Soil Vegetation Research Articles

Organic Fertilization Leads to N Limitation Rather than P Limitation in Both Vegetable Soils

Organic amendments are widely used to enhance soil fertility and nutrient cycling in greenhouse cultivation, but their effectiveness can vary depending on their origin and composition. This study investigated the impact of four organic materials (rice husk, coconut coir, biochar, and sheep manure) on nutrient cycling and enzyme activities in two of greenhouse tomato soils. The distribution of soil nutrients and enzyme activities was analyzed under controlled conditions during a pot experiment. The addition of organic amendments, regardless of their source, significantly altered the microbial resource allocation, reducing the carbon limitation while increasing the nitrogen demand. The effect on soil nutrient pools was largely determined by the chemical composition of the amendments. In clayey soils, biochar and rice husk additions most effectively promoted enzyme activities related to carbon, nitrogen, and phosphorus acquisition. Under sandy soil conditions, sheep manure increased carbon and phosphorus acquisition enzymes, while biochar most effectively enhanced nitrogen acquisition enzymes. Biochar emerged as a particularly effective amendment, enhancing organic carbon sequestration across different soil types. The chemical composition of the amendments, specifically, the content of carboxyl C, aromatic C, and O-alkyl C, played a crucial role in influencing soil nutrient limitations. In clayey soils, the mean C:N:P ratios for CK, T1, T2, T3, and T4 treatments were 1:1.375:0.625, 1:1.244:0.662, 1:0.839:0.610, 1:1.161:0.689, and 1:1.038:0.549, respectively. In sandy soils, the ratios were 1:1.117:0.698, 1:1.18:0.75, 1:1.096:0.731, 1:1.217:0.689, and 1:1.06:0.669, respectively. These findings suggest that the addition of organic amendments can improve nutrient retention and enzyme activities, but their effects on soil nutrient pools are influenced by both the composition of the amendments and the soil texture. This research enhances our understanding of organic amendments and soil nutrient transformations, and aids in optimizing the application of organic materials for improved soil management in greenhouse cultivation.

Flood Risk Assessment and Zoning for Niamey and Lokoja Metropolises in Niger and Nigeria

With the increasing frequency of floods in recent decades, particularly in West Africa, many regions have faced unusual and recurrent flooding events. Communities in flood-prone areas experience heightened insecurity, loss of property, and, in some cases, serious injuries or fatalities. Consequently, flood risk assessment and mitigation have become essential. This comparative study between Niamey and Lokoja employs Geographic Information Systems (GIS) and the Analytical Hierarchy Process (AHP) to delineate flood susceptibility, vulnerability, and risk zones. The study utilized a comprehensive range of thematic layers, with weight percentages assigned to each parameter as follows: 29% for elevation, 24% for slope, 15% for the Topographic Wetness Index (TWI), 9% for drainage density, 9% for distance from rivers, 4% for both precipitation and the Normalized Difference Water Index (NDWI), and 2% each for the Normalized Difference Vegetation Index (NDVI) and soil type. To validate these weightings, a consistency ratio was calculated, ensuring it remained below 10%. The findings reveal that 32% of the Niamey study area is at risk of flooding, compared to approximately 15% in Lokoja. The results highlight a very high flood potential, particularly in areas near the Niger River, with this potential decreasing as elevation increases. Given the current prevalence of extreme weather events in West Africa, it is crucial to employ effective tools to mitigate their adverse impacts. This research will assist decision-makers in quantifying the spatial vulnerability of flood-prone areas and developing effective flood risk assessment and mitigation strategies in the region.

Forest Soil Microbiomes: A Review of Key Research from 2003 to 2023

Forests have a key role in mitigating both non-biological and biological ecological disturbances. However, major disturbances (soil pollution, shift from native forest species to exoticones, forested watersheds and climate changes) can have different impacts on a forest’s soil microbiome. Because the soil microbial community of forests has a key role in a variety of ecosystem services that promote the forest’s health, this review tries to answer the following questions: (i) Which are the main ecological disturbances that drive the responses of the forest soil microbiome? (ii) How can we measure these changes? For this aim, the review summarizes details on the tree vegetation type, the microbial communities in forest ecosystems, and the mutual influence between plants, soil, and microbiomes. Microbial communities are shaped by factors such as soil type and composition, plant and vegetation types, nutrient levels and soil fertility, disturbance patterns, symbiotic associations, biotic interactions, and the progression of forest succession. Anthropogenic activities produce a rapid response in the microbial communities, leading to both short- and long-term alterations. Harvesting processes reduce drastically the microbiome diversity, forcing a shift from specialized to more generalist microorganisms. Restoration scenarios indicate a re-establishment of microbial communities to a level similar to the native forest, but with a high percentage of replaced native microorganisms. This review emphasizes that the forest soil microbiome is shaped by a range of environmental, ecological, and biotic factors. The primary drivers of the soil microbiome in forest ecosystems discussed in this review include soil composition and nutrient availability, plant community structure, microbial interactions within the soil, disturbances, succession, and temporal dynamics. When considered together, these factors interact in complex ways, influencing the diversity, function, and resilience of the soil microbiome in forest ecosystems.

Increasing Productivity and Recovering Nutritional, Organoleptic, and Nutraceutical Qualities of Major Vegetable Crops for Better Dietetics

The intensive use of chemical fertilizers for vegetable cultivation to achieve higher productivity causes soil degradation, resulting in an alarming decline (25–50%) in nutritional quality and a reduction in a wide variety of nutritionally essential minerals and nutraceutical compounds in high-yielding vegetable crops over the last few decades. To restore the physio-chemical and biological qualities of soil as well as the nutritional and nutraceutical qualities of fresh produce, there is a growing desire to investigate the remedial impacts of organic sources of nutrition. This study specifically focused on the impact of six different ratios of chemical fertilizers and organic sources with microbial inoculation on vegetable productivity, nutrition quality, and soil health parameters. Results show that replacing chemical fertilizers with organic sources in the presence of a microbial consortium supports the proliferation of the microbial population in the soil rhizosphere and improves the nutritional status and physico-chemical quality of soil, which is the area around the roots of plants where maximum nutrient uptake occurs. This combination of factors significantly recovers overall soil quality, increasing crop productivity by 13.58 to 18.32 percent in tomato, brinjal, and okra. Experimental findings likewise indicate that an assortment of organic sources with a microbial consortium significantly recovers the abundance of beneficial microbes and earthworms in the rhizosphere, which leads to an improvement in nutritional, organoleptic, and nutraceutical quality, with higher antioxidant contents in all three vegetables grown in arid climate conditions.

Analyzing the Impact of Geoenvironmental Factors on the Spatiotemporal Dynamics of Forest Cover via Random Forest

Understanding the dynamic relationships between geoenvironmental factors and forest vegetation cover is crucial for effective forest management and planning. This study investigates the spatiotemporal dynamics of forest cover in the Duhok District in the Kurdistan Region of Iraq over a decade (2013–2023), emphasizing the impact of geoenvironmental factors via Random Forest algorithms and Landsat data. This research integrates datasets including fractional vegetation cover (FVC), groundwater levels, climate data, topography, and soil moisture data, offering a comprehensive analysis of the factors influencing forest cover. The results show that in 2013, altitude and rainfall were the primary factors influencing FVC, with areas of higher altitudes and adequate rainfall exhibiting up to 30% denser forest cover. By 2023, soil moisture and groundwater levels had emerged as the dominant factors, with soil moisture levels accounting for 25% of the variation in FVC. This shift underscores the increasing importance of water management strategies to maintain forest health. The Random Forest model demonstrated high predictive accuracy, achieving an R2 value of 0.918 (RMSE of 0.016 and MAE of 0.013) for 2013 and 0.916 (RMSE of 0.018 and MAE of 0.014) for 2023, underscoring the model’s robustness in handling nonlinear ecological processes. This study’s insights are crucial for guiding sustainable forest management practices and assisting decision-makers in formulating strategies for resource management, environmental preservation, and future planning. This study underscores the necessity of adaptive management strategies that consider evolving climatic and hydrological conditions, emphasizing continuous monitoring and advanced technologies to ensure the resilience of forest ecosystems.

Modeling maize aflatoxins and fumonisins in a Tanzanian smallholder system: Accounting for diverse risk factors improves mycotoxin models.

Human exposure to mycotoxins is common and often severe in underregulated maize-based food systems. This study explored how monitoring of these systems could help to identify when and where outbreaks occur and inform potential mitigation efforts. Within a maize smallholder system in Kongwa District, Tanzania, we performed two food surveys of mycotoxin contamination at local grain mills, documenting high levels of aflatoxins and fumonisins in maize destined for human consumption. A farmer questionnaire documented diverse pre-harvest and post-harvest practices among smallholder farmers. We modeled maize aflatoxins and fumonisins as a function of diverse indicators of mycotoxin risk based on survey data, high-resolution geospatial environmental data (normalized difference vegetation index and soil quality), and proximal near-infrared spectroscopy. Interestingly, mixed linear models revealed that all data types explained some portion of variance in aflatoxin and fumonisin concentrations. Including all covariates, 2015 models explained 27.6% and 20.6% of variation in aflatoxin and fumonisin, and 2019 models explained 39.4% and 40.0% of variation in aflatoxin and fumonisin. This study demonstrates the value of using low-cost risk factors to model mycotoxins and provides a framework for designing and implementing mycotoxin monitoring within smallholder settings.

Exploring the Transmission Process of Carbon Sequestration Services and Its Applications: A Case Study of Hainan

The pressing need to address climate change and advance global sustainable development has heightened the emphasis on ecosystem services, especially carbon sequestration. This research assesses the supply and demand dynamics of carbon sequestration services on Hainan Island, China, highlighting its significant contributions to global biodiversity conservation and carbon balance. The analysis considers the spatial distribution and interrelation of these services in light of recent land use and ecological policy changes. The methodology incorporates land use and land cover data, the Normalized Difference Vegetation Index (NDVI), meteorological data, and soil data. A gravity model is employed to elucidate the supply–demand relationship for carbon sequestration services, examining the flow across different regions and identifying spatial connections and their intensities. The results indicate a notable increase in carbon sequestration supply in Hainan from 2000 to 2020, particularly in the central mountainous areas. Conversely, the demand for these services has risen, especially in the northern plains’ urban areas and southern coastal towns. The gravity model reveals a strong spatial interdependence between the central mountainous supply zones and the high-demand urban locales. This study underscores the disparities in carbon sequestration supply and demand on Hainan, emphasizing the need for the strategic management of these elements. It provides critical data for ecological compensation policies and offers insights into the roles of regional ecosystems in climate change mitigation. The research highlights the necessity of incorporating ecosystem services into land-use planning and decision-making to foster sustainable development and strengthen climate resilience.

Residual heavy metals and antibiotic pollution in abandoned breeding areas along the northeast coast of Hainan Island, China.

To assess the environmental status of an abandoned aquaculture and breeding area in the northeast coast of the Hainan Island, surface and well water, sediment and surface soils were sampled and analyzed for conventional physicochemical properties, heavy metals and antibiotics. Metagenome tests were also conducted to determine the composition and diversity of the microbial community in typical habitats. Affected by the discharge of wastewater from higher-place pond aquaculture, coastal freshwater rivers have undergone significant salinization, Cl- and Na+ were as high as 4.51×103 and 1.42×103mg/L. The 3 hand-pumped wells surveyed were also suffered from varying degrees of salinization and heavy metal pollution, especially the threat of arsenic pollution. Compared with the local background values, significantly higher valves of Cu, Zn, As and Cd were observed in the surface soil and sediment, and the average concentrations for Cu, Zn, As and Cd are 5.71, 17.6, 15.4 and 0.09mg/kg respectively. For As，the Nemerow index ranges from 7 to 16 and the geoaccumulation index is between 2 and 4, indicating moderate to severe pollution levels in surface soil. 14 antibiotics were detected in the soil and sediment samples, and the highest total amount was 73μg/kg, with tetracycline being the dominant antibiotic. Sediment and forest soil showed different microbial community and the genetic diversity index of sediment was lower than that of the forest soils. For typical vegetation soil, the genetic diversity followed the order as P. elliottii × P. caribaea>Eucalyptus > C. equisetifolia. Among the soil and sediment samples, the highest abundances of antibiotic resistance genes (ARGs) were associated with elfamycin, peptide, rifamycin, and the most common antibiotic resistance mechanisms were antibiotic target alteration (54.5%), antibiotic efflux (27.6%) and antibiotic target replacement (12.1%). The metal resistance genes (MRGs) for Cu, Fe, and Zn resistance were the main MRGs in the samples. This study identified the potential ecological environment risk factors in the abandoned coastal breeding areas, and suggested continuous monitoring and assessment of the residual pollutant abatement processes in the future.

Environmental and human health risk assessment of potentially toxic elements in rehabilitating iron mine lands in the Brazilian Amazon.

Waste pile substrates from Fe mining may carry potentially toxic elements (PTE). Rehabilitation efforts must maintain soil vegetation cover effectively, avoiding the dispersion of particulate matter and reducing the risk to the environment and human health. Therefore, this study aims to evaluate the pseudo-total and extractable contents, perform chemical fractionation, and assess the bioaccessibility and risk of PTE in waste piles of Fe mining in the Eastern Amazon. Soils were sampled from waste piles in different stages of environmental rehabilitation and from non-rehabilitated and native forest areas. The waste materials exhibit mean pseudo-total concentrations of Zn, Ni, Cr, and Cu that exceed the Brazilian soil quality threshold. However, they do not surpass reference values for human health safety. In addition, these elements are predominantly associated with the residual fraction, suggesting low availability. Among the 11 PTE evaluated, only Al, Cu, Fe, Mn, and Zn presented concentrations that were bioaccessible to the gastrointestinal tract. At the same time, Al, Fe, and Mn showed lung bioaccessibility. Soil properties limiting PTE extractability and bioaccessibility include pH and base saturation. Considering only elements above threshold levels, no environmental risk was observed, and the human health risk was considered insignificant for adult oral and inhalation exposure routes. Finally, the results show that high pseudo-total PTE contents in the analyzed Fe waste piles do not necessarily indicate high risks. However, substrate properties should be monitored over time to better understand their potential impacts and the main factors influencing their bioavailability.

Structures and determinants of soil microbiomes along a steep elevation gradient in Southwest China

Soil microbial communities play a vital role in accelerating nutrient cycling and stabilizing ecosystem functions in forests. However, the diversity of soil microbiome and the mechanisms driving their distribution patterns along elevational gradients in montane areas remain largely unknown. In this study, we investigated the soil microbial diversity along an elevational gradient from 650 m to 3,800 m above sea level in southeast Tibet, China, through DNA metabarcode sequencing of both the bacterial and fungal communities. Our results showed that the dominant bacterial phyla across elevations were Proteobacteria, Acidobacteriota and Actinobacteriota, and the dominant fungal phyla were Ascomycota and Basidiomycota. The Simpson indices of both soil bacteria and fungi demonstrated a hollow trend along the elevational gradient, with an abrupt decrease in bacterial and fungal diversity at 2,600 m a.s.l. in coniferous and broad-leaved mixed forests (CBM). Soil bacterial chemoheterotrophy was the dominant lifestyle and was predicted to decrease with increasing elevation. In terms of fungal lifestyles, saprophytic and symbiotic fungi were the dominant functional communities but their relative abundance was negatively correlated with increasing elevation. Environmental factors including vegetation type (VEG), altitude (ALT), soil pH, total phosphorus (TP), nitrate nitrogen (NO3−-N), and polyphenol oxidase (ppo) all exhibited significant influence on the bacterial community structure, whereas VEG, ALT, and the carbon to nitrogen ratio (C/N) were significantly associated with the fungal community structure. The VPA results indicated that edaphic factors explained 37% of the bacterial community variations, while C/N, ALT, and VEG explained 49% of the total fungal community variations. Our study contributes significantly to our understanding of forest ecosystems in mountainous regions with large elevation changes, highlighting the crucial role of soil environmental factors in shaping soil microbial communities and their variations in specific forest ecosystems.

Spatiotemporal Variation and Driving Factors of Carbon Sequestration Rate in Terrestrial Ecosystems of Ningxia, China

This study investigates the spatiotemporal variation characteristics and influencing factors of an ecosystem’s carbon sequestration rate (CSR) in the Ningxia region from 2001 to 2023, providing scientific evidence for assessing the regional carbon sequestration capacity and formulating carbon neutrality policies. Based on ground observation data and multimodal datasets, the optimal machine learning model (EXT) was used to invert a 30 m high-resolution vegetation and soil carbon density dataset for Ningxia from 2000 to 2023. Annual variation analysis and geographical detector methods were employed to assess the spatiotemporal distribution characteristics of the CSR from 2001 to 2023 and identify the primary influencing factors. The results show that from 2001 to 2023, the CSR of the Ningxia ecosystem exhibits a spatial distribution pattern characterized by higher values in the south and lower values in the north, with a mean value of 21.95 gC·m−2, and an overall fluctuating increasing trend, with an annual growth rate of 0.53 gC·m−2 a−1. Significant differences in the CSR exist across different ecological regions. In terms of land use types, the ranking of carbon sequestration capacity is forest > farmland > grassland > barren, while the ranking of the carbon sequestration enhancement capacity is farmland > forest > grassland > barren. Among land use change types, the carbon sequestration enhancement capacity significantly increased when grassland was converted to forest or shrubland, farmland to forest–grassland, and bare land to forest–grassland, with increases of 42.9%, 9.2%, and 34.6%, respectively. The NDVI is the primary driver of CSR spatiotemporal variation, while the interaction between the Enhanced Vegetation Index (EVI) and soil bulk density has a more significant explanatory power for CSR spatial differentiation. This study shows that ecological restoration projects, such as the conversion of cropland to forest (or grassland) and protective farmland measures, play a significant role in enhancing the carbon sequestration capacity in Ningxia.

Shifts in the ecological drivers influence the response of tree and soil carbon dynamics in central Himalayan forests.

Understanding and regulating global carbon relies crucially on comprehending the components and services of forest ecosystems. In particular, interactions that govern carbon storage in trees, soil, and microbes, driven by factors like vegetation structure, function, and soil characteristics, remain poorly understood, especially in the central Himalayas. To address this gap, we investigated carbon storage in tree aboveground biomass, root biomass, and soil across different vegetation types. We also examined how vegetation parameters {vegetation diversity (H'), diameter at breast height (DBH), basal area (BA), and biomass}, and soil characteristics {bulk density (BD), moisture (Mo), pH, and total nitrogen (N)} might influence forest carbon storage. Our study, based on 14 plots (0.1ha each) spanning four distinct vegetation types {Sal forest (SF, 3), Chir-pine forest (PF, 4), Nepalese-alder forest (AF, 3), and Banj-oak forest (OF, 4)} in the central Himalaya, revealed several key insights. Tree carbon storage ranged from ∼79 to 261MgC ha-1, accounting for 41-65% of forest carbon storage, while soil carbon storage ranged from ∼28 to 69MgC ha-1, contributing 35-58%. These values varied with vegetation types and were influenced by the vegetation and soil characteristics associated with each forest type. Important contributors to tree and soil carbon storage included soil Mo, N, and vegetation structural diversity (H', BA), explaining 8-64 % of the variation. Path analysis indicated that increased vegetation diversity, soil properties, and conservative traits (fine roots and leaves) strongly influence tree and soil carbon storage. The study highlights the potential complex system to optimizing carbon storage in natural forest ecosystems, offering valuable insight for managing carbon sinks. Further research is needed to fully understand ecosystem responses to carbon storage across different forest habitats and different spatial-temporal scales.

Vegetation development and soil conditions in reclaimed areas of former silica mines in Indonesia

To restore the function of ex-mining land according to its designation, the ex-mining area must be reclaimed. Ecological dynamics, especially soil conditions and the composition, structure, and biodiversity that occur in reclamation areas, need to be understood so that the reclamation area meets the criteria for the success of mine reclamation. This study aimed to understand the composition, structure, biodiversity, and soil conditions that influence growth in the reclamation area. The research was conducted in a silica mine reclamation area with a 17.8 m radius (r) circular plot (0.1 ha) of 10 plots (±1 ha) for vegetation and 9 points for soil sampling at a depth of 0-30 cm and 30-60 cm. In general, the results of the vegetation inventory show that the tree species recorded are dominated by Pinus merkusii, Hevea brasiliensis, and Enterolobium cyclocarpum with mean values of diameter, height, and low diversity conditions. The condition of soil physical and chemical properties in the reclamation area at both depths shows low mean values, which can affect the growth rate of plants in the reclamation area. The species of Schima wallichii that grow naturally in the reclamation area was found, indicating that the reclamation activities have created a good environment for other tree species to grow naturally. However, enrichment activities need to be carried out to increase biodiversity.

Independent Trends of Mountain Vegetation and Soil Properties Over 40 Years of Environmental Change

ABSTRACTQuestionsAlthough mountain ecosystems are key in providing numerous contributions to people, they are affected by environmental changes. The European Alps, in particular, although shaped by human land use for millennia, suffer pronounced impacts of climate change combined with continued land‐use changes and atmospheric nitrogen deposition. As a core component of ecosystem functioning, the soil–vegetation interface is especially sensitive to these environmental changes, and it is therefore crucial to understand its response. Although several studies have demonstrated the impacts of environmental change on vegetation or soil individually, it remains largely unknown whether they respond synchronously.LocationMontane and subalpine grasslands of the Western Swiss Alps.MethodsWe analysed changes and correlations of ecological indicators of vegetation and soil properties after 40 years in 86 re‐surveyed semi‐permanent plots.ResultsEcological indicators of vegetation releves changed, driven by an increase of species adapted to alkaline conditions and mowing or grazing. By contrast, we detected neither a trend of thermophilisation, nor an increase of nutriphilous species or those adapted to hemeroby. Organic carbon, nitrogen, organic matter content, carbon‐to‐nitrogen ratio and pH increased in the soil. Yet, these changes of vegetation and soil were so far independent of each other.ConclusionsOur findings suggest that mountain vegetation and soil have so far changed asymmetrically with potential knock‐on effects in the decades to come with implications for the conservation of mountain ecosystems and our capacity to predict their future trajectory.

Estimating Biomass Carbon Stocks of Inner Mongolia Grasslands Using Multi-Source Data

Accurate estimates of biomass C stocks of grasslands are crucial for grassland management and climate change mitigation efforts. Here, we estimated the mean C stocks of grasslands in the Inner Mongolia Autonomous Region (IMAR), China, in 2020 at a 10 m spatial resolution by combining multi-source data, including remote sensing, climate, topography, soil properties, and field surveys. We used the random forest model to estimate the aboveground biomass (AGB) of grasslands, achieving an R2 value of 0.83. We established a relationship between belowground biomass (BGB) and AGB using a power function based on field data, which allows us to estimate the BGB of grasslands from our AGB estimate. We estimated the mean AGB across IMAR to be 100.7 g m−2, with a total value of 1.4 × 108 t. The BGB of grasslands is much higher than AGB, with mean and total values of 526.0 g m−2 and 7.4 × 108 t, respectively. Consequently, our C stock estimates show that IMAR grasslands store significantly more C in their BGB (332.6 Tg C) compared to AGB (63.7 Tg C). Random forest model analyses suggested that remotely sensed vegetation indices and soil moisture are the most important predictors for estimating the AGB of grasslands in the IMAR. We highlight the important role of BGB for the C store in the Inner Mongolia grasslands.

Assimilating vegetation indices and canopy cover derived from remote sensing into the AquaCrop model for estimating rice yield

AbstractAccurate forecasting of rice yield using contemporary data is a paramount management tool. To achieve this objective, the AquaCrop, in conjunction with remote sensing (RS) data, was employed to predict yield in the paddy fields of SANRU. The required AquaCrop data, including ground (200 locations) and RS (Sentinel‐2, MODIS) data, were collected in two seasons, namely, 2020 and 2021. Leveraging Sentinel‐2 imagery, various vegetation indices (VIs) were computed, encompassing the normalized difference vegetation index (NDVI), rice growth vegetation index (RGVI) and soil adjusted vegetation index (SAVI). Additionally, MODIS and spatio‐temporal fusion algorithms (Spatial and Temporal Adaptive Reflectance Fusion Model [STARFM] and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model [ESTARFM]) were used to create a regular time pattern in satellite images taken on cloudy days. The results revealed that the yields observed for 2020 and 2021, on the basis of field data, were 4450 and 4370 kg/ha, respectively. Moreover, by leveraging the AquaCrop, the forecasted yield was ascertained both with and without the assimilation of RS data. Notably, the findings underscored that the incorporation of RS data significantly enhanced the model's predictive precision, particularly in estimating yield. The model's efficacy was demonstrated by its ability to forecast the end‐of‐season yield for the years 2020 and 2021, which yielded maximum RMSEs of 400 and 470 kg/ha, respectively.

Assessing the Post-Fire Recovery of Mined-Under Temperate Highland Peat Swamps on Sandstone

The Temperate Highland Peat Swamps on Sandstone (TPHSS) in the Sydney Basin of Australia provide critical ecological and hydrological services but are increasingly threatened by wildfires and human activities such as underground mining. The 2019–2020 wildfires severely impacted these swamps, raising concerns about their resilience and recovery. This study assessed the post-fire recovery of swamps and evaluated the ability of remote sensing techniques to determine recovery patterns. Specifically, it investigated differences in post-fire recovery patterns between swamps where groundwater levels and soil moisture contents were impacted by underground mining and those unimpacted by mining. Two mined and one non-mined swamp were studied. Soil moisture contents were monitored at five sites, and previously performed vegetation field surveys (2016–2022) were utilized. Remote sensing indices, including the Normalized Difference Vegetation Index (NDVI) and Soil Moisture Index (SMI), were calculated and compared with ground data to map post-fire responses. The results showed that hydrological conditions directly affect post-fire recovery, with slower recovery in mined swamps compared to non-mined ones. This study demonstrated that NDVI and SMI indices can effectively determine recovery patterns in terms of vegetation and hydrology. However, evaluating the recovery pattern of specific vegetation species requires more frequent field surveys.

Unraveling the non-linear relationship between seasonal deformation and permafrost active layer thickness

Accurate estimate of active layer thickness (ALT) is crucial for understanding permafrost and ecosystem responses to climate change. Interferometric Synthetic Aperture SAR (InSAR) technology can detect active layer freeze-thaw induced surface deformation with high accuracy, facilitating more accurate ALT estimation at the regional scale. Previous studies revealed a positive relationship between ALT and seasonal deformation in poorly drained Arctic soils. However, whether such relationship still holds in arid permafrost regions such as the Qinghai-Tibet Plateau (QTP) remains uncertain. Through synthesizing extensive field observations and remote sensing data, we find an overall negative correlation (r = -0.53, p < 0.01) between ALT and seasonal deformation in QTP, which tends to become more negative with sparser vegetation and drier soils, in contrast to the Arctic. After normalizing the climatic effect on ALT, we observe a decreasing sensitivity of seasonal deformation to active-layer changes with drier soils. Our study reveals a non-linear relationship between ALT and seasonal deformation across different permafrost regions, which helps to inform future development of InSAR-based permafrost applications.

Optimizing machine learning models for wheat yield estimation using a comprehensive UAV dataset

Timely and accurate wheat yield forecasts using Unmanned Aircraft Vehicles (UAV) are crucial for crop management decisions, food security, and ensuring the sustainability of agriculture worldwide. While traditional machine learning algorithms have already been used in crop yield modelling, previous research used machine learning algorithms with default parameters and did not take into account the complex, non-linear relationships between model variables. Especially, the combination of vegetation indices, soil properties, solar radiation, and wheat height at the field estimation has not been deeply analysed in scientific literature. We present a machine learning based wheat yield estimation model using comprehensive UAV datasets with the implementation of hyperparameter tuning to improve model performance. The performance of the models before and after optimisations was measured using the metrics RMSE, MAE and R2, and the results showed that the models improved after tuning. Furthermore, we find that the Random Forest (RF) and Extreme Gradient Boosting (XGBoost) models outperformed other examined models. Furthermore, a non-parametric Friedman test with a Nemenyi post-hoc test indicates that the best-performing algorithms for wheat yield estimation and prediction are RF and XGBoost models. In the final step, we utilised a SHapley Additive exPlanations approach to identify the direct impact of each input variable on the yield estimation model. Among the input variables, only the Red-Edge Chlorophyll Index, the Normalised Difference Red-Edge Index and wheat height were found to be of high explanatory power in predicting wheat yield. The optimised model is 7–12% more accurate in estimating wheat yields than traditional linear models.

Some strange substrates for macrofungi in the Pampa Biome - Brazil

Macrofungi usually are found growing on vegetal substrate, soil or dung, but sometimes they have the opportunity to explore some very unusual food. Studying mushrooms in São Gabriel municipality, southern Brazil (Pampa Biome) allow us to collect specimens in different substrata rarely or still not reported in the literature. The collections include Amanita muscaria subsp. flavivolvata growing in Eucalyptus plantation, Clitopilus argentinus growing on a wasp nest, Cheimonophyllum candidissimum growing on rock, Marasmius neosessilis on the shrub Asparagus officinalis (Angiosperm) and Panaeolina foenisecii in Capibara dung. The implications of such ecological relationship are discussed. To Clitopilus a review of the species reported to Brazil is presented.