Incidences of forest fires have increased in recent decades largely as a result of climate change and human factors, resulting in great environmental and socioeconomic losses. Post-fire forest restoration is therefore indispensable for maintaining forest ecological integrity and for the sustainability of the affected forest landscapes. In this study, we conduct a systematic review of the available literature on forest restoration in the past two decades (2002–2022) and propose a comprehensive framework for consideration in forest restoration after the occurrence of forest fires. The Preferred Reporting Items for Systemic Reviews and Meta-Analyses (PRISMA) model was adopted for this study, where three academic literature databases (Scopus, CAB Direct, Web of Science), the Google Scholar search engine, and specialized websites were used for literature searches. A final list of 36 records from the initial 732 was considered for this study after the screening stage and subsequent inclusion/exclusion of articles as per the stipulated eligibility criteria. The study findings reveal a dearth of information in the field of post-fire forest restoration in an integrated, balanced, and comprehensive manner, as there was no single methodology or unified protocol that guides post-fire forest restoration. There was also a notable bias in the geographical distribution of the relevant studies in restoration as influenced by economic prosperity, political stability, and scientific and technical advancement. This study recommends a 6-criteria comprehensive framework with 29 indicators for post-fire forest restoration based on the reviewed studies. The criteria integrate environmental, economic, social, cultural and aesthetic, management, infrastructure, and education objectives in their design and implementation for better outcomes in achieving the restoration goals.

Maize production in low-yielding regions is influenced by climate variability, poor soil fertility, suboptimal agronomic practices, and biotic influences, among other limitations. Therefore, the assessment of yields to various management practices is, among others, critical for advancing site-specific measures for production enhancement. In this study, we conducted a multiseason calibration and evaluation of the DSSAT–CERES-Maize model to assess the maize yield response of two common cultivars grown in Trans Nzoia County in Kenya under various agricultural strategies, such as sowing dates, nitrogen fertilization, and water management. We then applied the Mann–Kendall (MK), and Sen’s Slope Estimator (SSE) tests to establish the yield trends and magnitudes of the different strategies. The evaluated model simulated long-term yields (1984–2021) and characterized production under various weather regimes. The model performed well in simulating the growth and development of the two cultivars, as indicated by the model evaluation results. The RMSE for yield was 333 and 239 kg ha⁻¹for H614 and KH600-23A, respectively, representing a relative error (RRMSE) of 8.1 and 5.1%. The management strategies assessment demonstrated significant feedback on sowing dates, nitrogen fertilization, and cultivars on maize yield. The sowing date conducted in mid-February under fertilization of 100 kg of nitrogen per hectare proved to be the best strategy for enhancing grain yields in the region. Under the optimum sowing dates and fertilization rate, the average yield for cultivar KH600-23A was 7.1% higher than that for H614. The MK and SSE tests revealed a significant (p < 0.05) modest downwards trend in the yield of the H614 cultivar compared to the KH600-23A. The eastern part of Trans Nzoia County demonstrated a consistent downwards trend for the vital yield enhancement strategies. Medium to high nitrogen levels revealed positive yield trends for more extensive coverage of the study area. Based on the results, we recommend the adoption of the KH600-23A cultivar which showed stability in yields under optimum nitrogen levels. Furthermore, we recommend measures that improve soil quality and structure in the western and northern parts, given the negative model response on maize yield in these areas. Knowledge of yield enhancement strategies and their spatial responses is of utmost importance for precision agricultural initiatives and optimization of maize production in Trans Nzoia County.

The Mount Kenya forest ecosystem (MKFE), a crucial biodiversity hotspot and one of Kenya’s key water towers, is increasingly threatened by climate change, putting its ecological integrity and vital ecosystem services at risk. Understanding the interactions between climate extremes and forest dynamics is essential for conservation planning, especially in the Mount Kenya Forest Ecosystem (MKFE), where rising temperatures and erratic rainfall are altering vegetation patterns, reducing forest resilience, and threatening both biodiversity and water security. This study integrates remote sensing and machine learning to assess historical vegetation changes and predict areas at risk in the future. Landsat imagery from 2000 to 2020 was used to derive vegetation indices comprising the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil-Adjusted Vegetation Index (SAVI), and Bare Soil Index (BSI). Climate variables, including extreme precipitation and temperature indices, were extracted from CHIRPS and ERA5 datasets. Machine learning models, including Random Forest (RF), XGBoost, and Support Vector Machines (SVM), were trained to assess climate-vegetation relationships and predict future vegetation dynamics under the SSP245 climate scenario using Coupled Model Intercomparison Project Phase 6 (CMIP6) downscaled projections. The RF model achieved high accuracy (R ² = 0.82, RMSE = 0.15) in predicting the dynamics of vegetation conditions. Model projections show a 49–55% decline in EVI across forest areas by 2040, with the most pronounced losses likely in lower montane zones, which are more sensitive to climate-induced vegetation stress. Results emphasize the critical role of precipitation in sustaining forest health and highlight the urgent need for adaptive management strategies, including afforestation, sustainable land-use planning, and policy-driven conservation efforts. This study provides a scalable framework for modelling climate impacts on forest ecosystems globally and offers actionable insights for policymakers.

Studies on traditional use of medicinal plants can provide leads towards discovery of pharmaceutical drugs for treatment of both human and livestock diseases. This study sought to identify and document the use of medicinal plants by residents of Mwala Sub-county in Machakos County, Kenya. The research employed cross-sectional research design where simple random sampling was used to select the households to be sampled. A sample size of 61 households was used for the study and data collected by use of semi-structured questionnaires. Voucher specimens were collected, pressed, dried, mounted and identified at the Kenya Forestry Research Institute (KEFRI) Herbarium. Microsoft Excel Spreadsheet and Statistical Package for Social Sciences (SPSS) were used for data analysis and results presented in the form of tables and graphs. The study focused on identifying medicinal plants used, diseases treated, parts of the plant used, and methods of preparation and routes of administration. Research findings documented a total of 51 plants species from 31 families used to treat 38 different disease conditions in the study area. The commonly used plant species was Aloe secundiflora (11.1%) while Malaria (19.8%) was the most cited disease. The most used plant parts were the leaves (52.8%) and bark (19.4%). About 75.4% of the respondents acquired knowledge on medicinal plant use through apprenticeship from relatives. With the stocks of medicinal plants declining in the area, there is need for conservation, domestication and better management of key medicinal plants. Additionally, documentation of the indigenous knowledge is necessary to ensure intergenerational benefits from the herbal medicines.