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Agroteksos
Published by Universitas Mataram
ISSN : 08528268     EISSN : 26854368     DOI : https://doi.org/10.29303/agroteksos
Core Subject : Economy, Science, Agriculture, Social,
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences
Agroteksos merupakan jurnal pertama yang diterbitkan oleh Fakultas Pertanian Universitas Mataram. Agroteksos terdaftar di LIPI dengan p-ISSN (p-ISSN No. 0852-8268) pada 25 Mei 2007, dan e-ISSN (e-ISSN No. 2685-4368) pada 19 Juli 2019, Agroteksos menerbitkan minimal 6 artikel dalam satu edisi yang terbit 3 kali dalam setahun. Untuk peningkatan kualitas, Agroteksos telah menggunakan sistem jurnal online OJS 3. Agroteksos adalah Jurnal Ilmiah Pertanian yang memuat tulisan-tulisan hasil penelitian dan gagasan ilmiah di bidang Ilmu Pertanian, yang meliputi aspek Agronomi (termasuk kajian Pemuliaan, Produksi dan Perlindungan Tanaman, dan Ilmu Tanah), Teknologi dan Sosial Ekonomi Pertanian ( Agroteksos). Agroteksos diterbitkan oleh Fakultas Pertanian Universitas Mataram tiga kali dalam setahun (April, Agustus, dan Desember). Redaksi menerima artikel dalam bahasa Indonesia dan Inggris, baik dari universitas maupun luar universitas, baik dari dalam maupun dari luar negeri.
Arjuna Subject : Umum - Umum
Articles 491 Documents
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GOOGLE EARTH ENGINE UNTUK ESTIMASI PRODUKSI PADI Rio Valery Allen; Muhammad Syahfitra; Romy Aulia
AGROTEKSOS, Jurnal Ilmiah Ilmu Pertanian Vol 36 No 1 (2026): Jurnal Agroteksos April 2026
Publisher : Fakultas Pertanian Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/agroteksos.v36i1.1588

Abstract

The increasing global population and the complexity of food security challenges are driving an urgent need for accurate, fast, and large-scale rice production data. This study analyzes the potential of Google Earth Engine (GEE) a cloud computing platform that integrates a petabyte satellite data catalog with planetary-scale analysis capabilities as a primary tool for estimating rice production. The methodology studied focuses on a vegetation index (Normalized Difference Vegetation Index/NDVI)-based approach and a simple linear regression model. A case study in Sleman Regency demonstrates that a regression model can be developed, but critical analysis reveals a very low coefficient of determination (R2). This indicates that the linear vegetation index-based model is only able to explain a small portion of the variability in rice productivity in the field. This finding emphasizes that non-vegetative factors, such as rainfall and soil structure, play a much more significant role than vegetation indices alone can capture. While GEE offers a revolutionary solution to overcome the limitations of conventional methods (time and cost), its effectiveness as an accurate estimation tool is highly dependent on the development of more complex models. Future success requires the integration of multispectral data with multidisciplinary data (e.g., climate and soil). GEE, with its cloud computing architecture, is positioned as a key catalyst for the transition to proactive, data-driven precision agriculture.

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