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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Topp, C. (2015). Pesticide management in Scottish spring barley – insights from sowing dates (Vol. 5).
Abstract: Better management of pesticides is a potentially important strategy for reducing environmental impact while maintaining yields. Pesticide use is influenced by several drivers, including sowing date, which can directly impact disease burden. Analysis of sowing dates for spring barley was the first stage of this project, which aims to provide insight into areas of farm management which can be optimised to reduce environmental impact. Sowing dates were taken from the Adopt a Crop database, which contains data from 1983 onwards for commercial farms across Scotland. Work was carried out at three levels: national, to provide an overall picture of historical patterns; regional, to highlight differences within Scotland; and case study, to determine whether the national trend was visible in a single region. A general trend towards later sowing of spring barley in Scotland is visible – yet, this pattern is less pronounced in certain regions. Future work must therefore consider what factors have lead to this shift, to more fully understand interactions between sowing date and the environment. No Label
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Trnka, M., Hlavinka, P., Wimmerová, M., Pohanková, E., Rötter, R., Olesen, J. E., et al. (2017). Paper on model responses to selected adverse weather conditions (Vol. 10).
Abstract: Based on the Trnka et al. (2015) study that indicated that heat and drought will be the most important stress factors for most of the European what area the further effort focused on these two extremes. The crop model HERMES has been tested for its ability to replicate correctly drought stress, heat stress and combination of both stresses. While data on the drought stress were available for both field and growth chambers, heat stress and its combination with heat stress was available only for the growth chambers. The modified version of the HERMES crop model was developed by Dr. Kersebaum and is being currently prepared for the journal paper publication.
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Liu, X. (2015). Incentivising for climage change mitigation in the context of adaptation to climate and market changes at the farm level in North Savo region (Vol. 4).
Abstract: Authors: Lehtonen, H., Liu, X. & Purola, T. No Label
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Rolinski, S., & Sætnan, E. (2013). Uncertainties in climate change prediction and modelling (Vol. 1).
Abstract: As models become increasingly complex and integrated, uncertainty among model parameters, variables and processes become critical for evaluating model outcomes and predictions. A framework for understanding uncertainty in climate modelling has been developed by the IPCC and EEA which provides a framework for discussion of uncertainty in models in general. Here we report on a review of this framework along with the results of a survey of sources of uncertainty in livestock and grassland models. Along with the identification of key sources of uncertainty in livestock and grassland modelling, the survey highlighted the need for a development of a common typology for uncertainty. When collaborating across traditionally separate research fields, or when communicating with stakeholders, differences in understanding, interpretation or emphasis can cause confusion. Further work in MACSUR should focus on improving model intercomparison methods to better understand model uncertainties, and improve availability of high quality datasets which can reduce model uncertainties. No Label
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