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?

Genetic variation for grain nutritional profile and yield potential in sorghum and the possibility of selection for drought tolerance under irrigated conditions

BACKGROUND

Increasing grain nutritional value in sorghum (Sorghum bicolor) is a paramount breeding objective, as is increasing drought resistance (DR), because sorghum is grown mainly in drought-prone areas. The genetic basis of grain nutritional traits remains largely unknown. Marker-assisted selection using significant loci identified through genome-wide association study (GWAS) shows potential for selecting desirable traits in crops. This study assessed natural variation available in sorghum accessions from around the globe to identify novel genes or genomic regions with potential for improving grain nutritional value, and to study associations between DR traits and grain weight and nutritional composition.

RESULTS

We dissected the genetic architecture of grain nutritional composition, protein content, thousand-kernel weight (TKW), and plant height (PH) in sorghum through GWAS of 163 unique African and Asian accessions under irrigated and post-flowering drought conditions. Several QTLs were detected. Some were significantly associated with DR, TKW, PH, protein, and Zn, Mn, and Ca contents. Genomic regions on chromosomes 1, 2, 4, 8, 9, and 10 were associated with TKW, nutritional, and DR traits; colocalization patterns of these markers indicate potential for simultaneous improvement of these traits. In African accessions, markers associated with TKW were mapped to six regions also associated with protein, Zn, Ca, Mn, Na, and DR, suggesting the potential for simultaneous selection for higher grain nutrition and TKW. Our results indicate that it may be possible to select for increased DR on the basis of grain nutrition and weight potential.

CONCLUSIONS

This study provides a valuable resource for selecting landraces for use in plant breeding programs and for identifying loci that may contribute to grain nutrition and weight with the hope of producing cultivars that combine improved yield traits, nutrition, and DR.

Improving the cotton simulation model, GOSSYM, for soil, photosynthesis, and transpiration processes

GOSSYM, a mechanistic, process-level cotton crop simulation model, has a two-dimensional (2D) gridded soil model called Rhizos that simulates the below-ground processes daily. Water movement is based on gradients of water content and not hydraulic heads. In GOSSYM, photosynthesis is calculated using a daily empirical light response function that requires calibration for response to elevated carbon dioxide (CO₂). This report discusses improvements made to the GOSSYM model for soil, photosynthesis, and transpiration processes. GOSSYM's predictions of below-ground processes using Rhizos are improved by replacing it with 2DSOIL, a mechanistic 2D finite element soil process model. The photosynthesis and transpiration model in GOSSYM is replaced with a Farquhar biochemical model and Ball-Berry leaf energy balance model. The newly developed model (modified GOSSYM) is evaluated using field-scale and experimental data from SPAR (soil-plant-atmosphere-research) chambers. Modified GOSSYM better predicted net photosynthesis (root mean square error (RMSE) 25.5 versus 45.2 g CO₂ m^-2 day^-1; index of agreement (IA) 0.89 versus 0.76) and transpiration (RMSE 3.3 versus 13.7 L m^-2 day^-1; IA 0.92 versus 0.14) and improved the yield prediction by 6.0%. Modified GOSSYM improved the simulation of soil, photosynthesis, and transpiration processes, thereby improving the predictive ability of cotton crop growth and development.

Reproductive Stage Drought Tolerance in Wheat: Importance of Stomatal Conductance and Plant Growth Regulators

Drought stress requires plants to adjust their water balance to maintain tissue water levels. Isohydric plants (‘water-savers’) typically achieve this through stomatal closure, while anisohydric plants (‘water-wasters’) use osmotic adjustment and maintain stomatal conductance. Isohydry or anisohydry allows plant species to adapt to different environments. In this paper we show that both mechanisms occur in bread wheat (Triticum aestivum L.). Wheat lines with reproductive drought-tolerance delay stomatal closure and are temporarily anisohydric, before closing stomata and become isohydric at higher threshold levels of drought stress. Drought-sensitive wheat is isohydric from the start of the drought treatment. The capacity of the drought-tolerant line to maintain stomatal conductance correlates with repression of ABA synthesis in spikes and flag leaves. Gene expression profiling revealed major differences in the drought response in spikes and flag leaves of both wheat lines. While the isohydric drought-sensitive line enters a passive growth mode (arrest of photosynthesis, protein translation), the tolerant line mounts a stronger stress defence response (ROS protection, LEA proteins, cuticle synthesis). The drought response of the tolerant line is characterised by a strong response in the spike, displaying enrichment of genes involved in auxin, cytokinin and ethylene metabolism/signalling. While isohydry may offer advantages for longer term drought stress, anisohydry may be more beneficial when drought stress occurs during the critical stages of wheat spike development, ultimately improving grain yield.

Plant growth: the What, the How, and the Why

Growth is a widely used term in plant science and ecology, but it can have different meanings depending on the context and the spatiotemporal scale of analysis. At the meristem level, growth is associated with the production of cells and initiation of new organs. At the organ or plant scale and over short time periods, growth is often used synonymously with tissue expansion, while over longer time periods the increase in biomass is a common metric. At even larger temporal and spatial scales, growth is mostly described as net primary production. Here, we first address the question 'what is growth?'. We propose a general framework to distinguish between the different facets of growth, and the corresponding physiological processes, environmental drivers and mathematical formalisms. Based on these different definitions, we then review how plant growth can be measured and analysed at different organisational, spatial and temporal scales. We conclude by discussing why gaining a better understanding of the different facets of plant growth is essential to disentangle genetic and environmental effects on the phenotype, and to uncover the causalities around source or sink limitations of plant growth.

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.

Importance of the description of light interception in crop growth models

Canopy light interception determines the amount of energy captured by a crop, and is thus critical to modeling crop growth and yield, and may substantially contribute to the prediction uncertainty of crop growth models (CGMs). We thus analyzed the canopy light interception models of the 26 wheat (Triticum aestivum) CGMs used by the Agricultural Model Intercomparison and Improvement Project (AgMIP). Twenty-one CGMs assume that the light extinction coefficient (K) is constant, varying from 0.37 to 0.80 depending on the model. The other models take into account the illumination conditions and assume either that all green surfaces in the canopy have the same inclination angle (θ) or that θ distribution follows a spherical distribution. These assumptions have not yet been evaluated due to a lack of experimental data. Therefore, we conducted a field experiment with five cultivars with contrasting leaf stature sown at normal and double row spacing, and analyzed θ distribution in the canopies from three-dimensional canopy reconstructions. In all the canopies, θ distribution was well represented by an ellipsoidal distribution. We thus carried out an intercomparison between the light interception models of the AgMIP-Wheat CGMs ensemble and a physically based K model with ellipsoidal leaf angle distribution and canopy clumping (KellC). Results showed that the KellC model outperformed current approaches under most illumination conditions and that the uncertainty in simulated wheat growth and final grain yield due to light models could be as high as 45%. Therefore, our results call for an overhaul of light interception models in CGMs.

Use of hydraulic traits for modeling genotype-specific acclimation in cotton under drought

Understanding the genetic and physiological basis of abiotic stress tolerance under field conditions is key to varietal crop improvement in the face of climate variability. Here, we investigate dynamic physiological responses to water stress in silico and their relationships to genotypic variation in hydraulic traits of cotton (Gossypium hirsutum), an economically important species for renewable textile fiber production. In conjunction with an ecophysiological process-based model, heterogeneous data (plant hydraulic traits, spatially-distributed soil texture, soil water content and canopy temperature) were used to examine hydraulic characteristics of cotton, evaluate their consequences on whole plant performance under drought, and explore potential genotype × environment effects. Cotton was found to have R-shaped hydraulic vulnerability curves (VCs), which were consistent under drought stress initiated at flowering. Stem VCs, expressed as percent loss of conductivity, differed across genotypes, whereas root VCs did not. Simulation results demonstrated how plant physiological stress can depend on the interaction between soil properties and irrigation management, which in turn affect genotypic rankings of transpiration in a time-dependent manner. Our study shows how a process-based modeling framework can be used to link genotypic variation in hydraulic traits to differential acclimating behaviors under drought.

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.

Leveraging genome-enabled growth models to study shoot growth responses to water deficit in rice

Elucidating genotype-by-environment interactions and partitioning its contribution to phenotypic variation remains a challenge for plant scientists. We propose a framework that utilizes genome-wide markers to model genotype-specific shoot growth trajectories as a function of time and soil water availability. A rice diversity panel was phenotyped daily for 21 d using an automated, high-throughput image-based, phenotyping platform that enabled estimation of daily shoot biomass and soil water content. Using these data, we modeled shoot growth as a function of time and soil water content, and were able to determine the time point where an inflection in the growth trajectory occurred. We found that larger, more vigorous plants exhibited an earlier repression in growth compared with smaller, slow-growing plants, indicating a trade-off between early vigor and tolerance to prolonged water deficits. Genomic inference for model parameters and time of inflection (TOI) identified several candidate genes. This study is the first to utilize a genome-enabled growth model to study drought responses in rice, and presents a new approach to jointly model dynamic morpho-physiological responses and environmental covariates.

The case for improving crop carbon sink strength or plasticity for a CO2-rich future

Atmospheric CO₂ concentration [CO₂] has increased from 260 to 280μmolmol^-1 (level during crop domestication up to the industrial revolution) to currently 400 and will reach 550μmolmol^-1 by 2050. C3 crops are expected to benefit from elevated [CO₂] (e-CO₂) thanks to photosynthesis responsiveness to [CO₂] but this may require greater sink capacity. We review recent literature on crop e-CO₂ responses, related source-sink interactions, how abiotic stresses potentially interact, and prospects to improve e-CO₂ response via breeding or genetic engineering. Several lines of evidence suggest that e-CO₂ responsiveness is related either to sink intrinsic capacity or adaptive plasticity, for example, involving enhanced branching. Wild relatives and old cultivars mostly showed lower photosynthetic rates, less downward acclimation of photosynthesis to e-CO₂ and responded strongly to e-CO₂ due to greater phenotypic plasticity. While reverting to such archaic traits would be an inappropriate strategy for breeding, we argue that substantial enhancement of vegetative sink vigor, inflorescence size and/or number and root sinks will be necessary to fully benefit from e-CO₂. Potential ideotype features based on enhanced sinks are discussed. The generic 'feast-famine' sugar signaling pathway may be suited to engineer sink strength tissue-specifically and stage-specifically and help validate ideotype concepts. Finally, we argue that models better accounting for acclimation to e-CO₂ are needed to predict which trait combinations should be targeted by breeders for a CO₂-rich world.

An integrated research framework combining genomics, systems biology, physiology, modelling and breeding for legume improvement in response to elevated CO2 under climate change scenario

How unprecedented changes in climatic conditions will impact yield and productivity of some crops and their response to existing stresses, abiotic and biotic interactions is a key global concern. Climate change can also alter natural species' abundance and distribution or favor invasive species, which in turn can modify ecosystem dynamics and the provisioning of ecosystem services. Basic anatomical differences in C3 and C4 plants lead to their varied responses to climate variations. In plants having a C3 pathway of photosynthesis, increased atmospheric carbon dioxide (CO2) positively regulates photosynthetic carbon (C) assimilation and depresses photorespiration. Legumes being C3 plants, they may be in a favorable position to increase biomass and yield through various strategies. This paper comprehensively presents recent progress made in the physiological and molecular attributes in plants with special emphasis on legumes under elevated CO2 conditions in a climate change scenario. A strategic research framework for future action integrating genomics, systems biology, physiology and crop modelling approaches to cope with changing climate is also discussed. Advances in sequencing and phenotyping methodologies make it possible to use vast genetic and genomic resources by deploying high resolution phenotyping coupled with high throughput multi-omics approaches for trait improvement. Integrated crop modelling studies focusing on farming systems design and management, prediction of climate impacts and disease forecasting may also help in planning adaptation. Hence, an integrated research framework combining genomics, plant molecular physiology, crop breeding, systems biology and integrated crop-soil-climate modelling will be very effective to cope with climate change.

Impacts of Drought on Maize and Soybean Production in Northeast China During the Past Five Decades

Climate change has a distinct impact on agriculture in China, particularly in the northeast, a key agriculture area sensitive to extreme hydroclimate events. Using monthly climate and agriculture data, the influence of drought on maize and soybean yields-two of the main crops in the region-in northeast China since 1961 to 2017 were investigated. The results showed that the temperature in the growing season increased by 1.0 °C from the period 1998-2017 to the period 1961-1980, while the annual precipitation decreased slightly. However, precipitation trends varied throughout the growing season (May-September), increasing slightly in May and June, but decreasing in July, August and September, associated with the weakening of the East Asian summer monsoon. Consequently, the annual and growing season drought frequency increased by 15%, and 25%, respectively, in the period 1998-2017 relative to the period 1961-1980. The highest drought frequency (55%) was observed in September. At the same time, the drought intensity during the growing season increased by 7.8%. The increasing frequency and intensity of drought had negative influences on the two crops. During moderate drought years in the period 1961-2017, 3.2% and 10.4% of the provincial maize and soybean yields were lost, respectively. However, during more severe drought years, losses doubled for soybean (21.8%), but increased more than four-fold for maize (14.0%). Moreover, in comparison to the period 1961-1980, a higher proportion of the yields were lost in the period 1998-2017, particularly for maize, which increased by 15% (increase for soybean was 2.4%). This change largely depends on increasing droughts in August and September, when both crops are in their filling stages. The impact of drought on maize and soybean production was different during different growth stages, where a strong relationship was noted between drought and yield loss of soybean in its filling stage. Given the sensitivity of maize and soybean yields in northeast China to drought, and the observed production trends, climate change will likely have significant negative impacts on productivity in the future.

Towards a multiscale crop modelling framework for climate change adaptation assessment

Predicting the consequences of manipulating genotype (G) and agronomic management (M) on agricultural ecosystem performances under future environmental (E) conditions remains a challenge. Crop modelling has the potential to enable society to assess the efficacy of G × M technologies to mitigate and adapt crop production systems to climate change. Despite recent achievements, dedicated research to develop and improve modelling capabilities from gene to global scales is needed to provide guidance on designing G × M adaptation strategies with full consideration of their impacts on both crop productivity and ecosystem sustainability under varying climatic conditions. Opportunities to advance the multiscale crop modelling framework include representing crop genetic traits, interfacing crop models with large-scale models, improving the representation of physiological responses to climate change and management practices, closing data gaps and harnessing multisource data to improve model predictability and enable identification of emergent relationships. A fundamental challenge in multiscale prediction is the balance between process details required to assess the intervention and predictability of the system at the scales feasible to measure the impact. An advanced multiscale crop modelling framework will enable a gene-to-farm design of resilient and sustainable crop production systems under a changing climate at regional-to-global scales.

Impacts of climate change and crop management practices on soybean phenology changes in China

Crop phenology is determined by both climatic factors and agronomic management practices such as sowing date and cultivar characteristics. Exploring the interactive effects of climate change and crop management practices on crop phenology can be used to devise adaptation strategies to mitigate climate change. The objectives of this study were to: 1) examined trends in soybean (Glycine max L.) phenological development in China from 1981 to 2010; 2) isolate and quantify impacts of climate change and crop management on changes in soybean phenology; 3) determine the relative contribution of climate change and crop management to observed changes in soybean phenology; and 4) determine the relative contribution of temperature, precipitation, and sunshine hours to changes in soybean phenology. Changes in soybean phenology were observed across the major soybean producing area of eastern China during 1981-2010. Observed dates of sowing, emergence, anthesis, and maturity were delayed by an average of 1.78, 0.83, 0.19, and 0.62 days decade-1, respectively. Additionally, the lengths of the vegetative growth period and the soybean growing season were shortened by an average of 0.62 and 1.16 days decade-1, respectively. Conversely, the reproductive period was lengthened by an average of 0.43 days decade-1. Crop management practices had greater influence on sowing, emergence, and maturity dates than climate change. The direction of the changes to phenology trends created by management and climate change were opposite to each other. The relative influence of climate change on dates of anthesis, lengths of the vegetative and reproductive growth periods and growing season was larger than the influence of crop management practices. Mean temperature was the dominant climatic factor influencing most soybean phenological stages and phases. Delayed sowing dates and use of longer-duration cultivars are management adaptations that farmers have used to adapt to climate change occurring in past decades and that can continue to be used. These results indicate that farmers have a wider sowing window in spring and can select cultivars with long growing season duration and frost-tolerance to mitigate detrimental effects of a future warmer climate.

Can Metabolite- and Transcript-Based Selection for Drought Tolerance in Solanum tuberosum Replace Selection on Yield in Arid Environments?

Climate models predict an increased likelihood of drought, demanding efficient selection for drought tolerance to maintain yield stability. Classic tolerance breeding relies on selection for yield in arid environments, which depends on yield trials and takes decades. Breeding could be accelerated by marker-assisted selection (MAS). As an alternative to genomic markers, transcript and metabolite markers have been suggested for important crops but also for orphan corps. For potato, we suggested a random-forest-based model that predicts tolerance from leaf metabolite and transcript levels with a precision of more than 90% independent of the agro-environment. To find out how the model based selection compares to yield-based selection in arid environments, we applied this approach to a population of 200 tetraploid Solanum tuberosum ssp. tuberosum lines segregating for drought tolerance. Twenty-four lines were selected into a phenotypic subpopulation (PP_t) for superior tolerance based on relative tuber starch yield data from three drought stress trials. Two subpopulations with superior (MP_t) and inferior (MP_s) tolerance were selected based on drought tolerance predictions based on leaf metabolite and transcript levels from two sites. The 60 selected lines were phenotyped for yield and drought tolerance in 10 multi-environment drought stress trials representing typical Central European drought scenarios. Neither selection affected development or yield potential. Lines with superior drought tolerance and high yields under stress were over-represented in both populations selected for superior tolerance, with a higher number in PP_t compared to MP_t. However, selection based on leaf metabolites may still be an alternative to yield-based selection in arid environments as it works on leaves sampled in breeder's fields independent of drought trials. As the selection against low tolerance was ineffective, the method is best used in combination with tools that select against sensitive genotypes. Thus, metabolic and transcript marker-based selection for drought tolerance is a viable alternative to the selection on yield in arid environments.

Modelling tiller growth and mortality as a sink-driven process using Ecomeristem: implications for biomass sorghum ideotyping

BACKGROUND AND AIMS

Plant modelling can efficiently support ideotype conception, particularly in multi-criteria selection contexts. This is the case for biomass sorghum, implying the need to consider traits related to biomass production and quality. This study evaluated three modelling approaches for their ability to predict tiller growth, mortality and their impact, together with other morphological and physiological traits, on biomass sorghum ideotype prediction.

METHODS

Three Ecomeristem model versions were compared to evaluate whether tillering cessation and mortality were source (access to light) or sink (age-based hierarchical access to C supply) driven. They were tested using a field data set considering two biomass sorghum genotypes at two planting densities. An additional data set comparing eight genotypes was used to validate the best approach for its ability to predict the genotypic and environmental control of biomass production. A sensitivity analysis was performed to explore the impact of key genotypic parameters and define optimal parameter combinations depending on planting density and targeted production (sugar and fibre).

KEY RESULTS

The sink-driven control of tillering cessation and mortality was the most accurate, and represented the phenotypic variability of studied sorghum genotypes in terms of biomass production and partitioning between structural and non-structural carbohydrates. Model sensitivity analysis revealed that light conversion efficiency and stem diameter are key traits to target for improving sorghum biomass within existing genetic diversity. Tillering contribution to biomass production appeared highly genotype and environment dependent, making it a challenging trait for designing ideotypes.

CONCLUSIONS

By modelling tiller growth and mortality as sink-driven processes, Ecomeristem could predict and explore the genotypic and environmental variability of biomass sorghum production. Its application to larger sorghum genetic diversity considering water deficit regulations and its coupling to a genetic model will make it a powerful tool to assist ideotyping for current and future climatic scenario.

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.

Improving process-based crop models to better capture genotype×environment×management interactions

In spite of the increasing expectation for process-based crop modelling to capture genotype (G) by environment (E) by management (M) interactions to support breeding selections, it remains a challenge to use current crop models to accurately predict phenotypes from genotypes or from candidate genes. We use wheat as a target crop and the APSIM farming systems model (Holzworth et al., 2014) as an example to analyse the current status of process-based crop models with a major focus on need to improve simulation of specific eco-physiological processes and their linkage to underlying genetic controls. For challenging production environments in Australia, we examine the potential opportunities to capture physiological traits, and to integrate genetic and molecular approaches for future model development and applications. Model improvement will require both reducing the uncertainty in simulating key physiological processes and enhancing the capture of key observable traits and underlying genetic control of key physiological responses to environment. An approach consisting of three interactive stages is outlined to (i) improve modelling of crop physiology, (ii) develop linkage from model parameter to genotypes and further to loci or alleles, and (iii) further link to gene expression pathways. This helps to facilitate the integration of modelling, phenotyping, and functional gene detection and to effectively advance modelling of G×E×M interactions. While gene-based modelling is not always needed to simulate G×E×M, including well-understood gene effects can improve the estimation of genotype effects and prediction of phenotypes. Specific examples are given for enhanced modelling of wheat in the APSIM framework.

A multi-model framework for the Arabidopsis life cycle

Linking our understanding of biological processes at different scales is a major conceptual challenge in biology and aggravated by differences in research methods. Modelling can be a useful approach to consolidating our understanding across traditional research domains. The laboratory model species Arabidopsis is very widely used to study plant growth processes and has also been tested more recently in ecophysiology and population genetics. However, approaches from crop modelling that might link these domains are rarely applied to Arabidopsis. Here, we combine plant growth models with phenology models from ecophysiology, using the agent-based modelling language Chromar. We introduce a simpler Framework Model of vegetative growth for Arabidopsis, FM-lite. By extending this model to include inflorescence and fruit growth and seed dormancy, we present a whole-life-cycle, multi-model FM-life, which allows us to simulate at the population level in various genotype × environment scenarios. Environmental effects on plant growth distinguish between the simulated life history strategies that were compatible with previously described Arabidopsis phenology. Our results simulate reproductive success that is founded on the broad range of physiological processes familiar from crop models and suggest an approach to simulating evolution directly in future.

Phenotyping a Dynamic Trait: Leaf Growth of Perennial Ryegrass Under Water Limiting Conditions

Water limitation is one of the major factors reducing crop productivity worldwide. In order to develop efficient breeding strategies to improve drought tolerance, accurate methods to identify when a plant reduces growth as a consequence of water deficit have yet to be established. In perennial ryegrass (Lolium perenne L.), an important forage grass of the Poaceae family, leaf elongation is a key factor determining plant growth and hence forage yield. Although leaf elongation has been shown to be temperature-dependent under non-stress conditions, the impact of water limitation on leaf elongation in perennial ryegrass is poorly understood. We describe a method for quantifying tolerance to water deficit based on leaf elongation in relation to temperature and soil moisture in perennial ryegrass. With decreasing soil moisture, three growth response phases were identified: first, a "normal" phase where growth is mainly determined by temperature, second a "slow" phase where leaf elongation decreases proportionally to soil water potential and third an "arrest" phase where leaf growth terminates. A custom R function was able to quantify the points which demarcate these phases and can be used to describe the response of plants to water deficit. Applied to different perennial ryegrass genotypes, this function revealed significant genotypic variation in the response of leaf growth to temperature and soil moisture. Dynamic phenotyping of leaf elongation can be used as a tool to accurately quantify tolerance to water deficit in perennial ryegrass and to improve this trait by breeding. Moreover, the tools presented here are applicable to study the plant response to other stresses in species with linear, graminoid leaf morphology.

Computational aspects underlying genome to phenome analysis in plants

Recent advances in genomics technologies have greatly accelerated the progress in both fundamental plant science and applied breeding research. Concurrently, high-throughput plant phenotyping is becoming widely adopted in the plant community, promising to alleviate the phenotypic bottleneck. While these technological breakthroughs are significantly accelerating quantitative trait locus (QTL) and causal gene identification, challenges to enable even more sophisticated analyses remain. In particular, care needs to be taken to standardize, describe and conduct experiments robustly while relying on plant physiology expertise. In this article, we review the state of the art regarding genome assembly and the future potential of pangenomics in plant research. We also describe the necessity of standardizing and describing phenotypic studies using the Minimum Information About a Plant Phenotyping Experiment (MIAPPE) standard to enable the reuse and integration of phenotypic data. In addition, we show how deep phenotypic data might yield novel trait-trait correlations and review how to link phenotypic data to genomic data. Finally, we provide perspectives on the golden future of machine learning and their potential in linking phenotypes to genomic features.

Agronomic Management for Enhancing Plant Tolerance to Abiotic Stresses: High and Low Values of Temperature, Light Intensity, and Relative Humidity

Abiotic stresses have direct effects on plant growth and development. In agriculture, sub-optimal values of temperature, light intensity, and relative humidity can limit crop yield and reduce product quality. Temperature has a direct effect on whole plant metabolism, and low or high temperatures can reduce growth or induce crop damage. Solar radiation is the primary driver of crop production, but light intensity can also have negative effects, especially if concurrent with water stress and high temperature. Relative humidity also plays an important role by regulating transpiration and water balance of crops. In this review, the main effects of these abiotic stresses on crop performance are reported, and agronomic strategies used to avoid or mitigate the effects of these stresses are discussed.

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.

Dissecting the rootstock control of scion transpiration using model-assisted analyses in grapevine

How rootstocks contribute to the control of scion transpiration under drought is poorly understood. We investigated the role of root characteristics, hydraulic conductance and chemical signals (abscisic acid, ABA) in the response of stomatal conductance (gs) and transpiration (E) to drought in Cabernet Sauvignon (Vitis vinifera) grafted onto drought-sensitive (Vitis riparia) and drought-tolerant (Vitis berlandieri × Vitis rupestris 110R) rootstocks. All combinations showed a concomitant reduction in gs and E, and an increase in xylem sap ABA concentration during the drought cycle. Cabernet Sauvignon grafted onto 110R exhibited higher gs and E under well-watered and moderate water deficit, but all combinations converged as water deficit increased. These results were integrated into three permutations of a whole-plant transpiration model that couples both chemical (i.e., ABA) and hydraulic signals in the modelling of stomatal control. Model comparisons revealed that both hydraulic and chemical signals were important for rootstock-specific stomatal regulation. Moreover, model parameter comparison and sensitivity analysis highlighted two major parameters differentiating the rootstocks: (i) ABA biosynthetic activity and (ii) the hydraulic conductance between the rhizosphere and soil-root interface determined by root system architecture. These differences in root architecture, specifically a higher root length area in 110R, likely explain its higher E and gs observed at low and moderate water deficit.

Accelerating genetic gains in legumes for the development of prosperous smallholder agriculture: integrating genomics, phenotyping, systems modelling and agronomy

Grain legumes form an important component of the human diet, provide feed for livestock, and replenish soil fertility through biological nitrogen fixation. Globally, the demand for food legumes is increasing as they complement cereals in protein requirements and possess a high percentage of digestible protein. Climate change has enhanced the frequency and intensity of drought stress, posing serious production constraints, especially in rainfed regions where most legumes are produced. Genetic improvement of legumes, like other crops, is mostly based on pedigree and performance-based selection over the past half century. To achieve faster genetic gains in legumes in rainfed conditions, this review proposes the integration of modern genomics approaches, high throughput phenomics, and simulation modelling in support of crop improvement that leads to improved varieties that perform with appropriate agronomy. Selection intensity, generation interval, and improved operational efficiencies in breeding are expected to further enhance the genetic gain in experimental plots. Improved seed access to farmers, combined with appropriate agronomic packages in farmers' fields, will deliver higher genetic gains. Enhanced genetic gains, including not only productivity but also nutritional and market traits, will increase the profitability of farming and the availability of affordable nutritious food especially in developing countries.

The physiological and genetic basis of combined drought and heat tolerance in wheat

Drought and heat stress cause losses in wheat productivity in major growing regions worldwide, and both the occurrence and the severity of these events are likely to increase with global climate change. Water deficits and high temperatures frequently occur simultaneously at sensitive growth stages, reducing wheat yields by reducing grain number or weight. Although genetic variation and underlying quantitative trait loci for either individual stress are known, the combination of the two stresses has rarely been studied. Complex and often antagonistic physiology means that genetic loci underlying tolerance to the combined stress are likely to differ from those for drought or heat stress tolerance alone. Here, we review what is known of the physiological traits and genetic control of drought and heat tolerance in wheat and discuss potential physiological traits to study for combined tolerance. We further place this knowledge in the context of breeding for new, more tolerant varieties and discuss opportunities and constraints. We conclude that a fine control of water relations across the growing cycle will be beneficial for combined tolerance and might be achieved through fine management of spatial and temporal gas exchange.

Translating High-Throughput Phenotyping into Genetic Gain

Inability to efficiently implement high-throughput field phenotyping is increasingly perceived as a key component that limits genetic gain in breeding programs. Field phenotyping must be integrated into a wider context than just choosing the correct selection traits, deployment tools, evaluation platforms, or basic data-management methods. Phenotyping means more than conducting such activities in a resource-efficient manner; it also requires appropriate trial management and spatial variability handling, definition of key constraining conditions prevalent in the target population of environments, and the development of more comprehensive data management, including crop modeling. This review will provide a wide perspective on how field phenotyping is best implemented. It will also outline how to bridge the gap between breeders and 'phenotypers' in an effective manner.

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.

A 3-D functional-structural grapevine model that couples the dynamics of water transport with leaf gas exchange

Background and Aims

Predicting both plant water status and leaf gas exchange under various environmental conditions is essential for anticipating the effects of climate change on plant growth and productivity. This study developed a functional-structural grapevine model which combines a mechanistic understanding of stomatal function and photosynthesis at the leaf level (i.e. extended Farqhuhar-von Caemmerer-Berry model) and the dynamics of water transport from soil to individual leaves (i.e. Tardieu-Davies model).

Methods

The model included novel features that account for the effects of xylem embolism (fPLC) on leaf hydraulic conductance and residual stomatal conductance (g0), variable root and leaf hydraulic conductance, and the microclimate of individual organs. The model was calibrated with detailed datasets of leaf photosynthesis, leaf water potential, xylem sap abscisic acid (ABA) concentration and hourly whole-plant transpiration observed within a soil drying period, and validated with independent datasets of whole-plant transpiration under both well-watered and water-stressed conditions.

Key Results

The model well captured the effects of radiation, temperature, CO2 and vapour pressure deficit on leaf photosynthesis, transpiration, stomatal conductance and leaf water potential, and correctly reproduced the diurnal pattern and decline of water flux within the soil drying period. In silico analyses revealed that decreases in g0 with increasing fPLC were essential to avoid unrealistic drops in leaf water potential under severe water stress. Additionally, by varying the hydraulic conductance along the pathway (e.g. root and leaves) and changing the sensitivity of stomatal conductance to ABA and leaf water potential, the model can produce different water use behaviours (i.e. iso- and anisohydric).

Conclusions

The robust performance of this model allows for modelling climate effects from individual plants to fields, and for modelling plants with complex, non-homogenous canopies. In addition, the model provides a basis for future modelling efforts aimed at describing the physiology and growth of individual organs in relation to water status.

Towards a new generation of agricultural system data, models and knowledge products: Design and improvement

This paper presents ideas for a new generation of agricultural system models that could meet the needs of a growing community of end-users exemplified by a set of Use Cases. We envision new data, models and knowledge products that could accelerate the innovation process that is needed to achieve the goal of achieving sustainable local, regional and global food security. We identify desirable features for models, and describe some of the potential advances that we envisage for model components and their integration. We propose an implementation strategy that would link a "pre-competitive" space for model development to a "competitive space" for knowledge product development and through private-public partnerships for new data infrastructure. Specific model improvements would be based on further testing and evaluation of existing models, the development and testing of modular model components and integration, and linkages of model integration platforms to new data management and visualization tools.

The combined and separate impacts of climate extremes on the current and future US rainfed maize and soybean production under elevated CO2

Heat and drought are two emerging climatic threats to the US maize and soybean production, yet their impacts on yields are collectively determined by the magnitude of climate change and rising atmospheric CO2 concentrations. This study quantifies the combined and separate impacts of high temperature, heat and drought stresses on the current and future US rainfed maize and soybean production and for the first time characterizes spatial shifts in the relative importance of individual stress. Crop yields are simulated using the Agricultural Production Systems Simulator (APSIM), driven by high-resolution (12 km) dynamically downscaled climate projections for 1995-2004 and 2085-2094. Results show that maize and soybean yield losses are prominent in the US Midwest by the late 21st century under both Representative Concentration Pathway (RCP) 4.5 and RCP8.5 scenarios, and the magnitude of loss highly depends on the current vulnerability and changes in climate extremes. Elevated atmospheric CO2 partially but not completely offsets the yield gaps caused by climate extremes, and the effect is greater in soybean than in maize. Our simulations suggest that drought will continue to be the largest threat to US rainfed maize production under RCP4.5 and soybean production under both RCP scenarios, whereas high temperature and heat stress take over the dominant stress of drought on maize under RCP8.5. We also reveal that shifts in the geographic distributions of dominant stresses are characterized by the increase in concurrent stresses, especially for the US Midwest. These findings imply the importance of considering heat and drought stresses simultaneously for future agronomic adaptation and mitigation strategies, particularly for breeding programs and crop management. The modeling framework of partitioning the total effects of climate change into individual stress impacts can be applied to the study of other crops and agriculture systems.

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.

Proteomic and physiological approach reveals drought-induced changes in rapeseeds: Water-saver and water-spender strategy

The cultivar-dependent differences in Brassica napus L. seed yield are significantly affected by drought stress. Here, the response of leaf proteome to long-term drought (28days) was studied in cultivars (cvs): Californium (C), Cadeli (D), Navajo (N), and Viking (V). Analysis of twenty-four 2-D DIGE gels revealed 134 spots quantitatively changed at least 2-fold; from these, 79 proteins were significantly identified by MALDI-TOF/TOF. According to the differences in water use, the cultivars may be assigned to two categories: water-savers or water-spenders. In the water-savers group (cvs C+D), proteins related to nitrogen assimilation, ATP and redox homeostasis were increased under stress, while in the water-spenders category (cvs N+V), carbohydrate/energy, photosynthesis, stress related and rRNA processing proteins were increased upon stress. Taking all data together, we indicated cv C as a drought-adaptable water-saver, cv D as a medium-adaptable water-saver, cv N as a drought-adaptable water-spender, and cv V as a low-adaptable drought sensitive water-spender rapeseed. Proteomic data help to evaluate the impact of drought and the extent of genotype-based adaptability and contribute to the understanding of their plasticity. These results provide new insights into the provenience-based drought acclimation/adaptation strategy of contrasting winter rapeseeds and link data at gasometric, biochemical, and proteome level.

SIGNIFICANCE

Soil moisture deficit is a real problem for every crop. The data in this study demonstrates for the first time that in stem-prolongation phase cultivars respond to progressive drought in different ways and at different levels. Analysis of physiological and proteomic data showed two different water regime-related strategies: water-savers and spenders. However, not only water uptake rate itself, but also individual protein abundances, gasometric and biochemical parameters together with final biomass accumulation after stress explained genotype-based responses. Interestingly, under a mixed climate profile, both water-use patterns (savers or spenders) can be appropriate for drought adaptation. These data suggest, than complete "acclimation image" of rapeseeds in stem-prolongation phase under drought could be reached only if these characteristics are taken, explained and understood together.

Connecting Biochemical Photosynthesis Models with Crop Models to Support Crop Improvement

The next advance in field crop productivity will likely need to come from improving crop use efficiency of resources (e.g., light, water, and nitrogen), aspects of which are closely linked with overall crop photosynthetic efficiency. Progress in genetic manipulation of photosynthesis is confounded by uncertainties of consequences at crop level because of difficulties connecting across scales. Crop growth and development simulation models that integrate across biological levels of organization and use a gene-to-phenotype modeling approach may present a way forward. There has been a long history of development of crop models capable of simulating dynamics of crop physiological attributes. Many crop models incorporate canopy photosynthesis (source) as a key driver for crop growth, while others derive crop growth from the balance between source- and sink-limitations. Modeling leaf photosynthesis has progressed from empirical modeling via light response curves to a more mechanistic basis, having clearer links to the underlying biochemical processes of photosynthesis. Cross-scale modeling that connects models at the biochemical and crop levels and utilizes developments in upscaling leaf-level models to canopy models has the potential to bridge the gap between photosynthetic manipulation at the biochemical level and its consequences on crop productivity. Here we review approaches to this emerging cross-scale modeling framework and reinforce the need for connections across levels of modeling. Further, we propose strategies for connecting biochemical models of photosynthesis into the cross-scale modeling framework to support crop improvement through photosynthetic manipulation.

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

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

Do maize models capture the impacts of heat and drought stresses on yield? Using algorithm ensembles to identify successful approaches

Stresses from heat and drought are expected to increasingly suppress crop yields, but the degree to which current models can represent these effects is uncertain. Here we evaluate the algorithms that determine impacts of heat and drought stress on maize in 16 major maize models by incorporating these algorithms into a standard model, the Agricultural Production Systems sIMulator (APSIM), and running an ensemble of simulations. Although both daily mean temperature and daylight temperature are common choice of forcing heat stress algorithms, current parameterizations in most models favor the use of daylight temperature even though the algorithm was designed for daily mean temperature. Different drought algorithms (i.e., a function of soil water content, of soil water supply to demand ratio, and of actual to potential transpiration ratio) simulated considerably different patterns of water shortage over the growing season, but nonetheless predicted similar decreases in annual yield. Using the selected combination of algorithms, our simulations show that maize yield reduction was more sensitive to drought stress than to heat stress for the US Midwest since the 1980s, and this pattern will continue under future scenarios; the influence of excessive heat will become increasingly prominent by the late 21st century. Our review of algorithms in 16 crop models suggests that the impacts of heat and drought stress on plant yield can be best described by crop models that: (i) incorporate event-based descriptions of heat and drought stress, (ii) consider the effects of nighttime warming, and (iii) coordinate the interactions among multiple stresses. Our study identifies the proficiency with which different model formulations capture the impacts of heat and drought stress on maize biomass and yield production. The framework presented here can be applied to other modeled processes and used to improve yield predictions of other crops with a wide variety of crop models.

Optimization of Allelic Combinations Controlling Parameters of a Peach Quality Model

Process-based models are effective tools to predict the phenotype of an individual in different growing conditions. Combined with a quantitative trait locus (QTL) mapping approach, it is then possible to predict the behavior of individuals with any combinations of alleles. However the number of simulations to explore the realm of possibilities may become infinite. Therefore, the use of an efficient optimization algorithm to intelligently explore the search space becomes imperative. The optimization algorithm has to solve a multi-objective problem, since the phenotypes of interest are usually a complex of traits, to identify the individuals with best tradeoffs between those traits. In this study we proposed to unroll such a combined approach in the case of peach fruit quality described through three targeted traits, using a process-based model with seven parameters controlled by QTL. We compared a current approach based on the optimization of the values of the parameters with a more evolved way to proceed which consists in the direct optimization of the alleles controlling the parameters. The optimization algorithm has been adapted to deal with both continuous and combinatorial problems. We compared the spaces of parameters obtained with different tactics and the phenotype of the individuals resulting from random simulations and optimization in these spaces. The use of a genetic model enabled the restriction of the dimension of the parameter space toward more feasible combinations of parameter values, reproducing relationships between parameters as observed in a real progeny. The results of this study demonstrated the potential of such an approach to refine the solutions toward more realistic ideotypes. Perspectives of improvement are discussed.

Measurement of plant growth in view of an integrative analysis of regulatory networks

As the regulatory networks of growth at the cellular level are elucidated at a fast pace, their complexity is not reduced; on the contrary, the tissue, organ and even whole-plant level affect cell proliferation and expansion by means of development-induced and environment-induced signaling events in growth regulatory processes. Measurement of growth across different levels aids in gaining a mechanistic understanding of growth, and in defining the spatial and temporal resolution of sampling strategies for molecular analyses in the model Arabidopsis thaliana and increasingly also in crop species. The latter claim their place at the forefront of plant research, since global issues and future needs drive the translation from laboratory model-acquired knowledge of growth processes to improvements in crop productivity in field conditions.

Crop yield: challenges from a metabolic perspective

Considering the dual use of plants, as bio-factories for foods and feedstock for bio-refining, along with a rising world population, the plant biotechnology field is currently facing a dramatic challenge to develop crops with higher yield. Furthermore, convergent studies predict that global changes in climate will influence crop productivity by modifying most yield-associated traits. Here, we review recent advances in the understanding of plant metabolism directly or indirectly impacting on yield and provide an update of the different pathways proposed as targets for metabolic engineering aiming to optimize source-sink relationships.

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.

Phosphoproteomic analysis of the response of maize leaves to drought, heat and their combination stress

Drought and heat stress, especially their combination, greatly affect crop production. Many studies have described transcriptome, proteome and phosphoproteome changes in response of plants to drought or heat stress. However, the study about the phosphoproteomic changes in response of crops to the combination stress is scare. To understand the mechanism of maize responses to the drought and heat combination stress, phosphoproteomic analysis was performed on maize leaves by using multiplex iTRAQ-based quantitative proteomic and LC-MS/MS methods. Five-leaf-stage maize was subjected to drought, heat or their combination, and the leaves were collected. Globally, heat, drought and the combined stress significantly changed the phosphorylation levels of 172, 149, and 144 phosphopeptides, respectively. These phosphopeptides corresponded to 282 proteins. Among them, 23 only responded to the combined stress and could not be predicted from their responses to single stressors; 30 and 75 only responded to drought and heat, respectively. Notably, 19 proteins were phosphorylated on different sites in response to the single and combination stresses. Of the seven significantly enriched phosphorylation motifs identified, two were common for all stresses, two were common for heat and the combined stress, and one was specific to the combined stress. The signaling pathways in which the phosphoproteins were involved clearly differed among the three stresses. Functional characterization of the phosphoproteins and the pathways identified here could lead to new targets for the enhancement of crop stress tolerance, which will be particularly important in the face of climate change and the increasing prevalence of abiotic stressors.

Phosphoproteomic analysis of the response of maize leaves to drought, heat and their combination stress

Drought and heat stress, especially their combination, greatly affect crop production. Many studies have described transcriptome, proteome and phosphoproteome changes in response of plants to drought or heat stress. However, the study about the phosphoproteomic changes in response of crops to the combination stress is scare. To understand the mechanism of maize responses to the drought and heat combination stress, phosphoproteomic analysis was performed on maize leaves by using multiplex iTRAQ-based quantitative proteomic and LC-MS/MS methods. Five-leaf-stage maize was subjected to drought, heat or their combination, and the leaves were collected. Globally, heat, drought and the combined stress significantly changed the phosphorylation levels of 172, 149, and 144 phosphopeptides, respectively. These phosphopeptides corresponded to 282 proteins. Among them, 23 only responded to the combined stress and could not be predicted from their responses to single stressors; 30 and 75 only responded to drought and heat, respectively. Notably, 19 proteins were phosphorylated on different sites in response to the single and combination stresses. Of the seven significantly enriched phosphorylation motifs identified, two were common for all stresses, two were common for heat and the combined stress, and one was specific to the combined stress. The signaling pathways in which the phosphoproteins were involved clearly differed among the three stresses. Functional characterization of the phosphoproteins and the pathways identified here could lead to new targets for the enhancement of crop stress tolerance, which will be particularly important in the face of climate change and the increasing prevalence of abiotic stressors.