Sample Plots Research Articles

Short Communication: Estimation of aboveground biomass and carbon sequestration in KGPAA Mangkunagoro I Grand Forest Park, Central Java, Indonesia

Abstract. Indriyani S, Sari SP, Negari SIT, Prambudi SA, Nur AAI, Kusumaningrum L, Indrawan M, Sunarto, Sugiyarto, Budiharta S, Setyawan AD. 2024. Short Communication: Estimation of aboveground biomass and carbon sequestration in KGPAA Mangkunagoro I Grand Forest Park, Central Java, Indonesia. Biodiversitas 25: 2257-2263. Forests play an important role in climate change mitigation by absorbing carbon dioxide in the atmosphere and store it in the form of biomass. Valuation of carbon storage potential of a forest will provide insight on the importance of preserving it. This study aims to assess plant diversity and estimate aboveground biomass, carbon stocks, carbon dioxide sequestration potential, and the economic value of ecosystem services from carbon sequestration in KGPAA Mangkunagoro I Grand Forest Park, Central Java, Indonesia. We used a non-destructive method (i.e. allometric equation) in estimating aboveground biomass and carbon stock. We established 50 sampling plots to identify and measure trees (DBH >20 cm) and poles (10<DBH<20 cm). The results showed that there were 30 species from 20 families found in the observation plots. The potential aboveground biomass at the tree and pole level was 297.92 tons/ha and 57.22 tons/ha, respectively, while the total carbon stock for tree and pole was 140.02 tons/ha and 26.89 tons/ha, respectively. This was equivalent to carbon dioxide sequestration of 513.88 tons/ha and 98.70 tons/ha for trees and poles. The economic value of environmental services from carbon stock in the whole area of KGPAA Mangkunagoro I Grand Forest Park was estimated at IDR 21,289,040,253. The results of this research show that apart from playing a role in mitigating climate change by absorbing carbon, KGPAA Mangkunagoro I Grand Forest Park also has valuable economic value.

Comparison of Deep and Machine Learning Approaches for Quebec Tree Species Classification Using a Combination of Multispectral and LiDAR Data

Accessing tree species information is required for making appropriate decisions in forest management. 3D photo-interpretation using high spatial resolution aerial imagery is used to provide information on tree species in the province of Quebec. However, the shortage of qualified interpreters and the increasing costs of 3D photo-interpretation have affected the production of the forest inventory. In this study, we employed deep and machine learning models to classify nine tree species (i.e., paper birch, yellow birch, red maple, poplar, black spruce, white spruce, tamarack, jack pine, and balsam fir). We used a combination of spectral and vertical structural information extracted from 30-cm aerial imagery and airborne light detection and ranging (LiDAR) data in a 9 , 100 km 2 forested area in Quebec, Canada. The results indicated that Dense Convolution Network (DenseNet) achieved the best overall accuracy of 78%, outperforming machine learning methods by 5%. In addition, the models’ performance was independently assessed using permanent and temporary ground sample plots, acknowledging the superiority of DenseNet in terms of overall accuracy (73%) in predicting the dominant species. Our results suggest that the combination of aerial imagery and airborne LiDAR data, using deep learning approaches, can be applied to accurately map tree species.

Крупные древесные остатки в коренных и вторичных среднетаежных ельниках

Restoration of the pool of coarse woody debris after disturbances is one of the mechanisms for maintaining the stability of forest biogeocenoses. The studies of coarse woody debris have been carried out in the “Vepssky Forest” Reserve in the Leningrad Region on 8 sample plots established in primary forests (4 sample plots) and in secondary forests of the 1st generation after logging in 1973–1974 (4 sample plots), where the composition and structure of the stand, as well as the site conditions have been identical to those in primary forests. The coarse woody debris has been inventoried on transects. The stocks of coarse woody debris in primary stands have ranged from 104 to 233 m3 ha–1. Windfall and leaning trees have prevailed. The proportion of deadwood in both primary and secondary forests has been low. Clear cutting has significantly changed not only the stock of coarse woody debris, but also its distribution by decay classes and substrate categories. The stocks of coarse woody debris in secondary forests have ranged from 8 to 40 m3 ha–1, and have been mainly represented by stumps. The coarse woody debris of the 4th and 5th decay classes has almost been absent in primary forests, while in secondary forests the proportion of highly decomposed wood remaining after cutting has been about 50 %. The ratio of coarse woody debris and growing stocks has been on average 1:1 and 1:5 in the biogeocenoses of primary and secondary forests, respectively. The annual carbon balance of coarse woody debris (the difference in fluxes due to the loss of growing forest and the decay of coarse woody debris) has ranged from 0,40 to 2,80 t C ha–1 year–1, averaging 1,75 and 0,63 t C ha–1 year–1 in secondary and primary forests, respectively. The positive annual carbon balance in the coarse woody debris is due to the predominance of the rate of the loss of growing forest over the rate of the decay of coarse woody debris in primary forests as a result of wind disturbances and as a result of self-thinning of the stand in secondary forests.

Multi-objective early-stage design optimization of multifamily residential projects

The aim of this study is to find a building form and plan layout that can be used in the early stages of architectural design, where criteria such as daylight, view, sun-hour, sales area, and cost are optimized according to the different expectations of different housing type users. This study proposes a multi-objective early-stage design optimization for a real estate development project based on the NSGA2 genetic algorithm, considering weighted user preferences for different housing types. The framework is implemented using the platforms Rhino and Grasshopper; Wallacei is used for NSGA2, and Viktor.ai is used to deploy the app. Tested on six sample plots, the model was able to find architecturally optimized results that respond to different user expectations. While the model successfully demonstrated responsiveness to parameters, its focus on Pareto-optimal solutions limited the diversity of unit mixes generated. The model has been tested by professionals on a sample plot and is found to be important for architects and investors to generate ideas at an early stage of architectural design.

Biodiversity and Soil Properties of Saranda Sal Forest: Implications for Conservation and Management

Aims: This study is significant as it elucidates the relationship between floristic composition and soil nutrient availability of India's Saranda Sal (Shorea robusta Gaertn.) forest. Study Design: Vegetation sampling was done following a grid of 5 km x 5 km, and soil sampling was done in each sampling plot using a Z-pattern. Place and Duration of Study: The study was conducted in Saranda Forest of West Singhbhum district, Jharkhand, Eastern India, during 2021-2022 Methodology: Sampling has been designed as grid methods (5 km X 5 km) following a forest survey of India, and vegetation sampling was done by quadrat method. The soil samples were collected from three depths (i.e., surface: 0–30 cm, sub-surface: 30–60 cm and inner: 60-90 cm from each selected site. ANOVAs were used to compare the chemical properties of soil samples from various forests. Pearson correlation analyses to examine the effects of climatic variables on chemical properties of the soil of selected Sal forest as well as their relation with plant diversity. Results: A comprehensive analysis of 5432 vascular plants from 65 species and 34 families was conducted across the 17 sites. Fabaceae is the most dominant family with 07 species. The study also examined soil chemical parameters and micronutrients in different sites and established their relations with vegetation dynamics. Notably, tree density showed a significant positive correlation with soil pH (r=0.59, p<0.05), but a significant negative correlation with Shannon diversity (H') (r=-0.53, p<0.05). Similarly, there was a significant positive correlation of Organic carbon with copper (r=0.59, p<0.05) and iron (r=0.61, p<0.00); however, there was a significant negative correlation with Available Phosphorus (r=-0.52, p<0.05). Conclusion: Research findings underscore the importance of soil nutrients in promoting forest health and growth. Importantly, they can guide the formulation of practical and effective soil-forest management strategies for S. robusta and its associated forests, directly benefiting the forestry and environmental science community.

Модель формирования березово-еловых насаждений в условиях южной тайги Русской равнины

The results of 30 years or research on the formation and growth of birch forests from the subcanopy spruce population in the conditions of the Russian Plain southern taiga are presented. The studies have been carried out on 19 permanent sample plots (PSPs). They were laid in regionally representative sorrel-blueberry birch forests aged 15–115 years with high density and productivity. The birch plantations on the PSPs belong to the same natural age series. The majority of spruce trees appeared under the canopy of birch forests up to 40 years old. As the birch trees aged, the age structure of spruce trees changed from even-aged to conditionally even-aged. Based on the study of the dynamics of the number of trees, the vertical structure of stands, the growing stock and the total stem wood stock, the age stages of development of the spruce population in the birch forests up to 120 years old have been determined. They are regeneration, young growth, polewood and maturation. The end of the maturation stage occurs at the birch tree age of 120 years. At the same time, the average spruce tree age in the first and second layers is 90 years. The duration of the stages varies from 10 years (the young growth stage) to 40 years (the regeneration and maturation stages). To determine the time of onset and end of other age stages of the subcanopy spruce population, the studies in birch forests older than 120 years are needed. The work performed has made it possible to propose a model of the joint formation of birch forests and the subcanopy spruce population, in which the main taxation characteristics are given for all elements of the stand: age, number of trees, the sum of the cross-section of the stems, the stock of the growing and dead wood, as well as total productivity. According to the model, by the age of a birch of 120 years, the total productivity of modal plantations will be about 950 m3/ha and the growing stock of the stand will be about 490 m3/ha. The share of spruce wood will be 28 % or about 150 m3/ha. After the birch forest decay, a low-density spruce forest of the 3rd quality class can form in its place.

Characterizing Canopy Structure Variability in Amazonian Secondary Successions with Full-Waveform Airborne LiDAR

Full-waveform LiDAR (FWF) offers a promising advantage over other technologies to represent the vertical canopy structure of secondary successions in the Amazon region, as the waveform encapsulates the properties of all elements intercepting the emitted beam. In this study, we investigated modifications in the vertical structure of the Amazonian secondary successions across the vegetation gradient from early to advanced stages of vegetation regrowth. The analysis was performed over two distinct climatic regions (Drier and Wetter), designated using the Maximum Cumulative Water Deficit (MCWD). The study area was covered by 309 sample plots distributed along 25 LiDAR transects. The plots were grouped into three successional stages (early—SS1; intermediate—SS2; advanced—SS3). Mature Forest (MF) was used as a reference of comparison. A total of 14 FWF LiDAR metrics from four categories of analysis (Height, Peaks, Understory and Gaussian Decomposition) were extracted using the Waveform LiDAR for Forestry eXtraction (WoLFeX) software (v1.1.1). In addition to examining the variation in these metrics across different successional stages, we calculated their Relative Recovery (RR) with vegetation regrowth, and evaluated their ability to discriminate successional stages using Random Forest (RF). The results showed significant differences in FWF metrics across the successional stages, and within and between sample plots and regions. The Drier region generally exhibited more pronounced differences between successional stages and lower FWF metric values compared to the Wetter region, mainly in the category of height, peaks, and Gaussian decomposition. Furthermore, the Drier region displayed a lower relative recovery of metrics in the early years of succession, compared to the areas of MF, eventually reaching rates akin to those of the Wetter region as succession progressed. Canopy height metrics such as Waveform distance (WD), and Gaussian Decomposition metrics such as Bottom of canopy (BC), Bottom of canopy distance (BCD) and Canopy distance (CD), related to the height of the lower forest stratum, were the most important attributes in discriminating successional stages in both analyzed regions. However, the Drier region exhibited superior discrimination between successional stages, achieving a weighted F1-score of 0.80 compared to 0.73 in the Wetter region. When comparing the metrics from SS in different stages to MF, our findings underscore that secondary forests achieve substantial relative recovery of FWF metrics within the initial 10 years after land abandonment. Regions with potentially slower relative recovery (e.g., Drier regions) may require longer-term planning to ensure success in providing full potential ecosystem services in the Amazon.

An Investigation of the Magnitude of the Role of Different Plant Species in Grassland Communities on Species Diversity, China.

In this study, we selected four grassland plots in Altai forest area and used the field survey method of "two-valued occurrence" to obtain the occurrence data of each plant species in the plots so as to calculate the species diversity index value of the community as a whole and the species diversity index value of each plant species not present in the community and to make use of the difference between these two diversity indices to determine the role of each plant species in the overall species diversity of the community. The difference between these two diversity indices was used to investigate the role of each plant species in the overall species diversity of the community. The results show the following: (1) In the grassland of the Altai forest area in Xinjiang, Asteraceae, Poaceae, Fabaceae, Polygonaceae, and Rosaceae are the dominant families, among which the genera Puccinellia Parl, Taraxacum, Pharbitis, Lactuca, Geranium, and Alchemilla are the dominant genera. (2) The plant species with the greatest contribution to species diversity in the four grassland samples was not the first dominant species of the community, but rather the plant species whose dominance was in the second to sixth position. (3) The first dominant species was overwhelmingly dominant in the four sample plots, and it served to increase the overall diversity of the community. (4) The overall trend in the size of the role of species in diversity is unimodal, i.e., logarithmically increasing to a maximum as species dominance decreases and then exponentially or linearly decreasing and eventually converging to zero. The synthesis showed that it was not the first dominant species that played the largest role in species diversity in the different grassland communities and that the overwhelmingly dominant species reduced the species diversity of the community.

The effect of pre- and post-processing techniques on tree detection in young forest stands from images of snow cover using YOLO neural networks

A neural network model for individual tree detection was developed based on the YOLOv4 architecture, which underwent additional preprocessing and postprocessing steps. The preprocessing step involved expanding the dataset by randomly cutting fragments from images, calculating anchor frame sizes using the K-means clustering algorithm, and discarding anchor frames that were too small a priori. The existing post-processing block of the YOLO architecture was modified by giving more weight to false positives in the error function and using the non-maximum suppression algorithm. Baseline neural networks from the YOLOv4 and YOLOv5 architectures, each in two versions (pre-trained and not pre-trained on the MS COCO dataset), were used for comparison without any additional modifications. In the overgrown experimental field, multi-season aerial copter surveys and ground counts were conducted on several sample plots to gather data. Comparison of multi-season aerial photographs with ground-count data showed that the best images in terms of the percentage of visually identifiable trees were those taken during the snowy season and when there was no foliage. Using these images and some additional images, we manually created a dataset on which we trained and tested neural network models. The model we developed showed significantly better results (2 to 10 times better) on the mAP 0.5 metric compared to the alternatives we considered.

Integration of field measurements with unmanned aerial vehicle to predict forest inventory metrics at tree and stand scales in natural pure Crimean pine forests

ABSTRACT Inventorying forest ecosystems is an essential part of forest management planning. However, it is quite costly and time-consuming, particularly for larger areas. Recently, significant developments have been made in unmanned aerial vehicle (UAV) technology to improve the cost and time efficiency in forest inventory. Therefore, UAV images have become one of the inventory tools that produces data with high spatial resolution in determining forest resources. This study aims to investigate the contribution of UAV data to forest inventory in a case study area with a total of 30 sample plots located in pure and natural Crimean pine (Pinus nigra J.F. Arnold ssp. pallasiana (Lamb.) Holmboe) stands in the Black Sea backward region of Türkiye. Total tree height (h) and stem volume (v) were recorded at individual tree level (n = 367), and the number of trees (N), mean height (hmean), top height (htop), stand basal area (BA) and stand volume (V) were calculated at sample plot level (n = 30) from both the field and UAV-based data. Pearson’s correlation coefficients (r) for h and v were 0.96 and 0.72, respectively, the highest correlation at the sample plot level was observed for the hmean - htop (r = 0.96), while the lowest correlation was found for BA (r = 0.54). The suitability of the observation and prediction values was assessed using a t-test at both individual tree and sample plot levels. According to the t-test results, the observation and prediction values for h, v, hmean, htop, BA and V metrics were found to be compatible (p > 0.05), but not for N (p < 0.05). Overall results indicated that UAV technology has a potential to be used in forest inventory and can contribute to the determination of individual tree and stand metrics. Thereby, it saves cost and time in forest inventory studies and helps monitoring the dynamic structure of the forest ecosystem with an effective approach in forest inventory.

Square-grid sampling to address the vegetation patterns of declined Mediterranean forest ecosystems

Palo Laziale Wood is a relatively small biotope (129 ha) situated along the coast of the Metropolitan Area of Rome, Lazio region, Italy. Despite being one of the region's remaining patches of an ancient oak floodplain broad-leaved forest, it conserves numerous priority habitats and species of high conservation interest. The vegetation consists mainly of Turkey oak stands with small temporary ponds and flooded meadows. The forest underwent a dieback in 2003, triggered by a significantly hot and dry summer. In 2018, an ecological restoration project (LIFE PRIMED LIFE17 NAT/GR/000511) was initiated to restore Palo Laziale’s ecosystems. This paper presents the methodological approach employed to assess the vegetation ecology of a degraded forest ecosystem. Such an investigation was a key component of the Adaptative Restoration Plan of the Project. It provided the baseline necessary for designing and calibrating the planned direct conservation actions on the target habitat types (91M0: Pannonian-Balkanic turkey oak-sessile oak forests, 3170*: Mediterranean temporary ponds, 5230*: Arborescent matorral with Laurus nobilis, etc.) and establishing reference values to enable long-term monitoring. Plant taxa comparison from 1 ha square-grid sampling and multivariate analyses were carried out to group species and identify environmental and Ellenberg-based drivers. Six ecologically distinct units were found, eventually confirming the distinctive ecological heterogeneity of Mediterranean ecosystems. Amongst these, the hygrophilous vegetation has resulted to be the one mainly affected by the dieback outbreak. Due to the high heterogeneity, introduced by the massive tree mortality, the method of regular 1 ha squares turned out to be a reliable alternative to random vegetation sampling plots (e.g., phytosociological relevés) to disentangle ecological patterns of fragmented and disturbed habitats.

Pattern of plant biomass and carbon stock along different elevational forests in eastern Nepal

The primary aim of this investigation was to determine the biomass and carbon stock distribution pattern among different forest stands of diverse elevations in the Morang district of East Nepal. It is noteworthy to estimate carbon stock and biomass of relatively least underexplored forests in east Nepal. The data for estimating the biomass and carbon stocks of the five different forest sites, viz. Bhaunne, Raja-Rani, Murchungi, Adheri, and Sagma located between 100-1300m above the mean sea level, were acquired through the measurement of inventory plots selected randomly. Altogether, 50 sample plots were established within five forest stands located on different elevational zone; within each forest site, 10 sample plots of 20m × 20m size, were laid out for the measurement of trees. In the case of shrubs and herbs, nested plots of 5m × 5m and 1m ×1m, respectively were established. Calculation of the biomass of trees and shrubs was facilitated through the application of an allometric equation, while the biomass of herbs was determined by the harvest method. The carbon concentration in the plant materials was estimated using ash content method. The comprehensive analysis of the stand biomass in the Bhaunne, Raja-Rani, Murchungi, Adheri, and Sagma forest sites were: 815.86 Mg ha-1, 414.19 Mg ha-1, 606.81 Mg ha-1, 519.20 Mg ha-1, and 299.96 Mg ha-1, respectively, with minimum at the Sagma site (high-altitude forest) and maximum at the Bhaunne site (low-altitude forest). As per the variation in stand biomass, the carbon stocks in the forest sites also showed the same trend, but the values ranged from 140.19 Mg C ha-1 to 333.63 Mg C ha-1, with the minimum in the Sagma site and the maximum in the Bhaunne site. The application of the Friedman Test revealed statistically significant variation in the tree biomass between the Murchungi and Sagma sites and also in the shrub biomass between the Adheri and Sagma sites. Similarly, noteworthy variations were observed in the herb biomass of the Bhaunne, Raja-Rani, Murchungi, and Adheri sites as compared to that of the Sagma site. The present study contributes to the understanding of forest ecosystems in context to carbon management.

New Zealand's planted forests–Carbon stocks and yield in fast growing exotic tree plantations of the Southern Hemisphere

Every year New Zealand measures 1/5th of the permanent sample plots of the National Planted Forest Inventory administered by the Ministry for the Environment (MFE). This means that all plots are measured once during a five-year cycle to provide the full inventory, while each measurement year in its own right provides a random nationally-representative sample of New Zealand's plantation forests.Land-use change and planted forest area are highly dynamic in New Zealand compared to forests in many northern hemisphere countries and every year new plots are added to the inventory due to new afforestation areas. Over time, old plot data representing stands that have been recently harvested will be replaced with measurements of the new forests growing on these sites or will drop out altogether due to improvements in mapping or deforestation. This means that estimates from a single year's plot measurements can give different results from the previous year due to the changes that the plantation forest estate experiences from year to year.In this paper we analyse results for the latest measurement cycle (Panels 2016 - 2020) and identify any issues or trends in carbon stocks. We use all panel data plus data from previous periodic forest inventories to generate a fully representative yield table for all plantation forests in the sub-categories of Kyoto Protocol-compliant afforestation and other planted forests (“non-Kyoto”). Furthermore, we use an imputation approach to provide a statistically accurate representation of stock changes over time. The yield tables generated are used in MFE's Land Use Carbon Analysis System (LUCAS) to generate estimates of carbon stocks and stock changes for international reporting.The estimated mean carbon sequestration rate over the typical rotation for New Zealand's dominant plantation species is higher than the default range provided by the IPCC (Intergovernmental Panel on Climate Change). Predicted carbon sequestration in non-Kyoto planted forests (10.5 tC ha−1 yr−1) exceeds the estimated carbon sequestration in Kyoto-compliant planted forests (9.8 tC ha−1 yr−1) with both at the upper range of estimates for temperate plantation forests (IPCC). Current mean carbon stocks are significantly larger in Kyoto-compliant plantation forests (169 tC ha−1) than in non-Kyoto planted forests (108 tC ha−1) across New Zealand, largely because they are on average five years older. Estimated Mean annual increment, m3 ha−1 yr−1 expressed as a comparable index (“300Index”) indicates that Kyoto-compliant forest land has slightly higher productivity than non-Kyoto planted forests, but because the latter are composed of younger stands, greater carbon sequestration rates but lower current stocks are estimated. Plots with slower growing tree species such as Douglas-fir, cypresses and coastal redwood are now an increasing part of the Kyoto-compliant estate, reducing the overall sequestration rates for this forest type.

Population structure and regeneration of Tsuga dumosa and Abies spectabilis across altitudinal gradient in Rasuwa district, central Nepal

In Nepal, Tsuga dumosa thrives well between 2,100-3,600 m altitudes above the mean sea level, mostly in the temperate region while Abies spectabilis occurs between 3,000-4,200 m altitude above the mean sea level in the sub-alpine region. Research on mature T. dumosa-A. spectabilis forests in Nepal is limited. This study aimed to analyze the population structure and regeneration status of T. dumosa and A. spectabilis in the high-altitude mixed forests of Rasuwa, central Nepal. Conducted in 2023, a total of 61 concentric circular sample plots were laid out following a stratified systematic sampling method. The population-structure-curve displayed a consistent upward trend showing abnormality. The abnormal population structure with a lack of young trees and poor regeneration status in both species points towards potential threats like grazing and wildfires. The highest DBH class ranged from 90 cm to 120 cm DBH for T. dumosa while from 60 cm to 90 cm DBH for A. spectabilis. The study found that the seedling condition of T. dumosa species was ‘fair’ in the lower (2800-3100 m) and middle stratum (3100-3400 m), while it was ‘poor’ in the upper-elevation stratum (3400-3600 m). On the other hand, the seedling condition of A. spectabilis was found to be ‘poor’ in all the three elevation strata. Furthermore, the sapling condition of both the species were found to be ‘poor’. Therefore, the studied forest requires sustainable management along with a comprehensive strategy combining controlled grazing, zonation, monitoring, community engagement, regulation enforcement, restoration, ongoing research, and public awareness.

Weed Biology and Ecology Studies: Diversity, Dominance and Prediction of Yield Loss of Corn (Zea mays L.) Due to Broadleaf Weeds Competition in Dryland

The aim of the research was to determine the diversity, distribution, dominance, and yield loss of corn due to competition from broadleaf weeds. Descriptive research method with direct survey techniques of research objects. Data were collected using 10 sample plots measuring 1 m2, which were placed using random sampling. Observation parameters include the population and dry biomass weight of weeds and corn. The data was analyzed descriptively and comparatively by calculating density, frequency, and dominance to obtain an important value index and summation diminance ratio (SDR). The results of the research showed that 14 species of broad-leaved weeds were found with high species diversity, distribution, dominance, and abundance indices. The competitiveness and relative weighted dominance of six weed species, namely Amaranthus spinusus, Amaranthus gracilis, Synedrella nodiliflora, Acalypha indica, Ageratum conyzoides, and Alternanthera philoxeroides, are higher than those of other weed species, causing corn yield losses of 5.56%–21.90%. Therefore, these six weed species must be controlled.

Spectral Index-Based Estimation of Total Nitrogen in Forage Maize: A Comparative Analysis of Machine Learning Algorithms

Nitrogen plays a fundamental role as a nutrient for the growth of leaves and the process of photosynthesis, as it directly influences the quality and yield of corn. The importance of knowing the foliar nitrogen content through Machine Learning algorithms will help determine the efficient use of nitrogen fertilization in a context of sustainable agronomic management by avoiding Nitrogen loss and preventing it from becoming a pollutant for the soil and the atmosphere. The combination of machine learning algorithms with vegetation spectral indices is a new practice that helps estimate parameters of agricultural importance such as nitrogen. The objective of the present study was to compare random forest and neural network algorithms for estimating total plant nitrogen with spectral indices. Five spectral indices were obtained from remotely piloted aircraft systems and analyzed by mean, maximum and minimum from each sample plot to finally obtain 15 indices, and total nitrogen was estimated from the georeferenced points. The most important variables were selected with backward, forward and stepwise methods and total nitrogen estimates by laboratory were compared with random forest models and artificial neural networks. The most important indices were NDREmax and TCARImax. Using 15 spectral indices, total nitrogen with a variance of 79% and 81% with random forest and artificial neural network, respectively, was estimated. And only using NDREmax and TCARmax indices, 73% and 79% were explained by random forest and artificial neural network, respectively. It is concluded that it is possible to estimate nitrogen in forage maize with two indices and it is recommended to analyze by phenological stage and with a greater number of field data.

Does Central Asian forest growth benefit from a warming-wetting climate? Insights from tree-ring records

Climate warming has pronounced impacts on high-elevation regions, including arid Central Asia, and has multiple impacts on the environment. Forests in these mountainous areas provide essential services by regulating regional climate, sequestering carbon, and supporting soil and water conservation. However, trends in forest productivity and their response to climate change remain unclear. To address this knowledge gap, we collected tree cores from five sample plots in the western Tianshan region. We used tree-ring widths to reconstruct net primary productivity (NPP) and investigated the sensitivity of forest NPP to climate change by analysing weather station data and employing LASSO regression to identify climatic factors influencing forest growth. Our results demonstrate that the reconstructed forest NPP effectively captured significant carbon shifts and revealed a non-significant increase in forest productivity associated with climate warming and higher precipitation between 1970 and 2020 at low and middle elevations in the Tianshan mountains. Humidity is the primary limiting factor affecting forest growth in this region. Conversely, the relationship between temperature and forest growth is not consistent as precipitation increases. Our findings suggest that continued warming will exacerbate water stress in forests.

Changes in carbon stocks according to stand development stages in oriental beech forests in the Marmara Region of Türkiye.

This study was conducted to determine the changes in carbon stocks of oriental beech (Fagus orientalis) according to stand development stage in the Marmara Region of Türkiye. For this purpose, sample plots were taken from a total of 32 areas encompassing four stand development stages (young, middle age, mature and overmature stand). The diameter at breast height and height of all trees in the sample plots were measured, and only three dominant trees's ages per plot were determined. Aboveground carbon stock was calculated using equations developed for beech forests, while the coefficients in the Agriculture, Forestry and Other Land Use guide were used to determine belowground carbon stocks. A soil pit was dug in each plot and soil samples were taken at different depths (0-10, 10-30, 30-60, 60-100cm). In addition, litters were sampled from four different 25 × 25cm sections in each plot, and then the physical and chemical properties of the soil and litters were analysed. The variations in carbon stocks in above- and below-ground tree mass, litter and soil, and in ecosystem carbon stocks according to development stage were examined by analysis of variance and Duncan test, and the relationships between the carbon stocks were investigated by correlation analysis. Aboveground (AG) and belowground (BG) tree, soil and ecosystem carbon stocks showed significant differences between the four stand development stages (P < 0.05), but not the litter carbon stocks (P > 0.05). AG and BG tree and ecosystem carbon stocks increased with progressive stand development stages, while the soil carbon stock was the highest at the young stage. These findings will contribute to the preparation of forest management plans and the national greenhouse gas inventory.

Forest landscape restoration-induced changes in land cover and woody plant community structure in a degraded forest reserve in Ghana

Restoring degraded forests has become a global concern due to the high levels of forest disturbance arising from farming, logging, and mining. This study, conducted in the degraded Tain II Forest Reserve landscape in Ghana, examined the effects of forest landscape restoration (FLR) on land cover changes and woody plant community structure in the area. The study combined remote sensing, plot sampling techniques, and structured interviews in data collection. Landsat images were used to establish the land cover changes in the landscape. Plant community structure was assessed within thirty 50 m by 50 m plots that were laid randomly in equal numbers in Natural Forest (NF), Natural Regeneration (NR), and Assisted Regeneration (AR) sites. Ecosystem services and socio-economic benefits of the FLR to the local community were also assessed. To compare site attributes, one-way ANOVA analysis was used. The results showed that forestland increased from 45 to 70 % while bare land and cropland declined consistently from over 50 % to 28 % and 5 % to 0.7 %, respectively, within 10-year study period. Woody plant species totaling 120 belonging to 34 families, compared to a baseline of 61 species and 24 families, were enumerated. The NF and NR sites had higher flora diversity than the AR sites. NF composition was less homogenous compared to the NR and AR. The majority of the community is perceived to have socioeconomically gained from ecosystem services and engagement in the FLR. Effective land governance, stakeholder participation, community-led fire control, and strategic interventions positively influenced the rate of forest restoration. The FLR influenced the land cover and species composition of the landscape. The findings highlight the importance of ecological, social, and economic considerations in FLR for sustainable outcomes for people and the environment. FLR stakeholders should collaborate in planning and implementation of forest restoration to ensure sustainable outcomes.

Pollination and pollen plant diversity by Apis cerana cerana during olive flowering based on DNA metabarcoding

AbstractTo understand the role of Apis cerana cerana in olive tree (Olea europaea L.) pollination, pollinators and pollen carried by bees were collected from 11 experimental plots during the flowering period. The abundance and diversity of pollen plant species were investigated using DNA metabarcoding. A total of 583 pollinators were collected and A. cerana cerana accounts for 46.83%. Further analysis revealed a total of 56 families, 136 genera and 195 plant species in bee pollens. O. europaea is the dominant species accounting for 42.02 ± 34.89% and then followed by Sedum japonicum (8.70 ± 16.42%). Diversity index showed that the abundance and diversity of pollen plant species were the highest in Wanzhou (WZ) and lowest in Hechuan (HC). This suggests that Wanzhou sample plot has a greater abundance of plant species to supplement the bee diet. Excessive presence of miscellaneous flowering plants in olive plantations can distract bees from collecting olive tree pollens. Therefore, their abundances can be reduced appropriately while ensuring food diversity for the bees. Based on the abundance of plants in pollens, we recommend that appropriately planting of S. japonicum, Youngia japonica and Vicia villosa in olive plantations with fewer flowering plants could be an effective supplement of bee food. Our results not only contribute to future studies on olive tree pollination but also provide new ideas for enriching flowering plants in or around olive plantations.