Kurtz-Sohn, A. | Search bioRxiv

bioRxiv2026

Beyond Averaging: Pleiotropic effects of Dry2.2 expose the evolution of growth bet-hedging in barley

RationaleDeveloping climate-resilient crops requires understanding the developmental and physiological mechanisms governing responses to unpredictable environmental stresses. While agricultural selection historically favored phenotypic uniformity, highly volatile conditions can favor risk-spreading "bet-hedging". However, because standard genetic mapping relies on shifting plot-level averages, loci controlling population-level variance remain cryptic. MethodsWe investigated the pleiotropic effects of the Dry2.2 quantitative trait locus (candidate gene HvCEN) using an allelic series of wild barley (Hordeum vulgare ssp. spontaneum) introgressions housed in distinct cultivated backgrounds, evaluated via high-resolution single-plant physiological phenotyping and mini-plot field trials. ResultsSpecific wild alleles confer robust developmental canalization under water limitation, maintain harvest traits, stabilize vascular lignification, and drive a uniform senescence escape strategy. Conversely, carriers of cultivated alleles deploy a bet-hedging strategy under stress, more than doubling inter-plant developmental variation (volume, maturity timing). This variance-driven strategy relies on epistatic interactions, rendering Dry2.2 invisible to traditional mean-centric GWAS plots. ConclusionImproving crop resilience in volatile climates requires expanding selection focus beyond static, plot-level averages to include the active genetic design of population-level variance strategies.

Plant Biology↗

bioRxiv2025

Prediction of harvest-related traits in barley using high-throughput phenotyping data and machine learning

Developing crop varieties that maintain productivity under drought is essential for future food security. Here, we investigated the potential of time-resolved high-throughput phenotyping to predict harvest-related traits and identify drought-stressed plants. Six barley lines (Hordeum vulgare) were grown in a greenhouse environment with well-watered and drought treatments, and phenotyped using RGB, thermal infrared, chlorophyll fluorescence and hyperspectral imaging sensors. Temporal phenomic classification model accurately distinguished between drought-treated and control plants, achieving high accuracy (R2 [≥] 0.97) even when exclusively using predictors only from the early phase after drought induction. Canopy temperature depression at the early stage and RGB-derived plant size estimates at the late stage were identified as key classification features. Temporal phenomic prediction model of harvest-related traits achieved particularly high mean R2 values for total biomass dry weight (0.97) and total spike weight (0.93), with RGB plant size estimators emerging as important predictors. Prediction accuracy for these traits remained high (R2 [≥] 0.84) when using only predictors from the first half of the experiment. Models trained on pooled drought and control data outperformed single-treatment models and retained high accuracy when applied across treatments. These findings support the integration of high-throughput phenotyping and temporal modelling to enable timely and more cost-effective selection of drought-resilient genotypes, and illustrate the broader potential of phenomics-driven approaches in accelerating crop improvement under stress-prone conditions.

plant biology↗