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All Journal Journal of Tropical Soils JTSL (Jurnal Tanah dan Sumberdaya Lahan) Journal of Degraded and Mining Lands Management Habitat Jurnal Pengelolaan Laboratorium Pendidikan Jurnal Ilmiah Wahana Pendidikan Jurnal HPT (Hama Penyakit Tumbuhan) Jurnal Media dan Komunikasi
Aditya Nugraha Putra, Aditya Nugraha
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Religion Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Education Environmental Science Health Professions Social Sciences Other

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Journal : Journal of Tropical Soils

 1 
Response Macronutrient Content of Saline-Resistant Paddy to the Saline Source Distance Putra, Aditya Nugraha; Adelyanti, Martiana; Sitorus, Albert Fernando; Hakim, Qoid Luqmanul; Rahma, Melati Julia; Nita, Istika; Sudarto, Sudarto; Fibrianingtyas, Alia
JOURNAL OF TROPICAL SOILS Vol. 26 No. 2: May 2021
Publisher : UNIVERSITY OF LAMPUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5400/jts.2021.v26i2.63-74

Abstract

The impact of salinity on paddy production in Indonesia was pronounced with an average decline of 6.83% (2015-2019). Salinity interferes with macronutrients' absorption into plants, causing stunted growth (salinity contributed to a 42% decrease in paddy production). One solution to solve the salinity problem in paddy is to use saline varieties. There were very few studies on macronutrient content analysis in resistant varieties response to the salinity source's distance.  This research conducted in Jabon Sidoarjo, Indonesia, aims to see the macronutrient response and plant growth to the saline source's distance. This research was conducted in Jabon District, Sidoarjo Regency, using two transects with a length of 2 km and 3.4 km, respectively. The distance between the research location and the salinity source was 10.65 km.  The survey used a free grid to adjust paddy fields' location and the presence of resistant varieties. The results showed that the closer to the salinity source, the salinity indicators consisting of Electrical Conductivity, Sodium Adsorption Ratio, Exchangeable Sodium Percentage, and pH H2O would increase. The increase in salinity then affects the decrease in macronutrients (Nitrogen, Phosphor, and Kalium) in plants. However, tillers and leaves (length and number) were unaffected by high salinity levels in the soil.
Integrating Soil Properties and Vegetation Indices for Modeling Potato Productivity Sudarto, Sudarto; Putra, Aditya Nugraha; Fauziah, Dwi Christina; Nugroho, Agung; Suryoprojo, Adithya Riefanto; Prasetya, Novandi Rizky; Sugiarto, Michelle Talisia
JOURNAL OF TROPICAL SOILS Vol. 30 No. 3: September 2025
Publisher : UNIVERSITY OF LAMPUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5400/jts.2025.v30i3.159-173

Abstract

Global potato production reached approximately 383 million metric tons in 2025, with Indonesia contributing around 1.22 million metric tons (0.32% of global output). However, the sustainability of Indonesia’s potato production is increasingly threatened by soil quality degradation in key growing regions. Existing predictive studies have primarily focused on soil chemical properties, with limited incorporation of remote sensing technologies. This study investigates the potential of Unmanned Aerial Vehicle (UAV) as a high-resolution, non-destructive tool for estimating potato yield using vegetation index transformations. Utilizing a split-plot experimental design across elevation gradients, we integrated soil properties with UAV-derived vegetation indices—Visible Atmospherically Resistant Index (VARI), Green Leaf Index (GLI), and Normalized Green-Red Difference Index (NGRDI). Results reveal that total nitrogen, base saturation, and bulk density significantly influence yield variability, and can be accurately estimated using NGRDI, GLI, and a modified GLI (GLI CS), respectively. A multiple linear regression model was developed to predict potato yield = 24.22 + 7.26(NGRDI) + 9.87(GLI) + 28.42(GLI CS). This research demonstrates the efficacy of UAV-based spectral analysis in improving yield-prediction models, offering a scalable, precise approach for sustainable potato cultivation. Future work should incorporate machine learning to improve model robustness and assess applicability across varied agro-ecological contexts.
Co-Authors Abda Abda Achmad Noerkhaerin Putra Adelyanti, Martiana Adril, Adril Al Jauhary, Muhammad Rifqi Alia Fibrianingtyas Andhika, Yosi Ayuningtyas, Priska Danang Surya Candra, Danang Surya Didik Suprayogo Erekso Hadiwijoyo Evy Latifah Fachry Abda El Rahman Fata, Yulia Amirul Fauziah, Dwi Christina Hadi, Maruf Hidayatullah, Rifqi Rahmat Istika Nita Iva Dewi Lestariningsih, Iva Dewi Kartika Budi Utami Kristiawati, Wanda Kurniawan Sigit Wicaksono, Kurniawan Sigit Lestari, Nina Dwi Luqmanul Hakim Melati Julia Rahma Mochtar Luthfi Rayes Nugraha, Irfan Nur Priya Nugroho, Agung Prasetya, Novandi Rizky Pratiwi, Nisrina Salwa Puspitasari, Siti Riza, Sativandi Rizki Maulana Ishaq Romadhona, Mahimma Samudra, Ferdianto Budi Saputra, Danny Dwi Sari, Rika Ratna Savitri, Elvin Sektiono, Antok Wahyu Sitorus, Albert Fernando Sudarto Sudarto Sugeng Prijono Sugiarto, Michelle Talisia Sukamto, Ika Sumiyarsi Suryoprojo, Adithya Riefanto Syamsul Arifin