FACCE MACSUR Reports (Modelling European Agriculture with Climate Change for Food Security)
Not a member yet
471 research outputs found
Sort by
Applying adaptation response surfaces for managing wheat under perturbed climate and elevated CO2 in a Mediterranean environment
Full text linkThis study developed Adaptation Response Surfaces and applied them to a study case in North East Spain on winter crops adaptation, using rainfed winter wheat as reference crop. Crop responses to perturbed temperature, precipitation and CO2 were simulated by an ensemble of crop models. A set of combined changes on cultivars (on vernalisation requirements and phenology) and management (on sowing date and irrigation) were considered as adaptation options and simulated by the crop model ensemble. The discussion focused on two main issues: 1) the recommended adaptation options for different soil types and perturbation levels, and 2) the need of applying our current knowledge (AOCK) when building a crop model ensemble. The study has been published Agricultural Systems (Available online 25 January 2017, https://doi.org/10.1016/j.agsy.2017.01.009), and the text below consists on extracts from that paper
Heat stress impact on productive efficiency and GHG emission intensity in dairy cow
No full textIn this study, we assessed the effect of heat stress on greenhouse gas emission (GHG) intensity (emissions produced per kg fat and protein corrected milk: FPCM in kilogram of carbon dioxide equivalents: kg CO2e) in dairy cows. A commercial farm milking about 1,100 cows/day was considered. Data on milk yield, fat and protein as percentage, number of cows milked for day and digestible energy and protein of diet were used to estimate the enteric methane emissions under heat stress and thermo-neutral scenarios using the IPCC-based equations. Temperature Humidity Index (THI) was calculated from data recorded in the nearest weather station and used to define heat stress conditions. Months of June, July, August, September and October showed an average maximum THI greater than 70 unit and were considered under heat stress, while the other months were considered as thermo-neutral. Productive parameters considered were 27.4 liter/cow/day; 3.8% and 3.2% under heat stress compared to 28.9 liter/cow/day, 3.7% and 3.3 % under neutral climate for milk yield and for fat and protein as percentage, respectively. Diet did not change during the periods studied then 73% of digestible energy and 16.7% of protein were considered for both scenarios. Methane emission intensity, was found as 0.400 and 0.388 kg CO2eq/kg FPCM for heat stress and thermo-neutral scenario, respectively. Under heat stress, emissions were12 grams CO2eq/kg FPCM or about 60 tonsCO2eq (considering the total milk yield in the study period) higher than that of thermos-neutral conditions. The preliminary results suggest that the effect of heat stress on the production efficiency may affect the emission intensity of GHG. However, further investigations are needed to well understand how heat stress modules enteric methane and others sources of GHG emissions in dairy cows. Therefore, a further study will focus on using HolosNor, a farm scale model, to account for all significant GHG emissions and to compare different levels of heat stress on farm GHG emissions
Benefits of climate modeling for actors along the food chain - reflections for further engagement between science and practice
Full text linkIn the agricultural practice in Europe climate aspects are still rarely influencing decision-making at farm level, other aspects as short- term economics and legislative constraints being more relevant. But also in Europe farmers are facing shifts in weather patterns with weather extremes, thus showing that there is a need for more information regarding climate change. In this regard models which are able to describe climate phenomena and possible options for (pre-)adaptation are becoming more and more valuable for the farming community. This is in particularly relevant for long -term investments like for livestock buildings or irrigation infrastructures, but also for the choice of crops and management practices and related machinery. At the same time agriculture and in particular the livestock sector is pointed out as an important GHG emitent, in particular for methane. With the Paris agreement, EU Member States are asked to present strategies on how to reduce their emissions. There still is little knowledge about costeffective measures to reduce emissions at national, and especially at regional and farm level. Here sophisticated, consolidated climate models, able to present possible pathways for emission reductions and in particular its costs can be a very helpful tool for the selection of cost-effective mitigation measures. But in order to have realistic model predictions that are accepted by practitioners, it is important that the scenario- building is done in cooperation with those actors which are in the end asked to base their decisions on them. For the actors along the food chain it is very important not only to get information regarding overall benefits and costs, but at operational level. Still too seldom climate models are used to provide sound information about structural effects induced by climate changes as well as by climate change policies. Another important aspect is the consistency of model outcomes - too often there is heterogeneity in the quantitative as well as in the qualitative model results affecting the trust in agricultural model ing, in particular if not sufficiently explained. Here MACSUR has already made great progress by aligning scenario definitions and consolidations within and between crop, livestock and trade models, but still much work is necessary to further enforce the dialogue with stakeholders. This is particularly true for possible pathways to reduce livestock emissions without affecting productivity negatively - or even better looking for synergies. Another aspect that should be looked at in more detail are organic soils under agriculture land use and climate and water optimised fertilisation strategies. Climate models cannot only help farmers and other actors along the food chain, including input and food industries as well as the retail sector to better consider climate aspects in their economic decisions, but are a very powerful tool for decision- makers and for future climate change policies. Here it will become even more relevant in future to address leakage effects
Does collaborative farm-scale modelling address current challenges and future opportunities
No full textHistorically, farm-scale models have tended to be created, owned and maintained by a single person or research organisation. This modus operandi is proving increasingly fragile, when confronted with budgetary constraints and staff turnover. At the same time, rapid developments in sensors and communication technology mean that there are increasingly opportunities for data acquisition relating to farm-scale activities; data that could enable models to be parameterised for individual farms. Collaborative modelling is proving to be a viable alternative that has numerous advantages; it accesses a wider range of expertise – particularly relevant for farms with livestock, manure management systems and a range of crops, it allows costs to be shared, buffers budget and staff changes in individual organisations, increases quality control of model code and extends the biophysical and management dimensions of model testing. However, collaborative modelling itself presents practical and cultural challenges that must be overcome and also imposes some costs. The practical challenges include agreeing the choice of computing language (and often operating system), the need to develop QA/QC procedures and agreeing how costs should be shared. The cultural challenges include the need for research organisations to acknowledge the necessity of joint ownership of a flagship activity and for modellers to agree common quality criteria, and the willingness to accept criticism that this implies. We here reflect on the experience garnered through the development of two modelling platforms and assess their role in determining the future course of farm-scale modelling
Modelling the implications of variation in phenology and leaf canopy development for wheat adaptation to climate change.
Full text linkCrop models offer a great potential to quantitatively assess the impact of specific traits on crop yield and design ideotypes for target environments and future climatic conditions. The objectives of this study were to evaluate the capability of APSIM model for simulating two wheat cultivars contrasting in canopy development and phenology, and explore the implications of these traits for adaptation to climate change. A field experiment was conducted with a winter (Capo) and a facultative (Xenos) cultivar grown in Pannonian eastern Austria. Crops were sown at five sowing dates in 2013-14. Wheat yields ranged from 260 to 722 g m-2. Capo exhibited a more vigorous canopy growth and produced higher yields in autumn-sown plants, whereas Xenos performed better with spring sowing. The experimental dataset was used to parameterize the APSM model. While APSIM was capable of simulating the observed differences in phenology between the two cultivars, simulations of leaf canopy development were less accurate when the model default values for leaf appearance rate (phyllochron) and size were used. Adjusting these model parameters based on observed data improved the simulation results substantially. Thus, APSIM proved to be a robust modelling framework for capturing the differences in phenology and leaf canopy development in wheat and the resulting effects on crop water/N use and yield. The well-parameterised model was subsequently used to assess the potential value of genotypic variation in phenology and leaf canopy development for wheat adaptation to climate change by linking APSIM with climate change scenarios for the period 2035–65 in eastern Austria. The functional implications of variation in those plant traits on adaptation of wheat to future climatic conditions are discussed
Modelling Adaptation to climate change in agricultural systems
Full text linkModelling agricultural adaptation to climate change presents a range of challenges for modellers, but is vital to enabling decision makers to understand the potential costs and benefits of applying adaptation measures on-farm (or not) including risks and uncertainties associated with different actions. Here, the first stages of collaborative work undertaken at a workshop held in Braunschweig, Germany in autumn 2015, and subsequent analysis of findings, are reported. Subsequently, a second report will detail the development of these actions into a coherent overview of the state-of-the-art in modelling adaptation. Modellers and experimental researchers from a variety of disciplines (including biophysical and economic modellers from livestock, crop and grassland systems backgrounds) were asked to consider major climate impacts and associated adaptation options, and the challenges to modelling adaptations. Key modelling challenges fell into four main categories: information availability, accessibility of model outputs for stakeholders, technical challenges, and knowledge gaps. Within these categories, lists of specific challenges were compiled. The workshop revealed the diversity of approaches to modelling adaptation, and highlighted the different challenges associated with biophysical versus economic modelling. Understanding the state-of-the-art and key priorities for the modelling of climate change adaptation in agriculture is shown to be a complex and multi-faceted challenge. However, such an overview would provide a road map for stakeholder-driven improvement in modelling, with the potential to inform increased uptake of adaptation measures on-farm in Europe.(The main text will be published in a peer-reviewed journal
Spatial analysis of multifractal spectra of the MERRA II meteorological time series
No full textThe meteorological time series from the Modern Era Retrospective-Analysis for Research and Applications (MERRA) were analyzed using the Multifractal Detrended Fluctuation Analysis (MF-DFA) method for period 1979-2015 and in 248 grid points covering uniformly the Poland territory. MERRA combines observations distributed irregularly in space and time with an unchanging model and analysis system spanning the historical data record into a spatially complete gridded meteorological dataset (Rienecker et al., 2011). On the other hand the multifractal analysis is a powerful method to characterize long-range correlations within the time series through calculation of different scaling exponents for different parts of the series. The MF-DFA was widely used to analyze and compare the multifractality of recorded time series in various contexts, e.g. climate dynamics or aggregation effects (Baranowski et al. 2015, Hoffmann et al. 2017). Here, the MF-DFA is used for gridded daily air temperature, wind speed, wind direction and atmospheric pressure data and the objective of this study was to: a) verify whether, and to what extent multifractality occurs in the time series of meteorological variables from MERRA II data; b) compare the singularity spectra of the time series coming from different grid points spanned over Poland territory; c) analyze spatial patterns of multifractal properties and explain their similarities to the orographic features using geostatistical methods. The results show that MERRA II meteorological variables exhibit specific multifractal properties and spatial anisotropy
Concepts, approaches, and avenues for modelling crop health and crop losses
No full text(Main text in preparation for publication in a peer-reviewed journal
Web-based service of farm-level future climate and agro-information with RCP climate change scenarios
No full textFor the farm-level adaptation against climate change, we developed web-based information services for future climate and related to agricultural production. Firstly we developed high-definition digital climate maps (DCMs) of RCP8.5 and RCP4.5 scenarios, which were made by a combination of dynamical and statistical downscaling methods. For the monthly DCM of 30-yr normals of 30m or 270m resolutions, produced using micro-climate models for spatial interpolation among weather stations, we added future anomaly maps from normals with RCP scenarios of 12.5km resolution of 10-yr intervals, provided by Korea Meteorological Administration (KMA). Those models of altitude, topology, cold-air accumulation, temperature inversion and urban effects were incorporated in micro-climate models for maximum and minimum temperature maps, and modified PRISM (Parameter-elevation Relationships on Independent Slopes Model) was used for precipitation maps. Also we analyzed changes on crop growing zones based on proper ranges of temperature and precipitation for crop growing seasons of some temperate crops, such as apple, pear, persimmon, grape, kiwi fruit, using monthly DCMs. All of these information was provided through internet site (www.agdcm.kr) by putting farm address on the web
Observed impacts and adaptation in European cropping systems
No full textThe cultivation of crops, their productivity and quality, are directly dependent on different climaticfactors. Climate change is already having an impact on cropping systems in Europe, and farmersand other agricultural stakeholders are considering how to adapt to the ongoing changes inclimatic conditions. It is generally accepted that productivity of crops will increase in northernEurope due to a lengthened growing season and an extension of the frost-free period. Insouthern Europe, climate change is likely to negatively affect the productivity of crops and theirsuitability in certain regions primarily due to extreme heat events and an overall expectedreduction in precipitation and water availability. Year-to-year variability in yields is generallyexpected to increase throughout Europe, due to extreme climatic events and other factors,including pests and diseases. There is a large variation across the European continent in climaticconditions, soils, land use, infrastructure, and political and economic conditions, which greatlyinfluence the responsiveness to climatic change. Despite these many, diverse and ongoing effectsof climate change in Europe, there is little consolidated evidence on how climatic change affectscrops and cropping systems in Europe. Therefore, a questionnaire-based study of experts wasinitiated to give an overview of ongoing impacts and adaptations in European cropping systems.The study covers five major crops including wheat, oilseed rape, maize, potato and grapevine.The questionnaire takes individual European nations as the basis, but these are subdivided intoenvironmental zones, and responses are sought for each environmental zone. We present initialfindings of the questionnaire survey for major European crops