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Wallach, D.; Thorburn, P.; Asseng, S.; Challinor, A.J.; Ewert, F.; Jones, J.W.; Rötter, R.; Ruane, A. |
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Overview paper on comprehensive framework for assessment of error and uncertainty in crop model predictions |
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2016 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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8 |
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C4.1-D |
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MACSUR_ACK; CropM |
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Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. Several ways of quantifying prediction uncertainty have been explored in the literature, but there have been no studies of how the different approaches are related to one another, and how they are related to some overall measure of prediction uncertainty. Here we show that all the different approaches can be related to two different viewpoints about the model; either the model is treated as a fixed predictor with some average error, or the model can be treated as a random variable with uncertainty in one or more of model structure, model inputs and model parameters. We discuss the differences, and show how mean squared error of prediction can be estimated in both cases. The results can be used to put uncertainty estimates into a more general framework and to relate different uncertainty estimates to one another and to overall prediction uncertainty. This should lead to a better understanding of crop model prediction uncertainty and the underlying causes of that uncertainty. This study was published as (Wallach et al. 2016) |
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MA @ office @ |
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2954 |
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Kipling, R.; Scollan, N.; Bannink, A.; van Middelkoop, J. |
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Title |
From diversity to strategy: Livestock research for effective policy in a climate change world |
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2016 |
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FACCE MACSUR Reports |
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8 |
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H0.3-D1 |
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policy brief, networking |
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European livestock agriculture is extraordinarily diverse, and so are the challenges it faces. This diversity has contributed to the development of a fragmented set of research communities. As a result, livestock research is often under-represented at policy level, despite its high relevance for the environment and food security. Understanding livestock systems and how they can sustainably adapt to global change requires inputs across research areas, including grasslands, nutrition, health, welfare and ecology. It also requires experimental researchers, modellers and stakeholders to work closely together. Networks and capacity building structures are vital to enable livestock research to meet the challenges of climate change. They need to maintain shared resources and provide non-competitive arenas to share and synthesize results for policy support. ï‚· Long term strategic investment is needed to support such structures. Their leadership requires very different skills to those effective in scientific project coordination. |
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MA @ admin @ |
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2269 |
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Kipling, R. |
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LiveM2016: International livestock modelling conference – Modelling grassland-livestock systems under climate change |
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2016 |
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FACCE MACSUR Reports |
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8 |
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L0.1-D1 |
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MA @ admin @ |
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4841 |
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Bojar, W.; Żarski, J.; Knopik, L.; Kuśmierek-Tomaszewska, R.; Sikora, M.; Dzieża, G. |
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Markov Chain as a Model of Daily Total Precipitation and a Prediction of Future Natural Events |
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2016 |
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Berlin (Germany) |
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International Crop Modelling Symposium iCROPM 2016, 2016-05-15 to 2016-05-17, Berlin, Germany |
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MA @ admin @ |
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4911 |
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Heinschink et al. |
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Title |
Index-based costs of livestock production (INCAP.l) in Austria – the suckler cow and beef calf production activity |
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2016 |
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FACCE MACSUR Reports |
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8 |
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SP8-1 |
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Conference presentation PDF |
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LiveM2016: International livestock modelling conference – Modelling grassland-livestock systems under climate change |
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MA @ admin @ |
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4846 |
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