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

MACSUR Phase 1 Final Administrative Report: Public release

MACSUR's foremost charge is improving the methodology for integrative inter-disciplinary modelling of European agriculture. In addition to technical changes, improvements include the involvement of stakeholders for setting research priorities, scenarios (if-then evaluations), and model parameters to more realistic or region-specific values. The Knowledge Hub currently brings together 300 members from 18 countries and has generated 300 scientific papers, over 500 presentations and 20 workshops and conferences within the first three years. Scientific results are communicated in conferences and workshops, where policymakers take part by invitation or because of professional interest. These events also provide opportunities for direct dialogues between policy­makers and scientists. The primary form of output of the research network is scientific publications that are cited in policy documents by relevant administrative depart­ments, ministries, intergovern­mental agencies, and directorate-generals, and non-governmental interest groups. MACSUR members also contribute directly to policy documents as authors, e.g. the EEA's indicator report on CC impacts or the IPCC's 5th assessment report's chapter on food security.

MACSUR — Summary of research results, phase 1: 2012-2015

MACSUR — Modelling European Agriculture with Climate Change for Food Security — is a  knowledge hub that was formally created in June 2012 as a European scientific network.  The strategic aim of the knowledge hub is to create a coordinated and globally visible  network of European researchers and research groups, with intra- and interdisciplinary  interaction and shared expertise creating synergies for the development of scientific  resources (data, models, methods) to model the impacts of climate change on agriculture  and related issues. This objective encompasses a wide range of political and sociological  aspects, as well as the technical development of modelling capacity through impact  assessments at different scales and assessing uncertainties in model outcomes. We achieve  this through model intercomparisons and model improvements, harmonization and  exchange of data sets, training in the selection and use of models, assessment of benefits  of ensemble modelling, and cross-disciplinary linkages of models and tools. The project  engages with a diverse range of stakeholder groups and to support the development of  resources for capacity building of individuals and countries. Commensurate with this broad  challenge, a network of currently 300 scientists (measured by the number of individuals on  the central e-mail list) from 18 countries evolved from the original set of research groups  selected by FACCE.  In the spirit of creating and maintaining a network for intra- and interdisciplinary  knowledge exchange, network activities focused on meetings of researchers for sharing  expertise and, depending on group resources (both financial and personnel), development  of collaborative research activities. The outcome of these activities is the enhanced  knowledge of the individual researchers within the network, contributions to conference  presentations and scholarly papers, input to stakeholders and the general public, organised  courses for students, junior and senior scientists. The most visible outcome are the  scientific results of the network activities, represented in the contributions of MACSUR  members to the impressive number of more than 200 collaborative papers in peer-reviewed  publications.  Here, we present a selection of overview and cross-disciplinary papers which include  contributions from MACSUR members. It highlights the major scientific challenges  addressed, and the methodological solutions and insights obtained. Over and above these  highlights, major achievements have been reached regarding data collection, data  processing, evaluation, model testing, modelling assessments of the effects of agriculture  on ecosystem services, policy, and development of scenarios. Details on these  achievements in the context of MACSUR can be found in our online publication FACCE  MACSUR Reports at http://ojs.macsur.eu

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

Key ambition: TO DEVELOP: (1) a shared comprehensive information system on the impacts of climate change on European crop production and food security, (2) first shared pan-continental assessments and tools, (3) (Full) range of important crops and important crop rotations, (4) Improved management and analysis of data, (5) Model improvement (stresses and factors not yet accounted for), (6) Advanced scaling methods, (7) Advanced link to farm and sector models, (8) Comprehensive uncertainty assessment and reporting, (9) To train integrative crop modeler ; DATA ... FOR BETTER UNDERSTANDING AND MODELLING CLIMATE CHANGE IMPACT: (1) Evaluation of data quality (platinum, gold, silver), (2) Quantify data gaps for modelling, (3) Empirical analysis of crop responses to past climate variability and change, (4) Observed adaptation options and their efficacy, (5) Effect of extreme events (past analysis and projections), (6) Climate change scenarios, (7) Concept for data management, data journal ; UNCERTAINTY: (1) Methodology & protocols for uncertainty analysis, (2) Methodology for standardized model evaluation, (3) Local-scale climate scenarios & uncertainties in climate projections, (4) Basic methodology for probabilistic assessment of CC impacts using impact response surfaces, (5) Methodology for probabilistic evaluation of alternative adaptation options ; MAIN AIMS IN MACSUR2: (1) Improve crop model to better capture extremes, (2) Complement knowledge from crop models with empirical crop-weather analysis, (3) Consider management variables in simulations, (4) Full range of methods for analysing uncertainty in climate impact assessments, (5) Evaluate potential adaptation options, (6) Contributing to cross-cutting issues and case studies, (7) Further the links with other modelling activities, (8) Link local to European and global response

MACSUR Phase 1 Final Administrative Report (public release)

MACSUR's foremost charge is improving the methodology for integrative inter-disciplinary modelling of European agriculture. In addition to technical changes, improvements include the involvement of stakeholders for setting research priorities, scenarios (if-then evaluations), and model parameters to more realistic or region-specific values. The Knowledge Hub currently brings together 300 members from 18 countries and has generated 300 scientific papers, over 500 presentations and 20 workshops and conferences within the first three years. Scientific results are communicated in conferences and workshops, where policymakers take part by invitation or because of professional interest. These events also provide opportunities for direct dialogues between policy­makers and scientists. The primary form of output of the research network is scientific publications that are cited in policy documents by relevant administrative depart­ments, ministries, intergovern­mental agencies, and directorate-generals, and non-governmental interest groups. MACSUR members also contribute directly to policy documents as authors, e.g. the EEA's indicator report on CC impacts or the IPCC's 5th assessment report's chapter on food security

Differences in grazing pressure according to good trait-based indicators (PFTs).

<p>Panels A–F compare piosphere and pasture plots across tenure systems (commercial and communal) and biomes (savanna and grassland). All PFTs had a specific response to grazing at least in one biome (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104672#pone-0104672-g003" target="_blank">Figure 3</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104672#pone-0104672-t003" target="_blank">Table 3</a>). Broken lines connect piosphere and pasture plots of a tenure system within a biome, and different letters indicate significant differences (Tukey’s HSD; <i>p</i><0.05). Boxes show medians and 25^th to 75^th percentiles, whiskers stand for the non-outlier ranges of the data. Note the different scaling of the y-axis for panels E and F. HG lin = narrow-leaved perennial grasses, HG lan = broad-leaved perennial grasses, HG = perennial grasses, H = hemicryptophytes, TG = annual grasses, HF = perennial forbs.</p

Biome and tenure system characteristics, and soil differences between piosphere and pasture plots of the tenure systems in the two biomes.

<p>Climate data: Bloemfontein (29.10°S, 26.30°E), ca. 30–70 km distance to grassland sites; Kuruman (27.43°S, 23.45°E), ca. 35–75 km distance to savanna sites. MAP: mean annual precipitation of hydrological years (July–June); CV: coefficient of variation for MAP. For both meteorological stations, only years without data gaps were used for calculations; data source: <a href="http://climexp.knmi.nl" target="_blank">http://climexp.knmi.nl</a>. Bloemfontein data 1904–2011 (n = 100), Kuruman data 1905–1997 (n = 62). Soil types are given as WRB type <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104672#pone.0104672-Brser1" target="_blank">[74]</a>; grassland soils after <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104672#pone.0104672-Austin1" target="_blank">[50]</a>; savanna soils after <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104672#pone.0104672-Cowling1" target="_blank">[24]</a>. Vegetation characteristics of the grassland after <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104672#pone.0104672-Mucina1" target="_blank">[25]</a>; savanna: after <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104672#pone.0104672-Cowling1" target="_blank">[24]</a>. Herd composition, mobility and farm-specific stocking densities for 2011 were derived from pers. comm. with farmers and provided by C. Naumann. Please note for stocking density that a larger number reflects a lower density. Edaphic conditions are given for the topsoil (0–20 cm) of vegetation plots; letters indicate significant differences within a biome (Tukey’s HSD, <i>p</i><0.05; standardized data).</p

Response of plant aggregations to management and soil conditions in the grassland (A) and in the savanna biome (B).

<p>For each plant aggregation, bars denote the proportion of explained variance (given as effect sizes, η²) in best-fitting linear models, associated with biome-specific principal components and land tenure. Parameters are ordered by their effect sizes, starting with the grazing-related principal component. Arrows facing upwards indicate a positive response to increased grazing, and arrows facing downwards indicate a negative response. Note that negative or positive responses to grazing cannot be assigned to ordination axes. DCA 1 = plot scores on first DCA axis. For abbreviations of PFTs, refer to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104672#pone-0104672-t002" target="_blank">Table 2</a>.</p

Hierarchical, three-level approach for the definition of plant functional types (PFTs) based on categorical functional traits.

a<p>The percentage of plant species belonging to a certain PFT is given separately for the grassland/for the savanna biome.</p>b<p>PFT acronyms describe the hierarchical combination of traits. For single-trait PFTs, acronyms are based on the first 1–2 letters of Raunkiær’s life form classification <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104672#pone.0104672-Raunkir1" target="_blank">[15]</a>; for two-trait PFTs, acronyms for growth form are added (G = grasses, F = forbs); for three-trait PFTs, acronyms for leaf width are added (lin = linear (narrow-leaved), <5 mm; lan = lanceolate (broad-leaved), 5–10 mm, ov = ovate (very broad-leaved), >10 mm). PFTs which acronyms are in brackets were not included in further analyses due to their low frequency and low relative abundance on plots.</p>c<p>For two- and three-trait PFTs, names are only given if PFTs were retained in subsequent analyses, at least in one biome.</p

Response consistency of six PFTs which are good (specific) indicators for grazing pressure at least in one biome.

a<p>PFTs with a sensitive response to grazing had a significant contribution of the grazing-related PC to their final linear models, but other predictor variables had larger effect sizes. PFTs with a specific response to grazing also had a sensitive response, but responded stronger to grazing than to other predictor variables (largest effect size for the grazing-related PC). Insensitive PFTs had a non-significant contribution of grazing-related PC to their final linear models. Arrows indicate the direction of response (↑ positive response to increased grazing pressure, ↓negative response). For details of final linear models refer to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104672#pone.0104672.s002" target="_blank">Tables S2</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104672#pone.0104672.s003" target="_blank">S3</a>.</p