https://www.jurnal.uniraya.ac.id/index.php/Agrica <p align="justify">Jurnal Sapta Agrica adalah jurnal terbitan berkala yang diterbitkan oleh Fakultas Pertanian Universitas Nias Raya. Isi jurnal mencakup bidang keilmuan Agroteknologi meliputi Pemuliaan Tanaman,Ilmu Benih, Agronomi, Hortikultura, Hama Penyakit Tanaman, Ilmu Tanah, Bioteknologi, crop dataran rendah, crop dataran tinggi dan keilmuan pertanian secara luas. Jurnal ini diterbitkan sebagai sarana dan wadah para dosen, ilmuan, peneliti maupun pakar bidang pertanian mempublikasikan hasil-hasil penelitiannya untuk menunjang Tugas dan Program Tri Dharma Perguruan Tinggi secara Umum. Jurnal Sapta Agrica memiliki E-ISSN 2962-8210 yang dikeluarkan oleh LIPI. Jurnal Sapta Agrica terdaftar di BRIN dan Juga GARUDA. Periode terbit Jurnal ini dua kali setahun yaitu bulan Mei dan November.</p> Universitas Nias Raya en-US Jurnal Sapta Agrica 2962-8210 https://www.jurnal.uniraya.ac.id/index.php/Agrica/article/view/4917 <p><em>The sunflower plant (Helianthus annuus </em>L.)<em> is widely known as a source of vegetable oil </em>with an oil content reaching 40-50%<em>. This research was conducted to analyze the genetic relationships of several promising sunflower lines based on their phenotypic characters. The research was carried out in Srigading Village, Bantul Regency, Special Region of Yogyakarta, from December 2025 to March 2026. The study used a Randomized Block Design (RBD) with 9 promising lines and 3 replications, resulting in 27 experimental units. The observed characters consisted of 11 quantitative and 5 qualitative characters. The data were analyzed using the Unweighted Pair Group Method with Arithmetic Mean (UPGMA) based on Gower’s General Similarity Coefficient, as well as Principal Component Analysis (PCA). The results from the 9 lines using PCA were divided into two Principal Components with a cumulative diversity value of 83.03%, and the cluster analysis showed that the 9 promising lines formed 3 main clusters</em><em>.</em></p> Mukhammad Irwan Kurniadi Adellia Rahmatika Copyright (c) 2026 Mukhammad Irwan Kurniadi, Adellia Rahmatika 2026-05-26 2026-05-26 5 1 1 10 10.57094/jsa.v5i1.4917 https://www.jurnal.uniraya.ac.id/index.php/Agrica/article/view/4929 <p>This study aims to develop an integrated model for food crop productivity by combining natural sciences, remote sensing technology, and Nias local wisdom in South Nias Regency, Indonesia. The agricultural sector in this region faces challenges such as low productivity, soil fertility variability, and limited access to modern agricultural monitoring systems. To address these issues, a mixed-methods approach was applied, integrating quantitative geospatial analysis and qualitative ethnographic data. Remote sensing data from Sentinel-2 and Landsat satellites were used to extract vegetation indices (NDVI) for assessing crop health and spatial productivity patterns. Soil parameters and climatic variables from natural sciences were incorporated to explain biophysical factors influencing crop growth. In addition, local wisdom practices such as organic farming, mixed cropping, and traditional planting systems were quantified into a Local Wisdom Index (LWI). The data were analyzed using Geographic Information Systems and machine learning models to generate a predictive productivity map. The results show that the integrated model significantly improves prediction accuracy compared to single-source approaches, with strong spatial consistency between vegetation health, soil fertility, and traditional farming practices. This study demonstrates that combining scientific data, geospatial technology, and indigenous knowledge provides a more holistic and sustainable framework for agricultural productivity modeling in tropical rural regions.</p> Darmawan Harefa Copyright (c) 2026 Darmawan Harefa 2026-05-27 2026-05-27 5 1 11 22 10.57094/jsa.v5i1.4929