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All Journal Journal of Tropical Soils JTSL (Jurnal Tanah dan Sumberdaya Lahan) Journal of Degraded and Mining Lands Management Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Dikmas: Jurnal Pendidikan Masyarakat dan Pengabdian Journal of Applied Agricultural Science and Technology
Aditya Nugraha Putra
Faculty Of Agriculture, Brawijaya University
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Education Engineering Environmental Science

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PENERAPAN CITRA SENTINEL 2-A DALAM PENDUGAAN KALIUM PADA KENTANG Deffi Armita; Aditya Nugraha Putra; S Sudarto; Istika Nita; Hana Kusumawati; Dekan Rahmat Wahyudianto; Hanifah Ainur Dienna; Naafi Tiara Windari; Achmad Bima Fauzi; Ivena Hafshah Khairunnisa; Sri Agustiningsih; Rosy Lesmono Putri
Jurnal Tanah dan Sumberdaya Lahan Vol. 9 No. 1 (2022)
Publisher : Departemen Tanah, Fakultas Pertanian, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (540.849 KB) | DOI: 10.21776/ub.jtsl.2022.009.1.15

Abstract

Potato production in Indonesia decreased by 2.43% from 1,314,657 in 2019 and 1,282,768 tons in 2020. One of the causes of the decline in potato production is a lack of potassium. Potassium nutrient deficiency can be caused by fertilization that is not yet precise and is still done conventionally. The purpose of this study was to estimate the nutrient content of potassium using Sentinel 2-A. This study observed 50 points that were determined through the free grid method. Sentinel 2-A was transformed into GLI, GNDVI, NDVI which is the vegetation index and NDSI, and SAVI which is the soil index. The results showed that plant K correlated with GLI CS index (r = -0,46), NDVI CS (r = -0,48) and NDSI CS (r = -0,46). NDVI CS (R2 =2 3%) is the most accurate index in estimating the nutrient content of Potassium than GLI CS (R2 = 21%) and NDSI CS (R2 = 21%). Based on the results of the plant K regression test and NDVI CS, the regression equation y = 1,8003 + (-0,5716 NDVI CS) was obtained. The results of the validation test showed that the t table (-3.18) > t count (2.15) so that there is a significant difference in the estimation results of potassium with the results of potassium obtained in the field. Based on the results of the validation test which were significantly different, the productivity estimation model could not be used to estimate the potassium nutrient in potatoes.
Analysis and Prediction of Water Availability Criteria in Potato Using High-Resolution Aerial Photography Istika Nita; Aditya Nugraha Putra; Shofie Rindi Nurhutami; Michelle Talisia Sugiarto; Novandi Rizky Prasetya
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 15 No. 1 (2026): February 2026
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtepl.v15i1.198-212

Abstract

Indonesia’s potato fields are typically small and fragmented, making coarse resolution moisture products prone to spatial mismatch and limiting their usefulness for precision water management. This study developed plot scale, water based suitability information for potato by integrating UAV multispectral imagery with field measurements of soil water availability and plant height response. UAV imagery was processed into four vegetation indices, namely NDWI, SAVI, MSAVI, and SR, followed by geostatistical mapping. Relationships between indices and measured water availability were evaluated using correlation, linear regression, paired t test, and principal component analysis to examine inter index structure and redundancy. NDWI showed the most consistent performance, with a moderate positive correlation with measured water availability (r = 0.47), while SAVI and MSAVI were negatively correlated (r = −0.46) and SR showed the weakest association (r = −0.33). The NDWI based regression for water availability estimation was y = 0.50x + 29.68 with R² = 0.22. The paired t test indicated no significant difference between NDWI based estimates and field measurements, with mean values of 30.09 percent and 30.52 percent, respectively, across 17 observations. Water based land suitability classes were then refined using boundary line analysis linking water availability to plant height response, producing plot scale criteria suitable for precision zoning rather than landscape level evaluation.
Land-Use Scenario Modeling Using Remote Sensing and Cellular Automata and Its Impact on the Drought Hazard at the Sub-catchment Area Level Suryanta Junjungan Tua Sitio; Novandi Rizky Prasetya; Michelle Talisia Sugiarto; Aditya Nugraha Putra
Journal of Applied Agricultural Science and Technology Vol. 10 No. 2 (2026): Journal of Applied Agricultural Science and Technology
Publisher : Green Engineering Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55043/jaast.v10i2.467

Abstract

Rapid land-use change in upland watersheds alters hydrological processes and increases drought vulnerability. The Upper Brantas and Kali Konto sub-watersheds in East Java serve as important rainfall catchment and water storage areas, yet ongoing land conversion has progressively reduced their capacity to regulate water availability. However, the implications of future land-use trajectories for drought hazards at the sub-catchment scale remain insufficiently understood. This study aims to analyze land-use dynamics, simulate future land-use change, and evaluate alternative land-use planning scenarios to assess their potential influence on drought hazard distribution. Multi-temporal land-use data from 2017, 2019, 2021, and 2025 were analyzed using remote sensing, and future land-use patterns were projected for 2030 using the Artificial Neural Network–Cellular Automata–Markov (ANN–CA–Markov) model. The simulated land-use distribution was then evaluated under Regional Spatial Planning (RSP) and Land Capability Classification (LCC) scenarios to examine their implications for drought hazards. The results showed a substantial decline in natural forest from 12,600.35 ha (31.4%) in 2017 to 9,975.70 ha (24.9%) in 2025—a reduction of more than 20%—accompanied by the expansion of plantation systems, dryland farming, and built-up areas. Under the 2030 Business-as-Usual (BAU) scenario, moderate drought hazard areas increased from 44.0% to approximately 50.2% of the watershed area. Among the evaluated scenarios, the Land Capability Classification (LCC)-based planning approach showed the greatest potential to mitigate drought risk, reducing high drought hazard areas from 25.1% under the BAU scenario to 15.2%. These findings highlight the importance of integrating land capability considerations into spatial planning to support drought-resilient watershed management.
Co-Authors Achmad Bima Fauzi Achmad Wildanul Khakim Aftomi Firman Aditama Albert Fernando Sitorus Alia Fibrianingtyas Alia Fibrianingtyas Ananda Ginanthian Alpheratz Ridwan Arifin, Syamsul Bramantia Setiawan Deffi Armita Dekan Rahmat Wahyudianto Didik Suprayogo Dinna Hadi Solikah Dwi Okiyanto Erekso Hadiwijoyo Fata, Yulia Amirul Fikri Hadi Rusdianto Gali Gusira Hana Kusumawati Hanifah Ainur Dienna Hayyuna Khairina Albayani Iqbal Rizky Nugroho Istika Nita Iva Dewi Lestariningsih Ivena Hafshah Khairunnisa Khanza A’maladewi Sudharta Kurniawan Sigit Wicaksono Lukman Mei Widitya Martiana Adelyanti Melati Julia Rahma Michelle Talisia Sugiarto Naafi Tiara Windari Nina Dwi Lestari Novandi Rizky Prasetya Nurhutami, Shofie Rindi Priska Ayuningtyas Qoid Luqmanul Hakim Raushanfikr Bushron Rifqi Rahmat Hidayatullah Rizki Maulana Ishaq Rosy Lesmono Putri S Sudarto S Sudarto S Sudarto S Sudarto Sativandi Riza Sifa’ul Janahtin Sri Agustiningsih Sudarto Sudarto Sudarto Sudarto Suryanta Junjungan Tua Sitio Syahrul Kurniawan Syamsu Ridzal Indra Hadi Widianto Widianto