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Author |
Roggero, P.P. |
Title |
Managing Agricultural Greenhouse Gases Network (MAGGnet): Exploring Greenhouse Gas Mitigation Potential of Cropland Management Practices |
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Report |
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2016 |
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FACCE MACSUR Reports |
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9 C6 - |
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Sp9-8 |
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Global Research Alliance on Agricultural Greenhouse Gases Established: December 2009, United Nations Climate Change Conference, Copenhagen, Denmark•Purpose: Facilitate research, development and extension of technologies and practices that will help deliver ways to grow more food (and more climate-resilient food systems) without growing greenhouse gas emissions.•Current Membership: 46 countries (Europe, Americas, Asia Pacific, Africa) |
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MA @ admin @ |
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4840 |
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Gutierrez, L.; Piras, F.; Roggero, P.P. |
Title |
A global vector autoregression model for the analysis of wheat export prices |
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Journal Article |
Year |
2015 |
Publication |
American Journal of Agricultural Economics |
Abbreviated Journal |
American Journal of Agricultural Economics |
Volume |
97 |
Issue |
5 |
Pages |
1494-1511 |
Keywords |
Global dynamic models; price analysis; wheat market; lagged dependent-variables; commodity-markets; error-correction; food-prices; unit-root; regressors; tests; cointegration; dynamics; time |
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Food commodity price fluctuations have an important impact on poverty and food insecurity across the world. Conventional models have not provided a complete picture of recent price spikes in agricultural commodity markets, and there is an urgent need for appropriate policy responses. Perhaps new approaches are needed to better understand international spill-overs, the feedback between the real and the financial sectors, as well as the link between food and energy prices. In this article, we present the results from a new worldwide dynamic model that provides the short and long-run impulse responses of the international wheat price to various real and financial shocks. |
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0002-9092 1467-8276 |
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TradeM, ft_macsur |
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MA @ admin @ |
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4658 |
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Author |
Roggero, P.P. |
Title |
Oristano, Sardinia, Italy: Winners and losers from climate change in agriculture: a case study in the Mediterranean basin |
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Conference Article |
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2015 |
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6 |
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Sp6-7 |
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CropM |
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Focus questions • How to support effective adaptive responses to CC and stimulate proactive attitudes of farmers, policymakers & researchers? • How to co-construct the nature of the issues about CC adaptation? The «Oristanese» case study • Very diversified agricultural district in a Mediterranean context o Irrigated and rainfed farming systems o Variety of cropping systems, intensity levels, farm size • Multiple stakeholders o Cooperative agro-food system o Producers’ organizations (rice, horticulture) o Variety of extensive pastoral systems Emerging outcome • The dairy cattle coop is developing a new win-win pathway linking hi-input dairy cattle farming with low input beef cattle grazing systems • The local government is investing in the EIP for supporting the local beef production chain to reduce meat imports and enhance pasture biodiversity and ecosystem services (eg wildfire prevention) Emerging challenges Adaptive responses as co-evolution pathways • design social learning spaces for researchers, stakeholders and policy makers • combining integrated assessment modeling and social learning facilitation |
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Brussels |
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Climate-change impacts on farming systems in the next decades — why worry when you have CAP? A FACCE MACSUR workshop for policymakers, 2015-05-06 to 2015-05-06, Brussels |
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MA @ admin @ |
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2750 |
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Hoffmann, H.; Zhao, G.; van Bussel, L.G.J.; Enders, A.; Specka, X.; Sosa, C.; Yeluripati, J.; Tao, F.; Constantin, J.; Raynal, H.; Teixeira, E.; Grosz, B.; Doro, L.; Zhao, Z.; Wang, E.; Nendel, C.; Kersebaum, K.C.; Haas, E.; Kiese, R.; Klatt, S.; Eckersten, H.; Vanuytrecht, E.; Kuhnert, M.; Lewan, E.; Rötter, R.; Roggero, P.P.; Wallach, D.; Cammarano, D.; Asseng, S.; Krauss, G.; Siebert, S.; Gaiser, T.; Ewert, F. |
Title |
Variability of effects of spatial climate data aggregation on regional yield simulation by crop models |
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Journal Article |
Year |
2015 |
Publication |
Climate Research |
Abbreviated Journal |
Clim. Res. |
Volume |
65 |
Issue |
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Pages |
53-69 |
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spatial aggregation effects; crop simulation model; input data; scaling; variability; yield simulation; model comparison; input data aggregation; systems simulation; nitrogen dynamics; data resolution; n2o emissions; winter-wheat; scale; water; impact; apsim |
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Field-scale crop models are often applied at spatial resolutions coarser than that of the arable field. However, little is known about the response of the models to spatially aggregated climate input data and why these responses can differ across models. Depending on the model, regional yield estimates from large-scale simulations may be biased, compared to simulations with high-resolution input data. We evaluated this so-called aggregation effect for 13 crop models for the region of North Rhine-Westphalia in Germany. The models were supplied with climate data of 1 km resolution and spatial aggregates of up to 100 km resolution raster. The models were used with 2 crops (winter wheat and silage maize) and 3 production situations (potential, water-limited and nitrogen-water-limited growth) to improve the understanding of errors in model simulations related to data aggregation and possible interactions with the model structure. The most important climate variables identified in determining the model-specific input data aggregation on simulated yields were mainly related to changes in radiation (wheat) and temperature (maize). Additionally, aggregation effects were systematic, regardless of the extent of the effect. Climate input data aggregation changed the mean simulated regional yield by up to 0.2 t ha(-1), whereas simulated yields from single years and models differed considerably, depending on the data aggregation. This implies that large-scale crop yield simulations are robust against climate data aggregation. However, large-scale simulations can be systematically biased when being evaluated at higher temporal or spatial resolution depending on the model and its parameterization. |
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0936-577x 1616-1572 |
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CropM, ft_macsur |
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MA @ admin @ |
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4694 |
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Author |
Dono, G.; Cortignani, R.; Doro, L.; Roggero, P.P. |
Title |
The adaptation of farm and awareness of ongoing climate change (CC) |
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Conference Article |
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2014 |
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Farm 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. |
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FACCE MACSUR Mid-term Scientific Conference |
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3(S) Sassari, Italy |
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FACCE MACSUR Mid-term Scientific Conference, 2014-04-01 to 2014-04-04, Sassari, Italy |
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MA @ admin @ |
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5131 |
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