Vegetation Aboveground Biomass Research Articles

Mechanism underlying revetment effects on the spatial distribution of nitrogen removal and N2O emissions in riparian zones at summer

Study Region, Shanghai, China, Study FocusUrban riparian zones can mitigate or even control nitrogen pollution in rivers through nitrification and denitrification processes. However, these processes also produce N2O, a potent greenhouse gas. During urbanization, various types of revetments have been constructed in riparian zones. These revetments alter the exchange of materials and energy between the riparian zone and the river, which in turn affects the nitrification and denitrification processes in the riparian zone. However, the mechanisms by which revetments impact nitrogen removal and N2O emissions in riparian zones are not yet clear, and the ways in which revetments balance nitrogen removal and N2O emissions remain unknown. Therefore, in a single river section, natural (NR), permeable (PR), and impervious revetment (IR) types were replicated. Measurements are taken of nitrogen removal, N2O emissions, and related environmental indicators in both the river-riparian interface (RRI) and the riparian zone area outside the RRI (RZ). The study analyzes the differences in environmental factors, nitrification and denitrification potentials, and N2O emission rates among the different revetments to identify the main factors affecting nitrogen removal and N2O emissions in the riparian zone. It also explores the potential mechanisms by which revetments influence the spatial distribution of nitrogen removal and N2O emissions in the summer riparian zone.New Hydrological Insights for the Region: PR exhibit greater denitrification potential and lower N2O emission rates. The denitrification potential is higher in the RRI compared to RZ, while the N2O emission rate is lower than in RZ. nirK is the primary factor influencing denitrification potential, whereas nosZII predominantly affects N2O emissions. The lower soil carbon-to-nitrogen ratio in PR increases the abundance of nirK, thereby enhancing the riparian zone's denitrification potential. Moreover, greater aboveground vegetation biomass increases the abundance of nosZII, reducing the N2O emission rate. The study results could provide guidance for urban ecological restoration and greenhouse gas emission reduction.

Extraction of vegetation disturbance range using aboveground biomass estimated from Sentinel-2 imagery in coal mining areas with high groundwater table.

Coal mining in regions characterized by high groundwater table markedly predisposes to surface subsidence and water accumulation, thereby engendering substantial harm to surface vegetation, soil, and hydrological resources. Developing effective methods to extract surface disturbance information aids in quantitatively assessing the comprehensive impacts of coal mining on land, ecology, and society. Due to the shortcomings of traditional indicators in reflecting mining disturbance, vegetation aboveground biomass (AGB) is introduced as the primary indicator for extracting the mining disturbance range. Taking the Huaibei Coal Base as an example, Sentinel-2 MSI imagery is firstly used to calculate spectral factors and vegetation indices. Multiple machine learning algorithms are coupled to perform remote sensing estimation and spatial inversion of vegetation AGB based on measured samples of vegetation AGB. Secondly, an Orientation Distance-AGB (OD-AGB) curve is constructed outward from the center of subsidence water areas (SWA), with the Boltzmann function used for curve fitting. According to the location of the inflection point of the curve, the boundary points of vegetation disturbance are identified, and then the disturbance range is divided. The results show that (1) the TV-SVM model, utilizing total variables and support vector machine, achieves the highest estimation accuracy, with σMAE and σRMSE values of 208.47g/m2 and 290.19g/m2, respectively, for the validation set. (2) Thirty-six effective disturbance areas, totaling 29.89 km2, are identified; the Boltzmann function provides a good fit for the OD-AGB curve, with an R2 exceeding 0.8 for typical disturbance areas. (3) Analysis of general statistical laws indicates that disturbance distance conforms to the general characteristics of normal distribution, exhibiting boundedness and directional heterogeneity. The research is expected to provide scientific guidance for hierarchical zoning management, land reclamation, and ecological restoration in coal mining areas with high groundwater table.

Estimation of Coastal Wetland Vegetation Aboveground Biomass by Integrating UAV and Satellite Remote Sensing Data

Aboveground biomass (AGB) serves as a crucial indicator of the carbon sequestration capacity of coastal wetland ecosystems. Conducting extensive field surveys in coastal wetlands is both time-consuming and labor-intensive. Unmanned aerial vehicles (UAVs) and satellite remote sensing have been widely utilized to estimate regional AGB. However, the mixed pixel effects in satellite remote sensing hinder the precise estimation of AGB, while high-spatial resolution UAVs face challenges in estimating large-scale AGB. To fill this gap, this study proposed an integrated approach for estimating AGB using field sampling, a UAV, and Sentinel-2 satellite data. Firstly, based on multispectral data from the UAV, vegetation indices were computed and matched with field sampling data to develop the Field–UAV AGB estimation model, yielding AGB results at the UAV scale (1 m). Subsequently, these results were upscaled to the Sentinel-2 satellite scale (10 m). Vegetation indices from Sentinel-2 data were calculated and matched to establish the UAV–Satellite AGB model, enabling the estimation of AGB over large regional areas. Our findings revealed the AGB estimation model achieved an R2 value of 0.58 at the UAV scale and 0.74 at the satellite scale, significantly outperforming direct modeling from field data to satellite (R2 = −0.04). The AGB densities of the wetlands in Xieqian Bay, Meishan Bay, and Hangzhou Bay, Zhejiang Province, were 1440.27 g/m2, 1508.65 g/m2, and 1545.11 g/m2, respectively. The total AGB quantities were estimated to be 30,526.08 t, 34,219.97 t, and 296,382.91 t, respectively. This study underscores the potential of integrating UAV and satellite remote sensing for accurately assessing AGB in large coastal wetland regions, providing valuable support for the conservation and management of coastal wetland ecosystems.

Rapid and high-throughput determination of sorghum (Sorghum bicolor) biomass composition using near infrared spectroscopy and chemometrics

Compositional characterization of biomass is vital for the biofuel industry. Traditional wet chemistry-based methods for analyzing biomass composition are laborious, time-consuming, and require extensive use of chemical reagents as well as highly skilled personnel. In this study, near-infrared (NIR) spectroscopy was used to quickly assess the composition of above-ground vegetative biomass from 113 diverse, photoperiod-sensitive, biomass-type sorghum (Sorghum bicolor) accessions cultivated under field conditions in Central Illinois. Biomass samples were analyzed using NIR spectra collected in the spectral range of 867–2536 nm, with their chemical compositions determined following the National Renewable Energy Laboratory (NREL) protocol. Advanced spectral pre-treatment and band selection techniques were utilized to develop calibration models using partial least squares regression (PLSR). The models' effectiveness was assessed through cross-validation and independent data tests. The predictions for moisture, ash, extractives, glucan, xylan, acid-soluble lignin (ASL), acid-insoluble lignin (AIL), and total lignin were accurate and reliable, demonstrating the capability of NIR spectroscopy to provide rapid and precise characterization of sorghum biomass. The results demonstrated that NIR spectroscopy is an efficient tool for rapidly characterizing sorghum biomass, making it a sustainable option for screening desirable feedstock for biofuel or bioproduct production.

Biomass distribution patterns of salt marshes: A detailed spatial analysis in central China's coastal wetlands

Wetland vegetation plays a critical role in the health of coastal ecosystems, serving pivotal roles in disaster mitigation and blue carbon sequestration. While extensive research on the effects of sea-level rise on the aboveground biomass of salt marsh vegetation, the spatial patterns of biomass distribution related to surface elevation remain less explored. This study investigates the growth patterns and spatial distribution of Spartina alterniflora in central coast of China through field surveys and the analysis of remote sensing data via Google Earth Engine. We have found a significant parabolic relationship between vegetation biomass and distance from the sea, with the optimal biomass influenced by the location of the marsh front edge. Further, we observe that biomass distribution varies with surface elevation and is influenced by geomorphological features notably creeks, which introduce significant elevation differences and local biomass fluctuations. These findings offer new insights into the spatial distribution of tidal flat vegetation, which are vital for effective coastal management and for modeling the evolution of tidal flats.

Allometric estimation models for aboveground and belowground biomass of pre-fire and post-fire vegetation in Scots pine forests

Abstract Wildfires have recently destroyed large areas of forest in Central Europe. After a fire, the post-fire vegetation succession rebuilds previously lost biomass and carbon. Nevertheless, ground vegetation and tree regeneration, as well as belowground biomass, are largely neglected in allometric biomass models. Moreover, most models are calibrated for undisturbed forests, which differ from burned forests in terms of site conditions and species composition. We adapted a model using plant cover and shoot length of herb, graminoid and bryophyte species (PhytoCalc), and a single tree model using shoot length and root collar diameter of juvenile trees to estimate aboveground biomass (AGB) of post-fire species and growth groups, and extended them to estimate belowground biomass. We sampled the most dominant species from the herb and moss layer and the tree regeneration of burned and unburned Scots pine (Pinus sylvestris L.) forests in NE Germany. We successfully calibrated post-fire vegetation models and showed that models for undisturbed forests from the literature mostly underestimate post-fire AGB. Using models from the literature for clear-cuts often improved the estimation of post-fire AGB, but still failed to estimate AGB accurately for some species. Inaccurate estimation of post-fire biomass with these other models can be explained by high irradiance and increased nutrient availability on burned sites, which affect tissue and wood density. We showed that own species- and growth group-specific allometric models calibrated to post-fire vegetation are required to correctly estimate the total (above- and belowground) biomass of post-fire vegetation needed to calculate carbon storage.

Urban Above-Ground Biomass Estimation Using GEDI Laser Data and Optical Remote Sensing Images

Accurate estimating of above-ground biomass (AGB) of vegetation in urbanized areas is essential for urban ecosystem services. NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission can obtain precise terrestrial vegetation structure, which is very useful for AGB estimation in large forested areas. However, the spatial heterogeneity and sparse distribution of vegetation in urban areas lead to great uncertainty in AGB estimation. This study proposes a method for estimating vegetation heights by fusing GEDI laser observations with features extracted from optical images. GEDI is utilized to extract the accurate vegetation canopy height, and the optical images are used to compensate for the spatial incoherence of GEDI. The correlation between the discrete vegetation heights of GEDI observations and image features is constructed using Random Forest (RF) to obtain the vegetation canopy heights in all vegetated areas, thus estimating the AGB. The results in Xuzhou of China using GEDI observations and image features from Sentinel-2 and Landsat-8 satellites indicate that: (1) The method of combining GEDI laser observation data with optical images is effective in estimating AGB, and its estimation accuracy (R2 = 0.58) is higher than that of using only optical images (R2 = 0.45). (2) The total AGB in the shorter vegetation region is higher than the other two in the broadleaf forest and the coniferous forest, but the AGB per unit area is the lowest in the shorter vegetation area at 33.60 Mg/ha, and it is the highest in the coniferous forest at 46.60 Mg/ha. And the highest average AGB occurs in October–December at 59.55 Mg/ha in Xuzhou. (3) The near-infrared band has a greater influence on inverted AGB, followed by textural features. Although more precise information about vegetation should be considered, this paper provides a new method for the AGB estimation and also a way for the evaluation and utilization of urban vegetation space.

Deep Learning Model Effectiveness in Forecasting Limited-Size Aboveground Vegetation Biomass Time Series: Kenyan Grasslands Case Study

Timely forecasting of aboveground vegetation biomass is crucial for effective management and ensuring food security. However, research on predicting aboveground biomass remains scarce. Artificial intelligence (AI) methods could bridge this research gap and provide early warning to planners and stakeholders. This study evaluates the effectiveness of deep learning (DL) algorithms in predicting aboveground vegetation biomass with limited-size data. It employs an iterative forecasting procedure for four target horizons, comparing the performance of DL models—multi-layer perceptron (MLP), long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), and CNN-LSTM—against the traditional seasonal autoregressive integrated moving average (SARIMA) model, serving as a benchmark. Five limited-size vegetation biomass time series from Kenyan grasslands with values at 15-day intervals over a 20-year period were chosen for this purpose. Comparing the outcomes of these models revealed significant differences (p < 0.05); however, none of the models proved superior among the five time series and the four horizons evaluated. The SARIMA, CNN, and CNN-LSTM models performed best, with the statistical model slightly outperforming the other two. Additionally, the accuracy of all five models varied significantly according to the prediction horizon (p < 0.05). As expected, the accuracy of the models decreased as the prediction horizon increased, although this relationship was not strictly monotonic. Finally, this study indicated that, in limited-size aboveground vegetation biomass time series, there is no guarantee that deep learning methods will outperform traditional statistical methods.

Distribution Characteristics of Carbon Density in Plant–Soil System of Temperate Steppe and Temperate Desert in the Longzhong Loess Plateau

Grassland, as a key component of the carbon cycle in terrestrial ecosystems, is vital in confronting global climate change. Characterising the carbon density of grassland ecosystems in the Longzhong Loess Plateau is important for accurately assessing the contribution of grasslands to global climate change and achieving the goal of “peak carbon” and “carbon neutral”. In this study, the Longzhong Loess Plateau was used as the research object to explore changes in the plant–soil system carbon density in two grassland types by analysing the aboveground vegetation biomass carbon density, belowground vegetation biomass carbon density, 0–100 cm soil carbon density, and ecosystem carbon density of temperate steppe and temperate desert. The results showed that the vegetation biomass (standing and living, litter, and belowground biomass), soil, and ecosystem carbon densities of the temperate steppe were significantly higher than those of the temperate desert (p < 0.05). Their carbon densities were 700.51, 7612.95, and 8313.45 g·m−2, respectively. The vertical distribution of belowground biomass and soil carbon density in the temperate steppe was significantly higher than that in the temperate desert. The overall trend of belowground biomass carbon density in the temperate steppe and temperate desert showed a gradual decrease, whereas soil carbon density showed a steady increase. More than 91% and 96% of the carbon was stored in soil in the temperate steppe and temperate desert, respectively, and the belowground biomass carbon stock accounted for more than 84% of the total biomass carbon pools in both temperate steppe and temperate desert. Temperate steppe has a significant effect in improving the carbon stock of grassland ecosystems, so ecological protection and restoration of grassland should be strengthened in the future to enhance the capacity of grassland to sequester carbon and increase sinks.

Functional trait diversity and aboveground biomass of herbaceous vegetation in temperate forests of Kashmir Himalaya.

Studying functional trait diversity can provide crucial clues about the adaptive survival strategies of regional plant species pool. Despite large-scale trait datasets available worldwide, the plant trait data from many biodiversity hotpot regions, like the Himalaya is still scarce. In this study, we aimed to investigate the plant functional traits and aboveground biomass of understory herbaceous vegetation in temperate forests of Overa-Aru wildlife sanctuary of Kashmir Himalaya. We also investigate how these functional traits correlate and what is the magnitude of trait-biomass relationship across the herbaceous species pool. For this, we conducted field sampling and measured leaf functional traits and aboveground biomass of 38 plant species in the study region during peak growing season (July-August) in the year 2021. The results revealed a significant interspecific trait variability among the species studied. We observed a high variability in leaf size and type spectra of the species, with nanophyll and simple leaf lamina, respectively, the most common types among the species studied. The correlation analysis revealed that plant height was positively correlated with aboveground biomass. The variation partitioning analysis revealed that the plant height explained the maximum fraction of variation in aboveground biomass, while least by specific leaf area. Overall, the findings from the present study provide useful insights in understanding trait-trait relationship and trait-environment interaction at the regional scale and can also help in recognizing adaptive functional traits of plant species that determine plant survival under the changing climate in this Himalayan region.

Response of soil moisture and vegetation growth to precipitation under different land uses in the Northern Loess Plateau, China

The land-use change affects soil hydrological processes in semi-arid areas. Investigating the impact of precipitation change on soil moisture in different land use types (LUTs) is conducive to water resource management and ecological restoration. The volumetric water content (VWC) at 0–300 cm soil profile, meteorology data, and vegetation aboveground biomass were continuously observed from 2018 to 2022 in five terraced and three sloped LUTs. The results showed that soil moisture was affected by precipitation, vegetation, antecedent soil water, soil properties, and artificial management measures. There were significant differences in soil moisture among these land use types (P < 0.05), with the priority order of multi-year profile average VWC at 0–300 cm depth was farmland in the slope (FAS) (0.22 ± 0.01 cm3 cm−3) > bare land in the terrace (BAT) and farmland in the terrace (FAT) (0.19 ± 0.01 cm3 cm−3) > abandoned land in the slope (ABS) (0.18 ± 0.02 cm3 cm−3) > abandoned land in the terrace (ABT) (0.13 ± 0.02 cm3 cm−3) > forage land in the slope (FOS) (0.12 ± 0.02 cm3 cm−3) > forage land in the terrace (FOT) and shrubland in the terrace (SHT) (0.10 ± 0.01 cm3 cm−3). Except for BAT, soil water storage (SWS) in other LUTs decreased in 0–300 cm soil profile from 2018 to 2022. There were no or slight soil desiccations in FAS, BAT, ABS, and FAT, moderate soil desiccation in ABT, and serious soil desiccation in FOS, FOT, and SHT. The VWC, SWS, and soil desiccation index (SDI) of all LUTs were positively correlated with precipitation, especially at 0–100 cm depth. Precipitation threshold response to soil desiccation alleviation was determined to be 98–116 mm month−1. Moreover, aboveground biomass of FOT, ABT, and SHT exhibited a significant correlation with both soil moisture and precipitation (P < 0.05). Therefore, forage lands and shrublands have adverse effects on the mitigation of deep soil desiccation with decreasing rainfall. This study recommended farmland in the terrace and abandoned land in the slope for soil and water conservation and sustainable ecological restoration.

The above-ground biomass model of mangrove forest in Panai Hilir, Indonesia

Mangrove forest is one of the forests with high biomass storage in the tropics besides mainland forests. The mangrove ecosystem has an ecological function as a carbon reducer through a sequestration process (C-sequestration). One technique that can be used to calculate biomass is the use of remote sensing technology using satellite imagery and the application of transformation of the vegetation index. This study aims to obtain the best biomass equation model from several vegetation indices, analyze the estimated biomass value from Landsat 8 imagery in 2022, and map the distribution of the estimated distribution of average above-ground biomass of mangrove forest in Panai Hilir using Landsat 7 imagery and Landsat 8 in 2002 and 2022. This study used a systematic sampling method with a random start with 70 plots. Biomass calculations are carried out using allometric equations. Modeling uses the dependent variable (biomass) and the independent variable (vegetation index). The vegetation indices used are NDVI, GNDVI, and TDVI. The results of this study showed that the chosen equation model was the GNDVI (Green Normalized Difference Vegetation Index) linear equation model y = 11, 479x – 2,5569 with r2 of 0,404. The average distribution of above-ground biomass of mangrove vegetation at research sites 2002 was 109,66 tons/ha, with the highest biomass distribution being 200,48 tons/ha. The average above-ground biomass content of mangrove vegetation at the research location in 2022 is 98,28 tons/ha, with the highest biomass distribution of 213,10 tons/ha. In calculations using the selected model and index, in 2022, a total estimated average biomass was obtained of 405,52 tons/ha.

Long-term summer warming reduces post-fire carbon dioxide losses in an arctic heath tundra

The frequency and extent of wildfires in the Arctic has been increasing due to climate change. However, there is a lack of understanding about long-term impacts of climate warming on post-fire carbon dioxide (CO2) exchange in arctic tundra ecosystems. To investigate this, we conducted an experimental low-intensity fire in combination with summer warming (using open top chambers) in a dry heath tundra ecosystem in West Greenland. We report here on the impact four and five years after the fire. We also examined immediate effects of soil heating to three temperature levels (35, 55 and 80 °C), as a simulation of heat transfer during a typical tundra fire. Fire increased soil organic phosphorus concentrations up to at least four years after the burning. The burned areas remained a net CO2 source five years after the fire, mainly due to the lower aboveground vegetation biomass and reduced gross ecosystem production (GEP). However, with four to five years of summer warming, the GEP, ecosystem respiration and soil respiration significantly increased, and burned areas turned into a net CO2 sink. Ex-situ soil heating to the temperature of 55 °C, reaching the heat load comparable with in-situ burning, had minor effects on soil GHG fluxes. This suggests that soil GHG activities are not immediately affected by heat transfer and associated soil temperature increases during a typical low-intensity wildfire in arctic dry tundra. Overall, our results reveal that in a future warmer climate, vegetation is likely to recover more quickly from fires, resulting in a reduction in post-fire CO2 losses.

Integrating grafting and companion planting to improve crop performance in intensive high‐tunnel tomato production

AbstractIntroductionTomatoes are among the most popular horticultural crops cultivated in high tunnels. Due to space limitations in these enclosed environments, some tomatoes are produced in succession (i.e., without rotation) across years, which could lead to yield reductions over time. However, the impact of tomato monocultures on productivity in high tunnels and strategies to buffer against yield declines need further testing.Materials and MethodsWe used a 4‐year field experiment whereby heirloom tomatoes (cv. Cherokee Purple and Black Krim) were grown in the same high tunnel soil over time. We tested the impact of both tomato grafting and companion planting on aboveground vegetative biomass and fruit yield. For the grafting treatment, we used the commercially available Maxifort and wild tomato, Solanum pimpinellifolium, as rootstocks. For companion planting, we seeded open alleys with clover and spatially shifted crop beds each year such that tomatoes were transplanted into the previous year's clover planting.ResultsOur data indicate that the companion (clover) treatment had little to no impact on tomato production, while grafting to Maxifort rootstock had a significant positive effect on biomass and yield. However, beneficial effects of grafting were only observed over time, in years three (+71% inc. in biomass) and four (+77% inc. in biomass, +38% inc. in yield) of the experiment; not during the initial 2 years. Leaf (SPAD) and fruit (Brix) metrics were unaffected by any of the treatments.ConclusionOverall, our results suggest that grafting tomatoes with commercially available rootstock is an effective tool for maintaining production in high tunnel monocultures over time.

Biomass and Carbon Stocks in Post-Agriculture Secondary Forest in Manokwari, West Papua, Indonesia

Vegetations in post-agriculture forests (PAF) have the potential to absorb carbon dioxide and store it in to the form of biomass. However, information on carbon stocks in post-agriculture secondary forest in Papua, Indonesia still needs to be available. This study aimed to analyze the density of vegetation and its relationship to the carbon stock of aboveground vegetation biomass on PAF in the lowland of Manokwari, West Papua. This study was conducted on PAF aged 4 and 7 years using a one-factor, completely randomized design. Carbon stock is calculated using an allometric equation based on a diameter at breast height of 1.30 m (dbh ≥5cm). The results showed that 60% of the relative density of trees on the two PAF’s was composed by Premna odorata, Piper aduncum, and Macaranga tanarius. Carbon stocks on PAF aged 4 and 7 years reach 550 kg/ha and 140 kg/ha respectively. There is an increase in aboveground biomass and carbon stock with relative tree density and, tree diameter, which is significantly affected by the age of the PAF (P&lt;0,002). The low carbon stock on PAF aged four years indicates that there has been a significant decrease in soil fertility after several years of cultivation by the local Papuan community. Therefore, there must be a difference in land use management between natural secondary forest and post-agriculture forest.

Effects of rainfall amount and frequencies on soil net nitrogen mineralization in Gahai wet meadow in the Qinghai-Tibetan Plateau

Global climate change has led to a significant increase in the frequency of extreme rainfall events in the Qinghai-Tibetan Plateau (QTP), thus potentially increasing the annual rainfall amounts and, consequently, affecting the net soil nitrogen (N) mineralization process. However, few studies on the responses of the soil net N mineralization rates to the increases in rainfall amounts and frequencies in alpine wet meadows have been carried out. Therefore, the present study aims to assess the effects of rainfall frequency and amount changes on the N fixation capacity of wet meadow soils by varying the rainfall frequency and amount in the Gahai wet meadow in the northeastern margin of the QTP during the plant-growing season in 2019. The treatment scenarios consisted of ambient rain (CK) and supplementary irrigation at a rate of 25 mm, with different irrigation frequencies, namely weekly (DF1), biweekly (DF2), every three weeks (DF3), and every four weeks (DF4). According to the obtained results, the increased rainfall frequency and amount decreased the soil mineral N stock and increased the aboveground vegetation biomass (AB) amounts and soil water contents in the wet meadows of the QTP. Ammonium (NH4+-N) and nitrate N (NO3–-N) contributed similarly to the mineral N contents. However, the ammonification process played a major role in the soil mineralization process. The effects of increasing rainfall amount and frequency on N mineralization showed seasonal variations. The N mineralization rate showed a single-peaked curve with increasing soil temperature during the rapid vegetation growth phase, reaching the highest value in August. In addition, the N mineralization rates showed significant positive correlations with soil temperatures and NH4+-N contents and a significant negative correlation with AB (P < 0.05). The results of this study demonstrated the key role of low extreme rainfall event frequencies in increasing the net soil N mineralization rates in the vegetation growing season, which is detrimental to soil N accumulation, thereby affecting the effectiveness of soil N contents.

What influences does grazing bring about to stream nutrient fluxes in alpine meadows?

Non-point source pollution is a serious environmental issue and has attracted a lot of scientific attention. Currently, the influences of grazing on the water and nutrient cycles in alpine meadows, as well as corresponding underlying mechanisms, still remain unclear. The quantitative contributions of influence variables to the nutrient loading variations in rivers and streams in alpine-meadow watersheds need to be elucidated. In the study, the Soil and Water Assessment Tool (SWAT) was applied to a typical alpine-meadow watershed (the Buha River Basin, BRB) on the Tibetan Plateau, where non-intensive grazing and farming were developed, to reproduce the water and nutrient cycles. Results showed that SWAT model performed well in simulating monthly streamflow, TN, and TP fluxes from 2016 to 2022 (NSE > 0.6). Grazing slightly increased the streamflow by 4.27% because it reduced aboveground biomass and leaf area index of vegetation, which subsequently reduced the ET of grasslands. Grazing increased the annual NH4-N fluxes by 0.50% due to livestock manure inputs to the basin; in contrast, grazing reduced the annual fluxes of TN and TP of Buha River by 3.36% and 0.48%, respectively, during 2016 – 2022, which were driven by the increased plant uptake of NO3-N/soluble P from soil layer due to grazing. As a result, non-intensive grazing was beneficial for the water management and nutrient control in the studied alpine-meadow watershed. The hydrological, water-quality, and meteorological variables accounted for 30.78%, 28.27%, and 21.23% of the variations in N fluxes in Buha River, respectively. The dissolved oxygen (10.68%), soluble phosphorus (10.51%), precipitation (6.27%), overland flow (5.60%), and water temperature (5.53%) each explained more than 5% of the N flux variations. The present study quantified the effects of grazing and farming on stream nutrient fluxes based on mathematical modeling and statistical methods, and the findings could provide scientific supports for the managements of alpine-meadow watersheds.

Fast screening of total nutrient contents in strawberry leaves and spent growing media using NIRS.

In closed-loop soilless cultivation, the main nutrient sinks are nutrients retained either by the crop or in spent growing media. Measurement of nutrients in spent growing media and in the aboveground vegetative plant biomass at crop termination can be a tool for assessing and optimizing nutrient efficiency. The first aim of this study was to test the potential of near-infrared reflectance spectroscopy (NIRS) to forecast the various nutrient contents in strawberry leaves, which would then allow for assessment of crop nutrient status and total nutrient uptake by strawberry plants. The second aim was to test NIRS as a high throughput technique for assessing the N, K, Ca, Mg and organic matter (OM) content and the pH, EC and C:N and C:P ratios for a dataset of composts, plant fibers and spent growing media. The NIRS prediction model for fast screening of the total nutrient contents in spent growing media was compared with a single extraction method. A database with 369 dried and ground strawberry leaf samples with known contents of N, P, K, Ca, and Mg were scanned using NIRS. The database covered a range of leaf contents of 6-35 g N/kg dry matter (DM), 0.7-6.3 g P/kg DM and 2-29 g K/kg DM. A dataset of 458 samples of different types of materials used in growing media was validated with a dataset of 109 samples. Validation for the strawberry leaves indicated potential for this application, with R2 values of 0.90 or higher for N, K and Ca, and R2 values higher than 0.85 for P and Mg. Validation for the dataset of composts, plant fibers and spent growing media also indicated the potential for this application, with R2 values of 0.90 or higher for organic matter, and with R2 values of 0.85 or higher for total Ca, pH and C:N. A first test indicated potential for the calibration based on fresh samples of compost, plant fiber as well as spent growing media or dried (not ground) samples. Use of NIRS on fresh samples would eliminate the need for drying and grinding the samples and would reduce screening time. The ammonium acetate extraction is a reliable alternative to NIRS for fast screening of the total P, K, Ca, and Mg contents in composts, plant fibers and spent growing media.

Unexpected consequences of afforestation in degraded drylands: Divergent impacts on soil and vegetation

Forestry has long been considered an effective means of restoring degraded drylands worldwide. Often, afforestation in such lands relies on the establishment of runoff harvesting systems that are formed as contour bench terraces on hillslopes, increasing water availability for the planted trees and shrubs. The construction of terraces requires intensive earthworks by heavy machinery. This study assessed the long-term (>10 yrs) effects of forestry-related land-use change on soil properties and herbaceous vegetation in 16-year-old and 12-year-old afforestation sites (established in 2005 and 2009), and in nearby control (“natural”) areas in the semi-arid northern Negev, Israel. Mean herbaceous vegetation height in the 2005 afforestation sites (12.1 cm) was significantly (P = 0.0009) and 23% greater than in the control areas (9.8 cm), whereas in the 2009 afforestation sites (6.2 cm) it was 37% lesser than in the control areas. Mean herbaceous vegetation aboveground biomass was similar in the 2005 afforestation (0.39 Mg ha−1) and control areas (0.38 Mg ha−1), and almost significantly (P = 0.0510) and twofold greater than in the 2009 afforestation sites (0.19 Mg ha−1). The effect of hillslope aspect on these variables was substantial; their mean values were higher in the northern (mesic) hillslopes than in the southern (xeric) hillslopes. Soil samples were obtained from depths of 0–5 and 5–10 cm and physio-chemo-biological properties were assessed in the laboratory. The overall soil quality – as calculated by two soil quality indices (SQIs), including the generalized SQI (SQIgen) and the minimum dataset SQI (SQIMDS) – was significantly (P < 0.0001 for both indices) and 13–22% greater in the control areas (0.52 and 0.61, respectively) than that in the afforestation treatments (0.44–0.46 and 0.50–0.51, respectively). These results are generally attributed to the removal of soil's A-horizon during earthworks, and the exposure of the underlying B-horizon. The similar SQI values of both hillslope aspects, as well as of both soil depths, indicate the generally degraded state of the entire region. In conclusion, while contour bench terracing may facilitate the recovery of herbacaeous vegetation to some extent, the effectiveness of this practice for soil restoration is questionable. Overall, insights of this study demonstrate a caveat that converting natural drylands to forestry systems may not yield sufficient ecological benefits, and therefore should be implemented with caution.

Differential developmental rates and demographics in Red Kangaroo (Osphranter rufus) populations separated by the dingo barrier fence.

Decommissioning the dingo barrier fence has been suggested to reduce destructive dingo control and encourage a free transfer of biota between environments in Australia. Yet the potential impacts that over a century of predator exclusion might have had on the population dynamics and developmental biology of prey populations has not been assessed. We here combine demographic data and both linear and geometric morphometrics to assess differences in populations among 166 red kangaroos (Osphranter rufus)-a primary prey species of the dingo-from two isolated populations on either side of the fence. We also quantified the differences in aboveground vegetation biomass for the last 10 years on either side of the fence. We found that the age structure and growth patterns, but not cranial shape, differed between the two kangaroo populations. In the population living with a higher density of dingoes, there were relatively fewer females and juveniles. These individuals were larger for a given age, despite what seems to be lower vegetation biomass. However, how much of this biomass represented kangaroo forage is uncertain and requires further on-site assessments. We also identified unexpected differences in the ontogenetic trajectories in relative pes length between the sexes for the whole sample, possibly associated with male competition or differential weight-bearing mechanics. We discuss potential mechanisms behind our findings and suggest that the impacts of contrasting predation pressures across the fence, for red kangaroos and other species, merit further investigation.