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Schauberger, B.; Rolinski, S.; Müller, C. |
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Title |
A network-based approach for semi-quantitative knowledge mining and its application to yield variability |
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Journal Article |
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Year  |
2016 |
Publication |
Environmental Research Letters |
Abbreviated Journal |
Environ. Res. Lett. |
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Volume |
11 |
Issue |
12 |
Pages |
123001 |
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Keywords |
yield variability; crop models; interaction network; plant process; wheat; maize; rice; Global Food Security; Climate-Change; Crop Production; Stress Tolerance; Wheat Yields; Heat-Stress; Temperature Variability; Environmental-Factors; United-States; Elevated CO2 |
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Abstract |
Variability of crop yields is detrimental for food security. Under climate change its amplitude is likely to increase, thus it is essential to understand the underlying causes and mechanisms. Crop models are the primary tool to project future changes in crop yields under climate change. Asystematic overview of drivers and mechanisms of crop yield variability (YV) can thus inform crop model development and facilitate improved understanding of climate change impacts on crop yields. Yet there is a vast body of literature on crop physiology and YV, which makes a prioritization of mechanisms for implementation in models challenging. Therefore this paper takes on a novel approach to systematically mine and organize existing knowledge from the literature. The aim is to identify important mechanisms lacking in models, which can help to set priorities in model improvement. We structure knowledge from the literature in a semi-quantitative network. This network consists of complex interactions between growing conditions, plant physiology and crop yield. We utilize the resulting network structure to assign relative importance to causes of YV and related plant physiological processes. As expected, our findings confirm existing knowledge, in particular on the dominant role of temperature and precipitation, but also highlight other important drivers of YV. More importantly, our method allows for identifying the relevant physiological processes that transmit variability in growing conditions to variability in yield. We can identify explicit targets for the improvement of crop models. The network can additionally guide model development by outlining complex interactions between processes and by easily retrieving quantitative information for each of the 350 interactions. We show the validity of our network method as a structured, consistent and scalable dictionary of literature. The method can easily be applied to many other research fields. |
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2017-04-07 |
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English |
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1748-9326 |
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Review |
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CropM, ft_macsur |
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no |
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MA @ admin @ |
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4942 |
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Reidsma, P.; Bakker, M.M.; Kanellopoulos, A.; Alam, S.J.; Paas, W.; Kros, J.; W. de Vries, W. |
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Title |
Impacts of climate and socio-economic change at farm and landscape level in the Netherlands: climate smart agriculture |
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Conference Article |
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2015 |
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CropM |
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Climate-smart agriculture 2015. Global Science Conference. Montpellier, France, 2015-03-15 to 2015-03-18 |
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MA @ admin @ |
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2745 |
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Author |
Roggero, P.P. |
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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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no |
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MA @ admin @ |
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2750 |
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Rötter, R.P.; Höhn, J.K.; Palosuo, T.; Kassie, B.T.; Paff, K.; Tao, F.; Chen, Y.; Asseng, S.; et al. |
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Title |
Yield gap and variability analysis for different aro-technologies for maize and wheat (YGV study) |
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2015 |
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CropM; |
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Ithaca (U.S.A.) |
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2nd Global Food Security Conference, 2015-10-10- to 2015-10-15, Ithaca |
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no |
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MA @ admin @ |
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2770 |
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Ruiz-Ramos, M. |
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Title |
Les modèles de culture face au changement climatique : les enjeux des projets nationaux, européens et internationaux |
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2015 |
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CropM |
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Xe Colloque STICS. March 24-26, Rennes, France, 2015-03-24 to 2015-04-26 |
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MA @ admin @ |
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2772 |
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