Phenological Characteristics Research Articles

Vegetable Fields Mapping in Northeast China Based on Phenological Features

Developing vegetable agriculture is crucial for ensuring a balanced dietary structure and promoting nutritional health. However, remote sensing extraction in open-field vegetable planting areas faces several challenges, such as the mixing of target crops with natural vegetation caused by differences in climate conditions and planting practices, which hinders the development of large-scale vegetable field mapping. This paper proposes a classification method based on vegetable phenological characteristics (VPC), which takes into account the spatiotemporal heterogeneity of vegetable cultivation in Northeast China. We used a two-step strategy. First, Sentinel-2 satellite images and land use data were utilized to identify the optimal time and key indicators for vegetable detection based on the phenological differences in crop growth. Second, spectral analysis was integrated with three machine learning classifiers, which leveraged phenological and spectral features extracted from satellite images to accurately identify vegetable-growing areas. This combined approach enabled the generation of a high-precision vegetable planting map. The research findings reveal a consistent year-by-year increase in the planting area of vegetables from 2019 to 2023. The overall accuracy (OA) of the results ranges from 0.81 to 0.93, with a Kappa coefficient of 0.83. Notably, this is the first 10 m resolution regional vegetable map in China, marking a significant advancement in economic vegetable crop mapping.

Using a Phenocamera to Monitor Urban Forest Phenology

Under global climate change, fragmented urban vegetation is more susceptible to the external environment, and changes in vegetation phenology are one of the most apparent responses. In this study, phenological camera (phenocamera) photo data, Klosterman curve fitting, and a Gu model were employed to explore the phenological characteristics of an urban forest at different levels within different species. Differences between species and groups regarding the upturn date (UD), the stabilization date (SD), the downturn date (DD), the recession date (RD), and the length of the growing season (LOS) are displayed in detail. We found that the UD of Cinnamomum camphora groups began in late April (day of year 108th), the SD appeared in early May (121st), and the DD started in early October (283rd) and ended in late October (293rd), with an average LOS of 185 days. The phenological characteristics of the Cinnamomum camphora and Bischofia polycarpa groups differed significantly. The average LOS of Bischofia polycarpa was 47 days longer than that of Cinnamomum camphora. Between Cinnamomum camphora individuals and group levels, differences in the UD and the SD were not obvious, while differences in the DD, the RD, and the LOS were large (LOS > RD > DD). The LOS of Cinnamomum camphora was longer on the individual scale (209 days), while the average LOS on the group scale was 185 days. In conclusion, our results reflect the more refined quantitative results of urban vegetation phenology and will help to elucidate urban vegetation phenological changes, which has important theoretical and practical significance for future urban forest management practices.

АГРОБИОЛОГИЧЕСКИЙ АНАЛИЗ СТОЛОВЫХ ГИБРИДНЫХ ФОРМ ВИНОГРАДА СЕЛЕКЦИИ АЗОСВиВ

The purpose of the study is an agrobiological analysis of table hybrid forms of grapes of the AZOSViV selection. The results of studies of the agrobiological characteristics of new table hybrid forms of grapes, as well as their phenological characteristics in dynamics for 2019–2021 are presented. These hybrids of the Anapa Zonal Experimental Station of Vine and Winemaking (AZOSViV) are selected as elite, as they annually show stable yields, have high fruiting and fruiting rates, have ultra-early and early ripening, visually look healthy for many years. The task of AZOSViV scientists-breeders in creating table varieties is to obtain ultra-early and early large-fruited hybrid forms, transfer them to state variety testing and further recommendations for bringing these new varieties into production. These hybrids grow on the Anapa ampelographic collection in a hybrid plot. Methods used in the work: field, laboratory, statistical, analytical. Based on the results of many years of research, it was established that hybrids – GF-59-32, GF-62-10 and GF-59-43 are superearly, early – GF-59-6, GF-59-13 and GF-62-59; the highest fruiting coefficient in table hybrid forms GF-59-32 and GF-62-59 (1.8–1.9), and the lowest in GF-59-43; the highest coefficient of fruitfulness in table forms GF-59-32 and GF-62-59 (1.9–2.5); table hybrids GF-59-32 and GF-62-59 are the most productive of the studied table hybrid forms, yield per bush in 2020 and 2021 – 9.8 kg; the studied hybrid forms of the table direction of use are singled out in the elite and can be transferred for state variety testing. Table hybrid forms of grapes of the AZOSViV selection are the primary contenders for varieties, containing high economically useful traits.

Classification and Monitoring of Salt Marsh Vegetation in the Yellow River Delta Based on Multi-Source Remote Sensing Data Fusion.

Salt marsh vegetation in the Yellow River Delta, including Phragmites australis (P. australis), Suaeda salsa (S. salsa), and Tamarix chinensis (T. chinensis), is essential for the stability of wetland ecosystems. In recent years, salt marsh vegetation has experienced severe degradation, which is primarily due to invasive species and human activities. Therefore, the accurate monitoring of the spatial distribution of these vegetation types is critical for the ecological protection and restoration of the Yellow River Delta. This study proposes a multi-source remote sensing data fusion method based on Sentinel-1 and Sentinel-2 imagery, integrating the temporal characteristics of optical and SAR (synthetic aperture radar) data for the classification mapping of salt marsh vegetation in the Yellow River Delta. Phenological and polarization features were extracted to capture vegetation characteristics. A random forest algorithm was then applied to evaluate the impact of different feature combinations on classification accuracy. Combining optical and SAR time-series data significantly enhanced classification accuracy, particularly in differentiating P. australis, S. salsa, and T. chinensis. The integration of phenological features, polarization ratio, and polarization difference achieved a classification accuracy of 93.51% with a Kappa coefficient of 0.917, outperforming the use of individual data sources.

A Large-Scale Agricultural Land Classification Method Based on Synergistic Integration of Time Series Red-Edge Vegetation Index and Phenological Features.

Agricultural land classification plays a pivotal role in food security and ecological sustainability, yet achieving accurate large-scale mapping remains challenging. This study presents methodological innovations through a multi-level feature enhancement framework that transcends traditional time series analysis. Using Shandong Province, northern China's agricultural heartland, as a case study, we first established a foundation with time series red-edge vegetation indices (REVI) from Sentinel-2 imagery, uniquely combining the normalized difference red edge index (NDRE705) and plant senescence reflectance index (PSRI). Moving beyond conventional time series analysis, we innovatively amplified key temporal characteristics through newly designed spatial feature parameters (SFPs) and phenological feature parameters (PFPs). This strategic enhancement of critical temporal points significantly improved classification performance by capturing subtle spatial patterns and phenological transitions that are often overlooked in traditional approaches. The study yielded three significant findings: (1) The synergistic application of NDRE705 and PSRI significantly outperformed single-index approaches, demonstrating the effectiveness of our dual-index strategy; (2) The integration of SFPs and PFPs with time series REVI markedly enhanced feature discrimination at crucial growth stages, with PFPs showing superior capability in distinguishing agricultural land types through amplified phenological signatures; (3) Our optimal classification scheme (FC6), leveraging both enhanced spatial and phenological features, achieved remarkable accuracy (93.21%) with a Kappa coefficient of 0.9159, representing improvements of 4.83% and 0.0538, respectively, over the baseline approach. This comprehensive framework successfully mapped 120,996 km2 of agricultural land, differentiating winter wheat-summer maize rotation areas (39.44%), single-season crop fields (36.16%), orchards (14.49%), and facility vegetable fields (9.91%). Our approach advances the field by introducing a robust, scalable methodology that not only utilizes the full potential of time series data but also strategically enhances critical temporal features for improved classification accuracy, particularly valuable for regions with complex farming systems and diverse crop patterns.

Mutagenesis and analysis of contrasting wheat lines do not support a role for PFT in Fusarium head blight resistance.

Ambiguity about whether the histidine-rich calcium-binding protein-coding gene (HisR) or the pore-forming toxin-like gene (PFT) or both are responsible for Fusarium head blight (FHB) resistance conferred by the Fhb1 quantitative trait locus hinders progress toward elucidating Fhb1 resistance mechanisms. Here, with a series of developed lines with or without PFT but all possessing HisR and five HisR-carrying PFT mutant lines created via gene editing, we show that PFT does not confer FHB resistance and that the HisR resistance effect does not require PFT in the tested conditions. We also show that PFT mutations are not associated with morphological and phenological characteristics that often affect FHB severity.

Morphological and Phenological Characteristics of Ornamental Lianas (on the Example of Samarkand City)

Morphological and Phenological Characteristics of Ornamental Lianas (on the Example of Samarkand City)

Unraveling the impact of drought on waterbird community assembly and conservation strategies.

Drought-induced changes in lakes and wetlands, crucial habitats for migratory waterbirds, can greatly affect their foraging and habitat utilization. These changes lead to a decline in waterbird species richness and may cause shifts in community assembly from phylogenetic and functional trait perspectives. However, a gap remains between ecological mechanistic research about these changes and conservation applications. Here, we investigated the drought-induced phylogenetic and functional changes in waterbird community assembly over the past two decades in two lakes of the Yangtze River basin. Additionally, we explored conservation strategies to address the requirements of waterbirds during droughts. Results showed that drought modified the hydrological and phenological characteristics of waterbird habitats, leading to a decrease in waterbird species richness. As drought severity increased, species that were sensitive to drought exhibited higher degrees of similarity compared to the local species pool, leading to more divergent community assembly patterns. The mean values of body mass, tarsus length, bill length, and Evolutionary Distinctiveness (ED) of waterbirds in both lakes increased significantly under high drought stress conditions. Dabbling birds and small waders, which rely on habitats most susceptible to drought-induced changes, were the primary habitat preference groups affected. Additionally, the diversification of phylogenetic and functional community assembly in waterbirds and their associated conservation requirements indicated that effective conservation measures for waterbirds must be diversified and tailored to the specific requirements of different waterbird species, thereby preventing the loss of ecosystem functions and services. Conservation strategies should also be adaptive by incorporating periodic evaluations and adjustments to respond to environmental pressures and thus ensuring sustained effectiveness. In conclusion, as drought severity increased, functional and phylogenetic trait differences between species became more significant. Therefore, conservation measures must be diversified, tailored, and adaptive to effectively respond to the changes in waterbird community assembly.

Two-tier classification framework for mapping grassland types using multisource earth observation data

ABSTRACT The dynamic changes in grassland types are crucial for conserving grassland biodiversity, conducting comprehensive health assessments, and gaining insights into ecosystem evolution. However, accurately mapping grassland types remains an ongoing challenge, especially over large areas. In response, we developed a hierarchical classification framework using random forest to tackle this task. This framework was structured into two tiers: land cover (LC) classification and grassland-type mapping, with each tier using specific features tailored to its respective objectives. In this framework, LC samples were automatically generated using existing LC products combined with vegetation indices and phenological features. We fused spectral information, phenological features, and habitat factors such as topographic indices, soil, and climate from multisource earth observation (EO) data to enhance grassland-type mapping performance. This framework successfully generated distribution maps of grassland types on the Qinghai Plateau for the years 1990–2020. Our findings revealed the following: (1) Using the two-tier classification framework and fusing multisource features, an ideal distribution map of grassland types was obtained, with a macro-average F1 score (F1) of 91% and an overall accuracy (OA) of 96%; (2) Compared to a one-shot classification framework, the two-tier classification framework achieved higher accuracy, with F1 and OA increasing by 11% and 7%, respectively; (3) Climate, topographical, soil, and phenological features assisted in distinguishing grassland types with similar spectral characteristics, especially for zonal grasslands. Adding these features increased F1 by 21%, 10%, 7%, and 3%, respectively, while OA increased by 9%, 5%, 5%, and 2%, respectively. Among these features, relative humidity, total precipitation from May to September, geographic coordinates, and elevation had the greatest effect on grassland type differentiation. June and July were the optimal phenological periods for mapping; (4) Over the 30-year period, the grasslands on the Qinghai Plateau showed an expanding trend, with extensive areas of alpine steppes transforming into alpine meadows. The results of this study provide the first elucidation of grassland-type changes across the Qinghai Plateau from 1990 to 2020. Moreover, they underscore the potential of hierarchical classification frameworks and the integration of multisource EO data in mapping grassland-type distribution.

Assessment of Phenological and Yield Characteristics of Apple (Malus × domestica Borkh) Varieties

Phenology has gained recently a considerable emphasis as a method for assessing the impacts of changing climate on plant species. Shifting in phenological stages changes can have rippling impacts on environment. Quantifying the effects of these shifting changes on plants is therefore crucial. Knowledge of the dates and duration of particular phenological stages and their variations can be a valuable source of information for organizing, planning and implementation of some regular and special horticultural activities. The phenological study of apple is an essential component of the study as critical phenological stages of the apple are highly reliant on environment, they also have significant practical and scientific implications in the domain of selection. In particular, proper flowering and fruit ripening time are very critical to achieve optimum fruit yield and quality. Fruit yield is an important parameter that determines the acceptability of a cultivar by consumers. The present investigation was undertaken to evaluate the apple varieties for their phenology and yield under Kashmir conditions. Four varieties viz., Super Chief, Red Chief, Gala Redlum and Mitch Gala were used for study. Phenological characteristics revealed that Mitch Gala was earliest in all the phenological stages and Super Chief was late. The blooming period of the studied varieties indicated the synchronizing behaviour of the varieties, which is a prerequisite for effective pollination and adequate fruit set. As far as flowering is concerned, Mitch Gala was earliest to bloom and Gala Redlum was late. Fruit production characteristics revealed that Gala Redlum and Mitch Gala. In terms of yield characteristics, highest yield of 20.58 kg/tree was recorded in Gala Redlum followed by 19.40 kg/tree in Mitch Gala.

Simulation and Prediction of Thermokarst Lake Surface Temperature Changes on the Qinghai–Tibet Plateau

Thermokarst lakes are shallow bodies of freshwater that develop in permafrost regions, and they are an essential focus of international permafrost research. However, research regarding the mechanisms driving temperature fluctuations in thermokarst lakes and the factors that influence these changes is limited. We aimed to analyze seasonal variations in the surface water temperature, clarify historical trends in the phenological characteristics of lake ice, and predict future temperature changes in surface water of the thermokarst lakes using the air2water model. The results indicated that in comparison with air temperature, the thermokarst lake’s surface water temperature showed a certain lag and significantly higher values in the warm season. The warming rate of the thermokarst lake’s average surface water temperature based on historical data from 1957 to 2022 was 0.21 °C per decade, with a notably higher rate in August (0.42 °C per decade) than in other months. Furthermore, the ice-covered period steadily decreased by 2.12 d per decade. Based on the Coupled Model Intercomparison Project 6 projections, by 2100, the surface water temperatures of thermokarst lakes during the warm season are projected to increase by 0.38, 0.46, and 2.82 °C (under scenarios SSP126, SSP245, and SSP585), respectively. Compared with typical tectonic lakes on the Qinghai–Tibet Plateau, thermokarst lakes have higher average surface water temperatures during ice-free periods, and they exhibit a higher warming rate (0.21 °C per decade). These results elucidate the response mechanisms of thermokarst lakes’ surface water temperature and the phenological characteristics of lake ice in response to climate change.

Morphological and Phenological Attributes of Chickpea (Cicer arietinum L.) Affected by Different Growing Conditions, Zeolite and Nitrogen Applications

The present study investigated the effects of two different zeolite applications and different nitrogen-based fertilizers on chickpea’s yield and yield components in dry and irrigated conditions. The field experiment was conducted during 2019 and 2020 in the experimental area of the Faculty of Agriculture, Eskisehir Osmangazi University, Eskisehir, Türkiye. The experimental design was a split-split plot with four replicates. The main plots were grown under dry-irrigated conditions. At the same time, subplots received zeolite applications (zeolite+- zeolite-), and sub-sub plots received nitrogen applications [control, traditional, chemical, farmyard manure, and Isabion, (an animal collagen-derived biostimulant)]. The experiment found that irrigation caused a delay in phenological characters but had a favorable impact on morphological characters and yield. The effect of zeolite applications was different in the first and second years of the experiments for the investigated characters. In the first year, the application of zeolite had a significant impact on grain yield, but there was no discernible effect in the second year. The experiment demonstrated that both chemical fertilizer and farmyard manure positively impacted phenological and morphological characteristics. In both years, the farmyard manure plots produced the highest grain yield. Farmers in Türkiye are advised to apply nitrogen to their crops as the profitability of chickpeas has risen in recent years. If the high cost of farmyard manure renders its use impracticable, farmers can opt for chemical fertilizer as an alternative.

Machine Learning-Based Summer Crops Mapping Using Sentinel-1 and Sentinel-2 Images

Accurate crop type mapping using satellite imagery is crucial for food security, yet accurately distinguishing between crops with similar spectral signatures is challenging. This study assessed the performance of Sentinel-2 (S2) time series (spectral bands and vegetation indices), Sentinel-1 (S1) time series (backscattering coefficients and polarimetric parameters), alongside phenological features derived from both S1 and S2 time series (harmonic coefficients and median features), for classifying sunflower, soybean, and maize. Random Forest (RF), Multi-Layer Perceptron (MLP), and XGBoost classifiers were applied across various dataset configurations and train-test splits over two study sites and years in France. Additionally, the InceptionTime classifier, specifically designed for time series data, was tested exclusively with time series datasets to compare its performance against the three general machine learning algorithms (RF, XGBoost, and MLP). The results showed that XGBoost outperformed RF and MLP in classifying the three crops. The optimal dataset for mapping all three crops combined S1 backscattering coefficients with S2 vegetation indices, with comparable results between phenological features and time series data (mean F1 scores of 89.9% for sunflower, 76.6% for soybean, and 91.1% for maize). However, when using individual satellite sensors, S1 phenological features and time series outperformed S2 for sunflower, while S2 was superior for soybean and maize. Both phenological features and time series data produced close mean F1 scores across spatial, temporal, and spatiotemporal transfer scenarios, though median features dataset was the best choice for spatiotemporal transfer. Polarimetric S1 data did not yield effective results. The InceptionTime classifier further improved classification accuracy over XGBoost for all crops, with the degree of improvement varying by crop and dataset (the highest mean F1 scores of 90.6% for sunflower, 86.0% for soybean, and 93.5% for maize).

Uncovering the seasonal dynamics of terrestrial oil spills through multi-temporal and multi-frequency Synthetic Aperture radar (SAR) observations

The phenological characteristics of vegetation exposed to oil pollution can reveal how different vegetation types and species respond to the effects of hydrocarbons in crude oil. This can further inform the recovery status and remediation efforts on polluted sites. In this study, the potentials of various SAR frequencies (including multitemporal C band Sentinel-1, X band Cosmo Skymed, X band TanDEM-X, and L band ALOS PALSAR 2) were explored to analyse the characteristics of vegetation affected by hydrocarbons over time. The SAR backscatter characteristics of both oil-polluted and oil-free vegetation were systematically examined across different seasons to deduce the primary effects of oil pollution. Additionally, machine learning random forest (RF) classification and support vector machines (SVM) were implemented on seasonal image composites to assess spatial extent. Results show that stress caused by oil pollution on vegetation is more distinguishable during the wet season in the VV channel than in the VH channel of the multitemporal Sentinel 1. This was supported by the machine learning classification, as overall accuracy (OA) and Kappa (K) was also highest with the wet season SAR image composites. A further incorporation of L- and X-Band multifrequency SAR across the two seasons showed that the wet season composites significantly improved the classification accuracy, with Cropland, Grassland and Tree Cover Area (TCA) recording an increase in OA and K, to 82.3 % and 0.64, 66.67 % and 0.33, and 74.7 % and 0.49, respectively. Findings presented in this study represent a pioneering exploration of the capabilities of multi-temporal and multi-sensor SAR imagery in discriminating oil-impacted from healthy vegetation. This holds significant promise in evaluating the progress of environmental remediation, the regeneration of vegetation, and recovery efforts.

Агробиологическая оценка сорта-клона винограда Аг изюм урожайный в условиях южного Дагестана

Abstract. The article presents the results of studies on the evaluation of agrobiological and morphological characteristics of the Ag Izyum variety and its clone Ag Izyum Urozhaynyi. The studies were conducted on the ampelographic collection of the DBESV&VG – branch of the NCFSCHVW. The purpose of the research was to study the agrobiological and economically valuable traits and properties of the Ag Izyum Urozhaynyi table grape clone variety for further improvement of its performance in the conditions of the southern coastal zone of the Republic of Dagestan. The research methodology is generally accepted in viticulture. The object of the research was fruiting plants of the Ag Izyum variety and its clone Ag Izyum Urozhaynyi. Vineyards are own-rooted. Planting scheme is 3,0 x 1,5 m. The bushes are formed as a two-sided high-standard cordon. Based on the research results, it was revealed that, according to their phenological characteristics, the Ag Izyum Urozhaynyi clone variety and the control variety Ag Izyum belong to mid-late ripening varieties. The main agrobiological parameters of the clone variety Ag Izyum Urozhaynyi are at the level of or slightly higher than those of the control variety Ag Izyum. The percentage of opened buds in the clone variety Ag Izyum Urozhaynyi averaged 80 %, while in the variety Ag Izyum it was 76 %. The yield per bush is 9,96 kg and 7,4 kg; the yield capacity per 1 ha is 22,2 t/ha and 16,5 t/ha. The clone variety Ag Izyum Urozhaynyi surpasses the variety Ag Izyum (control) in having a larger bunch (289,8 g versus 243,3 g) and a denser constitution. The shoot productivity index of the clone Ag-Izyum Urozhaynyi is 25,5 % higher than that of the control variety Ag Izyum (170,98 g), while that of the control is 136,24 g. The quality indicators (mass concentration of sugars and titratable acids) are within the permissible conditions – 149 g /100 cm3 and 6,6 g / dm3 . Taking into account the duration of the production period (130-135 days, respectively) and the sum of active temperatures (2800-2900 °С), the Ag izum variety and the Ag izum urozhaynyi clone variety belong to the average ripening period. The Ag izum urozhaynyi clone variety is recommended for fresh consumption and for making compotes. Keywords: grapes, productivity, variety, variety plot, clone, clonal breeding, productivity index

Influence of environmental and biological factors on mercury accumulation in fish from the Atrato River Basin, Colombia

Understanding variations in total mercury (T-Hg) levels in fish is crucial for protecting aquatic biota and human health. This article evaluates the influence of environmental factors (temperature, pH) and biological variables (feeding habits, trophic level, total length, total weight), on T-Hg concentrations in fish from the Atrato River basin, Colombia. Utilizing a robust secondary data set of 842 fish samples from 16 species collected in 2019, we conducted a comprehensive analysis of these influences. We examined differences in T-Hg accumulation rates by habitat type (pelagic, benthopelagic and demersal) and probabilistically classified species based on their feeding habits and trophic levels. Our analysis identified a hierarchy of variables influencing T-Hg levels: feeding habits > total length > estimated total weight > trophic level > water temperature > pH, with temperature being the only predictor exerting a negative influence. Together, these variables accounted for over 60% of the variability in T-Hg accumulation in fish muscle tissue. Furthermore, fish in the Atrato River exhibited differential T-Hg based on habitat type, grouping into three distinct subpopulations stratified by feeding habits and trophic levels. These findings suggest that observed T-Hg accumulation patterns are driven by the functional ecology of the organisms, phenological characteristics, metabolism, contamination patterns, biogeography, land use, and the spatial and chemical configuration of the environmental matrices of the basin. Our results emphasize the importance of understand how biological and environmental factors influence T-Hg concentrations in fish, as these factors vary across aquatic systems. This knowledge is crucial for developing effective biodiversity management strategies. While we used a machine learning approach to identify key predictors of T-Hg accumulation, we also caution against potential biases in modeling T-Hg concentrations for aquatic biota management.

Effect of Seaweed Extract and Humic Acid Treatment on the Phenological and Yield Characteristics of Guava (Psidium guajava L.)

Aim: The main aim of this study was to evaluate the impact of seaweed and humic acid treatment at different doses on the phenological characteristics and yield of guava. Study Design: Randomized block design. Place and Duration of Study: A field experiment was conducted from December 2023 to July 2024 to assess the impact of bio-stimulants on the phenological and yield traits of guava (var. Arka Kiran) in the Poongunam village, Chengalpattu Dist, Tamil Nadu, India. Methodology: The experiment followed a Randomized Block Design with eight treatments: T₁ - Absolute control, T₂ - Water Spray, T₃ - 1% Seaweed Extract, T₄ - 1.5% Seaweed Extract, T₅ - 2% Seaweed Extract, T₆ - 1% Humic Acid, T₇ - 1.5% Humic Acid, and T₈ - 2% Humic Acid, each replicated three times. Results: Results showed that 2% Seaweed Extract (T₅) significantly improved flower retention (7.55 flowers per shoot), reduced the duration from flowering to fruit set (12.47 days), and shortened the time from fruit set to harvest (121.56 days). T₅ also led to the highest fruit set (68.65%), lowest fruit drop (48.72%), largest fruit weight (150.02 g), and greatest yield (13.35 kg/tree). Humic Acid at 2% (T₈) also improved fruit set (64.32%), fruit drop (49.54%), and yield (12.34 kg/tree), but was less effective than Seaweed Extract. Conclusion: This study demonstrates the potential of Seaweed Extract as a powerful bio-stimulant to enhance guava production, offering a sustainable alternative to traditional chemical treatments.

Exploration of the ecology and ex-situ conservation of Betula utilis D. Don: Insights from topography, edaphic factors, and nursery management practices

The global mean temperature is rising at an unprecedented rate, and the investigation of ecosystem functioning and phenological events offered some of the most compelling evidence for the impact of these changes on plant species in alpine regions. The present study assessed the impact of topographic and edaphic factors on treeline structure/diversity, regeneration behavior, phenological characteristics, and ex-situ conservation of endemic and relict species, Betula utilis D. Don in the Kashmir Himalaya, India. The data was collected using a stratified random sampling method along the three altitudinal gradients of 3000–3200 m, 3200–3400 m and 3400–3600 m asl. The results revealed that IVI of this species on the Southeastern and Southwestern aspect increase from lower to higher altitude. The lower IVI value of 156.92 and 181.85 % was observed at lower altitudes, followed by 186.05 and 208.80 percent at middle altitude and higher value of 236.58 and 260.04 % at the upper altitudinal range. On the eastern slope, the maximum and minimum numbers of recruits (1375 and 593 ha-1) were present at middle and upper altitudinal gradients, respectively. The established regeneration decreased with altitude and better regeneration was observed in the Southwest. The phenophases of B. utilis were completed earlier with an increase in altitude. Under the nursery conditions, the maximum germination of 89.11 % was recorded in the seeds subjected to 90 days of stratification while a minimum of 47.33 % was recorded under control. The satellite data observations on resource mapping showed the scattered distribution of B. utilis dominated forests at higher elevations within the Sindh Forest division in patches. B. utilis dominated forests are distributed over an area of 2746.58 ha (1.67 %) of the total 164,236.82 ha) geographical area of the division which makes its conservation highly significant. The Canonical Correspondence Analysis highlights the primary role of soil particularly sand, silt, and clay content well as bulk density in shaping the ecological distribution and regeneration patterns of B. utilis forest stands. The study's findings reveal field-based patterns related to numerous ecological characteristics of the endemic and relict species, B. utilis, which can be utilized in developing ecological restoration and conservation efforts in the region.

Functional groups of leaf phenology are key to build climate-resilience in cocoa agroforestry systems

Agroforestry has the potential to enhance climate change adaptation. While benefits from agroforestry systems consisting of cash crops and shade trees are usually attributed to the (shade) trees, the trees can also have negative impacts due to resource competition with crops. Our hypothesis is that leaf phenology and height of shade trees determine their seasonal effect on crops. We test this hypothesis by categorizing shade tree species into functional groups based on leaf phenology, shade tree canopy height and shade tree light (wet and dry season) interception as well as the effects. To this end, leaf phenology and the effects on microclimate (temperature, air humidity, intercepted photoactive radiation (PAR)), soil water, stomatal conductance and cocoa yield were monitored monthly during wet and dry seasons over a two-year period on smallholder cocoa plantations in the northern cocoa belt of Ghana. Seven leaf phenological groups were identified. In the wet season, highest buffering effect of microclimate was recorded under the trees brevi-deciduous before dry season. During dry season, high PAR and lowest reduction in soil moisture were observed under the trees in the group of completely deciduous during dry season. The evergreen groups also showed less reduction in soil water than the brevi-deciduous groups. In the wet season, shade tree effects on cocoa tree yields in their sub canopy compared to the respective control of outer canopy with full sun ranged from positive (+10 %) to negative (-15 %) for the deciduous groups, while yield reductions for the evergreen groups ranged from −20 % to −33 %. While there were negative yield impacts for all phenological groups in the dry season, the trees in completely deciduous during dry season group recorded least penalties (-12 %) and the trees with evergreen upper canopy the highest (-35 %). The function of shade trees in enhancing climate resilience is therefore strongly dependent on their leaf phenological characteristics. Our study demonstrates how the key trait leaf phenology can be applied to successful design of climate-resilient agroforestry systems.

Climatic sensitivity and the growth prospects of Silver fir and European larch in the Carpathians in the light of observed climate changes

One consequence of climate change is the rapid extinction of spruce monocultures in the Carpathians. Therefore, the need has arisen to replace spruce with other highly productive species that ensure the stability and biodiversity of mountain forests, such as the Abies alba and Larix decidua. These co-occurring conifers show different successional and phenological characteristics: L. decidua is a light-demanding species that dominates in the early successional stages, while A. alba is a shade-tolerant tree. This study attempts to identify climatic factors that determine the tree-ring width of fir and larch in order to assess the growth prospects of these species in the context of the ongoing climate change. As a measure of the vitality of trees, tree-ring widths of 42 sub-populations of fir and 36 of larch from the area of the Western Carpathians were used. Regional growth patterns for each species were used to estimate the relationship between temperature/precipitation and radial growth. Linear correlation analyses and nonlinear Random Forest models were used. Fir growth was significantly positively influenced by the high temperature of January–March and July and the high precipitation of February and July of the year of tree-ring formation. Larch growth was positively affected by the high temperature of May and the low precipitation of April and September of the year of tracheid formation. Unlike larch, fir growth increased if May and June of the preceding year were warm. The dominant climatic factor influencing fir growth was February temperature, whereas for larch growth, it was the May temperature of the year of tree-ring formation. Most of the recently observed changes in climatic factors that are important for the growth of both species are favorable. Thus, increasing their share in the Carpathian forests seems fully justified, as this will contribute to the stability, productivity, and biodiversity of the region’s forests.