<p> </p><p><strong> </strong></p><p> </p><p><strong>Background. </strong>Carbon lost in form of carbon dioxide contributes to climate change corresponding to altered soil chemical properties and plant growth. Land-uses that minimize carbon loses are highly encouraged. Unfortunately, in the Kakamega-Nandi Forest Complex, the Kenyan government continues to promote shamba systems (now Plantation Establishment for Livelihood Improvement Scheme) where the forest adjacent communities are allocated bush land plots to provide land for small-scale agriculture, cash crop farming and planting of tree seedlings for a specified period of time inside the forest. <strong>Objective. </strong>This study analyzed six land-uses and their effect on the dynamics of soil chemical parameters using landsat images and recent soil geochemical surveys. <strong>Methodology. </strong>Land cover and vegetation changes were determined using a series of multispectral Landsat images. A total of seven sets of image datasets were downloaded from the Glovis web portal (<a href="http://glovis.usgs.gov/">http://glovis.usgs.gov/</a>) for the years 1985 to 2015 with the cloud cover ranging from 1 to 10% taken in the dry season. The land-use/cover classification scheme adopted was based on expertise knowledge and literature of land-use/cover activities.  <strong>Results. </strong>The results show that (i) small-small agriculture has increased while bush-land has decreased between 1985 and 2015; (ii) smallholder farming of maize, pasture and sugarcane depleted soil organic carbon whereas perennial tree plantations (regenerated forests) increased soil carbon stocks; (iii) nitrogen decreased in all tested land-uses except in maize plantations; (iv) phosphorus remained unchanged in all land-uses, potassium significantly decreased in tea plantations while sugarcane and regenerated forests land-uses had decreased soil calcium stocks. <strong>Implications. </strong>The study provides evidence for the review of the shamba system. <strong>Conclusion. </strong>The study has shown that land-use changes through the application of the shamba system alter the dynamics of soil chemical parameters key among them are soil organic carbon, nitrogen and calcium. Cultivation of annual crops decreases soil carbon stocks which may lead to an influx of carbon dioxide in the atmosphere and increased vulnerability to climate change.</p>

Forest measurements, especially in natural forests are cumbersome and complex. 100% enumeration is costly and inefficient. This study sought to find out reliable, efficient and cost-effective sampling schemes for use in tropical rain forest (TRF), moist montane forest (MMF) and dry woodland forest (DWF) in Kenya. Forty-eight sampling schemes (each combining sampling intensity (5, 10, 20, 30%), plot size (25, 50, 100, 400 m2) and sampling technique (simple random sampling, systematic sampling along North-South and along East-West orientations) were generated for testing estimates of forest attributes such as regeneration through simulation using R-software. Sampling error and effort were used to measure efficiency of each sampling scheme in relation to actual values. Though forest sites differed in biophysical characteristics, cost of sampling increased with decreasing plot size regardless of the forest type and attribute. Accuracy of inventory increased with decreasing plot size. Plot sizes that captured inherent variability were 5mx5m for regeneration and trees ha-1 across forest types but varied between forest types for basal area. Different sampling schemes were ranked for relative efficiency through simulation techniques, using regeneration as an example. In many instances systematic sampling-based sampling schemes were most effective. Sub-sampling in one-hectare forest unit gave reliable results in TRF (e.g. SSV-5mx5m-30%) and DWF (e.g. SSV-10mx10m-30%) but not in MMF (5mx5m-100%). One-hectare-complete-inventory method was found inevitable for regeneration assessment in montane forest.

This study sought to establish an efficient inventory protocol to estimate regeneration stock and dynamics in natural forests. Field and computer outputs were integrated to develop complete inventory protocol for selected natural forest types in Kenya. Inventory cost and precision for four plot sizes (5 m × 5 m, 10 m × 5 m, 10 m × 10 m and 20 m × 20 m) were determined and compared. Specific objectives were to determine (i) precision levels of estimating tree seedling density using different plot sizes across forest types; and (ii) optimum plot size which minimise both sampling error and inventory effort for use in each forest type. Seedling counts and time taken per plot were recorded systematically over 400, 200, 100 and 25 plots ha -1 for the respective plot sizes. Larger plots and their data were created by merging smaller ones through programming with R Software. Smallest population mean-variance, was obtained using data from 5 m x 5 m plots. Both precision and inventory effort varied with plot size used, but in reverse directions. Seedlings population mean errors were 15.4% of the mean for rain forest, 14.7% for moist montane forest and 9.9 % for dry forest type. Inventory cost decreased with increasing data compilation unit size, e.g., 50.42 hrs ha -1 for 25 m 2 unit to 3.21 hrs ha -1 for 400 m 2 unit in rainforest. Similar trend was observed in other forest types. Recommended plot sizes for tree seedlings are 75 m 2 ; 62.4 m 2 and 88.4 m 2 for Kakamega rain forest, Mt Elgon montane forest and Loruk dry woodland forest, respectively. These plot sizes gave acceptable uncertainty levels between ±11% and ±17% of mean estimate ha -1 . Tree diameter distributions from 5 m x 5 m plots revealed that tree component recruitment was irregular over time across forest types.