Robotized indoor phenotyping allows genomic prediction of adaptive traits in the field

Breeding for resilience to climate change requires considering adaptive traits such as plant architecture, stomatal conductance and growth, beyond the current selection for yield. Robotized indoor phenotyping allows measuring such traits at high throughput for speed breeding, but is often considered as non-relevant for field conditions. Here, we show that maize adaptive traits can be inferred in different fields, based on genotypic values obtained indoor and on environmental conditions in each considered field. The modelling of environmental effects allows translation from indoor to fields, but also from one field to another field. Furthermore, genotypic values of considered traits match between indoor and field conditions. Genomic prediction results in adequate ranking of genotypes for the tested traits, although with lesser precision for elite varieties presenting reduced phenotypic variability. Hence, it distinguishes genotypes with high or low values for adaptive traits, conferring either spender or conservative strategies for water use under future climates.

Physiological and genetic control of transpiration efficiency in African rice, Oryza glaberrima Steud

Improving crop water use efficiency, the amount of carbon assimilated as biomass per unit of water used by a plant, is of major importance as water for agriculture becomes scarcer. In rice, the genetic bases of transpiration efficiency, the derivation of water use efficiency at the whole-plant scale, and its putative component trait transpiration restriction under high evaporative demand remain unknown. These traits were measured in 2019 in a panel of 147 African rice (Oryza glaberrima) genotypes known to be potential sources of tolerance genes to biotic and abiotic stresses. Our results reveal that higher transpiration efficiency is associated with transpiration restriction in African rice. Detailed measurements in a subset of highly contrasted genotypes in terms of biomass accumulation and transpiration confirmed these associations and suggested that root to shoot ratio played an important role in transpiration restriction. Genome wide association studies identified marker-trait associations for transpiration response to evaporative demand, transpiration efficiency, and its residuals, with links to genes involved in water transport and cell wall patterning. Our data suggest that root-shoot partitioning is an important component of transpiration restriction that has a positive effect on transpiration efficiency in African rice. Both traits are heritable and define targets for breeding rice with improved water use strategies.

Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits

BACKGROUND

Characterizing plant genetic resources and their response to the environment through accurate measurement of relevant traits is crucial to genetics and breeding. Spatial organization of the maize ear provides insights into the response of grain yield to environmental conditions. Current automated methods for phenotyping the maize ear do not capture these spatial features.

RESULTS

We developed EARBOX, a low-cost, open-source system for automated phenotyping of maize ears. EARBOX integrates open-source technologies for both software and hardware that facilitate its deployment and improvement for specific research questions. The imaging platform consists of a customized box in which ears are repeatedly imaged as they rotate via motorized rollers. With deep learning based on convolutional neural networks, the image analysis algorithm uses a two-step procedure: ear-specific grain masks are first created and subsequently used to extract a range of trait data per ear, including ear shape and dimensions, the number of grains and their spatial organisation, and the distribution of grain dimensions along the ear. The reliability of each trait was validated against ground-truth data from manual measurements. Moreover, EARBOX derives novel traits, inaccessible through conventional methods, especially the distribution of grain dimensions along grain cohorts, relevant for ear morphogenesis, and the distribution of abortion frequency along the ear, relevant for plant response to stress, especially soil water deficit.

CONCLUSIONS

The proposed system provides robust and accurate measurements of maize ear traits including spatial features. Future developments include grain type and colour categorisation. This method opens avenues for high-throughput genetic or functional studies in the context of plant adaptation to a changing environment.

High-throughput phenotyping reveals differential transpiration behaviour within the banana wild relatives highlighting diversity in drought tolerance

Crop wild relatives, the closely related species of crops, may harbour potentially important sources of new allelic diversity for (a)biotic tolerance or resistance. However, to date, wild diversity is only poorly characterized and evaluated. Banana has a large wild diversity but only a narrow proportion is currently used in breeding programmes. The main objective of this study was to evaluate genotype-dependent transpiration responses in relation to the environment. By applying continuous high-throughput phenotyping, we were able to construct genotype-specific transpiration response models in relation to light, VPD and soil water potential. We characterized and evaluated six (sub)species and discerned four phenotypic clusters. Significant differences were observed in leaf area, cumulative transpiration and transpiration efficiency. We confirmed a general stomatal-driven 'isohydric' drought avoidance behaviour, but discovered genotypic differences in the onset and intensity of stomatal closure. We pinpointed crucial genotype-specific soil water potentials when drought avoidance mechanisms were initiated and when stress kicked in. Differences between (sub)species were dependent on environmental conditions, illustrating the need for high-throughput dynamic phenotyping, modelling and validation. We conclude that the banana wild relatives contain useful drought tolerance traits, emphasising the importance of their conservation and potential for use in breeding programmes.

Simulating the effect of flowering time on maize individual leaf area in contrasting environmental scenarios

The quality of yield prediction is linked to that of leaf area. We first analysed the consequences of flowering time and environmental conditions on the area of individual leaves in 127 genotypes presenting contrasting flowering times in fields of Europe, Mexico, and Kenya. Flowering time was the strongest determinant of leaf area. Combined with a detailed field experiment, this experiment showed a large effect of flowering time on the final leaf number and on the distribution of leaf growth rate and growth duration along leaf ranks, in terms of both length and width. Equations with a limited number of genetic parameters predicted the beginning, end, and maximum growth rate (length and width) for each leaf rank. The genotype-specific environmental effects were analysed with datasets in phenotyping platforms that assessed the effects (i) of the amount of intercepted light on leaf width, and (ii) of temperature, evaporative demand, and soil water potential on leaf elongation rate. The resulting model was successfully tested for 31 hybrids in 15 European and Mexican fields. It potentially allows prediction of the vertical distribution of leaf area of a large number of genotypes in contrasting field conditions, based on phenomics and on sensor networks.

To clean or not to clean phenotypic datasets for outlier plants in genetic analyses?

Based on case studies, we discuss the extent to which genome-wide association studies (GWAS) are affected by outlier plants, i.e. those deviating from the expected distribution on a multi-criteria basis. Using a raw dataset consisting of daily measurements of leaf area, biomass, and plant height for thousands of plants, we tested three different cleaning methods for their effects on genetic analyses. No-cleaning resulted in the highest number of dubious quantitative trait loci, especially at loci with highly unbalanced allelic frequencies. A trade-off was identified between the risk of false-positives (with no-cleaning and/or a low threshold for minor allele frequency) and the risk of missing interesting rare alleles. Cleaning can lower the risk of the latter by making it possible to choose a higher threshold in GWAS.

Genotyping-by-sequencing and SNP-arrays are complementary for detecting quantitative trait loci by tagging different haplotypes in association studies

BACKGROUND

Single Nucleotide Polymorphism (SNP) array and re-sequencing technologies have different properties (e.g. calling rate, minor allele frequency profile) and drawbacks (e.g. ascertainment bias). This lead us to study their complementarity and the consequences of using them separately or combined in diversity analyses and Genome-Wide Association Studies (GWAS). We performed GWAS on three traits (grain yield, plant height and male flowering time) measured in 22 environments on a panel of 247 F1 hybrids obtained by crossing 247 diverse dent maize inbred lines with a same flint line. The 247 lines were genotyped using three genotyping technologies (Genotyping-By-Sequencing, Illumina Infinium 50 K and Affymetrix Axiom 600 K arrays).

RESULTS

The effects of ascertainment bias of the 50 K and 600 K arrays were negligible for deciphering global genetic trends of diversity and for estimating relatedness in this panel. We developed an original approach based on linkage disequilibrium (LD) extent in order to determine whether SNPs significantly associated with a trait and that are physically linked should be considered as a single Quantitative Trait Locus (QTL) or several independent QTLs. Using this approach, we showed that the combination of the three technologies, which have different SNP distributions and densities, allowed us to detect more QTLs (gain in power) and potentially refine the localization of the causal polymorphisms (gain in resolution).

CONCLUSIONS

Conceptually different technologies are complementary for detecting QTLs by tagging different haplotypes in association studies. Considering LD, marker density and the combination of different technologies (SNP-arrays and re-sequencing), the genotypic data available were most likely enough to well represent polymorphisms in the centromeric regions, whereas using more markers would be beneficial for telomeric regions.

Changes in the vertical distribution of leaf area enhanced light interception efficiency in maize over generations of selection

Breeders select for yield, thereby indirectly selecting for traits that contribute to it. We tested if breeding has affected a range of traits involved in plant architecture and light interception, via the analysis of a panel of 60 maize hybrids released from 1950 to 2015. This was based on novel traits calculated from reconstructions derived from a phenotyping platform. The contribution of these traits to light interception was assessed in virtual field canopies composed of 3D plant reconstructions, with a model tested in a real field. Two categories of traits had different contributions to genetic progress. (a) The vertical distribution of leaf area had a high heritability and showed a marked trend over generations of selection. Leaf area tended to be located at lower positions in the canopy, thereby improving light penetration and distribution in the canopy. This potentially increased the carbon availability to ears, via the amount of light absorbed by the intermediate canopy layer. (b) Neither the horizontal distribution of leaves in the relation to plant rows nor the response of light interception to plant density showed appreciable trends with generations. Hence, among many architectural traits, the vertical distribution of leaf area was the main indirect target of selection.

Genomic prediction of maize yield across European environmental conditions

The development of germplasm adapted to changing climate is required to ensure food security^1,2. Genomic prediction is a powerful tool to evaluate many genotypes but performs poorly in contrasting environmental scenarios^3-7 (genotype × environment interaction), in spite of promising results for flowering time⁸. New avenues are opened by the development of sensor networks for environmental characterization in thousands of fields^9,10. We present a new strategy for germplasm evaluation under genotype × environment interaction. Yield was dissected in grain weight and number and genotype × environment interaction in these components was modeled as genotypic sensitivity to environmental drivers. Environments were characterized using genotype-specific indices computed from sensor data in each field and the progression of phenology calibrated for each genotype on a phenotyping platform. A whole-genome regression approach for the genotypic sensitivities led to accurate prediction of yield under genotype × environment interaction in a wide range of environmental scenarios, outperforming a benchmark approach.

What is cost-efficient phenotyping? Optimizing costs for different scenarios

Progress in remote sensing and robotic technologies decreases the hardware costs of phenotyping. Here, we first review cost-effective imaging devices and environmental sensors, and present a trade-off between investment and manpower costs. We then discuss the structure of costs in various real-world scenarios. Hand-held low-cost sensors are suitable for quick and infrequent plant diagnostic measurements. In experiments for genetic or agronomic analyses, (i) major costs arise from plant handling and manpower; (ii) the total costs per plant/microplot are similar in robotized platform or field experiments with drones, hand-held or robotized ground vehicles; (iii) the cost of vehicles carrying sensors represents only 5-26% of the total costs. These conclusions depend on the context, in particular for labor cost, the quantitative demand of phenotyping and the number of days available for phenotypic measurements due to climatic constraints. Data analysis represents 10-20% of total cost if pipelines have already been developed. A trade-off exists between the initial high cost of pipeline development and labor cost of manual operations. Overall, depending on the context and objsectives, "cost-effective" phenotyping may involve either low investment ("affordable phenotyping"), or initial high investments in sensors, vehicles and pipelines that result in higher quality and lower operational costs.

Carbon isotope composition, water use efficiency, and drought sensitivity are controlled by a common genomic segment in maize

KEY MESSAGE

A genomic segment on maize chromosome 7 influences carbon isotope composition, water use efficiency, and leaf growth sensitivity to drought, possibly by affecting stomatal properties. Climate change is expected to decrease water availability in many agricultural production areas around the globe. Therefore, plants with improved ability to grow under water deficit are urgently needed. We combined genetic, phenomic, and physiological approaches to understand the relationship between growth, stomatal conductance, water use efficiency, and carbon isotope composition in maize (Zea mays L.). Using near-isogenic lines derived from a maize introgression library, we analysed the effects of a genomic region previously identified as affecting carbon isotope composition. We show stability of trait expression over several years of field trials and demonstrate in the phenotyping platform Phenodyn that the same genomic region also influences the sensitivity of leaf growth to evaporative demand and soil water potential. Our results suggest that the studied genomic region affecting carbon isotope discrimination also harbours quantitative trait loci playing a role in maize drought sensitivity possibly via stomatal behaviour and development. We propose that the observed phenotypes collectively originate from altered stomatal conductance, presumably via abscisic acid.

Phenomics allows identification of genomic regions affecting maize stomatal conductance with conditional effects of water deficit and evaporative demand

Stomatal conductance is central for the trades-off between hydraulics and photosynthesis. We aimed at deciphering its genetic control and that of its responses to evaporative demand and water deficit, a nearly impossible task with gas exchanges measurements. Whole-plant stomatal conductance was estimated via inversion of the Penman-Monteith equation from data of transpiration and plant architecture collected in a phenotyping platform. We have analysed jointly 4 experiments with contrasting environmental conditions imposed to a panel of 254 maize hybrids. Estimated whole-plant stomatal conductance closely correlated with gas-exchange measurements and biomass accumulation rate. Sixteen robust quantitative trait loci (QTLs) were identified by genome wide association studies and co-located with QTLs of transpiration and biomass. Light, vapour pressure deficit, or soil water potential largely accounted for the differences in allelic effects between experiments, thereby providing strong hypotheses for mechanisms of stomatal control and a way to select relevant candidate genes among the 1-19 genes harboured by QTLs. The combination of allelic effects, as affected by environmental conditions, accounted for the variability of stomatal conductance across a range of hybrids and environmental conditions. This approach may therefore contribute to genetic analysis and prediction of stomatal control in diverse environments.

A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform

BACKGROUND

In maize, silks are hundreds of filaments that simultaneously emerge from the ear for collecting pollen over a period of 1-7 days, which largely determines grain number especially under water deficit. Silk growth is a major trait for drought tolerance in maize, but its phenotyping is difficult at throughputs needed for genetic analyses.

RESULTS

We have developed a reproducible pipeline that follows ear and silk growths every day for hundreds of plants, based on an ear detection algorithm that drives a robotized camera for obtaining detailed images of ears and silks. We first select, among 12 whole-plant side views, those best suited for detecting ear position. Images are segmented, the stem pixels are labelled and the ear position is identified based on changes in width along the stem. A mobile camera is then automatically positioned in real time at 30 cm from the ear, for a detailed picture in which silks are identified based on texture and colour. This allows analysis of the time course of ear and silk growths of thousands of plants. The pipeline was tested on a panel of 60 maize hybrids in the PHENOARCH phenotyping platform. Over 360 plants, ear position was correctly estimated in 86% of cases, before it could be visually assessed. Silk growth rate, estimated on all plants, decreased with time consistent with literature. The pipeline allowed clear identification of the effects of genotypes and water deficit on the rate and duration of silk growth.

CONCLUSIONS

The pipeline presented here, which combines computer vision, machine learning and robotics, provides a powerful tool for large-scale genetic analyses of the control of reproductive growth to changes in environmental conditions in a non-invasive and automatized way. It is available as Open Source software in the OpenAlea platform.

The growth of vegetative and reproductive structures (leaves and silks) respond similarly to hydraulic cues in maize

The elongation of styles and stigma (silks) of maize (Zea mays) flowers is rapid (1-3 mm h(-1) ), occurs over a short period and plays a pivotal role in reproductive success in adverse environments. Silk elongation rate was measured using displacement transducers in 350 plants of eight genotypes during eight experiments with varying evaporative demand and soil water status. Measured time courses revealed that silk elongation rate closely followed changes in soil water status and evaporative demand, with day-night alternations similar to those in leaves. Day-night alternations were steeper with high than with low plant transpiration rate, manipulated via evaporative demand or by covering part of the leaf area. Half times of changes in silk elongation rate upon changes in evaporative demand or soil water status were 10-30 min, similar to those in leaves. The sensitivity of silk elongation rate to xylem water potential was genetically linked to that of leaf elongation rate. Lines greatly differed for these sensitivities. These results are consistent with a common hydraulic control of expansive growth in vegetative and reproductive structures upon changes in environmental conditions via a close connection with the xylem water potential. They have important implications for breeding, modelling and phenotyping.

Genome-Wide Analysis of Yield in Europe: Allelic Effects Vary with Drought and Heat Scenarios

Assessing the genetic variability of plant performance under heat and drought scenarios can contribute to reduce the negative effects of climate change. We propose here an approach that consisted of (1) clustering time courses of environmental variables simulated by a crop model in current (35 years × 55 sites) and future conditions into six scenarios of temperature and water deficit as experienced by maize (Zea mays L.) plants; (2) performing 29 field experiments in contrasting conditions across Europe with 244 maize hybrids; (3) assigning individual experiments to scenarios based on environmental conditions as measured in each field experiment; frequencies of temperature scenarios in our experiments corresponded to future heat scenarios (+5°C); (4) analyzing the genetic variation of plant performance for each environmental scenario. Forty-eight quantitative trait loci (QTLs) of yield were identified by association genetics using a multi-environment multi-locus model. Eight and twelve QTLs were associated to tolerances to heat and drought stresses because they were specific to hot and dry scenarios, respectively, with low or even negative allelic effects in favorable scenarios. Twenty-four QTLs improved yield in favorable conditions but showed nonsignificant effects under stress; they were therefore associated with higher sensitivity. Our approach showed a pattern of QTL effects expressed as functions of environmental variables and scenarios, allowing us to suggest hypotheses for mechanisms and candidate genes underlying each QTL. It can be used for assessing the performance of genotypes and the contribution of genomic regions under current and future stress situations and to accelerate breeding for drought-prone environments.

High-throughput estimation of incident light, light interception and radiation-use efficiency of thousands of plants in a phenotyping platform

Light interception and radiation-use efficiency (RUE) are essential components of plant performance. Their genetic dissections require novel high-throughput phenotyping methods. We have developed a suite of methods to evaluate the spatial distribution of incident light, as experienced by hundreds of plants in a glasshouse, by simulating sunbeam trajectories through glasshouse structures every day of the year; the amount of light intercepted by maize (Zea mays) plants via a functional-structural model using three-dimensional (3D) reconstructions of each plant placed in a virtual scene reproducing the canopy in the glasshouse; and RUE, as the ratio of plant biomass to intercepted light. The spatial variation of direct and diffuse incident light in the glasshouse (up to 24%) was correctly predicted at the single-plant scale. Light interception largely varied between maize lines that differed in leaf angles (nearly stable between experiments) and area (highly variable between experiments). Estimated RUEs varied between maize lines, but were similar in two experiments with contrasting incident light. They closely correlated with measured gas exchanges. The methods proposed here identified reproducible traits that might be used in further field studies, thereby opening up the way for large-scale genetic analyses of the components of plant performance.

Testing the link between genome size and growth rate in maize

Little is known about the factors driving within species Genome Size (GS) variation. GS may be shaped indirectly by natural selection on development and adaptative traits. Because GS variation is particularly pronounced in maize, we have sampled 83 maize inbred lines from three well described genetic groups adapted to contrasted climate conditions: inbreds of tropical origin, Flint inbreds grown in temperate climates, and Dent inbreds distributed in the Corn Belt. As a proxy for growth rate, we measured the Leaf Elongation Rate maximum during nighttime (LER_max) as well as GS in all inbred lines. In addition we combined available and new nucleotide polymorphism data at 29,090 sites to characterize the genetic structure of our panel. We found significant variation for both LER_max and GS among groups defined by our genetic structuring. Tropicals displayed larger GS than Flints while Dents exhibited intermediate values. LER_max followed the opposite trend with greater growth rate in Flints than in Tropicals. In other words, LER_max and GS exhibited a significantly negative correlation (r = − 0.27). However, this correlation was driven by among-group variation rather than within-group variation—it was no longer significant after controlling for structure and kinship among inbreds. Our results indicate that selection on GS may have accompanied ancient maize diffusion from its center of origin, with large DNA content excluded from temperate areas. Whether GS has been targeted by more intense selection during modern breeding within groups remains an open question.

Genetic and physiological controls of growth under water deficit

The sensitivity of expansive growth to water deficit has a large genetic variability, which is higher than that of photosynthesis. It is observed in several species, with some genotypes stopping growth in a relatively wet soil, whereas others continue growing until the lower limit of soil-available water. The responses of growth to soil water deficit and evaporative demand share an appreciable part of their genetic control through the colocation of quantitative trait loci as do the responses of the growth of different organs to water deficit. This result may be caused by common mechanisms of action discussed in this paper (particularly, plant hydraulic properties). We propose that expansive growth, putatively linked to hydraulic processes, determines the sink strength under water deficit, whereas photosynthesis determines source strength. These findings have large consequences for plant modeling under water deficit and for the design of breeding programs.

The growths of leaves, shoots, roots and reproductive organs partly share their genetic control in maize plants

We have tested to what extent the growth ability of several organs of maize share a common genetic control. Every night, leaf elongation rate reaches a maximum value (LERmax ) that has a high heritability, is repeatable between experiments and is correlated with final leaf length. Firstly, we summarized quantitative trait loci (QTLs) of LERmax and of leaf length in three mapping populations. Among the 14 consensus QTLs (cQTLs) of leaf length, seven co-located with cQTLs of LERmax with consistent allelic effects. Nine cQTLs of LERmax (4% of the genome) were highly reliable and confirmed by introgression lines. We then compared these QTLs with those affecting the growths of leaves, shoots, roots or young reproductive organs, detected with the same mapping populations in three field experiments or in literature datasets. Five of the nine most reliable cQTLs of LERmax co-located with QTLs involved in the growth of other organs (but not in flowering time) with consistent allelic effects. Reciprocally, two-thirds of the 20 QTLs of growth of different organs co-located with cQTLs of LERmax . Hence, LERmax , as determined in a phenotyping platform, is an indicator of the growth ability of other organs of the plant in controlled or in-field conditions.

A common genetic determinism for sensitivities to soil water deficit and evaporative demand: meta-analysis of quantitative trait Loci and introgression lines of maize

Evaporative demand and soil water deficit equally contribute to water stress and to its effect on plant growth. We have compared the genetic architectures of the sensitivities of maize (Zea mays) leaf elongation rate with evaporative demand and soil water deficit. The former was measured via the response to leaf-to-air vapor pressure deficit in well-watered plants, the latter via the response to soil water potential in the absence of evaporative demand. Genetic analyses of each sensitivity were performed over 21 independent experiments with (1) three mapping populations, with temperate or tropical materials, (2) one population resulting from the introgression of a tropical drought-tolerant line in a temperate line, and (3) two introgression libraries genetically independent from mapping populations. A very large genetic variability was observed for both sensitivities. Some lines maintained leaf elongation at very high evaporative demand or water deficit, while others stopped elongation in mild conditions. A complex architecture arose from analyses of mapping populations, with 19 major meta-quantitative trait loci involving strong effects and/or more than one mapping population. A total of 68% of those quantitative trait loci affected sensitivities to both evaporative demand and soil water deficit. In introgressed lines, 73% of the tested genomic regions affected both sensitivities. To our knowledge, this study is the first genetic demonstration that hydraulic processes, which drive the response to evaporative demand, also have a large contribution to the genetic variability of plant growth under water deficit in a large range of genetic material.

Simulating the yield impacts of organ-level quantitative trait loci associated with drought response in maize: a "gene-to-phenotype" modeling approach

Under drought, substantial genotype-environment (G x E) interactions impede breeding progress for yield. Identifying genetic controls associated with yield response is confounded by poor genetic correlations across testing environments. Part of this problem is related to our inability to account for the interplay of genetic controls, physiological traits, and environmental conditions throughout the crop cycle. We propose a modeling approach to bridge this "gene-to-phenotype" gap. For maize under drought, we simulated the impact of quantitative trait loci (QTL) controlling two key processes (leaf and silk elongation) that influence crop growth, water use, and grain yield. Substantial G x E interaction for yield was simulated for hypothetical recombinant inbred lines (RILs) across different seasonal patterns of drought. QTL that accelerated leaf elongation caused an increase in crop leaf area and yield in well-watered or preflowering water deficit conditions, but a reduction in yield under terminal stresses (as such "leafy" genotypes prematurely exhausted the water supply). The QTL impact on yield was substantially enhanced by including pleiotropic effects of these QTL on silk elongation and on consequent grain set. The simulations obtained illustrated the difficulty of interpreting the genetic control of yield for genotypes influenced only by the additive effects of QTL associated with leaf and silk growth. The results highlight the potential of integrative simulation modeling for gene-to-phenotype prediction and for exploiting G x E interactions for complex traits such as drought tolerance.

Leaf growth rate per unit thermal time follows QTL-dependent daily patterns in hundreds of maize lines under naturally fluctuating conditions

We have analysed daily patterns of leaf elongation rate (LER) in large data sets with 318 genotypes placed in naturally fluctuating temperature and evaporative demand, and examined the effect of targeted alleles on these patterns. The method consisted, firstly, in expressing elongation rate per unit thermal time, so it became temperature independent; secondly, in a joint analysis of diurnal fluctuations of elongation rate and of micrometeorological conditions in several experiments, and finally, in a comparison of daily patterns between groups of genotypes possessing targeted alleles. (1) Conditions for using thermal time at a time step of 15 min were first tested successfully in 318 recombinant inbred lines (RILs) of three mapping populations. (2) An analysis of 1989 time courses revealed a robust daily pattern of LER per unit thermal time (LERth) over several experiments. LERth was constant during the night and was reproducible (for a given RIL) over up to 10 consecutive nights in different experiments. It declined rapidly during the early morning, closely following the daily pattern of transpiration rate. (3) Groups of RILs carrying alleles conferring a high response to temperature had markedly higher night-time plateau of LER than those with low responses. Groups of RILs with high response to evaporative demand had rapid decreases in elongation rate at the transition between night and day, while this decrease was slower in groups of RILs with low response. These results open the way for using kinetics of responses to environmental stimuli as a phenotyping tool in genetic analyses.

Are source and sink strengths genetically linked in maize plants subjected to water deficit? A QTL study of the responses of leaf growth and of Anthesis-Silking Interval to water deficit

Leaf growth and Anthesis-Silking Interval (ASI) are the main determinants of source and sink strengths of maize via their relations with light interception and yield, respectively. They depend on the abilities of leaves and silks to expand under fluctuating environmental conditions, so the possibility is raised that they may have a partly common genetic determinism. This possibility was tested in a mapping population which segregates for ASI. Maximum leaf elongation rate per unit thermal time (parameter a) and the slopes of its responses to evaporative demand and soil water status (parameters b and c) were measured in greenhouse and growth chamber experiments, in two series of 120 recombinant inbred lines (RILs) studied in 2004 and 2005 with 33 RILs in common both years. ASI was measured in three and five fields under well-watered conditions and water deficit, respectively. For each RIL, the maximum elongation rate per unit thermal time was reproducible over several experiments in well-watered plants. It was accounted for by five QTLs, among which three co-localized with QTLs of ASI of well-watered plants. The alleles conferring high leaf elongation rate conferred a low ASI (high silk elongation rate). The responses of leaf elongation rate to evaporative demand and to predawn leaf water potential were linear, allowing each RIL to be characterized by the slopes of these response curves. These slopes had three QTLs in common with ASI of plants under water deficit. The allele for leaf growth maintenance was, in all cases, that for shorter ASI (maintained silk elongation rate). By contrast, other regions influencing ASI had no influence on leaf growth. These results may have profound consequences for modelling the genotype x environment interaction and for designing drought-tolerant ideotypes.

Linking physiological and genetic analyses of the control of leaf growth under changing environmental conditions

Decrease in leaf growth rate under water deficit can be seen as an adaptive process. The analysis of its genetic variability is therefore important in the context of drought tolerance. Several mechanisms are widely believed to drive the reduction in leaf growth rate under water deficit, namely leaf carbon balance, incomplete turgor maintenance, and decrease in cell wall plasticity or in cell division rate, with contributions from hormones such as abscisic acid or ethylene. Each of these mechanisms is still controversial, and involves several families of genes. It is argued that gene regulatory networks are not feasible for modelling such complex systems. Leaf growth can be modelled via response curves to environmental conditions, which are considered as ‘meta-mechanisms’ at a higher degree of organisation. Response curves of leaf elongation rate to meristem temperature, atmospheric vapour pressure deficit, and soil water status were established in recombinant inbred lines (RILs) of maize in experiments carried out in the field and in the greenhouse. A quantitative trait locus (QTL) analysis was conducted on the slopes of these responses. Each parameter of the ecophysiological model could then be computed as the sum of QTL effects, allowing calculation of parameters of new RILs, either virtual or existing. Leaf elongation rates of new RILS were simulated and were similar to measurements in a growth chamber experiment. This opens the way to the simulation of virtual genotypes, known only by their alleles, in any climatic scenario. Each genotype is therefore represented by a set of response parameters, valid in a large range of conditions and deduced from the alleles at QTLs.

Maize introduction into Europe: the history reviewed in the light of molecular data

The resolution that can be obtained from molecular genetic markers affords new prospects for understanding the dispersion of agricultural species from their primary origin centres. In order to study the introduction and the dispersion of maize in Europe, we have characterised a large and representative set of maize populations of both American and European origins for their variation at 29 restriction fragment length polymorphism loci. Polymorphism was higher for American populations than for European populations (respectively, 12.3 and 9.6 alleles per locus, on average), and only a few alleles were specific to European populations. Investigation of genetic similarity between populations from both continents made it possible to identify various types of American maize introduced into Europe at different times or in different places and which have given rise to distinctive European races. Beyond confirming the importance of Caribbean germplasm, the first maize type to be introduced into Europe, this research revealed that introductions of Northern American flint populations have played a key role in the adaptation of maize to the European climate. According to a detailed historical investigation, the introduction of these populations must have occurred shortly after the discovery of the New World.