Integration of biophysical and socio‐economic factors to assess soil erosion hazard in the Upper Kaligarang Watershed, Indonesia

Abstract

Soil erosion is one form of land degradation, which is caused by the interacting effects of numerous factors such as biophysical characteristics and socio-economic condition of a particular watershed. Previous erosion studies focused on the use of soil erosion models (e.g. USLE, EUROSEM, SLEMSA etc.), which have been developed under local conditions (e.g. United States, Europe, Africa, etc) and mostly use only biophysical factors as inputs to the models. In this study, a methodology that integrates both biophysical and socio-economic aspects into a framework for soil erosion hazard assessment using principal component analysis (PCA) is described. The analysis is done at the land unit level. With the particular conditions of the study area that is characterized by Inceptisols and Alfisols soil types, nine different land uses with mixed vegetation and forest area dominant in the steep slope, high annual rainfall (>2500 mm), high population with mostly low income and low education, were considered. These were used in formulating a soil erosion hazard index (EHI) equation which relates a number of key factors consisting of biophysical and socio-economic variables, namely soil texture, slope steepness, land cover, soil conservation practices, income and farmers' knowledge. Weighting and scoring of these key factors were used to develop the EHI equation and to calculate an index value of erosion hazard for every land unit. Results indicate that more than 60% of the area has erosion hazard ranging from moderate to very severe, and most of the land units with high erosion hazard were found at the mountain areas. It was also found that erosion hazard was severe in areas with high silt content, followed by high rainfall and steep slope, low crop cover without any soil conservation practices coupled with lack of awareness on soil erosion and low income. The key factors identified and level of erosion hazard obtained can be used to formulate conservation measures in critical areas which are prone to soil erosion.

Principal Component and Multiple Regression Analyses for the Estimation of Suspended Sediment Yield in Ungauged Basins of Northern Thailand

“…Moreover, the PCA has been used in the present study as the preliminary step in the development of a prediction model [62]. The sequence of the main steps involved in the PCA, as applied by Halim et al [38], were adapted and are described below:…”

“…The PCA with Varimax rotation is performed to identify the principal components (PCs) or subsets from a larger data set. For selecting the dominant factors, Kaiser's criterion or the eigenvalues rule, i.e., only components with eigenvalues of 1.0 or more are retained for further investigation [38,67,68], was employed.…”

“…The statistical method for reducing a large number of interrelated variables into a smaller number of dominant variables is called principal components analysis (PCA) and has been used in many areas of scientific research [17,[34][35][36][37][38][39][40]. Recently, Tayfur et al [41] investigated sediment load prediction and generalization from the laboratory scale to the field scale using principle component analysis (PCA) in conjunction with data-driven methods of artificial neural networks and genetic algorithms.…”

Trade-Offs in Multi-Purpose Land Use under Land Degradation

“…Agriculture is the prime consumer of the world's water and land resources and is an important source of greenhouse gas (GHG) emissions [21]. Land conversion to agriculture is the principal driver behind deforestation worldwide.…”

“…For example, in only five years, around 1 Mha (about 5%) of arable land in Indonesia was converted to urban use to meet the increasing demands of industrial and infrastructural development [21]. The sealing of agricultural land surfaces leads to a shift in the trade-off curve in Figure 3 towards the origin when it comes to the delivery of ESSs.…”