Search Results (177)

Long-term observations of the seasonal growth of Scots pine (Pinus sylvestris L.) tree rings in the arid conditions of the Khakass-Minusinsk Basin (southern Siberia) revealed that in 2024, trees had formed a tree ring with a typical intra-annual density fluctuation (IADF) in the transition wood. An analysis of the timing and causes of this wood structure anomaly was conducted using a combination of three approaches: (1) analyzing images of cross-sections of the forming tree ring throughout the season; (2) comparing the timing of anomalous cells’ differentiation with daily climate data; (3) comparing seasonal growth observations with calculated characteristics of the modeled growth rate and its derivatives: soil moisture and transpiration. We found that during the most severe heat wave and drought (from 22 June to 9 July), the last normal earlywood cells were yet expanding, IADF cells were being produced in the cambial zone, and the first of them began expansion, while normal cells began being produced again immediately after the subsiding of environmental stress. Apparently, low soil moisture and very high temperatures mainly impacted cells in the cambial zone, marking it as the primary target of external factors influencing tree-ring formation and structure, which is important for dendroclimatology and digital wood anatomy. This result is supported by both indirect and limited direct evidence from other sources. Full article

Cortaderia selloana is an invasive grass spreading rapidly and becoming a serious environmental concern in many areas of the world. The species expanded to the Iberian Peninsula, including its eastern coast, where it increasingly occupies diverse ecosystems. This is the first evaluation of C. selloana’s tolerance to salinity and water deficit, combined with heat stress, during two key developmental stages: germination and early vegetative growth. Experimental trials were conducted using seeds and juvenile plants from two populations. Elevated temperature reduced germination, biomass accumulation, and shoot elongation, particularly when combined with water or salt stress. Drought exerted the strongest inhibitory effect on photosynthetic pigments, whereas salinity mainly affected carotenoid content, mostly in one of the populations analysed. Proline accumulation increased under drought and salinity, reaching up to 70 µmol·g⁻¹ DW, but to a lesser extent when combined with a heat treatment, suggesting enhanced proline catabolism at high temperature. Total soluble sugars tended to increase under water deficit (from ~75 to >100 mg equivalent of glucose g⁻¹ DW), indicating a potential osmoprotective shift from proline to carbohydrates. These results highlight intraspecific variability in stress tolerance and emphasise that C. selloana’s success in Mediterranean environments depends on its capacity to withstand transient but not prolonged combined stresses. Full article

Lentil (Lens culinaris Medik. subsp. culinaris) is a Mediterranean legume crop of high value due to nutritional quality and adaptability; however, its cultivation is increasingly threatened due to climate uncertainty and reduction in genetic diversity in modern cultivars. The present research study evaluated 31 Greek lentil accessions (twenty-two landraces and nine commercial cultivars of both small and large seed types) in a semi-arid environment of Central Greece, over two cropping seasons, focusing on phenological, morphological, yield, and quality traits. The great diversity observed at the morpho-phenological and qualitative levels implies the high genotypic diversity of these genetic resources. Small-seeded landraces performed better in seed and biological yield, harvest index, and protein content, having greater phenological stability and tolerance to the Mediterranean environments. In particular, the highest seed yield was observed in LAX small-seeded landrace (1930 kg ha⁻¹), followed by TSO (1559 kg ha⁻¹), DIG (1449 kg ha⁻¹), and EGL (1437 kg ha⁻¹) small-seeded landraces. As for the regression analysis, seed yield was positively correlated with days to flowering (TF: r = 0.076, p < 0.01), plant height (PH: r = 0.143, p < 0.05), number of pods per plant (NPP: r = 0.941, p < 0.001), number of seeds per pod (NPP: r = 0.432, p < 0.001), number of branches (NPB: r = 0.234, p < 0.01), biological yield (BY: r = 0.683, p < 0.001), and harvest index (HI: r = 0.650, p < 0.001). Principal component analysis (PCA) distinguished small-seeded landraces associated with adaptive and yield traits from large-seeded cultivars associated with seed size. Greek lentil landraces, especially the small-seeded genotypes (e.g., LAX and DIG), have great potential for use in the development of climate-tolerant and high-yielding lentil varieties adapted for sustainable Mediterranean production. Breeding programs can target the crossing of landraces with large-seeded cultivars (e.g., IKAm and THEm) to develop varieties that combine stress tolerance, adaptation, and high productivity with adaptation to different seed sizes. Subsequent studies on drought tolerance and heat resistance are still important for continued improvement in lentil productivity in a changing climate. Full article

Against the backdrop of growing climatic variability, the identification of genotypes combining high yield with stability and resilience to stress factors has become a central objective of contemporary wheat breeding. Therefore, the objective of this work was to assess the stability and adaptability of a collection of 13 wheat–wheatgrass hybrids (WWHs, lines) (Triticum aestivum L. (2n = 42)) in comparison with 10 commercial spring bread wheat (Tr. aestivum L.) cultivars under various meteorological conditions. This study was conducted in one location (Moscow region, Russia) over three growing seasons (2020, 2021, and 2022), which included a highly stressful year (2021) characterized by a severe combination of drought and heat during critical growth stages. Statistical analysis employed analysis of variance (ANOVA), clustering, and modern models for assessing the genotype-by-environment interaction (GEI)—AMMI (Additive Main Effects and Multiplicative Interaction). The results showed a significant effect of year conditions on all yield components. Under the stressful conditions of 2021, most genotypes exhibited a 30–70% decrease in productivity. Cluster analysis revealed a dynamic regrouping of genotypes depending on the conditions of the growing season. The AMMI model identified genotypes with high stability, such as Sudarinya (ASV = 9.3) and WWH 200 (ASV = 11.2), as well as genotypes specifically adapted to certain conditions: KWS Akvilon (ASV = 52.1) to stressful conditions and WWH 127 (ASV = 55.9) to favorable conditions. Under stress, lines WWH 107, WWH 127, and WWH 2430 exhibited the most adaptive strategies, including compensatory mechanisms, making these hybrids promising for further breeding. In conclusion, although wheat–wheatgrass hybrids demonstrate high productive potential under favorable conditions, their successful use in breeding requires the selection of genotypes that combine productivity and stress resistance. The identified stable and adaptive genotypes are valuable for developing new competitive cultivars under changing climatic conditions. Full article

Global climate change has led to frequent extreme weather events such as high temperatures and droughts, severely threatening the heat and water balance during the growing season of summer maize. To adapt to these changes, adjusting planting dates to optimize crop development has become a key agronomic measure for mitigating climate stress and ensuring yield. Against this backdrop, precise monitoring of leaf area index (LAI) is crucial for evaluating the effectiveness of planting date regulation and achieving precision management. To reveal the impact of planting date variations on summer maize LAI inversion and address the limitations of single data sources in comprehensively reflecting complex environmental conditions affecting crop growth, this study examined summer maize at different planting dates across the North China Plain. Through stepwise regression analysis (SRA), multiple vegetation indices (VIs) and 0–2nd order fractional order derivatives (FODs), spectral parameters were dynamically screened. These were then integrated with effective accumulated temperature (EAT) to optimize model inputs. Partial Least Squares Regression (PLSR), Random Forest (RF), Support Vector Regression (SVR), and Adaptive Boosting Regression (AdaBoot) algorithms were employed to construct LAI inversion models for summer maize across different planting dates and mixed planting dates. Results indicate that, compared to empirical VIs and “tri-band” parameters, randomly selected dual-band combination VIs exhibit the strongest correlation with summer maize LAI. Key bands identified through SRA screening concentrated in the 0.7–1.2 order range, primarily distributed across the red edge and near-infrared bands. Multi-feature models incorporating EAT significantly improved retrieval accuracy compared to single-feature models. Optimal models and feature combinations varied across planting dates. Overall, the VIs + EAT combination exhibited the highest stability across all models. Ensemble learning algorithms RF and AdaBoost performed exceptionally well, achieving average R² values of 0.93 and 0.92, respectively. The model accuracy for the 20-day delayed planting (S4) decreased significantly, with an average R² of 0.62, while the average R² for other planting dates exceeded 0.90. This indicates that the altered environmental conditions during the later growth stages of LAI due to delayed planting hindered LAI estimation. This study provides an effective method for estimating summer maize LAI across different planting dates under climate change, offering scientific basis for optimizing adaptive cultivation strategies for maize in the North China Plain. Full article

Global warming increases the frequency with which drought and heat stress occur simultaneously, especially in semi-arid regions. Such combined stress imposes a non-additive and more severe impact on plant growth, yield, and quality than either stress alone. Here, we integrate recent physiological, biochemical, and multi-omics studies to compare individual and combined stress responses and to dissect the underlying signal transduction networks. We show that drought-dominated phases rapidly elevate ABA concentrations and activate SnRK2–AREB cascades, whereas heat pulses trigger jasmonic acid and ethylene signals that antagonize ABA-driven stomatal closure. Under combined stress, these hormonal modules converge on a “competitive TF marketplace”, where ABA, JA, and GA cis-elements co-regulate invertase–sugar checkpoints, heat shock factor/ROS oscillators, and chromatin-remodeling events that determine reproductive fate. Recent advances using multi-omics approaches and systems biology have further elucidated these complex networks. These insights will inform future breeding strategies aiming to develop stress-tolerant crops. We highlight emerging tools—weighted gene co-expression networks, kinetic multi-omics, and cis-regulatory CRISPR editing—that can exploit these signaling hubs for breeding crops with improved combined stress tolerance. Full article

Plants continuously face multiple abiotic stresses, including drought, salinity, heat, cold, and heavy metal, that challenge cellular homeostasis and threaten global crop productivity. Recent research reveals that these stress responses are not isolated but interconnected through shared hormonal, redox, and transcriptional networks. This review provides an integrative synthesis of current advances in stress signaling, emphasizing how perception, transduction, and memory layers are hierarchically organized across distinct stress types. We outline key regulatory hubs—such as ABA-centered hormonal crosstalk, chloroplast-nucleus redox communication, and epigenetic priming—that coordinate systemic tolerance. Furthermore, we highlight emerging evidence for stress-specific modules that operate under combined stresses (e.g., drought–heat, salinity–cold), providing a unified framework for understanding how plants integrate multi-dimensional signals. This synthesis offers a conceptual perspective linking signaling architecture to adaptive outcomes, aiming to inform future strategies for engineering multi-stress-resilient crops. Full article

This study sought to screen 45 maize (Zea mays L.) inbred lines for tolerance to combined drought and heat stress (CDHS) and identify the leaf proteome patterns of two inbred lines with contrasting stress response at early vegetative stage. Biomass accumulation was significantly reduced under CDHS compared to optimum conditions. Furthermore, CDHS-tolerant inbred lines exhibited significantly lower (p < 0.05) leaf temperatures (28.6 °C) and higher sub-stomatal CO₂ concentration (9012 mol mol⁻¹) and photosynthetic yield (0.69) under stress. The tolerant (CIM18) and susceptible (QS21) inbred lines were exposed to stress by maintaining a field capacity of 25% for 7 days and increasing the daily ambient temperature by 5 °C from 25 °C to 40 °C. Conventional two-dimensional electrophoresis analysis was used to compare leaf protein expression profiles, and significant differences (p < 0.05) were observed. Out of a total of 505 proteins, 114 showed significant quantitative variation. Of these, 62 proteins had a twofold upregulation in CIM18, while 52 were downregulated. Twenty upregulated proteins were selected for amino acid micro-sequencing, and 11 proteins were uniquely expressed in CIM18. The other nine proteins had ≥ twofold upregulation in CIM18 compared to QS21. The functions of the identified proteins included defence, metabolism, photosynthesis and structure. Full article

The operation of modern power networks is increasingly exposed to overlapping climate extremes and volatile system conditions, making it essential to adopt scheduling approaches that are resilient as well as economical. In this study, a two-stage stochastic formulation is advanced, where indicators of system adaptability are embedded directly into the optimization process. The objective integrates standard operating expenses—generation, reserve allocation, imports, responsive demand, and fuel resources—with a Conditional Value-at-Risk component that reflects exposure to rare but damaging contingencies, such as extreme heat, severe cold, drought-related hydro scarcity, solar output suppression from wildfire smoke, and supply chain interruptions. Key adaptability dimensions, including storage cycling depth, activation speed of demand response, and resource ramping behavior, are modeled through nonlinear operational constraints. A stylized test system of 30 interconnected areas with a 46 GW demand peak is employed, with more than 2000 climate-informed scenarios compressed to 240 using distribution-preserving reduction techniques. The results indicate that incorporating risk-sensitive policies reduces expected unserved demand by more than 80% during compound disruptions, while the increase in cost remains within 12–15% of baseline planning. Pronounced spatiotemporal differences emerge: evening reserve margins fall below 6% without adaptability provisions, yet risk-adjusted scheduling sustains 10–12% margins. Transmission utilization curves further show that CVaR-based dispatch prevents extreme flows, though modest renewable curtailment arises in outer zones. Moreover, adaptability provisions promote shallower storage cycles, maintain an emergency reserve of 2–3 GWh, and accelerate the mobilization of demand-side response by over 25 min in high-stress cases. These findings confirm that combining stochastic uncertainty modeling with explicit adaptability metrics yields measurable gains in reliability, providing a structured direction for resilient system design under escalating multi-hazard risks. Full article

Grain legume crops are rich in nutritional value and play a crucial role in global food sustainability. Like many other crops, they are affected by various abiotic stresses that reduce yield and seed quality, thereby threatening food security. Several strategies have been proposed to mitigate these effects and enhance yield. Among them, the use of biostimulants offers a sustainable and efficient approach to improving stress tolerance in the short term. However, the molecular mechanisms underlying the effects of individual or combined molecules remain poorly understood and could significantly influence the development of edited crops with enhanced stress tolerance in the long term. Melatonin (MT) has emerged as a versatile biostimulant, providing multiple benefits across different crop species. Given its key role in plant physiological processes, along with endogenous production, receptor identification, and signaling functions, it has been suggested to act as a hormone-like molecule. Nonetheless, the molecular response triggered by its application remains under investigation, particularly in grain legume species. This review explores the current state of MT applications for alleviating abiotic stress in grain legume crops, with emphasis on drought, salinity, metals/metalloids, and heat stress. We integrate biochemical, molecular, and physiological evidence to highlight the main scientific gaps regarding MT function in grain legumes. Finally, we discuss the biotechnological prospects of combining MT with modern breeding tools, as well as strategies for its delivery and sustainable production. Full article

Although rhizobacteria are known to improve plant adaptation to abiotic stressors, their possible contribution to the inherent resilience exhibited by crops such as Sorghum bicolor is still poorly quantified. Here, three sorghum pre-release lines and three check varieties were established and evaluated at two low-altitude sites of less than 600 masl. Treatments were laid out in a randomized complete block design, replicated two times. Twenty-four rhizospheric soil samples comprising six sorghum genotypes with two replications across two sites were collected, processed using Zymo Research DNA extraction protocols, and the 16S rRNA amplicon sequences were generated for bacterial diversity quantifications following the Divisive Amplicon Denoising Algorithm 2 (DADA2) workflow. Grain yield data were also recorded and expressed in tonnes per hectare. Rhizobacteria recruitment and GY performance significantly (p < 0.05) varied with sorghum genotypes. Bacterial abundance significantly (p < 0.05) associated with sorghum grain yield performance with Actinobacteriota and Firmicutes being identified to be of economic importance, explaining between 52.23 and 85.64% of the variation in grain yield performance. The modelled relationships between rhizobacteria and grain yield performance revealed R² predicted values of up to 75.25% and a 10-fold R² of 75.54%, implying no model overfitting. Sorghum genotypes did not consistently exhibit direct variation between genetic worth values and grain yield performance. Superior grain yield performers, namely ICSV111IN, CHITICHI, and SV4, consistently associated with high incidences of occurrence of the bacteria phyla Chloroflexi (class = Chloroflexia) and Firmicutes (class = Bacilli), whilst integrating the conventional selection method with microbial diversity data, changed the genotype performance ranking, in which all the three pre-release lines, namely, IESV91070DL, ASARECA12-3-1, and ICSV111IN, exhibited superiority over the check varieties. The results demonstrated that the inherent stress resilience exhibited by some sorghum genotypes under climate change-induced stresses such as CDHS may be influenced by specific bacterial taxa recruited in the rhizosphere environment of the plants. Hence, more effort should be made to further exploit these beneficial plant–microbe interactions for enhanced sorghum productivity under abiotic stress conditions. Full article

Magnesium (Mg) release (MGR) proteins play a crucial role in maintaining Mg²⁺ homeostasis in plant cells. However, MGR family genes have not yet been explored in crops. This study identified the wheat MGR (TaMGR) family members via BlastP alignment. A total of 15 MGR genes were mapped to 12 chromosomes. Cis-element prediction in the promoter region revealed that the ABA-responsive element (ABRE) was 100% conserved among all family members. Collinearity analysis indicates that MGR genes in monocot plants may have higher conservation compared to dicot plants. Expression profiling analyses uncovered the expression patterns of TaMGR genes across diverse tissues and under various stresses. Our results demonstrated that TaMGR5D and TaMGR5A.2 were significantly induced by both powdery mildew and stripe rust pathogen infections, whereas TaMGR4A transcript levels were upregulated in response to drought, heat and their combined stress. These findings indicate that TaMGRs may contribute coordinately to the regulation of wheat growth and development as well as adaptive responses to adverse conditions through member-specific expression patterns. This study systematically identified and analyzed the evolution and expression regulation characteristics of TaMGRs, providing a theoretical basis for in-depth research on the functional mechanisms of the TaMGRs and for improving the Mg use efficiency and stress adaptability of wheat via molecular approaches. Full article

As climate extremes increasingly threaten global food security, precision tools for early detection of crop stress have become vital, particularly for root crops such as potato (Solanum tuberosum L.) and sweet potato (Ipomoea batatas L. Lam.), which are especially susceptible to environmental stressors throughout their life cycles. In this study, plants were monitored from the initial onset of seasonal stressors, including spring drought, heat, and episodes of excessive rainfall, through to harvest, capturing the full range of physiological and biochemical responses under seasonal, simulated conditions in greenhouses. The spectral data were obtained from regions of interest (ROIs) of each cultivar’s leaves, with over 3000 data points extracted per cultivar; these data were subsequently used for model development. A comprehensive classification framework was established by employing machine learning models, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Partial Least Squares-Discriminant Analysis (PLS-DA), to detect stress across various growth stages. Furthermore, severity levels were objectively defined using photoreflectance indices and principal component analysis (PCA) data visualizations, which enabled consistent and reliable classification of stress responses in both individual cultivars and combined datasets. All models achieved high classification accuracy (90–98%) on independent test sets. The application of the Successive Projections Algorithm (SPA) for variable selection significantly reduced the number of wavelengths required for robust stress classification, with SPA-PLS-DA models maintaining high accuracy (90–96%) using only a subset of informative bands. Furthermore, SPA-PLS-DA-based chemical imaging enabled spatial mapping of stress severity within plant tissues, providing early, non-invasive insights into physiological and biochemical status. These findings highlight the potential of integrating hyperspectral imaging and machine learning for precise, real-time crop monitoring, thereby contributing to sustainable agricultural management and reduced yield losses. Full article

Climate change is intensifying the co-occurrence of drought and heat stresses, which substantially constrain global crop yields and threaten food security. Developing climate–resilient crop varieties requires a comprehensive understanding of the physiological and molecular mechanisms underlying combined drought–heat stress tolerance. This review systematically summarizes recent advances in integrating multi-scale remote-sensing phenomics with multi-omics approaches—genomics, transcriptomics, proteomics, and metabolomics—to elucidate stress response pathways and identify adaptive traits. High-throughput phenotyping platforms, including satellites, UAVs, and ground-based sensors, enable non-invasive assessment of key stress indicators such as canopy temperature, vegetation indices, and chlorophyll fluorescence. Concurrently, omics studies have revealed central regulatory networks, including the ABA–SnRK2 signaling cascade, HSF–HSP chaperone systems, and ROS-scavenging pathways. Emerging frameworks integrating genotype × environment × phenotype (G × E × P) interactions, powered by machine learning and deep learning algorithms, are facilitating the discovery of functional genes and predictive phenotypes. This “pixels-to-proteins” paradigm bridges field-scale phenotypes with molecular responses, offering actionable insights for breeding, precision management, and the development of digital twin systems for climate-smart agriculture. We highlight current challenges, including data standardization and cross-platform integration, and propose future research directions to accelerate the deployment of resilient crop varieties. Full article

Heat-exacerbated drought stress is becoming increasingly common in crop production systems, including peanuts, yet limited information exists on how peanut cultivars respond to this combined stress. While controlled environments allow for the isolation of these stress effects, their relevance to field conditions remains unclear. In this study, five Virginia-type peanut cultivars were evaluated under four treatments in a growth chamber environment, i.e., control, heat, drought, and combined heat and drought stress; and under two treatments in the field environment, i.e., rainfed control, and combined heat and drought stress using rainout shelters. The physiological traits assessed included stomatal conductance and transpiration rate, as well as leaf temperature difference. In both environments, combined heat and drought resulted in a significant decline in physiological performance compared to control conditions. On average, stomatal conductance decreased by 65% in the growth chamber and 21% in the field under combined heat and drought stress, while transpiration was reduced by 49% and 24%, respectively. In the growth chamber, leaf temperature difference increased by 40% under combined stress, whereas it was not statistically different under field conditions. Correlations of the physiological responses between growth chamber and field were stronger under combined stress conditions than under control conditions. Principal component analysis revealed clear genotypic separation based on gas exchange and thermal traits, with NC 20 and Sullivan consistently associated with higher stomatal conductance and transpiration under stress across environments, indicating greater physiological resilience, while Emery clustered with traits linked to stress susceptibility. These findings underscore the significant impacts of combined stress in peanut production and highlight the importance of evaluating cultivar responses under both controlled and field environments to guide crop improvement strategies. Full article