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Author |
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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ISSN |
1748-9326 |
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Medium |
Review |
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Notes |
CropM, ft_macsur |
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Call Number |
MA @ admin @ |
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4942 |
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Liu, B.; Asseng, S.; Müller, C.; Ewert, F.; Elliott, J.; Lobell, D. B.; Martre, P.; Ruane, A. C.; Wallach, D.; Jones, J. W.; Rosenzweig, C.; Aggarwal, P. K.; Alderman, P. D.; Anothai, J.; Basso, B.; Biernath, C.; Cammarano, D.; Challinor, A.; Deryng, D.; Sanctis, G. D.; Doltra, J.; Fereres, E.; Folberth, C.; Garcia-Vila, M.; Gayler, S.; Hoogenboom, G.; Hunt, L. A.; Izaurralde, R. C.; Jabloun, M.; Jones, C. D.; Kersebaum, K. C.; Kimball, B. A.; Koehler, A.-K.; Kumar, S. N.; Nendel, C.; O’Leary, G. J.; Olesen, J. E.; Ottman, M. J.; Palosuo, T.; Prasad, P. V. V.; Priesack, E.; Pugh, T. A. M.; Reynolds, M.; Rezaei, E. E.; Rötter, R. P.; Schmid, E.; Semenov, M. A.; Shcherbak, I.; Stehfest, E.; Stöckle, C. O.; Stratonovitch, P.; Streck, T.; Supit, I.; Tao, F.; Thorburn, P.; Waha, K.; Wall, G. W.; Wang, E.; White, J. W.; Wolf, J.; Zhao, Z.; Zhu, Y. |
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Title |
Similar estimates of temperature impacts on global wheat yield by three independent methods |
Type |
Journal Article |
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Year |
2016 |
Publication |
Nature Climate Change |
Abbreviated Journal |
Nat. Clim. Change |
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Volume |
6 |
Issue |
12 |
Pages |
1130-1136 |
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1758-678x |
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article |
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CropM |
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MA @ admin @ |
Serial |
4965 |
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Boote, K.J.; Porter, C.; Jones, J.W.; Thorburn, P.J.; Kersebaum, K.C.; Hoogenboom, G.; White, J.W.; Hatfield, J.L. |
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Title |
Sentinel site data for crop model improvement—definition and characterization |
Type |
Book Chapter |
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Year |
2016 |
Publication |
Improving Modeling Tools to Assess Climate Change Effects on Crop Response |
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Crop models are increasingly being used to assess the impacts of future climate change on production and food security. High quality, site-specific data on weather, soils, management, and cultivar are needed for those model applications. Also important is that model development, evaluation, improvement, and calibration require additional high quality, site-specific measurements on crop yield, growth, phenology, and ancillary traits. We review the evolution of minimum data set requirements for agroecosystem modeling and then describe the characteristics and ranking of sentinel site data needed for crop model improvement, calibration, and application. We in the Agricultural Model Intercomparison and Improvement Project (AgMIP), propose to rank sentinel site data sets as platinum, gold, silver, and copper, based on the degree of true site-specific measurement of weather, soils, management, crop yield, as well as the quality, comprehensiveness, quantity, accuracy, and value. For example, to be ranked platinum, the weather and soil characterization must be measured on-site, and all management inputs must be known. Dataset ranking will be lower for weather measured off-site or soil traits estimated from soil mapping. Ranking also depends on the intended purposes for data use. If the purpose is to improve a crop model for response to water or N, then additional observations are necessary, such as initial soil water, initial soil inorganic N, and plant N uptake during the growing season to be ranked platinum. Rankings are enhanced by presence of multiple treatments and sites. Examples of platinum-, gold-, and silver-quality data sets for model improvement and calibration uses are illustrated. |
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Hatfield, J.L.; Fleisher, D. |
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Advances in Agricultural Systems Modeling |
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Series Volume |
7 |
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CropM |
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MA @ admin @ |
Serial |
4980 |
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Author |
Kersebaum, K.; Kroes, J.; Gobin, A.; Takáč, J.; Hlavinka, P.; Trnka, M.; Ventrella, D.; Giglio, L.; Ferrise, R.; Moriondo, M.; Dalla Marta, A.; Luo, Q.; Eitzinger, J.; Mirschel, W.; Weigel, H.-J.; Manderscheid, R.; Hoffmann, M.; Nejedlik, P.; Iqbal, M.; Hösch, J. |
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Title |
Assessing uncertainties of water footprints using an ensemble of crop growth models on winter wheat |
Type |
Journal Article |
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Year |
2016 |
Publication |
Water |
Abbreviated Journal |
Water |
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Volume |
8 |
Issue |
12 |
Pages |
571 |
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Abstract |
Crop productivity and water consumption form the basis to calculate the water footprint (WF) of a specific crop. Under current climate conditions, calculated evapotranspiration is related to observed crop yields to calculate WF. The assessment of WF under future climate conditions requires the simulation of crop yields adding further uncertainty. To assess the uncertainty of model based assessments of WF, an ensemble of crop models was applied to data from five field experiments across Europe. Only limited data were provided for a rough calibration, which corresponds to a typical situation for regional assessments, where data availability is limited. Up to eight models were applied for wheat. The coefficient of variation for the simulated actual evapotranspiration between models was in the range of 13%–19%, which was higher than the inter-annual variability. Simulated yields showed a higher variability between models in the range of 17%–39%. Models responded differently to elevated CO2 in a FACE (Free-Air Carbon Dioxide Enrichment) experiment, especially regarding the reduction of water consumption. The variability of calculated WF between models was in the range of 15%–49%. Yield predictions contributed more to this variance than the estimation of water consumption. Transpiration accounts on average for 51%–68% of the total actual evapotranspiration. |
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2073-4441 |
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CropM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
4987 |
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Author |
Wang, X.; Biewald, A.; Dietrich, J.P.; Schmitz, C.; Lotze-Campen, H.; Humpenöder, F.; Bodirsky, B.L.; Popp, A. |
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Title |
Taking account of governance: Implications for land-use dynamics, food prices, and trade patterns |
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Journal Article |
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Year |
2016 |
Publication |
Ecological Economics |
Abbreviated Journal |
Ecol. Econ. |
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Volume |
122 |
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Pages |
12-24 |
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Abstract |
Highlights • Governance impacts on land use dynamics are modeled at the global scale with an agro-economic dynamic optimization model. • Improved governance performance lowers deforestation, reduces cropland expansion and increases agricultural yield. • Good governance makes a decisive difference in investment for increasing yields in developing regions. • Weak governance increases food prices, particularly in Sub-Saharan Africa and Southeast Asia. • Improving governance performance has significant impacts on poverty reduction. Abstract Deforestation, mainly caused by unsustainable agricultural expansion, results in a loss of biodiversity and an increase in greenhouse gas emissions, as well as impinges on local livelihoods. Countries’ governance performance, particularly with respect to property rights security, exerts significant impacts on land-use patterns by affecting agricultural yield-related technological investment and cropland expansion. This study aims to incorporate governance factors into a recursive agro-economic dynamic model to simulate governance impacts on land-use patterns at the global scale. Due to the difficulties of including governance indicators directly into numerical models, we use lending interest rates as discount rates to reflect risk-accounting factors associated with different governance scenarios. In addition to a reference scenario, three scenarios with high, low and mixed divergent discount rates are formed to represent weak, strong and fragmented governance. We find that weak governance leads to slower yield growth, increased cropland expansion and associated deforestation, mainly in Latin America, Sub-Saharan Africa, South Asia and Southeast Asia. This is associated with increasing food prices, particularly in Sub-Saharan Africa and Southeast Asia. By contrast, strong governance performance provides a stable political and economic situation which may bring down deforestation rates, stimulate investment in agricultural technologies, and induce fairly strong decreases in food prices. |
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0921-8009 |
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TradeM, ftnotmacsur |
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MA @ admin @ |
Serial |
5002 |
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