Search Results (529)

Background: The implementation of advanced agronomical strategies, including the use of antitranspirant, in order to mitigate the negative effects of environmental stress, particularly heat stress on plants, has become a focal area of research in the Mediterranean basin. This region is characterized by hot and dry summer that affects plant physiology. Methods: The experiment was carried out in Sicily (South Italy) on 12-year-old avocado cv. Hass grafted onto Walter Hole rootstock. Two subplots each of forty homogenous trees were selected and treated (1) with calcium carbonate (DECCO Shield^®) and (2) with water (control) at the following phenological phases: 711, 712 and 715 BBCH. The climatic parameters were recorded throughout the year. Physiological measurements (leaf transpiration, net photosynthesis, stomatal conductance, leaf water potential) were measured at 105, 131 and 168 days after full bloom. Fruit growth was monitored, and physico-chemical analyses were carried out at harvest. Results: The antitranspirant increased photosynthesis and stomatal conductance and reduced leaf transpiration (−26.1%). Fruit growth rate increased during summer, although no morphological and qualitative difference was observed at harvest. PCA highlighted the positive effect of the calcium carbonate on overall plant physiology. Conclusions: Antitranspirant foliar application reduced heat stress effects by improving physiological responses of avocado trees. Full article

Accurate and non-destructive monitoring of leaf chlorophyll content (LCC) is essential for assessing crop photosynthetic activity and nitrogen status in precision agriculture. This study introduces a phenology-aware machine learning framework that combines hyperspectral reflectance data with various regression models to estimate leaf chlorophyll content (LCC) in cotton at six key reproductive stages. Field experiments utilized synchronized spectral and SPAD measurements, incorporating spectral transformations—such as vegetation indices (VIs), first-order derivatives, and trilateration edge parameters (TEPs, a new set of geometric metrics for red-edge characterization)—for evaluation. Five regression approaches were evaluated, including univariate and multivariate linear models, along with three machine learning algorithms: Random Forest, K-Nearest Neighbor, and Support Vector Regression. Random Forest consistently outperformed the other models, achieving the highest R² (0.85) and the lowest RMSE (4.1) during the bud stage. Notably, the optimal prediction accuracy was achieved with fewer than five spectral features. The proposed framework demonstrates the potential for scalable, stage-specific monitoring of chlorophyll dynamics and offers valuable insights for large-scale crop management applications. Full article

High nighttime temperature (HNT) due to asymmetric diurnal warming threatens wheat productivity. This study evaluated the effect of HNT on wheat phenology, physiology, and yield through field and controlled environment experiments in Central Queensland, Australia. Two wheat genotypes, Faraday and AVT#6, were assessed under three sowing dates—1 May (Early), 15 June (Mid), and 1 August (Late)—within the recommended sowing window for the region. In a parallel growth chamber study, the plants were exposed to two nighttime temperature regimes, of 15 °C (normal) and 20 °C (high), with consistent daytime conditions from booting to maturity. Late sowing resulted in shortened vegetative growth and grain filling periods and increased exposure to HNT during the reproductive phase. This resulted in elevated floret sterility, lower grain weight, and up to 40% yield loss. AVT#6 exhibited greater sensitivity to HNT despite maturing earlier. Leaf gas exchange analysis revealed increased nighttime respiration (Rn) and reduced assimilation (A), resulting in higher Rn/A ratio for late-sown crops. The results from controlled environment chambers resembled trends of the field experiment, producing lower grain yield and biomass under HNT. Cumulative nighttime hours above 20 °C correlated more strongly with yield losses than daytime heat. These findings highlight the need for HNT-tolerant genotypes and optimized sowing schedules under future climate scenarios. Full article

Drought disturbances are becoming more frequent with global warming. Accurately assessing the regulatory effect of drought on vegetation phenology is key to understanding terrestrial ecosystem response mechanisms in the context of climate change. Previous studies on cumulative and lagged effects of drought on vegetation growth have mostly focused on a single vegetation type or the overall vegetation NDVI, overlooking the possible influence of different adaptation strategies of different vegetation types and differences in drought effects on different phenological nodes. This study investigates the cumulative and lagged effects of drought on vegetation phenology across a region of East Asia from 2001 to 2020 using NDVI data and the Standardized Precipitation Evapotranspiration Index (SPEI). We analyzed the start of growing season (SOS) and end of growing season (EOS) responses to drought across four vegetation types: deciduous needleleaf forests (DNFs), deciduous broadleaf forests (DBFs), shrublands, and grasslands. Results reveal contrasting phenological responses: drought delayed SOS in grasslands through a “drought escape” strategy but advanced SOS in forests and shrublands. All vegetation types showed earlier EOS under drought stress. Cumulative drought effects were strongest on DNFs, SOS, and shrubland SOS, while lagged effects dominated DBFs and grassland SOS. Drought impacts varied with moisture conditions: they were stronger in dry regions for SOS but more pronounced in humid areas for EOS. By confirming that drought effects vary by vegetation type and phenology node, these findings enhance our understanding of vegetation adaptation strategies and ecosystem responses to climate stress. Full article

Root phenology significantly influences ecosystem processes yet remains poorly characterized across biomes. This study synthesized data from 59 studies spanning Arctic to tropical ecosystems to identify woody plants root phenological patterns and their environmental drivers. The analysis revealed distinct climate-specific patterns. Arctic regions had a short growing season with remarkably low temperature threshold for initiation of root growth (0.5–1 °C). Temperate forests displayed pronounced spring-summer growth patterns with root growth initiation occurring at 1–9 °C. Mediterranean ecosystems showed bimodal patterns optimized around moisture availability, and tropical regions demonstrate seasonality primarily driven by precipitation. Root-shoot coordination varies predictably across biomes, with humid continental ecosystems showing the highest synchronous above- and belowground activity (57%), temperate regions exhibiting leaf-before-root emergence (55%), and Mediterranean regions consistently showing root-before-leaf patterns (100%). Winter root growth is more widespread than previously recognized (35% of studies), primarily in tropical and Mediterranean regions. Temperature thresholds for phenological transitions vary with climate region, suggesting adaptations to environmental conditions. These findings provide a critical, region-specific framework for improving models of terrestrial ecosystem responses to climate change. While our synthesis clarifies distinct phenological strategies, its conclusions are drawn from data focused primarily on Northern Hemisphere woody plants, highlighting significant geographic gaps in our current understanding. Bridging these knowledge gaps is essential for accurately forecasting how belowground dynamics will influence global carbon sequestration, nutrient cycling, and ecosystem resilience under changing climatic regimes. 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

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

Leaf phenology is key to ecosystem functioning by regulating carbon, water, and energy fluxes and influencing vegetation productivity. Yet, detecting land surface phenology (LSP) in drylands using remote sensing remains particularly challenging due to sparse and heterogeneous vegetation cover, high spatiotemporal variability, and complex spectral signals. Unlike the Northern Hemisphere, these challenges are further compounded in the Southern Hemisphere (SH), where several regions experience year-round moderate temperatures. When combined with irregular rainfall, this leads to highly variable vegetation activity throughout the year. However, LSP dynamics in the SH remain poorly understood. This study presents a review of remote sensing-based phenology research in drylands, integrating (i) a synthesis of global methodological advances and (ii) a systematic analysis of peer-reviewed studies published from 2015 through April 2025 focused on SH drylands. This review reveals a research landscape still dominated by conventional vegetation indices (e.g., NDVI) and moderate-spatial-resolution sensors (e.g., MODIS), though a gradual shift toward higher-resolution sensors such as PlanetScope and Sentinel-2 has emerged since 2020. Despite the widespread use of start- and end-of-season metrics, their accuracy varies greatly, especially in heterogeneous landscapes. Yet, advanced products such as solar-induced chlorophyll fluorescence or the fraction of absorbed photosynthetically active radiation were rarely employed. Gaps remain in the representation of hyperarid zones, grass- and shrub-dominated landscapes, and large regions of Africa and South America. Our findings highlight the need for multi-sensor approaches and expanded field validation to improve phenological assessments in dryland environments. The accurate differentiation of vegetation responses in LSP is essential not only for refining phenological metrics but also for enabling more realistic assessments of ecosystem functioning in the context of climate change and its impact on vegetation dynamics. 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

Three purple prairie clover (PPC; Dalea purpurea Vent.) varieties, namely Common seed (CS), AC Lamour (ACL) and Bismarck (BIS), were established in plots of irrigated land (rain-fed plus irrigation, Lethbridge, AB) and dryland (rain-fed only, Swift Current, SK) to assess its agronomic characteristics and nutritive value under different ecoclimate and growing conditions in Western Canada. Each seed source was replicated in four test plots arranged as a randomized complete block design at each experimental site. Forage mass on dry matter (DM) basis, canopy height, proportions of leaf and stem and nutritive value were determined at vegetative (VEG), full flower (FF) and late flower (LF) phenological stages. The forage masses of the three PPC varieties were similar (p < 0.05) at each phenological stage with the mean values for VFG, FF and LF being 4739, 4988 and 6753 kg DM/ha under the Lethbridge irrigated conditions, and 1423, 2014 and 2297 kg DM/ha under the Swift Current dryland conditions. The forage mass was higher (p < 0.001) under Lethbridge irrigation than under Swift Current dryland conditions and increased (p < 0.05) with maturity. The three varieties had similar concentrations of organic matter (OM), neutral detergent fibre (NDF), acid detergent fibre (ADF) and crude protein (CP) and in vitro DM digestibility (DMD) at each phenological stage, but CP concentration and in vitro DMD decreased (p < 0.001) whilst NDF and ADF concentration increased (p < 0.001) with maturity. Purple prairie clover grown at Lethbridge irrigated land had higher (p < 0.001) DMD, OM and CP, but lower (p < 0.001) NDF, ADF and condensed tannin concentrations than that grown at Swift Current dryland conditions. These results indicate that PPC has great potential as an alternative legume forage for the cattle industry. Full article

This study presents the MSPAT (Multispectral Soil Plant Analysis Tool), a device designed for assessing leaf nitrogen concentrations in maize crops under field conditions. The MSPAT includes the AS7265x sensor, which has 18 bands and covers the spectrum from 410 to 940 nm. This device was designed to be portable, using the ESP32 microcontroller and incorporating such functionalities as data storage on a MicroSD card, communication with a smartphone via Wi-Fi, and geolocation of acquired data. The MSPAT was evaluated in an experiment conducted at the Federal University of Ceará (UFC), where maize was subjected to different doses of nitrogen fertiliser (0, 60, 90, 120, 150, and 180 kg·ha⁻¹ N). Spectral readings were taken at three phenological stages (V5, V10, and R2) using the MSPAT, an SPAD-502 chlorophyll meter, and a FieldSpec PRO FR3 spectroradiometer. After the optical measurements were taken, the nitrogen concentrations in the leaves were determined in a laboratory by using the Kjeldahl method. The data analysis included the calculation of normalised ratio indices (NRIs) using linear regression and the application of multivariate statistical methods (PLSR and PCR) for predicting leaf nitrogen concentrations (LNCs). The best performance for the MSPAT index (NRI) was obtained using the 900 nm and the 560 nm bands (R² = 0.64) at stage V10. In the validation analysis, the MSPAT presented an R² of 0.79, showing performance superior to that of SPAD-502, which achieved an R² of 0.70. This confirms the greater potential of the MSPAT compared to commercial equipment and makes it possible to obtain results similar to those obtained using the reference spectroradiometer. The PLSR model with data from the FieldSpec 3 provided important validation metrics when using reflectance data with first-derivative transformation (R² = 0.88, RMSE = 1.94 and MAE = 1.28). When using the MSPAT, PLSR (R² = 0.75, RMSE = 2.77 and MAE = 2.26) exhibited values of metrics similar to those for PCR (R² = 0.75, RMSE = 2.78 and MAE = 2.26). This study validates the use of MSPAT as an effective tool for monitoring the nutritional status of maize to optimize the use of nitrogen fertilisers. Full article

Tagetes erecta L. is an ornamental crop known for its medicinal qualities. Large amounts of waste are produced in the commercial usage of T. erecta flowers, including leaves that could be used to develop new eco-friendly phenolic extracts with additional value for the food industry. To maximize the phenol content in the leaf extracts, this study used a Box–Behnken design with Response Surface Methodology, considering three extraction methods (Soxhlet distillation, heat, and vacuum-assisted extraction), three cropping practices (without fertilizer, chemical fertilizer, and vermicompost), and three phenological stages (plants without buds, with buds, and in flower). Extracts from plants fertilized with vermicompost (Eisenia foetida, 10 t ha⁻¹), collected during the blossoming stage and extracted via Soxhlet distillation, exhibited the highest phenol content (25.66 mg GAE/g). Further chemical characterization of the optimized extract (UV-Vis, UV-fluorescence, FTIR, GC-MS, HPLC) confirmed the occurrence of polyphenols in the extract, including quercetin, chlorogenic, gallic, p-coumaric, 3-hydroxycinnamic, and caffeic acids. This underscores the significance of T. erecta leaf residues as a valuable source of bioactive molecules, highlighting the importance of integrating agricultural practices and chemical extraction methods to enhance the phenolic content in leaf extracts from this species. Full article

Precise estimation of the leaf area index (LAI) is vital in efficient maize growth monitoring and precision farming. Traditional LAI measurement methods are often destructive and labor-intensive, while techniques relying solely on spectral data suffer from limitations such as spectral saturation. To overcome these difficulties, the study integrated computer vision techniques with UAV-based remote sensing data to establish a rapid and non-invasive method for estimating the LAI in maize. Multispectral imagery of maize was acquired via UAV platforms across various phenological stages, and vegetation features were derived based on the Excess Green (ExG) Index and the Hue–Saturation–Value (HSV) color space. LAI standardization was performed through edge detection and the cumulative distribution function. The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R² values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively. When HSV-based optimization was applied, VisLAI achieved even better performance, with R² values of 0.92, 0.90, and 0.85, and RMSE values of 0.19, 0.23, and 0.22 at the respective stages. The estimation results were validated against ground-truth data collected using the LAI-2200C plant canopy analyzer and compared with six machine learning algorithms, including Gradient Boosting (GB), Random Forest (RF), Ridge Regression (RR), Support Vector Regression (SVR), and Linear Regression (LR). Among these, GB achieved the best performance, with R² values of 0.88, 0.88, and 0.65, and RMSE values of 0.22, 0.25, and 0.34. However, VisLAI consistently outperformed all machine learning models, especially during the grain filling stage, demonstrating superior robustness and accuracy. The VisLAI model proposed in this study effectively utilizes UAV-captured visible light imagery and computer vision techniques to achieve accurate, efficient, and non-destructive estimation of maize LAI. It outperforms traditional and machine learning-based approaches and provides a reliable solution for real-world maize growth monitoring and agricultural decision-making. 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

In the context of increasing consumer demand for high-quality, residue-free fruits and the growing emphasis on sustainable postharvest technologies, identifying effective, eco-friendly treatments to maintain grape quality during storage has become a critical focus in modern viticulture. Over the course of this study, we examined the influence of seaweed extract (derived from Ascophyllum nodosum) and ethanol-based postharvest treatments on the postharvest quality of the ‘Tarsus Beyazı’ grape. The seaweed extract was applied at six specific phenological stages according to the BBCH scale: BBCH 13 (3rd–4th leaf stage, 0.40%), BBCH 60 (first flower sheath opening, 0.50%), BBCH 71 (fruit set, 0.50%), BBCH 75 (chickpea-sized berries, 0.50%), BBCH 81 (start of ripening, 0.60%), and BBCH 89 (harvest maturity, 0.60%). After harvest, grape clusters were subjected to four different postharvest treatments: untreated control, control + ethanol (20% ethanol immersion for 10 s), seaweed extract alone (preharvest applications only), and seaweed extract + ethanol (combining both preharvest and postharvest treatments). Grapes were stored at 0–1 °C and 90–95% RH for three weeks, followed by a shelf-life evaluation period of three days at 20 °C and 60–65% RH. The findings revealed that seaweed treatments, especially when applied during cluster formation and berry development, effectively mitigated physiological deterioration, preserving stem turgidity and enhancing berry firmness. In contrast, ethanol showed variable responses, occasionally exerting negative effects, with only marginal benefits observed when applied at optimal developmental stages. Both the type and timing of application emerged as critical determinants of key quality attributes such as weight loss, decay incidence, and must properties (TSS, pH, TA). Correlation and heat map analyses indicated the interrelationships among these parameters and the differential impacts of treatments. These results suggest that phenological-stage-specific seaweed applications hold significant potential as a sustainable strategy to extend the storage life and maintain the market quality of ‘Tarsus Beyazı’ grapes. Full article