Support vector machines and generalized linear models for quantifying soil dehydrogenase activity in agro-forestry system of mid altitude central Himalaya

Abstract

In natural ecosystems, the linkages between inputs of carbon from plants, soil moisture (SM) and microbial activity are central to our understanding of nutrient cycling. Predictions of microbial activities in soil are important as they indicate the potential of the soil to support biochemical processes that are essential for the maintenance of soil fertility as well as productivity. The dehydrogenase activity (DHA) in soil provides information on microbial activities of the soil. However, estimation of DHA activity over complex terrain such as soils of the central Himalaya is not always possible due to very harsh environment and climatic conditions. In this study, the attempts were made to estimate the DHA in the soil of mid altitude central Himalaya using computational intelligence techniques. The linear and non-linear correlation results indicate that the fluctuations in SM and organic carbon (OC) in the root zone affect DHA and can be used as predictors for DHA. Therefore, the performances of support vector machines (SVMs) and generalized linear models (GLMs) were attempted for the prediction of DHA over mid altitude central Himalaya using information of SM and OC. The results showed that the SVM was giving a much better performance than GLM using SM and OC and could be promising and cost effective approach for soil DHA prediction over complex ecosystem. Our results are also of considerable scientific and practical value to the wider scientific community, given the number of practical applications and research studies in which SM and OC datasets are used.