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Journal of Tropical Soils
Published by Universitas Lampung
ISSN : 0852257X     EISSN : 20866682     DOI : http://dx.doi.org/10.5400/jts.v25i1
Core Subject : Agriculture, Social,
Agriculture, Biological Sciences & Forestry Environmental Science
Journal of Tropical Soils (JTS) publishes all aspects in the original research of soil science (soil physic and soil conservation, soil mineralogy, soil chemistry and soil fertility, soil biology and soil biochemical, soil genesis and classification, land survey and land evaluation, land development and management environmental), and related subjects in which using soil from tropical areas.
Arjuna Subject : Ilmu Lingkungan (semua ketegori) - Konservasi Alam dan Lahan
Articles 812 Documents
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From Rice to Oil Palm: The Historical Evolution of Peatland Reclamation in the Berbak Delta, Indonesia Widjaja, Hermanu
JOURNAL OF TROPICAL SOILS Vol. 30 No. 3: September 2025 (in Progress)
Publisher : UNIVERSITY OF LAMPUNG

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Abstract

The Berbak Delta in Jambi Province, Indonesia, underwent large-scale peatland reclamation in the 1970s to develop rice fields through a transmigration program. Initially, rice production averaged 3–4 tons/ha but declined to less than 1 ton/ha due to the conversion of peat soils into acid sulfate soils. This decline was attributed to soil acidification resulting from pyrite oxidation, leading to a decrease in soil pH. Currently, rice cultivation persists in areas near rivers influenced by tidal brackish water. In response to declining yields, a technological package for acid sulfate soil management, including canal water management, pH improvement, and fertilizer application, was introduced. This increased rice yields to over 5 tons/ha in a 100-ha pilot project in 2005. However, by 2008, farmers began shifting to oil palm plantations due to higher income potential. This study examines the land-use change from rice fields to oil palm plantations and its impact on the livelihoods of the Berbak Delta communities. Land cover change analysis from 2013 to 2023 revealed a decrease in rice fields from 58.30% to 25.79% and an increase in oil palm plantations from 15.37% to 55.70%. Oil palm cultivation has demonstrated sustainability on reclaimed acid sulfate soils, leading to economic prosperity through improved infrastructure, housing, and increased private vehicle ownership.
Advanced Modeling of Potato Productivity Using Soil Physical Properties and Vegetation Index Transformations Sudarto, Sudarto; Putra, Aditya Nugraha; Fauziah, Dwi Christina; Nugroho, Agung; Suryoprojo, Adithya Riefanto; Prasetya, Novandi Rizky; Sugiarto, Michelle Talisia
JOURNAL OF TROPICAL SOILS Vol. 30 No. 3: September 2025 (in Progress)
Publisher : UNIVERSITY OF LAMPUNG

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Abstract

Global potato production reached approximately 383 million metric tons in 2025, with Indonesia contributing around 1.22 million metric tons (0.32% of global output). However, the sustainability of Indonesia’s potato production is increasingly threatened by soil quality degradation in key growing regions. Existing predictive studies have largely focused on soil physical properties, with limited incorporation of remote sensing technologies. This study investigates the potential of Unmanned Aerial Vehicle (UAV) as a high-resolution, non-invasive tool to estimate potato yield through vegetation index transformations. Utilizing a split-plot experimental design across elevation gradients, we integrated soil physical properties with UAV-derived vegetation indices—Visible Atmospherically Resistant Index (VARI), Green Leaf Index (GLI), and Normalized Green-Red Difference Index (NGRDI). Results reveal that Total Nitrogen (TN), Base Saturation, and Bulk Density significantly influence yield variability, and can be accurately estimated using NGRDI, GLI, and a modified GLI (GLI CS), respectively. A multiple linear regression model was developed to predict potato yield: y = 24.22 + 7.26(NGRDI) + 9.87(GLI) + 28.42(GLI CS). This research demonstrates the efficacy of UAV-based spectral analysis in enhancing yield prediction models, offering a scalable and precise approach for sustainable potato cultivation. Future work should incorporate machine learning to improve model robustness and assess applicability across varied agro-ecological contexts.

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