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 smallholder farming systems in Sub-Saharan Africa (SSA) are highly diverse and heterogeneous in terms of biophysical and socio-economic characteristics. This study was conducted in upper Eastern Kenya (UEK) to categorize farm households and determine the influence of socio-economic characteristics (SeC) and soil fertility management practices (SFMP) on soil fertility across farms. Conditioned Latin hypercube sampling (cLHS) was performed to determine 69 soil sampling sites within Meru and Tharaka Nithi counties. From each household (whose field soil sample was obtained), data relating to resource endowment and soil fertility management were collected through a household questionnaire survey. Standard laboratory procedures were used to analyse soil samples. Data reduction was performed using categorical principal component analysis (CATPCA) (for SeC and SFMP) and standard principal component analysis (PCA) (for soil properties). Two-step cluster analysis identified three distinct farm categories or farm types (FT), namely, low fertility farms (FT1), moderately fertile farms (FT2), and fertile farms (FT3). The correlation of clusters against soil properties was significant across pH, soil organic carbon (SOC), cation exchange capacity (CEC), available P, plant available K, and exchangeable bases. FT1 had low SOC, pH, CEC and available P (soil characteristics), low usage of fertilizer and manure (soil fertility management), and smaller household size, lower income, and smaller farm size (socio-economic). FT2 had lower SOC (compared to FT3) and available P. In terms of soil fertility management, FT2 had higher cases of fallowing and composting with moderate fertilizer usage. Households in this category had moderate income, family size, and land size (socio-economic). FT3 had relatively high SOC, pH, CEC, and mineral nutrients. This farm type was characterized by high fertilizer use (soil fertility management) as well as larger household size, higher income, and larger farm size (socio-economic). The results indicate the importance of nutrient management in enhancing soil quality. Delineation and characterization of farms based on the various parameters including resource endowment reveal imbalanced farm resource flows, suggesting a need for locally tailored interventions suited for location-specific conditions to facilitate improved targeting of soil fertility-enhancing technologies and sustainable crop production regimes. While fertilizer is one of the most critical inputs for enhancing agricultural production, it is a major contributor to nitrous oxide emissions from agriculture and can have negative environmental effects on soil biota and water sources. Farmers’ knowledge on the use of fertilizer is thus necessary in developing strategies (such as integrated approach) to promote its efficient use and minimize its detrimental impacts.

Soil exchangeable acidity (EA) is an indicator of aluminium toxicity potential in acidic soils. Predicting the distribution and dynamics of EA is needed for the identification and management of acidic soils. In this study, we used datasets of 355 pedons from across Ghana and the Cubist rule‐based algorithm to generate pedotransfer functions (PTFs) of EA. Eight soil properties (pH, organic carbon, calcium, sodium, magnesium, total exchangeable bases, cation exchange capacity [CEC] to clay ratio and soil depth) were used to predict EA. We first used the whole dataset to construct generic PTFs and then stratified the dataset based on World Reference Base‐Reference Soil Groups (WRB‐RSGs) to generate soil‐specific PTFs. Goodness‐of‐fit statistics comprising the root mean squared error (RMSE), Lin's concordance correlation coefficient () and coefficient of determination ( R ² ) were used to evaluate the prediction accuracy and reliability of the developed PTF models on both calibration and validation datasets. The Fluvisols EA‐PTF exhibited lower performance metrics in the validation (RMSE = 0.17 cmol _c kg ⁻¹ , = 0.19, R ² = 0.24), whereas the EA‐PTFs for all other WRB‐RSGs and whole dataset had above‐average performance metrics in the validation (0.05 ≤ RMSE ≤ 0.97 cmol _c kg ⁻¹ ; 0.34 ≤ ≤ 0.94; 0.52 ≤ R ² ≤ 0.96). Soil pH sufficed for predicting EA in soils with pH above 5.0, but in soils with a pH < 5.0, the levels of exchangeable bases (e.g., Na ⁺ , K ⁺ , Mg ²⁺ , Ca ²⁺ ), CEC to clay ratio (CCR) and soil depth improved the prediction of EA. The developed EA‐PTFs are useful for estimating the missing values of EA in soil databases.

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