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SUMMARY To find a principal component (PC) that quantifies the degree of soil degradation, we analyzed various physicochemical characteristics of soils over a land degradation gradient related to aboveground vegetation in the Sakacrat Environmental Research Station (SERS), Thailand. The aboveground vegetative types representing the degradation gradient were bare ground (BG, highly degraded), dry dipterocarp forest (DDF, moderately disturbed) and dry evergreen forest (DEF, the original vegetation). Soils under these vegetative types were sampled in February (dry season). March just after temporal precipitation) and June (rainy season) 2001. Through the period of this research, the degradation was consistently explained by sandy texture, high bulk density, lower pH, high exchangeable acidity, poor mineral and organic nutrients and dryness. Principal component analysis (PCA) was applied to determine significant principal components (PCs) that clarify the differences in soil properties between the vegetative ...

Multivariate profiling of soil bacterial communities provides information that will possibly contribute to the conservation and rehabilitation of the environment. In the Sakaerat Environmental Research Station, Thailand, the land degradation gradient, represented by a dry evergreen forest (DEF) and bare ground (BG) as a result of deforestation and subsequent land degradation was determined. Profiling was done with the antibiotic resistance most probable number (MPN) method (method 1), the antibiotic disc diffusion method (method 2) and the Biolog method. Compread to the DEF soil, the BG soil had significantly lower moisture content and pH values (at p=0.05), and total C and bacterial MPN count (at p=0.10). Profiles of the soils obtained by the antibiotic and the Biolog methods were analyzed. The profiling methods scored Wilk’s lambda values of 0.001 (method 1, p=0.086), 0.004 (method 2, p=0.157) and 0.000 to 0.110 (Biolog, p=0.040 to 0.708 at 0.2 to 1.4 average well color development values, AWCDs), then gave principal component score plots showing that all the methods successfully determined the degradation gradient with comparable efficiency. Redundancy analysis ordination diagrams for the above data sets indicated that the soil environmental factors that significantly correlated to the bacterial community profiles were: moisture content (p

Soil dehydrogenase activity is a good indicator of overall microbial activity in soil, and it can serve as a good indicator of soil condition. However, seasonal changes in soil moisture content may have an effect on soil dehydrogenase activity, making an accurate assessment of soil condition difficult. In this study, we attempted to determine the significance of soil dehydrogenase activity for assessing soil condition, and we attempted to find a way to account for the influence of soil moisture content on soil dehydrogenase activity. Soils were sampled in dry evergreen forest (original vegetation), bare ground (severely degraded) and Acacia plantation plots established on bare ground in 1986 and 1987 in Sakaerat, Thailand. Soil physico-chemical characteristics and dehydrogenase activity in the Acacia plantation soil had few differences from those in the evergreen forest soil. Soil dehydrogenase activity varied significantly between the bare ground and the forests regardless of the season (wet or dry), while the season did not produce a significant variation in soil dehydrogenase activity, as determined by repeated measures analysis of variance (p=0.077). The physico-chemical data provided the first principal component as a good measure of soil fertility. Values of soil dehydrogenase activity significantly cor- related to scores of the soil samples of the first principal component (R=0.787, p

In this study, we attempted multivariate color profiling of soils over a land degradation gradient represented by dry evergreen forest (original vegetation), dry deciduous forest (moderately disturbed by fire), and bare ground (severely degraded) in Sakaerat, Thailand. The soils were sampled in a dry-to-wet seasonal transition. Values of the red-green-blue (RGB), cyan-magenta-yellow-key black (CMYK), L*a*b*, and hue-intensity-saturation (HIS) color models were determined using the digital software Adobe Photoshop. Land degradation produced significant variations (p