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

Exploring the genetic architecture of powdery mildew resistance in wheat through QTL meta-analysis

Powdery mildew (PM), caused by Blumeria graminis f. sp. tritici, poses a significant threat to wheat production, necessitating the development of genetically resistant varieties for long-term control. Therefore, exploring genetic architecture of PM in wheat to uncover important genomic regions is an important area of wheat research. In recent years, the utilization of meta-QTL (MQTL) analysis has gained prominence as an essential tool for unraveling the complex genetic architecture underlying complex quantitative traits. The aim of this research was to conduct a QTL meta-analysis to pinpoint the specific genomic regions in wheat responsible for governing PM resistance. This study integrated 222 QTLs from 33 linkage-based studies using a consensus map with 54,672 markers. The analysis revealed 39 MQTLs, refined to 9 high-confidence MQTLs (hcMQTLs) with confidence intervals of 0.49 to 12.94 cM. The MQTLs had an average physical interval of 41.00 Mb, ranging from 0.000048 Mb to 380.71 Mb per MQTL. Importantly, 18 MQTLs co-localized with known resistance genes like Pm2, Pm3, Pm8, Pm21, Pm38, and Pm41. The study identified 256 gene models within hcMQTLs, providing potential targets for marker-assisted breeding and genomic prediction programs to enhance PM resistance. These MQTLs would serve as a foundation for fine mapping, gene isolation, and functional genomics studies, facilitating a deeper understanding of molecular mechanisms. The identification of candidate genes opens up exciting possibilities for the development of PM-resistant wheat varieties after validation.

Regulation of a single inositol 1-phosphate synthase homeologue by HSFA6B contributes to fibre yield maintenance under drought conditions in upland cotton

Drought stress substantially impacts crop physiology resulting in alteration of growth and productivity. Understanding the genetic and molecular crosstalk between stress responses and agronomically important traits such as fibre yield is particularly complicated in the allopolyploid species, upland cotton (Gossypium hirsutum), due to reduced sequence variability between A and D subgenomes. To better understand how drought stress impacts yield, the transcriptomes of 22 genetically and phenotypically diverse upland cotton accessions grown under well-watered and water-limited conditions in the Arizona low desert were sequenced. Gene co-expression analyses were performed, uncovering a group of stress response genes, in particular transcription factors GhDREB2A-A and GhHSFA6B-D, associated with improved yield under water-limited conditions in an ABA-independent manner. DNA affinity purification sequencing (DAP-seq), as well as public cistrome data from Arabidopsis, were used to identify targets of these two TFs. Among these targets were two lint yield-associated genes previously identified through genome-wide association studies (GWAS)-based approaches, GhABP-D and GhIPS1-A. Biochemical and phylogenetic approaches were used to determine that GhIPS1-A is positively regulated by GhHSFA6B-D, and that this regulatory mechanism is specific to Gossypium spp. containing the A (old world) genome. Finally, an SNP was identified within the GhHSFA6B-D binding site in GhIPS1-A that is positively associated with yield under water-limiting conditions. These data lay out a regulatory connection between abiotic stress and fibre yield in cotton that appears conserved in other systems such as Arabidopsis.

GWAS and Meta-QTL Analysis of Yield-Related Ear Traits in Maize

Maize ear traits are an important component of yield, and the genetic basis of ear traits facilitates further yield improvement. In this study, a panel of 580 maize inbred lines were used as the study material, eight ear-related traits were measured through three years of planting, and whole genome sequencing was performed using the maize 40 K breeding chip based on genotyping by targeted sequencing (GBTS) technology. Five models were used to conduct a genome-wide association study (GWAS) on best linear unbiased estimate (BLUE) of ear traits to find the best model. The FarmCPU (Fixed and random model Circulating Probability Unification) model was the best model for this study; a total of 104 significant single nucleotide polymorphisms (SNPs) were detected, and 10 co-location SNPs were detected simultaneously in more than two environments. Through gene function annotation and prediction, a total of nine genes were identified as potentially associated with ear traits. Moreover, a total of 760 quantitative trait loci (QTL) associated with yield-related traits reported in 37 different articles were collected. Using the collected 760 QTL for meta-QTL analysis, a total of 41 MQTL (meta-QTL) associated with yield-related traits were identified, and 19 MQTL detected yield-related ear trait functional genes and candidate genes that have been reported in maize. Five significant SNPs detected by GWAS were located within these MQTL intervals, and another three significant SNPs were close to MQTL (less than 1 Mb). The results provide a theoretical reference for the analysis of the genetic basis of ear-related traits and the improvement of maize yield.

Deciphering spike architecture formation towards yield improvement in wheat

Wheat is the most widely grown crop globally, providing 20% of the daily consumed calories and protein content around the world. With the growing global population and frequent occurrence of extreme weather caused by climate change, ensuring adequate wheat production is essential for food security. The architecture of the inflorescence plays a crucial role in determining the grain number and size, which is a key trait for improving yield. Recent advances in wheat genomics and gene cloning techniques have improved our understanding of wheat spike development and its applications in breeding practices. Here, we summarize the genetic regulation network governing wheat spike formation, the strategies used for identifying and studying the key factors affecting spike architecture, and the progress made in breeding applications. Additionally, we highlight future directions that will aid in the regulatory mechanistic study of wheat spike determination and targeted breeding for grain yield improvement.

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.

Meta-analysis of the quantitative trait loci associated with agronomic traits, fertility restoration, disease resistance, and seed quality traits in pigeonpea (Cajanus cajan L.)

A meta-analysis of quantitative trait loci (QTLs), associated with agronomic traits, fertility restoration, disease resistance, and seed quality traits was conducted for the first time in pigeonpea (Cajanus cajan L.). Data on 498 QTLs was collected from 9 linkage mapping studies (involving 21 biparental populations). Of these 498, 203 QTLs were projected onto "PigeonPea_ConsensusMap_2022," saturated with 10,522 markers, which resulted in the prediction of 34 meta-QTLs (MQTLs). The average confidence interval (CI) of these MQTLs (2.54 cM) was 3.37 times lower than the CI of the initial QTLs (8.56 cM). Of the 34 MQTLs, 12 high-confidence MQTLs with CI (≤5 cM) and a greater number of initial QTLs (≥5) were utilized to extract 2255 gene models, of which 105 were believed to be associated with different traits under study. Furthermore, eight of these MQTLs were observed to overlap with several marker-trait associations or significant SNPs identified in previous genome-wide association studies. Furthermore, synteny and ortho-MQTL analyses among pigeonpea and four related legumes crops, such as chickpea, pea, cowpea, and French bean, led to the identification of 117 orthologous genes from 20 MQTL regions. Markers associated with MQTLs can be employed for MQTL-assisted breeding as well as to improve the prediction accuracy of genomic selection in pigeonpea. Additionally, MQTLs may be subjected to fine mapping, and some of the promising candidate genes may serve as potential targets for positional cloning and functional analysis to elucidate the molecular mechanisms underlying the target traits.

Unraveling genomic regions and candidate genes for multiple disease resistance in upland cotton using meta-QTL analysis

Verticillium wilt (VW), Fusarium wilt (FW) and Root-knot nematode (RKN) are the main diseases affecting cotton production. However, many reported quantitative trait loci (QTLs) for cotton resistance have not been used for agricultural practices because of inconsistencies in the cotton genetic background. The integration of existing cotton genetic resources can facilitate the discovery of important genomic regions and candidate genes involved in disease resistance. Here, an improved and comprehensive meta-QTL analysis was conducted on 487 disease resistant QTLs from 31 studies in the last two decades. A consensus linkage map with genetic overall length of 3006.59 cM containing 8650 markers was constructed. A total of 28 Meta-QTLs (MQTLs) were discovered, among which nine MQTLs were identified as related to resistance to multiple diseases. Candidate genes were predicted based on public transcriptome data and enriched in pathways related to disease resistance. This study used a method based on the integration of Meta-QTL, known genes and transcriptomics to reveal major genomic regions and putative candidate genes for resistance to multiple diseases, providing a new basis for marker-assisted selection of high disease resistance in cotton breeding.

Comprehensive meta-QTL analysis for dissecting the genetic architecture of stripe rust resistance in bread wheat

BACKGROUND

Yellow or stripe rust, caused by the fungus Puccinia striiformis f. sp. tritici (Pst) is an important disease of wheat that threatens wheat production. Since developing resistant cultivars offers a viable solution for disease management, it is essential to understand the genetic basis of stripe rust resistance. In recent years, meta-QTL analysis of identified QTLs has gained popularity as a way to dissect the genetic architecture underpinning quantitative traits, including disease resistance.

RESULTS

Systematic meta-QTL analysis involving 505 QTLs from 101 linkage-based interval mapping studies was conducted for stripe rust resistance in wheat. For this purpose, publicly available high-quality genetic maps were used to create a consensus linkage map involving 138,574 markers. This map was used to project the QTLs and conduct meta-QTL analysis. A total of 67 important meta-QTLs (MQTLs) were identified which were refined to 29 high-confidence MQTLs. The confidence interval (CI) of MQTLs ranged from 0 to 11.68 cM with a mean of 1.97 cM. The mean physical CI of MQTLs was 24.01 Mb, ranging from 0.0749 to 216.23 Mb per MQTL. As many as 44 MQTLs colocalized with marker-trait associations or SNP peaks associated with stripe rust resistance in wheat. Some MQTLs also included the following major genes- Yr5, Yr7, Yr16, Yr26, Yr30, Yr43, Yr44, Yr64, YrCH52, and YrH52. Candidate gene mining in high-confidence MQTLs identified 1,562 gene models. Examining these gene models for differential expressions yielded 123 differentially expressed genes, including the 59 most promising CGs. We also studied how these genes were expressed in wheat tissues at different phases of development.

CONCLUSION

The most promising MQTLs identified in this study may facilitate marker-assisted breeding for stripe rust resistance in wheat. Information on markers flanking the MQTLs can be utilized in genomic selection models to increase the prediction accuracy for stripe rust resistance. The candidate genes identified can also be utilized for enhancing the wheat resistance against stripe rust after in vivo confirmation/validation using one or more of the following methods: gene cloning, reverse genetic methods, and omics approaches.

Responses of dry matter accumulation and partitioning to drought and subsequent rewatering at different growth stages of maize in Northeast China

INTRODUCTION

Dry matter accumulation (DMA) and dry matter partitioning (DMP) are important physiological processes determining crop yield formation. Deep understanding of the DMA and DMP processes and their responses to drought are limited by difficulty in acquiring total root biomass.

METHODS

Pot experiments with treatments quitting and ceasing ear growth (QC) and controlling soil water (WC) during vegetative (VP) and reproductive (RP) growth stages of maize (Zea mays) were conducted in Jinzhou in 2019 and 2020 to investigate the effects of drought and rewatering on DMW and DMP of different organs.

RESULTS

The response of DMW of reproductive organ to drought was more sensitive than those of vegetative organs, and was maintained after rehydration. Drought during VP (VPWC) reduced more sharply DMW of stalk than of leaves, and that during RP (RPWC) decreased more substantially leaves DMW. The effect of drought on DMPR was inconsistent with that on DMW for each organ. The DMP patterns of maize in different growth stages have adaptability to some level of water stress, and their responses increased with drought severity. Drought increased significantly DMP rates (DMPRs) of vegetative organs and reduced the ear DMPR and harvest index (HI), attributing to the suppressed photosynthates partitioning into ear and dry matter redistribution (DMRD) of vegetative organs, especially for stalk DMRD decreasing 26%. The persistence of drought impact was related to its occurrence stage and degree as well as the duration during rewatering to maturity. The aftereffect of drought during different growth periods on DMP were various, and that of VPWC enlarged and drastically induced the reduction of HI, also was larger than that of RPWC which demonstrated obvious alleviation in the previous responses of DMP and HI. Root-shoot ratio (RSR) increased under VPWC and RPWC and subsequent rehydration.

DISCUSSION

The DMWs of stalk, roots and leaves were affected by VPWC in order from large to small, and were close to or larger than the controls after rehydration, indicating the compensation effect of rewatering after drought. The DMPRs, RSR AND HI are the important parameters in agricultural production, and are often used as the constants, but in fact they vary with plant growth. In addition, the interannual differences in ear and stalk DMPRs in response to drought were probably caused by the difference in degree and occurrence stage of drought, further reflecting the variation in response of allometry growth among organs to the environment. Besides, the persistence of drought impact was related to the occurrence stage and degree of drought, which is also associated with the duration during rewatering to maturity. Notably, the effect of drought on DMW was inconsistent with that on DMPR for each organ meaning that the two variables should be discussed separately. The QC did not affect total DMW but increased RSR, changed and intensified the effect and aftereffect of RPWC on DMP, respectively, indicating that the DMP pattern and its response to drought occur change under the condition of QC.

Physiological adaptive traits are a potential allele reservoir for maize genetic progress under challenging conditions

Combined phenomic and genomic approaches are required to evaluate the margin of progress of breeding strategies. Here, we analyze 65 years of genetic progress in maize yield, which was similar (101 kg ha^-1 year^-1) across most frequent environmental scenarios in the European growing area. Yield gains were linked to physiologically simple traits (plant phenology and architecture) which indirectly affected reproductive development and light interception in all studied environments, marked by significant genomic signatures of selection. Conversely, studied physiological processes involved in stress adaptation remained phenotypically unchanged (e.g. stomatal conductance and growth sensitivity to drought) and showed no signatures of selection. By selecting for yield, breeders indirectly selected traits with stable effects on yield, but not physiological traits whose effects on yield can be positive or negative depending on environmental conditions. Because yield stability under climate change is desirable, novel breeding strategies may be needed for exploiting alleles governing physiological adaptive traits.

Potassium regulates diel leaf growth of Brassica napus by coordinating the rhythmic carbon supply and water balance

Carbon and water are two main factors limiting leaf expansion. Restriction of leaf growth by low availability of carbon or water is among the earliest visible effects of potassium (K) deficiency. It is not known how K is involved in regulating the rhythmic supply of these two substrates, which differ remarkably across the day-night cycle, affecting leaf expansion. We investigated the effects of different K regimes on the time courses of leaf expansion, carbon assimilation, carbohydrates, and hydraulic properties of Brassica napus. Potassium supply increased leaf area, predominantly by promoting night-time leaf expansion (>60%), which was mainly associated with increased availability of carbohydrates from photosynthetic carbon fixation and import from old leaves rather than improvement of leaf hydraulics. However, sufficient K improved leaf hydraulic conductance to balance diurnal evaporative water loss and increase the osmotic contribution of water-soluble carbohydrates, thereby maintaining leaf turgor and increasing the daytime expansion rate. The results also indicated an ontogenetic role of K in modifying the amplitude of circadian expansion; almost 80% of the increase in leaf area occurred before the area reached 66.9% of the mature size. Our data provide mechanistic insight into K-mediated diel coordination of rhythmic carbon supply and water balance in leaf expansion.

Unravelling consensus genomic regions associated with quality traits in wheat using meta-analysis of quantitative trait loci

Meta-analysis in wheat for three major quality traits identified 110 meta-QTL (MQTL) with reduced confidence interval (CI). Five GWAS validated MQTL (viz., 1A.1, 1B.2, 3B.4, 5B.2, and 6B.2), each involving more than 20 initial QTL and reduced CI (95%) (< 2 cM), were selected for quality breeding programmes. Functional characterization including candidate gene mining and expression analysis discovered 44 high confidence candidate genes associated with quality traits. A meta-analysis of quantitative trait loci (QTL) associated with dough rheology properties, nutritional traits, and processing quality traits was conducted in wheat. For this purpose, as many as 2458 QTL were collected from 50 interval mapping studies published during 2013-2020. Of the total QTL, 1126 QTL were projected onto the consensus map saturated with 249,603 markers which led to the identification of 110 meta-QTL (MQTL). These MQTL exhibited an 18.84-fold reduction in the average CI compared to the average CI of the initial QTL (ranging from 14.87 to 95.55 cM with an average of 40.35 cM). Of the 110, 108 MQTL were physically anchored to the wheat reference genome, including 51 MQTL verified with marker-trait associations (MTAs) reported from earlier genome-wide association studies. Candidate gene (CG) mining allowed the identification of 2533 unique gene models from the MQTL regions. In-silico expression analysis discovered 439 differentially expressed gene models with > 2 transcripts per million expressions in grains and related tissues, which also included 44 high-confidence CGs involved in the various cellular and biochemical processes related to quality traits. Nine functionally characterized wheat genes associated with grain protein content, high-molecular-weight glutenin, and starch synthase enzymes were also found to be co-localized with some of the MQTL. Synteny analysis between wheat and rice MQTL regions identified 23 wheat MQTL syntenic to 16 rice MQTL associated with quality traits. Furthermore, 64 wheat orthologues of 30 known rice genes were detected in 44 MQTL regions. Markers flanking the MQTL identified in the present study can be used for marker-assisted breeding and as fixed effects in the genomic selection models for improving the prediction accuracy during quality breeding. Wheat orthologues of rice genes and other CGs available from MQTLs can be promising targets for further functional validation and to better understand the molecular mechanism underlying the quality traits in wheat.

Integration of Crop Growth Models and Genomic Prediction

Crop growth models (CGMs) consist of multiple equations that represent physiological processes of plants and simulate crop growth dynamically given environmental inputs. Because parameters of CGMs are often genotype-specific, gene effects can be related to environmental inputs through CGMs. Thus, CGMs are attractive tools for predicting genotype by environment (G×E) interactions. This chapter reviews CGMs, genetic analyses using these models, and the status of studies that integrate genomic prediction with CGMs. Examples of CGM analyses are also provided.

Sensitivities to temperature and evaporative demand in wheat relatives

There is potential sources of alleles and genes currently locked into wheat-related species that could be introduced into wheat breeding programs for current and future hot and dry climates. However, neither the intra- nor the inter-specific diversity of the responses of leaf growth and transpiration to temperature and evaporative demand have been investigated in a large diversity of wheat-related species. By analysing 12 groups of wheat-related sub-species, we questioned the n-dimensional structure of the genetic diversity for traits linked to plant vegetative structures and development, leaf expansion and transpiration together with their responses to "non-stressing" range of temperature and evaporative demand. In addition to provide new insight on how genome type, ploidy level, phylogeny and breeding pressure together structure this genetic diversity, this study provides new mathematical formalisms and the associated parameters of trait responses in the large genetic diversity of wheat-related species. This potentially allow crop models predicting the impact of this diversity on yield, and indicate potential sources of varietal improvement for modern wheat germplasms, through interspecific crosses.

Large-scale integration of meta-QTL and genome-wide association study discovers the genomic regions and candidate genes for yield and yield-related traits in bread wheat

Based on the large-scale integration of meta-QTL and Genome-Wide  Association Study, 76 high-confidence MQTL regions and 237 candidate genes that affected wheat yield and yield-related traits were discovered. Improving yield and yield-related traits are key goals in wheat breeding program. The integration of accumulated wheat genetic resources provides an opportunity to uncover important genomic regions and candidate genes that affect wheat yield. Here, a comprehensive meta-QTL analysis was conducted on 2230 QTL of yield-related traits obtained from 119 QTL studies. These QTL were refined into 145 meta-QTL (MQTL), and 89 MQTL were verified by GWAS with different natural populations. The average confidence interval (CI) of these MQTL was 2.92 times less than that of the initial QTL. Furthermore, 76 core MQTL regions with a physical distance less than 25 Mb were detected. Based on the homology analysis and expression patterns, 237 candidate genes in the MQTL involved in photoperiod response, grain development, multiple plant growth regulator pathways, carbon and nitrogen metabolism and spike and flower organ development were determined. A novel candidate gene TaKAO-4A was confirmed to be significantly associated with grain size, and a CAPS marker was developed based on its dominant haplotype. In summary, this study clarified a method based on the integration of meta-QTL, GWAS and homology comparison to reveal the genomic regions and candidate genes that affect important yield-related traits in wheat. This work will help to lay a foundation for the identification, transfer and aggregation of these important QTL or candidate genes in wheat high-yield breeding.

Drought responses and phenotypic plasticity of Maprounea guianensis populations in humid and dry tropical forests

Abstract The wide distribution of Maprounea guianensis populations in contrasting environments (dry and humid forests) in the Chapada Diamantina, northeastern Brazil, can indicate the phenotypic plasticity of this species in relation to seasonal rainfall, drought regimes, and soil characteristics at different sites. Functional traits were measured in five individuals in each vegetation types. Water potential, succulence, thickness and density leaf, were evaluated during the dry and rainy periods; wood density and the saturated water content of the wood were evaluated in rainy period. Rainfall was monitored monthly for two years. The functional traits and the phenotypic plasticity indices (PPI) were submitted to analysis of variance. Our results demonstrated seasonal and spatial variations in plant functional traits. We found a low capacity for storing water in leaves and woody tissues, associated with soil properties and the seasonal rainfall/drought regimes, conditioning water potential variations that were greatest during the rainy season. Local environmental parameters influenced variations in the functional traits of M. guianensis populations, reflecting phenotypic plasticity. We highlight the connections between drought regimes and plant responses, demonstrating the importance of functional traits associated with water availability (especially water potential). Our study evidences the factors associated with the wide distribution of M. guianensis.

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.

The Genetic Architecture for Phenotypic Plasticity of the Rice Grain Ionome

The ionome of the rice grain is crucial for the health of populations that consume rice as a staple food. However, the contribution of phenotypic plasticity to the variation of rice grain ionome and the genetic architecture of phenotypic plasticity are poorly understood. In this study, we investigated the rice grain ionome of a rice diversity panel in up to eight environments. A considerable proportion of phenotypic variance can be attributed to phenotypic plasticity. Then, phenotypic plasticity and mean phenotype were quantified using Bayesian Finlay-Wilkinson regression, and a significant correlation between them was observed. However, the genetic architecture of mean phenotype was distinct from that of phenotypic plasticity. Also, the correlation between them was mainly attributed to the phenotypic divergence between rice subspecies. Furthermore, the results of whole-genome regression analysis showed that the genetic loci related to phenotypic plasticity can explain a considerable proportion of the phenotypic variance in some environments, especially for Cd, Cu, Mn, and Zn. Our study not only sheds light on the genetic architecture of phenotypic plasticity of the rice grain ionome but also suggests that the genetic loci which related to phenotypic plasticity are valuable in rice grain ionome improvement breeding.

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.

Modelling strategies for assessing and increasing the effectiveness of new phenotyping techniques in plant breeding

New types of phenotyping tools generate large amounts of data on many aspects of plant physiology and morphology with high spatial and temporal resolution. These new phenotyping data are potentially useful to improve understanding and prediction of complex traits, like yield, that are characterized by strong environmental context dependencies, i.e., genotype by environment interactions. For an evaluation of the utility of new phenotyping information, we will look at how this information can be incorporated in different classes of genotype-to-phenotype (G2P) models. G2P models predict phenotypic traits as functions of genotypic and environmental inputs. In the last decade, access to high-density single nucleotide polymorphism markers (SNPs) and sequence information has boosted the development of a class of G2P models called genomic prediction models that predict phenotypes from genome wide marker profiles. The challenge now is to build G2P models that incorporate simultaneously extensive genomic information alongside with new phenotypic information. Beyond the modification of existing G2P models, new G2P paradigms are required. We present candidate G2P models for the integration of genomic and new phenotyping information and illustrate their use in examples. Special attention will be given to the modelling of genotype by environment interactions. The G2P models provide a framework for model based phenotyping and the evaluation of the utility of phenotyping information in the context of breeding programs.

The use of thermal time in plant studies has a sound theoretical basis provided that confounding effects are avoided

The use of thermal time is essential in plant studies and crop growth modeling because correcting time for temperature allows working in fluctuating conditions as if temperature was constant. However, thermal time is often seen as a loose concept because of a multitude of thermal functions and case-specific parameter values. Our hypothesis is that these different formalisms and parameterization could emerge from common principles and a common response of plant development to temperature, but with several counfounding factors which are not taken into account. We first show that these calculations of thermal time are based on sound common principles and mathematical formalisms. We test, via a modelling exercise of nine case studies using maize plants grown in three field sites, how a given "ground truth" response of plant development rate to temperature can be affected if an experimenter either considers or ignores confounding factors. We also show that apparent differences in temperature responses between phenological stages of the growth cycle, between day and night, or between plant genotypes may be due to the confounding effects of evaporative demand, the range of temperatures, and the time interval at which measurements are taken. On the basis of our findings, we propose that the critical point in the use of a given formalism of thermal time calculation is to ensure that the chosen model is compatible with the temporal definition, temperature range, and environmental scenario in the considered dataset.

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.

Plant Phenomics, From Sensors to Knowledge

Major improvements in crop yield are needed to keep pace with population growth and climate change. While plant breeding efforts have greatly benefited from advances in genomics, profiling the crop phenome (i.e., the structure and function of plants) associated with allelic variants and environments remains a major technical bottleneck. Here, we review the conceptual and technical challenges facing plant phenomics. We first discuss how, given plants' high levels of morphological plasticity, crop phenomics presents distinct challenges compared with studies in animals. Next, we present strategies for multi-scale phenomics, and describe how major improvements in imaging, sensor technologies and data analysis are now making high-throughput root, shoot, whole-plant and canopy phenomic studies possible. We then suggest that research in this area is entering a new stage of development, in which phenomic pipelines can help researchers transform large numbers of images and sensor data into knowledge, necessitating novel methods of data handling and modelling. Collectively, these innovations are helping accelerate the selection of the next generation of crops more sustainable and resilient to climate change, and whose benefits promise to scale from physiology to breeding and to deliver real world impact for ongoing global food security efforts.

The Physiological Basis of Drought Tolerance in Crop Plants: A Scenario-Dependent Probabilistic Approach

Drought tolerance involves mechanisms operating at different spatial and temporal scales, from rapid stomatal closure to maintenance of crop yield. We review how short-term mechanisms are controlled for stabilizing shoot water potential and how long-term processes have been constrained by evolution or breeding to fit into acclimation strategies for specific drought scenarios. These short- or long-term feedback processes participate in trade-offs between carbon accumulation and the risk of deleterious soil water depletion. Corresponding traits and alleles may therefore have positive or negative effects on crop yield depending on drought scenarios. We propose an approach that analyzes the genetic architecture of traits in phenotyping platforms and of yield in tens of field experiments. A combination of modeling and genomic prediction is then used to estimate the comparative interests of combinations of alleles depending on drought scenarios. Hence, drought tolerance is understood probabilistically by estimating the benefit and risk of each combination of alleles.

Quantifying Wheat Sensitivities to Environmental Constraints to Dissect Genotype × Environment Interactions in the Field

Yield is subject to strong genotype-by-environment (G × E) interactions in the field, especially under abiotic constraints such as soil water deficit (drought [D]) and high temperature (heat [H]). Since environmental conditions show strong fluctuations during the whole crop cycle, geneticists usually do not consider environmental measures as quantitative variables but rather as factors in multienvironment analyses. Based on 11 experiments in a field platform with contrasting temperature and soil water deficit, we determined the periods of sensitivity to drought and heat constraints in wheat (Triticum aestivum) and determined the average sensitivities for major yield components. G × E interactions were separated into their underlying components, constitutive genotypic effect (G), G × D, G × H, and G × H × D, and were analyzed for two genotypes, highlighting contrasting responses to heat and drought constraints. We then tested the constitutive and responsive behaviors of two strong quantitative trait loci (QTLs) associated previously with yield components. This analysis confirmed the constitutive effect of the chromosome 1B QTL and explained the G × E interaction of the chromosome 3B QTL by a benefit of one allele when temperature rises. In addition to the method itself, which can be applied to other data sets and populations, this study will support the cloning of a major yield QTL on chromosome 3B that is highly dependent on environmental conditions and for which the climatic interaction is now quantified.

Predictable 'meta-mechanisms' emerge from feedbacks between transpiration and plant growth and cannot be simply deduced from short-term mechanisms

Growth under water deficit is controlled by short-term mechanisms but, because of numerous feedbacks, the combination of these mechanisms over time often results in outputs that cannot be deduced from the simple inspection of individual mechanisms. It can be analysed with dynamic models in which causal relationships between variables are considered at each time-step, allowing calculation of outputs that are routed back to inputs for the next time-step and that can change the system itself. We first review physiological mechanisms involved in seven feedbacks of transpiration on plant growth, involving changes in tissue hydraulic conductance, stomatal conductance, plant architecture and underlying factors such as hormones or aquaporins. The combination of these mechanisms over time can result in non-straightforward conclusions as shown by examples of simulation outputs: 'over production of abscisic acid (ABA) can cause a lower concentration of ABA in the xylem sap ', 'decreasing root hydraulic conductance when evaporative demand is maximum can improve plant performance' and 'rapid root growth can decrease yield'. Systems of equations simulating feedbacks over numerous time-steps result in logical and reproducible emergent properties that can be viewed as 'meta-mechanisms' at plant level, which have similar roles as mechanisms at cell level.

Nighttime evaporative demand induces plasticity in leaf and root hydraulic traits

Increasing evidence suggests that nocturnal transpiration rate (TR_N ) is a non-negligible contributor to global water cycles. Short-term variation in nocturnal vapor pressure deficit (VPD_N ) has been suggested to be a key environmental variable influencing TR_N . However, the long-term effects of VPD_N on plant growth and development remain unknown, despite recent evidence documenting long-term effects of daytime VPD on plant anatomy, growth and productivity. Here we hypothesized that plant anatomical and functional traits influencing leaf and root hydraulics could be influenced by long-term exposure to VPD_N . A total of 23 leaf and root traits were examined on four wheat (Triticum aestivum) genotypes, which were subjected to two long-term (30 day long) growth experiments where daytime VPD and daytime/nighttime temperature regimes were kept identical, with variation only stemming from VPD_N , imposed at two levels (0.4 and 1.4 kPa). The VPD_N treatment did not influence phenology, leaf areas, dry weights, number of tillers or their dry weights, consistently with a drought and temperature-independent treatment. In contrast, vein densities, adaxial stomata densities, TR_N and cuticular TR, were strongly increased following exposure to high VPD_N . Simultaneously, whole-root system xylem sap exudation and seminal root endodermis thickness were decreased, hypothetically indicating a change in root hydraulic properties. Overall these results suggest that plants 'sense' and adapt to variations in VPD_N conditions over developmental scales by optimizing both leaf and root hydraulics.

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.

Gravimetric phenotyping of whole plant transpiration responses to atmospheric vapour pressure deficit identifies genotypic variation in water use efficiency

There is increasing interest in rapidly identifying genotypes with improved water use efficiency, exemplified by the development of whole plant phenotyping platforms that automatically measure plant growth and water use. Transpirational responses to atmospheric vapour pressure deficit (VPD) and whole plant water use efficiency (WUE, defined as the accumulation of above ground biomass per unit of water used) were measured in 100 maize (Zea mays L.) genotypes. Using a glasshouse based phenotyping platform with naturally varying VPD (1.5-3.8kPa), a 2-fold variation in WUE was identified in well-watered plants. Regression analysis of transpiration versus VPD under these conditions, and subsequent whole plant gas exchange at imposed VPDs (0.8-3.4kPa) showed identical responses in specific genotypes. Genotype response of transpiration versus VPD fell into two categories: 1) a linear increase in transpiration rate with VPD with low (high WUE) or high (low WUE) transpiration rate at all VPDs, 2) a non-linear response with a pronounced change point at low VPD (high WUE) or high VPD (low WUE). In the latter group, high WUE genotypes required a significantly lower VPD before transpiration was restricted, and had a significantly lower rate of transpiration in response to VPD after this point, when compared to low WUE genotypes. Change point values were significantly positively correlated with stomatal sensitivity to VPD. A change point in stomatal response to VPD may explain why some genotypes show contradictory WUE rankings according to whether they are measured under glasshouse or field conditions. Furthermore, this novel use of a high throughput phenotyping platform successfully reproduced the gas exchange responses of individuals measured in whole plant chambers, accelerating the identification of plants with high WUE.

Foliar Abscisic Acid-To-Ethylene Accumulation and Response Regulate Shoot Growth Sensitivity to Mild Drought in Wheat

Although, plant hormones play an important role in adjusting growth in response to environmental perturbation, the relative contributions of abscisic acid (ABA) and ethylene remain elusive. Using six spring wheat genotypes differing for stress tolerance, we show that young seedlings of the drought-tolerant (DT) group maintained or increased shoot dry weight (SDW) while the drought-susceptible (DS) group decreased SDW in response to mild drought. Both the DT and DS groups increased endogenous ABA and ethylene concentrations under mild drought compared to control. The DT and DS groups exhibited different SDW response trends, whereby the DS group decreased while the DT group increased SDW, to increased concentrations of ABA and ethylene under mild drought, although both groups decreased ABA/ethylene ratio under mild drought albeit at different levels. We concluded that SDW of the DT and DS groups might be distinctly regulated by specific ABA:ethylene ratio. Further, a foliar-spray of low concentrations (0.1 μM) of ABA increased shoot relative growth rate (RGR) in the DS group while ACC (1-aminocyclopropane-1-carboxylic acid, ethylene precursor) spray increased RGR in both groups compared to control. Furthermore, the DT group accumulated a significantly higher galactose while a significantly lower maltose in the shoot compared to the DS group. Taken all together, these results suggest an impact of ABA, ethylene, and ABA:ethylene ratio on SDW of wheat seedlings that may partly underlie a genotypic variability of different shoot growth sensitivities to drought among crop species under field conditions. We propose that phenotyping based on hormone accumulation, response and hormonal ratio would be a viable, rapid, and an early-stage selection tool aiding genotype selection for stress tolerance.

A comprehensive meta-analysis of plant morphology, yield, stay-green, and virus disease resistance QTL in maize (Zea mays L.)

The meta-QTL and candidate genes will facilitate the elucidation of molecular bases underlying agriculturally important traits and open new avenues for functional markers development and elite alleles introgression in maize breeding program. A large number of QTLs attributed to grain productivity and other agriculturally important traits have been identified and deposited in public repositories. The integration of fruitful QTL becomes a major issue in current plant genomics. To this end, we first collected QTL for six agriculturally important traits in maize, including yield, plant height, ear height, leaf angle, stay-green, and maize rough dwarf disease resistance. The meta-analysis method was then employed to retrieve 113 meta-QTL. Additionally, we also isolated candidate genes for target traits by the bioinformatic technique. Several candidates, including some well-characterized genes, GA3ox2 for plant height, lg1 and lg4 for leaf angle, zfl1 and zfl2 for flowering time, were co-localized with established meta-QTL intervals. Intriguingly, in a relatively narrow meta-QTL region, the maize ortholog of rice yield-related gene GW8/OsSPL16 was believed to be a candidate for yield. Leveraging results presented in this study will provide further insights into the genetic architecture of maize agronomic traits. Moreover, the meta-QTL and candidate genes reported here could be harnessed for the enhancement of stress tolerance and yield performance in maize and translation to other crops.

LeasyScan: a novel concept combining 3D imaging and lysimetry for high-throughput phenotyping of traits controlling plant water budget

In this paper, we describe the thought process and initial data behind the development of an imaging platform (LeasyScan) combined with lysimetric capacity, to assess canopy traits affecting water use (leaf area, leaf area index, transpiration). LeasyScan is based on a novel 3D scanning technique to capture leaf area development continuously, a scanner-to-plant concept to increase imaging throughput and analytical scales to combine gravimetric transpiration measurements. The paper presents how the technology functions, how data are visualised via a web-based interface and how data extraction and analysis is interfaced through 'R' libraries. Close agreement between scanned and observed leaf area data of individual plants in different crops was found (R(2) between 0.86 and 0.94). Similar agreement was found when comparing scanned and observed area of plants cultivated at densities reflecting field conditions (R(2) between 0.80 and 0.96). An example in monitoring plant transpiration by the analytical scales is presented. The last section illustrates some of the early ongoing applications of the platform to target key phenotypes: (i) the comparison of the leaf area development pattern of fine mapping recombinants of pearl millet; (ii) the leaf area development pattern of pearl millet breeding material targeted to different agro-ecological zones; (iii) the assessment of the transpiration response to high VPD in sorghum and pearl millet. This new platform has the potential to phenotype for traits controlling plant water use at a high rate and precision, of critical importance for drought adaptation, and creates an opportunity to harness their genetics for the breeding of improved varieties.

Modelling the coordination of the controls of stomatal aperture, transpiration, leaf growth, and abscisic acid: update and extension of the Tardieu-Davies model

Stomatal aperture, transpiration, leaf growth, hydraulic conductance, and concentration of abscisic acid in the xylem sap ([ABA]xyl) vary rapidly with time of day. They follow deterministic relations with environmental conditions and interact in such a way that a change in any one of them affects all the others. Hence, approaches based on measurements of one variable at a given time or on paired correlations are prone to a confusion of effects, in particular for studying their genetic variability. A dynamic model allows the simulation of environmental effects on the variables, and of multiple feedbacks between them at varying time resolutions. This paper reviews the control of water movement through the plant, stomatal aperture and growth, and translates them into equations in a model. It includes recent progress in understanding the intrinsic and environmental controls of tissue hydraulic conductance as a function of transpiration rate, circadian rhythms, and [ABA]xyl. Measured leaf water potential is considered as the water potential of a capacitance representing mature tissues, which reacts more slowly to environmental cues than xylem water potential and expansive growth. Combined with equations for water and ABA fluxes, it results in a dynamic model able to simulate variables with genotype-specific parameters. It allows adaptive roles for hydraulic processes to be proposed, in particular the circadian oscillation of root hydraulic conductance. The script of the model, in the R language, is included together with appropriate documentation and examples.

Circadian rhythms of hydraulic conductance and growth are enhanced by drought and improve plant performance

Circadian rhythms enable plants to anticipate daily environmental variations, resulting in growth oscillations under continuous light. Because plants daily transpire up to 200% of their water content, their water status oscillates from favourable during the night to unfavourable during the day. We show that rhythmic leaf growth under continuous light is observed in plants that experience large alternations of water status during an entrainment period, but is considerably buffered otherwise. Measurements and computer simulations show that this is due to oscillations of plant hydraulic conductance and plasma membrane aquaporin messenger RNA abundance in roots during continuous light. A simulation model suggests that circadian oscillations of root hydraulic conductance contribute to acclimation to water stress by increasing root water uptake, thereby favouring growth and photosynthesis. They have a negative effect in favourable hydraulic conditions. Climate-driven control of root hydraulic conductance therefore improves plant performances in both stressed and non-stressed conditions.

Circadian rhythms allow plants to respond to diurnal fluctuations in the environment. Here Caldeira et al. find that circadian control of hydraulic conductance, aquaporin expression and leaf growth are entrained by oscillations of plant water status and promote water uptake in drought-stressed plants.

Can current crop models be used in the phenotyping era for predicting the genetic variability of yield of plants subjected to drought or high temperature?

A crop model with genetic inputs can potentially simulate yield for a large range of genotypes, sites, and years, thereby indicating where and when a given combination of alleles confers a positive effect. We discuss to what extent current crop models, developed for predicting the effects of climate or cultivation techniques on a reference genotype, are adequate for ranking yields of a large number of genotypes in climatic scenarios with water deficit or high temperatures. We compare here the algorithms involved in 19 crop models. Marked differences exist in the representation of the combined effects of temperature and water deficit on plant development, and in the coordination of these effects with biomass production. The current literature suggests that these differences have a small impact on the yield prediction of a reference genotype because errors on the effects of different traits compensate each other. We propose that they have a larger impact if the crop model is used in a genetic context, because the model has to account for the genetic variability of studied traits. Models with explicit genetic inputs will be increasingly feasible because model parameters corresponding to each genotype can now be measured in phenotyping platforms for large plant collections. This will in turn allow prediction of parameter values from the allelic composition of genotypes. It is therefore timely to adapt crop models to this new context to simulate the allelic effects in present or future climatic scenarios with water or heat stresses.

A hydraulic model is compatible with rapid changes in leaf elongation under fluctuating evaporative demand and soil water status

Plants are constantly facing rapid changes in evaporative demand and soil water content, which affect their water status and growth. In apparent contradiction to a hydraulic hypothesis, leaf elongation rate (LER) declined in the morning and recovered upon soil rehydration considerably quicker than transpiration rate and leaf water potential (typical half-times of 30 min versus 1-2 h). The morning decline of LER began at very low light and transpiration and closely followed the stomatal opening of leaves receiving direct light, which represent a small fraction of leaf area. A simulation model in maize (Zea mays) suggests that these findings are still compatible with a hydraulic hypothesis. The small water flux linked to stomatal aperture would be sufficient to decrease water potentials of the xylem and growing tissues, thereby causing a rapid decline of simulated LER, while the simulated water potential of mature tissues declines more slowly due to a high hydraulic capacitance. The model also captured growth patterns in the evening or upon soil rehydration. Changes in plant hydraulic conductance partly counteracted those of transpiration. Root hydraulic conductivity increased continuously in the morning, consistent with the transcript abundance of Zea maize Plasma Membrane Intrinsic Protein aquaporins. Transgenic lines underproducing abscisic acid, with lower hydraulic conductivity and higher stomatal conductance, had a LER declining more rapidly than wild-type plants. Whole-genome transcriptome and phosphoproteome analyses suggested that the hydraulic processes proposed here might be associated with other rapidly occurring mechanisms. Overall, the mechanisms and model presented here may be an essential component of drought tolerance in naturally fluctuating evaporative demand and soil moisture.

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.

Selection strategies for the development of maize introgression populations

Introgression libraries are valuable resources for QTL detection and breeding, but their development is costly and time-consuming. Selection strategies for the development of introgression populations with a limited number of individuals and high-throughput (HT) marker assays are required. The objectives of our simulation study were to design and compare selection strategies for the development of maize introgression populations of 100 lines with population sizes of 360–720 individuals per generation for different DH and crossing schemes. Pre-selection for complete donor chromosomes or donor chromosome halves reduced the number of simultaneous backcross programs. The investigated crossing and selection schemes differed considerably with respect to their suitability to create introgression populations with clearly separated, evenly distributed target donor chromosome segments. DH crossing schemes were superior to crossing schemes, mainly due to complete homozygosity, which greatly reduced the total number of disjunct genome segments in the introgression populations. The crossing schemes were more flexible with respect to selection and provided economic alternatives to DH crossing schemes. For the DH crossing schemes, increasing population sizes gradually over backcross generations was advantageous as it reduced the total number of required HT assays compared to constant population sizes. For the crossing schemes, large population sizes in the final backcross generation facilitated selection for the target segments in the final backcross generation and reduced fixation of large donor chromosome segments. The suggested crossing and selection schemes can help to make the genetic diversity of exotic germplasm available for enhancing the genetic variation of narrow-based breeding populations of crops.

Water: the most important 'molecular' component of water stress tolerance research

Water deficit is the main yield-limiting factor across the Asian and African semiarid tropics and a basic consideration when developing crop cultivars for water-limited conditions is to ensure that crop water demand matches season water supply. Conventional breeding has contributed to the development of varieties that are better adapted to water stress, such as early maturing cultivars that match water supply and demand and then escape terminal water stress. However, an optimisation of this match is possible. Also, further progress in breeding varieties that cope with water stress is hampered by the typically large genotype×environment interactions in most field studies. Therefore, a more comprehensive approach is required to revitalise the development of materials that are adapted to water stress. In the past two decades, transgenic and candidate gene approaches have been proposed for improving crop productivity under water stress, but have had limited real success. The major drawback of these approaches has been their failure to consider realistic water limitations and their link to yield when designing biotechnological experiments. Although the genes are many, the plant traits contributing to crop adaptation to water limitation are few and revolve around the critical need to match water supply and demand. We focus here on the genetic aspects of this, although we acknowledge that crop management options also have a role to play. These traits are related in part to increased, better or more conservative uses of soil water. However, the traits themselves are highly dynamic during crop development: they interact with each other and with the environment. Hence, success in breeding cultivars that are more resilient under water stress requires an understanding of plant traits affecting yield under water deficit as well as an understanding of their mutual and environmental interactions. Given that the phenotypic evaluation of germplasm/breeding material is limited by the number of locations and years of testing, crop simulation modelling then becomes a powerful tool for navigating the complexity of biological systems, for predicting the effects on yield and for determining the probability of success of specific traits or trait combinations across water stress scenarios.

Flooding tolerance in interspecific introgression lines containing chromosome segments from teosinte (Zea nicaraguensis) in maize (Zea mays subsp. mays)

BACKGROUND AND AIMS

Nicaraguan teosinte (Zea nicaraguensis), a species found in frequently flooded areas, provides useful germplasm for breeding flooding-tolerant maize (Z. mays subsp. mays). The objective of this study was to select flooding-tolerant lines using a library of introgression lines (ILs), each containing a chromosome segment from Z. nicaraguensis in the maize inbred line Mi29.

METHODS

To produce the ILs, a single F1 plant derived from a cross between maize Mi29 and Z. nicaraguensis was backcrossed to Mi29 three times, self-pollinated four times and genotyped using simple sequence repeat markers. Flooding tolerance was evaluated at the seedling stage under reducing soil conditions.

KEY RESULTS

By backcrossing and selfing, a series of 45 ILs were developed covering nearly the entire maize genome. Five flooding-tolerant lines were identified from among the ILs by evaluating leaf injury. Among these, line IL#18, containing a Z. nicaraguensis chromosome segment on the long arm of chromosome 4, showed the greatest tolerance to flooding, suggesting the presence of a major quantitative trait locus (QTL) in that region. The presence of the QTL was verified by examining flooding tolerance in a population segregating for the candidate region of chromosome 4. There was no significant relationship between the capacity to form constitutive aerenchyma and flooding tolerance in the ILs, indicating the presence of other factors related to flooding tolerance under reducing soil conditions.

CONCLUSIONS

A flooding-tolerant genotype, IL#18, was identified; this genotype should be useful for maize breeding. In addition, because the chromosome segments of Z. nicaraguensis in the ILs cover nearly the entire genome and Z. nicaraguensis possesses several unique traits related to flooding tolerance, the ILs should be valuable material for additional QTL detection and the development of flooding-tolerant maize lines.

Transpiration sensitivities to evaporative demand and leaf areas vary with night and day warming regimes among wheat genotypes

Warmer climates are already contributing to significant decreases in wheat (Triticum spp.) yields worldwide, highlighting the need for more adapted germplasm. Although many studies have addressed the effects of warmer climates on grain physiology and photosynthesis, only a few have considered temperature effects on other key yield-related traits such as the sensitivity of transpiration rate (TR) to vapour pressure deficit (VPD)-a function of air temperature and relative humidity. In wheat, no reports are available to document such influences. More importantly, the relative contributions of heat-stress night and day conditions on such sensitivity and the plant's evaporative surface remain to be investigated. The objective of this study was to assess the response of these two physiological processes to long-term (i.e. 3 weeks) exposures to six warming scenarios, consisting of a combination of three target growth-period VPD (2, 2.7 and 4kPa), and two night temperature (20 and 30°C) regimes among 11 diverse bread and durum wheat lines having different origins. The study revealed (i) a large genetic variability in those responses; (ii) non-linear interactions between the effects of day and night conditions; and (iii) compensation mechanisms between leaf areas and transpiration sensitivities to VPD together with differential acclimation strategies of these sensitivities with respect to increasingly warmer scenarios. These findings open the way to implementing breeding strategies that can improve wheat yields under different warming scenarios.

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.

Plant response to environmental conditions: assessing potential production, water demand, and negative effects of water deficit

This paper reviews methods for analyzing plant performance and its genetic variability under a range of environmental conditions. Biomass accumulation is linked every day to available light in the photosynthetically active radiation (PAR) domain, multiplied by the proportion of light intercepted by plants and by the radiation use efficiency. Total biomass is cumulated over the duration of the considered phase (e.g., plant cycle or vegetative phase). These durations are essentially constant for a given genotype provided that time is corrected for temperature (thermal time). Several ways of expressing thermal time are reviewed. Two alternative equations are presented, based either on the effect of transpiration, or on yield components. Their comparative interests and drawbacks are discussed. The genetic variability of each term of considered equations affects yield under water deficit, via mechanisms at different scales of plant organization and time. The effect of any physiological mechanism on yield of stressed plants acts via one of these terms, although the link is not always straightforward. Finally, I propose practical ways to compare the productivity of genotypes in field environments, and a "minimum dataset" of environmental data and traits that should be recorded for that.

Plant response to environmental conditions: assessing potential production, water demand, and negative effects of water deficit

This paper reviews methods for analyzing plant performance and its genetic variability under a range of environmental conditions. Biomass accumulation is linked every day to available light in the photosynthetically active radiation (PAR) domain, multiplied by the proportion of light intercepted by plants and by the radiation use efficiency. Total biomass is cumulated over the duration of the considered phase (e.g., plant cycle or vegetative phase). These durations are essentially constant for a given genotype provided that time is corrected for temperature (thermal time). Several ways of expressing thermal time are reviewed. Two alternative equations are presented, based either on the effect of transpiration, or on yield components. Their comparative interests and drawbacks are discussed. The genetic variability of each term of considered equations affects yield under water deficit, via mechanisms at different scales of plant organization and time. The effect of any physiological mechanism on yield of stressed plants acts via one of these terms, although the link is not always straightforward. Finally, I propose practical ways to compare the productivity of genotypes in field environments, and a "minimum dataset" of environmental data and traits that should be recorded for that.

Phenotyping for drought tolerance of crops in the genomics era

Improving crops yield under water-limited conditions is the most daunting challenge faced by breeders. To this end, accurate, relevant phenotyping plays an increasingly pivotal role for the selection of drought-resilient genotypes and, more in general, for a meaningful dissection of the quantitative genetic landscape that underscores the adaptive response of crops to drought. A major and universally recognized obstacle to a more effective translation of the results produced by drought-related studies into improved cultivars is the difficulty in properly phenotyping in a high-throughput fashion in order to identify the quantitative trait loci that govern yield and related traits across different water regimes. This review provides basic principles and a broad set of references useful for the management of phenotyping practices for the study and genetic dissection of drought tolerance and, ultimately, for the release of drought-tolerant cultivars.

Temperature responses of developmental processes have not been affected by breeding in different ecological areas for 17 crop species

• Rates of tissue expansion, cell division and progression in the plant cycle are driven by temperature, following common Arrhenius-type response curves. • We analysed the genetic variability of this response in the range 6-37°C in seven to nine lines of maize (Zea mays), rice (Oryza spp.) and wheat (Triticum aestivum) and in 18 species (17 crop species, different genotypes) via the meta-analysis of 72 literature references. • Lines with tropical or north-temperate origins had common response curves over the whole range of temperature. Conversely, appreciable differences in response curves, including optimum temperatures, were observed between species growing in temperate and tropical areas. • Therefore, centuries of crop breeding have not impacted on the response of development to short-term changes in temperature, whereas evolution over millions of years has. This slow evolution may be a result of the need for a synchronous shift in the temperature response of all developmental processes, otherwise plants will not be viable. Other possibilities are discussed. This result has important consequences for the breeding and modelling of temperature effects associated with global changes.