FACCE MACSUR Reports (Modelling European Agriculture with Climate Change for Food Security)
Not a member yet
471 research outputs found
Sort by
A comparison of farm-scale models to estimate greenhouse gas emissions from dairy farms in Europe
Get PDFFarm-scale models quantify the cycling of nitrogen (N) and carbon (C) so are powerful tools for assessing the impact of management-related decisions on greenhouse gas (GHG) emissions, especially on dairy cattle farms, where the internal cycling is particularly important. Farm models range in focus (economic, environmental) and the detail with which they represent C and N cycling. We compared four models from this range in terms of on-farm production and emissions of GHGs, using standardized scenarios. The models compared were SFarMod, DairyWise, FarmAC and HolosNor. The scenarios compared were based on two soil types (sandy clay versus heavy clay), two roughage systems (grass only versus grass and maize), and two climate types (Eindhoven versus Santander). Standard farm characteristics were; area (50 ha), milk yield (7000 kg/head/year), fertiliser (275 kg N and 150 kg N/ha/year for grass and maize, respectively). Potential yields for grass 10t dry matter (DM)/ha/year in both areas, maize 14 t DM/ha/ year in Eindhoven and 18t DM/ha/ year in Santander. The import of animal feed and the export/import manure and forages was minimized. Similar total farm direct GHG emissions for all models disguised a variation between models in the contribution of the different on-farm sources. There were large differences between models in the predictions of indirect GHG emission from nitrate leaching. Results could be explained by differences between models in the assumptions made and detail with which underlying processes were represented. We conclude that the choice of an appropriate farm model is highly dependent upon the role it should play and the context within which it will operate, so the current diversity of farm models will continue into the future
Factors underlying changes in population of Phytophthora infestans in Poland
No full textPhytophthora 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: Młochów, Boguchwała and Siedlce, in three years 2010, 2011 and 2012. In the region of Młochów intensively protected fields are dominating. There are mainly small gardens and experimental fields near Boguchwała. 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
Empirical analysis on crop-weather relationships
No full textThere have been several studies, where process-based crop models are developed, used and compared in order to project crop production and corresponding model uncertainties under climate change. Despite many advances in this field, there are some correlations between climate variables and crop growth, such as pest and diseases, that is often absent in process-based models. Such relationships can be simulated using empirical models. In this study, several statistical techniques were applied on winter oilseed rape data collected in some European countries. The empirical models were then used to predict yield of winter oilseed rape in the field experiments during more than 20 years, up to 2013. Results suggest that newly developed regression techniques such as shrinkage methods work well both in yield projections and finding the influential climatic variables. Many of regression techniques agree in terms of yield prediction; however, choice of significant climate variables is rather sensitive to the choice of regression technique
Models for regional scale farming system evaluation of climate change mitigation options and environmental impact assessment
No full textThe aim of the present paper is to exemplify and discuss the importance of farm scale modeling in relation to The EU Joint Programming Initiative (JPI-FACCE) knowledge hub on Agriculture, Food Security and Climate Change project.In particular, livestock production systems include complex interactions, with non-linear relationships between input factors, production, emissions, local climate as well as natural resources (e.g. soil types, rotational land versus permanent grasslands etc.). Moreover, management options pursued by the different types of farmers and other relevant decision makers are important to integrate. Consequently, results of regional scale impact assessments depend on the farming systems model approach, the approach to upscale results, and the inclusion of the relevant stakeholders and decision makers at the scales considered.Different farming systems models are reviewed, including the existing dynamic and static biophysical models. Finally, procedures for upscaling and validity testing of synthesized model results at regional scales are presented. Based on a discussion of these procedures, recommendations for hot-spot analyses in farming systems with regard to integrated climate change adaptation and mitigation for a sustainable food production are synthesized, and the potentials for integration of recommended policies and farm management options into overarching models in order to assess their impact on the regional to global scales are discussed
Three years of collaboration in TradeM – Agricultural markets and prices
Get PDFSome 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.
Integrated Assessment of Climate Change Mitigation and Adaptation Impacts at Landscape level: Mostviertel, Austria
Get PDFConclusionsIncreasing productivity can increase intensification pressuresThreatened permanent (extensive) grasslands and landscape elements, butsubject to resource constraints, costs and prices andfuture production potential to increase global food supplyFuture RDP and environmental policy design (e.g. WFD) should take changing productivity into accountHeterogeneity matters at farm and regional levelChanging relative competitiveness of farmsFuture research: analyze uncertaintie
Inventory of data and data sharing mechanism for model linking and scaling exercises
Get PDFThis deliverable lays out the work as done as part of MACSUR CropM on ‘Inventory of data and data sharing mechanism for model linking and scaling exercises’. In summary not much work was done, as it was found that there was not real demand for the activity in this task. The task in itself was servicing the other work as part of MACSUR, and as the service was not in demand, it was decided to take a low profile and wait for specific requests by partners for data in relation to model linking and upscaling
Inter-model variability in wheat yield responses to changes in climate in the IRS1 model experiment
Get PDF
The greenhouse gas emissions intensity of herds with mastitis
No full textMastitis is an inflammatory disease of milking cows, causing production and economic losses in dairy farms. The main pathogens causing majority of the intramammary infections are Staphylococcus aureus, Streptococcus dysgalactiae, Escherichia coli and coagulase-negative Staphylococci. Here, we analysed the effect of mastitis on herd parameters such as milk yield, feed intake, replacement rate, gross margin and greenhouse gas emissions. The data were collected from the Norwegian Dairy Herd Recording System between 2010 and 2012. The farm data were recorded from 20 farms in Norway, based on health, fertility and breeding characteristics. SimHerd, a computer simulation model was used to estimate the impact of the observed levels of mastitis on herd parameters which were then fed into a whole farm model, HolosNor, to calculate the greenhouse gas emissions on the farm. The standard values provided in the SimHerd except for mastitis occurrence were applied in the scenario simulations. A further study is planned to parameterize each herd with specific herd characteristics in SimHerd so that herd specific estimates of the effect of mastitis on greenhouse gas emissions can be performed