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

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

Marker-based crop model-assisted ideotype design to improve avoidance of abiotic stress in bread wheat.

Wheat phenology allows escape from seasonal abiotic stresses including frosts and high temperatures, the latter being forecast to increase with climate change. The use of marker-based crop models to identify ideotypes has been proposed to select genotypes adapted to specific weather and management conditions and anticipate climate change. In this study, a marker-based crop model for wheat phenology was calibrated and tested. Climate analysis of 30 years of historical weather data in 72 locations representing the main wheat production areas in France was performed. We carried out marker-based crop model simulations for 1019 wheat cultivars and three sowing dates, which allowed calculation of genotypic stress avoidance frequencies of frost and heat stress and identification of ideotypes. The phenology marker-based crop model allowed prediction of large genotypic variations for the beginning of stem elongation (GS30) and heading date (GS55). Prediction accuracy was assessed using untested genotypes and environments, and showed median genotype prediction errors of 8.5 and 4.2 days for GS30 and GS55, respectively. Climate analysis allowed the definition of a low risk period for each location based on the distribution of the last frost and first heat days. Clustering of locations showed three groups with contrasting levels of frost and heat risks. Marker-based crop model simulations showed the need to optimize the genotype depending on sowing date, particularly in high risk environments. An empirical validation of the approach showed that it holds good promises to improve frost and heat stress avoidance.

A data-driven simulation platform to predict cultivars' performances under uncertain weather conditions.

In most crops, genetic and environmental factors interact in complex ways giving rise to substantial genotype-by-environment interactions (G×E). We propose that computer simulations leveraging field trial data, DNA sequences, and historical weather records can be used to tackle the longstanding problem of predicting cultivars' future performances under largely uncertain weather conditions. We present a computer simulation platform that uses Monte Carlo methods to integrate uncertainty about future weather conditions and model parameters. We use extensive experimental wheat yield data (n = 25,841) to learn G×E patterns and validate, using left-trial-out cross-validation, the predictive performance of the model. Subsequently, we use the fitted model to generate circa 143 million grain yield data points for 28 wheat genotypes in 16 locations in France, over 16 years of historical weather records. The phenotypes generated by the simulation platform have multiple downstream uses; we illustrate this by predicting the distribution of expected yield at 448 cultivar-location combinations and performing means-stability analyses.

Using environmental clustering to identify specific drought tolerance QTLs in bread wheat (T. aestivum L.).

KEY MESSAGE: Environmental clustering helps to identify QTLs associated with grain yield in different water stress scenarios. These QTLs could be useful for breeders to improve grain yields and increase genetic resilience in marginal environments. Drought is one of the main abiotic stresses limiting winter bread wheat growth and productivity around the world. The acquisition of new high-yielding and stress-tolerant varieties is therefore necessary and requires improved understanding of the physiological and genetic bases of drought resistance. A panel of 210 elite European varieties was evaluated in 35 field trials. Grain yield and its components were scored in each trial. A crop model was then run with detailed climatic data and soil water status to assess the dynamics of water stress in each environment. Varieties were registered from 1992 to 2011, allowing us to test timewise genetic progress. Finally, a genome-wide association study (GWAS) was carried out using genotyping data from a 280 K SNP chip. The crop model simulation allowed us to group the environments into four water stress scenarios: an optimal condition with no water stress, a post-anthesis water stress, a moderate-anthesis water stress and a high pre-anthesis water stress. Compared to the optimal water condition, grain yield losses in the stressed conditions were 3.3%, 12.4% and 31.2%, respectively. This environmental clustering improved understanding of the effect of drought on grain yields and explained 20% of the G × E interaction. The greatest genetic progress was obtained in the optimal condition, mostly represented in France. The GWAS identified several QTLs, some of which were specific of the different water stress patterns. Our results make breeding for improved drought resistance to specific environmental scenarios easier and will facilitate genetic progress in future environments, i.e., water stress environments.

Functional mapping of N deficiency-induced response in wheat yield-component traits by implementing high-throughput phenotyping.

As overfertilization leads to environmental concerns and the cost of N fertilizer increases, the issue of how to select crop cultivars that can produce high yields on N-deficient soils has become crucially important. However, little information is known about the genetic mechanisms by which crops respond to environmental changes induced by N signaling. Here, we dissected the genetic architecture of N-induced phenotypic plasticity in bread wheat (Triticum aestivum L.) by integrating functional mapping and semiautomatic high-throughput phenotyping data of yield-related canopy architecture. We identified a set of quantitative trait loci (QTLs) that determined the pattern and magnitude of how wheat cultivars responded to low N stress from normal N supply throughout the wheat life cycle. This analysis highlighted the phenological landscape of genetic effects exerted by individual QTLs, as well as their interactions with N-induced signals and with canopy measurement angles. This information may shed light on our mechanistic understanding of plant adaptation and provide valuable information for the breeding of N-deficiency tolerant wheat varieties.

Predictions of heading date in bread wheat (Triticum aestivum L.) using QTL-based parameters of an ecophysiological model.

Prediction of wheat phenology facilitates the selection of cultivars with specific adaptations to a particular environment. However, while QTL analysis for heading date can identify major genes controlling phenology, the results are limited to the environments and genotypes tested. Moreover, while ecophysiological models allow accurate predictions in new environments, they may require substantial phenotypic data to parameterize each genotype. Also, the model parameters are rarely related to all underlying genes, and all the possible allelic combinations that could be obtained by breeding cannot be tested with models. In this study, a QTL-based model is proposed to predict heading date in bread wheat (Triticum aestivum L.). Two parameters of an ecophysiological model (V sat and P base , representing genotype vernalization requirements and photoperiod sensitivity, respectively) were optimized for 210 genotypes grown in 10 contrasting location × sowing date combinations. Multiple linear regression models predicting V sat and P base with 11 and 12 associated genetic markers accounted for 71 and 68% of the variance of these parameters, respectively. QTL-based V sat and P base estimates were able to predict heading date of an independent validation data set (88 genotypes in six location × sowing date combinations) with a root mean square error of prediction of 5 to 8.6 days, explaining 48 to 63% of the variation for heading date. The QTL-based model proposed in this study may be used for agronomic purposes and to assist breeders in suggesting locally adapted ideotypes for wheat phenology.

Anthesis date mainly explained correlations between post-anthesis leaf senescence, grain yield, and grain protein concentration in a winter wheat population segregating for flowering time QTLs.

The genetic variability of the duration of leaf senescence during grain filling has been shown to affect both carbon and nitrogen acquisition. In particular, maintaining green leaves during grain filling possibly leads to increased grain yield, but its associated effect on grain protein concentration has not been studied. The aim of this study was to dissect the genetic factors contributing to correlations observed at the phenotypic level between leaf senescence during grain filling, grain protein concentration, and grain yield in winter wheat. With this aim in view, an analysis of quantitative trait locus (QTL) co-locations for these traits was carried out on a doubled haploid mapping population grown in a large multienvironment trial network. Pleiotropic QTLs affecting leaf senescence and grain yield and/or grain protein concentration were identified on chromosomes 2D, 2A, and 7D. These were associated with QTLs for anthesis date, showing that the phenotypic correlations with leaf senescence were mainly explained by flowering time in this wheat population. Study of the allelic effects of these pleiotropic QTLs showed that delaying leaf senescence was associated with increased grain yield or grain protein concentration depending on the environments considered. It is proposed that this differential effect of delaying leaf senescence on grain yield and grain protein concentration might be related to the nitrogen availability during the post-anthesis period. It is concluded that the benefit of using leaf senescence as a selection criterion to improve grain protein concentration in wheat cultivars may be limited and would largely depend on the targeted environments, particularly on their nitrogen availability during the post-anthesis period.

Deviation from the grain protein concentration-grain yield negative relationship is highly correlated to post-anthesis N uptake in winter wheat.

In plants, carbon and nitrogen (N) economies are intimately linked at the physiological and biochemical level. The strong genetic negative correlation between grain yield and grain protein concentration observed in various cereals is an illustration of this inter-relationship. Studies have shown that deviation from this negative relationship (grain protein deviation or GPD) has a genetic basis, but its physiological basis is still poorly understood. This study analysed data on 27 genotypes grown in multienvironment field trials, representing a wide range of agricultural practices and climatic conditions. The objective was to identify physiological processes related to the genetic variability in GPD. Under most environments, GPD was significantly related to post-anthesis N uptake independently of anthesis date and total N at anthesis. The underlying physiological trait might be related to genotypic differences in either access to soil N, regulation of N uptake by plant N status, or ability to maintain root activity during the grain-filling period. GPD is an interesting potential target in breeding as it appears to be relatively robust across different environments and would be valuable in increasing total N uptake by maturity.

Differential patterns of reactive oxygen species and antioxidative mechanisms during atrazine injury and sucrose-induced tolerance in Arabidopsis thaliana plantlets.

BACKGROUND: Besides being essential for plant structure and metabolism, soluble carbohydrates play important roles in stress responses. Sucrose has been shown to confer to Arabidopsis seedlings a high level of tolerance to the herbicide atrazine, which causes reactive oxygen species (ROS) production and oxidative stress. The effects of atrazine and of exogenous sucrose on ROS patterns and ROS-scavenging systems were studied. Simultaneous analysis of ROS contents, expression of ROS-related genes and activities of ROS-scavenging enzymes gave an integrative view of physiological state and detoxifying potential under conditions of sensitivity or tolerance. RESULTS: Toxicity of atrazine could be related to inefficient activation of singlet oxygen (1O2) quenching pathways leading to 1O2 accumulation. Atrazine treatment also increased hydrogen peroxide (H2O2) content, while reducing gene expressions and enzymatic activities related to two major H2O2-detoxification pathways. Conversely, sucrose-protected plantlets in the presence of atrazine exhibited efficient 1O2 quenching, low 1O2 accumulation and active H2O2-detoxifying systems. CONCLUSION: In conclusion, sucrose protection was in part due to activation of specific ROS scavenging systems with consequent reduction of oxidative damages. Importance of ROS combination and potential interferences of sucrose, xenobiotic and ROS signalling pathways are discussed.