Africa, specifically Nigeria, has witnessed a dramatic increase in population over the last century, prompting efforts to ensure sustainable food production and quality. Concerns for soil sustainability and food security have led to the exploration of cost-effective methods, such as biochar, to enhance soil quality. Researchers in Nigeria and Africa as a whole have investigated biochar’s potential to improve soil fertility and crop performance across various agroecological zones. This paper aims to review recent biochar research priorities on soil fertility and crop performance with an emphasis on various sole biochar applications and combinations with fertilizers to determine the research gaps that need to be developed more in biochar research in Nigeria. From the papers reviewed, sole biochar applications and biochar + macronutrients and biochar + manure combinations were studied more dominantly, while biochar + micronutrients research projects were scanty despite their low content in the semi-arid soils of Nigeria. The studies were spread across the country with the majority taking place in derived savanna and humid forest, while Sudan savanna and Sahel savanna received less research attention despite being characterized by a low-fertile soil and vast area of land. Research involving BC in the context of Sahel savanna (SLS) and Sudan savanna (SS) soils is strongly encouraged in Nigeria. This research should encompass a wide range of investigations, including sole BC applications and combinations of BC with macronutrients, micronutrients, and manure, as well as exploring its potential as a slow-release fertilizer. Incorporating exclusive biochar in substantial amounts appears economically unfeasible within the context of local biochar production. However, it can be utilized in the synthesis of slow-release fertilizers, requiring smaller quantities and potentially offering cost-effectiveness. This approach enhances soil condition and crop productivity. Challenges are faced due to less commercial production as a result of inadequate power and structural facilities. Exploring the modification of local biochar for slow-release fertilizers through future research offers potential profitability.

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.

Accurate forest biomass estimation is essential for quantifying carbon stocks, guiding sustainable forest management, and informing climate change mitigation strategies. Kenya’s forests are diverse, ranging from Afromontane and mangrove ecosystems to dryland woodlands and plantations, each presenting unique challenges for biomass measurement. This review synthesizes literature on field-based, remote sensing, and machine learning approaches applied in Kenya, highlighting their effectiveness, limitations, and integration potential. A systematic search across multiple databases identified peer-reviewed studies published in the last decade, screened against defined inclusion and exclusion criteria. The main findings are (1) Field-based techniques (e.g., allometric equations, quadrat sampling) provide reliable and site-specific estimates but are labor-intensive and limited in scalability. (2) Remote sensing methods (LiDAR, UAVs, multispectral and radar imagery) enable large-scale and repeat assessments, though they require extensive calibration and investment. (3) Machine learning and hybrid approaches enhance prediction accuracy by integrating multi-source data, but their success depends on data availability and methodological harmonization. This review identifies opportunities for integrating field and remote sensing data with machine learning to strengthen biomass monitoring. Establishing a national biomass inventory, supported by robust policy frameworks, is critical to align Kenya’s forest management with global climate and biodiversity goals.

A three-year field trial was conducted between 2014 and 2017 in the Ellembelle and Jomoro districts of the Western region of Ghana where rubber production is common to determine the optimum population density of plantain when grown in combination with immature rubber tree crops. The trials were arranged in a Randomized Complete Block Design with 3 replications. The treatments were sole rubber, sole plantain, and three intercrops of one row of plantain in between two rows of rubber, two rows of plantain in between two rows of rubber, and three rows of plantain in between two rows of rubber. The rubber clone used was GT1 while the variety of plantain used was false horn. The results showed that population density of plantain had significant effect on the growth of the associated rubber. Growing plantain at closer spacing of 1.5m under the high-density plantain treatment significantly increased plantain yield compared to the other cropping systems. There was a significant positive relationship between population density of plantain and the rubber tree growth and development. The optimum population density of plantain when intercropped with rubber was 1,666/ha. The study showed intercropping was advantageous over sole cropping for both crops.