Biology subjects

Chenu, K.

Publications and source records attributed to Chenu, K..

13 recordsLinked to original sources

bioRxiv2026

Leveraging synthetic environmental covariates to explore crop genotype by environment interactions

Environments are unpredictable. Genotype by environment interactions (GEI) further complicate any assessment of genotype performance or stability across diverse environments posing a challenge for plant breeders. Several approaches exist to address this challenge. However existing approaches present a trade-off between scale and interpretability often acting in a 'black box' manner. Here we extend the Finlay-Wilkinson regression on synthetic environmental covariates of Piepho and Blancon (2023) to dissect GEI at the level of individual developmental windows, resolving which growth stages drive GEI and how genotype stability varies across them. Yield data for 961 genotypes from a barley breeding program, tested across 12 environments, is used as a case study to illustrate how this approach can provide valuable insight into the GEI present. Exploring the dynamic impact of environmental variables across growth stages revealed that environmental factors during early grain fill contributed the most to yield GEI variance. Stability was found to be dynamic across development windows, presenting growth-stage-specific yield stability as a new potential breeding target. This approach yields new, growth-stage-resolved measures of yield stability and a new means of dissecting GEI in complex traits, while also opening a route to environmental clustering based on stage-specific GEI patterns.

genetics↗

bioRxiv2026

Additive Effects Dominate Legume Responses to Combined Heat and Drought Stress: A Quantitative Review

Concurrent exposure to heat and drought stress compromises legume productivity, yet their combined effects are rarely quantified systematically. We compiled a database of 18 studies covering seven legume species. From these, we extracted 929 physiological, biochemical, and yield-related traits and calculated actual-to-additive ratios to classify heat-drought interactions as antagonistic (ratio < 1), additive (ratio = 1), or synergistic (ratio > 1). Additive heat-drought relationships accounted for 59 % of all classifiable observations, 37% relationships were antagonistic, and 4% synergistic. The relationship varied with species, genotype, trait, and experimental conditions highlighting the complexity of combined abiotic stress effects. The results challenge the common assumption that concurrent stresses invariably exacerbate damage and underscore the need for more realistic, quantitatively defined stress treatments as well as frameworks that integrate trait-level responses into predictive models of crop growth and development. Our synthesis provides a quantitative foundation to understand legume phenotypes under the increasingly frequent co-occurrence of heat and drought stress and identifies research areas where further work is needed to improve insight into combined stress responses. HighlightsO_LICombined heat and drought responses were mainly additive or antagonistic. C_LIO_LIEvidence is biased toward few legumes and controlled environments. C_LIO_LIField-based, multi-species studies are needed to identify adaptive traits. C_LI

plant biology↗

bioRxiv2026

Evaluating crop models for future climate scenarios: wheat yield predictions using APSIM and STICS under combined CO2, warming, and water deficit conditions

Crop models are essential for predicting climate change impacts on agriculture, yet their validation under multi-stress conditions remains limited. This study evaluated two widely-used wheat models, APSIM and STICS, using data from three Free-Air CO2 Enrichment (FACE) experiments (USA, Germany, Australia) combining elevated CO2 (eCO2), water deficit, and warming. Environmental characterisation using simulation-based stress indices revealed that intended "controls" frequently experienced hidden heat and water stress, meaning models were calibrated on crops already undergoing physiological adjustments. Evaluation of simulated yield and components revealed a clear hierarchy in prediction errors (RRMSE): unlimited conditions (3-9%) < single stress (4-27%, with a need to improve response to heat stress) < combined stress (17-123%). Elevated CO2 generally increased prediction uncertainty for crops experiencing water stress. Our results suggest that current stress functions from the models fail to capture the synergistic coupling between drought and heat stress. This highlights the urgent need for more mechanistic modelling to improve the reliability of climate change impact assessments.

plant biology↗

bioRxiv2026

Trait stability of diverse kabuli chickpea germplasm from delayed sowing in a rainfed environment

Structured AbstractO_ST_ABSContext and ObjectiveC_ST_ABSDelayed sowing can expose chickpea crops to stress during the critical period for yield determination, but the effect of yield components and phenology to grain yield variation is not well characterised in diverse germplasm under rainfed conditions. Identifying genetic resources for grain yield improvement requires integration of multi-environment trial and genomic analyses to disentangle direct yield effects from indirect effects of phenology. This study aimed to (1) characterise genotype by environment interaction patterns for grain yield, yield components and phenology across times of sowing and seasons, and (2) identify genomic regions associated with improved grain yield that are present in genebank accessions but absent from current Australian commercial cultivars. MethodsA diversity panel of 141 kabuli chickpea genotypes, including six commercial Australian cultivars and 135 genebank accessions, was evaluated across six rainfed trials at Narrabri, New South Wales over three seasons (2018 to 2020) under typical (MAIN) and delayed (LATE) sowing. Multi-environment trial analyses with factor analytic models partitioned genotype by environment interactions for grain yield, 100-seed weight, seed number, and thermal time to flowering, podding, and maturity. Haplotype block analysis identified high variance blocks associated with seed number, classified by their overlap with high variance thermal time to flowering blocks, to distinguish from effects mediated by phenology. Results and ConclusionsDelayed sowing reduced grain yield by up to 1.04 t ha-{superscript 1}, driven primarily by reductions in seed number rather than 100-seed weight. Accelerated phenology was a key component of adaptation among commercial cultivars. Four haploblocks with high block variance for seed number were identified across all six trials. SignificanceSeed number was the dominant driver of grain yield variation in this diverse kabuli chickpea panel. Targeted introgression of rare superior haplotypes from genebank accessions provides an opportunity to broaden the genetic base of Australian kabuli chickpea and improve yield through higher seed number, with relevance to chickpea production systems facing similar climate variability. HighlightsDelayed sowing reduced grain yield in diverse kabuli chickpea germplasm by up to 1.04 t ha-1 across three years and six trials in northern New South Wales. Seed number, not seed weight, was the dominant driver of grain yield variation, and a shorter phenological duration was associated with higher seed number. Across all six trials, haplotype block analysis identified four genomic regions in high linkage disequilibrium with high variance for seed number and low variance for flowering time. The accession FLIP 94 62C uniquely carried rare superior haplotypes at two chromosome 4 blocks, the haplotype at the 17.0 Mb block was the most superior haplotype in all trials while the haplotype at the 8.5Mb block was most superior only in the most heat stressed environment.

plant biology↗

bioRxiv2026

Identifying water stress response haplotypes in barley using latent environmental covariates

PurposeGenotype-by-environment (G x E) interactions represent a major obstacle to increasing genetic gain in crop breeding, with the underlying physiological drivers often remaining obscured within conventional statistical models. This case study presents a novel framework that transforms the latent factors from Factor Analytic (FA) multi-environment trial (MET) models into heritable quantitative traits, enabling the genetic dissection of adaptive response patterns. MethodsA Factor Analytical Linear Mixed Model (FA-LMM) was fit to plot-level yield data for 1,036 barley genotypes across eight Australian trials. ResultsCorrelation of the factor loadings with APSIM-simulated environmental covariates demonstrated that the second latent factor FA2 was strongly correlated with the Water Stress Index (r = -0.83) during the critical flowering period, establishing water availability as the main biological axis of crossover Gx E. Genotypic scores for the derived traits, Overall Performance (OP) and Water Stress Response (WSR), were subjected to high-resolution haplotype-based mapping using local Genomic Estimated Breeding Values (GEBV). ConclusionThis analysis successfully identified major genomic regions that accounted for a substantial proportion of the additive genetic variance. Gene Ontology enrichment of candidate genes within the top haploblocks implicated fundamental pathways related to energy homeostasis, root development, and stress response, with notable candidates including FTsH11, BPS1, and TDP1. The distribution of favourable Haplotypes of Interest (HOI) in elite cultivars suggested a historical signature of inadvertent selection for these adaptive mechanisms. This framework provides an explicit bridge between statistical modelling and functional genomics, offering breeders actionable genetic targets for accelerated development of climate-resilient cereals.

plant biology↗

bioRxiv2025

Saving water to get 'more crop per drop' - A new phenotyping framework revealed wide plasticity in wheat

Improving transpiration efficiency (TE) offers a pathway to increase yield in drought-prone environments. This study examined genotypic variation in TE and its physiological determinants across diverse wheat lines. An initial experiment with six cultivars was expanded to 105 genetically-diverse genotypes evaluated under well-watered conditions and fluctuating vapour pressure deficit (VPD). Using a high-throughput lysimeter platform, transpiration rates were recorded every 10 minutes and normalised daily at low VPD to account for genotypic variations in canopy size. TE was strongly associated with reduced normalised transpiration rate at high VPD (TRnorm-highVPD), while no significant relationship was found with maximum photosynthetic capacity. High-TE lines achieved either greater biomass with similar water use or similar biomass with lower water use, reflecting a water-saving strategy under high evaporative demand. A complementary experiment under low VPD revealed limited genotypic variation in intrinsic TE, reinforcing the value of TRnorm-highVPD as a screening trait. Consistent correlations between TE and TRnorm-highVPD across experiments highlight the potential of TRnorm-highVPD as a robust phenotyping target. Several high-TE lines outperformed modern cultivars, offering promising sources of novel alleles. These findings provide a scalable framework to identify drought-resilient genotypes and support breeding strategies to improve water productivity and achieve more crop per drop. HIGHLIGHTWheat genotypes with reduced normalised transpiration rate under high evaporative demand achieved greater transpiration efficiency. A new automatised high-throughput phenotyping framework is proposed to assist drought-resilient breeding.

physiology↗

bioRxiv2025

Envirotyping Facilitates Understanding of Genotype x Environment Interactions and Highlights the Potential of Stay-green Traits in Wheat

Better understanding genotype by environment interaction (GxE) can help breeding for better adapted varieties. Envirotyping for environmental water status was applied to assist interpretation of GxE interactions for wheat yield in multi-environment trials conducted in drought-prone Australian environments. Genotypes from a multi-reference parent nested association mapping (MR-NAM) population were tested in 10 trials across the Australian wheatbelt. Genotype yield and phenology were measured in all trials, while traits associated with the stay-green phenotype were assessed for a subset of 5 trials. Envirotyping was conducted by characterizing water stress experienced by genotypes at each trial using crop modelling. Envirotyping facilitated the understanding of GxE interactions by explaining 75, 67, and 66% of the genotypic variance for yield in severe water-limited (ET3), mild terminal water-stress (ET2), and water-sufficient (ET1) environments, respectively. Yield and stay-green were negatively correlated with flowering time in most trials. However, when focusing on genotypes flowering at similar times within a trial, no significant correlation was found between yield and flowering. Importantly stay-green traits remained significantly correlated with yield. Stay-green traits such as delayed onset of senescence and slower senescence rate benefited yield by 0.2 to 1.1 t ha-1 across environments, highlighting the breeding potential for stay-green traits in both water-sufficient and water-limited environments. Hence, sustaining green leaf area during grain filling helped to enhance yield. Envirotyping to better understand GxE interactions for yield, coupled with screening for traits exhibiting superior adaptive mechanisms, are powerful assets in assisting plant breeders to select more effectively drought adapted genotypes. HighlightsGenotype x environment interaction for yield could be reduced with envirotyping. Envirotyping enabled a 75% gain in genotypic variance in severe drought conditions. Envirotyping clarified breeding potential of stay-green traits across environments. Stay-green has potential to enhance wheat yield across diverse environments.

physiology↗

bioRxiv2025

Plant plasticity in the face of climate change - CO2 offsetting effects to warming and water deficit in wheat. A review.

Future crop production will depend on plant plasticity in response to increases in atmospheric CO2, mean temperature, heatwave and drought events. The present review intends to highlight the impact of interactions between high CO2 levels, warming and water deficit in existing published experimental data in the case of wheat. To do so, we identified experiments quantifying the effects of such interactions on traits related to crop productivity and water use. We used the collected data to estimate plasticity indices assessing compensation and interaction between elevated CO2 and adverse climatic conditions, bringing a new perspective on the matter. In the studied data, even though there is an important variability, we found that crop productivity tends to decrease despite the positive effects of the rise in CO2 concentration. Conversely, with elevated CO2, water consumption tends to decrease despite the warmer conditions. We hypothesized that the positive effect of CO2 on crop productivity is greater under drought conditions, which is confirmed in 54% of the experiments. This review highlights the need to acquire further experimental data under possible future conditions to calibrate and validate crop models: their range of validity requires more thorough testing under the wide range of projected environmental conditions.

plant biology↗

bioRxiv2024

Phenotyping the hidden half: Combining UAV phenotyping and machine learning to predict barley root traits in the field

Improving crop root systems for enhanced adaptation and productivity remains challenging due to limitations in scalable non-destructive phenotyping approaches, inconsistent translation of root phenotypes from controlled environment to the field, and a lack of understanding of the genetic controls. This study serves as a proof of concept, evaluating a panel of Australian barley breeding lines and cultivars (Hordeum vulgare L) in two field experiments. Integrated ground-based root and shoot phenotyping was performed at key growth stages. UAV-captured vegetation indices (VIs) were explored for their potential to predict root distribution and above-ground biomass. Machine learning models, trained on a subset of 20 diverse lines, with the most accurate model applied to predict traits across a broader panel of 395 lines. Unlike previous studies focusing on above-ground traits or indirect proxies, this research directly predicts root traits in field conditions using VIs, machine learning and root phenotyping. Root trait predictions for the broader panel enabled genomic analysis using a haplotype-based approach, identifying key genetic drivers, including EGT1 and EGT2 which regulate root gravitropism. This approach offers the potential to advance root research across various crops and integrate root traits into breeding programs, fostering the development of varieties adapted to future environments. HighlightIntegrating UAV phenotyping and machine learning can be used to predict RSA traits non-destructively and offers a new approach to support root research and crop improvement.

physiology↗

bioRxiv2023

Does late water deficit induce root growth or senescence in wheat?

Drought frequently limits productivity in rain-fed systems. To investigate water-stress impacts on post-anthesis root development in wheat, three experiments were conducted with two cultivars, Scout and Mace, grown in 1.5m tubes under well-watered conditions or post-anthesis water-stress. Shallow roots of both genotypes appeared to senesce between heading and maturity under well-watered conditions, whereas moderate water stress tended to increase post-heading shallow-root senescence in Mace but stimulated growth in Scout. For deep roots, net growth in biomass was observed for both genotypes under well-watered conditions between heading and maturity, whereas under moderate water stress, only Scout maintained net growth, with net senescence being observed for Mace. Severe water stress resulted in root senescence at all depths for both genotypes. Above ground, Scout retained leaf greenness for only slightly longer than Mace under well-watered conditions. In contrast, under moderate water stress, Mace showed rapid post-anthesis leaf senescence while Scout was affected little if at all. Grain biomass per plant was similar between genotypes in well-watered conditions but more reduced for Mace under moderate stress. Post-anthesis differences in root growth and senescence can strongly influence water use and grain filling in drought-prone environments. Screening for this could assist breeding for drought tolerant varieties. HighlightWater deficit induced post-heading root senescence and/or growth in wheat dependant on genotypes and conditions. Root senescence was associated to leaf senescence, earlier plant maturity and reduced plant yield.

physiology↗

bioRxiv2022

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

Photosynthetic manipulation provides new opportunities for enhancing crop yield. However, understanding and quantifying effectively how the seasonal growth and yield dynamics of target crops might be affected over a wide range of environments is limited. Using a state-of-the-art cross-scale model we predicted crop-level impacts of a broad list of promising photosynthesis manipulation strategies for C3 wheat and C4 sorghum. The manipulation targets have varying effects on the enzyme-limited (Ac) and electron transport-limited (Aj) rates of photosynthesis. In the top decile of seasonal outcomes, yield gains with the list of manipulations were predicted to be modest, ranging between 0 and 8%, depending on the crop type and manipulation. To achieve the higher yield gains, large increases in both Ac and Aj are needed. This could likely be achieved by stacking Rubisco function and electron transport chain enhancements or installing a full CO2 concentrating system. However, photosynthetic enhancement influences the timing and severity of water and nitrogen stress on the crop, confounding yield outcomes. Strategies enhancing Ac alone offers more consistent but smaller yield gains across environments, Aj enhancement alone offers higher gains but is undesirable in less favourable environments. Understanding and quantifying complex cross-scale interactions between photosynthesis and crop yield will challenge and stimulate photosynthesis and crop research. Summary StatementLeaf-canopy-crop prediction using a state-of-the-art cross-scale model improves understanding of how photosynthetic manipulation alters wheat and sorghum growth and yield dynamics. This generates novel insights for quantifying impacts of photosynthetic enhancement on crop yield across environments.

plant biology↗

bioRxiv2021

Physiological and genetic drivers underpinning canopy development are associated with durum wheat yield in rainfed environments

New durum wheat (Triticum turgidum L. ssp. Durum) cultivars with improved adaptation to variable rainfall environments are required to sustain productivity in the face of climate change. Physiological traits related to canopy development underpin the production of biomass and yield, as they interact with solar radiation and affect the timing of water use throughout the growing season. This study explored the temporal canopy dynamics of durum wheat using a nested-association mapping population evaluated for longitudinal normalized difference vegetation index (NDVI) measurements. Association mapping was performed to identify quantitative trait loci (QTL) for time-point NDVI and spline-smoothed NDVI trajectory traits. Yield effects associated with QTL for canopy development were investigated using data from four rainfed field trials. Four QTL associated with slower canopy closure, improved yield in specific environments, and notably, were not associated with a yield penalty in any environment. This was likely due to optimised timing of water-use and pleiotropic effects on yield component traits, including spike number and spike length. Overall, this study suggests that slower canopy closure is beneficial for durum wheat production in rainfed environments. Selection for traits or loci associated with canopy development may improve yield stability of durum wheat in water limited environments.

genetics↗

bioRxiv2021

A robust field-based method to screen heat tolerance in wheat

Wheat is highly sensitive to elevated temperatures, particularly during pollen meiosis and early-to-mid grain filling. The impact of heat stress greatly depends on the plant developmental stage. Thus, germplasm ranking for heat tolerance in field trials may be confounded by variations in developmental phase between genotypes at the time of heat events. A photoperiod-extension method (PEM) was developed allowing screening of 35 diverse genotypes at matched developmental phase despite phenological variations. Paired trials were conducted to compare the new PEM against conventional field screening in plots. In the PEM, plants were sown in single rows or small plots. Artificial lighting was installed at one end of each row or plot to extend day length, inducing a gradient of flowering times with distance from the lights. Individual stems or plot quadrats of each genotype were tagged at flowering. Late-sown plants received more heat shocks during early to mid grain filling than earlier sowings, suffering reductions in both individual grain weight (IGW) and yield. IGW was reduced by 1.5 mg for each additional post-flowering day with temperature > 30{degrees}C. Significant genotypic differences in heat tolerance ranking were observed between PEM versus conventional plot screening. Strong correlations between trials experiencing similar degree of heat were found both for IGW and for total grain weight with the PEM either with individual-stem tagging (e.g. average r of 0.59 and 0.54, respectively for environments with moderate postflowering heat) or quadrat tagging (r of 0.53 and 0.47). However, correlations for IGW and yield in these environments were either poor or negative for conventional trials (e.g. average r of 0.11 and 0.12, respectively for environments with moderate postflowering heat). Accordingly, a PCA grouped genotypes consistently for heir performance across environments with similar heat stress in PEM trials but not in conventional trials. In this study, most consistent genotype ranking for heat tolerance was achieved with the PEM with tagging and harvesting individual spikes at matched developmental phase. The PEM with quadrat sampling provided slightly less consistent rankings but appears overall more suitable for high-throughput phenotyping. The method promises to improve the efficiency of heat tolerance field screening, particularly when comparing genotypes of different maturity types.

plant biology↗