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All Journal SAINS TANAH - Journal of Soil Science and Agroclimatology Caraka Tani: Journal of Sustainable Agriculture
Soemarno Soemarno
Department of Soil Science, Faculty of Agriculture, Brawijaya University, Malang
Agriculture, Biological Sciences & Forestry Education

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Soil nutrient improvement with organic amendments: a basis for lemon orchard management Raushanfikr Bushron; Atiqah Aulia Hanuf; Alfian Tri Yulianto; M. Wasilul Lutfi; Dinda Mahartian Yunita; Retno Suntari; Soemarno Soemarno
SAINS TANAH - Journal of Soil Science and Agroclimatology Vol 22, No 2 (2025): December
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/stjssa.v22i2.99868

Abstract

Lemon trees require the nutrients they extract from the soil. This research aims to analyze the impact of organic matter application on enhancing soil nutrient availability and improving soil chemical properties using a pot-scale incubation experiment. This study used a completely randomized design with eight treatments and four replications. The pot treatment used 10 kg of air-dry soil per pot mixed with an organic matter dosage of 30 tons ha-1 and was observed at 2, 4, 8 weeks after applications. The treatment consisted of P1 (topsoil, control), P2 (subsoil, control), P3 (topsoil + compost), P4 (subsoil + compost), P5 (topsoil + cow manure), P6 (subsoil + cow manure), P7 (topsoil + goat manure), and P8 (subsoil + goat manure). The results indicated that compost and manure fertilizer had a significant effect in increasing soil chemical properties (pH, organic carbon content, cation exchange capacity, total-N, available-P, and exchangeable-K), with topsoil treatment having the highest value compared to the subsoil treatment, almost at all parameters. The topsoil treatment + 30 tons ha⁻¹ cow manure significantly increased the N-total by 44.44% at 8 and 12 WAA on the control treatment. The topsoil treatment + goat manure 30 tons ha-1 significantly increased P-available by 13.63 - 29.74% and exchangeable-K by 40.61 - 62.88% at 4, 8, and 12 WAA against the control treatment. Based on these findings, the best fertilizer method of topsoil treatment + 30 tons ha⁻¹ of manure is recommended to increase the soil fertility of the lemon tree soil.
New Emerging and Comprehensive Land Mapping Unit at Detailed Scale: Integrating Random Forest Analysis and Remote Sensing Techniques for Sustainable Land Management Aditya Nugraha Putra; Reni Ustiatik; Novandi Rizky Prasetya; Erza Aulia Adara; Istika Nita; Syamsu Ridzal Indra Hadi; Soemarno Soemarno; Sudarto Sudarto; Sri Rahayu Utami; Mochammad Munir; Mochtar Lutfi Rayes
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 Aditya Nugraha Putra Alfian Tri Yulianto Atiqah Aulia Hanuf Dinda Mahartian Yunita Erza Aulia Adara Istika Nita Lailatul Fitria M. Wasilul Lutfi Mochammad Munir Mochtar Lutfi Rayes Novandi Rizky Prasetya Raushanfikr Bushron Retno Suntari Sri Rahayu Utami Sudarto Sudarto Syamsu Ridzal Indra Hadi Ustiatik, Reni