FACCE MACSUR Reports (Modelling European Agriculture with Climate Change for Food Security)
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Laboratory and field scale: two approaches for the evaluation of GHG emissions from dairy cows
No full textThe agricultural sector is an important contributor to emissions of greenhouse gases (GHG). At global scale GHG emissions from agricultural systems are estimated at 10-12% of the total anthropogenic. Modifying the diets of the dairy cows is a possible way to mitigate the emissions. Besides the methane (CH4) emissions from enteric fermentation, also the nitrous oxide (N2O) emissions from feed production have to be considered.This study (INNO-Mil-CH4) aims to evaluate the GHG emissions in dairy farms, through the analysis of the effect of different diets on the emissions from dairy cows at laboratory scale and the use of a model to estimate the GHG emissions at real farm conditions. Twenty dairy cows were fed with four diets different for fiber and starch content and the addition of extruded linseed. CH4 emissions were measured in respiration chambers. In parallel, at field scale, data on cow diets, manure management, fertilization and milk production were collected from 21 farms located in three regions in Germany over a period of 24 months. CH4 and N2O emissions for each farm were estimated, and the effects of the dietary components and the farm management were evaluated and correlated to the emissions.The results confirmed that CH4 emissions are strongly affected by the dry matter intake (DMI) and the linseed supplementation. A lower fiber and higher starch content in the diet reduced CH4 emission per kg milk by ~ 14%. The linseed supplementation (~ 8% of DMI) decreased CH4 emission per kg milk by ~ 12%. CH4 from enteric fermentation ranged between 12 and 15 g kg-1 milk-1 measured in the respiration chambers, and between 13 and 25 g kg-1 milk-1 estimated for the farm data. N2O emissions from feed production on the dairy farms ranged between 0.37 and 0.90 g kg-1 milk-1
Multi-criteria tools for the assessment and implementation of geographically targeted measures to mitigate nutrient losses and adapt to climate change - examples from Denmark
No full textLike most livestock dense agricultural areas in North-Western Europe, the Danish macsur.eu study site around Norsminde Fjord, and Danish livestock agriculture in general, have significant problems with nutrient losses and greenhouse gas emissions. Consequently, challenging policy targets have been set for the reduction of nitrogen and phosphorus losses as defined in the EU Nitrates and Water Framework Directives, and in action plans for related reductions in greenhouse gasses. Climate change, with expected more winter rain and higher temperatures, potentially makes this problem worse, and mitigation options are urgently needed.The present paper presents a suite of tools for the assessment of mitigation measure implementation to deal with this nutrient loss, greenhouse gas emission and climate adaption problems. In common for the studies presented are the integration of geographically targeted measures at the landscape level and experiences with stakeholder interactions. This also include multi-criteria assessment of the various effects of measures. Especially, the case of buffer strips as a geographically targeted measure is discussed, based on findings from the www.Buffertech.dk research project, and as one of the measures in the www.dNmark.org research alliance, landscape level impact assessment model presented. Finally, these results are discussed in the context of the www.macsur.eu joint programming research in livestock systems (LIVE-M) and in relation to the specific MACSUR case studies in Denmark and other European countries
Climate change. From an Integrated Farm Management perspective
Full text linkIntegrated Farm Management (IFM) is a holistic approach characterised by a continuous improvement process in the delivery of more sustainable agriculture. Members of EISA collaborate on this in order to create a united voice for sustainable agriculture and its practical implementation on the ground through the development and promotion of IFM. In this presentation impacts from integrated farmers who are associated by EISAs national members LEAF (UK) and Skylark (the Netherlands) will be highlighted. What measures do they take, how is the adoption rate, what are the innovations the farmers are working on and how does it work in between these two regions that are characterised as mostly intensive agricultural areas? The main question for debate is whether farming is able to deliver solutions to the aims of the Paris Agreement and what MACSUR can do in order to support the farmers in achieving this
Integrating the impact of climate change, price changes and recent CAP orientation on Mediterranean farming systems
Full text linkIt is of interest to compare the possible impact of climate change (CC) on agriculture with thepossible effects of changes in the agricultural policies and regulations, as well as marketconditions. In this regard, recent studies show that the impact of changes in the policies,regulations and market conditions may be even larger than that of the CC and may determinethe changes in land use and livelihood strategies of farms in highly vulnerable areas to CC. Newtechnologies could compensate for the adverse impacts of increased occurrence of negativeconditions. On the other hand, changes in the ratios between commodity and factor pricesinteract with CC, in some cases balancing its impacts, in other cases accentuating them. Inaddition, new market regulation such as the abolishment of the milk quota and many measuresoriginating from the recent orientation of the CAP may contribute to improved adaptation to theCC.In this paper we review the analysis of the impact of CC in the Oristano MACSUR study area, tointegrate the influence of elements, other than CC, on the management and adaptationstrategies of local farming systems. We focus on milk quota abolition, CAP reform, with new directpayments, new price conditions and technological improvements as provided by the CAPRInetwork. The study represents the productive conditions of the area with a discrete stochasticprogramming model specified for its main farms types, irrigated and rainfed. This version of theOristano model allows adjustment of herd and flock characteristics, acreage of tree crops andother structural elements. The assessment verifies the relationship between impact of CC and theinfluence of policy, and of new technological and market conditions
Effects of nutrient supply on mitigation in a long-term experiment.
No full textClimate change and the possible solutions for stopping it are important questions of research, industry and agriculture. In the last two centuries the carbon dioxide (CO2) concentration has increased significantly due to human activity. About 20 % of the greenhouse gases come from agriculture which is 5% of carbon-dioxide. Due to the climate change the CO2 emission should be reduced in agriculture nevertheless the sector should supply the world's population with food in good quantity and quality.Evolving the HURO0802/092_AF project calculation of soil carbon stocks, examination the effects of plant nutrition methods and soil microbial activity on carbon cycle, characterization of adaptation capacity of the main plant species and studying the possibilities of decreasing CO2-emission based on the adaptation of plants were studied. The common research base allows soil and plant analysis connected to the carbon cycle in Westsik's crop rotation experiment which represent the typical farming systems of Eastern Hungarian Region, respectively and the experiment is appropriate for studying long-term effects of farming (straw, farmyard, main and second crop green manure). The results could contribute to the development of methods for decreasing the quantity of CO2 efflux from agriculture and for increasing the quantity of carbon stored in the soil. The experiences of study could contribute to the development of new agrotechnical methods for conservation of organic matter content of soil and they could result in new innovations to decrease the sensitivity to climate change
Assessing priorities for enhancing adaptive capacity of agricultural systems to climate change using fuzzy logic-based approaches
Full text linkThis study outlines the development of a composite indicator of the adaptive capacity (ACI) toclimate change of rural communities in the Oristanese district (Sardinia, Italy). Farming systemsinclude intensive dairy cattle, rainfed dairy sheep, cereals and irrigated horticulture. Twenty-oneindicators of AC were derived from an array of several priorities, initially identified by aninterdisciplinary team of scientists and then extended and scored (on a rank from 1 to 5) by 31experts (agronomic scientists, farmers, advisors and consumers). The extended list of prioritieswas reduced to a set of indicators that could be quantified using data from different sources. Theindicators were organized into seven determinants (Infrastructure, Technology, Economicpower, Flexibility, Knowledge, Sensitivity, Social capital), in turn organized in three components:Ability, Action and Awareness. AC calculations required that 1) scores for each basic indicator benormalized and aggregated to a determinant value, 2) determinants aggregated to a componentvalue, 3) components aggregated to an AIC (best, 0≤ACI≤1, worst). A fuzzy logic inferring systemwas used based on the importance of the basic indicators and their aggregation intodeterminants and components. Favourable/unfavourable thresholds for each indicator were setfollowing expert knowledge and/or survey/census/literature data, while the priority scores wereused to assign weighting factors. Results for the Oristanese district indicate a low-medium AC(ACI=0.61) with social capital (0.27) being the strongest determinant and economic power (0.80)the weakest. These findings provide insights for enhancing effective, locally meaningful andfeasible strategies by increasing the AC of Oristanese rural communities
Modelling European ruminant production systems: Facing the challenges of climate change
Full text linkRuminant production systems are important producers of food, support rural communities and culture, and help to maintain a range of ecosystem services including the sequestering of carbon in grassland soils. However, these systems also contribute significantly to climate change through greenhouse gas (GHG) emissions, while intensi- fication of production has driven biodiversity and nutrient loss, and soil degradation. Modeling can offer insights into the complexity underlying the relationships between climate change, management and policy choices, food production, and the maintenance of ecosystem services. This paper 1) provides an overview of how ruminant systems modeling supports the efforts of stakeholders and policymakers to predict, mitigate and adapt to climate change and 2) provides ideas for enhancing modeling to fulfil this role. Many grassland models can predict plant growth, yield and GHG emissions from mono-specific swards, but modeling multi-species swards, grassland quality and the impact of management changes requires further development. Current livestock models provide a good basis for predicting animal production; linking these with models of animal health and disease is a prior- ity. Farm-scale modeling provides tools for policymakers to predict the emissions of GHG and other pollutants from livestock farms, and to support the management decisions of farmers from environmental and economic standpoints. Other models focus on how policy and associated management changes affect a range of economic and environmental variables at regional, national and European scales. Models at larger scales generally utilise more empirical approaches than those applied at animal, field and farm-scales and include assumptions which may not be valid under climate change conditions. It is therefore important to continue to develop more realistic representations of processes in regional and global models, using the understanding gained from finer-scale modeling. An iterative process of model development, in which lessons learnt from mechanistic models are ap- plied to develop ‘smart’ empirical modeling, may overcome the trade-off between complexity and usability. De- veloping the modeling capacity to tackle the complex challenges related to climate change, is reliant on closer links between modelers and experimental researchers, and also requires knowledge-sharing and increasing technical compatibility across modeling disciplines. Stakeholder engagement throughout the process of model development and application is vital for the creation of relevant models, and important in reducing problems re- lated to the interpretation of modeling outcomes. Enabling modeling to meet the demands of policymakers and other stakeholders under climate change will require collaboration within adequately-resourced, long-term inter-disciplinary research network
Targeting and prioritization of interventions for reducing enteric methane emissions: findings and lessons from 13 countries.
No full textGlobally, ruminants (dairy and beef cattle, goats, and sheep) constitute the largest source of anthropogenic emissions of methane (CH4) and, based on the activity of CH4 in the atmosphere, livestock CH4 emissions have been responsible for close to 20% of the warming the earth has experienced since the beginning of the industrial revolution. Ruminant livestock produce about 2.7 Gt CO2 eq. of CH4 annually, of which FAO estimate that about 500 Mt CO2-eq. can be mitigated through widespread adoption of known good practices that increase productivity. A range of technological options for interventions exists that have varying environmental and economic impacts and costs. Identifying appropriate interventions requires understanding the trade-offs across levels from farmers to sub-national and national policy makers and consideration about what is appropriate for given contexts. Targeting and prioritizing approaches narrows an extensive list of possible practices down to a range of best-bet options that can be scaled out. This paper will present the approach applied to assess and prioritize interventions for reducing enteric CH4 in 13 countries (Argentina, Uruguay, Sri Lanka, Bangladesh, Ethiopia, Kenya, Uganda, Tanzania, Senegal, Benin, Niger, Mali and Burkina Faso). More specifically, it will present (i) detailed baseline estimates of enteric CH4 emissions from ruminant systems estimated using the Global Livestock and Environment Assessment Model (GLEAM) and comprehensive locally-obtained data; (ii) potential mitigation packages developed by local experts and assessed for both their ability to reduce GHG emissions and their cost effectiveness; (iii) results from prioritization of mitigation interventions based on their impacts on enteric CH4, productivity and profitability
Comparison of two calibration levels on the simulation of soil water content using nine crop models under different rotation schemes in five European sites.
No full textDiversification of crop rotations is a basic agronomic practice recommended to increase the resilience of agroecosystems, especially in a context of climate change. The majority of crop simulation studies have focused in simulating single crops during singles years. In a long term perspective, it makes more sense to simulate rotations than single crops because they can also characterize the carry-over effects of the previous crop, providing much better arguments for impact and adaptation studies.The aim of this study is to compare two levels of crop model calibration on the projections of soil water availability using nine different crop models (using rotation and single year simulations) in five sites and under different rotation schemes. The low calibration level contained only information related to soil water content and soil mineral N at a date close to sowing day. High calibration contained detailed information about soil parameters, management, so as plant phenology. Data sets from five European sites including ten crops under different crop rotation schemes were used. The targeted variable was soil water content during different periods along the crop season.Results indicate that, for the majority of crops, the high calibration does improve the modelling performance of the dynamics of soil water content for all models when compared with low calibration. When comparing models using rotation or single year simulation, it could be perceived that rotation models can better mimic the long term dynamics of soil water availability. Site conditions also play a role in the quality of the simulation. Overall, models capable of simulating rotation schemes perform better than single year models, if the objective is to assess the soil water dynamic in long term