CropM - progress overview

Activities in the first 1 ½ years of CropM were related to key issues identified as critical at the beginning of the FACCE MACSUR the knowledge Hub. These include: Model intercomparison,Generation of new data for model improvement, Methods for scaling and model linking, Uncertainty analysis, Building research capacity, Climate scenario data for crop models. The key ambition of CropM has been to develop scientific excellence on methods for a comprehensive assessment of climate change impact, adaptation and policy on European crop production, agriculture and food security. Much progress has been made in developing a first shared continental assessment and tool for: A range of important crops, Important crop rotations, Advanced scaling methods, Advanced link to farm and sector models, Novel impact uncertainty assessment and reporting, State-of-the-art scenario construction. A number of concrete studies towards this aim have been launched in CropM workpackages (WPs): WP1-2: Two multi-facetted studies on crop rotation, launched in summer 2013, WP3: comprehensive scaling exercises, launched in March 2013, WP4: Studies on (a) Climate scenario development, (b) impact response surface method and (c) Extremes, launched in summer 2013, WP5: Analysis of transect across Europe with temperature effect (Space for Time). In addition, extended activities related to capacity building including several PhD courses (WP5) workshops (in WPs1-4) and an International Symposium (10-12 Feb, Oslo, Norway) have been organized. Present and future work is and will be focused on framing and advancing crop modelling as integrated part of comprehensive climate risk assessment and modelling of agricultural systems for food security from farm to supra-national level

Is a Space Laundry Needed for Exploration?

Future human space exploration missions will lengthen to years, and keeping crews clothed without a huge resupply burden is an important consideration for habitation systems. A space laundry system could be the solution; however, the resources it uses must be accounted for and must win out over the very reliable practice of bringing along enough spare underwear. Through NASA's Logistics Reduction and Repurposing project, trade off studies have been conducted to compare current space clothing systems, life extension of that clothing, traditional water based clothes washing and other sanitizing techniques. The best clothing system of course depends on the mission and assumptions, but in general, analysis results indicate that washing clothes on space missions will start to pay off as mission durations push past a year

Impact of spatial soil and climate input data aggregation on regional yield simulations

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations

Evaluation of scaling methods for other crops, regions and scaling methods

The MACSUR WP3 Scaling Exercise predominantly investigated the effects of spatially aggregating or sampling model input data for large scale model assessments. This was first carried out for the region of North Rhine-Westphalia (NRW) and predominantly evaluated for water-limited yield simulations of winter wheat grain yield. In this report, specific findings from NRW are compared to findings from a larger population of simulation settings / environmental conditions, extending the analysis to further crops, regions and impact variables. Similar aggregation errors and spatial patterns of silage maize and winter wheat yield have been found. When verifying findings with a different region, partially similar error patterns were observed for Tuscany, Italy. While the aggregation error is strongly related to the spatial heterogeneity of the data, other influences as e.g. the climate may be less relevant if the cropping system is adapted to local conditions. Findings for different output variables (NPP, N-leaching, water use efficiency, etc.) largely confirm findings from crop yield with regard to error patterns. However, absolute values and thresholds partially differed considerably across output variables. The findings give a first empiric insight towards a possible generalization of aggregation errors.The report contains material from published papers. Therefore only the abstract is made available

Report on results of scaling exercise

The MACSUR scaling exercise investigates the effects of scaling crop model data in combination with different data types (climate, soil and management). For this purpose the effect of aggregating model input as well as spatial sampling schemes were tested with a range of crop models under varying conditions. From findings for winter wheat yield of the region of North Rhine-Westphalia (Germany) it can be concluded for most models, that regional water-limited yield simulations in a temperate humid region are on average little affected by aggregating soil or climate data up to 100 km resolution. However, some models showed considerably larger biases. Consequently, models need to be assessed individually for their robustness to input data aggregation when simulating regional yields. Aggregating soils partially led to aggregation effects larger than from averaged climate data, in the range or larger than the inter-annual yield variability or differences between models. This can thus be a dominant source of uncertainty when assessing spatial yield patterns of heterogeneous regions. Simultaneous use of aggregated climate and soil data is likely to increase these aggregation effects further. However, large negative aggregation effects were found in areas with soils characterized by high available water holding capacity and large positive aggregation effects in areas with soils of predominantly low available water holding capacity. This indicates that the direction and magnitude of aggregation effects may be estimated from a limited number of soil variables.Similarly, the precision of simple random sampling (SimRS) and variations of stratified random sampling (StrRS) schemes in estimating regional mean water-limited yields were evaluated. We found that the precision gains of StrRS varied considerably across stratification methods and crop models. Precision gains for compact geographical stratification were positive, stable and consistent across crop models. Stratification with soil water holding capacity had very high precision gains for twelve models, but resulted in negative gains for two models. Increasing the sample size monotonously decreased the sampling errors for all the sampling schemes. We conclude that compact geographical stratification can modestly but consistently improve the precision in estimating regional mean yields. Using the most influential environmental variable for stratification can notably improve the sampling precision, when the sensitivity behaviour of a crop model is known.The report contains parts from published journal articles, therfore, only the abstract is made available

Report on results of application of scaling methods for integrated modelling

Defining and estimating uncertainty in simulations is essential in order to quantify the reliability of the outcomes or when model improvement is sought. Several general definitions of uncertainty are given for model-based simulations. By defining the uncertainty from different sources, these can be quantified and assessed separately, as well as eventually their absolute or relative contribution to the total uncertainty. Therefore, different types and sources of uncertainty are given. Furthermore, the choice of method when assessing the uncertainty of a given simulation may depend on the purpose and the type of uncertainty to be assessed. Approaches of assessing uncertainty in process-based models are described in general and more specifically for crop models. As a simplistic method, the already established approach of variance decomposition is suggested.The report contains parts of published papers, therefore only the abstract is made available

Review on scaling methods for crop models

Agricultural systems cover a range of organisational levels and spatial and temporal scales. To capture multi-scale problems of sustainable management in agricultural systems, Integrated assessment modelling (IAM) including crop models is often applied which require methods of scale changes (scaling methods). Scaling methods, however, are often not well understood and are therefore sources of uncertainty in models. The present report summarizes scaling methods as developed and applied in recent years (e.g. in SEAMLESS-IF and MACSUR) in a classification scheme based on Ewert et al. (2011, 2006). Scale changes refer to different spatial, temporal and functional scales with changes in extent, resolution, and coverage rate. Accordingly, there are a number of different scaling methods that can include data extrapolation, aggregation and disaggregation, sampling and nested simulation. Comparative quantitative analysis of alternative scaling methods are currently under way and covered by other reports in MACSUR and several publications (e.g. Ewert et al., 2014; Hoffmann et al., 2015; Zhao et al., 2015). The following classification of scaling methods assists to structure such analysis. Improved integration of scaling methods in IAM may help to overcome modelling limitations that are related to high data demand, complexity of models and scaling methods considered

Uncertainties in Scaling-Up Crop Models for Large-Area Climate Change Impact Assessments

Problems related to food security and sustainable development are complex (Ericksenet al., 2009) and require consideration of biophysical, economic, political, and social factors, as well as their interactions, at the level of farms, regions, nations, and globally. While the solution to such societal problems may be largely political, there is a growing recognition of the need for science to provide sound information to decision-makers (Meinke et al., 2009). Achieving this, particularly in light of largely uncertain future climate and socio-economic changes, will necessitate integrated assessment approaches and appropriate integrated assessment modeling (IAM) tools to perform them. Recent (Ewertet al., 2009; van Ittersumet al., 2008) and ongoing (Rosenzweiget al., 2013) studies have tried to advance the integrated use of biophysical and economic models to represent better the complex interactions in agricultural systems that largely determine food supply and sustainable resource use. Nonetheless, the challenges for model integration across disciplines are substantial and range from methodological and technical details to an often still-weak conceptual basis on which to ground model integration (Ewertet al., 2009; Janssenet al., 2011). New generations of integrated assessment models based on well-understood, general relationships that are applicable to different agricultural systems across the world are still to be developed. Initial efforts are underway towards this advancement (Nelsonet al., 2014; Rosenzweiget al., 2013). Together with economic and climate models, crop models constitute an essential model group in IAM for large-area cropping systems climate change impact assessments. However, in addition to challenges associated with model integration, inadequate representation of many crops and crop management systems, as well as a lack of data for model initialization and calibration, limit the integration of crop models with climate and economic models (Ewertet al., 2014). A particular obstacle is the mismatch between the temporal and spatial scale of input/output variables required and delivered by the various models in the IAM model chain. Crop models are typically developed, tested, and calibrated for field-scale application (Booteet al., 2013; see also Part 1, Chapter 4 in this volume) and short time-series limited to one or few seasons. Although crop models are increasingly used for larger areas and longer time-periods (Bondeauet al., 2007; Deryng et al., 2011; Elliottet al., 2014) rigorous evaluation of such applications is pending. Among the different sources of uncertainty related to climate and soil data, model parameters, and structure, the uncertainty from methods used to scale-up crop models has received little attention, though recent evaluations indicate that upscaling of crop models for climate change impact assessment and the resulting errors and uncertainties deserve attention in order to advance crop modeling for climate change assessment (Ewertet al., 2014; R¨ otteret al., 2011). This reality is now reflected in the scientific agendas of new international research projects and programs such as the Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweiget al., 2013) and MACSUR (MACSUR, 2014). In this chapter, progress in evaluation of scaling methods with their related uncertainties is reviewed. Specific emphasis is on examining the results of systematic studies recently established in AgMIP and MACSUR. Main features of the respective simulation studies are presented together with preliminary results. Insights from these studies are summarized and conclusions for further work are drawn

A Novel Multi Slit X-Ray Backscatter Camera Based on Synthetic Aperture Focusing

A special slit collimator was developed earlier for fast acquisition of X-ray back scatter images. The design was based on a twisted slit design (ruled surfaces) in a Tungsten block to acquire backscatter images. The comparison with alternative techniques as the flying spot and the coded aperture pin hole technique could not prove the expected higher contrast sensitivity. In analogy to the coded aperture technique, a novel multi slit camera was designed and tested. Several twisted slits were parallelly arranged in a metal block. The CAD design of different multi-slit cameras was evaluated and optimized by the computer simulation packages aRTist and McRay. The camera projects a set of equal images per slit to the digital detector array, which are overlaying each other. Afterwards, the aperture is corrected based on a deconvolution algorithm to focus the overlaying projections into a single representation of the object. Furthermore, a correction of the geometrical distortions due to the slit geometry is performed. The expected increase of the contrast-to-noise ratio is proportional to the square root of the number of parallel slits in the camera. However, additional noise has to be considered originating from the deconvolution operation. The slit design, functional principle, and the expected limits of this technique will be discussed

CropM: Understanding and Modelling Impacts of Climate Change on Crop Production

Key ambition:To developa shared comprehensive information system on the impacts of climate change on European crop production and food securityfirst shared pan-continental assessments and tools(Full) range of important crops and important crop rotationsImproved management and analysis of dataModel improvement (stresses and factors not yet accounted for)Advanced scaling methodsAdvanced link to farm and sector modelsComprehensive uncertainty assessment and reportingTo train integrative crop modelerData ... for better understanding and modelling climate change impactEvaluation of data quality (platinum, gold, silver)Quantify data gaps for modellingEmpirical analysis of crop responses to past climate variability and changeObserved adaptation options and their efficacyEffect of extreme events (past analysis and projections)Climate change scenariosConcept for data management, data journalUncertaintyMethodology & protocols for uncertainty analysisMethodology for standardized model evaluationLocal-scale climate scenarios & uncertainties in climate projectionsBasic methodology for probabilistic assessment of CC impacts using impact response surfacesMethodology for probabilistic evaluation of alternative adaptation options Main aims in MACSUR2:Improve crop model to better capture extremesComplement knowledge from crop models with empirical crop-weather analysisConsider management variables in simulationsFull range of methods for analysing uncertainty in climate impact assessmentsEvaluate potential adaptation optionsContributing to cross-cutting issues and case studies.Further the links with other modelling activitiesLink local to European and global response