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Ruane, A.C.; Hudson, N.I.; Asseng, S.; Camarrano, D.; Ewert, F.; Martre, P.; Boote, K.J.; Thorburn, P.J.; Aggarwal, P.K.; Angulo, C.; Basso, B.; Bertuzzi, P.; Biernath, C.; Brisson, N.; Challinor, &rew J.; Doltra, J.; Gayler, S.; Goldberg, R.; Grant, R.F.; Heng, L.; Hooker, J.; Hunt, L.A.; Ingwersen, J.; Izaurralde, R.C.; Kersebaum, K.C.; Kumar, S.N.; Müller, C.; Nendel, C.; O’Leary, G.; Olesen, J.E.; Osborne, T.M.; Palosuo, T.; Priesack, E.; Ripoche, D.; Rötter, R.P.; Semenov, M.A.; Shcherbak, I.; Steduto, P.; Stöckle, C.O.; Stratonovitch, P.; Streck, T.; Supit, I.; Tao, F.; Travasso, M.; Waha, K.; Wallach, D.; White, J.W.; Wolf, J. |
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Title |
Multi-wheat-model ensemble responses to interannual climate variability |
Type |
Journal Article |
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Year |
2016 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
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Volume |
81 |
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Pages  |
86-101 |
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Keywords |
Crop modeling; Uncertainty; Multi-model ensemble; Wheat; AgMIP; Climate; impacts; Temperature; Precipitation; lnterannual variability; simulation-model; crop model; nitrogen dynamics; winter-wheat; large-area; systems simulation; farming systems; yield response; growth; water |
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Abstract |
We compare 27 wheat models’ yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981-2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models’ climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R-2 <= 0.24) was found between the models’ sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts. Published by Elsevier Ltd. |
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1364-8152 |
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CropM, ft_macsur |
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no |
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Call Number |
MA @ admin @ |
Serial |
4769 |
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Author |
Ghaley, B.B.; Porter, J.R. |
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Title |
Determination of biomass accumulation in mixed belts of Salix, Corylus and Alnus species in combined food and energy production system |
Type |
Journal Article |
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Year |
2014 |
Publication |
Biomass and Bioenergy |
Abbreviated Journal |
Biomass and Bioenergy |
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63 |
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86-91 |
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Keywords |
allometric equation; destructive and non-destructive method; stool and biomass yield; bio-energy belts; food and fodder crops; short rotation woody crops; short-rotation forestry; willow; plantations; sweden; coppice; equations; growth; poplar; trees; yield |
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Abstract |
Given the energetic, demographic and the climatic challenges faced today, we designed a combined food and energy (CFE) production system integrating food, fodder and mixed belts of Salix, Alnus and Corylus sp. as bioenergy belts. The objective was to assess the shoot dry weight-stem diameter allometric relationship based on stem diameter at 10 (SD10) and 55 cm (SD55) from the shoot base in the mixed bioenergy belts. Allometric relations based on SD10 and SD55 explained 90-96% and 90-98% of the variation in shoot dry weights respectively with no differences between the destructive and the non-destructive methods. The individual stool yields varied widely among the species and within willow species with biomass yield range of 37.60-92.00 oven dry tons (ODT) ha (1) in 4-year growth cycle. The biomass yield of the bioenergy belt, predicted by allometric relations was 48.84 ODT ha 1 in 4-year growth cycle corresponding to 12.21 ODT ha (1) year (1). The relatively high biomass yield is attributed to the border effects and the ‘fertilizing effect’ of alder due to nitrogen fixation, benefitting other SWRC components. On termination of 4-year growth cycle, the bioenergy belts were harvested and the biomass yield recorded was 12.54 ODT ha (1) year (1), in close proximity to the biomass yield predicted by the allometric equations, lending confidence and robustness of the model for biomass yield determination in such integrated agro-ecosystem. (C) 2014 Elsevier Ltd. All rights reserved. |
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0961-9534 |
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CropM |
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MA @ admin @ |
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4624 |
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Nelson, G.C.; van der Mensbrugghe, D.; Ahammad, H.; Blanc, E.; Calvin, K.; Hasegawa, T.; Havlik, P.; Heyhoe, E.; Kyle, P.; Lotze-Campen, H.; von Lampe, M.; Mason, d’C., Daniel; van Meijl, H.; Müller, C.; Reilly, J.; Robertson, R.; Sands, R.D.; Schmitz, C.; Tabeau, A.; Takahashi, K.; Valin, H.; Willenbockel, D. |
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Title |
Agriculture and climate change in global scenarios: why don’t the models agree |
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Journal Article |
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Year |
2014 |
Publication |
Agricultural Economics |
Abbreviated Journal |
Agric. Econ. |
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45 |
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1 |
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85-85 |
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Keywords |
climate change impacts; economic models of agriculture; scenarios; system model; demand; cmip5 |
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Agriculture is unique among economic sectors in the nature of impacts from climate change. The production activity that transforms inputs into agricultural outputs involves direct use of weather inputs (temperature, solar radiation available to the plant, and precipitation). Previous studies of the impacts of climate change on agriculture have reported substantial differences in outcomes such as prices, production, and trade arising from differences in model inputs and model specification. This article presents climate change results and underlying determinants from a model comparison exercise with 10 of the leading global economic models that include significant representation of agriculture. By harmonizing key drivers that include climate change effects, differences in model outcomes were reduced. The particular choice of climate change drivers for this comparison activity results in large and negative productivity effects. All models respond with higher prices. Producer behavior differs by model with some emphasizing area response and others yield response. Demand response is least important. The differences reflect both differences in model specification and perspectives on the future. The results from this study highlight the need to more fully compare the deep model parameters, to generate a call for a combination of econometric and validation studies to narrow the degree of uncertainty and variability in these parameters and to move to Monte Carlo type simulations to better map the contours of economic uncertainty. |
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2016-10-31 |
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0169-5150 |
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CropM, TradeM, ft_macsur |
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MA @ admin @ |
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4796 |
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Nelson, G.C.; van der Mensbrugghe, D.; Ahammad, H.; Blanc, E.; Calvin, K.; Hasegawa, T.; Havlik, P.; Heyhoe, E.; Kyle, P.; Lotze-Campen, H.; von Lampe, M.; Mason, d’C., Daniel; van Meijl, H.; Müller, C.; Reilly, J.; Robertson, R.; Sands, R.D.; Schmitz, C.; Tabeau, A.; Takahashi, K.; Valin, H.; Willenbockel, D. |
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Title |
Agriculture and climate change in global scenarios: why don’t the models agree |
Type |
Journal Article |
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Year |
2014 |
Publication |
Agricultural Economics |
Abbreviated Journal |
Agric. Econ. |
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Volume |
45 |
Issue |
1 |
Pages |
85-101 |
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Keywords |
climate change impacts; economic models of agriculture; scenarios; system model; demand; CMIP5 |
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Abstract |
Agriculture is unique among economic sectors in the nature of impacts from climate change. The production activity that transforms inputs into agricultural outputs involves direct use of weather inputs (temperature, solar radiation available to the plant, and precipitation). Previous studies of the impacts of climate change on agriculture have reported substantial differences in outcomes such as prices, production, and trade arising from differences in model inputs and model specification. This article presents climate change results and underlying determinants from a model comparison exercise with 10 of the leading global economic models that include significant representation of agriculture. By harmonizing key drivers that include climate change effects, differences in model outcomes were reduced. The particular choice of climate change drivers for this comparison activity results in large and negative productivity effects. All models respond with higher prices. Producer behavior differs by model with some emphasizing area response and others yield response. Demand response is least important. The differences reflect both differences in model specification and perspectives on the future. The results from this study highlight the need to more fully compare the deep model parameters, to generate a call for a combination of econometric and validation studies to narrow the degree of uncertainty and variability in these parameters and to move to Monte Carlo type simulations to better map the contours of economic uncertainty. |
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0169-5150 |
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CropM, TradeM, ft_macsur |
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MA @ admin @ |
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4536 |
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Podhora, A.; Helming, K.; Adenäuer, L.; Heckelei, T.; Kautto, P.; Reidsma, P.; Rennings, K.; Turnpenny, J.; Jansen, J. |
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The policy-relevancy of impact assessment tools: Evaluating nine years of European research funding |
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Journal Article |
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2013 |
Publication |
Environmental Science & Policy |
Abbreviated Journal |
Environmental Science & Policy |
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31 |
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85-95 |
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impact assessment tools; policy appraisal; science policy interface; sustainable development; european commission; affecting land-use; of-the-art; integrated assessment; sustainable development; agricultural systems; analytical framework; union; part |
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Since 2002, the European Commission has employed the instrument of ex-ante impact assessments (IA) to help focus its policy-making process on implementing sustainable development. Scientific tools should play an essential role of providing the evidence base to assess the impacts of alternative policy options. To identify the contribution of research funding for IA tool development, this paper analysed the variety of IA tools designed in projects funded by European Framework Programmes (FPs) 6 and 7. The paper is based on project information available on the European Cordis website, individual project websites and a verification of the results by the project coordinators. We analysed the projects from the interests of IA practitioners as tool users (European policy and impact areas addressed by the tools, jurisdictional application levels and tool categories). Out of the 7.781 projects funded in FP6 and FP7, 203 could be identified that designed tools for the IA process. Nearly half of them applied to environmental, agricultural and transport policy areas. Within these areas, the tools primarily addressed environmental impact areas, less economic and least social impact areas. The IA tools focused on European policies. Models represented the largest tool category, whereas approximately half of the tools could not be clearly categorized. Concerning our analysis criteria, the tool descriptions available on the internet were often unclear and thus may limit the application potential of the tools because of a mismatch of technical terms and categorisation criteria between tool providers and tool users. Future IA tools require a joint political and scientific typology and a narrowing of the gaps, e.g., with view to multi-jurisdictional application and a clear reference to the steps of the IA process. (C) 2013 Elsevier Ltd. All rights reserved. |
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1462-9011 |
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TradeM |
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MA @ admin @ |
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4500 |
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