Mapping the race between crop phenology and climate risks for wheat in France under climate change

Climate change threatens food security by affecting the productivity of major cereal crops. To date, agroclimatic risk projections through indicators have focused on expected hazards exposure during the crop's current vulnerable seasons, without considering the non-stationarity of their phenology under evolving climatic conditions. We propose a new method for spatially classifying agroclimatic risks for wheat, combining high-resolution climatic data with a wheat's phenological model. The method is implemented for French wheat involving three GCM-RCM model pairs and two emission scenarios. We found that the precocity of phenological stages allows wheat to avoid periods of water deficit in the near future. Nevertheless, in the coming decades the emergence of heat stress and increasing water deficit will deteriorate wheat cultivation over the French territory. Projections show the appearance of combined risks of heat and water deficit up to 4 years per decade under the RCP 8.5 scenario. The proposed method provides a deep level of information that enables regional adaptation strategies: the nature of the risk, its temporal and spatial occurrence, and its potential combination with other risks. It's a first step towards identifying potential sites for breeding crop varieties to increase the resilience of agricultural systems.

A Generic Model to Estimate Wheat LAI over Growing Season Regardless of the Soil-Type Background

It is valuable to develop a generic model that can accurately estimate the leaf area index (LAI) of wheat from unmanned aerial vehicle-based multispectral data for diverse soil backgrounds without any ground calibration. To achieve this objective, 2 strategies were investigated to improve our existing random forest regression (RFR) model, which was trained with simulations from a radiative transfer model (PROSAIL). The 2 strategies consisted of (a) broadening the reflectance domain of soil background to generate training data and (b) finding an appropriate set of indicators (band reflectance and/or vegetation indices) as inputs of the RFR model. The RFR models were tested in diverse soils representing varying soil types in Australia. Simulation analysis indicated that adopting both strategies resulted in a generic model that can provide accurate estimation for wheat LAI and is resistant to changes in soil background. From validation on 2 years of field trials, this model achieved high prediction accuracy for LAI over the entire crop cycle (LAI up to 7 m² m^-2) (root mean square error (RMSE): 0.23 to 0.89 m² m^-2), including for sparse canopy (LAI less than 0.3 m² m^-2) grown on different soil types (RMSE: 0.02 to 0.25 m² m^-2). The model reliably captured the seasonal pattern of LAI dynamics for different treatments in terms of genotypes, plant densities, and water-nitrogen managements (correlation coefficient: 0.82 to 0.98). With appropriate adaptations, this framework can be adjusted to any type of sensors to estimate various traits for various species (including but not limited to LAI of wheat) in associated disciplines, e.g., crop breeding, precision agriculture, etc.

A cross-scale analysis to understand and quantify the effects of photosynthetic enhancement on crop growth and yield across environments

Photosynthetic manipulation provides new opportunities for enhancing crop yield. However, understanding and quantifying the importance of individual and multiple manipulations on the seasonal biomass growth and yield performance of target crops across variable production environments is limited. Using a state-of-the-art cross-scale model in the APSIM platform we predicted the impact of altering photosynthesis on the enzyme-limited (A_c ) and electron transport-limited (A_j ) rates, seasonal dynamics in canopy photosynthesis, biomass growth, and yield formation via large multiyear-by-location crop growth simulations. A broad list of promising strategies to improve photosynthesis for C₃ wheat and C₄ sorghum were simulated. In the top decile of seasonal outcomes, yield gains were predicted to be modest, ranging between 0% and 8%, depending on the manipulation and crop type. We report how photosynthetic enhancement can affect the timing and severity of water and nitrogen stress on the growing crop, resulting in nonintuitive seasonal crop dynamics and yield outcomes. We predicted that strategies enhancing A_c alone generate more consistent but smaller yield gains across all water and nitrogen environments, A_j enhancement alone generates larger gains but is undesirable in more marginal environments. Large increases in both A_c and A_j generate the highest gains across all environments. Yield outcomes of the tested manipulation strategies were predicted and compared for realistic Australian wheat and sorghum production. This study uniquely unpacks complex cross-scale interactions between photosynthesis and seasonal crop dynamics and improves understanding and quantification of the potential impact of photosynthesis traits (or lack of it) for crop improvement research.

Unsupervised Plot-Scale LAI Phenotyping via UAV-Based Imaging, Modelling, and Machine Learning

High-throughput phenotyping has become the frontier to accelerate breeding through linking genetics to crop growth estimation, which requires accurate estimation of leaf area index (LAI). This study developed a hybrid method to train the random forest regression (RFR) models with synthetic datasets generated by a radiative transfer model to estimate LAI from UAV-based multispectral images. The RFR models were evaluated on both (i) subsets from the synthetic datasets and (ii) observed data from two field experiments (i.e., Exp16, Exp19). Given the parameter ranges and soil reflectance are well calibrated in synthetic training data, RFR models can accurately predict LAI from canopy reflectance captured in field conditions, with systematic overestimation for LAI<2 due to background effect, which can be addressed by applying background correction on original reflectance map based on vegetation-background classification. Overall, RFR models achieved accurate LAI prediction from background-corrected reflectance for Exp16 (correlation coefficient (r) of 0.95, determination coefficient (R²) of 0.90~0.91, root mean squared error (RMSE) of 0.36~0.40 m² m⁻², relative root mean squared error (RRMSE) of 25~28%) and less accurate for Exp19 (r =0.80~0.83, R² = 0.63~0.69, RMSE of 0.84~0.86 m² m⁻², RRMSE of 30~31%). Additionally, RFR models correctly captured spatiotemporal variation of observed LAI as well as identified variations for different growing stages and treatments in terms of genotypes and management practices (i.e., planting density, irrigation, and fertilization) for two experiments. The developed hybrid method allows rapid, accurate, nondestructive phenotyping of the dynamics of LAI during vegetative growth to facilitate assessments of growth rate including in breeding program assessments.

How does post-flowering heat impact grain growth and its determining processes in wheat?

Wheat grain yield is anticipated to suffer from the increased temperatures expected under climate change. In particular, the effects of post-anthesis temperatures on grain growth and development must be better understood in order to improve crop models. Grain growth and development involve several processes, and we hypothesized that some of the most important processes, namely grain dry biomass and water accumulation, grain volume expansion, and endosperm cell proliferation, will have different thermal sensitivity. To assess this, we established temperature-response curves of these processes for steady post-anthesis temperatures between 15 °C and 36 °C. From anthesis to maturity, grain dry mass, water mass, volume, and endosperm cell number were monitored, whilst considering grain temperature. Different sensitivities to heat of these various processes were revealed. The rate of grain dry biomass accumulation increased linearly up to 25 °C, while the reciprocal of its duration increased linearly up to at least 32 °C. In contrast, the growth rates of traits contributing to grain expansion, such as increase in grain volume and cell numbers, had higher optimum temperatures, while the reciprocal of their durations were significantly lower. These temperature-response curves can contribute to improve current crop models, and allow targeting of specific mechanisms for genetic and genomic studies.

Early vigour in wheat: Could it lead to more severe terminal drought stress under elevated atmospheric [CO2 ] and semi-arid conditions?

Early vigour in wheat is a trait that has received attention for its benefits reducing evaporation from the soil surface early in the season. However, with the growth enhancement common to crops grown under elevated atmospheric CO₂ concentrations (e[CO₂ ]), there is a risk that too much early growth might deplete soil water and lead to more severe terminal drought stress in environments where production relies on stored soil water content. If this is the case, the incorporation of such a trait in wheat breeding programmes might have unintended negative consequences in the future, especially in dry years. We used selected data from cultivars with proven expression of high and low early vigour from the Australian Grains Free Air CO₂ Enrichment (AGFACE) facility, and complemented this analysis with simulation results from two crop growth models which differ in the modelling of leaf area development and crop water use. Grain yield responses to e[CO₂ ] were lower in the high early vigour group compared to the low early vigour group, and although these differences were not significant, they were corroborated by simulation model results. However, the simulated lower response with high early vigour lines was not caused by an earlier or greater depletion of soil water under e[CO₂ ] and the mechanisms responsible appear to be related to an earlier saturation of the radiation intercepted. Whether this is the case in the field needs to be further investigated. In addition, there was some evidence that the timing of the drought stress during crop growth influenced the effect of e[CO₂ ] regardless of the early vigour trait. There is a need for FACE investigations of the value of traits for drought adaptation to be conducted under more severe drought conditions and variable timing of drought stress, a risky but necessary endeavour.

From QTLs to Adaptation Landscapes: Using Genotype-To-Phenotype Models to Characterize G×E Over Time

Genotype by environment interaction (G×E) for the target trait, e.g. yield, is an emerging property of agricultural systems and results from the interplay between a hierarchy of secondary traits involving the capture and allocation of environmental resources during the growing season. This hierarchy of secondary traits ranges from basic traits that correspond to response mechanisms/sensitivities, to intermediate traits that integrate a larger number of processes over time and therefore show a larger amount of G×E. Traits underlying yield differ in their contribution to adaptation across environmental conditions and have different levels of G×E. Here, we provide a framework to study the performance of genotype to phenotype (G2P) modeling approaches. We generate and analyze response surfaces, or adaptation landscapes, for yield and yield related traits, emphasizing the organization of the traits in a hierarchy and their development and interactions over time. We use the crop growth model APSIM-wheat with genotype-dependent parameters as a tool to simulate non-linear trait responses over time with complex trait dependencies and apply it to wheat crops in Australia. For biological realism, APSIM parameters were given a genetic basis of 300 QTLs sampled from a gamma distribution whose shape and rate parameters were estimated from real wheat data. In the simulations, the hierarchical organization of the traits and their interactions over time cause G×E for yield even when underlying traits do not show G×E. Insight into how G×E arises during growth and development helps to improve the accuracy of phenotype predictions within and across environments and to optimize trial networks. We produced a tangible simulated adaptation landscape for yield that we first investigated for its biological credibility by statistical models for G×E that incorporate genotypic and environmental covariables. Subsequently, the simulated trait data were used to evaluate statistical genotype-to-phenotype models for multiple traits and environments and to characterize relationships between traits over time and across environments, as a way to identify traits that could be useful to select for specific adaptation. Designed appropriately, these types of simulated landscapes might also serve as a basis to train other, more deep learning methodologies in order to transfer such network models to real-world situations.

Combining Crop Growth Modeling and Statistical Genetic Modeling to Evaluate Phenotyping Strategies

Genomic prediction of complex traits, say yield, benefits from including information on correlated component traits. Statistical criteria to decide which yield components to consider in the prediction model include the heritability of the component traits and their genetic correlation with yield. Not all component traits are easy to measure. Therefore, it may be attractive to include proxies to yield components, where these proxies are measured in (high-throughput) phenotyping platforms during the growing season. Using the Agricultural Production Systems Simulator (APSIM)-wheat cropping systems model, we simulated phenotypes for a wheat diversity panel segregating for a set of physiological parameters regulating phenology, biomass partitioning, and the ability to capture environmental resources. The distribution of the additive quantitative trait locus effects regulating the APSIM physiological parameters approximated the same distribution of quantitative trait locus effects on real phenotypic data for yield and heading date. We use the crop growth model APSIM-wheat to simulate phenotypes in three Australian environments with contrasting water deficit patterns. The APSIM output contained the dynamics of biomass and canopy cover, plus yield at the end of the growing season. Each water deficit pattern triggered different adaptive mechanisms and the impact of component traits differed between drought scenarios. We evaluated multiple phenotyping schedules by adding plot and measurement error to the dynamics of biomass and canopy cover. We used these trait dynamics to fit parametric models and P-splines to extract parameters with a larger heritability than the phenotypes at individual time points. We used those parameters in multi-trait prediction models for final yield. The combined use of crop growth models and multi-trait genomic prediction models provides a procedure to assess the efficiency of phenotyping strategies and compare methods to model trait dynamics. It also allows us to quantify the impact of yield components on yield prediction accuracy even in different environment types. In scenarios with mild or no water stress, yield prediction accuracy benefitted from including biomass and green canopy cover parameters. The advantage of the multi-trait model was smaller for the early-drought scenario, due to the reduced correlation between the secondary and the target trait. Therefore, multi-trait genomic prediction models for yield require scenario-specific correlated traits.

Risk assessment of frost damage to sugar beet simulated under cold and semi-arid environments

In the semi-arid climatic conditions, water shortage is a key factor to generate crop production. Planting in autumn and winter and using precipitation can help cope with the problem. But in the semi-arid areas with cold winter, frost is another limited factor affecting crop production. For this purpose, in the present study, a simple and universal crop growth simulator (SUCROS) model was used to estimate the potential yield of sugar beets and frost damage from 1993 to 2009 for four autumn sowing dates (2 October, 17 October, 1 November, and 16 November) and two spring dates (6 March and 6 May) in eight locations (Birjand, Bojnord, Ghaen, Mashhad, Torbat-e Heydarieh, Neyshabor, Torbat-e Jam, and Ghochan) of the Khorasan province in northeastern Iran as a semi-arid and cold area. There was a large variability between locations and years in terms of frost damage. The crop failure from frost for the autumn sowing dates ranged from 62.5 to 100% at Neyshabor and Ghochan, respectively. Although autumn sowing dates performed better than spring sowing dates in terms of fresh storage organ yield (~ 109.9 t ha^-1 vs. ~ 78.4 t ha^-1), the risk of frost stress under autumn sowing dates was high at all studied locations. To maximize potential yield and minimize frost risk, sugar beet farmers under semi-arid and frost-prone conditions in the world such as Khorasan province should choose optimum sowing dates outside the high frost risk period to avoid crop damage. The last frost day under these areas normally happened between the 15th and 28th of February, after which no frost events occurred. Accordingly, it is recommended to farmers to sow sugar beet after the period during which no frost risk for sugar beet occurred.

A low-cost method to rapidly and accurately screen for transpiration efficiency in wheat

BACKGROUND

Wheat (Triticum aestivum L.) productivity is commonly limited by the availability of water. Increasing transpiration efficiency (biomass produced per unit of water used, TE) can potentially lead to increased grain yield in water-limited environments ('more crop per drop'). Currently, the ability to screen large populations for TE is limited by slow, low-throughput and/or expensive screening procedures. Here, we propose a low-cost, low-technology, rapid, and scalable method to screen for TE. The method uses a Pot-in-Bucket system that allows continuous watering of the pots and frequent monitoring of water use. To investigate the robustness of the method across environments, and to determine the shortest trial duration required to get accurate and repeatable TE estimates in wheat, plants from 11 genotypes varying in phenology were sown at three dates and grown for different durations in a polyhouse with partial environmental control.

RESULTS

The method revealed significant genotypic variations in TE among the 11 studied wheat genotypes. Genotype rankings for TE were consistent when plants were harvested the same day, at the flag-leaf stage or later. For these harvests, genotype rankings were consistent across experiments despite changes in environmental conditions, such as evaporative demand.

CONCLUSIONS

These results indicate that (1) the Pot-In-Bucket system is suitable to screen TE for breeding purposes in populations with varying phenology, (2) multiple short trials can be carried out within a season to allow increased throughput of genotypes for TE screening, and (3) root biomass measurement is not required to screen for TE, as whole-plant TE and shoot-only TE are highly correlated, at least in wheat. The method is particularly relevant in developing countries where low-cost and relatively high labour input may be most applicable.

Selection in Early Generations to Shift Allele Frequency for Seminal Root Angle in Wheat

A current challenge for plant breeders is the limited ability to phenotype and select for root characteristics to enhance crop productivity. The development of a high-throughput phenotyping method has recently offered new opportunities for the selection of root characteristics in breeding programs. Here, we investigated prospects for phenotypic and molecular selection for seminal root angle (SRA), a key trait associated with mature root system architecture in wheat ( L.). We first investigated genetic diversity for this trait in a panel of 22 wheat lines adapted to Australian environments. The angle between the first pair of seminal roots ranged from 72 to 106°. We then evaluated selection gain via direct phenotypic selection in early generations by comparing the resulting shift in population distribution in tail populations selected for "narrow" and "wide" root angle. Overall, two rounds of selection significantly shifted the mean root angle as much as 10°. Furthermore, comparison of allele frequencies in the tail populations revealed genomic regions under selection, for which marker-assisted selection appeared to be successful. By combining efficient phenotyping and rapid generation advance, lines enriched with alleles for either narrow or wide SRA were developed within only 18 mo. These results suggest that there is a valuable source of allelic variation for SRA that can be harnessed and rapidly introgressed into elite wheat lines.

Integrating modelling and phenotyping approaches to identify and screen complex traits: transpiration efficiency in cereals

Following advances in genetics, genomics, and phenotyping, trait selection in breeding is limited by our ability to understand interactions within the plant and with the environment, and to identify traits of most relevance to the target population of environments. We propose an integrated approach that combines insights from crop modelling, physiology, genetics, and breeding to characterize traits valuable for yield gain in the target population of environments, develop relevant high-throughput phenotyping platforms, and identify genetic controls and their value in production environments. This paper uses transpiration efficiency (biomass produced per unit of water used) as an example of a complex trait of interest to illustrate how the approach can guide modelling, phenotyping, and selection in a breeding programme. We believe that this approach, by integrating insights from diverse disciplines, can increase the resource use efficiency of breeding programmes for improving yield gains in target populations of environments.

Projected impact of future climate on water-stress patterns across the Australian wheatbelt

Drought frequently limits Australian wheat production, and the expected future increase in temperatures and rainfall variability will further challenge productivity. A modelling approach captured plant×environment×management interactions to simulate water-stress patterns experienced by wheat crops at representative locations across the Australian wheatbelt for 33 climate model projections, considering the 'business as usual' emission scenario RCP8.5. The results indicate that projections of future water-stress patterns are region specific. Significant variations in projected impacts were found across climate models, providing local ranges of uncertainty to consider in planning efforts. Most climate models projected an increase in the frequency of severe water-stress conditions in the Western area, the largest producing region, and fewer severe water stresses in other regions. Where found, reductions in water-stress conditions were largely due to shorter crop cycles (a result of warmer temperatures), increased water use efficiency (resulting from increased CO2 levels), and, in some cases, increased local rainfall. Overall, simulations indicate that all areas of the Australian wheatbelt will continue to experience severe water-stress conditions (43.9, 42.6, and 40.2% for 2030, 2050, and 2070 compared with 42.8% for 1990). Given projected frequencies of severe water stress and warmer conditions, efforts towards maintaining or improving yields are essential.

Contribution of Crop Models to Adaptation in Wheat

With world population growing quickly, agriculture needs to produce more with fewer inputs while being environmentally friendly. In a context of changing environments, crop models are useful tools to simulate crop yields. Wheat (Triticum spp.) crop models have been evolving since the 1960s to translate processes related to crop growth and development into mathematical equations. These have been used over decades for agronomic purposes, and have more recently incorporated advances in the modeling of environmental footprints, biotic constraints, trait and gene effects, climate change impact, and the upscaling of global change impacts. This review outlines the potential and limitations of modern wheat crop models in assisting agronomists, breeders, and policymakers to address the current and future challenges facing agriculture.

Stay-green traits to improve wheat adaptation in well-watered and water-limited environments

A stay-green phenotype enables crops to retain green leaves longer after anthesis compared with senescent types, potentially improving yield. Measuring the normalized difference vegetative index (NDVI) during the whole senescence period allows quantification of component stay-green traits contributing to a stay-green phenotype. These objective and standardized traits can be compared across genotypes and environments. Traits examined include maximum NDVI near anthesis (Nmax), senescence rate (SR), a trait integrating senescence (SGint), plus time from anthesis to onset (OnS), mid-point (MidS), and near completion (EndS) of senescence. The correlation between stay-green traits and yield was studied in eight contrasting environments ranging from well watered to severely water limited. Environments were each classified into one of the four major drought environment types (ETs) previously identified for the Australian wheat cropping system. SGint, OnS, and MidS tended to have higher values in higher yielding environments for a given genotype, as well as for higher yielding genotypes within a given environment. Correlation between specific stay-green traits and yield varied with ET. In the studied population, SGint, OnS, and MidS strongly correlated with yield in three of the four ETs which included well-watered environments (0.43-0.86), but less so in environments with only moderate water-stress after anthesis (-0.03 to 0.31). In contrast, Nmax was most highly correlated with yield under moderate post-anthesis water stress (0.31-0.43). Selection for particular stay-green traits, combinations of traits, and/or molecular markers associated with the traits could enhance genetic progress toward stay-green wheats with higher, more stable yield in both well-watered and water-limited conditions.

Velocity of temperature and flowering time in wheat - assisting breeders to keep pace with climate change

By accelerating crop development, warming climates may result in mismatches between key sensitive growth stages and extreme climate events, with severe consequences for crop yield and food security. Using recent estimates of gene responses to vernalization and photoperiod in wheat, we modelled the flowering times of all 'potential' genotypes as influenced by the velocity of climate change across the Australian wheatbelt. In the period 1957-2010, seasonal increases in temperature of 0.012 °C yr(-1) were recorded and changed flowering time of a mid-season wheat genotype by an average -0.074 day yr(-1) , with flowering 'velocity' of up to 0.95 km yr(-1) towards the coastal edges of the wheatbelt; this is an estimate of how quickly the given genotype would have to be 'moved' across the landscape to maintain its original flowering time. By 2030, these national changes are projected to accelerate by up to 3-fold for seasonal temperature and by up to 5-fold for flowering time between now and 2030, with average national shifts in flowering time of 0.33 and 0.41 day yr(-1) between baseline and the worst climate scenario tested for 2030 and 2050, respectively. Without new flowering alleles in commercial germplasm, the life cycle of wheat crops is predicted to shorten by 2 weeks by 2030 across the wheatbelt for the most pessimistic climate scenario. While current cultivars may be otherwise suitable for future conditions, they will flower earlier due to warmer temperatures. To allow earlier sowing to escape frost, heat and terminal drought, and to maintain current growing period of early-sown wheat crops in the future, breeders will need to develop and/or introduce new genetic sources for later flowering, more so in the eastern part of the wheatbelt.

Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis

A crop can be viewed as a complex system with outputs (e.g. yield) that are affected by inputs of genetic, physiology, pedo-climatic and management information. Application of numerical methods for model exploration assist in evaluating the major most influential inputs, providing the simulation model is a credible description of the biological system. A sensitivity analysis was used to assess the simulated impact on yield of a suite of traits involved in major processes of crop growth and development, and to evaluate how the simulated value of such traits varies across environments and in relation to other traits (which can be interpreted as a virtual change in genetic background). The study focused on wheat in Australia, with an emphasis on adaptation to low rainfall conditions. A large set of traits (90) was evaluated in a wide target population of environments (4 sites × 125 years), management practices (3 sowing dates × 3 nitrogen fertilization levels) and CO2 (2 levels). The Morris sensitivity analysis method was used to sample the parameter space and reduce computational requirements, while maintaining a realistic representation of the targeted trait × environment × management landscape (∼ 82 million individual simulations in total). The patterns of parameter × environment × management interactions were investigated for the most influential parameters, considering a potential genetic range of +/- 20% compared to a reference cultivar. Main (i.e. linear) and interaction (i.e. non-linear and interaction) sensitivity indices calculated for most of APSIM-Wheat parameters allowed the identification of 42 parameters substantially impacting yield in most target environments. Among these, a subset of parameters related to phenology, resource acquisition, resource use efficiency and biomass allocation were identified as potential candidates for crop (and model) improvement.

The shifting influence of drought and heat stress for crops in northeast Australia

Characterization of drought environment types (ETs) has proven useful for breeding crops for drought-prone regions. Here, we consider how changes in climate and atmospheric carbon dioxide (CO2 ) concentrations will affect drought ET frequencies in sorghum and wheat systems of northeast Australia. We also modify APSIM (the Agricultural Production Systems Simulator) to incorporate extreme heat effects on grain number and weight, and then evaluate changes in the occurrence of heat-induced yield losses of more than 10%, as well as the co-occurrence of drought and heat. More than six million simulations spanning representative locations, soil types, management systems, and 33 climate projections led to three key findings. First, the projected frequency of drought decreased slightly for most climate projections for both sorghum and wheat, but for different reasons. In sorghum, warming exacerbated drought stresses by raising the atmospheric vapor pressure deficit and reducing transpiration efficiency (TE), but an increase in TE due to elevated CO2 more than offset these effects. In wheat, warming reduced drought stress during spring by hastening development through winter and reducing exposure to terminal drought. Elevated CO2 increased TE but also raised radiation-use efficiency and overall growth rates and water use, thereby offsetting much of the drought reduction from warming. Second, adding explicit effects of heat on grain number and grain size often switched projected yield impacts from positive to negative. Finally, although average yield losses associated with drought will remain generally higher than that for heat stress for the next half century, the relative importance of heat is steadily growing. This trend, as well as the likely high degree of genetic variability in heat tolerance, suggests that more emphasis on heat tolerance is warranted in breeding programs. At the same time, work on drought tolerance should continue with an emphasis on drought that co-occurs with extreme heat.

Frost trends and their estimated impact on yield in the Australian wheatbelt

Radiant spring frosts occurring during reproductive developmental stages can result in catastrophic yield loss for wheat producers. To better understand the spatial and temporal variability of frost, the occurrence and impact of frost events on rain-fed wheat production was estimated across the Australian wheatbelt for 1957-2013 using a 0.05 ° gridded weather data set. Simulated yield outcomes at 60 key locations were compared with those for virtual genotypes with different levels of frost tolerance. Over the last six decades, more frost events, later last frost day, and a significant increase in frost impact on yield were found in certain regions of the Australian wheatbelt, in particular in the South-East and West. Increasing trends in frost-related yield losses were simulated in regions where no significant trend of frost occurrence was observed, due to higher mean temperatures accelerating crop development and causing sensitive post-heading stages to occur earlier, during the frost risk period. Simulations indicated that with frost-tolerant lines the mean national yield could be improved by up to 20% through (i) reduced frost damage (~10% improvement) and (ii) the ability to use earlier sowing dates (adding a further 10% improvement). In the simulations, genotypes with an improved frost tolerance to temperatures 1 °C lower than the current 0 °C reference provided substantial benefit in most cropping regions, while greater tolerance (to 3 °C lower temperatures) brought further benefits in the East. The results indicate that breeding for improved reproductive frost tolerance should remain a priority for the Australian wheat industry, despite warming climates.

High-throughput phenotyping of seminal root traits in wheat

BACKGROUND

Water availability is a major limiting factor for wheat (Triticum aestivum L.) production in rain-fed agricultural systems worldwide. Root system architecture has important functional implications for the timing and extent of soil water extraction, yet selection for root architectural traits in breeding programs has been limited by a lack of suitable phenotyping methods. The aim of this research was to develop low-cost high-throughput phenotyping methods to facilitate selection for desirable root architectural traits. Here, we report two methods, one using clear pots and the other using growth pouches, to assess the angle and the number of seminal roots in wheat seedlings- two proxy traits associated with the root architecture of mature wheat plants.

RESULTS

Both methods revealed genetic variation for seminal root angle and number in the panel of 24 wheat cultivars. The clear pot method provided higher heritability and higher genetic correlations across experiments compared to the growth pouch method. In addition, the clear pot method was more efficient - requiring less time, space, and labour compared to the growth pouch method. Therefore the clear pot method was considered the most suitable for large-scale and high-throughput screening of seedling root characteristics in crop improvement programs.

CONCLUSIONS

The clear-pot method could be easily integrated in breeding programs targeting drought tolerance to rapidly enrich breeding populations with desirable alleles. For instance, selection for narrow root angle and high number of seminal roots could lead to deeper root systems with higher branching at depth. Such root characteristics are highly desirable in wheat to cope with anticipated future climate conditions, particularly where crops rely heavily on stored soil moisture at depth, including some Australian, Indian, South American, and African cropping regions.

Phenotyping novel stay-green traits to capture genetic variation in senescence dynamics

Stay-green plants retain green leaves longer after anthesis and can have improved yield, particularly under water limitation. As senescence is a dynamic process, genotypes with different senescence patterns may exhibit similar final normalised difference vegetative index (NDVI). By monitoring NDVI from as early as awn emergence to maturity, we demonstrate that analysing senescence dynamics improves insight into genotypic stay-green variation. A senescence evaluation tool was developed to fit a logistic function to NDVI data and used to analyse data from three environments for a wheat (Triticum aestivum L.) population whose lines contrast for stay-green. Key stay-green traits were estimated including, maximum NDVI, senescence rate and a trait integrating NDVI variation after anthesis, as well as the timing from anthesis to onset, midpoint and conclusion of senescence. The integrative trait and the timing to onset and mid-senescence exhibited high positive correlations with yield and a high heritability in the three studied environments. Senescence rate was correlated with yield in some environments, whereas maximum NDVI was associated with yield in a drought-stressed environment. Where resources preclude frequent measurements, we found that NDVI measurements may be restricted to the period of rapid senescence, but caution is required when dealing with lines of different phenology. In contrast, regular monitoring during the whole period after flowering allows the estimation of senescence dynamics traits that may be reliably compared across genotypes and environments. We anticipate that selection for stay-green traits will enhance genetic progress towards high-yielding, stay-green germplasm.

Plot size matters: interference from intergenotypic competition in plant phenotyping studies

Genetic and physiological studies often comprise genotypes diverse in vigour, size and flowering time. This can make the phenotyping of complex traits challenging, particularly those associated with canopy development, biomass and yield, as the environment of one genotype can be influenced by a neighbouring genotype. Limited seed and space may encourage field assessment in single, spaced rows or in small, unbordered plots, whereas the convenience of a controlled environment or greenhouse makes pot studies tempting. However, the relevance of such growing conditions to commercial field-grown crops is unclear and often doubtful. Competition for water, light and nutrients necessary for canopy growth will be variable where immediate neighbours are genetically different, particularly under stress conditions, where competition for resources and influence on productivity is greatest. Small hills and rod-rows maximise the potential for intergenotypic competition that is not relevant to a crop's performance in monocultures. Response to resource availability will typically vary among diverse genotypes to alter genotype ranking and reduce heritability for all growth-related traits, with the possible exception of harvest index. Validation of pot experiments to performance in canopies in the field is essential, whereas the planting of multirow plots and the simple exclusion of plot borders at harvest will increase experimental precision and confidence in genotype performance in target environments.

Large-scale characterization of drought pattern: a continent-wide modelling approach applied to the Australian wheatbelt--spatial and temporal trends

Plant response to drought is complex, so that traits adapted to a specific drought type can confer disadvantage in another drought type. Understanding which type(s) of drought to target is of prime importance for crop improvement. Modelling was used to quantify seasonal drought patterns for a check variety across the Australian wheatbelt, using 123 yr of weather data for representative locations and managements. Two other genotypes were used to simulate the impact of maturity on drought pattern. Four major environment types summarized the variability in drought pattern over time and space. Severe stress beginning before flowering was common (44% of occurrences), with (24%) or without (20%) relief during grain filling. High variability occurred from year to year, differing with geographical region. With few exceptions, all four environment types occurred in most seasons, for each location, management system and genotype. Applications of such environment characterization are proposed to assist breeding and research to focus on germplasm, traits and genes of interest for target environments. The method was applied at a continental scale to highly variable environments and could be extended to other crops, to other drought-prone regions around the world, and to quantify potential changes in drought patterns under future climates.

Breeding for the future: what are the potential impacts of future frost and heat events on sowing and flowering time requirements for Australian bread wheat (Triticum aestivium) varieties?

Extreme climate, especially temperature, can severely reduce wheat yield. As global warming has already begun to increase mean temperature and the occurrence of extreme temperatures, it has become urgent to accelerate the 5-20 year process of breeding for new wheat varieties, to adapt to future climate. We analyzed the patterns of frost and heat events across the Australian wheatbelt based on 50 years of historical records (1960-2009) for 2864 weather stations. Flowering dates of three contrasting-maturity wheat varieties were simulated for a wide range of sowing dates in 22 locations for 'current' climate (1960-2009) and eight future scenarios (high and low CO2 emission, dry and wet precipitation scenarios, in 2030 and 2050). The results highlighted the substantial spatial variability of frost and heat events across the Australian wheatbelt in current and future climates. As both 'last frost' and 'first heat' events would occur earlier in the season, the 'target' sowing and flowering windows (defined as risk less than 10% for frost (<0 °C) and less than 30% for heat (>35 °C) around flowering) would be shifted earlier by up to 2 and 1 month(s), respectively, in 2050. A short-season variety would require a shift in target sowing window 2-fold greater than long- and medium-season varieties by 2050 (8 vs. 4 days on average across locations and scenarios, respectively), but would suffer a lesser decrease in the length of the vegetative period (4 vs. 7 days). Overall, warmer winters would shorten the wheat season by up to 6 weeks, especially during preflowering. This faster crop cycle is associated with a reduced time for resource acquisition, and potential yield loss. As far as favourable rain and modern equipment would allow, early sowing and longer season varieties (i.e. in current climate) would be the best strategies to adapt to future climates.

A multisite managed environment facility for targeted trait and germplasm phenotyping

Field evaluation of germplasm for performance under water and heat stress is challenging. Field environments are variable and unpredictable, and genotype×environment interactions are difficult to interpret if environments are not well characterised. Numerous traits, genes and quantitative trait loci have been proposed for improving performance but few have been used in variety development. This reflects the limited capacity of commercial breeding companies to screen for these traits and the absence of validation in field environments relevant to breeding companies, and because little is known about the economic benefit of selecting one particular trait over another. The value of the proposed traits or genes is commonly not demonstrated in genetic backgrounds of value to breeding companies. To overcome this disconnection between physiological trait breeding and uptake by breeding companies, three field sites representing the main environment types encountered across the Australian wheatbelt were selected to form a set of managed environment facilities (MEFs). Each MEF manages soil moisture stress through irrigation, and the effects of heat stress through variable sowing dates. Field trials are monitored continuously for weather variables and changes in soil water and canopy temperature in selected probe genotypes, which aids in decisions guiding irrigation scheduling and sampling times. Protocols have been standardised for an essential core set of measurements so that phenotyping yield and other traits are consistent across sites and seasons. MEFs enable assessment of a large number of traits across multiple genetic backgrounds in relevant environments, determine relative trait value, and facilitate delivery of promising germplasm and high value traits into commercial breeding programs.

Environment characterization as an aid to wheat improvement: interpreting genotype-environment interactions by modelling water-deficit patterns in North-Eastern Australia

Genotype-environment interactions (GEI) limit genetic gain for complex traits such as tolerance to drought. Characterization of the crop environment is an important step in understanding GEI. A modelling approach is proposed here to characterize broadly (large geographic area, long-term period) and locally (field experiment) drought-related environmental stresses, which enables breeders to analyse their experimental trials with regard to the broad population of environments that they target. Water-deficit patterns experienced by wheat crops were determined for drought-prone north-eastern Australia, using the APSIM crop model to account for the interactions of crops with their environment (e.g. feedback of plant growth on water depletion). Simulations based on more than 100 years of historical climate data were conducted for representative locations, soils, and management systems, for a check cultivar, Hartog. The three main environment types identified differed in their patterns of simulated water stress around flowering and during grain-filling. Over the entire region, the terminal drought-stress pattern was most common (50% of production environments) followed by a flowering stress (24%), although the frequencies of occurrence of the three types varied greatly across regions, years, and management. This environment classification was applied to 16 trials relevant to late stages testing of a breeding programme. The incorporation of the independently-determined environment types in a statistical analysis assisted interpretation of the GEI for yield among the 18 representative genotypes by reducing the relative effect of GEI compared with genotypic variance, and helped to identify opportunities to improve breeding and germplasm-testing strategies for this region.

Detection and use of QTL for complex traits in multiple environments

QTL mapping methods for complex traits are challenged by new developments in marker technology, phenotyping platforms, and breeding methods. In meeting these challenges, QTL mapping approaches will need to also acknowledge the central roles of QTL by environment interactions (QEI) and QTL by trait interactions in the expression of complex traits like yield. This paper presents an overview of mixed model QTL methodology that is suitable for many types of populations and that allows predictive modeling of QEI, both for environmental and developmental gradients. Attention is also given to multi-trait QTL models which are essential to interpret the genetic basis of trait correlations. Biophysical (crop growth) model simulations are proposed as a complement to statistical QTL mapping for the interpretation of the nature of QEI and to investigate better methods for the dissection of complex traits into component traits and their genetic controls.

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

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

Estimation of light interception in research environments: a joint approach using directional light sensors and 3D virtual plants applied to sunflower (Helianthus annuus) and Arabidopsis thaliana in natural and artificial conditions

Light interception is a major factor influencing plant development and biomass production. Several methods have been proposed to determine this variable, but its calculation remains difficult in artificial environments with heterogeneous light. We propose a method that uses 3D virtual plant modelling and directional light characterisation to estimate light interception in highly heterogeneous light environments such as growth chambers and glasshouses. Intercepted light was estimated by coupling an architectural model and a light model for different genotypes of the rosette species Arabidopsis thaliana (L.) Heynh and a sunflower crop. The model was applied to plants of contrasting architectures, cultivated in isolation or in canopy, in natural or artificial environments, and under contrasting light conditions. The model gave satisfactory results when compared with observed data and enabled calculation of light interception in situations where direct measurements or classical methods were inefficient, such as young crops, isolated plants or artificial conditions. Furthermore, the model revealed that A. thaliana increased its light interception efficiency when shaded. To conclude, the method can be used to calculate intercepted light at organ, plant and plot levels, in natural and artificial environments, and should be useful in the investigation of genotype-environment interactions for plant architecture and light interception efficiency.

Relative contributions of light interception and radiation use efficiency to the reduction of maize productivity under cold temperatures

Maize (Zea mays L.) is a chill-susceptible crop cultivated in northern latitude environments. The detrimental effects of cold on growth and photosynthetic activity have long been established. However, a general overview of how important these processes are with respect to the reduction of productivity reported in the field is still lacking. In this study, a model-assisted approach was used to dissect variations in productivity under suboptimal temperatures and quantify the relative contributions of light interception (PAR_c) and radiation use efficiency (RUE) from emergence to flowering. A combination of architectural and light transfer models was used to calculate light interception in three field experiments with two cold-tolerant lines and at two sowing dates. Model assessment confirmed that the approach was suitable to infer light interception. Biomass production was strongly affected by early sowings. RUE was identified as the main cause of biomass reduction during cold events. Furthermore, PAR_c explained most of the variability observed at flowering, its relative contributions being more or less important according to the climate experienced. Cold temperatures resulted in lower PAR_c, mainly because final leaf length and width were significantly reduced for all leaves emerging after the first cold occurrence. These results confirm that virtual plants can be useful as fine phenotyping tools. A scheme of action of cold on leaf expansion, light interception and radiation use efficiency is discussed with a view towards helping breeders define relevant selection criteria.

Using a 3-D virtual sunflower to simulate light capture at organ, plant and plot levels: contribution of organ interception, impact of heliotropism and analysis of genotypic differences

BACKGROUND AND AIMS

Light interception is a critical factor in the production of biomass. The study presented here describes a method used to take account of architectural changes over time in sunflower and to estimate absorbed light at the organ level.

METHODS

The amount of photosynthetically active radiation absorbed by a plant is estimated on a daily or hourly basis through precise characterization of the light environment and three-dimensional virtual plants built using AMAP software. Several treatments are performed over four experiments and on two genotypes to test the model, quantify the contribution of different organs to light interception and evaluate the impact of heliotropism.

KEY RESULTS

This approach is used to simulate the amount of light absorbed at organ and plant scales from crop emergence to maturity. Blades and capitula were the major contributors to light interception, whereas that by petioles and stem was negligible. Light regimen simulations showed that heliotropism decreased the cumulated light intercepted at the plant scale by close to 2.2% over one day.

CONCLUSIONS

The approach is useful in characterizing the light environment of organs and the whole plant, especially for studies on heterogeneous canopies or for quantifying genotypic or environmental impacts on plant architecture, where conventional approaches are ineffective. This model paves the way to analyses of genotype-environment interactions and could help establish new selection criteria based on architectural improvement, enhancing plant light interception.

Short-term responses of leaf growth rate to water deficit scale up to whole-plant and crop levels: an integrated modelling approach in maize

Physiological and genetic studies of leaf growth often focus on short-term responses, leaving a gap to whole-plant models that predict biomass accumulation, transpiration and yield at crop scale. To bridge this gap, we developed a model that combines an existing model of leaf 6 expansion in response to short-term environmental variations with a model coordinating the development of all leaves of a plant. The latter was based on: (1) rates of leaf initiation, appearance and end of elongation measured in field experiments; and (2) the hypothesis of an independence of the growth between leaves. The resulting whole-plant leaf model was integrated into the generic crop model APSIM which provided dynamic feedback of environmental conditions to the leaf model and allowed simulation of crop growth at canopy level. The model was tested in 12 field situations with contrasting temperature, evaporative demand and soil water status. In observed and simulated data, high evaporative demand reduced leaf area at the whole-plant level, and short water deficits affected only leaves developing during the stress, either visible or still hidden in the whorl. The model adequately simulated whole-plant profiles of leaf area with a single set of parameters that applied to the same hybrid in all experiments. It was also suitable to predict biomass accumulation and yield of a similar hybrid grown in different conditions. This model extends to field conditions existing knowledge of the environmental controls of leaf elongation, and can be used to simulate how their genetic controls flow through to yield.

Day length affects the dynamics of leaf expansion and cellular development in Arabidopsis thaliana partially through floral transition timing

BACKGROUND AND AIMS

Plant aerial development is well known to be affected by day length in terms of the timing and developmental stage of floral transition. Arabidopsis thaliana is a 'long day' plant in which the time to flower is delayed by short days and leaf number is increased. The aim of the work presented here was to determine the effects of different day lengths on individual leaf area expansion. The effect of flower emergence per se on the regulation of leaf expansion was also tested in this study.

METHODS

Care was taken to ensure that day length was the only source of micro-meteorological variation. The dynamics of individual leaf expansion were analysed in Ler and Col-0 plants grown under five day lengths in five independent experiments. Responses at cellular level were analysed in Ler plants grown under various day lengths and treatments to alter the onset of flowering.

KEY RESULTS

When the same leaf position was compared, the final leaf area and both the relative and absolute rates of leaf expansion were decreased by short days, whereas the duration of leaf expansion was increased. Epidermal cell number and cell area were also altered by day-length treatments and some of these responses could be mimicked by manipulating the date of flowering.

CONCLUSIONS

Both the dynamics and cellular bases of leaf development are altered by differences in day length even when visible phenotypes are absent. To some extent, cell area and its response to day length are controlled by whole plant control mechanisms associated with the onset of flowering.

Simulations of virtual plants reveal a role for SERRATE in the response of leaf development to light in Arabidopsis thaliana

The SERRATE gene (SE) was shown to determine leaf organogenesis and morphogenesis patterning in Arabidopsis thaliana. The se-1 mutant was used here to investigate the role of SE in leaf development in response to incident light. Virtual plants were modelled to analyse the phenotypes induced by this mutation. Plants were grown under various levels of incident light. The amount of light absorbed by the plant was estimated by combining detailed characterizations of the radiative environment and virtual plant simulations. Four major changes in leaf development were induced by the se-1 mutation. Two constitutive leaf growth variables were modified, with a lower initial expansion rate and a higher duration of expansion. Two original responses to a reduced incident light were identified, concerning the leaf-initiation rate and the duration of leaf expansion. The se-1 mutation dramatically affects both changes in the leaf development pattern and the response to reduced incident light. Virtual plants helped to reveal the combined effects of the multiple changes induced by this mutation.

Integrated responses of rosette organogenesis, morphogenesis and architecture to reduced incident light in Arabidopsis thaliana results in higher efficiency of light interception

Plants have a high phenotypic plasticity in response to light. We investigated changes in plant architecture in response to decreased incident light levels in Arabidopsis thaliana (L.) Heynh, focusing on organogenesis and morphogenesis, and on consequences for the efficiency of light interception of the rosette. A. thaliana ecotype Columbia plants were grown under various levels of incident photosynthetically active radiation (PAR), with blue light (BL) intensity proportional to incident PAR intensity and with a high and stable red to far-red light ratio. We estimated the PAR absorbed by the plant, using data from precise characterisation of the light environment and 3-dimensional simulations of virtual plants generated with AMAPsim software. Decreases in incident PAR modified rosette architecture; leaf area decreased, leaf blades tended to be more circular and petioles were longer and thinner. However, the efficiency of light interception by the rosette was slightly higher in plants subjected to lower PAR intensities, despite the reduction in leaf area. Decreased incident PAR delayed leaf initiation and slowed down relative leaf expansion rate, but increased the duration of leaf expansion. The leaf initiation rate and the relative expansion rate during the first third of leaf development were related to the amount of PAR absorbed. The duration of leaf expansion was related to PAR intensity. The relationships identified could be used to analyse the phenotypic plasticity of various genotypes of Arabidopsis. Overall, decreases in incident PAR result in an increase in the efficiency of light interception.

PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit

The high-throughput phenotypic analysis of Arabidopsis thaliana collections requires methodological progress and automation. Methods to impose stable and reproducible soil water deficits are presented and were used to analyse plant responses to water stress. Several potential complications and methodological difficulties were identified, including the spatial and temporal variability of micrometeorological conditions within a growth chamber, the difference in soil water depletion rates between accessions and the differences in developmental stage of accessions the same time after sowing. Solutions were found. Nine accessions were grown in four experiments in a rigorously controlled growth-chamber equipped with an automated system to control soil water content and take pictures of individual plants. One accession, An1, was unaffected by water deficit in terms of leaf number, leaf area, root growth and transpiration rate per unit leaf area. Methods developed here will help identify quantitative trait loci and genes involved in plant tolerance to water deficit.

Individual leaf development in Arabidopsis thaliana: a stable thermal-time-based programme

In crop species, the impact of temperature on plant development is classically modelled using thermal time. We examined whether this method could be used in a non-crop species, Arabidopsis thaliana, to analyse the response to temperature of leaf initiation rate and of the development of two leaves of the rosette. The results confirmed the large plant-to-plant variability in the studied isogenic line of the Columbia ecotype: 100-fold differences in leaf area among plants sown on the same date were commonly observed at a given date. These differences disappeared in mature leaves, suggesting that they were due to a variability in plant developmental stage. The whole population could therefore be represented by any group of synchronous plants labelled at the two-leaf stage and followed during their development. Leaf initiation rate, duration of leaf expansion and maximal relative leaf expansion rate varied considerably among experiments performed at different temperatures (from 6 to 26 degrees C) but they were linearly related to temperature in the range 6-26 degrees C, with a common x-intercept of 3 degrees C. Expressing time in thermal time with a threshold temperature of 3 degrees C unified the time courses of leaf initiation and of individual leaf development for plants grown at different temperatures and experimental conditions. The two leaves studied (leaf 2 and leaf 6) had a two-phase development, with an exponential phase followed by a phase with decreasing relative elongation rate. Both phases had constant durations for a given leaf position if expressed in thermal time. Changes in temperature caused changes in both the rate of development and in the expansion rate which mutually compensated such that they had no consequence on leaf area at a given thermal time. The resulting model of leaf development was applied to ten experiments carried out in a glasshouse or in a growth chamber, with plants grown in soil or hydroponically. Because it predicts accurately the stage of development and the relative expansion rate of any leaf of the rosette, this model facilitates precise planning of sampling procedures and the comparison of treatments in growth analyses.