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All Journal Caraka Tani: Journal of Sustainable Agriculture Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering)
Hadi, Syamsu Ridzal Indra
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Agriculture, Biological Sciences & Forestry Engineering

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Bio-physico-chemical Soil Characteristic: Intensive Tillage vs. No Tillage Ustiatik, Reni; Ariska, Ayu Putri; Ramadhan, Resa Kharisma; Aziz, Novryanti Rizqi; Hadi, Syamsu Ridzal Indra; Nugroho, R Muhammad Yusuf Adi Pujo; Rinandy, Maydella Vista Putri; Hidayat, Muhammad Taufik; Nugroho, Wikan Agung; Kurniawan, Syahrul
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 13 No. 4 (2024): December 2024
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtep-l.v13i4.1196-1205

Abstract

Soil tillage has both positive and negative impacts on soil quality and crop productivity. Efforts to reduce the negative impacts of intensive soil tillage are urgently needed. This study aims to analyze the impact of intensive soil tillage on soil fertility parameters (pH, available P, organic-C, soil compaction, aggregate stability, and soil biodiversity). This research was conducted in two land uses: agriculture and forest land. The research design was descriptive-explorative through surveys and direct field observations. The sample points were determined using stratified random sampling with 3 replications (24 samples). Parameters analyzed in this study were soil compaction, aggregate stability, soil pH, soil available-P, and soil biodiversity (total microbial, soil meso-and-macrofauna). The results showed that intensive tillage affected the soil microbial population, aggregate stability, pH, and available-P (p<0.05). The negative impact of intensive soil tillage reduced total soil microbes by 59.37%. The soil macro and mesofauna found at the study site were earthworms and mycorrhizae, which had a higher density on non-tillage land, with trees as the main vegetation. This encourages efforts to introduce conservation soil tillage to maintain soil biodiversity before more severe damage occurs. Keywords: Intensive agriculture, Soil degradation, Soil fertility, Soil quality, Soil structure.
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 Ariska, Ayu Putri Aziz, Novryanti Rizqi Istika Nita Mochammad Munir Mochtar Lutfi Rayes Muhammad Taufik Hidayat Nugroho, Wikan Agung Prasetya, Novandi Rizky R Muhammad Yusuf Adi Pujo Nugroho Ramadhan, Resa Kharisma Rinandy, Maydella Vista Putri Soemarno Soemarno Sri Rahayu Utami Sudarto Sudarto Syahrul Kurniawan Ustiatik, Reni