Multi-criteria tools for the assessment and implementation of geographically targeted measures to mitigate nutrient losses and adapt to climate change - examples from Denmark

Like 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

Challenges and research gaps in the area of integrated climate change risk assessment for European agriculture and food security

Priorities in addressing research gaps and challenges should follow the order of im­por­tance, which in itself would be a matter of defining goals and metrics of importance, e.g. the extent, impact and likelihood of occurrence. For improving assessments of cli­mate change impacts on agriculture for achieving food security and other sustainable develop­ment goals across the European continent, the most important research gaps and challen­ges appear to be the agreement on goals with a wide range of stakeholders from policy, science, producers and society, better reflection of political and societal prefer­ences in the modelling process, and the reflection of economic decisions in farm manage­ment within models. These and other challenges could be approached in phase 3 of MACSUR

Extending the BASGRA model for timothy grass with functions to simulate impacts of climate change and sward management on yield and nutritive value.

Grass-based dairy and meat production constitute the economic backbone of agriculture in Northern Europe including Scandinavia. Timothy (Phleum pratense L.) is one the most important forage grasses in Sandinavia as well as in high latitude regions in North America and Japan. Grassland productivity is expected to be affected by climate change. Process-based models for weather dependent grass growth can assist farmers and plant breeders in adapting to climate change by simulating different options. These models can also be used to investigate different management options such as the prediction of the optimal harvest time for use in tactical planning at farm level under prevailing conditions. The BASGRA model was originally developed to investigate the interaction between the weather, soil and cutting regime on forage dry-matter yield. Recently, BASGRA was extended with functions for simulating nutritive value including crude protein, NDF fibres and fibre digestibility. The aim of this presentation is to give a brief overview of the new version of BASGRA, and to show an example of application of the model to multi-year simulation of timothy growth, yield and nutritive value at two sites in Norway under current and projected future climate conditions, including different fertilizer levels and cutting regimes. Information about the impact of climate change and management on sward nutritional value from such simulations is of particular importance to understand the interaction between these factors and livestock production, and thus to design livestock production systems for future climates

Effect of climate changes on plant disease under simulated conditions: challenges and limits

Increases in CO2 and temperatures are expected to induce complex effects on plant pathogens. Different approaches were used to study the effect of climate on plant diseases, including laboratory and/or field studies, as well as modeling-based assessments and simulations under phytotrons. During the last 10 years, the impact of climate changes such as increased CO2 and temperature on pathogens affecting grapevine, basil, rocket, beet, lettuce, zucchini, radish, bean and geranium was assessed under phytotrons. Plants were grown under different simulated climatic conditions, at standard (400-450 ppm), average (600 ppm) and high (800 ppm) CO2 concentration and at standard (ranging from 18 to 22/24°C) and elevated temperature (4°C higher than standard). Variable effects were observed when individual parameters were taken into consideration. An increase of downy mildew on grapes, of powdery mildew on zucchini, of Alternaria leaf spot on rocket salad, of black spot on basil and of Phoma leaf spot on garden beet was observed when both CO2 level and temperature increased. Powdery mildew of grape was not influenced by increasing carbon dioxide and temperature. Downy mildew of basil and rusts affecting bean and geranium increased at higher CO2 levels, but only at lower temperatures, while the combination of high CO2 and high temperature lead to a reduction of the diseases. Regarding the effects of climate changes on Fusarium wilt of lettuce and rocket, the soil fungal and bacterial development was not affected by the different CO2 and temperature levels, while an increasing disease incidence was observed at high CO2 and high temperature, probably through plant-mediated effects. The role of phytotrons in the study of climate changes is discussed

Spatial aggregation for crop modelling at regional scales: the effects of soil variability

Modelling agriculture production and adaptation to the environment at regional or global scale receives much interest in the context of climate change (CC). One concern is to take into account the spatial variability of the environmental conditions (e.g. climate, soils, management practices) used as model input because the impacts of CC on cropping systems depend strongly on the site conditions [1]. For example CC effects on yield can be either negative or positive depending on the soil type [2]. Additionally, the use of different methods of upscaling and downscaling adds new sources of modelling uncertainties [3].In the present study, the effect of aggregating soil data by area majority of soil mapping units was explored for regional simulations with the soil-vegetation model CoupModel for a region inGermany (North Rhine-Westphalia). Data aggregation effects (DAE) were analysed for wheat yield, water drainage, soil carbon mineralisation and nitrogen leaching below the root zone. DAE were higher for soil C and N variables than for yield and drainage and were strongly related to the presence of specific soils within the study region. These 'key soils' were identified by a model sensitivity analysis to soils present in the region. The spatial aggregation of the key soils additionally influenced the DAE. A spatial analysis of the pattern of these key soils (i.e. presence/ absence, coverage and aggregation) can help in defining the appropriate grid-resolution that would minimize the error caused by aggregated soil input data in regional model simulations. In a second step the method will be applied and evaluated with respect to another European region(Tuscany) which is characterised by a warmer and drier climate.[1] Kersebaum, K.C., Nendel, C., 2014. Site-specific impacts of climate change on wheat production across regions ofGermany using different CO2 response functions. Eur. J. Agron. 52, 22–32. doi:10.1016/j.eja.2013.04.005[2] Folberth, C. et al, 2016. Uncertainty in soil data can outweigh climate impact signals in global crop yield simulations. Nat. Commun. 7, 11872. doi:10.1038/ncomms11872[3] Ewert et al., 2011. Scale changes and model linking methods for integrated assessment of agri-environmental systems. Agric. Ecosyst. Environ. 142, 6–17. doi:10.1016/j.agee.2011.05.01

Is a green tax on red meat a feasible strategy to achieve Norwegian GHG-emission targets for agriculture

Norway has decided to follow the EU in setting ambitious targets for reducing greenhouse gas (GHG) emissions from agriculture. The aim is to reduce GHG-emissions by 40 per cent by 2030. The paper discusses three policy measures to achieve this target in Norway: Reduced direct payments to red meat (beef, sheep, and lamb), a consumption fee for red meat, and informational measures that align red meat consumption with official public health recommendations.The per capita consumption of red meat has shown a negative development in recent years. A continuation of that trend will positively contribute in the challenge to reach the emission target. However, there is currently a significant import of red meat that is expected to be reduced before domestic production eventually will fall.Model results based on the sector model Jordmod indicate that all policy options have significant effects on Norwegian agriculture. The current level of the EU carbon tax is used as a proxy for the reduced direct payments and the consumption fee. The implicit amount of 410 (820) nkr per ton CO2-equivalent translates into a reduction of between 5 (7) per cent and is far from achieving the 40 per cent target. The result is partly based on some stickiness in the model that prevents an immediate fall in production due to lower profitability. A moderate change in the diet from red meat to white meat follows from the implementation of the policies. The consumption fee and the reduced payments have, in principle, the same effect on agriculture. This result relies on the assumption that import protection is no longer prohibitive at a commodity basis, and only partially prohibitive at the processed food level

Bringing together grassland and farm scale modelling. Part 1. Characterizing grasslands in farm scale modelling

This report provides an overview of how grasslands are represented in six different farmscale  models represented in MACSUR. A survey was conducted, followed by a workshop in  which modellers discussed the results of the survey, and identified research challenges and  knowledge gaps. The workshop was attended by grassland as well as livestock specialists.  The investigated models differed largely with respect to how grasslands were represented,  e.g. as regards weather and management factors accounted for, spatial and temporal  resolution, and output variables. All models had grassland modules that simulate DM yield  and herbage N content (or crude protein (CP) content = N content x 6.25). Many models  also simulate P content, whereas only one simulate K content. About half of the model  simulate herbage energy value and/or herbage fibre content and fibre and/or dry matter  digestibility. Critical input data required from grassland models to simulate ruminant  productivity and GHG emissions at farm scale was identified by the workshop participants.  The different types of input data required were ranked in order of importance as regards  their influence on important system outputs. For simulation of ruminant productivity and  GHG emissions, herbage DM yield was ranked as the most important input variable from  grassland models, followed by CP content together with at least one variable describing  herbage fibre characteristics. These findings suggest that work on improving the ability of  the current grassland models with respect to simulation of fibre/energy should be  prioritized in farm-scale modelling aiming at quantifying livestock production and GHG  emissions under different management regimes and climate conditions. More work is also needed on model evaluation, a task that has not been prioritized yet for some models

Implications of input data aggregation on upscaling of soil organic carbon changes

Dynamic process models are increasingly used to predict changes in soil organic carbon (SOC) stocks of agricultural soils on the large scale. This study examines the aggregation effects of climate and soil data on regional SOC modeling for varying simulation periods based on a multi model ensemble. For a NUTS2 region in central Europe (North Rhine-Westphalia) data on soil properties and daily weather available on a spatial resolution of 1 km have been aggregated to 10, 25, 50 and 100 km resolution. Soil data aggregation (DA) showed a bigger effect on modeled SOC stock changes than climate DA, which was one order of magnitude smaller. The DA effect determine the spatial resolution of model output (scale of interest). Model errors, calculated as the difference between respective DA level and 1 km outputs, were high at low model output DA level (scale of interest: 1 km) and decreased with increasing scale of interest (10-100 km). Additionally, a large variability of simulated SOC contents amongst models was observed. Contrary to model errors induced by input DA, this variability was not leveled out by increasing the scale of output data. The regionalization of SOC stocks and changes is highly influenced by input DA. Factors like the length of the modeling period, the modeling region and the type of input DA control the resulting errors. The presented study describe a detail of these relationships

Rethinking farm-scale modelling to meet new challenges and possibilities

Historically, agricultural models have tended to be created, owned and maintained by a single person or research organisation. This modus operandi is often proving fragile, when confronted with budget constraints and staff turnover. Collaborative modelling is proving to be a viable alternative that has numerous advantages; 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. We here reflect on the experience garnered through the development of two modelling platforms: APSIM and RECORD