Roggero, P. P. (2016). Managing Agricultural Greenhouse Gases Network (MAGGnet): Exploring Greenhouse Gas Mitigation Potential of Cropland Management Practices (Vol. 9 C6 -).
Abstract: 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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Rötter, L. R. (2016). Introduction to MACSUR — methodology for integrated assessment.. Rotterdam (Netherlands).
Abstract: Presentation SC 2.10 Farming systems. Introduction to MACSUR – methodology for integrated assessment, Reimund R�tter, Natural Resources Institute Finland (LUKE), Finland (2016). Presented at the international conference Adaptation Futures 2016, Rotterdam, the Netherlands. No Label
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Rötter, R. P. A. U. -, J.H., Kassie, B. T., Paff, K., Palosuo, T. A. U. -, C.Y., Tao, F., Ewert, F., et al. (2016). Analysis of crop yield variability and yield gaps for maize and wheat in diverse climatic zones.. Berlin (Germany).
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Ruane, A. C., Hudson, N. I., Asseng, S., Camarrano, D., Ewert, F., Martre, P., et al. (2016). Multi-wheat-model ensemble responses to interannual climate variability. Env. Model. Softw., 81, 86–101.
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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Ruget, F., Buis, S., Irfan, K., Delmotte, S., Mouret, J. - C., Ridaura, S. L., et al. (2016). Parametrization of a crop model using a regional agronomical database: rice in Camargue with STICS.. Berlin (Germany).
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