Search Results (644)

This study combined field survey data with Gaofen 5 (GF-5) satellite hyperspectral images of the Yuncheng Basin (China), considering 15 environmental variables. Random forest (RF) was used to select the optimal satellite hyperspectral model, sequentially introducing natural and farmland management factors into the model to analyze the spatial distribution of farmland soil organic carbon (SOC). Furthermore, RF factorial experiments determined the contributions of farmland management, climate, vegetation, soil, and topography to the SOC. Structural equation modeling (SEM) elucidated the driving mechanisms of SOC variations. Integrating satellite hyperspectral data and environmental variables improved the prediction accuracy and SOC-mapping precision of the model. The integration of natural variables significantly improved the RF model performance (R² = 0.78). The prediction accuracy enhanced with the introduction of crop phenology (R² = 0.81) and farmland management factors (R² = 0.87). The model that incorporated all 15 variables demonstrated the highest prediction accuracy (R² = 0.89) and greatest spatial SOC variability, with minimal uncertainty. Farmland management activities exerted the strongest influence on SOC (0.38). The proposed method can support future investigations on soil carbon sequestration processes in river basins worldwide. Full article

Quinoa (Chenopodium quinoa Willd.) has emerged as a climate-resilient, nutrient-dense crop with increasing global popularity because of its adaptability under current environmental variations. To address the limited understanding of quinoa’s genotypic performance under local agro-environmental conditions, this study hypothesized that elite genotypes would exhibit significant variation in agronomic, physiological, and biochemical traits. This study aimed to elucidate genotypic variability among 23 elite quinoa lines under field conditions in Faisalabad, Pakistan, using a multidimensional framework that integrated phenological, physiological, biochemical, root developmental, and yield-related attributes. The results revealed that significant variation was observed across all measured parameters, highlighting the diverse adaptive strategies and functional capacities among the tested genotypes. More specifically, genotypes Q4, Q11, Q15, and Q126 demonstrated superior agronomic potential and canopy-level physiological efficiencies, including high biomass accumulation, low infrared canopy temperatures and sustained NDVI values. Moreover, Q9 and Q52 showed enhanced accumulation of antioxidant compounds such as phenolics and anthocyanins, suggesting potential for functional food applications and breeding program for improving these traits in high-yielding varieties. Furthermore, root trait analysis revealed Q15, Q24, and Q82 with well-developed root systems, suggesting efficient resource acquisition and sufficient support for above-ground plant parts. Moreover, principal component analysis further clarified genotype clustering based on trait synergistic effects. These findings support the use of multidimensional phenotyping to identify ideotypes with high yield potential, physiological efficiency and nutritional value. The study provides a foundational basis for quinoa improvement programs targeting climate adaptability and quality enhancement. Full article

Snow serves as a crucial water source for vegetation growth on the Mongolian Plateau, and its temporal and spatial variations exert profound influences on terrestrial vegetation phenology. In recent years, global climate change has led to significant changes in snow and vegetation start of growing season (SOS). Therefore, it is necessary to study the mechanism of snow cover on vegetation growth and changes on the Mongolian Plateau. The study found that the spatial snow cover fraction (SCF) of the Mongolian Plateau ranged from 50% to 60%, and the snow melt date (SMD) ranged from day of the year (DOY) 88 to 220, mainly concentrated on the northwest Mongolian Plateau mountainous areas. Using different SOS methods to calculate the vegetation SOS distribution map. Vegetation SOS occurs earlier in the eastern part compared to the western part of the Mongolian Plateau. In this study, we assessed spatiotemporal distribution characteristics of snow on the Mongolian Plateau over the period from 2001 to 2023. The results showed that the SOS of the Mongolian Plateau was mainly concentrated on DOY 71-186. The Cox survival analysis model system established SCF and SMD on vegetation SOS. The SCF standard coefficient is 0.06, and the SMD standard coefficient is 0.02. The SOS_NDVI coefficient is −0.15, and the SOS_NDGI coefficient is −0.096. The results showed that the vegetation SOS process exhibited differential response characteristics to snow driving factors. These research results also highlight the important role of snow in vegetation phenology and emphasize the importance of incorporating the unique effects of vegetation SOS on the Mongolian Plateau. Full article

Leaf phenology of trees responds to temperature and photoperiod cues, mediated by underlying genes and plasticity. However, uncertainties remain regarding how smaller-scale phenological variation in cold-limited regions has been affected by modified selection pressures from herbivores, pathogens, and climate conditions, and whether this leaves genetic signatures allowing for projections of future responses. We investigated environmental correlates and genetic variation putatively associated with spring and autumn leaf phenology in northern range margin oak (Quercus robur L.) populations in Sweden (55.6° N–60.8° N). Results suggested that budburst occurred later at higher latitudes and in locations with colder spring (April) temperatures, whereas leaf senescence occurred earlier at higher latitudes. Several candidate loci associated with phenology were identified (n = 40 for budburst and 47 for leaf senescence), and significant associations between these loci and latitude were detected. Functions associated with some of the candidate loci, as identified in previous studies, included host defence and heat stress tolerance. The proportion of polymorphic candidate loci associated with budburst decreased with increasing latitude, towards the range margin. Overall, the Swedish oak population seems to comprise genetic diversity in phenology-related traits that may provide resilience to a rapidly changing climate. Full article

Mango is highly sensitive to heat stress, which directly affects the yield and quality. The extreme heat waves of 2024, with temperatures reaching 41–47 °C over 25 days, caused significant impacts on sensitive cultivars. The impact of heat waves on ten commercial cultivars from subtropical regions viz.,‘Dashehari’, ‘Langra’, ‘Chausa’, ‘Bombay Green’, ‘Himsagar’, ‘Amrapali’, ‘Mallika’, ‘Sharda Bhog’, ‘Kesar’, and ‘Rataul’, and thirteen selected elite hybrids H-4208, H-3680, H-4505, H-3833, H-4504, H-1739, H-3623, H-1084, H-4264, HS-01, H-949, H-4065, and H-2805, is reported. The predominant effects that were observed include the following: burning symptoms or blackened tips, surrounded by a yellow halo, with premature ripening in affected parts and, in severe cases, tissue mummification. Among commercial cultivars, viz., ‘Amrapali’ (25%), ‘Mallika’ (30%), ‘Langra’ (30%), ‘Dashehari’ (50%), and ‘Himsagar’ and ‘Bombay Green’ had severe impacts, with ~80% of fruits being affected, followed by ‘Sharda Bhog’. In contrast, mid-maturing cultivars like ‘Kesar’, ‘Rataul’, and late-maturing elite hybrids, which were immature during the stress period, showed no symptoms, indicating they are tolerant. Biochemical analyses revealed significantly elevated total soluble solids (TSS > 25 °B) in affected areas of sensitive genotypes compared to non-affected tissues and tolerant genotypes. Aroma profiling indicated variations in compounds such as caryophyllene and humulene between affected and unaffected parts. The study envisages that the phenological maturity scales are indicators for the selection of climate-resilient mango varieties/hybrids and shows potential for future breeding programs. Full article

Olive tree leaf is a phenol-rich, high-potential-value biomass that can be used to formulate food additives and supplements. Leaf phenolic content varies depending on numerous factors, like cultivar, geographical origin, year, and season of harvest. The aim of this research was to evaluate the variations in phenolic profile of four major Tuscan cultivars (Frantoio, Leccio del Corno, Leccino, and Moraiolo) over four different phenological phases and across two years. All 96 olive leaf samples were harvested from trees grown in the same orchard located in Florence. After drying, their phenolic profile was characterized using HPLC-DAD-MS, and the obtained data were processed by ANOVA, GA-LDA, and RF methods. A total of 25 phenolic derivatives were analyzed, with total contents ranging 16,674.0–50,594.3 mg/kg and oleuropein (4570.0–27,547.7 mg/kg) being the predominant compound regardless of cultivar, year, and season of harvest. Oleuropein and hydroxytyrosol glucoside showed inverse proportionality and similar behavior across years in all cultivars, and therefore were highlighted as main phenolic compounds correlated with the seasonal variability in studied cultivars. Interesting behavior was also pointed out for apigenin rutinoside. Application of GA-LDA and RF methods allowed pointing out the excellent performance of leaf phenols in discriminating samples based on cultivar, harvest year, and harvesting season. Full article

Winter wheat yield prediction is critical for optimizing field management plans and guiding agricultural production. To address the limitations of conventional manual yield estimation methods, including low efficiency and poor interpretability, this study innovatively proposes an intelligent yield estimation method based on a convolutional neural network (CNN). A comprehensive two-factor (fertilization × irrigation) controlled field experiment was designed to thoroughly validate the applicability and effectiveness of this method. The experimental design comprised two irrigation treatments, sufficient irrigation (C) at 750 m³ ha⁻¹ and deficit irrigation (M) at 450 m³ ha⁻¹, along with five fertilization treatments (at a rate of 180 kg N ha⁻¹): (1) organic fertilizer alone, (2) organic–inorganic fertilizer blend at a 7:3 ratio, (3) organic–inorganic fertilizer blend at a 3:7 ratio, (4) inorganic fertilizer alone, and (5) no fertilizer control. The experimental protocol employed a DJI M300 RTK unmanned aerial vehicle (UAV) equipped with a multispectral sensor to systematically acquire high-resolution growth imagery of winter wheat across critical phenological stages, from heading to maturity. The acquired multispectral imagery was meticulously annotated using the Labelme professional annotation tool to construct a comprehensive experimental dataset comprising over 2000 labeled images. These annotated data were subsequently employed to train an enhanced CNN model based on ResNet50 architecture, which achieved automated generation of panicle density maps and precise panicle counting, thereby realizing yield prediction. Field experimental results demonstrated significant yield variations among fertilization treatments under sufficient irrigation, with the 3:7 organic–inorganic blend achieving the highest actual yield (9363.38 ± 468.17 kg ha⁻¹) significantly outperforming other treatments (p < 0.05), confirming the synergistic effects of optimized nitrogen and water management. The enhanced CNN model exhibited superior performance, with an average accuracy of 89.0–92.1%, representing a 3.0% improvement over YOLOv8. Notably, model accuracy showed significant correlation with yield levels (p < 0.05), suggesting more distinct panicle morphological features in high-yield plots that facilitated model identification. The CNN’s yield predictions demonstrated strong agreement with the measured values, maintaining mean relative errors below 10%. Particularly outstanding performance was observed for the organic fertilizer with full irrigation (5.5% error) and the 7:3 organic-inorganic blend with sufficient irrigation (8.0% error), indicating that the CNN network is more suitable for these management regimes. These findings provide a robust technical foundation for precision farming applications in winter wheat production. Future research will focus on integrating this technology into smart agricultural management systems to enable real-time, data-driven decision making at the farm scale. Full article

Accurate estimation of rice leaf nitrogen content (LNC) is essential for optimizing nitrogen management in precision agriculture. However, challenges such as spectral saturation and canopy structural variations across different growth stages complicate this task. This study proposes a robust framework for LNC estimation that integrates both spectral and texture features extracted from UAV-based multispectral imagery through the development of novel Spectral–Texture Fusion Indices (STFIs). Field data were collected under nitrogen gradient treatments across three critical growth stages: heading, early filling, and late filling. A total of 18 vegetation indices (VIs), 40 texture features (TFs), and 27 STFIs were derived from UAV images. To optimize the feature set, a two-stage feature selection strategy was employed, combining Pearson correlation analysis with model-specific embedded selection methods: Recursive Feature Elimination with Cross-Validation (RFECV) for Random Forest (RF) and Extreme Gradient Boosting (XGBoost), and Sequential Forward Selection (SFS) for Support Vector Regression (SVR) and Deep Neural Networks (DNNs). The models—RFECV-RF, RFECV-XGBoost, SFS-SVR, and SFS-DNN—were evaluated using four feature configurations. The SFS-DNN model with STFIs achieved the highest prediction accuracy (R² = 0.874, RMSE = 2.621 mg/g). SHAP analysis revealed the significant contribution of STFIs to model predictions, underscoring the effectiveness of integrating spectral and texture information. The proposed STFI-based framework demonstrates strong generalization across phenological stages and offers a scalable, interpretable approach for UAV-based nitrogen monitoring in rice production systems. Full article

Understanding how flowering phenology varies across spatial scales is essential for assessing plant responses to environmental heterogeneity under climate change. In this study, we investigated the flowering phenology of the plant species Taraxacum officinale across five sites in an agricultural region of Wanju, Republic of Korea. Each site contained five 1 m × 1 m quadrats, where the number of flowering heads was recorded at 1- to 2-day intervals during the spring flowering period (February to May). We applied the nlstimedist package in R to model flowering distributions and to estimate key phenological metrics including flowering onset (5%), peak (50%), and end (95%). The results revealed substantial variation in flowering timing and duration at both the intra-site (quadrat-level) and inter-site (site-level) scales. Across all sites, the mean onset, peak, end, and duration of flowering were day of year (DOY) 89.6, 101.5, 117.6, and 28.0, respectively. Although flowering onset showed relatively small variation across sites (DOY 88 to 92), flowering peak (DOY 97 to 108) and end dates (DOY 105 to 128) exhibited larger differences at the site level. Sites with dry soils and regularly mowed Zoysia japonica vegetation with minimal understory exhibited shorter flowering durations, while those with moist soils, complex microtopography, and diverse slope orientations showed delayed and prolonged flowering. These findings suggest that microhabitat variability—including landform type, slope direction, soil water content, and soil temperature—plays a key role in shaping local flowering dynamics. Recognizing this fine-scale heterogeneity is essential for improving phenological models and informing site-specific climate adaptation strategies. Full article

Crop height is a key indicator of plant development and growth dynamics, offering valuable insights for temporal crop monitoring. However, modeling its variation across phenological stages remains challenging due to canopy structural changes. This study aimed to predict wheat height throughout the growth cycle by integrating radar remote sensing data with a phenology-informed clustering approach. The research was conducted in three wheat fields in Umbria, Italy, from 30 January to 10 June 2024, using in-field height measurements, phenological observations, and Sentinel-1 acquisitions. Backscatter variables (VH, VV, and CR) were processed using two speckle filters (Lee 7 × 7 and Refined Lee), alongside additional radar-derived parameters (entropy, anisotropy, alpha, and RVI). Fuzzy C-means clustering enabled the classification of observations into two phenological groups, supporting the development of stage-specific linear regression models. Results demonstrated high accuracy during early growth stages (tillering to stem elongation), with R² values of 0.76 (RMSE = 6.88) for Lee 7 × 7 and 0.79 (RMSE = 6.35) for Refined Lee. In later stages (booting to maturity), model performance declined, with Lee 7 × 7 outperforming Refined Lee (R² = 0.51 vs. 0.33). These findings underscore the potential of phenology-based modeling approaches to enhance crop height estimation and improve radar-driven crop monitoring. Full article

Banana is an important cash and food crop worldwide. Recent outbreaks of banana diseases are threatening the global banana industry and smallholder livelihoods. Remote sensing data offer the potential to detect the presence of disease, but formal analysis is needed to compare inferred disease data with observed disease data. In this study, we present a novel remote-sensing-based framework that combines Landsat-8 imagery with meteorology-informed phenological models and machine learning to identify anomalies in banana crop health. Unlike prior studies, our approach integrates domain-specific crop phenology to enhance the specificity of anomaly detection. We used a pixel-level random forest (RF) model to predict 11 key vegetation indices (VIs) as a function of historical meteorological conditions, specifically daytime and nighttime temperature from MODIS and precipitation from NASA GES DISC. By training on periods of healthy crop growth, the RF model establishes expected VI values under disease-free conditions. Disease presence is then detected by quantifying the deviations between observed VIs from Landsat-8 imagery and these predicted healthy VI values. The model demonstrated robust predictive reliability in accounting for seasonal variations, with forecasting errors for all VIs remaining within 10% when applied to a disease-free control plantation. Applied to two documented outbreak cases, the results show strong spatial alignment between flagged anomalies and historical reports of banana bunchy top disease (BBTD) and Fusarium wilt Tropical Race 4 (TR4). Specifically, for BBTD in Australia, a strong correlation of 0.73 was observed between infection counts and the discrepancy between predicted and observed NDVI values at the pixel with the highest number of infections. Notably, VI declines preceded reported infection rises by approximately two months. For TR4 in Mozambique, the approach successfully tracked disease progression, revealing clear spatial spread patterns and correlations as high as 0.98 between VI anomalies and disease cases in some pixels. These findings support the potential of our method as a scalable early warning system for banana disease detection. Full article

As climate change alters subarctic ecosystems and human activities in Alaska, ecological baselines are critical for long-term conservation. We applied an ecoacoustic approach to characterize the ecological conditions of a rapidly deglaciating region in Kenai Fjords National Park, Alaska. Using automated recording units deployed at increasing distances from a road, we collected over 120,000 one-minute audio samples during the tourist seasons of 2021 and 2022. Ecoacoustic indices—Sonic Heterogeneity Index (SHI_tf), Spectral Sonic Signature (SSS), Weighted Proportion of Occupied Frequencies (wPOF), and Normalized Difference Sonic Heterogeneity Index (NDSHI)—were used to measure spatio-temporal patterns of the sonoscape. Results revealed higher sonic heterogeneity near the road attributed to technophony (vehicles) and geophony (wind) that spanned across the frequency spectrum, masking mid-high frequency biophony. Seasonal phenology and diel variations reflected ecological and human rhythms, including biophony from the dawn chorus from May–June, technophony from vehicle-based tourism from July–September, and decreased sonic activity in the form of geophonic ambience in October. Low-frequency geophonies were prevalent throughout the sonoscape with more natural sounds at greater distances from the road. Our findings demonstrate the benefits of using ecoacoustic methods to assess ecosystem dynamics for establishing ecological baselines useful for future comparisons in rapidly changing environments. Full article

Capparis spinosa L. (caper) is an important medicinal plant whose bioactive compounds vary significantly depending on its growth stage. This directly affects its pharmaceutical and nutritional value. Collecting C. spinosa at the optimal growth stage is essential to achieving high phytochemical quality and meeting consumer needs. This study aimed to evaluate the variation of these active compounds in the aerial parts of C. spinosa across four phenological stages (vegetative, flowering, unripe fruit, and ripe fruit). The result showed that EO content was highest in unripe fruits (0.18%) and lowest in the flowering stage (0.07%) in leaves, while extract yield was highest in leaves of the ripe fruit stage (14.65%) followed by the flowering stage in flowers (12.66%). Flowering stage leaves showed the highest total phenol (56.20 mg GAE/g) and flavonoid (17.10 mg QE/g) content, while the lowest concentrations were found in the ripe fruit stage of the leaves. EO analysis showed that methyl isothiocyanate reached the highest concentration in flowers at the flowering stage (41.6%), while isopropyl isothiocyanate reached the highest concentration in leaves at the ripe fruit stage (36.2%). Isobutyl isothiocyanate was found exclusively in fruits, with the highest concentration in ripe fruits (9.2%). Dimethyltrisulphide showed a maximum concentration in leaves at the vegetative stage, decreasing by 76.6% as the plant developed towards the ripe fruit stage. The dominant phenolic acids varied between phenological stages: cinnamic acid at the vegetative stage; rosmarinic and cinnamic acids at the flowering stage in leaves; caffeic and cinnamic acids in flowers; vanillic, cinnamic, and rosmarinic acids at the unripe fruit stage in leaves and fruits; and rosmarinic, cinnamic, and vanillic acids in ripe fruits. The results indicate that harvesting C. spinosa at the vegetative stage and in the leaves of the flowering stage is optimal for maximum secondary metabolite yield, providing valuable guidance for growers targeting food and pharmaceutical applications. Full article

Daylily (Hemerocallis spp.) are perennial herbaceous flowers with high ornamental and medicinal value. Currently, the breeding of new daylily cultivars was mainly achieved through hybrid breeding, but issues such as self-incompatibility, hybridization barriers, and asynchronous reproductive phenology severely hinder the breeding process. Understanding pollen viability was essential for daylily breeding and cultivar improvement. In this study, we systematically investigated the effects of pollen viability determination methods, collection time, medium combinations, culture temperature and storage conditions on the pollen germination characteristics of daylily, using five daylily cultivars introduced in the Zhanjiang region of China as materials. Comparing the Iodine-potassium iodide (I₂-KI) staining and Acetocarmine staining, the results of 2,3,5-Triphenyltetrazolium Chloride (TTC) staining showed a significant positive correlation (p < 0.05) with the in vitro germination rate, which is suitable for the rapid detection of daylily pollen vigor. The daylily variation of pollen vigor was significant in different cultivars, and most cultivars had the highest vigor at 9:00–12:00 a.m., which was suitable for artificial pollination. The in vitro germination experiment showed that sucrose concentration was the key factor for daylily pollen germination and pollen tube growth, and the optimal medium for pollen in vitro germination was 50 g/L⁻¹ sucrose + 0.1 g/L⁻¹ H₃BO₃ + 0.06 g/L⁻¹ KNO₃ + 0.2 g/L⁻¹ Ca(NO₃)₂. The temperature experiment showed that the optimum temperature for pollen germination was 24.1–26.7 °C, and the optimum range for pollen tube growth was 24.1–25.7 °C, and the high temperature significantly inhibited the elongation rate of pollen tube. Storage experiments showed that low temperature (−40 °C) combined with drying treatment could significantly prolong pollen life, and the “Water Dragon” variety still maintained 41.29% vigor after 60 days of dry storage. This study provides theoretical basis and technical support for the introduction and domestication of daylily in South China, hybridization and garden application. Full article

Barnyard grass is the most problematic weed in paddy fields in Ningxia. Its substantial morphological variation complicates both identification and control, yet the genetic diversity of barnyard grass infesting paddy fields in Ningxia has not been thoroughly studied. In this research, we analyzed the genetic diversity of 46 barnyard grass populations from Ningxia’s paddy fields based on the assessment of morphological traits, DNA barcoding, and SCoT-targeted gene markers. Nine morphological traits were quantitatively analyzed, among which three phenological traits, i.e., leaf length, stem diameter, and plant height, exhibited notable variations. Correlational analysis revealed a positive relationship between morphological traits and multi-herbicide resistance profiles. To assess genetic diversity, four DNA barcodes (ITS, psbA, matK, and trnL-F) were used, among which ITS demonstrated the strongest potential in single-gene barcoding for barnyard grass species identification. Cluster analysis based on ITS barcode sequences was performed to group the populations into five main categories. Additionally, SCoT marker analysis using six primers was performed to classify the 46 barnyard grass samples into five groups. The results showed that the predominant barnyard grass species in Ningxia were E. colona, E. crus-galli var. Formosensis, E. crusgalli, E. oryzoides, and E. crusgalli var. Zelayensis, with E. colona being the most prevalent. The differences observed between the morphological and molecular marker-based classifications were method-dependent. However, both SCoT molecular marker technology and DNA barcoding contributed to identifying the genetic diversity of barnyard grass. Taken together, our study revealed significant morphological and genetic variations among barnyard grass populations, which correlated with herbicide sensitivity in Ningxia’s paddy fields, underscoring the necessity for an integrated weed management approach to combat this troublesome weed species. Full article