Modelling Memory: do crop models need to become nostalgic?

International audienceIncreased frequency of stress events such as heat waves has been observed for the last decades. Based on the last IPCC report, they are expected to be more frequent, to last longer and to increase in intensity during the reproductive phase of economically important crops. Many recent studies pointed out induced memory effects of stressing events when plants are challenged several times with similar stresses throughout the crop season. These memory effects were shown to be potentially beneficial since the plants are 'primed' and thus more prepared to develop an earlier, more rapid, intense and/or sensitive response when the stress recurs [1]. Therefore, the new climatic patterns prompts to take into account stress memory into predictive crop modelling approaches so as to estimate the effects of repeated stresses and their consequences on crop yield, quality of harvested products. During the last decades, the use of crop models have been enlarged to climate change driven predictions [2]. While evidence for improving crop climate models and especially the temperature response functions in order to reduce uncertainty in yield simulations before any decision making in agriculture, no modelling studies have attempted to decipher and interpret simulation bias in the light of stress memory nor they focused on methodologies to take into account stress memory effects

Modelling Memory: do crop models need to become nostalgic?

International audienceIncreased frequency of stress events such as heat waves has been observed for the last decades. Based on the last IPCC report, they are expected to be more frequent, to last longer and to increase in intensity during the reproductive phase of economically important crops. Many recent studies pointed out induced memory effects of stressing events when plants are challenged several times with similar stresses throughout the crop season. These memory effects were shown to be potentially beneficial since the plants are 'primed' and thus more prepared to develop an earlier, more rapid, intense and/or sensitive response when the stress recurs [1]. Therefore, the new climatic patterns prompts to take into account stress memory into predictive crop modelling approaches so as to estimate the effects of repeated stresses and their consequences on crop yield, quality of harvested products. During the last decades, the use of crop models have been enlarged to climate change driven predictions [2]. While evidence for improving crop climate models and especially the temperature response functions in order to reduce uncertainty in yield simulations before any decision making in agriculture, no modelling studies have attempted to decipher and interpret simulation bias in the light of stress memory nor they focused on methodologies to take into account stress memory effects

QTL analysis of seed germination and pre-emergence growth at extreme temperatures in Medicago truncatula

Enhancing the knowledge on the genetic basis of germination and heterotrophic growth at extreme temperatures is of major importance for improving crop establishment. A quantitative trait loci (QTL) analysis was carried out at sub- and supra-optimal temperatures at these early stages in the model Legume Medicago truncatula. On the basis of an ecophysiological model framework, two populations of recombinant inbred lines were chosen for the contrasting behaviours of parental lines: LR5 at sub-optimal temperatures (5 or 10°C) and LR4 at a supra-optimal temperature (20°C). Seed masses were measured in all lines. For LR5, germination rates and hypocotyl growth were measured by hand, whereas for LR4, imbibition and germination rates as well as early embryonic axis growth were measured using an automated image capture and analysis device. QTLs were found for all traits. The phenotyping framework we defined for measuring variables, distinguished stages and enabled identification of distinct QTLs for seed mass (chromosomes 1, 5, 7 and 8), imbibition (chromosome 4), germination (chromosomes 3, 5, 7 and 8) and heterotrophic growth (chromosomes 1, 2, 3 and 8). The three QTL identified for hypocotyl length at sub-optimal temperature explained the largest part of the phenotypic variation (60% together). One digenic interaction was found for hypocotyl width at sub-optimal temperature and the loci involved were linked to additive QTLs for hypocotyl elongation at low temperature. Together with working on a model plant, this approach facilitated the identification of genes specific to each stage that could provide reliable markers for assisting selection and improving crop establishment. With this aim in view, an initial set of putative candidate genes was identified in the light of the role of abscissic acid/gibberellin balance in regulating germination at high temperatures (e.g. ABI4, ABI5), the molecular cascade in response to cold stress (e.g. CBF1, ICE1) and hypotheses on changes in cell elongation (e.g. GASA1, AtEXPA11) with changes in temperatures based on studies at the whole plant scale

How does early leaf reduction impact on development of adaptation strategies to low phosphorus availability in Zea Mays L.?

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How does early leaf reduction impact on development of adaptation strategies to low phosphorus availability in Zea Mays L.?

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Impact of early growth traits on further genotypic performance during the vegetative growth of maize (Zea mays L.) in response to phosphorous (P) availability

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Simulation analysis for optimizing S fertilization in a context of increased spring temperatures with SuMoToRI model

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La conception des idéotypes,

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Epigenetics for crop adaptation to climate change

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