| Records |
Author  |
Barber, H.M.; Lukac, M.; Simmonds, J.; Semenov, M.A.; Gooding, M.J. |
| Title |
Temporally and Genetically Discrete Periods of Wheat Sensitivity to High Temperature |
Type |
Journal Article |
| Year |
2017 |
Publication |
Frontiers in Plant Science |
Abbreviated Journal |
Front. Plant Sci |
| Volume |
8 |
Issue |
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Pages |
51 |
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| Abstract |
Successive single day transfers of pot-grown wheat to high temperature (35/30°C day/night) replicated controlled environments, from the second node detectable to the milky-ripe growth stages, provides the strongest available evidence that the fertility of wheat can be highly vulnerable to heat stress during two discrete peak periods of susceptibility: early booting [decimal growth stage (GS) 41-45] and early anthesis (GS 61-65). A double Gaussian fitted simultaneously to grain number and weight data from two contrasting elite lines (Renesansa, listed in Serbia, Ppd-D1a, Rht8; Savannah, listed in UK, Ppd-D1b, Rht-D1b) identified peak periods of main stem susceptibility centered on 3 (s.e. = 0.82) and 18 (s.e. = 0.55) days (mean daily temperature = 14.3°C) pre-GS 65 for both cultivars. Severity of effect depended on genotype, growth stage and their interaction: grain set relative to that achieved at 20/15°C dropped below 80% for Savannah at booting and Renesansa at anthesis. Savannah was relatively tolerant to heat stress at anthesis. A further experiment including 62 lines of the mapping, doubled-haploid progeny of Renesansa × Savannah found tolerance at anthesis to be associated with Ppd-D1b, Rht-D1b, and a QTL from Renesansa on chromosome 2A. None of the relevant markers were associated with tolerance during booting. Rht8 was never associated with heat stress tolerance, a lack of effect confirmed in a further experiment where Rht8 was included in a comparison of near isogenic lines in a cv. Paragon background. Some compensatory increases in mean grain weight were observed, but only when stress was applied during booting and only where Ppd-D1a was absent. |
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1664-462x |
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CropM, ftnotmacsur |
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no |
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MA @ admin @ |
Serial |
4974 |
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Barbier-Brygoo, H.; Chilliard, Y.; Durand, J.-L.; Elmayan, T.; Goldringer, I.; Porter, J.R. |
| Title |
Rapport du groupe de traveil sur la Propriétè Intellectuelle dans le végétal, du conseil scientifique nationale de l’INRA |
Type |
Report |
| Year |
2014 |
Publication |
Rapport de L’INRA Conseil Scientifique |
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43 |
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CropM |
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Paris, France |
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MA @ admin @ |
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2074 |
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Bassu, S.; Brisson, N.; Durand, J.-L.; Boote, K.; Lizaso, J.; Jones, J.W.; Rosenzweig, C.; Ruane, A.C.; Adam, M.; Baron, C.; Basso, B.; Biernath, C.; Boogaard, H.; Conijn, S.; Corbeels, M.; Deryng, D.; De Sanctis, G.; Gayler, S.; Grassini, P.; Hatfield, J.; Hoek, S.; Izaurralde, C.; Jongschaap, R.; Kemanian, A.R.; Kersebaum, K.C.; Kim, S.-H.; Kumar, N.S.; Makowski, D.; Müller, C.; Nendel, C.; Priesack, E.; Pravia, M.V.; Sau, F.; Shcherbak, I.; Tao, F.; Teixeira, E.; Timlin, D.; Waha, K. |
| Title |
How do various maize crop models vary in their responses to climate change factors |
Type |
Journal Article |
| Year |
2014 |
Publication |
Global Change Biology |
Abbreviated Journal |
Glob. Chang. Biol. |
| Volume |
20 |
Issue |
7 |
Pages |
2301-2320 |
| Keywords |
Carbon Dioxide/metabolism; *Climate Change; Crops, Agricultural/growth & development/metabolism; Geography; Models, Biological; Temperature; Water/*metabolism; Zea mays/*growth & development/*metabolism; AgMIP; [Co2]; climate; maize; model intercomparison; simulation; uncertainty |
| Abstract |
Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2 ], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per °C. Doubling [CO2 ] from 360 to 720 μmol mol(-1) increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2 ] among models. Model responses to temperature and [CO2 ] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information. |
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English |
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1354-1013 |
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CropM |
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no |
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MA @ admin @ |
Serial |
4510 |
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| Author |
Bellocchi, G.; Ma, S.; Köchy, M.; Braunmiller, K. |
| Title |
Datasets classification and criteria for data requirements |
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Report |
| Year |
2013 |
Publication |
FACCE MACSUR Reports |
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2 |
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Pages |
D-L2.1.2 |
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This deliverable focuses on the collation, screening, and consolidation of data for selected grassland sites in Europe and peri-Mediterranean regions. No Label |
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MA @ admin @ |
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2245 |
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Bellocchi, G.; Ma, S.; Köchy, M.; Braunmiller, K. |
| Title |
Identified grassland-livestock production systems and related models |
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Report |
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2013 |
Publication |
FACCE MACSUR Reports |
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2 |
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D-L2.1.1 |
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This report describes grassland-livestock production systems, as selected for model-basedstudies. A list of grassland models was identified for evaluation against such datasets(WP2) and application at reference farm (WP3) and regions (WP4) across Europe and peri-European countries. No Label |
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MA @ admin @ |
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2244 |
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