FACCE MACSUR Reports (Modelling European Agriculture with Climate Change for Food Security)
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LiveM WP4: Methods for regional scale farming systems modelling and uncertainty assessment – sustainability assessment case studies of production, nutrient losses and greenhouse gas emissions from grassland based systems
No full textIn the EU Joint-Programming-Initiative: Modelling European Agriculturewith Climate Change for Food Security (MACSUR, LiveM: http://www.macsur.eu/index.php/livestock-modelling) we develop a research frameworkfor the modelling and sustainability assessment of livestock and grasslandbased farming systems at farm and regional scales.Based on results from related research and model development in Denmark,methodologies used for regional scaling, the description of data requirementsand sources, and methods to predict the effect and effectiveness of climate-and environment related policy measures are developed. In this study we present results from farm modelling in a study areaaround Viborg, Western Denmark using the http://www.Farm-N.dk/ model (Env.Pol. 159 3183-3192), including thedistribution of N-surpluses into different types of losses, and a comparisonwith empirical studies of farm nitrogen balances in the Danish study and fiveadditional European landscapes (Biogeosciences 9, 5303–5321). Based on this,methods and development needs for the mapping and uncertainty assessment ofnutrient losses and greenhouse gas emissions are discussed, referring to the presentdevelopment of the Farm-AC model and ongoing scenario studies in e.g. the www.dNmark.org project. In these scenarios, regional-scale policy measures areimplemented via the responses of a range of stakeholders, such as farmers,public interest groups, regulators and politicians. When modelling the outcomeof the policy measures implementation, it is often assumed that stakeholdersrespond as economically rational entities. However, social and cultural factorsare also known to play a role and modelling methods that permit these factorsto be taken into account will also be discussed
Regional Pilot Case Study Mostviertel – AT: Preliminary Results
Get PDFAn integrated modelling framework (IMF) is developed to analyse impacts of climate andpolicy changes on farm welfare and the environment. The IMF is applied on two contrasting grassland (south) and cropland (north) dominated Austrian landscapes. The IMF combines the crop rotation model CropRota, the bio-physical process model EPIC and the bio-economic farm model FAMOS[space] and applies combined climate change and policy scenarios. Changing policies reduce farm gross margins by -36% and -5% in the two landscapes respectively. Climate change increases gross margins and farms can reach pre-reform levels on average. Climate induced intensification such as removing of landscape elements andincreasing fertilization can be moderated by an agri-environmental program (AEP). However, productivity gains from climate change increase the opportunity costs for AEP participation
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
Uncertainty analysis and management in the regional pilot case study 'Mostviertel region'
Get PDFAn integrated modelling framework (IMF) is developed to analyse impacts of climate andpolicy changes on farm welfare and the environment. The IMF is applied on two contrasting grassland (south) and cropland (north) dominated Austrian landscapes. The IMF combines the crop rotation model CropRota, the bio-physical process model EPIC and the bio-economic farm model FAMOS[space] and applies combined climate change and policy scenarios. Changing policies reduce farm gross margins by -36% and -5% in the two landscapes respectively. Climate change increases gross margins and farms can reach pre-reform levels on average. Climate induced intensification such as removing of landscape elements andincreasing fertilization can be moderated by an agri-environmental program (AEP). However, productivity gains from climate change increase the opportunity costs for AEP participation
Responses of soil N2O emissions and nitrate leaching on climate input data aggregation: a biogeochemistry model ensemble study
No full textModels are increasingly used to estimate greenhouse gas emissions at site to regional and national scales and are outlined as the most advanced methodology for national emission reporting in the framework of UNFCCC. Process-based models incorporate the major processes of the carbon and nitrogen cycle and are thus thought to be widely applicable at various spatial and temporal scales. The definition of the spatial scale is determined by the objectives. GHG emission reporting requests spatially and temporally aggregated information whereas for the assessment of mitigation options on hot spots and hot moments of emissions a high spatial simulation resolution is required. In addition, other input data also determine the simulation scale. Low resolution simulations needs less effort in computation and data management, but important details could be lost during the process of data aggregation associated with high uncertainties of the simulation results. This study presents the aggregation effects of climate input data on the simulations of soil N2O emissions and nitrate leaching by comparing different biogeochemistry models. Using process-based models (DailyDayCent, LandscapeDNDC, Stics, Mode, Coup, Epic), we simulated a 30-year cropping system for two crops (winter wheat and maize monocultures) under water- and nutrient-limited conditions based on a 1 km resolution climate dataset. We aggregated the climate data to resolutions of 10, 25, 50, and 100 km and repeated the simulations on these spatial scales. We calculated the N2O emissions as well as the nitrate leaching on all scales. Results will be presented and discussed.
Land use dynamics and the environment
Get PDFThis paper builds a benchmark framework to study optimal land use, encompassing land use activities and environmental degradation. We focus on the spatial externalities of land use as drivers of spatial patterns: land is immobile by nature, but location’s actions affect the whole space since pollution flows across locations resulting in both local and global damages. We prove that the decision maker problem has a solution, and characterize the corresponding social optimum trajectories by means of the Pontryagin conditions. We also show that the existence and uniqueness of steady-state solutions are not straightforward. Finally, a global dynamic algorithm is proposed in order to illustrate the spatial-dynamic richness of the model. We find that our simple set-up already reproduces a great variety of spatial patterns related to the interaction between land use activities and the environment. In particular, abatement technology turns out to play a central role as pollution stabilizer, allowing the economy to achieve stable steady-states that are spatially heterogeneous
Parameter-induced uncertainty quantification of a regional N2O and NO3 inventory using the biogeochemical model LandscapeDNDC
No full textIn this study we quantify regional parameter-induced model uncertainty on nitrous oxide (N2O) emissions and nitrate (NO3) leaching from arable soils of Saxony (Germany) using the biogeochemical model LandscapeDNDC. For this we calculate a regional inventory using a joint parameter distribution for key parameters describing microbial C and N turnover processes as obtained by a Bayesian calibration study. We representatively sampled 400 different parameter vectors from the discrete joint parameter distribution comprising approximately 400,000 parameter combinations and used these to calculate 400 individual realizations of the regional inventory. The spatial domain (represented by 4042 polygons) is set up with spatially explicit soil and climate information and a region-typical 3-year crop rotation consisting of winter wheat, rape- seed, and winter barley. Average N2O emission from arable soils in the state of Saxony across all 400 realizations was 1.43 ± 1.25 [kg N / ha] with a median value of 1.05 [kg N / ha]. Using the default IPCC emission factor approach (Tier 1) for direct emissions reveal a higher average N2O emission of 1.51 [kg N / ha] due to fertilizer use. In the regional uncertainty quantification the 20% likelihood range for N2O emissions is 0.79 – 1.37 [kg N / ha] (50% likelihood: 0.46 – 2.05 [kg N / ha]; 90% likelihood: 0.11 – 4.03 [kg N / ha]). Respective quantities were calculated for nitrate leaching
Cross-cutting uncertainties in MACSUR impact projections
No full textProjections into the future, such as climate change impact projections on crop production for a given region, or, on global food prices and trade are inherently uncertain. Uncertainty does not fall within a single discipline but is dealt with by a wide variety of disciplines, themes and problem domains. Model uncertainty pertaining to the impact modelling chain from climate via crop and livestock to economic and trade modelling is only part of the overall uncertainty*. There is also scenario uncertainty and many other known and unknown “unknowns”1 to be considered in efforts such as MACSUR and its themes (CropM, LiveM, TradeM) to advance model-based integrated assessment of climate change risk assessment for agriculture and food security. Propagation of uncertainties along the climate change impact modelling chain has been portrayed as “uncertainty cascade” 2. We will present different basic approaches for evaluating uncertainty in models. So far, studies addressing quantification and reporting of uncertainties in impact projections still largely focus on two major sources, i.e. the shares originating from climate modelling and from crop modelling. However, a more comprehensive treatment of uncertainty and how it is reported is urgently needed
The state-contingent approach to production and choice under uncertainty: usefulness as a basis for economic modeling
Get PDFThe state-contingent approach developed by Chambers and Quiggin (2000) constitutes an attractive blend of a theory of production analysis under uncertainty and a theory of decision-making under uncertainty. One of the goals of this contribution is to introduce the reader to the approach by outlining its contents while comparing and contrasting it to related theories. With respect to production analysis: an emphasis is made on the ability of the approach to deliver well defined cost functions corresponding to stochastic production technologies. With respect to decision-making under uncertainty: the comparison with other theories consistent with a rational agent emphasizes the production theoretical basis of the state-contingent approach. It is the author’s belief that appropriately categorizing the state-contingent approach serves the primary goal of this work: to explore its usefulness as a basis for economic modeling. Some challenges regarding an empirical implementation are discussed: challenges in estimating the parameters of a state-contingent technology representation in general, as well as challenges arising from the fact that the approach is constructed around the argument pioneered by Leonard J Savage: that probabilities underlying economic decision-making are inherently subjective.(The financial support of ScienceCampus Halle is gratefully acknowledged.
The adaptation of farm and awareness of ongoing climate change (CC)
No full textFarm planning is based on awareness of climate variability, here assumed to depend on experience gained over the years, and to generate expectations on climatic variables. Expectations are based on probability distributions (pdfs) estimated on climate data and used to generate managing choices by means of Discrete Stochastic Programming. The model simulates the income losses in case farmers do not recognize the ongoing CC, and continue to plan assuming climate stability. In particular, the use of resources in 2010 is simulated based on the pdfs of the early 2000s, despite CC has changed the probabilities of the various states of nature. The model, calibrated with Positive Mathematical Programming, generates a 0.9% income increase when is allowed to adapt to 2010 climate pdfs. The model is also calibrated according to pdfs of 2010, i.e. recognizing CC: in this case income falls of 0.7% when farmers are simulated to use their soil mistakenly based of the 2000 pdfs. Given the short period of CC, the differences represent an appreciable error that farmers may be already committing. Properly specifying with the CC at local level can help building farmers' awareness on it, and to properly manage their resources, recovering profitability.