Biology subjects

Conaty, W.

Publications and source records attributed to Conaty, W..

3 recordsLinked to original sources

bioRxiv2026

Identification of environmental factors and growth stages in the prediction of fibre yield and fibre quality traits in rain-grown cotton

ContextUnderstanding how and when environmental conditions influence overall crop performance is crucial for optimising the development of genotypes to a specific breeding target environment. We focused on economically important traits of Australian rain-grown cotton including fibre yield and quality traits, which have not been investigated comprehensively. The aim of the study was to identify relevant environmental factors, and the timing and extent of their impact on rain-grown cotton production. MethodsWe used a data driven approach to analyse the relationship between ten climate related environmental factors across various plant growth stages and eight fibre yield and quality traits, using a large-scale field dataset of 9,283 records collected over 23 years at 4 locations, with 53 unique year-location combinations. We applied eight complementary statistical models including stepwise, penalised and Bayesian linear regression, regression-tree based ensemble methods and deep learning frameworks to (1) select the most essential environmental covariates affecting rain-grown cotton production, and (2) evaluate the predictive performance of these models. ResultsThe environmental impacts on rain-grown cotton production were trait and growth-stage specific. Number of rainy days and solar radiation were identified as the most influential environmental factors for fibre yield traits, vapour pressure deficit at maximum daily temperature was the most influential factor for majority of fibre quality traits. However, each analysed trait was influenced by multiple environmental factors across multiple growth stages (rather than a single factor or a single growth stage). These influential covariates explained a wide range of variation in the traits, accounting for 5.8% to 68.2%. Using the best-fit random forest model, our findings revealed non-linear relationships between key environmental covariates and the traits. ConclusionsEnvironmental factors at different rain-grown cotton growth stages are key determinants for the performance of end-of-season fibre yield and fibre quality parameters. These findings highlight the need to account for environment conditions when developing cotton varieties optimised for rain-grown production systems. Potential strategies are proposed whereby these key environmental factors can be used to increase the rate of genetic gain in rain-grown cotton production systems. ImplicationsThe results of this study will be crucial for future genetic evaluations and analyses of genotype-by-environment interaction effects in rain-grown cotton, which must account for the influence of the environment on plant performance. Furthermore, these methods can be applied to other species to identify critical growth stages and environmental factors which most influence crop performance.

bioinformatics↗

bioRxiv2026

Novel linkage disequilibrium-based genotype-by-environmental interaction method for genomic prediction of cotton yield and fibre quality traits

Genomic prediction (GP) across diverse environments has a potential to accelerate genetic gain in cotton breeding programs. A major challenge in GP is modelling genotype-by-environment interactions (GEI), which is essential for selecting stable and high-performing genotypes under variable production conditions. However, incorporating GEI into GP models increases the dimensionality and computational complexity, risking complex models that are impractical to use on commercial breeding-scale data sets because of run times and computational demands. This study addresses two primary aims. Firstly, we evaluate the practical benefits of GEI-informed GP for predicting economically important cotton traits. Second, advanced statistical modelling strategies are developed and assessed for integrating genomic and environmental data at scale. We propose a dimensionality reduction approach that combines linkage disequilibrium network analysis with principal component techniques to reduce redundancy while preserving informative variation. Using this reduced dataset, we implement Bayesian linear regression models and, for comparison, deep residual neural networks for genomic prediction. Analyses were conducted on a large multi-environment dataset from the CSIRO cotton breeding program, comprising 3,236 breeding lines, 54 environmental covariates, and 8,049 yield and fibre quality phenotype records collected over 10 years and 9 locations representing 41 year-location combinations. Results demonstrate that generally Bayesian linear regression approaches outperform BG-BLUP models, with all three linear/linear mixed methods providing clearly more reliable performance than the deep learning models. These findings highlight the value of using interpretable statistical models for integrating genomic and environmental information to support selection decisions under diverse environmental conditions.

plant biology↗

bioRxiv2026

Trade-offs between photosynthetic capacity, mesophyll conductance stability and leaf anatomy shape heat and water deficit resilience in Gossypium.

O_LIMesophyll conductance (gm) governs CO2 diffusion to Rubisco and is a key determinant of photosynthetic performance, yet the mechanisms underlying its sensitivity to heat and water stress remain unresolved. C_LIO_LIWe quantified gm temperature responses across diverse Gossypium species and examined anatomical drivers of gm plasticity in cultivated cotton (G. hirsutum) and the wild Australian species G. bickii under elevated temperature and soil water deficit. C_LIO_LISpecies exhibited contrasting gm strategies: G. hirsutum exhibited high gm and carbon assimilation near thermal optima but showed greater sensitivity under combined heat and water deficit, whereas G. bickii maintained comparatively stable gm and photosynthesis across stress conditions. C_LIO_LIUnder water deficit, structural adjustments in G. hirsutum (increased leaf porosity, cell wall thickness and mesophyll surface exposure to intercellular airspaces) were insufficient to sustain gm, suggesting that liquid-phase resistances impose dominant constraints on CO2 diffusion under extreme climatic stress. C_LIO_LIThese results identify gm as a dynamic, multi-component trait and a key physiological vulnerability in cotton, shaped by coordinated anatomical characteristics and potentially cell wall properties and membrane-associated processes, with major implications for mechanistic photosynthesis modelling and improving climate resilience in cotton and other C3 species. C_LI

plant biology↗