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Journal of Degraded and Mining Lands Management
Published by Universitas Brawijaya
ISSN : 2339076X     EISSN : 25022458     DOI : -
Core Subject : Science, Agriculture,
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology
Journal of Degraded and Mining Lands Management is managed by the International Research Centre for the Management of Degraded and Mining Lands (IRC-MEDMIND), research collaboration between Brawijaya University, Mataram University, Massey University, and Institute of Geochemistry, Chinese Academy of Sciences-China Papers dealing with result of original research, and critical reviews on aspects directed to the management of degraded and mining lands covering topography of a landscape, soil and water quality, biogeochemistry, ecosystem structure and function, and environmental, economic, social and health impacts are welcome with no page charge
Arjuna Subject : -
Articles 24 Documents
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Search results for , issue "Vol 10, No 3 (2023)" : 24 Documents clear
Optimization of critical land empowerment through coffee plant extensification as an effort to improve the economic level of coffee farmers in Indonesia Eri Yusnita Arvianti; Ratna Wati; Cakti Indra Gunawan; Karunia Setyowati
Journal of Degraded and Mining Lands Management Vol 10, No 3 (2023)
Publisher : Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15243/jdmlm.2023.103.4457

Abstract

Critical land in Indonesia is the result of weather disturbances, natural disasters, farming behavior without considering the preservation of nature, and the unwise use of chemical fertilizers. Critical land tends to be acidic and has a soil structure that does not support cultivation. Coffee plants are flexible plants, and their root systems and ecology can improve soil structure. The need for coffee at home and abroad tends to increase along with the development of coffee consumption as a lifestyle for Generation Z (Gen-Z). The economic value of coffee, which tends to increase, opens the insight of farmers to continue to develop this coffee plantation area. In the development of planting areas, knowledge of critical land optimization is needed, which is a principal factor as the basis for implementing critical land extensification. For this reason, the purpose of this study was to determine the level of knowledge of farmers on optimizing critical land into strategic land and efforts to develop coffee agribusiness in critical land. This study used a quantitative descriptive method and used the SmartPLS3 analysis tool. The results showed that internal factors, external factors, and motivation of farmers affect the level of knowledge about optimization of critical land, critical land management must meet ecological conservation and improve the community's economy in a structured manner, the extensification of critical land using coffee plants is one of the strategic steps for critical land optimization, as well as the development of coffee agribusiness, both seeds and waste as an effort to increase farmers' income.
Landslide hazard assessment and their application in land management in Kendari, Southeast Sulawesi Province, Indonesia La Ode Restele; Ahmad Hidayat; Fitra Saleh; L M Iradat Salihin
Journal of Degraded and Mining Lands Management Vol 10, No 3 (2023)
Publisher : Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15243/jdmlm.2023.103.4349

Abstract

Kendari is the capital of Southeast Sulawesi Province which is prone to landslides. Good land management needs to be done to minimize the impact of landslides. This study aimed to map the Kendari landslide hazard that can be used as an input into land management strategy, especially in vulnerable to the threat of landslides. The primary data used in this study were DEMNAS and Sentinel-2. Landslide detection was carried out using a Process Hierarchy Analysis (AHP) approach and validated by field surveys. Land capability analysis was based on landform analysis using land system data. Land management directions were carried out based on the integration of landslide hazard analysis with the ability of the land to be calibrated with actual land cover. The analysis showed that areas with high and very high landslide hazards reached 2654.09 ha (9.64%) and 4354.78 ha (15.82%). Capability class of VII is spread over structural hills to the north and south of Kendari with an area of 7,215.81 ha (26.21%). Land management in areas with very high landslide hazards and land capability class VII is to add cover crops on land that is not protected by a canopy. Cover crops that can be added are the grass type to minimize the danger of erosion that can trigger landslides.
Mercury-resistant biofilm-forming bacteria and local plants in phytoremediation of small-scale gold mine tailings in Lombok Island, Indonesia Siska Nurfitriani; Endang Arisoesilaningsih; Yulia Nuraini
Journal of Degraded and Mining Lands Management Vol 10, No 3 (2023)
Publisher : Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15243/jdmlm.2023.103.4305

Abstract

Small-scale gold mining is one of the sectors that contribute to the world's largest mercury contamination through the tailings it produces. Many efforts have been made to reduce mercury concentrations from tailings, one of which is by utilizing a combination of plants and bacteria. This study aims to analyze the combination of mercury-resistant biofilm-forming bacteria and local plants in the phytoremediation of small-scale gold mine tailings. This study used ten plant species divided into three groups and three biofilm-forming mercury-resistant bacteria (Bacillus toyonensis, Burkholderia cepacia, and Microbacterium chocolatum). Parameters observed included plant biomass, total chlorophyll, plant mercury content and media. The results showed that adding bacteria to each plant in the treatment had a different effect. Some plants with the addition of biofilm-forming bacteria had a higher wet weight than others. However, the addition of bacteria was not effective in increasing plant dry weight. The combination of biofilm-forming bacteria in the first and second plant groups reduced tailings mercury concentrations better than without the addition of bacteria. The combination of plants and bacteria in the third group gave higher media and plant mercury concentrations. This study shows that the addition of biofilm-forming bacteria can lead to increased remediation by plants. The second plant group treatment with a combination of P. indica, P. conjugatum, and S. sesban plants was the most effective in reducing tailings mercury content.
Integration of remote sensing and geophysical data to enhance lithological mapping utilizing the Random Forest classifier: a case study from Komopa, Papua Province, Indonesia Hary Nugroho; Ketut Wikantika; Satria Bijaksana; Asep Saepuloh
Journal of Degraded and Mining Lands Management Vol 10, No 3 (2023)
Publisher : Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15243/jdmlm.2023.103.4417

Abstract

Lithological information is important in mineral resource exploration, geological observations, mine planning or degradation vulnerability assessment. Currently, lithology mapping can be performed in a fast, inexpensive, and easy way using remote sensing data and machine learning. Remote sensing techniques have become a valuable and promising tool for mapping lithological units and searching for minerals. Typically, the integration of remote sensing data with geophysical data provides a better diagnosis to lithological units than single-source mapping methodologies. Accordingly, this study used a combination of remote sensing and airborne geophysical data utilizing the Random Forest algorithm with small training samples to enhance lithology mapping in Komopa, Papua Province, Indonesia. Geophysical data consisting of magnetic, electromagnetic, and radiometric were added one by one gradually to the remote sensing data, which includes Sentinel 2A, ALOS PALSAR, and DEM (digital elevation model) to compare the accuracy of the classification results from each dataset. The results showed that the model that combined remote sensing data and the three types of geophysical data produced the best classification, with an overall accuracy of 0.81, precision of 0.66, recall of 0.47, and F1 score of 0.52. This fused data can increase the accuracy of the classification results by 8% overall accuracy, 6% precision, 11% recall, and 13% F1 score when compared to the model that only used remote sensing data.

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