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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.
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