FACCE MACSUR Reports (Modelling European Agriculture with Climate Change for Food Security)
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Bayesian inference of a dynamic vegetation model for grassland
No full textAs a part of the MACSUR task L2.4, we probabilistically calibrated the CARAIB dynamic vegetation model by Markov chain Monte Carlo (MCMC) simulation with the DREAMZS sampler.. CARAIB is a mechanistic model that calculates the carbon assimilation of the vegetation as a function of the soil and climatic conditions, and can thus be used for simulating grassland production under cutting or grazing management. Bayesian model inversion was performed at 4 grassland sites across Europe: Oensingen, CH; Grillenburg, DE; Laqueuille, FR and Monte-Bodone, IT. Four daily measured variables from these sites: the Gross Primary Productivity (GPP), Net Ecosystem Exchange (NEE), Evapotranspiration (ET) and Soil Water Content (SWC) were used to sample 10 parameters related to rooting depth, stomatal conductance, specific leaf area, carbon-nitrogen ratio and water stresses. The maximized likelihood function therefore involved four objectives, whereas the applied Bayesian framework allowed for assessing the so called parameter posterior probability density function (pdf), which quantifies model parameter uncertainty caused by measurement and model errors. Sampling trials were performed using merged data from all sites (all-sites-sampling) and for each site (site-specific sampling) separately. The derived posterior parameter pdfs from the all-sites sampling and site-specific sampling runs showed differences in relation with the specificities of each site. Analysis of these distributions also revealed model sensitivity to parameters conditioned on the measured data, as well as parameter correlations
Interrelationship between evaluation metrics to assess agro-ecological models
Get PDFWhen evaluating the performances of simulation models, the perception of the quality of the outputs may depend on the statistics used to compare simulated and observed data. In order to have a comprehensive understanding of model performance, the use of a variety of metrics is generally advocated. However, since they may be correlated, the use of two or more metrics may convey the same information, leading to redundancy. This study intends to investigate the interrelationship between evaluation metrics, with the aim of identifying the most useful set of indicators, for assessing simulation performance. Our focus is on agro-ecological modelling. Twenty-three performance indicators were selected to compare simulated and observed data of four agronomic and meteorological variables: above-ground biomass, leaf area index, hourly air relative humidity and daily solar radiation . Indicators were calculated on large data sets, collected to effectively apply correlation analysis techniques. For each variable, the interrelationship between each pair of indicators was evaluated, by computing the Spearman’s rank correlation coefficient. A definition of “stable correlation” was proposed, based on the test of heterogeneity, allowing to assess whether two or more correlation coefficients are equal. An optimal subset of indicators was identified, striking a balance between number of indicators, amount of provided information and information redundancy. They are: Index of Agreement, Squared Bias, Root Mean Squared Relative Error, Pattern Index, Persistence Model Efficiency and Spearman’s Correlation Coefficient. The present study was carried out in the context of CropM-LiveM cross-cutting activities of MACSUR knowledge hub
CO2 flux measurements in the vegetation period of winter wheat in Lubelskie province
No full textThe assessment of net ecosystem exchange and respiration of ecosystem of terrestrial ecosystems is necessary to improve our knowledge about carbon cycle in nature. Here we present measurements of CO2 fluxes for a winter wheat temperate climate ecosystem (buckwheat in the previous years) located in the Lubelskie province (eastern Poland) using a closed dynamic chamber system over a 2013 vegetation season. Measurements of carbon dioxide emission from soils and its assimilation by plants were carried out on a typical for Lubelskie highland arable land located in the Stany Nowe (N50o49’17.0555”, E22o16’28.51”, height 243m above sea level) using the set of two chambers (transparent and dark). Carbon dioxide fluxes have been measured by EGM-4 PP Systems sensor during fixed stages of the plant growing season. During the experiment carbon emission from soil ranged from 151 to 764 mg C·m-2·h-1 and its assimilation by plants ranged from -148 (emission) to 1585 mg C·m-2·h-1. We found substantial differences in emission and assimilation of carbon in the winter wheat ecosystem. This, along with other measurements (meteorological factors and soil and plant parameters) carried out in the Stany Nowe can be used as a high quality data to verify various models of emission of greenhouse gases. The chamber technique occurs to be a useful tool for determining carbon dioxide exchange between ecosystem surface and the atmosphere
The development of cereals and oilseed production until 2050 under different socioeconomic conditions in Finland
No full textWe will use Finland as an example of a small, developed country with difficult climatic conditions to show how changes in global food consumption patterns and global population growth will influence local production. In order to do so we use two different models. First, an agricultural sector model for Finland, and second, a regionally adapted version of a global, spatially explicit agroeconomic land use model. We use both models to investigate how Finnish cereals and oilseed production develops under different socioeconomic conditions, as defined in the Socioeconomic Pathways (SSPs). We find that without a major improvement of oilseed yields, oilseed production will be disrupted by 2050 and even with a major increase in oilseeds yields only in the SSP2 scenario production of oilseeds can be sustained. Cereal production on the other hand does not change in the simulations with the global model, but does almost decrease by half in the simulations with the national model. This shows that even with an enormous global population growth and reduced international trade, Finland will not become a major agricultural producer
Exploring yield gaps in the EU, concept and data
No full textAgreeing that increased future global food demand will have to be met by production intensification rather than land use expansion (Hertel, 2011), scientists have moved to empirically analysing the causes for differences between potentially attainable yields and actually realized yields – the yield gap (Neumann et al., 2010). We aim at disentangling the effects of biophysical, economic and political impacts and farmers’ response to them on crop yields based on reviewing theoretical and empirical literature and some descriptive data analysis
Combining modeling and stakeholder involvement to build community adaptive responses to climate change in a Mediterranean agricultural district
Get PDFThe case study area (54,000 ha) is located at Oristano, Italy. The main cropping systems are based on forages (silage maize, Italian ryegrass and alfalfa under irrigation, winter cereals and grasslands under rainfed conditions), rainfed cereals (durum wheat, barley), vegetables (e.g. artichokes), rice, citrus, olives and vineyards. Some 36,000 ha are served by irrigation. The area includes the dairy cows cooperative system of Arborea (30,000 cows, 5500 ha, nitrate vulnerable zone). The rainfed dairy sheep includes 372,000 sheep and a number of small milk processing plants. The research aims to support adaptive responses to climate change through the combination of modeling approaches and stakeholder engagement. Present (2000-2010) and future (2020-2030) climatic scenarios were developed by combining global climate models with Regional Atmospheric Modelling Systems to produce calibrated time series of daily temperature and precipitation for the case study. The EPIC model was calibrated to simulate the impact of climate scenarios on the main cropping systems. The impact of THIndex on milk yield, milk quality and mortality was also simulated for dairy cows. A territorial farm-type Discrete Stochastic Programming model was implemented to simulate choices for thirteen farming typologies as influenced by crop yields and water consumptions. Participatory activities, including field experiments, interviews, focus groups and interactive workshops, involved farmers and other stakeholders in the most critical phases of the research. The assessment of uncertainties and opportunities were proposed as a basis for discussion with policy makers to identify priorities for agro-climatic measures in 2014-2020
Modelling approach and first results on irrigation as climate change adaptation strategy of the project NaLaMa-nT
No full textThe project NaLaMa-nT examines in the context of climate change sustainable development paths of land use in four different rural districts in Northern Germany. These districts were chosen along a soil-climate gradient from west to east with increasing water deficit for plant growth caused by both: decreasing rain fall and decreasing soil quality. In front of this background different trends and developments of agricultural production can be derived from analysing, modelling and comparing existing production systems and conditions of the different regions. One assumption developed from existing climate projections is that climate change will cause increasing water deficits for plant growth – especially in the eastern part of Germany. An obvious solution is to intensify agricultural production using existing irrigation methods that can reduce the yield risk and thus stabilize income from agriculture by avoiding yield failures and increasing the overall yield level. Therefore we build a modelling approach which allows an economic analysis both on the crop production activity level as well on the farm level. The data base comprises data representing recent production techniques and added optional irrigation techniques. The yields and input level changes are derived from literature studies and expert interviews. The farm structure is represented and modeled based on typical farms chosen from an IACS-data farm typology with different production potentials and patterns. First results will be presented in April
Curriculum for training course on policy impact assessment
Get PDFA one-week MACSUR training course on policy impact assessment was held in March 2014 at Haifa University in Israel. The course was organised by ZALF (Hannes König, Katharina Helming) and Haifa University (Ofira Ayalon, Edan Benami, Ruslana Palatnik), targeting at the participation of Post-Docs and PhD students associated to the MACSUR consortium. The Framework for Participatory Impact Assessment (FoPIA) was used as the main method for the course to support structuring the policy impact assessment. The Israelian MACSUR case study of the Ramat Menashe Biosphere was used the test case of assessing alternative policy options and sustainability trade-offs
Incorporating uncertainty in a deterministic agricultural sector model
No full textClimate-induced uncertainty in crop yields is introduced in the Norwegian sector model Jordmod. The model is comprised of a supply module in which profits for more than 300 regional farms are maximized and a market module which maximizes social welfare in the agricultural sector. In the supply module, farmers determine their plant decisions and crop input levels (N-fertilizer) subject to a discrete number of weather outcomes affecting crop yields. After that, a specific weather distribution is chosen determining crop yields. The resulting input-output mix at farm level makes up the supply side of the commodity markets which together with linear demand functions determine equilibrium levels. The procedure is repeated for each discrete weather outcome. Note that plant decisions and crop input levels remain the same for all weather outcomes as farmers face the same uncertainty during all repetitions, but crop yield will vary. Hence, equilibrium prices and quantities will vary as well allowing their representation as stochastic distributions. In a preliminary empirical application, the stochastic results are contrasted with the deterministic results based on the mean values of the weather outcomes. This comparison will shed light on the potential error made by neglecting uncertainty at the farm level
How does a crop model calibrated to national yield data perform on the field scale?
No full textCrop models used as parts of integrated assessments often need to be run on regional, national and global scales. Calibration is an important step in the application procedure, yet on scales like this the process needs to be simplified in order to meet data requirements and computational limits. The question arises if a model calibrated in such a “simple” fashion still performs adequately at field scale, and if parameters not calibrated in the process can nevertheless be used with some confidence in later stages of the assessment. To answer this question, we applied the crop model EPIC to the simulation of sugarcane in Sao Paulo, Brazil. We once calibrated the model using Bayesian calibration to data on yield, aboveground biomass, and root weight measured in four years on two field trials in Sao Paulo. For the second calibration we used a simplified approach and calibrated the model only to FAOSTAT yield data for the whole of Brazil. Both calibrated models were applied to the simulation of stalk yield, aboveground biomass and root weight on a third field trial, and to the simulation of mean yields in Sao Paulo. The results showed that both models were able to adequately depict yields on both scales, but that the model calibrated to only national yield data was not able to accurately simulate root biomass, and to a lesser degree aboveground biomass. We conclude that a simplified calibration performs adequately on both scales, but that non-calibrated parameters may only be used with caution