Climate variability, particularly freeze stress, poses a substantial challenge to crop yields worldwide. This study examined the impact of early 2024 freeze stress on rapeseed yields in the Yangtze River Basin, China, and assessed yield responses to nitrogen (N), phosphorus (P), and potassium (K) fertilizer rates. Six field experiments with varying N, P, and K fertilizer rates were conducted from 2022 to 2024 at two sites. In 2023-2024, a severe freeze event caused yield losses ranging from 13.4% to 63.3%, depending on nutrient fertilizer rates and sites. The effect of N fertilization on mitigating freeze stress varied across different sites, while high P fertilizer rates were associated with a reduced yield decline under freeze stress. The K fertilizer application also decreased the yield reductions caused by the freeze stress. Freeze stress disproportionately affected yield components, particularly the number of siliques per plant. Membership function values (MFV) were used as a comprehensive indicator of yield-related traits to quantify the combined effects of freeze stress and fertilization on rapeseed yield. The optimal fertilizer rates that maximized MFV were 343 ​kg ​N ​ha⁻¹, 118 ​kg P₂O₅ ​ha⁻¹, and 166 ​kg K₂O ha⁻¹ for 2022-2023 and 239 ​kg ​N ​ha⁻¹, 110 ​kg P₂O₅ ​ha⁻¹, and 169 ​kg K₂O ha⁻¹ for 2023-2024. These results highlight the importance of balanced nutrient management in improving rapeseed resilience to freeze stress and provide practical recommendations for optimizing nutrient management in cold-prone regions.

Achieving rice yield-quality synergy, which is critical for breeding and agronomic practice, is hindered by dynamic regulatory gaps due to methodological constraints, while high-throughput unmanned aerial vehicle (UAV) phenotyping can enable breakthroughs by decoding dynamic traits at scale. This study conducted five experiments (EXP, 2022-2024; including nitrogen fertilization, multi-cultivar, and breeding material experiments) with UAV-based phenotyping to establish trait estimation models (EXP1-EXP3), enabling dissection of trait-specific contributions to yield-quality synergies via regression, multi-objective optimization, and path analysis (EXP4-EXP5), and identifying diagnostic traits in practice. Using UAV data, effective regression models were developed to monitor five rice traits: plant height (R² ​= ​0.89), aboveground biomass (R² ​= ​0.84), leaf area index (R² ​= ​0.61), canopy nitrogen content (R² ​= ​0.68), and leaf nitrogen content (R² ​= ​0.83), thereby systematically establishing 37 critical plant traits across the growth stages. Furthermore, feature importance analysis using extreme gradient boosting (R² ​= ​0.99) assessed the importance of these traits for yield and grain quality, and four common traits that were crucial for both yield and grain quality were identified. Notably, the synergistic yield-quality group exhibited 26.38-51.76% higher net assimilation rate (NAR) than the low-performance group (validated by multi-objective optimization), positioning NAR as a diagnostic marker for yield-quality synergistic enhancement. Path analysis revealed that NAR exerted positive effects on yield and grain quality, while yield indirectly influenced grain quality through eating quality. Overall, this study integrated UAV-based phenotyping and trait analysis, providing a novel insight into the synergistic enhancement of yield and grain quality.

Early sowing of spring wheat increases the risk of frost injury due to premature spikelet initiation under low temperatures. While trampling has been reported to delay spikelet initiation, its physiological mechanism remains unclear. We attempted to explain the delayed spikelet initiation caused by trampling in terms of the involvement of ethylene. The role of ethylene in trampling was investigated in the spring wheat cultivar Ayahikari by measuring ethylene production after trampling and applying the ethylene-releasing agent ethephon and the ethylene action inhibitor 1-methylcyclopropene (1-MCP). The effects of trampling and ethylene on the expression of VERNALIZATION1 (Vrn1) and FLOWERING LOCUS T (FT), key regulators of spikelet initiation, were also examined. Trampling significantly increased ethylene production and delayed young spike development. Exogenous application of ethephon mimicked the effect of trampling on young spike development, while 1-MCP reversed this effect. Furthermore, trampling suppressed the expression of Vrn1 and FT, similar to the effects observed in ethephon-treated plants. The present study indicated that ethylene mediated the effect of trampling on young spike development likely through the suppression of Vrn1 and FT expression, ultimately causing a delay in spikelet initiation. Our findings provide a mechanistic explanation and highlight trampling as a potential agronomic strategy for mitigating frost risk in early-sown spring wheat.

High-throughput field phenotyping offers an efficient solution for identifying and selecting genotypes of interest in plant breeding. This study aimed to develop multivariate models using spectral reflectance data to estimate physiological and yield traits in spring wheat genotypes exposed to different water regimes. Fifteen spring wheat varieties and one triticale genotype were evaluated in sixteen environments, which were generated by combining data from over four seasons in two Mediterranean locations in Chile, along with two water regimes (irrigated and water deficit). Measured traits were leaf pigments, leaf area index (LAI), leaf water potential (Ψ_leaf), gas exchange, chlorophyll fluorescence, grain yield, and carbon isotope composition (δ¹³C). Hyperspectral reflectance was recorded at the leaf level and canopy level (45° and 90°) at anthesis and grain filling and used to generate predictive models using partial least squares (PLS), least absolute shrinkage and selection operator (LASSO), and elastic net (E.net) regression. Models explained over 60% of the trait variation (R²) for 70% of traits analysed. Fluorescence parameters (R² ​= ​0.78-0.88), δ¹³C (R² ​= ​0.80), leaf pigments (R² ​= ​0.50-0.74), Ψ_leaf (R² ​= ​0.72), and LAI (R² ​= ​0.68) had the most robust predictions. LASSO regression showed the highest R² and accuracy, while canopy-level spectra at 90° excelled in predicting grain yield and LAI, and leaf-level spectra were best for fluorescence traits. These methods facilitated the identification of genotypes with superior water-deficit adaptation and yield potential, accelerating breeding, enhancing crop resilience to climate change, and improving food security.