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All Journal Journal of Tropical Soils Caraka Tani: Journal of Sustainable Agriculture
Prasetya, Novandi Rizky
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Agriculture, Biological Sciences & Forestry Environmental Science

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Integrating Soil Properties and Vegetation Indices for Modeling Potato Productivity 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
Publisher : UNIVERSITY OF LAMPUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5400/jts.2025.v30i3.159-173

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 primarily focused on soil chemical properties, with limited incorporation of remote sensing technologies. This study investigates the potential of Unmanned Aerial Vehicle (UAV) as a high-resolution, non-destructive tool for estimating potato yield using vegetation index transformations. Utilizing a split-plot experimental design across elevation gradients, we integrated soil 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, 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 = 24.22 + 7.26(NGRDI) + 9.87(GLI) + 28.42(GLI CS). This research demonstrates the efficacy of UAV-based spectral analysis in improving yield-prediction models, offering a scalable, precise approach for sustainable potato cultivation. Future work should incorporate machine learning to improve model robustness and assess applicability across varied agro-ecological contexts.
New Emerging and Comprehensive Land Mapping Unit at Detailed Scale: Integrating Random Forest Analysis and Remote Sensing Techniques for Sustainable Land Management Putra, Aditya Nugraha; Ustiatik, Reni; Prasetya, Novandi Rizky; Adara, Erza Aulia; Nita, Istika; Hadi, Syamsu Ridzal Indra; Soemarno, Soemarno; Sudarto, Sudarto; Utami, Sri Rahayu; Munir, Mochammad; Rayes, Mochtar Lutfi
Caraka Tani: Journal of Sustainable Agriculture Vol 40, No 3 (2025): July
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/carakatani.v40i3.97530

Abstract

Precise and detailed land mapping is essential for sustainable land management, environmental conservation, and regional planning, especially in complex and diverse landscapes. This study aims to present an innovative framework for the development of Land Mapping Units (LMUs) at a detailed scale (1:20,000), through the integration of Random Forest (RF) analysis and high-resolution remote sensing data. This study was conducted in the South Malang Plateau, Indonesia (the area characterized by karst, tectonic, volcanic, and alluvial landforms) from June to December 2024. As part of the methodology, the study utilized a combination of geospatial data, including geological maps, DEM-derived topographical indices, and remote sensing indices (Normalized Difference Soil Index/NDSI, Soil Adjusted Vegetation Index/SAVI, Normalized Difference Water Index/NDWI, Modified Soil Adjusted Vegetation Index/MSAVI). A total of 10,903 field observation points were analyzed, with 70% used for model training and 30% for validation. The results show that RF-based LMUs achieved R2 of 0.93 and Root Mean Square Error (RMSE) of 0.645, which is reliable to use. The LMUs provide a comprehensive understanding of landform-specific characteristics, including soil fertility linked to parent material, erosion sensitivity, and slope variability. These insights support applications in precision agriculture, disaster mitigation, and environmental planning. Moreover, the result can guide informed decision-making to prioritize sustainable land management that effectively prevents land degradation in the South Malang Plateau region, as stated in the Sustainable Development Goals (SDGs). The study demonstrates the potential of combining machine learning and remote sensing to refine spatial analysis and address the limitations of manual mapping methods. The proposed framework is scalable and adaptable to other diverse landscapes, making it a valuable tool for advancing sustainable land management in a rapidly changing world.
Co-Authors Adara, Erza Aulia Aditya Nugraha Putra, Aditya Nugraha Fauziah, Dwi Christina Hadi, Syamsu Ridzal Indra Istika Nita Mochammad Munir Mochtar Lutfi Rayes Nugroho, Agung Soemarno Soemarno Sri Rahayu Utami Sudarto Sudarto Sugiarto, Michelle Talisia Suryoprojo, Adithya Riefanto Ustiatik, Reni