| Records |
| Author |
Sándor, R.; Barcza, Z.; Acutis, M.; Doro, L.; Hidy, D.; Köchy, M.; Minet, J.; Lellei-Kovács, E.; Ma, S.; Perego, A.; Rolinski, S.; Ruget, F.; Sanna, M.; Seddaiu, G.; Wu, L.; Bellocchi, G. |
| Title |
Multi-model simulation of soil temperature, soil water content and biomass in Euro-Mediterranean grasslands: Uncertainties and ensemble performance |
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Journal Article |
| Year |
2016 |
Publication |
European Journal of Agronomy |
Abbreviated Journal |
European Journal of Agronomy |
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| Keywords |
Biomass; Grasslands; Modelling; Multi-model ensemble; Soil processes |
| Abstract |
• We simulate biomass, soil water content (SWC) and temperature (ST) in grasslands. • We compare nine models to the multi-model median (MMM) at nine sites. • With model calibration, we obtain satisfactory estimates of ST, less of SWC and biomass. • We observe discrepancies across models in the simulation of grassland processes. • We improve performance with multi-model approach. This study presents results from a major grassland model intercomparison exercise, and highlights the main challenges faced in the implementation of a multi-model ensemble prediction system in grasslands. Nine, independently developed simulation models linking climate, soil, vegetation and management to grassland biogeochemical cycles and production were compared in a simulation of soil water content (SWC) and soil temperature (ST) in the topsoil, and of biomass production. The results were assessed against SWC and ST data from five observational grassland sites representing a range of conditions – Grillenburg in Germany, Laqueuille in France with both extensive and intensive management, Monte Bondone in Italy and Oensingen in Switzerland – and against yield measurements from the same sites and other experimental grassland sites in Europe and Israel. We present a comparison of model estimates from individual models to the multi-model ensemble (represented by multi-model median: MMM). With calibration (seven out of nine models), the performances were acceptable for weekly-aggregated ST (R² > 0.7 with individual models and >0.8–0.9 with MMM), but less satisfactory with SWC (R² < 0.6 with individual models and < ∼ 0.5 with MMM) and biomass (R² < ∼0.3 with both individual models and MMM). With individual models, maximum biases of about −5 °C for ST, −0.3 m3 m−3 for SWC and 360 g DM m−2 for yield, as well as negative modelling efficiencies and some high relative root mean square errors indicate low model performance, especially for biomass. We also found substantial discrepancies across different models, indicating considerable uncertainties regarding the simulation of grassland processes. The multi-model approach allowed for improved performance, but further progress is strongly needed in the way models represent processes in managed grassland systems. |
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1161-0301 |
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LiveM |
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MA @ admin @ |
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4768 |
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König, H.J.; Helming, K.; Seddaiu, G.; Kipling, R.; Köchy, M.; Graversgaard, M.; van den Pol-van Dasselaar, A.; Nguyen, T.P.L.; Quaranta, G.; Salvia, R.; Sieber, S.; Ithes, S.; Kjeldsen, C.; Turner, K.G.; Dalgaard, T.; Roggero, P.P. |
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Stakeholder participation in agricultural research: Who should be involved, why, and how? |
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Research in sustainable agricultural management requires appropriate participatory processes and tools enabling efficient dialogue and cooperation to allow researchers and stakeholders to co-produce knowledge. Research approaches that encourage stakeholder participation are in high demand because they allow a better understanding of human-nature interactions and interdependencies between actors. Participatory approaches also support multiple goals of agricultural management: improved productivity, food security, climate change adaptation, environmental conservation, rural development and policy decision making. Approaches to stakeholder engagement in the field of agricultural management research are manifold. Therefore, selecting the “right” approach depends on the specific purpose and contextualized issues at stake. We analyzed ten stakeholder approaches and propose a new framework with which to identify and select appropriate approaches for stakeholder engagement. The framework consists of three components: whom to engage (i.e., stakeholder type and mandate), why to engage (i.e., research purpose: consult, inform, collaborate), and how to engage (i.e., different methodological approaches). We identified different stakeholder groups (who?): farmers, agricultural actors, land users, and policymakers; different purposes (why?): facilitate engagement process, inform stakeholders, and obtain stakeholder perceptions; and different types of engagement methods (how?): participatory field experiments, desk simulations, interviews, panel discussions and different types of workshops. The framework was applied to arrange these approaches, organize them to improve understanding of their main strengths, weaknesses and supports for identifying and selecting an appropriate approach. We conclude that understanding the different facets of available approaches is crucial for selecting an appropriate stakeholder engagement approach. ; |
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MA @ admin @ |
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2564 |
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Knopik, L.; Bojar, W. |
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Mozliwosci zastosowania metody wielo – agentowej w analizie wybranych modeli (Possibilities of multiagent appliacation for analysis of selected models) |
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Book Chapter |
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2013 |
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199-208 |
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TradeM |
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Warsaw Technical University |
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Warsaw |
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Rostek, K. |
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Zarzadzanie wiedza w tworzeniu przewagi konkurencyjnej (Knowledge management in creating comparative advantage) |
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MA @ admin @ |
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2547 |
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Klatt, S.; Kraus, D.; Rahn, K.-H.; Werner, C.; Kiese, R.; Butterbach-Bahl, K.; Haas, E. |
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Parameter-induced uncertainty quantification of a regional N2O and NO3 inventory using the biogeochemical model LandscapeDNDC |
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2014 |
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CropM |
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MA @ admin @ |
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2542 |
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Bennetzen, E.H.; Smith, P.; Porter, J.R. |
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Agricultural production and greenhouse gas emissions from world regions—The major trends over 40 years |
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Journal Article |
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2016 |
Publication |
Global Environmental Change |
Abbreviated Journal |
Glob. Environ. Change |
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37 |
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43-55 |
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Agriculture; Greenhouse gas intensity; Climate change; Kaya-Porter; identity; Decoupling emissions; Kaya-identity; land-use change; carbon-dioxide emissions; sustainable intensification; livestock production; forest transitions; global agriculture; crop; production; food security; deforestation; mitigation |
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Since 1970, global agricultural production has more than doubled with agriculture and land-use change now responsible for similar to 1/4 of greenhouse gas emissions from human activities. Yet, while greenhouse gas (GHG) emissions per unit of agricultural product have been reduced at a global level, trends in world regions have been quantified less thoroughly. The KPI (Kaya-Porter Identity) is a novel framework for analysing trends in agricultural production and land-use change and related GHG emissions. We apply this to assess trends and differences in nine world regions over the period 1970-2007. We use a deconstructed analysis of emissions from the mix of multiple sources, and show how each is changing in terms of absolute emissions on a per area and per produced unit basis, and how the change of emissions from each source contributes to the change in total emissions over time. The doubling of global agricultural production has mainly been delivered by developing and transitional countries, and this has been mirrored by increased GHG emissions. The decoupling of emissions from production shows vast regional differences. Our estimates show that emissions per unit crop (as kg CO2-equivalents per Giga Joule crop product), in Oceania, have been reduced by 94% from 1093 to 69; in Central & South America by 57% from 849 to 362; in sub-Saharan Africa by 27% from 421 to 309, and in Europe by 56% from 86 to 38. Emissions per unit livestock (as kg CO2-eq. GJ(-1) livestock product) have reduced; in sub-Saharan Africa by 24% from 6001 to 4580; in Central & South America by 61% from 3742 to 1448; in Central & Eastern Asia by 82% from 3,205 to 591, and; in North America by 28% from 878 to 632. In general, intensive and industrialised systems show the lowest emissions per unit of agricultural production. (C) 2016 Elsevier Ltd. All rights reserved. |
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English |
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0959-3780 |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4758 |
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