively. Harvestable yield distributions from the crop models were fed into a stochastic version of the economic sector model Jordmod. Distributions of the rates of agricultural subsidies rates were assessed based on past policy changes and prospective reforms. The model was used to assess the effects of both climate and policy uncertainty on agricultural production, land use, and national food security. Jordmod is comprised of a supply module in which stochastic profits for more than 300 regional farms are maximized and a deterministic market module which maximizes social welfare in the agricultural sector. Socio-economic scenarios were developed around the level of ambition of Norwegian agricultural policy makers. The model results were contrasted with the deterministic results based on average yield and payment rates. The innovation of this paper lays in assessing the combined effects of future climate and policy uncertainty for the agricultural sector in Norway. It also highlights the potential errors made by neglecting these types of uncertainty in economic modelling. No Label
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Nendel, C. (2013). Data classification and criteria catalogue for data requirements (Vol. 1).
Abstract: Data requirements for calibration and validation of agro-ecosystem models were elaborated and a classification scheme for the suitability of experimental data for model testing and improvement has been developed. The scheme enables to evaluate datasets and to classify datasets upon their quality to be used in crop modelling. No Label
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Niemi, J. (2016). Framework of stochastic gross margin volatility modeling of crop rotation with farm management practices (Vol. 9 C6 -).
Abstract: DP models with risk aversion through meanvariancespecification is already implemented inLuke and applied in North Savo regionHOWEVER climate change, e.g. changes in mean andvariance of crop yiels, still not yet taken into account– Recently, such crop modelling results have becomeavailble for wheat as well, not only for barley– Still CC impact available for 2 cereals crops only, whilemost farms cultivate more than 2 crops Some early conclusions• The suggested approach is consistent in terms of DPprinciples and mean-variance approach and can provideconsistent results for farm scale risk analysis• It is however hard to utilise the approach except assuming afarm with only few crops (those with crop modelling / otherresults of climate change effects on mean and (co-variance)© Natural Resources Institute Finland• Assuming no change in price (co)variability is a majorsimplification results show farm level (or local) effects ofchanges in mean yields and yield (co)variability only
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Nieróbca, A. (2015). The regional trends in maize yield in Poland and its prediction according regional GLOBIOM –CAPRI baseline analysis for 2010, 2030 and 2050 (Vol. 5).
Abstract: The increase of maize production in Polish agriculture is considered as one of the indicators showing temporal climate change impact. The sowing area of grain maize in Poland increased from 152 thousand hectares in 2000 to 614 thousand hectares in 2013. In the same time, the area of maize production for forage in Poland has increased from 162 to 462 thousand hectares. There is observed increase of yield level but the regional differentiation of this trend is also visible. In the paper we discuss the temporal regional trends in maize yield in Poland connected to the limitation factors (soil, climate, fertilisation) and prognosis for further climate change impact using GLOBIOM-CAPRI regional simulations. The data for the analysis of regional trends for maize production level in 16th regions in Poland (NUTS2) were taken from National Statistical Offices Reports. The GLOBIOM-CAPRI regional simulations for baseline analysis 2010, 2030 and 2050 were obtained from MASCUR knowledge-hab evaluation exercises. As a limitation factors for of maize yield we considered the index for suitable soil and climate suitability index for climate developed in Poland and fertilisation. We have identified that observed positive trend in yield level at NUTS2 is correlated with the climate suitability index and level of NPK fertilisation, whilst there is no statistical relations with soil quality index. The GLOBIOM –CAPRI regional simulation for 2030 shows that the maize yield in Poland will further increase and it can be explained by realisation of existing trends. In simulations for the baseline 2050 year there is visible negative trend in yield level in some regions, where even in current climate there is high probability of deficit precipitation (eg. Wielkopolskia region). No Label
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