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Brylinska, M. (2015). Factors underlying changes in population of Phytophthora infestans in Poland (Vol. 5).
Abstract: Phytophthora infestans (Mont.) de Bary belongs to Oomycetes and it causes the most destructive potato disease worldwide – late blight. It originates from Mexico but it has spread wherever potatoes are grown. P. infestans populations are diversified, sexual or asexual and their composition may be affected by climate changes. Mating type, mitochondrial haplotype, Simple Sequence Repeats (SSR) markers, sensitivity to metalaxyl and virulence were evaluated to monitor changes in Polish P. infestans population.Samples of potato leaflets with single late blight lesions were collected from fields located in three regions of Poland: Mlochów, Boguchwala and Siedlce, in three years 2010, 2011 and 2012. In the region of Mlochów intensively protected fields are dominating. There are mainly small gardens and experimental fields near Boguchwala. In Siedlce region early and starch potatoes are cultivated. Total number of isolates tested was 365. Mating type, mitochondrial haplotype and SSR were evaluated using a PCR method. Sensitivity to metalaxyl was tested on rye A agar media. Virulence was tested on detached potato leaflets.Polish P. infestans population is diverse. We did not observe major clonal lineages. A1 mating type (69%) and Ia mitochondrial haplotype (72.7%) dominated. Most of the isolates were sensitive to metalaxyl (66%). We noted differences in population composition between the regions which indicate that cultivation system has an impact on the population of P. infestans. No Label
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Bojar, W. (2013). Factsheets of the models (Vol. 1).
Abstract: The exploration of adaptation and mitigation measures in the context of global challenges like climate change, food security and expected demographic boom is an field of research of growing importance. Over the last decades many research groups have been developing economic-trade models to analyse consequences on farm welfare, market supply and trade, some of them also address food security and other global concerns. There are many different ways to tackle these issues and the specific advantages and limitations of alternative modelling strategies are not yet well understood. The objective of the WP1 T1.1 task within TradeM theme of MACSUR is to use the results of a survey on trade and economic models of MACSUR Consortium partners to show which topics are currently addressed in the different models, which methods are used and how well these tools are prepared for an integration with other models like climate, crop and livestock models. This work was co-financed by NCBiR, Contract no. FACCE JPI/04/2012 – P100 PARTNER No Label
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Kässi, P., Niskanen, O., & Känkänen, H. (2014). Farm level approach to manage grass yield variation in changing climate in Jokioinen and St. Petersburg (Vol. 3).
Abstract: Cattle’s feeding is based on grass silage in Northern Europe, but grass growth is highly dependent on weather conditions. In farms decision making, grass area is usually determined by the variation of yield. To be adequate in every situation, the lowest expected yield level determines the cultivated area. Other way to manage the grass yield risk is to increase silage storage capacity over annual consumption. Variation of grass yield in climate data from years 1961-1990 was compared with 15 different climate scenario models simulating years 2046-2065. A model was developed for evaluating the inadequacy risk in terms of cultivated area and storing capacity. The cost of risk is presented and discussed.In northern Europe a typical farm has storage for roughage consumption of almost one year. In addition, there can be a buffer storage. The extra storage is to be used before and during the harvest season. New harvest will be fed to animals only after the buffer empty. Shortage in the buffer storage is possible to be filled, when the yield exceeds the target level. For risk management, two alternative mechanisms are given: forage buffer and possibility to alter the field area.According to our results, there are no significant adverse effects in the cost of risk and implied farm profitability due to climate change. Selecting the risk management scenario of 30 % grass yield risk turned out to be the least cost solution. No Label
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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 (Vol. 3).
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. No Label
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Eory, V., & Hutchings, N. (2016). Farm management and sustainability indicators: What and how to include in farm scale models (Vol. 8).
Abstract: Conference presentation PDF
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