Understanding how elevation gradients and soil depths influence soil organic carbon stocks (SOCS) and total nitrogen stocks (TNS) is essential for sustainable forest management (SFM) and climate change mitigation. This study investigated the effects of elevation and soil depth on SOCS and TNS in the Mount Kenya East Forest (MKEF). A stratified systematic sampling approach was applied, involving collection of 38 soil samples from two depths (0–20 cm and 20–40 cm) across three elevation zones: Lower Forest (1700–2000 m), Middle Forest (2000–2350 m), and Upper Forest (2350–2650 m). Samples were analysed for bulk density (BD), pH, texture, soil organic carbon (SOC), and total nitrogen (TN), using standard laboratory methods. In topsoil (0–20 cm), SOCS ranged from 109.28 ± 23.41 to 151.27 ± 17.61 Mg C ha⁻¹, while TNS varied from 8.89 ± 1.77 to 12.00 ± 2.46 Mg N ha⁻¹. In subsoil (20–40 cm), SOCS ranged from 72.03 ± 19.90 to 132.23 ± 11.80 Mg C ha⁻¹, with TNS varying between 5.71 ± 1.63 and 10.50 ± 1.90 Mg N ha⁻¹. SOCS and TNS increased significantly with elevation (p < 0.05), exhibiting the following trend: Lower Forest < Middle Forest < Upper Forest. Topsoil consistently stored significantly higher SOCS than subsoil (p < 0.05), emphasizing the critical role of surface soils in carbon sequestration. Regression analysis revealed a significant positive relationship between SOCS and TNS (R² = 0.84, p < 0.001). Both SOCS and TNS were positively correlated with elevation, SOC, TN, and total annual precipitation (TAP), but negatively correlated with BD and mean annual temperature (MAT). These findings provide baseline data for monitoring SOCS and TNS in the MKEF, offering insights into sustainable forest management strategies to improve soil health and enhance climate change mitigation efforts.

The present study aimed to contribute to the diagnosis and advance the knowledge of the impacts of land use change and climate change on the tropical longleaf forest biome at the continental scale in South America (Biome 1 according to the WWF classification) for realizing scientific progress in the search for convincing strategies and actions by different actors for the preservation of forests in the continent. The status and climate of the area, which harbors the tropical longleaf forests of South America, were assessed. Moreover, volumetric soil moisture (VSM) was evaluated through maps and simulation using the autoregressive integrated moving average model (ARIMA). Furthermore, future climate scenarios were predicted based on El Niño–Southern Oscillation phenomena, meteorological systems, and scientific evidence, such as the shared socioeconomic pathways (SSPs) and sociopolitical dynamics evident in the region from the case analysis of the Brazilian states of Acre and Rio de Janeiro. An increase was noted in the temperature and range of precipitation variation in the biome. ARIMA analysis indicated changes of up to 0.24 m³ m⁻³ and an increased range of future VSM values. The December–January–February (DJF) quarter recorded the highest VSM median with the measurement scale of 0.05 to 0.44 m³ m⁻³, while the June–July–August (JJA) quarter recorded the lowest value. The regions of the biome with the lowest VSM values included southern Amazon (Ecuador, Peru, and the Brazilian states of Acre, Mato Grosso, Pará, and Maranhão), Brazilian Atlantic Forest, Southeast Region, and the Brazilian state of Bahia.

Although numerous studies have reported an increased yield upon adoption to improved agricultural practices (IAPs), yet smallholder farmers face limited access to this information. The objective of this study was to assess smallholder farmers' awareness and adoption of IAPs. A total of 206 active and registered households were surveyed by mixed sampling in the Mbeya region. The data was collected from smallholder farmers using the ODK collect tool through a well-structured questionnaire. The probit model and One-way ANOVA test were performed to identify predictor variables. Results pointed out that farming period, top dressing, flood exposure, fallowing time, soil information on IAPs, and knowledge about soil type showed a significant difference (p < 0.05) on the farmers’ adoption of IAPs. Moreover, 81% and 72% of the smallholder farmers surveyed showed a lack of awareness regarding the specific soil type and general soil information related to their farmland, respectively. Only 24% of farmers relied on agricultural experts (extension service officers) to get information on IAPs whereas 65% of the respondents were unfamiliar with neither Agricultural Extension Officers, non-governmental organizations (NGOs), Tanzania Agricultural Research Institute (TARI), nor Researchers from Higher Learning Institution (RHLI). About 59% of the female-headed households were found to have a better soil information on IAPs than men at a 5% significant level. Age and education level do not affect farmers’ adoption of IAPs with 72.5% of respondents motivated to adopt various IAPs in the surveyed area. This study highlights the need to highly consider agricultural extension officers as important agents in linking smallholder farmers with NGOs programs, agricultural research findings, and government programs to improve their livelihood. Graphical abstract

Reliable national-scale, multi-depth soil property maps remain scarce across much of Africa, particularly in arid regions. Here, we present the first national-scale digital soil mapping (DSM) for Namibia: sixteen physical and chemical soil properties predicted on a 90 m prediction grid for three depth intervals (0–30, 30–60 and 60–100 cm). The framework draws on 4958 legacy profiles and augerings and 65 covariates, including locally produced datasets that capture soil–environment relationships not fully represented by global covariates. A semi-automated workflow combined Random Forest modelling in Google Earth Engine with R-based depth harmonisation, Boruta-feature selection and hyperparameter tuning. Performance and bootstrap variability were quantified over 20 bootstrap iterations per property–depth combination, with pedological evaluation and independent validation. pH was the most consistently predicted property, and base saturation, sand and silt captured useful broad spatial patterns, whereas bulk density, organic carbon, phosphorus, magnesium and clay were less predictable, and electrical conductivity and sodium showed low predictive skill. Performance generally declined with depth, and bootstrap variability was highest in sparsely sampled regions and deeper layers. Against independent samples from arable land, root mean square error was lower than SoilGrids in all 14 property–depth combinations evaluated and lower than iSDA in 8 of 12. Predicted patterns were consistent with known Namibian soil–landscape relationships and pedogenic processes, except for sodium. National-scale DSM is therefore feasible in data-sparse arid environments when harmonised legacy data are combined with global and locally relevant covariates, using simple but operationally robust methods. The products are intended for national and regional assessment, rather than site-specific decisions, and the modular workflow supports future updates and is potentially adaptable to other data-limited regions.