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Author |
Pilbeam, D.J. |
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Title  |
Breeding crops for improved mineral nutrition under climate change conditions |
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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 |
3511-3421 |
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Keywords |
Breeding/*methods; *Climate Change; Crops, Agricultural/*growth & development; Environment; Minerals/*metabolism; *Nutritional Physiological Phenomena; Micronutrient; nitrogen; nutrient availability; nutrient use efficiency; phosphorus; quantitative trait loci (QTLs) |
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Improvements in understanding how climate change may influence chemical and physical processes in soils, how this may affect nutrient availability, and how plants may respond to changed availability of nutrients will influence crop breeding programmes. The effects of increased atmospheric CO2 and warmer temperatures, both individually and combined, on soil microbial activity, including mycorrhizas and N-fixing organisms, are evaluated, together with their implications for nutrient availability. Potential changes to plant growth, and the combined effects of soil and plant changes on nutrient uptake, are discussed. The organization of research on the efficient use of macro- and micronutrients by crops under climate change conditions is outlined, including analysis of QTLs for nutrient efficiency. Suggestions for how the information gained can be used in plant breeding programmes are given. |
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English |
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1460-2431; 0022-0957 |
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Review |
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CropM |
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MA @ admin @ |
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4575 |
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Author |
Höglind, M.; Persson, T.; van Oijen, M. |
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Title |
Breeding forage grasses: simulation modelling as a tool to identify important cultivar characteristics for winter survival and yield under future climate conditions in Norway |
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Conference Article |
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2014 |
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CropM |
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MACSUR CropM International Symposium and Workshop: Modelling climate change impacts on crop production for food security, Oslo, Norway, 2014-02-10 to 2014-02-12 |
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MA @ admin @ |
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2487 |
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Author |
Höglind, M.; Persson, T.; van Oijen, M. |
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Title |
Breeding forage grasses: simulation modelling as a tool to identify important cultivar characteristics for winter survival and yield under future climate conditions in Norway |
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Conference Article |
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2014 |
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CropM |
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Conference on Genetic Resources for Food and Agriculture in a Changing Climate, Lillehammer, Norway., 2014-01-27 to 2014-01-29 |
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MA @ admin @ |
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2488 |
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Author |
Acutis, M.; Bellocchi, G. |
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Title |
Briefing on CropM-LiveM model intercomparison protocol |
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Conference Article |
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2013 |
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CropM |
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JPI FACCE MACSUR CropM and LiveM cross-cutting activity Helsinki, Finland, 2013-05-06 to 2013-05-06 |
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no |
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Call Number |
MA @ admin @ |
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2272 |
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Author |
Höglind, M.; the partners of LiveM task L1.3 |
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Title |
Bringing together grassland and farm scale modelling. Part 1. Characterizing grasslands in farm scale modelling |
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Report |
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2017 |
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FACCE MACSUR Reports |
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10 |
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L1.3-D |
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This report provides an overview of how grasslands are represented in six different farmscale models represented in MACSUR. A survey was conducted, followed by a workshop in which modellers discussed the results of the survey, and identified research challenges and knowledge gaps. The workshop was attended by grassland as well as livestock specialists. The investigated models differed largely with respect to how grasslands were represented, e.g. as regards weather and management factors accounted for, spatial and temporal resolution, and output variables. All models had grassland modules that simulate DM yield and herbage N content (or crude protein (CP) content = N content x 6.25). Many models also simulate P content, whereas only one simulate K content. About half of the model simulate herbage energy value and/or herbage fibre content and fibre and/or dry matter digestibility. Critical input data required from grassland models to simulate ruminant productivity and GHG emissions at farm scale was identified by the workshop participants. The different types of input data required were ranked in order of importance as regards their influence on important system outputs. For simulation of ruminant productivity and GHG emissions, herbage DM yield was ranked as the most important input variable from grassland models, followed by CP content together with at least one variable describing herbage fibre characteristics. These findings suggest that work on improving the ability of the current grassland models with respect to simulation of fibre/energy should be prioritized in farm-scale modelling aiming at quantifying livestock production and GHG emissions under different management regimes and climate conditions. More work is also needed on model evaluation, a task that has not been prioritized yet for some models. |
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LiveM |
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MA @ admin @ |
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4957 |
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