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Tao, F.; Rötter, R.P.; Palosuo, T.; Höhn, J.; Peltonen-Sainio, P.; Rajala, A.; Salo, T. |
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Title |
Assessing climate effects on wheat yield and water use in Finland using a super-ensemble-based probabilistic approach |
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Journal Article |
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Year |
2015 |
Publication |
Climate Research |
Abbreviated Journal |
Clim. Res. |
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Volume |
65 |
Issue |
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Pages |
23-37 |
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Keywords  |
adaptation; drought; evapotranspiration; heat stress; risk; uncertainties; northern agriculture; model; weather; variability; precipitation; uncertainty; adaptation; simulation; dynamics; impacts |
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Abstract |
We adapted a large area crop model, MCWLA-Wheat, to winter wheat Triticum aestivum L. and spring wheat in Finland. We then applied Bayesian probability inversion and a Markov Chain Monte Carlo technique to analyze uncertainties in parameter estimations and to optimize parameters. Finally, a super-ensemble-based probabilistic projection system was updated and applied to project the effects of climate change on wheat productivity and water use in Finland. The system used 6 climate scenarios and 20 sets of crop model parameters. We projected spatiotemporal changes of wheat productivity and water use due to climate change/variability during 2021-2040, 2041-2070, and 2071-2100. The results indicate that with a high probability wheat yields will increase substantially in Finland under the tested climate change scenarios, and spring wheat can benefit more from climate change than winter wheat. Nevertheless, in some areas of southern Finland, wheat production will face increasing risk of high temperature and drought, which can offset the benefits of climate change on wheat yield, resulting in an increase in yield variability and about 30% probability of yield decrease for spring wheat. Compared with spring wheat, the development, photosynthesis, and consequently yield will be much less enhanced for winter wheat, which, together with the risk of extreme weather, will result in an up to 56% probability of yield decrease in eastern parts of Finland. Our study explicitly para meterized the effects of extreme temperature and drought stress on wheat yields, and accounted for a wide range of wheat cultivars with contrasting phenological characteristics and thermal requirements. |
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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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4667 |
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Holman, I.P.; Brown, C.; Carter, T.R.; Harrison, P.A.; Rounsevell, M. |
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Title |
Improving the representation of adaptation in climate change impact models |
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Journal Article |
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Year |
2019 |
Publication |
Regional Environmental Change |
Abbreviated Journal |
Reg. Environ. Change |
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19 |
Issue |
3 |
Pages |
711-721 |
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Keywords |
Adaptive capacity; Limits; Water; Land; Decision making; Integrated assessment; Land-Cover Change; Global Change; River-Basin; Integrated Assessment; Adaptive Capacity; Vulnerability; Variability; Precautionary; Agriculture; Management |
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Climate change adaptation is a complex human process, framed by uncertainties and constraints, which is difficult to capture in existing assessment models. Attempts to improve model representations are hampered by a shortage of systematic descriptions of adaptation processes and their relevance to models. This paper reviews the scientific literature to investigate conceptualisations and models of climate change adaptation, and the ways in which representation of adaptation in models can be improved. The review shows that real-world adaptive responses can be differentiated along a number of dimensions including intent or purpose, timescale, spatial scale, beneficiaries and providers, type of action, and sector. However, models of climate change consequences for land use and water management currently provide poor coverage of these dimensions, instead modelling adaptation in an artificial and subjective manner. While different modelling approaches do capture distinct aspects of the adaptive process, they have done so in relative isolation, without producing improved unified representations. Furthermore, adaptation is often assumed to be objective, effective and consistent through time, with only a minority of models taking account of the human decisions underpinning the choice of adaptation measures (14%), the triggers that motivate actions (38%) or the time-lags and constraints that may limit their uptake and effectiveness (14%). No models included adaptation to take advantage of beneficial opportunities of climate change. Based on these insights, transferable recommendations are made on directions for future model development that may enhance realism within models, while also advancing our understanding of the processes and effectiveness of adaptation to a changing climate. |
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2019-04-27 |
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1436-3798 |
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TradeM, ft_macsur |
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MA @ admin @ |
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5220 |
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Dietrich, J.P.; Popp, A.; Lotze-Campen, H. |
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Reducing the loss of information and gaining accuracy with clustering methods in a global land-use model |
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Journal Article |
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2013 |
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Ecological Modelling |
Abbreviated Journal |
Ecol. Model. |
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263 |
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233-243 |
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Keywords |
aggregation; downscaling; clustering; information conservation; land use model; scale; scales; agriculture; simulation; dynamics; pattern |
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Global land-use models have to deal with processes on several spatial scales, ranging from the global scale down to the farm level. The increasing complexity of modern land-use models combined with the problem of limited computational resources represents a challenge to modelers. One solution of this problem is to perform spatial aggregation based on a regular grid or administrative units such as countries. Unfortunately this type of aggregation flattens many regional differences and produces a homogenized map of the world. In this paper we present an alternative aggregation approach using clustering methods. Clustering reduces the loss of information due to aggregation by choosing an appropriate aggregation pattern. We investigate different clustering methods, examining their quality in terms of information conservation. Our results indicate that clustering is always a good choice and preferable compared to grid-based aggregation. Although all the clustering methods we tested delivered a higher degree of information conservation than grid-based aggregation, the choice of clustering method is not arbitrary. Comparing outputs of a model fed with original data and a model fed with aggregated data, bottom-up clustering delivered the best results for the whole range of numbers of clusters tested. (C) 2013 Elsevier B.V. All rights reserved. |
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0304-3800 |
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TradeM |
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MA @ admin @ |
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4488 |
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Author |
Bojar, W.; Knopik, L.; Żarski, J.; Sławiński, C.; Baranowski, P.; Żarski, W. |
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Title |
Impact of extreme climate changes on the forecasted agriculture production |
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Journal Article |
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2014 |
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Acta Agrophysica |
Abbreviated Journal |
Acta Agrophysica |
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21 |
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4 |
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415-431 |
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agricultural economics; agriculture; climate change; crop production; integrating assessments |
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The paper presents general characteristics of resources and outputs of agriculture in the Kujawsko-Pomorskie and Lubelskie Regions, based on statistical databases and literature review. Some specific features of the regions, with special consideration for the predicted extreme climate changes, are also included. Next, some statistically significant dependencies between the climatic parameters and yields of selected important crops in the abovementioned regions were worked out on the basis of empirical survey conducted in the University of Technology and Life Sciences, Bydgoszcz, and the Institute of Agrophysics in Lublin. Creating an appropriate method of forecasting long series of ten days without precipitation was necessary to find the desired dependencies. Third, some efforts were taken to make integrated assessments of forecast agricultural outputs influenced by climate extreme phenomena on the basis of the yield-precipitation relations obtained and on the data coming from wide area model regional outputs such as prices of farmland and produce. |
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CropM, TradeM, ft_macsur |
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MA @ admin @ |
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4619 |
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Elliott, J.; Deryng, D.; Müller, C.; Frieler, K.; Konzmann, M.; Gerten, D.; Glotter, M.; Flörke, M.; Wada, Y.; Best, N.; Eisner, S.; Fekete, B.M.; Folberth, C.; Foster, I.; Gosling, S.N.; Haddeland, I.; Khabarov, N.; Ludwig, F.; Masaki, Y.; Olin, S.; Rosenzweig, C.; Ruane, A.C.; Satoh, Y.; Schmid, E.; Stacke, T.; Tang, Q.; Wisser, D. |
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Constraints and potentials of future irrigation water availability on agricultural production under climate change |
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Journal Article |
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2013 |
Publication |
Proceedings of the National Academy of Sciences of the United States of America |
Abbreviated Journal |
Proc. Natl. Acad. Sci. U. S. A. |
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111 |
Issue |
9 |
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3239-3244 |
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Agricultural Irrigation/economics/*methods; Agriculture/economics/*methods; Carbon Dioxide/analysis; *Climate Change; Computer Simulation; Forecasting; *Models, Theoretical; Water Supply/*statistics & numerical data; adaptation; agriculture; hydrology; uncertainty |
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We compare ensembles of water supply and demand projections from 10 global hydrological models and six global gridded crop models. These are produced as part of the Inter-Sectoral Impacts Model Intercomparison Project, with coordination from the Agricultural Model Intercomparison and Improvement Project, and driven by outputs of general circulation models run under representative concentration pathway 8.5 as part of the Fifth Coupled Model Intercomparison Project. Models project that direct climate impacts to maize, soybean, wheat, and rice involve losses of 400-1,400 Pcal (8-24% of present-day total) when CO2 fertilization effects are accounted for or 1,400-2,600 Pcal (24-43%) otherwise. Freshwater limitations in some irrigated regions (western United States; China; and West, South, and Central Asia) could necessitate the reversion of 20-60 Mha of cropland from irrigated to rainfed management by end-of-century, and a further loss of 600-2,900 Pcal of food production. In other regions (northern/eastern United States, parts of South America, much of Europe, and South East Asia) surplus water supply could in principle support a net increase in irrigation, although substantial investments in irrigation infrastructure would be required. |
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2016-10-31 |
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0027-8424 1091-6490 |
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CropM |
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MA @ admin @ |
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4790 |
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