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Author |
Lehtonen, H. |
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Title |
Evaluating adaptation and the production development of Finnish agriculture in climate and global change |
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Journal Article |
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Year |
2015 |
Publication |
Agricultural and Food Science |
Abbreviated Journal |
Agricultural and Food Science |
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Volume |
24 |
Issue |
3 |
Pages |
219-234 |
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Keywords |
agricultural sector modelling; economic adjustment; global prices; climate change; finnish agriculture; crop production; land-use; challenge; ensembles; Finland; Europe; policy |
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Abstract |
Agricultural product prices and policies influence the development of crop yields under climate change through farm level management decisions. On this basis, five main scenarios were specified for agricultural commodity prices and crop yields. An economic agricultural sector model was used in order to assess the impacts of the scenarios on production, land use and farm income in Finland. The results suggest that falling crop yields, if realized due to low prices and restrictive policies, will result in decreasing crop and livestock production and increasing nutrient surplus. Slowly increasing crop yields could stabilise production and increase farm income. Significantly higher crop prices and yields are required, however, for any marked increase in production in Finland. Cereals production would increase relatively more than livestock production, if there were high prices for agricultural products. This is explained by abundant land resources, a high opportunity cost of labour and policies maintaining current dairy and beef production. |
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2016-07-22 |
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English |
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ISSN |
1459-6067 |
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Article |
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TradeM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
4750 |
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Author |
Rötter, R.P.; Tao, F.; Höhn, J.G.; Palosuo, T. |
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Title |
Use of crop simulation modelling to aid ideotype design of future cereal cultivars |
Type |
Journal Article |
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Year |
2015 |
Publication |
Journal of Experimental Botany |
Abbreviated Journal |
J. Experim. Bot. |
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Volume |
66 |
Issue |
12 |
Pages |
3463-3476 |
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Keywords |
Breeding/*methods; Climate Change; *Computer Simulation; Ecotype; Edible Grain/*growth & development; *Models, Theoretical; cereals; climate extremes; crop growth simulation; ensemble modelling; future cultivars; genetic modelling; ideotype breeding; model improvement; model-aided design |
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Abstract |
A major challenge of the 21st century is to achieve food supply security under a changing climate and roughly a doubling in food demand by 2050 compared to present, the majority of which needs to be met by the cereals wheat, rice, maize, and barley. Future harvests are expected to be especially threatened through increased frequency and severity of extreme events, such as heat waves and drought, that pose particular challenges to plant breeders and crop scientists. Process-based crop models developed for simulating interactions between genotype, environment, and management are widely applied to assess impacts of environmental change on crop yield potentials, phenology, water use, etc. During the last decades, crop simulation has become important for supporting plant breeding, in particular in designing ideotypes, i.e. ‘model plants’, for different crops and cultivation environments. In this review we (i) examine the main limitations of crop simulation modelling for supporting ideotype breeding, (ii) describe developments in cultivar traits in response to climate variations, and (iii) present examples of how crop simulation has supported evaluation and design of cereal cultivars for future conditions. An early success story for rice demonstrates the potential of crop simulation modelling for ideotype breeding. Combining conventional crop simulation with new breeding methods and genetic modelling holds promise to accelerate delivery of future cereal cultivars for different environments. Robustness of model-aided ideotype design can further be enhanced through continued improvements of simulation models to better capture effects of extremes and the use of multi-model ensembles. |
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2016-10-31 |
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0022-0957 1460-2431 |
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Review |
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CropM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
4804 |
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Author |
Ruiz-Ramos, M.; Rodriguez, A.; Dosio, A.; Goodess, C.M.; Harpham, C.; Minguez, M.I.; Sanchez, E. |
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Title |
Comparing correction methods of RCM outputs for improving crop impact projections in the Iberian Peninsula for 21st century |
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Journal Article |
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Year |
2016 |
Publication |
Climatic Change |
Abbreviated Journal |
Clim. Change |
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Volume |
134 |
Issue |
1-2 |
Pages |
283-297 |
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Keywords |
regional climate model; bias correction; weather generator; circulation model; simulations; temperature; precipitation; ensemble; uncertainty; extremes |
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Abstract |
Assessment of climate change impacts on crops in regions of complex orography such as the Iberian Peninsula (IP) requires climate model output which is able to describe accurately the observed climate. The high resolution of output provided by Regional Climate Models (RCMs) is expected to be a suitable tool to describe regional and local climatic features, although their simulation results may still present biases. For these reasons, we compared several post-processing methods to correct or reduce the biases of RCM simulations from the ENSEMBLES project for the IP. The bias-corrected datasets were also evaluated in terms of their applicability and consequences in improving the results of a crop model to simulate maize growth and development at two IP locations, using this crop as a reference for summer cropping systems in the region. The use of bias-corrected climate runs improved crop phenology and yield simulation overall and reduced the inter-model variability and thus the uncertainty. The number of observational stations underlying each reference observational dataset used to correct the bias affected the correction performance. Although no single technique showed to be the best one, some methods proved to be more adequate for small initial biases, while others were useful when initial biases were so large as to prevent data application for impact studies. An initial evaluation of the climate data, the bias correction/reduction method and the consequences for impact assessment would be needed to design the most robust, reduced uncertainty ensemble for a specific combination of location, crop, and crop management. |
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2016-10-31 |
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ISSN |
0165-0009 |
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Article |
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CropM, ft_macsur |
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Call Number |
MA @ admin @ |
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4805 |
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Author |
Bindi, M.; Palosuo, T.; Trnka, M.; Semenov, M.A. |
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Title |
Modelling climate change impacts on crop production for food security INTRODUCTION |
Type |
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 |
3-5 |
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Keywords |
Crop production; Climate change impact and adaptation assessments; Upscaling; Model ensembles |
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Abstract |
Process-based crop models that synthesise the latest scientific understanding of biophysical processes are currently the primary scientific tools available to assess potential impacts of climate change on crop production. Important obstacles are still present, however, and must be overcome for improving crop modelling application in integrated assessments of risk, of sustainability and of crop-production resilience in the face of climate change (e.g. uncertainty analysis, model integration, etc.). The research networks MACSUR and AGMIP organised the CropM International Symposium and Workshop in Oslo, on 10-12 February 2014, and present this CR Special, discussing the state-of-the-art-as well as future perspectives-of crop modelling applications in climate change risk assessment, including the challenges of integrated assessments for the agricultural sector. |
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2016-10-31 |
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English |
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Series Editor |
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Series Volume |
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Edition |
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ISSN |
0936-577x |
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Editorial Material |
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Notes |
CropM, ftnotmacsur |
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Call Number |
MA @ admin @ |
Serial |
4785 |
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Author |
Ruane, A.C.; Hudson, N.I.; Asseng, S.; Camarrano, D.; Ewert, F.; Martre, P.; Boote, K.J.; Thorburn, P.J.; Aggarwal, P.K.; Angulo, C.; Basso, B.; Bertuzzi, P.; Biernath, C.; Brisson, N.; Challinor, &rew J.; Doltra, J.; Gayler, S.; Goldberg, R.; Grant, R.F.; Heng, L.; Hooker, J.; Hunt, L.A.; Ingwersen, J.; Izaurralde, R.C.; Kersebaum, K.C.; Kumar, S.N.; Müller, C.; Nendel, C.; O’Leary, G.; Olesen, J.E.; Osborne, T.M.; Palosuo, T.; Priesack, E.; Ripoche, D.; Rötter, R.P.; Semenov, M.A.; Shcherbak, I.; Steduto, P.; Stöckle, C.O.; Stratonovitch, P.; Streck, T.; Supit, I.; Tao, F.; Travasso, M.; Waha, K.; Wallach, D.; White, J.W.; Wolf, J. |
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Title |
Multi-wheat-model ensemble responses to interannual climate variability |
Type |
Journal Article |
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Year |
2016 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
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Volume |
81 |
Issue |
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Pages |
86-101 |
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Keywords |
Crop modeling; Uncertainty; Multi-model ensemble; Wheat; AgMIP; Climate; impacts; Temperature; Precipitation; lnterannual variability; simulation-model; crop model; nitrogen dynamics; winter-wheat; large-area; systems simulation; farming systems; yield response; growth; water |
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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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English |
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ISSN |
1364-8152 |
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CropM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
4769 |
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