Jurnal Ilmu Tanah dan Lingkungan

Analysis of The Chemical Properties of Ultisol After Addition of Solid Compost and Coal Fly Ash

2025-06-01T21:08:31+07:00

This study aimed to evaluate the effects of applying solid compost and coal fly ash on the improvement of Ultisol chemical properties and to determine the optimal dosage for its application. Ultisol, known for its high acidity, low organic matter content, and limited nutrient availability, requires specific amendments for optimization. The study was conducted using a factorial completely randomized design (CRD) with 12 treatment combinations: four levels of solid compost (0 t.ha^-1, 2,5 t.ha^-1, 5 t.ha^-1 dan 7,5 t.ha^-1) and three levels of fly ash (0 t.ha^-1, 3 t.ha^-1and 6 t.ha^-1), each with three replications. The soil that has been treated is incubated for two weeks, then the chemical properties of the soil are analyzed. The results showed that the combination of solid compost and fly ash significantly improved Ultisol chemical properties, including increasing soil pH, reducing Al-dd levels, and enhancing P-available, organic carbon (organic C), total nitrogen (total N), cation exchange capacity (CEC), and total potassium (total K). The combination of 7,5 t.ha^-1solid compost and 6 t.ha^-1 fly ash provided the best results, achieving a soil pH of 5.15, an increase available P to 23.7 ppm, and a high CEC level. This combination effectively improved Ultisol quality, making it more supportive for plant growth.

Effect of Bio-Organic Fertilizer on Soil Organism in Experimental Oil Palm Plantation Cikabayan, Bogor, Indonesia

2025-06-01T21:08:11+07:00

The use of chemical-based fertilizer continuously without addition of organic matter may have hazardous effects on the environment. Numerous studies have shown that chemical-based fertilizers may disrupt the balance of soil properties, including physical, chemical, and biological properties. Bio-organic fertilizers have become one of the alternative answers to oil palm sustainability and a replacement for chemical-based fertilizers to promote and provide a better service to the soil and environment. This study aims to analyze the effect of application of biofertilizers combined with different dosages of organic matter (bio-organic fertilizers), i.e. 0 kg tree^-1, 6 kg tree^-1, and 12 kg tree^-1, respectively. The study was conducted in Cikabayan Experimental Oil Palm Plantation, Bogor, West Java with 12 year-old-plant oil palm, from May to November 2023. The study showed an increase in the population density of the soil organisms after application of bio-organic fertilizers, especially with the dose of 6 kg tree^-1. The change in the soil fauna community structure was also observed in this study, where Collembola dominated the ecosystem after fertilization. These findings offer practical strategies to mitigate the negative impacts of traditional fertilizers. Moreover, the study highlights the role of bio-organic fertilizers in enhancing soil biodiversity by increasing the populations of beneficial soil organisms.

Mapping of Soil pH and NPK Nutrients on Peat Agricultural Land in Kalampangan Village, Palangka Raya City

2025-06-01T21:07:52+07:00

Kalampangan Village is one of the villages in Sebangau District, Palangka Raya City, Central Kalimantan Province. Kalampangan Village has an area of 5000Ha with a population of 4000 people. The main livelihood of the population is farming. Farmers convert peatlands into agricultural land to grow food crops. The main challenge of farming on peatlands is the high acidity value of peat soil which causes low soil nutrient content and decreases soil fertility. Therefore, this study aims to map the distribution of nutrient content and acidity of peat agricultural soil in Kelampangan Village. Data measurement in agricultural land was carried out using the Soil Sensor 5 Pin Probe RS485 NPKPHCTH-HMI043 by inserting the sensor feet into the ground and the results will be displayed on the HMI touch screen. While the coordinate points of data collection use the Global Positioning System. The data measurements obtained were analyzed using the Geographic Information System (GIS) application to produce a thematic map of the distribution of soil pH and NPK nutrients. The results show that peat agricultural land in Kalampangan tends to be slightly acidic (pH 4.48-6.64) with majority low N (0-1.24 mg/kg), P (0-18.44 mg/kg), and K (0-14.61 mg/kg) content. The resulting thematic map can be used as a reference for farmers to process peat agricultural land to be more productive, for local governments in formulating agricultural policies, and contribute to the development of science in the field of soil science.

Land Cover/Use Classification at Landsat 8 with Random Forest and Support Vector Machine Algorithm in Sumedang Regency, West Java

2025-06-01T21:18:17+07:00

Sumedang Regency is one of the selected regencies in the national strategic project, in the form of the construction of the Jatigede Reservoir and the Cisumdawu Toll Road. This development has a negative impact on the agricultural sector because it causes a change in the function of agricultural land, so that measurable monitoring of land cover/use is needed. This study aims to identify the pattern of reflectance values for each land cover/use and compare the RF and SVM approaches in the classification of land cover/use in Sumedang Regency in 2023. The pattern of reflectance values for each land cover/use is unique, but the use of rice fields and fields/dry fields has a similar pattern and is more susceptible to misclassification. The RF and SVM approaches produce high classification accuracy, which is 93.6% for RF and 98% for SVM. The difference in RF and SVM classification results is 34.64%. This difference occurs due to differences in the way the classification works.

Keywords : national strategic project, reflectance pattern, machine learning

Classification of Horticultural Commodities based on Multispectral UAV Image with k-Nearest Neighbor and Minimum Distance Approach.

2025-06-01T21:21:00+07:00

Horticulture is an agricultural subsector that includes fruit, vegetable, flower and ornamental plants that play an important role in supporting the national economy. Monitoring horticultural cultivation to maintain and improve the quality of horticultural production can utilize remote sensing technology. Remote sensing imagery varies from low resolution such as MODIS (1 km), medium such as Landsat (30 m) and Sentinel-2A (20, 10 m), to high such as IKONOS (1– 4 m). In horticultural agricultural land with a small area, UAV can be used as an alternative because it has high resolution. This study aims to analyze the spectral patterns of horticultural crops based on multispectral UAV images, to map horticultural crops using the k-Nearest Neighbor (k-NN) and Minimum Distance Classification (MDC) methods, and to analyze the level of accuracy of horticultural crop classification using both methods, The research location is at the Pasir Sarongge Experimental Garden, Cianjur Regency using field observation data and multispectral UAV imagery acquired on November 6, 2022. The analysis of the sample spectral patterns was carried out by taking sample areas from 11 classes including horticulture and non-horticulture which were then used to create spectral characteristic curves. Image classification using the k-NN and MDC methods was assessed based on overall accuracy through an error matrixs. The results of the study showed that the spectral response of horticulture was low in visible light where the green band was slightly higher than the red band, while in the red edge band there was a significant increase and continued to increase in the NIR band. The classification of horticultural plants in both methods shows differences in area due to differences in classifier algorithms. The order of the largest to smallest area in k-NN is chili, carrot, potato, banana, cabbage, onion, and tomato. Meanwhile, the order of the largest to smallest in MDC is chili, banana, carrot, cabbage, potato, onion, and tomato. The overall classification accuracy for k-NN and MDC is 89.37% and 51.48% respectively.

Land Use Planning for Local Banana in Flood Prone Areas of Katingan Regency

2025-06-01T21:14:12+07:00

Bananas are the fourth most crucial commodity globally and in Indonesia, significantly contributing to the horticulture sector. The high demand for bananas requires a substantial increase in production, making land resources crucial for development, such as in the Katingan Regency. The purpose of this research is to evaluate the potential areas for the expansion of local banana cultivation in Katingan Regency. Land suitability is evaluated using a Multi-Criteria Decision Making (MCDM) approach with the Analytical Network Process (ANP) and spatial analysis. Land availability is analyzed by integrating land status and spatial planning while also considering different levels of flood-prone areas. Soil type, soil texture, soil drainage, and rainfall are critical criteria in determining land suitability for local bananas. The results indicate that suitable land for local bananas accounts for 79% or 16,079.5 (102) ha, with the suitability class predominantly in class S2 (60%). Available and suitable land for local bananas with varying levels of flood risk covers 12% (2,418.1 (102) of the total study area. The Overall results show a high potential for land allocation for local banana production, with recommendations to expand agricultural development areas in Katingan Regency. Furthermore, it provides a foundation for stakeholders to sustainably increase local banana production, support the local economy, and improve the welfare of the community in Katingan Regency.