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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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Brouwer, F., & Sinabell, F. (2015). Three years of collaboration in TradeM – Agricultural markets and prices. In FACCE MACSUR Reports (Vol. 6, pp. SP6–4). Brussels.
Abstract: Some farmers may claim that climate change adaptation is easy compared to the difficulties caused by policiesAction based on weather observations only, is insufficient for farmers to respond to climate change. Researchers need support from farmers in understanding the responses in practice.Policies might be too slow to respond to needs for change in agriculture. Winners and losers seem to be observed everywhere.The impacts of climate change is heterogeneous among farm types and regionsEffects beyond 2050 remain largely unclear, mainly because the effects of extreme events are not consideredVariability of yields is important to farm incomes, but most studies only consider average changesFarmers are ready to design their site-specific adaptation response providing that new knowledge and learning spaces are available. A learning process based on integrated models, assessment of short- and long-term effects, is needed for farmers to adapt to climate change, price fluctuations and policy change. No Label
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Ahmadi, V. (2015). Impacts of Common Agricultural Policy 2015 reforms on animal health and welfare of Scottish dairy herds (Vol. 5).
Abstract: The latest Common Agricultural Policy (CAP) 2015 reforms bring a substantial change in the way farm support is paid in Scotland where previous direct CAP payments were largely based on historical entitlements. Under the new payment scheme, three rates of payment are designated based on land uses and capabilities. As a result, it is anticipated that, average large dairy farms will lose out up to 32% of their farm net margins, while small dairy farms will lose out between 7-20% of their farm net margins. Such reductions of payment support may force dairy farmers to cut costs of production on farms especially livestock variable costs including labour costs and costs of prevention, control, treatment and management of livestock diseases and welfare conditions. This will have direct and indirect consequences on health and welfare of dairy cattle. This study aims to assess the impact of new support payments under CAP 2015 reforms on financial capabilities of dairy herds in tackling three conditions namely: infertility, mastitis and lameness. A detailed inventory of 42 commercial dairy farms in Scotland that contains both physical (i.e. farm area, nutrition and labour supply, etc.) and health data collected in 2013 and was used to parameterise an optimisation model. The model is a linear programme (LP) model which optimises farm net margin under limiting farm resources. The model also consists of feed demand and supply components that are used to determine monthly feed requirements for each of the animals on a farm as well as grass yield for pasture area of the land. The model is run for both ‘healthy’ and ‘diseased’ herds under previous and future CAP support payments. Details of the model and the dataset used as well as some results will be presented at the conference. No Label
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Kipling, R., Scollan, N., Bannink, A., & van Middelkoop, J. (2016). From diversity to strategy: Livestock research for effective policy in a climate change world (Vol. 8).
Abstract: European livestock agriculture is extraordinarily diverse, and so are the challenges it faces. This diversity has contributed to the development of a fragmented set of research communities. As a result, livestock research is often under-represented at policy level, despite its high relevance for the environment and food security. Understanding livestock systems and how they can sustainably adapt to global change requires inputs across research areas, including grasslands, nutrition, health, welfare and ecology. It also requires experimental researchers, modellers and stakeholders to work closely together. Networks and capacity building structures are vital to enable livestock research to meet the challenges of climate change. They need to maintain shared resources and provide non-competitive arenas to share and synthesize results for policy support. ï‚· Long term strategic investment is needed to support such structures. Their leadership requires very different skills to those effective in scientific project coordination.
Keywords: policy brief, networking
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Semenov, M. A., & Stratonovitch, P. (2016). Local-scale CMIP5-based climate scenarios for MACSUR2 (Vol. 8).
Abstract: Climate sensitivity of GCMs was used to select 5 GCMs from the CMIP5 ensemble for impact studies in MACSUR2. Selected GCMs for MACSUR2 are EC-EARTH (7), GFDL-CM3 (8) HadGEM2-ES (10), MIROC5 (13), and MPI-ESM-MR (15). These GCMs are evenly distributed among CMIP5 (Fig 1) and should capture, in principal, climate uncertainty of the CMIP5 ensemble. Using 5 GCMs will enable us to assess uncertainties in impacts related to uncertainty in climate projections. The selection of GCMs in MACSUR2 has a good overlap with selections of GCMs used in CORDEX and AgMIP projects. We used the LARS-WG generator to construct local-scale CMIP5-based climate scenarios for Europe (Semenov & Stratonovitch, 2015). Fifteen sites were selected in Europe for MACSUR2. For each site and each selected GCM, 100 yrs climate daily data were generated by LARS-WG for RCP4.5 and RCP8.5 emission scenarios and for baseline and 3 future periods: near-term (2021-2040), mid-term (2041-2060) and long-term (2081-2100).
Keywords: CropM
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