Search Results (1,530)

High-resolution soil depth maps are valuable for environmental modelling, yet reliable data remains scarce in the tropics. This study evaluates the feasibility of mapping depth to bedrock (DTB) in Sri Lanka using a legacy dataset (n = 88) and global environmental covariates (n = 247). A robust machine learning workflow was employed—including feature selection, hyperparameter tuning, and a stacked ensemble of four algorithms (Random Forest, XGBoost, Cubist, SVM)—to test the limits of global data for local mapping. Despite rigorous optimization, the final ensemble model achieved a performance of R² = 0.197 (RMSE = 35.4 cm) under spatial cross-validation. While still modest, this result significantly outperforms existing global products and quantifies the “prediction gap” inherent in using ~1 km resolution global covariates to model micro-scale soil variability. An initial exploration involved log-transforming the target variable; however, following rigorous testing, the untransformed depth was modelled directly to avoid bias in back-transformation. A robustness experiment was further conducted, reducing predictors from 24 to 12, which degraded performance, confirming that the model captures complex, physically meaningful climatic interactions rather than fitting noise. The study concludes that while global covariates can capture regional meso-scale trends (explaining ~20% of variance), they are insufficient for resolving local micro-relief (<50 m). The resulting map and uncertainty products provide a critical “baseline” for national planning, but effectively demonstrate that future improvements will require investment in higher-resolution local covariates (e.g., LiDAR) rather than more complex algorithms. Full article

Litter input, including aboveground and belowground plant residues such as leaves, branches, and roots, is a major pathway of carbon return to forest soils. The prevailing paradigm in forest carbon management emphasizes litter input as the primary driver of soil organic carbon (SOC) sequestration. Here, litter input refers specifically to experimental litter manipulation, including litter-addition and litter-removal treatments. Although numerous experimental studies have examined the effects of litter manipulation on SOC, several limitations remain. By synthesizing 1555 global observations, we demonstrate that climate zone, not litter manipulation per se, is the dominant moderator of SOC fraction responses. Litter addition significantly increased labile fractions (light fraction: +60%) but left MAOC largely unchanged. Conversely, litter removal depleted labile pools yet failed to destabilize MAOC. This universal inertia of MAOC challenges the assumption that litter management directly enhances long-term carbon stability. Furthermore, we reveal a critical climate dependency: tropical forests show attenuated carbon gains under litter addition, while temperate systems are more responsive. Our findings necessitate a paradigm shift from uniform litter-based strategies to climate-zone-specific forest management, prioritizing the protection of existing soil carbon in vulnerable biomes over indiscriminate litter augmentation. Full article

This study demonstrates a hierarchical spectral modelling approach for predicting pasture nutrition metrics using TabPFN (Tabular Prior-Data Fitted Network), a transformer-based machine learning architecture. In the face of climate variability, aligning stocking rates with pasture resources is crucial for sustainable livestock grazing, requiring accurate assessments of both pasture biomass and nutrient composition. Our research, conducted across diverse growth stages at five tropical and subtropical savanna rangeland properties in Queensland, Australia, with native and introduced C4 grasses, employed a hierarchical sampling and modelling strategy that scales from laboratory spectroscopy to Sentinel-2 satellite predictions via uncrewed aerial vehicle (UAV) hyperspectral imaging. Spectral data were collected from leaf (laboratory spectroscopy) through field (point measurements), UAV hyperspectral imaging, and Sentinel-2 satellite imagery. Traditional laboratory wet chemistry methods determined plant leaf and stem nutrient content, from which crude protein (CP = total nitrogen (TN) × 6.25) and dry matter digestibility (DMD = 88.9–0.779 × acid detergent fibre (ADF)) were derived. TabPFN models were trained at each spatial scale, achieving validation ${\mathrm{R}}^2$ of 0.76 for crude protein at the leaf scale, 0.95 at the UAV scale, and 0.92 at the Sentinel-2 satellite scale. For dry matter digestibility, validation ${\mathrm{R}}^2$ was 0.88 at the UAV scale and 0.73 at the Sentinel-2 scale. A pasture classification masking approach using a deep neural network with 98.6% accuracy (7 classes) was implemented to focus predictions on productive pasture areas, excluding bare soil and woody vegetation. The Sentinel-2 models were trained on 462 samples from 19 site–date combinations across 11 field sites. The TabPFN architecture provided notable advantages over traditional neural networks: no hyperparameter tuning required, faster training, and superior generalisation from limited training samples. These results demonstrate the potential for accurate and efficient prediction and mapping of pasture quality across large areas (100 s–1000 s ${\mathrm{k}\mathrm{m}}^2$) using freely available satellite imagery and open-source machine learning frameworks. Full article

Vegetation net primary productivity (NPP) is a key indicator of ecosystem functioning in tropical rainforests and has important implications for carbon cycling and ecosystem stability. Examining the spatial and temporal variation in vegetation NPP and the factors associated with it can help inform ecosystem management and responses to climate change. In this study, Hainan Tropical Rainforest National Park (HTR), China, was selected as a representative tropical rainforest ecosystem. MODIS NPP data, Landsat imagery, meteorological variables, topographic factors, soil data, and socioeconomic indicators were integrated to analyze the spatiotemporal evolution of vegetation NPP from 2000 to 2023. The Theil–Sen Median trend analysis and Mann–Kendall test were applied to detect temporal trends, while the Optimal Parameter Geographical Detector (OPGD) model was used to identify dominant driving factors and their nonlinear interactions. The results showed that vegetation NPP in HTR exhibited an overall increasing trend during the study period, although short-term fluctuations occurred. Spatially, NPP was higher in the west and south and lower in the east and north. Elevation, soil type, and land use type were the main variables associated with this pattern. Moreover, interactions between natural and human-related factors accounted for more of the spatial variation in NPP than individual factors considered separately. These findings improve the understanding of vegetation productivity dynamics in tropical rainforest ecosystems and provide scientific insights for carbon sequestration enhancement, ecological conservation, and sustainable ecosystem management in tropical rainforests under global climate change. Full article

Tropical grasses are increasingly present in farming systems in Brazil. However, a national-scale assessment of this practice’s impact on soil health (SH) and soybean yield has been lacking. In this study, we conducted a meta-analysis of 55 studies published until February 2026, comprising field trials run in 33 locations in Brazil, aiming to assess the effects of deep-rooted tropical grasses as preceding crops on biological indicators of SH and soybean yield. Results showed that grasses (Urochloa spp. and Megathyrsus maximus) promote soybean yield by 15%, representing an average increase of 515 kg ha⁻¹ and an additional revenue of US$198 ha⁻¹. The analysis of forage grass species used, management system (single or intercropped), soybean cultivar (growth habit, life cycle, genetic modification), and edaphoclimatic controlling factors revealed positive effects of tropical grasses on soybean yield under all the study conditions and yield ranges. SH indicators also showed sizeable increment, notably the activity of arylsulfatase (+35%) and β-glucosidase (+31%), followed by acid phosphatase activity (+20%), microbial biomass carbon (+24%), and organic carbon (+11%). The results confirmed the beneficial effects of deep-rooted tropical grasses, highlighting their contribution to sustainable intensification in tropical farming systems due to their ability to enhance SH. This, in turn, leads to increased soybean yield under most agronomic and environmental conditions. Full article

Maize cultivation in tropical Oxisols during the second growing season faces significant climatic risks, where spatial heterogeneity in soil water retention often dictates economic viability. This study integrated a trimodal sensing approach, combining multispectral, thermal, and LiDAR data, with proximal physiological measurements to map isohydric responses and yield variability. Conducted in the Brazilian Cerrado, the research monitored a one-hectare maize field using UAV-based sensors alongside ground truth evaluations of gas exchange, leaf water potential, and soil moisture. Results revealed high yield variability (6.6 to 13.4 Mg ha⁻¹) primarily governed by clay content-mediated water availability. Maize exhibited strict isohydric behavior, maintaining homeostatic leaf water potential through preventive stomatal closure, which limited CO₂ assimilation in zones with lower water retention. A significant statistical decoupling was observed between plant height and final grain yield, as water stress impacted reproductive stages more severely than vegetative growth. Furthermore, the Temperature Vegetation Dryness Index (TVDI) served as a robust proxy for biomass vigor rather than mere water deficit. These results confirm that yield variability in tropical Oxisols was not a product of hydraulic failure, but rather a consequence of carbon limitation necessitated by the crop’s conservative hydraulic management to maintain leaf water potential within safe thresholds. Full article

Nitrogen (N) fertilization plays a key role in pasture productivity but also contributes to environmental losses, especially under tropical conditions. This study evaluated the effects of four N rates (0, 50, 75, and 100 kg N ha⁻¹) as urea on soil N dynamics, ammonia (NH₃) volatilization, nitrous oxide (N₂O) emissions, and biomass accumulation in Brachiaria brizantha cv. Piatã, cultivated in a clayey Oxisol in the Brazilian Savanna. The experiment was conducted over two pasture growth cycles during the late summer and early fall. NH₃ volatilization increased with the N rate and showed significant differences in the initial samplings of both cycles. N₂O emissions were low, strongly influenced by rainfall, and resulted in emission factors ≤ 0.3%. Soil NH₄⁺ and NO₃⁻ concentrations did not differ statistically among treatments. Biomass production increased over time on Cycle 2 but plateaued at greater doses, with no significant differences between treatments. The limited biomass response suggests physiological saturation or environmental constraints. Findings indicate that N losses and use efficiency are shaped by rainfall and plant demand. Full article

Maize–tropical grass intercropping has been adopted during the dry season as a strategy for soil cover; however, a knowledge gap remains regarding adequate nitrogen (N) supply and the efficiency of this system in degraded pasture areas. The objective of this study was to evaluate dry biomass, grain yield, and macronutrient concentrations in maize–tropical grass intercropping as a function of N rates applied as side-dressing in the dry season. The experimental design consisted of a randomized complete block design in a split-plot arrangement with four replications. Main plots comprised maize monoculture, maize intercropped with Urochloa ruziziensis (Congo grass), and maize intercropped with Megathyrsus maximus cv. Aruana (Aruana Guinea grass). Subplots consisted of N rates (0, 50, 100, and 150 kg ha⁻¹). Maize–Aruana intercropping showed a positive linear response to N rates for grain yield; specifically, the nitrogen rate of 150 kg ha⁻¹ resulted in a 71.71% increase in grain yield compared to the lack of nitrogen supply. Conversely, maize monoculture showed a negative linear response, where the highest N rate (150 kg ha⁻¹) resulted in a 68.83% reduction in grain yield compared to the lack of nitrogen supply. Despite yield potential being capped by seasonal water deficits and frost events, the intercropping systems maintained essential growth dynamics. Aruana grass provided a protective effect for maize development under stress. The findings demonstrate that N side-dressing in the maize–Aruana intercropping system in a minimum of 71.83 kg ha⁻¹ is an adequate strategy to enhance grain yield and biomass production. Full article

Legumes (Fabaceae) are key functional components of tropical forests due to their role in nitrogen fixation and nutrient cycling. This study provides an integrated assessment of forest legume diversity and its relationship with soil physicochemical properties across three protected areas in the Peruvian upper Amazon: the Alto Mayo Protected Forest (BPAM), the Cordillera Escalera Regional Conservation Area (ACR-CE), and the Shunté and Mishollo Forests Regional Conservation Area (ACR-BOSHUMI). Floristic studies were conducted in nine sectors ranging from 618 to 1729 m a.s.l. Soil samples were analyzed, and vegetation cover was quantified using high-resolution drone imagery with four vegetation indices derived from RGB data. We recorded eleven legume species from eight genera within the sampling plots, identifying Inga as the most frequent genus. Species diversity was highest in the ACR-CE, whereas BPAM showed lower richness and abundance. Multivariate analyses revealed that legume diversity was positively associated with higher soil pH, cation concentrations, and cation exchange capacity, but negatively associated with elevated Al³⁺ and Fe³⁺ levels. Vegetation indices effectively distinguished between vegetated and degraded areas, indicating higher legume occurrence in sites with greater canopy cover. These findings emphasize that soil fertility and vegetation structure are key drivers of legume diversity, with significant implications for conservation in the upper Amazon. Full article

The synergistic impacts of land use/land cover (LULC) transformations and weather pattern variabilities (WPV) represent a primary driver of hydro-geological instability, threatening agricultural productivity, soil conservation, and water quality. Disentangling the discrete contributions of these stressors to runoff and sediment yield (SY) remains a significant challenge, particularly in complex, confluence-proximal watersheds lacking major hydraulic regulations. This study investigates the Tirumakudalu Narasipura watershed in Karnataka, India, an agriculturally intensive system undergoing rapid peri-urbanization. Leveraging the process-based geospatial interface of the Water Erosion Prediction Project (GeoWEPP), we analyzed hydrological responses over a 24-year period (2000–2023) and projected future trajectories through 2030. To overcome the traditional constraints of GeoWEPP, which was developed for small-scale watersheds (<260 ha), we present a novel upscaling framework utilizing a multi-site multivariate temporal calibration of hydrological response variables to exploit its process-based precision in capturing distributed soil erosion and landscape heterogeneity. This approach is further reinforced by an ancillary data validation to minimize error propagation while model-upscaling. Our findings reveal projected increases in runoff and SY of 14.69% and 49.23%, respectively, between 2000 and 2030. Notably, the sub-decadal acceleration from 2023 to 2030 (17.32% for runoff and 18.51% for SY) underscores a shifting dominance where LULC-driven surface modifications now outweigh climatic variance in forcing hydrologic change. Furthermore, the study quantifies how anthropogenic interventions such as strategic crop selection, tillage intensity, and irrigation regimes act as critical determinants of topsoil preservation. These results provide a scalable, economically feasible framework for precision land stewardship and sustainable watershed management in rapidly developing tropical landscapes. Full article

Secondary succession in tropical forests is traditionally described as a linear process driven by time since disturbance. However, growing evidence suggests that recovery pathways depend strongly on historical and environmental contexts. We evaluated how disturbance legacies and edaphic constraints interact to shape successional trajectories in a tropical karst landscape of the Maya Forest, Mexico. We sampled 100 plots along a chronosequence, quantifying vegetation structure, floristic diversity, biomass (NDVI), disturbance legacies, and soil properties. Using unsupervised clustering (K-means) and multivariate ordination, we identified four contrasting ecological typologies that represent distinct successional states rather than transient stages. Our results show a pronounced dichotomy in vegetation dynamics following the abandonment of land-use practices: while some sites are experiencing diverse development due to positive forest legacies (Typology B), others remain stalled (Typology C), dominated by lianas, where biotic barriers inhibit tree regeneration despite decades of abandonment. Additionally, we documented an asynchronous recovery between floristic recovery and vertical development; in sites with edaphic constraints, forests reach high diversity and biomass but exhibit stunted growth (Typology D). This suggests that severe abiotic constraints—specifically high rockiness and shallow soils—limit the dominance of highly competitive species, thereby acting as a filter that maintains high levels of diversity despite structural limitations. Edaphic analysis confirmed that chemical fertility and physical constraints (rockiness and shallow depth) act as orthogonal filters. This explains the persistence of structurally constrained yet functionally mature forests as stable, edaphically determined outcomes. Overall, secondary succession in tropical karst is nonlinear and path-dependent, governed by a hierarchical filtering model where historical land use dictates community identity and physical substrate limits structural architecture. These findings highlight the need for trajectory-specific management and the abandonment of uniform expectations of forest recovery in karst landscapes. Full article

Predictive Digital Soil Mapping (PDSM) in Eastern Amazonia faces challenges due to its environmental complexity, difficult access, and scarce legacy data. While legacy soil maps contain valuable tacit knowledge, updating them requires methods that can handle uncertainty. This study evaluates the integration of old soil maps with machine learning to update soil information in Tracuateua, Pará, with a specific focus on the performance of ensemble learning and the explicit incorporation of uncertainty metrics in soil mapping units under hydromorphic influence, which, in addition to being difficult to access, are influenced by complex pedogenetic processes. We combined 270 sampling points, equivalent to the total pixels that captured the variability of soil mapping units, with environmental covariates and historical data. Several algorithms were tested, including an ensemble approach, to predict mapping units and quantify uncertainty through entropy and confusion indices. The ensemble model demonstrated improved stability and reduced classification uncertainty compared to single models, particularly in challenging hydromorphic environments. Although accuracy gains were modest, the models captured soil–environment relationships, with climate as: Annual Mean Temperature 22,000 years ago (Tmean_22k), relief: Channel Network Base Level (CNBL and altitude) and organism variables: Land Surface Temperature (LST) emerging as the main predictors. Spatialized uncertainty estimates, expressed through entropy and the confusion index, provide a practical decision-support tool for guiding field surveys and identifying areas of low mapping reliability. By explicitly transferring the pedologist’s mental model—encoded as tacit knowledge in legacy soil maps—into ensemble learning, this study presents a robust and transferable framework for updating soil maps in data-scarce tropical regions, balancing predictive performance, spatial consistency, and uncertainty-aware interpretation. Full article

Tropical cyclones kill thousands and inflict vast destruction annually. While ocean temperatures and atmospheric conditions dominate their formation and behaviour, forests’ potential influence has received little systematic attention. This review examines whether and how forests may affect tropical cyclone frequency, intensity, and behaviour. Support varies by mechanism and stage. Post-landfall effects have the strongest support: forests slow storms, moderate wind speeds and curb flooding through enhanced soil infiltration. Forests also influence storm tracks, though magnitudes are uncertain. Pre-landfall effects are less certain. These include processes that modify offshore humidity, temperature, and aerosols. The Biotic Pump theory proposes that forest cover creates pressure gradients drawing moisture inland, reducing its availability for ocean storms. Forest influences are likely to be most evident near thresholds for storm formation or intensification, where small perturbations in conditions can alter outcomes. This context-dependency reconciles divergent findings and aids the integration of forests into climate risk assessments. Forest conservation provides clear post-landfall protection; pre-landfall effects, while uncertain, further strengthen the case for protection and highlight research priorities. Full article

This study presents an Internet of Things (IoT)-assisted semi-open hydroponic system for cultivating Andrographis paniculata under tropical conditions, aiming to enhance biomass productivity, andrographolide (AG) yield, and production efficiency. IoT-assisted hydroponics, non-IoT hydroponics, and soil-based cultivation were compared in 10 m² greenhouses. The IoT system enabled real-time monitoring and adaptive regulation of temperature, relative humidity, light intensity, nutrient solution pH, and electrical conductivity (EC). IoT-assisted hydroponics achieved earlier harvest (≈90 days) and the highest fresh biomass yield (0.409 ± 0.014 kg m⁻²) while maintaining per-plant productivity (15.74 ± 0.54 g plant⁻¹) comparable to soil-based cultivation. Andrographolide concentration reached 25.58 ± 3.36 mg g⁻¹ DW (2.56% w/w), meeting pharmacopeial requirements. Owing to stable environmental regulation and tolerance to high planting density, the IoT system produced the highest areal AG productivity (209.5 mg m⁻²), representing a four- to tenfold increase over the other systems. Despite higher operational costs, IoT-assisted hydroponics achieved the lowest AG unit cost (≈6.77 USD g⁻¹). While most previous studies emphasize tissue-level AG concentration, system-level productivity and cost efficiency under realistic cultivation conditions remain insufficiently explored. Overall, IoT-enabled semi-open hydroponics provides a scalable and economically viable approach for medicinal plant production, bridging the gap between open-field cultivation and fully controlled plant factory systems. Full article

Granitic soils in the Highlands support the cultivation of Arabica coffee in northern Thailand; however, their geochemical and radiological properties are inadequately defined. This study examined major oxides, trace elements, natural radionuclides, and extractable phosphorus in granitic-derived coffee soils from the Agricultural Innovation Research, Integration, Demonstration, and Training Center (AIRID) in Chiang Mai. Twenty soil samples were obtained from 10 locations at two depth intervals (0–30 cm and 30–60 cm). Major and trace elements were analyzed via X-ray fluorescence (XRF), natural radionuclides were analyzed through high-purity germanium (HPGe) gamma spectrometry, and extractable phosphorus was determined using the Bray II method. The soils demonstrate remarkably high ⁴⁰K activity concentrations (1.2–1.9 kBq kg⁻¹) and increased K₂O contents (4.9–7.8 wt%), about three to five times more than worldwide soil averages according to Reimann & de Caritat, indicating enrichment from potassium-rich granitic rocks. Major oxide compositions suggest extensive tropical weathering, characterized by elevated SiO₂ (>60 wt%) and Al₂O₃ (>14 wt%), alongside significant depletion of CaO and MgO (<1 wt%). In topsoil, Bray II–extractable phosphorus constitutes 10–25% of total phosphorus and has a robust positive connection with P₂O₅ (R² = 0.95, p < 0.001), signifying surface accumulation and restricted vertical mobility. Multivariate analysis indicates lithogenic grouping of trace elements with negligible vertical redistribution. These findings establish a geochemical and radiological baseline for highland coffee soils in northern Thailand, with implications for soil fertility assessment, soil–plant transfer research, and evaluations of natural radioactive exposure related to coffee production. Full article