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.

Natural alleles of the abscisic acid catabolism gene ZmAbh4 modulate water use efficiency and carbon isotope discrimination in maize.

Altering plant water use efficiency (WUE) is a promising approach for achieving sustainable crop production in changing climate scenarios. Here, we show that WUE can be tuned by alleles of a single gene discovered in elite maize (Zea mays) breeding material. Genetic dissection of a genomic region affecting WUE led to the identification of the gene ZmAbh4 as causative for the effect. CRISPR/Cas9-mediated ZmAbh4 inactivation increased WUE without growth reductions in well-watered conditions. ZmAbh4 encodes an enzyme that hydroxylates the phytohormone abscisic acid (ABA) and initiates its catabolism. Stomatal conductance is regulated by ABA and emerged as a major link between variation in WUE and discrimination against the heavy carbon isotope (Δ13C) during photosynthesis in the C4 crop maize. Changes in Δ13C persisted in kernel material, which offers an easy-to-screen proxy for WUE. Our results establish a direct physiological and genetic link between WUE and Δ13C through a single gene with potential applications in maize breeding.

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.

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.

The plasma membrane aquaporin ZmPIP2;5 enhances the sensitivity of stomatal closure to water deficit.

Increasing stomatal movement is beneficial to improve plant water use efficiency and drought resilience. Contradictory results indicate that aquaporins might regulate stomatal movement. Here, we tested whether the maize plasma membrane PIP2;5 aquaporin affects stomatal closure under water deficit, abscisic acid (ABA) or vapour pressure deficit (VPD) treatment in intact plants, detached leaves or peeled epidermis. Transpiration, stomatal conductance (gs ) and aperture and reactive oxygen species (ROS) in stomatal complexes were studied in maize lines with increased or knocked down (KD) PIP2;5 gene expression. In well-watered conditions, the PIP2;5 overexpressing (OE) plants transpired more than wild types (WTs), while no significant difference in transpiration was observed between pip2;5 KD and WT. Upon mild water deficit or low ABA concentration treatments, transpiration and gs decreased more in PIP2;5 OE lines and less in pip2;5 KD lines, in comparison with WTs. In the detached epidermis, ABA treatment induced faster stomatal closing in PIP2;5 OE lines compared to WTs, while pip2;5 KD stomata were ABA insensitive. These phenotypes were associated with guard cell ROS accumulation. Additionally, PIP2;5 is involved in the transpiration decrease observed under high VPD. These data indicate that maize PIP2;5 is a key actor increasing the sensitivity of stomatal closure to water deficit.

Different avenues for progress apply to drought tolerance, water use efficiency and yield in dry areas.

Drought tolerance, water use efficiency (WUE) and yield in dry areas are often considered as synonyms. However, they correspond to markedly different suites of physiological mechanisms, based on combinations of alleles constrained by evolution into consistent strategies. Improving (i) drought tolerance, sensu stricto, involves extreme conservative strategy with protection and repair mechanisms; (ii) WUE most often results in small plants but avenues exist with lower penalties for growth, that is, by reducing night transpiration; (iii) yield for drought prone areas involves both constititutive traits (e.g. phenology or plant architecture), favourable for most environmental scenarios, and adaptive physiological traits whose effects suited to a given scenario. Genetic improvement of the latter would requires identification of scenario-dependent combinations of alleles, involving phenomics, modelling and genomic prediction.

In pursuit of a better world: crop improvement and the CGIAR.

The CGIAR crop improvement (CI) programs, unlike commercial CI programs, which are mainly geared to profit though meeting farmers' needs, are charged with meeting multiple objectives with target populations that include both farmers and the community at large. We compiled the opinions from >30 experts in the private and public sector on key strategies, methodologies, and activities that could the help CGIAR meet the challenges of providing farmers with improved varieties while simultaneously meeting the goals of: (i) nutrition, health, and food security; (ii) poverty reduction, livelihoods, and jobs; (iii) gender equality, youth, and inclusion; (iv) climate adaptation and mitigation; and (v) environmental health and biodiversity. We review the crop improvement processes starting with crop choice, moving through to breeding objectives, production of potential new varieties, selection, and finally adoption by farmers. The importance of multidisciplinary teams working towards common objectives is stressed as a key factor to success. The role of the distinct disciplines, actors, and their interactions throughout the process from crop choice through to adoption by farmers is discussed and illustrated.

Breeding custom-designed crops for improved drought adaptation.

The current pace of crop improvement is inadequate to feed the burgeoning human population by 2050. Higher, more stable, and sustainable crop production is required against a backdrop of drought stress, which causes significant losses in crop yields. Tailoring crops for drought adaptation may hold the key to address these challenges and provide resilient production systems for future harvests. Understanding the genetic and molecular landscape of the functionality of alleles associated with adaptive traits will make designer crop breeding the prospective approach for crop improvement. Here, we highlight the potential of genomics technologies combined with crop physiology for high-throughput identification of the genetic architecture of key drought-adaptive traits and explore innovative genomic breeding strategies for designing future crops.

Maize adaptation across temperate climates was obtained via expression of two florigen genes.

Expansion of the maize growing area was central for food security in temperate regions. In addition to the suppression of the short-day requirement for floral induction, it required breeding for a large range of flowering time that compensates the effect of South-North gradients of temperatures. Here we show the role of a novel florigen gene, ZCN12, in the latter adaptation in cooperation with ZCN8. Strong eQTLs of ZCN8 and ZCN12, measured in 327 maize lines, accounted for most of the genetic variance of flowering time in platform and field experiments. ZCN12 had a strong effect on flowering time of transgenic Arabidopsis thaliana plants; a path analysis showed that it directly affected maize flowering time together with ZCN8. The allelic composition at ZCN QTLs showed clear signs of selection by breeders. This suggests that florigens played a central role in ensuring a large range of flowering time, necessary for adaptation to temperate areas.

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.

Enabling reusability of plant phenomic datasets with MIAPPE 1.1.

Enabling data reuse and knowledge discovery is increasingly critical in modern science, and requires an effort towards standardising data publication practices. This is particularly challenging in the plant phenotyping domain, due to its complexity and heterogeneity. We have produced the MIAPPE 1.1 release, which enhances the existing MIAPPE standard in coverage, to support perennial plants, in structure, through an explicit data model, and in clarity, through definitions and examples. We evaluated MIAPPE 1.1 by using it to express several heterogeneous phenotyping experiments in a range of different formats, to demonstrate its applicability and the interoperability between the various implementations. Furthermore, the extended coverage is demonstrated by the fact that one of the datasets could not have been described under MIAPPE 1.0. MIAPPE 1.1 marks a major step towards enabling plant phenotyping data reusability, thanks to its extended coverage, and especially the formalisation of its data model, which facilitates its implementation in different formats. Community feedback has been critical to this development, and will be a key part of ensuring adoption of the standard.

Modification of the Expression of the Aquaporin ZmPIP2;5 Affects Water Relations and Plant Growth.

The plasma membrane intrinsic protein PIP2;5 is the most highly expressed aquaporin in maize (Zea mays) roots. Here, we investigated how deregulation of PIP2;5 expression affects water relations and growth using maize overexpression (OE; B104 inbred) or knockout (KO; W22 inbred) lines. The hydraulic conductivity of the cortex cells of roots grown hydroponically was higher in PIP2;5 OE and lower in pip2;5 KO lines compared with the corresponding wild-type plants. While whole-root conductivity decreased in the KO lines compared to the wild type, no difference was observed in OE plants. This paradox was interpreted using the MECHA hydraulic model, which computes the radial flow of water within root sections. The model hints that the plasma membrane permeability of the cells is not radially uniform but that PIP2;5 may be saturated in cell layers with apoplastic barriers, i.e. the endodermis and exodermis, suggesting the presence of posttranslational mechanisms controlling the abundance of PIP in the plasma membrane in these cells. At the leaf level, where the PIP2;5 gene is weakly expressed in wild-type plants, the hydraulic conductance was higher in the PIP2;5 OE lines compared with the wild-type plants, whereas no difference was observed in the pip2;5 KO lines. The temporal trend of leaf elongation rate, used as a proxy for that of xylem water potential, was faster in PIP2;5 OE plants upon mild stress, but not in well-watered conditions, demonstrating that PIP2;5 may play a beneficial role in plant growth under specific conditions.

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.

Genomic prediction of maize yield across European environmental conditions.

The development of germplasm adapted to changing climate is required to ensure food security1,2. Genomic prediction is a powerful tool to evaluate many genotypes but performs poorly in contrasting environmental scenarios3-7 (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 fields9,10. 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.

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.

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.

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.

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.

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.