FACCE MACSUR Reports (Modelling European Agriculture with Climate Change for Food Security)
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Modelling grassland vulnerability to climate change
No full textA model-based methodology for vulnerability assessment in agriculture is illustrated, with applications to grassland sites and large regions, which reflects the experiences of MACSUR-LiveM (linked to other projects and initiatives). The most recent developments include a multi-metric indicator for assessing the adaptive capacity of agricultural districts, whose potential is illustrated with an exemplary application to the pilot case study of Arborea (Italy)
Yield gaps of cereals across Europe.
No full textTo find proper compromises between land productivity, resource use efficiency and environmental impact, benchmarking of yields is a helpful starting point. Yield gaps are defined as the difference between potential or water-limited yield and actual yield. The GYGA project applies a consistent bottom-up approach to estimate yield gaps per country. Here we focus on the application for wheat, barley and maize in Europe. For each country, a climate zonation is overlaid with a crop area map. Within climate zones with important crop areas, weather stations are selected with at least 10 years of daily data. For the dominant soil types within a 100 km zone around the weather stations, the potential and water-limited yields are simulated with the WOFOST crop model, using location-specific knowledge on crop systems. Data from variety trials or other experiments, potential or water-limited yields, are used for validation and calibration of the model. Actual yields are taken from sub-national statistics. Yields and yield gaps are scaled up to climate zones and subsequently to countries. The average national simulated potential wheat yields under rainfed conditions varied from around 5 to 6 t/ha/year in the Mediterranean to nearly 12 t/ha/year on the British Isles and in the Low Countries. The average actual wheat yield varied from around 2 to 3 t/ha/year in the Mediterranean and some countries in East Europe to nearly 9 t/ha/year on the British Isles and in the Low Countries. The average relative yield gaps varied from around 10% to 30% in many countries in northwest Europe to around 50% to 70% in some countries in the Mediterranean and eastern Europe. For an initial understanding of yields and yield gaps, we assess differences between climate zones, soils and in relation to nitrogen input
Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change
Full text linkCrop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (−2 to +9°C) and precipitation (−50 to +50%). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses. The model ensemble was used to simulate yields of winter and spring wheat at sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern. The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes, Figure 1) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description. Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index. Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities. The full manuscript of this study is currently under revision (Fronzek et al. 2017)
Future climate change, yield variation, and impacts on farm management: a case study at a pilot regions in Finland
Full text linkCrop production in northern regions such as in Finland is projected to benefit from longergrowing seasons brought by future climate change. However, production is also facing multiplechallenges under more frequent and extreme weather. More frequent drought stress, heat stressand other environment-related constraints may lead to higher yield variability in different regionsand increase the yield risks faced by farmers. Changes in yield potential and relative profitabilitybetween crops caused by climate change is likely to be different in different regions. The purposeof this paper is to develop a method to evaluate the impacts of adaptation and mitigation optionson farms with different socio-economic characteristics. Both socio-economic and biophysicalfactors affect rational decision-making process at a farm level and production decisions. Based onthe results from carefully chosen climate models under three SRES scenarios, together withdifferent market price scenarios, we attempt to identify how future changes in mean yields andyield variation caused by climate change in two regions in Finland may affect local farm landallocation and farming management practice. We study how management choices such as cropchoice, crop rotation, fertilization, crop protection and liming are affected and if these changesare in synergy or in conflict with mitigation. This study contributes to the development ofintegrated modelling methods needed to assess impacts of global changes on farming systems
Farm-scale model linkage for ruminant systems
Full text linkThis report describes the findings of the first workshop and associated actions of task L1.4. The findings detailed below, along with the outputs of a second workshop (L1.4-D2) are currently being synthesized into an article for submission as a peer reviewed paper. The work presented here addresses the scientific/conceptual issues related to model linkage
Watch It Grow, an innovative platform for a sustainable growth of the Belgian potato production.
Full text linkBelgium is the largest exporter of frozen potato products in the world. Each year, Belgian companies process over four million tons of potatoes into French fries, potato chips and other products. To ensure a sustainable growth of the potato sector, a higher potato production is needed. In this context, expansion of agricultural land is not an option.Potato processors, traders and packers largely work with potato contracts. The close follow up of contracted parcels is important to improve the quantity and quality of the crop and reduce risks related to storage, packaging or processing. The use of geo-information by the sector is limited, notwithstanding the great benefits that this type of information may offer. At the same time, new sensor-based technologies continue to gain importance and farmers increasingly invest in these technologies.The combination of geo-information and crop modelling might strengthen the competitiveness of the Belgian potato chain in a global market.In the frame of the iPot project, financed by the Belgian Science Policy Office (BELSPO), a commercial webtool called Watch iT Grow helping potato traders, the processing industry as well as farmers to monitor the potato growth has been developed.By using weather data, satellite images, aerial images (taken with drones) and data from ground measurements, users are for instance able to follow whether the crops emerge properly from the ground, how the growth is developing, whether diseases might be present or when farmers can start harvesting. The collected data are combined into crop growth models allowing the webtool to propose as well yields estimations and predictions per plot
The problem of series of days without rainfall in a view of efficiency of agricultural output under climate change
Full text linkoai:ojs.ojs.macsur.eu:article/526Modelling future is key issue in studying CC impacts on agriculture across disciplines and scales. Improving models basing on empirical data coming from diverse micro regions let obtain synergic effects important in shaping food security. Especially, rainfall distribution is most important factor determining agricultural output.The amount of cereal yield depends on an occurrence of long series of days without rain during a growing season. Based on statistical analysis of daily totals it was found that in Central Poland the length of series of days without rainfall during growing season is 40 days. Statistical analysis was done for years 1971-2015. The data allowed finding empirical probability distribution of a length of the series. Average value of the length of series is 4.31 while SD is 4.41. Values of parameters of gamma distribution estimated by the likelihood method are: α=0.9542, β=4.5150. Value of the parameter α (shape parameter) suggests that distribution of the length of series is similar to exponential distribution.Goodness of fit test with gamma distribution was carried out using λ-Kolmogorov and χ2-Pearson tests. Both prove high conformity between empirical and gamma distribution. Based on assumption that gamma distribution can be accepted as distribution of the length of rainless series, further is determined distribution of the length of the longest series in n-element random sample. On the theory of distributions of asymptotic order statistics it is known that the random variable T(n) with appropriate normalization has asymptotic double exponential distribution. Based on that one can conclude that probability to occur 30-day rainless series or longer equals approx. to 0.48. This is useful in forecasting agricultural output depended on rainfall distribution
Modelling production and environmental impacts of perennial cropping systems with the STICS model
No full textThe Seventh Environment Action Program of the European commission commits the European Union to "increase efforts to reduce soil erosion and increase organic matter". Use of perennial crops in crop rotation could be a way to meet this objective.Perennial crops differed from annual crops due to their ability to recycle C and N from one year to another. They could also increase C and N storage in soils due to perennial organs death and root system turn-over. We recently improved the STICS model to allow long term simulation of perennial cropping systems, matching with its objective of genericity for crops (Brisson et al., 1998; 2003). We added to the model new formalisms allowing the simulation of C and N fluxes between perennial and non-perennial organs (Strullu et al., 2014) and the simulation of root system turn-over by distinguishing fine and coarse roots (Strullu et al., 2015).The model was able to simulate with accuracy biomass production and N content of different perennial crops in various climate and soil conditions. Moreover, taking into account C and N inputs to the soil due to crop residues allowed a realistic simulation of the evolution of soil organic carbon and nitrogen (SOC and SON respectively).We realized a sensitivity analysis of the evolution of SOC, SON and mineral N in the soil to C and N inputs due to crop residues quantity and quality. Results highlighted the primary role of roots and perennial organs turn-over on C and N storage in soil.Improvements brought to the model allow the simulation on the long term of perennial cropping systems biomass production and environmental impacts. These modifications will also be useful to simulate alternative cropping systems
Creating a dynamical farmer population model at country scale level.
No full textWestern Europe has a long agrarian history; shaping the landscape and the environment for centuries. In Belgium, high population density together with lack of spatial planning during the first half of the 20th century led to urbanization of the countryside. Due to limited availability of land and other socio-economic reasons, farmers were forced to either specialize and intensify, or quit. Total farmed area in contrast, only decreased slightly since 1980, resulting in increased average farm size. This is a trend that can be observed throughout Western Europe.The remaining farmers stay under pressure requiring constant adaptation and investment, resulting in continued agricultural land use changes.Understanding these significant trends and their impact on the land use and environment requires a deeper understanding on the mechanisms behind the decreasing number of farms and the impacts of different policy measures.To this effect, a farmer model (FarmMo) was created in order to gain insights into these trends and explore the effect of certain policy measures. The model works at the parcel scale and outputs the number of farms, the size of the farms and the crops on the fields.After calibration, a plausible evolution of farms, was obtained for a sub-region in Belgium.Further tests will prove whether the model continues to be reliable on the country scale level and will give more information on the reliability of the crop decisions process.The model will then allow to test the impact of different strategies for subsidizing agriculture (e.g. based on farms, farmers, area, crops) on farms and the farmer population.The presentation will describe the model, the results and the possibility for policy makers to use the model in their decision-making process
How does the projected climate change impact on dry matter yields, greenhouse gas emissions and economics in Norwegian dairy farming systems
No full textFuture climate projections showing increases in the air temperature and the number of rainydays in Norway will require changes in feed-base to adapt to climate change. A large number ofstudies have used single models to quantify the effects of management-related changes onproductivity, greenhouse gas (GHG) emissions and profitability. Here, we combined four models:BASGRA and CSM-CERES-Wheat, HolosNor and JORDMOD to estimate the impacts of projectedclimate conditions on grass and wheat dry matter (DM) yields, farm level GHG emissions andprofits. Simulations were carried out for baseline (1961-1990) and future (2046-2065) climateconditions projected based on two climate models and for production conditions with andwithout a milk quota. We compared four locations with different climate conditions for low, andmedian and high yielding years. The spring wheat grain DM yields simulated for the sameweather conditions within each climate projection varied between 2200 kg and 6800 kg DM perha. The GHG emissions intensities (kilogram carbon dioxide equivalent: kgCO2e emissions per kgfat and protein corrected milk: FPCM) varied between 0.82 kg and 1.25 kg CO2e per kg FPCM,with the lowest and highest emissions found in central Norway and south-east Norway,respectively. The farm profitability expressed by total national land rents varied from 1900 millionNorwegian krone (NOK) for median yields under baseline climate conditions up to 3900 millionNOK for median yields under future projected climate conditions. The projected future changein climate evaluated here decelerated the production of GHG emissions from dairy production inthe locations assessed due to higher milk yields per cow and partly to higher crop yields