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KARAKTERISASI INDIKATOR KESESUAIAN LAHAN KOMODITI NANAS LOKAL (Ananas comosus) KABUPATEN MAJENE Sanjaya, Muhammad Fahyu; Arham, Ihsan; Irlan, Irlan; Mahendra, Yusril; Irwansyah, Irwansyah
Jurnal Tanah dan Sumberdaya Lahan Vol. 11 No. 1 (2024)
Publisher : Departemen Tanah, Fakultas Pertanian, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jtsl.2024.011.1.24

Indonesia boasts a high biodiversity, intricately linked to the various ecosystems within its territories. In Majene Regency, West Sulawesi Province, for instance, there is a unique commodity in the form of pineapple, locally known as 'pondang'. One of the efforts to preserve this unique pineapple commodity is to understand the characteristics of the cultivation land that has sustained it over time. This research aimed to characterize the land in the cultivation areas of Majene's local pineapple as fundamental information for farmers to comprehend the plant growth ecosystem and manage the land optimally. The research utilized purposive random sampling based on the cultivation locations of Majene's local pineapple. Observations revealed that the cultivation land conditions for Majene's local pineapple had an average temperature ranging from 25.65 to 29.75 ºC, rainfall between 1,488.05 and 2,820.50 mm, with humidity ranging from 63.38 to 89.00%. Additionally, soil fertility conditions in the research locations indicated high nutrient retention, as shown by slightly acidic to neutral soil pH values and high Cation Exchange Capacity (CEC), although some available nutrients indicated very low to low soil fertility levels, such as potassium. Soil management significantly influenced nutrient availability, soil salinity, and alkalinity in the research locations.

EKSPLORASI FUNGI MIKORIZA ARBUSKULA PADA TINGKAT KELERENGAN LAHAN BERBEDA DI LAHAN KONSERVASI TANAMAN NANAS LOKAL Sanjaya, Muhammad Fahyu; Arham, Ihsan; Sukmawati, Sri; Irlan; Kurniati; Burhan, Abd Rukman
Jurnal Tanah dan Sumberdaya Lahan Vol. 12 No. 1 (2025)
Publisher : Departemen Tanah, Fakultas Pertanian, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jtsl.2025.012.1.13

This study aimed to explore the characteristics of Arbuscular Mycorrhizal Fungi (AMF) across varying land slope gradients in the conservation area of local pineapple plantations in Majene Regency. The analysis was conducted on five slope categories: flat (0-8%), gentle (8-15%), moderately steep (15-25%), steep (25-45%), and very steep (>45%), to examine the spore density and morphology of AMF as well as to see its relationship to ecological factors such as climate and topography. The results revealed that slope gradients significantly influenced AMF spore density, with the highest density observed on moderately steep and steep slopes. In contrast, lower densities were recorded on flat, gentle, and very steep slopes. Four AMF spore genera were identified: Glomus, Acaulospora, Gigaspora, and Scutellospora. Glomus was dominant across all slopes, Acaulospora was more prevalent on moderate slopes, and Gigaspora preferred steep slopes. Scutellospora was detected in limited quantities on extreme slopes. Environmental factors, including stable temperatures (27.61 °C-27.77 °C), high relative humidity (79.44%-80.41%), and varying precipitation levels, influenced AMF spore distribution and morphology. These findings emphasize the critical role of topography and climate in supporting AMF sustainability in management strategies to conserve AMF biodiversity and enhance crop productivity.

Will Indonesia's Forests Survive Development Pressure? Machine Learning Predictions for Energy-Critical Tropical Watersheds Utami A, Widyanti; Irlan, Irlan; Syahrir, Nur Hilal A; Rosmaeni, Rosmaeni
Jurnal Wasian Vol. 12 No. 01 (2025): June
Publisher : Forestry Department, University of Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62142/hjs6a555

Land Use and Land Cover (LULC) changes play an important role in influencing the hydrological conditions of a watershed. The conversion of land such as forests, shrubs and grasslands into agricultural land can disrupt the hydrological balance of the watershed. The availability of information related to LULC dynamics in the future is needed to assist sustainable watershed management planning. Machine learning technology, such as Cellular Automata, can provide accurate predicting. The objective of this research is to simulate LULC based on machine learning in the Mamasa Sub-watershed. Two model combinations were employed to simulate LULC: Artificial Neural Network-Cellular Automata (ANN-CA) and Logistic Regression-Cellular Automata (LR-CA). The research results found that the ANN-CA model achieved percent of correctness and overall kappa of 83.6745 and 0.75412, respectively, which were higher than those of the LR-CA model (82.3498 and 0.73361). The prediction results of both model combinations still fall below the actual LULC values, especially in the case of large LULC classes such as forests, range-shrub, rice, and pasture. Conversely, higher accuracy is observed for smaller classes such as wetlands-forested, orchard, residential, and oak. However, it should be noted that this research did not include several socio-economic variables, such as population and income level, which are considered to influence changes in LULC. Future research is expected to analyse the influence of each variable and include some socio-economic variables that may have a significant influence on LULC change.

Biomassa dan Karbon Tersimpan Diatas Tanah Pada Hutan Rakyat Agroforestri Di Kecamatan Bulo Kabupaten Polman. Irundu, Daud; Andi Irmayanti Idris; Prayogi Sudiatmoko; Irlan
Jurnal Hutan dan Masyarakat VOLUME 15 NO 1, JULI 2023
Publisher : Fakultas Kehutanan, Universitas Hasanuddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24259/jhm.v15i1.26365

Global warming has a huge negative impact on all living things, both humans, animals and plants. The cause of global warming is due to an increase in the concentration of carbon dioxide (CO2). Agroforestry pattern community forests have a good role in efforts to reduce gas emissions in the atmosphere. Vegetation found in community forests can absorb carbon through photosynthesis and release carbon through respiration, in the process of photosynthesis vegetation produces O2 and energy and some is stored in the form of biomass. This study aims to determine the potential of biomass and carbon stored in agroforestry-patterned community forests in Polewali Mandar District, West Sulawesi. Data collection was carried out in 9 villages in Bulo District with a total of 27 plots measuring 20 x 20 m. The data collected included tree diameter, tree height and tree species. data analysis using allometric equations according to tree species to obtain biomass values. while for the stored carbon value obtained from biomass products of 0.47. The results showed that the plant that dominated the highest value of biomass and carbon was durian. The total biomass in the community forest with the agroforestry pattern in Polewali Mandar Regency is 90.62 ton/ha while the total value of stored carbon is 42.59 ton/ha.

PEMODELAN DEBIT SUNGAI MENGGUNAKAN SOIL AND WATER ASSESSMENT TOOL DI SUBDAS MAMASA Irlan, Irlan; A, Widyanti Utami; Rosmaeni, Rosmaeni; Samsu, Andi Khairil A.; Irundu, Daud; Mas'ud, Emban Ibnurusyd
Jurnal Eboni Vol. 5 No. 2 (2023): November
Publisher : Program Studi Kehutanan Universitas Muslim Maros

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46918/eboni.v5i2.2106

ABSTRACT Issues related to watershed have received primary attention in the past few decades. The hydrological conditions of the watershed area are considered crucial as a source of life. In efforts to achieve sustainable watershed management, understanding the complex hydrological dynamics is very important. The objective of this research is to simulate river discharge using the Soil and Water Assessment Tools (SWAT) model. This study was conducted in the Mamasa Sub-watershed area. The results show that the river discharge in the Mamasa Sub-watershed has increased as it approaches the outlet (downstream) through the accumulation of discharge in larger order rivers. The Mamasa Sub-watershed also experiences a trend of increasing average annual discharge at a rate of 0.74 m3/s per year. Improvement in the discharge simulation results was achieved through a calibration process using 11 parameters. The calibration results indicate that the calibrated discharge has a higher R2 value compared to the initial simulation discharge, showing that the model calibration successfully improved the quality of the expected discharge results to reach 79.50 percent. However, the calibration results still have a low R2 value, influenced by the selection of appropriate parameters and accurate observational data. Keywords: Modeling, River discharge, SWAT, Watershed

KARAKTERISASI INDIKATOR KESESUAIAN LAHAN KOMODITI NANAS LOKAL (Ananas comosus) KABUPATEN MAJENE Sanjaya, Muhammad Fahyu; Arham, Ihsan; Irlan, Irlan; Mahendra, Yusril; Irwansyah, Irwansyah
Jurnal Tanah dan Sumberdaya Lahan Vol. 11 No. 1 (2024)
Publisher : Departemen Tanah, Fakultas Pertanian, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jtsl.2024.011.1.24

Indonesia boasts a high biodiversity, intricately linked to the various ecosystems within its territories. In Majene Regency, West Sulawesi Province, for instance, there is a unique commodity in the form of pineapple, locally known as 'pondang'. One of the efforts to preserve this unique pineapple commodity is to understand the characteristics of the cultivation land that has sustained it over time. This research aimed to characterize the land in the cultivation areas of Majene's local pineapple as fundamental information for farmers to comprehend the plant growth ecosystem and manage the land optimally. The research utilized purposive random sampling based on the cultivation locations of Majene's local pineapple. Observations revealed that the cultivation land conditions for Majene's local pineapple had an average temperature ranging from 25.65 to 29.75 ºC, rainfall between 1,488.05 and 2,820.50 mm, with humidity ranging from 63.38 to 89.00%. Additionally, soil fertility conditions in the research locations indicated high nutrient retention, as shown by slightly acidic to neutral soil pH values and high Cation Exchange Capacity (CEC), although some available nutrients indicated very low to low soil fertility levels, such as potassium. Soil management significantly influenced nutrient availability, soil salinity, and alkalinity in the research locations.

Individual Tree Segmentation in TropicalNatural Forest Based on Point CloudGenerated from UAV RGB Image Irlan, Irlan; Adzkia, Ulfa; Suhartono, Suhartono; Meliani, Meliani; Jenos, Alpri Sri; Bimantara, Teguh; A, Chairil
Jurnal Wasian Vol. 12 No. 02 (2025): December
Publisher : Forestry Department, University of Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62142/kx7bhn83

Different techniques have been developed for segmenting individual trees using point clouds from UAVs and other remote sensing technologies. A more accurate and reasonably priced method is still required, nevertheless, especially for tropical natural forests. This study evaluates the accuracy of individual tree segmentation using point clouds derived from RGB images in Indonesian natural forests. Compared to other sensors like LiDAR, RGB-based point clouds are significantly more cost-effective. We employed a point cloud-based segmentation algorithm, which has demonstrated superior performance over raster-based or hybrid methods. The results show that this approach is feasible for segmenting individual trees, although it tends to produce over-segmentation. This was attributed to the constraints of incomplete ground measurements resulting from dense canopy cover. The method achieved an overall segmentation accuracy of r (0.68), p (0.76), and F (0.72). Tree position accuracy had an RMSE of 1.95 meters, while the RMSE for crown radius was 1.59 meters. Future work will focus on enhancing the quality of RGB point clouds and improving algorithms to increase segmentation accuracy in natural forests.

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