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Pedotransfer functions for predicting tropical soil water retention: A case study in upper Citarum watershed, Indonesia

Mulyono Asep, Suriadikusumah Abraham, Harryanto Rachmat, Djuwansah Muhammad R.

Journal of Water and Land Development

2020

no. 45

76--85

Tropical regions such as Java, Indonesia, still lack publication of soil water retention (SWR) information, particularly at upper Citarum watershed. The SWR is one of the critical elements in water storage and movement in the soil and very important to solve ecological and environmental problems. However, getting the access requires a lot of laboratory measurement that is time-consuming and expensive. Therefore, utilizing pedotransfer functions (PTFs) to estimate the water in the soil is needed. This study aims to define soil properties related to the SWR and to evaluate the performance of existing PTFs in predicting SWR. The study was carried out at agroforestry land system soil at upper Citarum watershed, Indonesia. Ten point and two continuous existing PTFs developed for tropical regions were applied in this study. Pearson's correlation (r), mean error (ME), root mean square error (RMSE), and modelling efficiency (EF) were used for evaluation. Cation exchange capacity (CEC), organic carbon (OC), bulk density (BD), and clay were considered as potential soil properties for soil water retention prediction. The performance of PTFs by MINASNY, HARTEMINK [2011] at matric potential of –10 kPa and BOTULA [2013] at matric potential of –33 kPa and –1500 kPa were recommended for point PTFs, while PTFs by HODNETT, TOMASELLA [2002] was for continuous PTFs in predicting SWR. The accuracy of the point PTFs is almost better than the continuous PTFs in predicting SWR in agroforestry land system soil at upper Citarum watershed, Indonesia.

Soil Quality under Agroforestry Trees Pattern in Upper Citarum Watershed, Indonesia

Mulyono Asep, Suriadikusumah Abraham, Harriyanto Rachmat, Djuwansah Muhammad Rahman

Journal of Ecological Engineering

2019

Vol. 20, nr 1

203--213

This paper discusses the setting up of a multivariate statistical method in selecting the useful soil quality indicators for soil quality assessment under agroforestry pattern. The of soil quality has been recognized as a tool to determine the sustainability of land resources, especially in agroforestry development. The study was carried out at Upper Citarum Watershed of Bandung district, West Java province, Indonesia. The soil samples were taken with purposive sampling under agroforestry pattern. Principal component analysis (PCA) was used as the multivariate statistical method to identify the minimum data set (MDS); scoring of each indicator, and data integration in the index of soil quality. The MDS consisted of four soil chemical indicators and represented 83.6% of the variability of data, i.e., pH, and exchangeable Calcium (exch Ca), organic Carbon (org C), and exchangeable Natrium (exch Na) respectively. The soil quality index (SQI) was categorized under agroforestry pattern as moderate. The artificial agroforestry-based coffee with an intercropping system (timber woods, multi purpose trees and horticultures) provides better soil quality.