Sample Plots Research Articles

Estimation of Maize Residue Cover Using Remote Sensing Based on Adaptive Threshold Segmentation and CatBoost Algorithm

Maize residue cover (MRC) is an important parameter to quantify the degree of crop residue cover in the field and its spatial distribution characteristics. It is also a key indicator of conservation tillage. Rapid and accurate estimation of maize residue cover (MRC) and spatial mapping are of great significance to increasing soil organic carbon, reducing wind and water erosion, and maintaining soil and water. Currently, the estimation of maize residue cover in large areas suffers from low modeling accuracy and poor working efficiency. Therefore, how to improve the accuracy and efficiency of maize residue cover estimation has become a research hotspot. In this study, adaptive threshold segmentation (Yen) and the CatBoost algorithm are integrated and fused to construct a residue coverage estimation method based on multispectral remote sensing images. The maize planting areas in and around Sihe Town in Jilin Province, China, were selected as typical experimental regions, and the unmanned aerial vehicle (UAV) was employed to capture maize residue cover images of sample plots within the area. The Yen algorithm was applied to calculate and analyze maize residue cover. The successive projections algorithm (SPA) was used to extract spectral feature indices from Sentinel-2A multispectral images. Subsequently, the CatBoost algorithm was used to construct a maize residue cover estimation model based on spectral feature indices, thereby plotting the spatial distribution map of maize residue cover in the experimental area. The results show that the image segmentation based on the Yen algorithm outperforms traditional segmentation methods, with the highest Dice coefficient reaching 81.71%, effectively improving the accuracy of maize residue cover recognition in sample plots. By combining the spectral index calculation with the SPA algorithm, the spectral features of the images are effectively extracted, and the spectral feature indices such as NDTI and STI are determined. These indices are significantly correlated with maize residue cover. The accuracy of the maize residue cover estimation model built using the CatBoost model surpasses that of traditional machine learning models, with a maximum determination coefficient (R2) of 0.83 in the validation set. The maize residue cover estimation model constructed based on the Yen and CatBoost algorithms effectively enhances the accuracy and reliability of estimating maize residue cover in large areas using multispectral imagery, providing accurate and reliable data support and services for precision agriculture and conservation tillage.

Comparative Study of Single-Wood Biomass Model at Plot Level Based on Multi-Source LiDAR

Forests play an important role in promoting carbon cycling and mitigating the urban heat island effect as one of the world’s major carbon storages. Scientifically quantifying tree biomass is the basis for assessing tree carbon storage and other ecosystem functions. In this study, a sample plot of Populus tomentosa plantation in the Olympic Forest Park in Beijing was selected as the research object. Point cloud data from three types of laser scanners, including terrestrial laser scanner (TLS), backpack laser scanner (BLS), and handheld laser scanner (HLS), were used to estimate the biomass of single tree trunks, branches, leaves, and aboveground total biomass based on the Allometric Biomass Model (ABM) and Advanced Quantitative Structure Model (AdQSM). The following conclusions were drawn from the estimation results: (1) For the three types of laser scanner point clouds, the biomass estimation values obtained using the AdQSM model were generally higher than those obtained using the Allometric Biomass Model. However, the estimation values obtained using the two models were similar, especially for tree trunks and total biomass. (2) For total biomass and individual biomass components of single trees, the results obtained from handheld and terrestrial laser scanner point clouds are consistent; however, they show some differences from the results obtained from backpack-mounted point clouds. This study further enriches the methodological system for estimating forest biomass, providing a theoretical basis and reference for more accurate estimates of forest biomass and more sustainable forest management.

Stand growth, Biomass and Carbon sequestration potentials of Parkia biglobosa (jacq.) Bench plantation in South-Western Nigeria

This study assessed tree growth variables, above (AGB), below ground biomass (BGB) and total carbon content (TC) sequestered by Parkia biglobosa (Jacq.) Bench. Plantation in Wasangare, Oyo State using non-destructive ground base survey. Tree growth data (Diameter at breast height, Dbh and Tree height, Th) were collected using lacer ace hypsometer and diameter girth tape from 20 temporary sampling plots of size 25 m X 25 m established through systematic transect lines. Diameter size classes (DSC) for the plantation was examined, carbon stock for each DSC was also determine while basal area (m2 ha-1), volume (m3 ha-1), Biomass (Mg ha-1) and Carbon (Mg ha-1) were also estimated. Results showed mean Dbh of 18.7 + 0.25 cm with 8.14 + 0.10 m, 0.033 + 0.00 m2 ha-1 and 0.320 + 0.01 m3 ha-1 for tree height, basal area and volume respectively. AGB and BGB were 10.877 + 0.39 Mgha-1 and 2.175 + 0.08 Mgha-1 respectively while TC was 6.527 + 0.24 Mgha-1. The percentage carbon stock proportion for each DSC revealed class size 25-29-9 cm (19.02%) as the highest while the least proportion was observed in less than 5 cm class with 0.04% of carbon. The DSC showed majority of the tree Dbh in lower Dbh classes with fewer trees in higher classes forming almost a normal bell shape. The study provides information that can help the management in planning silvicultural activities and selective removal from the stand (harvesting schedule). Tree Dbh, height, basal area, volume and biomass are the determinant characteristics for forest carbon assessment. In conclusion, the plantation actively sequesters carbon showing potentials for indigenous trees in climate change mitigation.

Bayesian approach for modeling diameter distributions of patula pine (Pinus patula Schiede ex Schltdl. & Cham.) stands in Southern Mexico

Characterization of the diameter distribution of forest stands managed for timber production provides accurate estimates of existing wood volume by diameter class and area unit. It provides the basis for determining the stand's ecological and economic value, its structure, the potential for ecosystem services, and appropriate management practices. Patula pine is one of the most important conifers in southern Mexico and requires biometric tools to improve management strategies. This study aimed to use the Bayesian approach to estimate the parameters of Johnson's SB (JSB) and Weibull (W) probability density functions (pdf) and to model the diameter distribution of patula pine stands in southern Mexico. We used data from 462 diameter distributions derived from 66 permanent 400 m2 sampling plots. The results indicated that the Bayesian approach was appropriate for estimating the JSB and W pdf parameters. Due to the similarity between the nonparametric tests and the goodness-of-fit statistics of the functions, the graphical analysis helped to select the W function as the better option to adequately model the diameter distribution of the analyzed stands. The equations developed for parameter prediction of the W function with stand variables allowed us to characterize and project the theoretical distribution of diameter classes in a simple accurate way with variables easily obtained from forest inventories. The Bayesian approach proved to be an effective method for estimating the pdf parameters used. This allowed for the correct characterization of the observed diameter distribution, and its implementation was easy for executing the procedure. Diameter distribution models are a powerful tool for forest inventory and management of patula pine stands analyzed in southern Mexico.

Simultaneous Models for the Estimation of Main Forest Parameters Based on Airborne LiDAR Data

This study aimed to develop simultaneous models with universal applicability for the estimation of the main factors of forest stands based on airborne LiDAR data and to provide a reference for standardizing the approach and evaluation indices of main forest factor modeling. Using airborne LiDAR and field survey data from 190 sample plots in spruce (Picea spp.), fir (Abies spp.), and spruce–fir mixed forests in Northeast China, the simultaneous models for estimating the main factors of forest stands were developed. To develop the models, the relationships between mean tree height, stand basal area, stand volume, and the main metrics of the LiDAR data and the correlations between eight quantitative factors of forest stands were considered, and the error-in-variable simultaneous equations approach was employed to fit the models. The results showed that the mean prediction errors (MPEs) of eight forest stand factors estimated by the simultaneous models were mostly within 5%, and only the MPE of the number of trees per hectare exceeded 5%. The mean percentage standard errors (MPSEs) of the estimates, including the mean diameter at the breast height (DBH), mean tree height, and mean dominant tree height, were within 15%; the MPSEs of the estimates of the stand basal area, volume, biomass, and carbon stock per hectare were within 25%; and only the MPSE of the estimated number of trees per hectare exceeded 30%. The coefficients of determination (R2) of the core prediction models for the volume, biomass, and carbon storage were all greater than 0.7. It can be concluded that estimating the main factors of forest stands based on the combination of LiDAR and field survey data is technically feasible, and the simultaneous models developed in this study for the estimation of the eight main stand factors of spruce–fir forests can meet the precision requirements of forest resource inventory, except for the number of trees, indicating that the models can be applied in practice.

Distance and T-square sampling for spatial measures of tree diversity

Distance sampling and its statistically improved variant, T-square sampling, are important sampling methods in plant ecology. They have often been applied in the context of plant density estimations and are comparatively easy to implement, since they intuitively follow the nearest-neighbour principle and thus do not require the layout of sample plots. Previous research studying distance sampling suggested that T-square sampling may also lead to an improved estimation of spatial tree diversity indices. We simulated distance and T-square sampling in six large fully mapped forest areas for seven tree diversity indices of which some competed for the same diversity aspect, i.e. tree location (dispersion), tree species and tree size diversity. Our results demonstrated that both distance and T-square sampling are indeed robust methods for sampling spatial measures of tree diversity. The sample size required for a sampling error of 10% does not exceed 20% of the total number of trees in a sampling area. T-square sampling has the ability to adapt to different spatial patterns of tree locations and this ability is key to the way the method controls estimation bias. The sample size required for species mingling and size differentiation clearly depends on the underlying spatial tree pattern in the sampling area. With most diversity indices, sample size reductions between 0.06% and 40% could be achieved by the application of T-square sampling compared to traditional distance sampling. All other conditions being equal, we could identify the uniform angle index, the species mingling index and the size differentiation index as those diversity indices achieving lower sampling error values than their competitors. For tree density estimations the Diggle and Byth estimators performed best. Based on our results, T-square sampling can be considered a robust sampling method for spatial tree diversity indices that is easy to apply in the field.

Modelling and Mapping of Aboveground Carbon of Oluwa Forest Reserve Using LandSat 8 TM and Forest Inventory Data

This study was conducted in Oluwa Forest Reserve to assess and predict its aboveground carbon sequestration potentials using LandSat Thematic Mapper data. The Oluwa Forest Reserve, Ondo State, Nigeria, is recognized for its rich biodiversity and extensive size. To estimate its forest aboveground biomass and carbon should be complex and costly endeavour requiring the expertise of various professionals and equipment. Consequently, this study explored the use of Geographic Information System (GIS) and Remote Sensing (RS) technology using LandSat bands to estimate spectral indices in fitting linear models to predict the aboveground carbon sequestration potentials of the tropical rainforest ecosystem of Oluwa Forest Reserve. The observed aboveground carbon from sample plots and the estimated spectral indices were used to model the spread of aboveground carbon of Oluwa Forest Reserve. Positive linear relationship exists between the observed and the spectral indices data estimated. Therefore, linear models were fitted and the best-fit was determined using statistical measures. The aboveground carbon average estimated from the sample plots and the predicted were 150.70 t/ha and 149.80 t/ha, respectively. The coefficient of determination 94% and Root Mean Square Error = 6.38E-16, respectively were obtained statistically. The chosen model predicts the aboveground carbon spread of Oluwa Forest Reserve adequately. The study revealed that spectral data, GIS and RS are critical for large forest aboveground carbon modelling and mapping for efficiency.

The Effect of Spatial Scale on the Prediction of Tropical Forest Attributes from Image Texture

The availability of high-resolution satellite imagery has boosted the modelling of tropical forest attributes based on texture metrics derived from grey-level co-occurrence matrices (GLCMs). This procedure has shown that GLCM metrics are good predictors of vegetation attributes. Nonetheless, the procedure is also sensitive to the scale of analysis (image resolution and plot size). This study aimed to analyse the effect of spatial scale on the modelling of forest attributes, and to provide some ecological insight into such effect. Nineteen 32 × 32 m sampling plots were used to quantify forest structure (basal area: BA; mean height: H; standard deviation of height, HSD; density, D; and aboveground biomass, AGB). The 19 plots were subdivided into four 16 × 16 m, one of which was subdivided into four 8 × 8 m plots. To match this design, 12 GLCM metrics were calculated from a GeoEye-1 image (pixel size ≤ 2 m) using a 5-, 9-, and 21-pixel window from the R, NIR, NDVI, and EVI bands. For each of the windows, we modelled the five structural variables as linear combinations of the 12 metrics through linear models. The modelling potential ranged from high (R2 = 0.70) to low (0.11). H was the best-predicted attribute; this occurred at the smallest scale, with increasing scales producing lower R2 values. The second best-predicted attribute was HSD, which peaked at the intermediate scale. D and AGB displayed a similar pattern. BA was the only attribute best predicted at the largest scale. Thus, in predicting tropical forest attributes from GLCM-derived texture metrics, the spatial scale to be used should reflect the spatial scale at which ecological processes occur. Therefore, understanding how ecological processes express themselves in a remotely sensed image becomes a critical task.

Developing kNN forest data imputation for Catalonia

The combined use of LiDAR (Light Detection And Ranging) scanning and field inventories can provide spatially continuous wall-to-wall information on forest characteristics. This information can be used in many ways in forest mapping, scenario analyses, and forest management planning. This study aimed to find the optimal way to obtain continuous forest data for Catalonia when using kNN imputation (kNN stands for “k nearest neighbors”). In this method, data are imputed to a certain location from k field-measured sample plots, which are the most similar to the location in terms of LiDAR metrics and topographic variables. Weighted multidimensional Euclidean distance was used as the similarity measure. The study tested two different methods to optimize the distance measure. The first method optimized, in the first step, the set of LiDAR and topographic variables used in the measure, as well as the transformations of these variables. The weights of the selected variables were optimized in the second step. The other method optimized the variable set as well as their transformations and weights in one single step. The two-step method that first finds the variables and their transformations and subsequently optimizes their weights resulted in the best imputation results. In the study area, the use of three to five nearest neighbors was recommended. Altitude and latitude turned out to be the most important variables when assessing the similarity of two locations of Catalan forests in the context of kNN data imputation. The optimal distance measure always included both LiDAR metrics and topographic variables. The study showed that the optimal similarity measure may be different for different regions. Therefore, it was suggested that kNN data imputation should always be started with the optimization of the measure that is used to select the k nearest neighbors.

Response of the root anatomical structure of Carex moorcroftii to habitat drought in the Western Sichuan Plateau of China

Main conclusionThe anatomical structures of Carex moorcroftii roots showing stronger plasticity during drought had a lower coefficient of variation in cell size in the same habitats, while those showing weaker plasticity had a higher coefficient of variation. The complementary relationship between these factors comprises the adaptation mechanism of the C. moorcroftii root to drought.To explore the effects of habitat drought on root anatomy of hygrophytic plants, this study focused on roots of C. moorcroftii. Five sample plots were set up along a soil moisture gradient in the Western Sichuan Plateau to collect experimental materials. Paraffin sectioning was used to obtain root anatomy, and one-way ANOVA, correlation analysis, linear regression analysis, and RDA ranking were applied to analyze the relationship between root anatomy and soil water content. The results showed that the root transverse section area, thickness of epidermal cells, exodermis and Casparian strips, and area of aerenchyma were significantly and positively correlated with soil moisture content (P < 0.01). The diameter of the vascular cylinder and the number and total area of vessels were significantly and negatively correlated with the soil moisture content (P < 0.01). The plasticity of the anatomical structures was strong for the diameter and area of the vascular cylinder and thickness of the Casparian strip and epidermis, while it was weak for vessel diameter and area. In addition, there was an asymmetrical relationship between the functional adaptation of root anatomical structure in different soil moisture and the variation degree of root anatomical structure in the same soil moisture. Therefore, the roots of C. moorcroftii can shorten the water transport distance from the epidermis to the vascular cylinder, increase the area of the vascular cylinder and the number of vessels, and establish a complementary relationship between the functional adaptation of root anatomical structure in different habitats and the variation degree of root anatomical structure in the same habitat to adapt to habitat drought. This study provides a scientific basis for understanding the response of plateau wetland plants to habitat changes and their ecological adaptation strategies. More scientific experimental methods should be adopted to further study the mutual coordination mechanisms of different anatomical structures during root adaptation to habitat drought for hygrophytic plants.

Evaluation of chemical attributes of soils: definition of management zones in silvipastoral system

Soil chemical analysis is a fundamental to achieve optimal cultivation conditions in a productive silvopastoral system, essential for the planning and management of this activity. The objective of this research is to map the spatial distribution of nutrients in the soil in a 5-year-old silvopastoral system composed of brachiaria-brizantha and jacarandá-da-bahia, and to analyze the effect of tree density and canopy on nutrient levels. The study was conducted at the experimental area of the Capixaba Institute for Research, Technical Assistance, and Rural Extension (INCAPER) in the district of Pacotuba, ES. The delimitation of the work area was done using geographic information systems, to create a regular grid of points to enable systematic soil sampling. A total of 75 disturbed soil samples were collected at depths of 0-0.10 m to obtain quantitative soil nutrient data. High-quality images obtained using a remotely piloted aircraft (RPA) allowed for the overlay of soil nutrient data, pasture data, forest inventory data, and sample plot data to assist in comparative analyses. This enabled identification of the areas with lower canopy development, particularly in the northeast portion. By cross-referencing the spatialized soil nutrient data with the data obtained through RPA, it was possible to correlate them with the lower or higher development of the system. The use of RPA, together with a georeferenced database, proved to be an innovative and powerful tool to support decision-making, allowing for the monitoring of system evolution and serving as a basis for evaluating the relationship between chemical elements and tree canopies. Keywords: geostatistics, precision agriculture, soil nutrients.

Soil-Microbial CNP Content and Ecological Stoichiometry Characteristics of Typical Broad-Leaved Tree Communities in Fanjing Mountain in Spring

This study aims to investigate the impact of diverse forest stand types and soil depths on soil ecological stoichiometry characteristics, shedding light on nutrient limitations and cycling patterns within the mid-subtropical forest ecosystem in southwest China during spring. The research focused on four representative forest stands situated in Fanjing Mountain: Castanopsis fargesii (C. fargesii), Cyclobalanopsis multiervis (C. multiervis), Cyclobalanopsis argyrotricha (C. argyrotricha), and Rhododendron argyrophyllum Franch (R. argyrophyllum). Sample plots were established in these forest types, and soil samples were collected from the 0–20 cm and 20–40 cm soil layers in March, spring of 2023. Various soil parameters, including pH, soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), soil microbial biomass carbon (MBC), soil microbial nitrogen (MBN), and soil microbial phosphorus (MBP) were measured, and their stoichiometric ratios were calculated. The findings of the study were as follows: (1) In the 0–20 cm soil layer, C. argyrotricha exhibited the highest soil organic carbon, followed by C. fargesii, C. multiervis, and R. argyrophyllum with the lowest content. No significant differences in soil organic carbon were observed among the four forests in the 20–40 cm soil layer. Additionally, C. argyrotricha displayed a significantly higher soil C:N ratio compared to other forest types in different soil layers. In the typical broad-leaved forest area of Fanjing Mountain, the TP was classified as deficient. (2) In the 0–20 cm soil layer, the MBC of C. fargesii surpassed C. multiervis, C. argyrotricha, and R. argyrophyllum by 26.59%, 42.92%, and 24.67%, respectively. There were no significant differences in soil MBC:MBP ratio and MBN:MBP ratio, regardless of forest species and soil depths. The low availability of soil nitrogen in different forest stand types in Fanjing Mountain strongly limits soil microorganism biomass. (3) The correlation between SOC, TN, TP, and their stoichiometric ratios varied across different soil layers. Therefore, in managing the Fanjing Mountain forest area, attention should be paid to supplementing N and P in the soil.

Comparative Carbon Stock Potential of Indigenous Agroforestry Systems in Silte Wereda, Southern Ethiopia

Agroforestry system (AFS) is described as one of the promising mitigation options for climate change through its high carbon sequestration capacity. This study was conducted in Silite District; Southern Ethiopia to assess the unaccounted carbon stock potential of selected traditional agroforestry systems. The study assessed the carbon stock potential of AFS biomass and soil carbon pools. Biomass and soil samples were taken from temporary plots laid for this study, 20 × 20 m for home garden, 50 × 100 m for parkland, and 10 × 10 m for woodlot AFS. Height (H) and diameter at breast height (DBH) were taken from the sample plots to estimate biomass carbon. Litter, herb, and grass samples were collected from 1 m2 quadrant within the main plot. The findings show that the total biomass carbon ranged from (1.28-7 Mg ha -1) though there was no significant difference among the systems and higher biomass carbon was attributed by parkland AFS while the lowest was woodlot. A significantly higher amount of SOC was recorded in home garden AFS along the two depths (82.5 Mg ha -1) than the other two systems and the lowest was attributed to parkland (41.7 Mg ha -1). Therefore, this traditional AFS should be supported for their contributions in climate change mitigation schemes as they can sequester a reasonable amount of carbon

Vegetation communities and soil properties along the restoration process of the Jinqianghe mine site in the Qilian Mountains, China.

The study explores the impact of mine grassland restoration on plant communities and soil properties in alpine grasslands, a subject of significant interest due to the observed relationship between grassland changes, plant communities, and soil properties. While prior research has mainly focused on the consequences of grassland degradation on plant diversity and soil characteristics, the specific effects of varying restoration degrees in alpine mining grasslands at the regional scale remain poorly understood. To address this knowledge gap, we established 15 sampling plots (0.5m×0.5m) across five different restoration degrees within alpine mining grasslands in the Qilian Mountains, China. Our objective was to assess the variations in plant diversity and soil properties along these restoration gradients. We conducted comprehensive analyses, encompassing soil properties [soil water content (SWC), available nitrogen (AN), total phosphorus (TP), nitrate nitrogen (NO3-N), ammonium nitrogen (NH4-N), total nitrogen (TN), available phosphorus (AP), soil organic carbon (SOC), nitrate nitrogen, soil pH, and electrical conductivity (EC)], plant characteristics (height, density, frequency, coverage, and aboveground biomass), and plant diversity indices (Simpson, Shannon-Wiener, Margalef, Dominance, and Evenness indexes). Our findings included the identification and collection of 18 plant species from 11 families and 16 genera across the five restoration degrees: Very Low Restoration Degree (VLRD), Low Restoration Degree (LRD), Moderate Restoration Degree (MRD), High Restoration Degree (HRD), and Natural Grassland (NGL). Notably, species like Carex duriuscula, Cyperus rotundus, and Polygonum viviparum showed signs of recovery. Principal component analysis and Pearson correlation analysis revealed that soil pH, SWC, SOC, NO3-N, and AN were the primary environmental factors influencing plant communities. Specifically, soil pH and EC decreased as restoration levels increased, while SWC, AN, TP, NH4-N, TN, AP, SOC, and NO3-N exhibited a gradual increase with greater restoration efforts. Furthermore, the HRD plant community demonstrated similarities to the NGL, indicating the most effective natural recovery. In conclusion, our study provides valuable insights into the responses of plant community characteristics, plant diversity, and soil properties across varying restoration degrees to environmental factors. It also elucidates the characteristics of plant communities along recovery gradients in alpine grasslands.

The distribution of tree biomass carbon within the Pacific Coastal Temperate Rainforest, a disproportionally carbon dense forest

Spatially explicit global estimates of forest carbon storage are typically coarsely scaled. While useful, these estimates do not account for the variability and distribution of carbon at management scales. We asked how climate, topography, and disturbance regimes interact across and within geopolitical boundaries to influence tree biomass carbon, using the perhumid region of the Pacific Coastal Temperate Rainforest, an infrequently disturbed carbon dense landscape, as a test case. We leveraged permanent sample plots in southeast Alaska and coastal British Columbia and used multiple quantile regression forests and generalized linear models to estimate tree biomass carbon stocks and the effects of topography, climate, and disturbance regimes. We estimate tree biomass carbon stocks are either 211 (SD = 163) Mg C ha−1 or 218 (SD = 169) Mg C ha−1. Natural disturbance regimes had no correlation with tree biomass but logging decreased tree biomass carbon and the effect diminished with increasing time since logging. Despite accounting for 0.3% of global forest area, this forest stores between 0.63% and 1.07% of global aboveground forest carbon as aboveground live tree biomass. The disparate impact of logging and natural disturbance regimes on tree biomass carbon suggests a mismatch between current forest management and disturbance history.

Eco-Custodians: A Contemporary Analysis of Tree Species Composition and Regeneration Health in Ramsagar and Singra National Parks, Bangladesh

This study systematically assessed tree species composition and regeneration status within two protected national parks in Bangladesh: Singra National Park and Ramsagar National Park. We employed a stratified random quadrat method across 100 sample plots (50 quadrats from each national park of 10 m x 10 m). Our findings revealed 45 tree species belonging to 25 families. Myrtaceae and Dipterocarpaceae were the most dominant family, having a maximum number of species. We identified 16 exotic species of 10 families and 29 native species of 19 families. Phytosociological parameters like density, frequency, abundance, and importance value index (IVI) were determined. Shorea robusta with an IVI of 47.38 dominated Singra National Park, while Eucalyptus camaldulensis with an IVI of 22.67 was most important in Ramsagar National Park. Regeneration status was good for 15.56% of species, fair for 24.44%, poor for 28.89%, and absent for 17.78%. This study implied the need for collaborative conservation strategies involving policymakers, conservationists, and local communities to ensure the parks’ sustainable eco-custodianship. Keywords: biodiversity, conservation, forest, national park, regeneration

Влияние климата на возрастную структуру древостоев в экотоне северной границы леса на полуострове Ямал

The spatial and temporal dynamics of forest-tundra communities was studied on the Yamal Peninsula within the northern treeline ecotone. The paper presents the results of surveying 12 sample plots and more than 1000 trees of Siberian larch and Siberian spruce. Based on cross-dated tree-ring chronologies, the age structure and dynamics of stands for the last 150 years were obtained. Dynamic processes in stands of the northern treeline ecotone are most pronounced in the sparse forests, where there has been a sharp increase in stand density since the middle of the 20th century. Correlation analysis of tree establishment time with instrumental observations of climatic variables showed that the establishment of new larch and spruce individuals in different stand types is significantly influenced by the amount of precipitation in June-August.

Identification of geographic, climatic, and soil factors dominating Stevia rebaudiana yield and quality

Raising crop yields and quality through plant ecological research has become an important approach to sustainably improving crop products. Stevia rebaudiana is widely introduced and cultivated around the world because of the continuous increase in demand for steviol glycosides. When selecting regions or plots suitable for high-yield and high-quality cultivation, it is necessary to understand the main ecological factors that contribute to the formation of S. rebaudiana yield and quality. In this study, sampling plots from different geographic and climatic regions in Xinjiang and Jiangsu were selected for S. rebaudiana cultivation. The geographic, climatic and soil ecological factors were analyzed based on a PLSR (partial least squares regression) model to discover the potentially influential factors in the yield and quality formation of the crop. The results suggest that not all geographic and climatic ecological factors contributed to yield formation; however, these factors, such as latitude, altitude and annual sunshine duration, had high or moderate influences on the concentration of each steviol glycoside and total steviol glycoside. Soil organic matter (OM), total potassium (TK), available potassium (AK), zinc (Zn), and catalase (CAT) had high or moderate effects on the concentrations of each steviol glycoside and total steviol glycoside. However, the other soil factors showed variability in the extent to which they drove the steviol glycosides. The study contributes to a more profound understanding of the ecological driving mechanisms of S. rebaudiana yield and quality formation. The yield or steviol glycoside concentrations of sweetleaf may be improved by modifying certain key ecological factors.

Recovery status of coastal vegetation in Mozambique: Insights from floristic composition and soil organic carbon analysis

The growing demand for land for slash-and-burn cultivation, driven by human population growth, has led to the creation of scattered cultivated fields and a mosaic of fallow areas in various stages of regeneration. While tropical forests have demonstrated the ability to rebound regarding tree species diversity and forest structure following disturbances, the extent of recovery in coastal vegetation in southern Africa remains uncertain and poorly understood. To address this knowledge gap, our study collected biophysical data from smallholder fields, fallow land, and forests to evaluate the structure and functioning of coastal vegetation in Gaza province, southern Mozambique. We established 90 sample plots of 0.1 ha each at different stages of plant succession using opportunistic sampling approach. Within these plots, we recorded the diameter at breast height, total height, and species name of all trees and shrubs. Litter and soil samples were collected in 1 m² quadrats embedded within the main plot. Our findings indicate a high capacity for regeneration following the disturbance caused by slash-and-burn cultivation. Species composition varied significantly across different stages of succession, with 52 species identified in forests (FL), 34 in old fallow (OF), 29 in middle fallow (MF), 28 in initial fallow (IF), and 15 in cropland (CL). Richness and diversity increased over time after the practice of slash-and-burn cultivation, with a noticeable enhancement from the initial fallow stage. We observed a gradual recovery of soil carbon stocks in young fallow areas, while litter biomass decreased throughout the recovery phase of the fallow lands. Our study highlights that areas abandoned after agriculture can regenerate in terms of structure, floristic composition, and tree carbon content.

Polyaspartic Acid Urea Increased Maize Yield by Enhancing Leaf N Turnover Efficiency and Soil Microbial Diversity

The release rates of different nitrogen (N) fertilizers and their matching with plant demand determine crop yields. A field experiment was conducted to investigate the effect of using no fertilizer (N0), regular urea applied at rates of 180 kg ha−1 (N180) and 240 kg ha−1 (N240), controlled-release urea applied at a rate of 180 kg ha−1 (H180), and polyaspartic acid urea (PASP) on maize seed yield, soil microbial community diversity, and leaf N-converting enzymes. XianYu 688 was selected as the test maize variety. All cobs in the sample plots were collected per unit area to estimate maize yield. The enzyme-linked immunosorbent assay (ELISA) was used to determine leaf N-converting enzyme activities. Soil DNA was extracted using the Power Max Soil DNA Isolation Kit and subsequently sequenced using the Illumina HiSeq platform (PE 2500) to determine the microbial diversity and communities. The results showed that the highest seed yields were obtained under N240 and PASP180 treatments. The N-partial factor productivity of the PASP180 fertilizer was significantly higher than that of the other treatments. PASP treatment significantly increased maize seed yield due to the potential of storing more N in the ear leaves. Additionally, partial N productivity showed a significant positive correlation with the soil microbial Shannon, Chao1, and ACE indices, indicating that increased soil microbial diversity promoted N efficiency in maize. Further analysis revealed that PASP treatment increased seed yield by promoting leaf N-converting enzyme activity and soil microbial diversity. The results revealed that nitrate reductase (NR), glutamate synthase (GOGAT), and glutaminase (GLNS) enzyme activities in maize leaves were higher under the PASP treatment than under other fertilizer treatments. The PASP treatment significantly enhanced soil microbial diversity at different maize stages. Our study revealed the effects of using different N fertilizers on seed yield by examining their impact on soil microbial diversity and leaf N-converting enzyme activity. This study provides essential insights into maize production and soil fertility maintenance in the North China Plain.