Search Results (725)

Drought is a critical constraint for legume production in semi-arid regions, yet breeding for drought tolerance in faba bean through induced mutagenesis remains largely unexplored. To our knowledge, this is the first EMS-derived mutant population in faba bean specifically developed for drought tolerance, comprising 45 M₂/M₃ lines derived from small-seeded cv. Zina and large-seeded cv. Aguadulce Superlonga), evaluated under two irrigation regimes—100% field capacity (well-watered control) and 40% field capacity (severe stress)—over two consecutive growing seasons in a randomized complete block design with three replications. Drought stress caused severe yield losses, reducing mean seed number per plant by 42.2% and mean seed weight per plant by 47.1%. Analysis of variance revealed highly significant effects of genotype, irrigation, and generation/year on both yield components. The non-significant genotype × irrigation interaction indicated similar proportional drought response across genotypes, while the non-significant three-way interaction suggested relatively consistent genotype rankings across generations/growing seasons. Among the ten drought tolerance indices evaluated, seed-number-based mean productivity (MPn) and stress tolerance index (STIn) were the most discriminating, whereas weight-based indices failed to differentiate genotypes due to the inherent seed-size contrast between botanical backgrounds. Dunnett’s comparisons identified genotype 23 (Zina-derived) as the top performer, significantly exceeding its parent for both MPn and STIn; genotypes 22, 24, 12, 3, and 15 similarly outperformed controls. Cluster analysis broadly distinguished three groups: a tolerant cluster dominated by Zina-derived lines, a moderately tolerant cluster (Zina wild-type), and a sensitive cluster of Aguadulce Superlonga-derived lines. These findings suggest that EMS mutagenesis generated potentially heritable and exploitable variation for drought tolerance, with selected lines representing promising candidates for further multi-environment validation. Full article

Winter wheat production in the extremely arid oasis region of Xinjiang relies heavily on irrigation and fertilization, but conventional high-input management can induce luxury transpiration and non-productive water consumption, limiting the coordinated improvement of grain yield, water use efficiency (WUE), and economic benefits. To identify the threshold at which water–fertilizer inputs shift from efficient use to inefficient water consumption and to define a robust management range, a two-year field experiment was conducted in southern Xinjiang during the 2022–2023 and 2023–2024 growing seasons. Four irrigation levels, corresponding to 60%, 80%, 100%, and 120% of crop evapotranspiration (ET_c), and four fertilization levels were established to evaluate the effects of water–fertilizer interactions on canopy development, leaf gas exchange, evapotranspiration, yield, WUE, and economic benefits. Appropriate water and nutrient supply promoted canopy establishment and maintained higher photosynthetic capacity, thereby increasing grain yield, WUE, and net return. However, excessive inputs weakened yield gains and failed to synchronously improve WUE and economic benefits. Linear plateau models revealed clear thresholds in both the crop-stand scale evapotranspiration (ET)–dry matter accumulation (DM) relationship and the leaf-scale transpiration rate (Tr)–net photosynthetic rate (Pn) relationship. The seasonal ET thresholds were 504.59 and 553.87 mm in the two growing seasons, respectively, and the Tr threshold was 4.83 mmol m⁻² s⁻¹. Beyond these thresholds, additional water consumption was not effectively converted into photosynthetic assimilation or biomass accumulation, indicating luxury transpiration. Year-specific response surface analysis and TOPSIS evaluation showed that I3F3, namely 100% ET_c combined with 210–195–75 kg ha⁻¹ N–P₂O₅–K₂O, together with its adjacent range, sustained high grain yield, WUE, and economic benefits, with I3F3 achieving the best overall performance in both years. The intersection of the two-year high-performance regions further defined a robust interannual feasible range with an irrigation amount of 506.21–545.09 mm and a total fertilizer input of 369.54–628.33 kg ha⁻¹. Overall, maintaining water and fertilizer inputs within the I3F3-adjacent range can reduce non-productive water consumption and luxury transpiration risk while synergistically improving grain yield, WUE, and economic benefits in winter wheat. Full article

Accurate mapping of winter rapeseed cultivation areas is crucial for food security assessment and agricultural resource management, yet remains a persistent challenge in mountainous regions characterized by complex topography and highly fragmented field parcels. To address these challenges, this study develops a progressive deep learning framework using single growing-season data from the Hanzhong Basin. We conducted a structured comparison of remote sensing indices, machine learning, and deep learning approaches for rapeseed identification in heterogeneous landscapes. First, sensitivity analysis of the Flowering Index for Rapeseed was performed to identify the optimal parameterization, yielding high inter-class separability (ND = 0.959) during peak flowering and a threshold-based overall accuracy (OA) of 94.41%. Second, a multidimensional feature space was constructed by integrating Sentinel-2 spectral bands, image texture metrics, and topographic variables; Random Forest-based feature importance selection subsequently enhanced Support Vector Machine classification performance to an OA of 90.70%. Third, we proposed an innovative three-stage progressive UNet++ architecture: Stage1 focuses on binary rapeseed/non-rapeseed classification to establish spatial priors; Stage2 refines discrimination among spectrally similar vegetation classes (rapeseed and other vegetation); and Stage3 achieves comprehensive seven-class semantic segmentation. A weighted focal loss function combined with a weight inheritance mechanism was employed to mitigate class imbalance and facilitate inter-stage knowledge transfer. The final model attained an OA of 98.65% and a mean intersection over union of 95.29%, while effectively suppressing salt-and-pepper noise artifacts in geometrically fragmented parcels. Our findings demonstrate the substantial advantages of progressive deep learning strategies for crop monitoring in topographically constrained environments. Full article

The ecological restoration of degraded sandy land in the Yarlung Zangbo River Valley is constrained by the metabolic functions of soil microorganisms. This study investigates the dynamic mechanisms of microbial elemental use efficiency in walnut plantations, with a focus on seasonal variations in soil chemical stoichiometry, extracellular enzyme activity, and microbial nutrient efficiency in rhizosphere and bulk soils. This paper explores the effects of conventional organic fertilizer (CF) and organic–inorganic compound fertilizer (OIF) on microbial nutrient use strategies and their seasonal dynamics. The results showed significant seasonal fluctuations in soil active nutrients and microbial biomass, while the total nutrient content remained stable. OIF enhanced microbial chemical stoichiometric homeostasis but simultaneously triggered a “carbon–phosphorus metabolic trade-off”, leading to a restraint of microbial carbon use efficiency (CUE) during the growing season. Microbial elemental use efficiency (EUE) exhibited clear seasonal differentiation: CUE was higher in summer, promoting biomass accumulation, whereas NUE and PUE increased in winter and spring, reflecting a nutrient conservation strategy. The EUE pathways were decoupled between rhizosphere and non-rhizosphere microenvironments. The rhizosphere was more directly driven by soil chemical stoichiometry and microbial biomass, while the non-rhizosphere was influenced by nutrient limitation states, represented by vector characteristics. This study provides insights into the seasonal adaptability and microenvironmental heterogeneity of microbial metabolism during the restoration of cold sandy land. It is suggested that future ecological management should focus on N-P balanced fertilization and consider the differential responses between rhizosphere and non-rhizosphere zones to enhance ecosystem productivity and soil carbon, nitrogen, and phosphorus sequestration potential. Full article

The present paper suggests a federated learning-based distributed solar forecasting model based on gated recurrent unit (GRU) networks (FL-GRU) to smart buildings in Muscat, Oman. The growing adoption of rooftop photovoltaic (PV) systems in urban settings needs precise, privatizing, and scalable forecasting models able to manage geographically dispersed and statistically heterogeneous data. The suggested solution will include federated learning and GRU networks to train a global forecasting model across several smart buildings and avoid the exchange of raw energy data to overcome these challenges. The local GRU models are trained on local PV generation data and only parameters of the model are relayed to a central aggregation server. This provides privacy of data without compromising the effectiveness of collaborative learning. The proposed framework is tested in a variety of realistic scenarios such as scalability analysis, non-identically distributed (non-IID) data, client dropout, communication constraints, seasonal variability, and privacy saving noise injection. Simulation outcomes show that the proposed FL-GRU model presents a final RMSE of 0.129, MAE of 0.100 and forecasting accuracy of 97%. When increasing the number of clients involved in the process, 2 to 10, RMSE decreases to 0.129, which supports the high scalability advantages. In non-IID scenarios, RMSE ranges between 0.129 and 0.167, and even with half of the clients dropping, the system is robust with an RMSE of 0.172. The proposed FL-GRU is better than the benchmark models, Local GRU, centralized GRU, FL-LSTM, and FL-ANN with a maximum improvement of 22.29% in RMSE reduction. Also, the best predictive consistency is found with correlation analysis with R² = 0.957. On the whole, the suggested approach can offer an efficient, privacy-aware, and scalable solution to distributed solar energy prediction in smart cities. Full article

Forest soils serve as critical terrestrial carbon sinks. While broad hydrothermal controls on soil respiration (R_s) are established, uncertainties persist regarding high-frequency temporal dynamics and moisture-dependent variations in temperature sensitivity (Q₁₀). Specifically, conventional reliance on discrete, clear-day sampling obscures how precipitation disrupts diurnal patterns. To address this, we continuously monitored R_s and environmental factors in two Northeast Chinese mixed forests (Korean pine, Pinus koraiensis (KP), and Dahurian larch, Larix gmelinii (DL)) to quantify weather-driven daily dynamics and carbon fluxes. Precipitation primarily drove daily variability, but more importantly, it reshaped day–night asymmetry. Under clear-day conditions, R_s exhibited a consistent daytime-dominant pattern, with daytime fluxes being significantly higher than nighttime fluxes (p < 0.05). However, precipitation events fundamentally neutralized this asymmetry, resulting in no significant day–night differences across most phenological stages. Annual R_s effluxes (759 and 965 g C m⁻² yr⁻¹ for KP and DL, respectively) lacked significant inter-stand or temporal variations. Seasonal emissions peaked unimodally in July, with the non-growing season contributing merely 5%–8%. Notably, spring freeze–thaw R_s in the KP stand surged interannually by 143%. While R_s correlated positively with temperature (p < 0.001), Q₁₀ was co-regulated by forest stand and moisture. Under moderate moisture, the KP stand’s Q₁₀ (2.72) was significantly lower than the DL stand’s (3.81); however, this divergence neutralized under low moisture. Consequently, soil moisture acts as both a direct R_s driver and a fundamental regulator of its temperature sensitivity. These empirical findings provide critical data to calibrate forest carbon models, improving predictions of soil carbon feedbacks under future climate scenarios. Full article

Traffic accidents pose a persistent challenge for urban mobility, public safety, and sustainable development in smart cities, particularly in rapidly growing urban environments. This study presents a data-driven spatiotemporal analysis of traffic accidents in Ecuador, aimed at supporting evidence-based urban traffic management and road safety planning. Using large-scale historical accident records, the proposed approach combines spatial clustering and temporal forecasting techniques to characterize accident concentration patterns and temporal dynamics at national and metropolitan scales. Spatial accident hotspots are identified using Density-Based Spatial Clustering of Applications with Noise (DBSCAN), enabling the detection of high-risk zones without imposing assumptions on cluster shape or size. This analysis reveals strong spatial concentration of accidents, with a limited number of clusters accounting for a substantial proportion of fatalities and injuries. Complementary temporal analysis is conducted using a multi-model ensemble framework to examine accident trends and seasonal patterns. This approach integrates SARIMA for linear stochastic modeling and Prophet for additive trend analysis, alongside two Long Short-Term Memory (LSTM) architectures: a direct 12-month vector output and a recursive horizon-3 model. By synthesizing these statistical and neural network-based methods through inverse-RMSE weighting, the study captures both stable seasonal cycles and non-linear, short-to-medium-term variations in accident frequency. Results show that traffic accidents in Ecuador exhibit stable diurnal and seasonal structures, alongside pronounced spatial heterogeneity across urban regions. The combined spatial and temporal insights provide a coherent representation of accident risk patterns, facilitating the prioritization of critical zones and high-risk periods. The resulting hotspot maps and multi-model forecasting horizons offer actionable information for smart city stakeholders, supporting targeted infrastructure interventions, adaptive enforcement strategies, and data-informed urban mobility policies. This work contributes to the broader understanding of traffic safety analytics as a core component of smart city decision-support systems. Full article

Lodging is a major constraint on the stable production of high-quality japonica rice in the Yangtze River Delta. This study evaluated whether different concentrations of prohexadione calcium (Pro-Ca) could improve lodging resistance while maintaining grain yield in high-quality japonica rice. Field experiments were conducted in the 2024 and 2025 growing seasons, with TA 1 cultivated in 2024 and TA 1, SY 28, and HR 1212 cultivated in 2025. Pro-Ca was applied at the jointing stage at four concentrations: CK (water spray), P1 (15 mg L⁻¹), P2 (30 mg L⁻¹) and P3 (45 mg L⁻¹). Rice yield and its components, lodging parameters, culm morphological traits, and non-targeted metabolomic profiles were analyzed. Compared with CK, the P1 treatment significantly reduced the lodging index without a significant reduction in grain yield. In contrast, the P2 and P3 treatments further decreased the lodging index by 14.0–48.1% but decreased grain yield by 6.7–17.9%, mainly due to reductions in effective panicle number and spikelets per panicle. Pro-Ca treatment significantly increased internode diameter and culm wall thickness by 4.9–29.3% and 11.7–76.5%, respectively, and promoted the accumulation of lignin by 5.4–17.7% and cellulose by 3.0–8.6%, thereby enhancing the structural reinforcement of the rice stem. A metabolomic analysis showed that Pro-Ca treatment was associated with changes in carbon- and nitrogen-related metabolites, including metabolites linked to the tricarboxylic acid (TCA) cycle and amino acid biosynthesis. These changes were accompanied by increased accumulation of phenylpropanoid pathway intermediates and lignin-related precursors, including sinapyl alcohol and coniferyl aldehyde. Therefore, in our study, 15 mg L⁻¹ Pro-Ca showed the most favorable balance between lodging resistance and yield, indicating its potential for further evaluation; however, its agronomic and economic feasibility requires additional investigation before practical recommendation. Full article

Non-flooding irrigation is widely promoted as a carbon–water co-benefit strategy in paddy rice, but field-scale trials overlook return flow compensation within irrigation districts and therefore overstate water-saving potential. To reconcile this scale mismatch, we developed a semi-distributed multi-scale water balance model coupled with a carbon footprint and full-component blue–green–grey water footprint framework and applied it across field, district, and provincial scales in Heilongjiang Province—a leading cold-region japonica rice region in Northeast China—using the Qinglongshan Irrigation District on the Sanjiang Plain as the focal case, supported by two growing seasons of field observations and 35 years of provincial records. Under alternate wetting and drying, apparent field-level water savings of 50–60% converge to 33% after return flow correction, implying that field-based indicators overestimate savings by 40–50%. Carbon mitigation is decoupled from water volume: CH₄ suppression dominates total abatement and is governed by drying frequency rather than water saved. At the provincial scale, the water footprint has shifted from grey- to blue-water dominance, suggesting that blue-water efficiency now represents a principal remaining lever for further cold-region carbon–water co-benefits. Two-season coverage and fixed parameter assumptions affect magnitudes but not directions. Water-saving irrigation in cold-region paddy systems should therefore be evaluated at the district scale where data permit, rather than relying solely on field-scale indicators. Full article

Agricultural waste streams represent an underutilized source of bioactive compounds with potential to enhance crop resilience under climate stress. We previously showed that volatile compounds (VCs) emitted from waste shiitake fungi beds (WSFBs) promote early rice seedling growth under controlled conditions. Here, we evaluated whether these early-stage effects persist after transplanting and translate into agronomic benefits under field conditions, including the record high temperatures (HTs) of the 2023 growing season in Niigata, Japan. Seedlings of two japonica cultivars, Nipponbare and Koshihikari, were exposed to WSFBs-derived VCs using a non-contact system and subsequently grown in paddy fields across two seasons (2023–2024). WSFBs-VCs-treated (+VCs) plants exhibited enhanced seedling vigor, increased tiller and panicle numbers, higher grain yield per plant, greater 1000-grain weight, and reduced grain chalkiness. Gas exchange measurements at the reproductive stage during the 2023 record HT showed that +VCs plants maintained higher net photosynthetic rate, stomatal conductance, intercellular CO₂ concentration, and transpiration rate, while intrinsic water-use efficiency showed a modest decline consistent with transpirational cooling. Controlled-environment assays revealed enhanced physiological stability supported by upregulation of cytokinin and stress-responsive genes under acute heat stress. Together, these results demonstrate that short-term exposure to WSFBs-derived VCs enhances rice performance under field conditions, including during extreme heat, and highlight their potential as low-cost, waste-derived biostimulants that support sustainable, circular, and climate-resilient rice production. Full article

Fine-scale crop type information is essential for agricultural monitoring, irrigation management, and food security assessment. This study mapped three major crops—wheat, corn, and sunflower—in the Hetao Irrigation District, China, using multi-temporal Sentinel-2 optical imagery and Sentinel-1 SAR observations at the parcel scale. A multi-source feature set, including spectral bands, vegetation and red-edge indices, moisture-related variables, radar backscatter coefficients, and derived radar features, was constructed from the full growing season. An LSTM network was used to learn temporal representations of crop phenological dynamics, and the resulting embeddings were then combined with traditional machine learning classifiers, including Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), for final classification. The results show that the hybrid framework substantially improves classification performance compared with the corresponding non-LSTM classifiers. Among all tested models, XGBoost + LSTM achieved the best performance, with an overall accuracy of 93.61%, a Kappa coefficient of 91.66%, and a mean IoU of 87.41%. The class-wise F1-scores were 85.61% for wheat, 97.22% for corn, and 87.27% for sunflower. Additional experiments further confirmed the advantages of parcel-based aggregation in improving spatial consistency and reducing mixed-field noise. The proposed framework provides a promising parcel-scale workflow for crop type mapping in fragmented irrigation districts, while its transferability across years and regions still requires further validation. Full article

The increasing burden of dengue represents a growing global public health concern. Among the factors associated with rising dengue incidence, climate change, particularly increasing temperatures, has been frequently highlighted, alongside other environmental, biological, and social determinants. The emergence of dengue in previously non-endemic areas and its sustained increase in incidence have become increasingly common in recent decades. Objective: The aim of this study was to describe national dengue case trends in Mexico from 1990 to 2023 and to assess their association with temperature over the same period using a descriptive, retrospective analysis of epidemiological surveillance and temperature data. Methods: Epidemiological data on confirmed dengue cases and incidence were obtained from the Morbidity Yearbook of the General Directorate of Epidemiology (DGE) of the Mexican Ministry of Health. These data were used to construct epidemic curves and to analyze the geographic distribution of incidence using quartiles. Temperature data were derived from the national annual mean calculated from monthly reports issued by the National Water Commission (CONAGUA). Associations between temperature and dengue cases and incidence were explored over the study period. Results: Temporal analysis revealed a significant increase in both dengue cases and incidence in Mexico, with a positive association with temperature during the same period. Quartile-based geographic analysis showed that state-level classifications remained relatively stable across periods, with several states clustering within or tending toward the group considered endemic. Conclusions: The results of this study show an increase in cases and incidence of dengue over time, as well as a positive association between cases/incidence of dengue in Mexico and the increase in the national average temperature during the study period; however, due to its descriptive and retrospective design, causal inference is not possible. Dengue transmission is inherently multifactorial, and the observed trends likely reflect the combined influence of climatic conditions, historical expansion of transmission cycles, vector establishment, and unmeasured socio-epidemiological factors. The absence of entomological indicators, additional climatic variables, and spatially or seasonally disaggregated analyses limits the ability to capture localized dynamics. Overall, temperature should be interpreted as a contributing factor within a complex system rather than as the sole driver of dengue trends, underscoring the need for integrated surveillance and control strategies in both endemic and non-endemic regions. Full article

In the ecologically fragile West Liao River Basin, characterizing groundwater dynamics is crucial for sustainable water management. Using 2000–2016 groundwater level data, this study applies Seasonal-Trend decomposition using Loess (STL) and change-point detection to analyse trends. Driving factors are quantified via random forest combined with SHapley Additive exPlanations (SHAP) analysis, and a novel STL–Long Short-Term Memory (STL-LSTM) hybrid model is developed for forecasting. Key findings include: (1) Groundwater levels declined persistently, with a significant change point in 2009. The post-2009 decline rate accelerated to −0.749 m/yr, a 55.7% increase. (2) Statistical attribution reveals that soil moisture (43.5%) and climatic factors (29.0%) are the primary predictors of groundwater variability. The dominance of soil moisture highlights the key role of agricultural irrigation, which strongly modifies soil water dynamics during the growing season. (3) The STL-LSTM model achieves optimal predictive performance (R² = 0.8805, RMSE = 0.7081 m), demonstrating enhanced accuracy for non-stationary sequences. This integrated framework combines trend diagnosis, driver interpretation, and hybrid modelling, offering scientific support for precise groundwater management in semi-arid agricultural basins. Full article

Integrating agricultural remote sensing and phenomics for full-growth-period rice quality prediction is vital for early non-destructive screening and breeding; however, studies integrating genomic and multi-source phenotypic data across multiple environments remain limited. This study addressed this gap by integrating genomic SNP data, UAV-based spectral data, and individual multidimensional phenotypic data of 61 indica rice varieties (field and greenhouse environments). As a proof-of-concept study, feature selection methods (LASSO, MI, RFE, SPA) were used to mitigate overfitting and the “p >> n” problem, with further validation needed in larger populations. The results showed that amylose content is genetically dominated, protein content is genetically determined and influenced by gene-environment interactions, and chalkiness traits are determined by three combined factors. For amylose content, SNP data under the Random Forest model at the population level (phenomics data from field UAV remote sensing of variety populations) achieved optimal performance (R² = 0.92; MAE = 1.1; RMSE = 1.5), while the Stacking Ensemble method enhanced accuracy at the individual level (phenomics data from greenhouse single-plant phenotyping per variety). Chalky grain rate and chalkiness degree showed SNP-comparable prediction accuracy, with Stacking significantly improving performance at the population level (R² = 0.89 and 0.85, respectively). Protein content prediction remained relatively low (optimal R² = 0.56) due to strong environmental sensitivity and complex interactions. This framework extends traditional single-environment/single-data-source approaches, providing an effective strategy for early, high-throughput, non-destructive rice quality screening. Further validation with larger datasets, more growing seasons, or independent populations is required for reliable application in breeding-related practices. Full article

Understanding the soil–plant transfer of both essential and non-essential elements is crucial for evaluating the crop nutritional quality, environmental interactions, and food safety. This study delivered a multiyear and multielement assessment under field conditions of the element uptake, translocation, and accumulation in raspberry (Rubus idaeus L.), based on data collected over two growing seasons (2024–2025) in two contrasting Romanian agroecosystems. Two commercial cultivars (Opal and Delniwa) were investigated under fertilized and unfertilized conditions. The concentrations of essential macroelements such as Ca, Mg, Na, and K, as well as trace elements (Li and Sr), were determined in soils and fruits using ICP-OES and AAS. The soil–fruit transfer was quantified through the transfer factor, assisted by a robust statistical framework which integrated spatial–temporal variability and non-parametric analysis. The results highlighted two contrasting accumulation regimes. The essential macroelements revealed a dynamic uptake pattern driven by the physiological demand, soil availability, and fertilization. K exhibited the highest transfer capacity, while Ca had a restricted translocation to the fruits, due to the intrinsic transport limitations. On the other hand, Li and Sr revealed a constrained accumulation, characterized by low concentrations, weak responsiveness to fertilization, and a strong dependence on the soil geochemical background and interannual dilution processes. The spatial variability between the cultivation sites and year-to-year changes in the dilution intensity was evidenced as the dominant driver of the transfer efficiency, while the varietal differences had a secondary but detectable role, mainly for the Ca–Sr discrimination. Overall, the results evidenced that the multielement accumulation in the raspberries was governed by the interplay between the soil geochemistry, physiological transport constraints, and environmental variability. Furthermore, the research provided a field-based, multiyear evidence supporting improved soil management, cultivar selection, as well as the strategies that may increase the fruit nutritional quality while minimizing the trace element risks. Full article