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Ikhsan Nendi
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Journal of Agricultural Economy and Technology Development
Published by Politeknik Siber Cerdika Internasional
ISSN : -     EISSN : 30897440     DOI : https://doi.org/10.59261/jaetd.v1i1
Core Subject : Economy, Agriculture,
Agriculture, Biological Sciences & Forestry Economics, Econometrics & Finance
Scope of the journal includes but is not limited to: Agricultural economics and agribusiness Agricultural policy and rural development Farm management and production economics Agricultural finance and marketing Food security and supply chain analysis Agricultural innovation and technology transfer Precision agriculture and smart farming Mechanization and automation in agriculture Post-harvest technology and logistics Environmental and sustainability issues in agriculture ICT and digital tools for agricultural development Agricultural biotechnology and renewable resources Climate change impact and adaptation strategies in agriculture
Arjuna Subject : Ilmu Ekonomi, Ekonometrika dan Keuangan (semua kategori) - Ilmu Ekomomi dan Ekonometrika
Articles 15 Documents
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Drone-Assisted Precision Fertilization in Shallot Cultivation: Cost-Efficiency Evaluation in Brebes Regency, Central Java Gilang Prananda, Narendra
Journal of Agricultural Economy and Technology Development Vol. 2 No. 2 (2025): Journal of Agricultural Economy and Technology Development
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jaetd.v2i2.41

Abstract

Brebes Regency in Central Java, Indonesia, is recognized as the largest shallot production center nationally, contributing approximately 65% of provincial output. However, conventional fertilization methods face critical challenges including high operational costs, labor intensity, and uneven distribution patterns that compromise crop productivity. This study evaluates the cost-efficiency of implementing drone-assisted precision fertilization technology in shallot cultivation. A comparative field experiment was conducted across 20 hectares during the 2023-2024 growing season, contrasting drone-based application systems with traditional manual methods. Key performance indicators measured included operational costs per hectare, time efficiency, fertilizer distribution uniformity, labor requirements, and resultant crop yields. Results demonstrate that drone-based fertilization achieved a 28.3% reduction in operational costs (from $842/ha to $604/ha), a 73.5% decrease in labor hours (from 34 to 9 hours/ha), and a 35.7% improvement in fertilizer distribution uniformity coefficient (from 0.68 to 0.92). Furthermore, shallot productivity increased by 14.2% (from 11.8 to 13.5 tons/ha) under drone application. Economic analysis revealed a favorable benefit-cost ratio of 2.34 and a payback period of 2.3 years for drone technology adoption. Despite promising outcomes, implementation constraints include initial capital requirements, technical expertise demands, regulatory compliance, and terrain-specific operational limitations. This research provides empirical evidence supporting drone technology as a viable precision agriculture solution for enhancing cost-efficiency and productivity in Indonesian shallot cultivation systems.
Machine Learning-Based Disease Detection in Cocoa Plantations: Economic Viability Study in Luwu Regency, South Sulawesi Millatul Maula, Indi
Journal of Agricultural Economy and Technology Development Vol. 2 No. 2 (2025): Journal of Agricultural Economy and Technology Development
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jaetd.v2i2.42

Abstract

Disease-related yield losses represent a critical constraint to cocoa productivity in Indonesia, particularly in South Sulawesi Province where endemic infections cause 30-70% production declines annually. This study evaluated the economic viability of implementing machine learning-based disease detection systems in cocoa plantations in Luwu Regency through an 18-month mixed-methods research design integrating technical validation, randomized controlled field trials, and comprehensive economic analysis. A convolutional neural network model was developed using 12,450 labeled images and deployed across 30 cocoa farms stratified by size and disease pressure, with 15 treatment farms receiving ML-based detection technology and 15 control farms continuing conventional monitoring practices. The ML model achieved 93.7% diagnostic accuracy for detecting Cocoa Pod Borer, Vascular Streak Dieback, and Black Pod Disease. Treatment farms demonstrated significantly higher yields (1,247 kg/ha vs. 942 kg/ha, 32.4% increase), reduced disease incidence (8.7% vs. 23.1%), and improved bean quality (73.2% Grade A vs. 58.4%). Economic analysis revealed highly favorable investment returns with Internal Rate of Return of 47.3% for individual adoption and 52.6% for cooperative models, Net Present Value of $2,847 per farm, Benefit-Cost Ratio of 3.68, and Payback Period of 2.8 years. The findings demonstrate that ML-based disease detection achieves economic viability in smallholder cocoa farming contexts, offering a transformative solution for enhancing agricultural productivity and farmer incomes in disease-endemic tropical plantation systems.  
Internet of Things (IoT) Implementation in Tilapia Aquaculture: Profitability Assessment of Smart Pond Management in Subang District, West Java Prahitaningtyas, Sherina
Journal of Agricultural Economy and Technology Development Vol. 2 No. 2 (2025): Journal of Agricultural Economy and Technology Development
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jaetd.v2i2.44

Abstract

This study aims to evaluate the effectiveness and profitability of implementing an Internet of Things (IoT)–based smart pond management system in tilapia aquaculture within Subang District, West Java. A controlled field experiment compared three IoT-enabled ponds equipped with real-time pH, temperature, dissolved oxygen, and turbidity sensors against three traditionally managed ponds (using manual monitoring) across a complete 120-day production cycle. Results show that IoT monitoring significantly improved water quality stability, increasing fish survival rates by 12.5% (from 80% to 92.5%), biomass production by 15.3%, and feed conversion efficiency by 8.7%, thereby enhancing operational performance. Economic analysis revealed that the IoT system produced favorable financial indicators, including higher Net Present Value (NPV), Internal Rate of Return (IRR), and Revenue–Cost (R/C) ratios, despite requiring a larger initial investment. These findings indicate that IoT adoption can provide substantial technical and economic benefits to smallholder aquaculture when applied under appropriate management conditions. Overall, the study concludes that smart pond systems represent a viable pathway for increasing productivity, reducing production risks, and improving profitability in Indonesian freshwater aquaculture.
Hydroponics-Based Romaine Lettuce Production in Peri- Urban Areas: Market Competitiveness Study in Greater Bandung Metropolitan Region Adnan Rivaldo, Muhammad Is'raj
Journal of Agricultural Economy and Technology Development Vol. 2 No. 2 (2025): Journal of Agricultural Economy and Technology Development
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jaetd.v2i2.46

Abstract

This study investigated the market competitiveness of hydroponics-based romaine lettuce production in peri-urban areas of the Greater Bandung Metropolitan Region, Indonesia, and addressed critical knowledge gaps regarding economic viability and strategic positioning of controlled environment agriculture in rapidly urbanizing tropical contexts. Employing a comprehensive mixed-methods approach, the research integrated technical performance data from fifteen commercial hydroponic farms over six production cycles spanning 18 months, detailed economic analysis of production costs and profitability indicators, competitive market assessment through Porter's Five Forces and SWOT frameworks, and consumer preference surveys administered to 384 respondents across diverse socioeconomic segments. Results demonstrated that hydroponic systems achieved superior technical performance with 35.6% higher yields, 87.3% marketable quality rates, and 11.4 annual production cycles compared to conventional cultivation, translating into substantial economic returns averaging 58.4% ROI and 2.12 revenue-cost ratios despite capital requirements nearly five times higher than traditional systems. Market analysis revealed that competitive advantage emerged through integrated management of production efficiency, premium retail channel access commanding 130.7% price premiums, and medium-scale operations, with consumer willingness to pay significantly influenced by quality perceptions, food safety concerns, and information provision. The study concludes that hydroponic romaine lettuce production represents an economically viable and competitive agricultural strategy in peri-urban metropolitan regions when technical proficiency, strategic marketing, and adequate scale are simultaneously achieved, though success requires coordinated support addressing knowledge gaps, market access barriers, and capital constraints.
Blockchain-Based Traceability Systems for Arabica Coffee Supply Chains: Economic Impact Analysis in Gayo Highland Farmers, Aceh Province Purnama Sari, Anisa
Journal of Agricultural Economy and Technology Development Vol. 2 No. 2 (2025): Journal of Agricultural Economy and Technology Development
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jaetd.v2i2.47

Abstract

This study investigates blockchain-based traceability implementation among smallholder Arabica coffee farmers in Gayo Highlands, Aceh Province, Indonesia. Using mixed-methods quasi-experimental design, 150 treatment farmers adopting blockchain technology and 150 control farmers with traditional practices were tracked over 18 months (January 2024–June 2025). Data encompassed structured surveys (three time points), blockchain transaction records (2,847 coffee lots), semi-structured interviews (60 stakeholders), and focus groups (52 participants). Difference-in-differences regression revealed statistically significant price premiums of USD 0.35/kg attributable to blockchain (p < 0.001), yielding USD 356 average annual household income increases (13.0% gain). Enhanced market access emerged with 41% of treatment farmers establishing new buyer relationships versus 12% control farmers (χ² = 28.4, p < 0.001), and supply chain disintermediation reduced intermediary stages from 4.3 to 3.1. Implementation challenges included infrastructure constraints (62% connectivity issues), digital literacy gaps (41% requiring assistance), and heterogeneous impacts favoring larger farms and higher-quality producers. Findings demonstrate that appropriately designed blockchain systems generate measurable economic benefits for smallholder producers while requiring sustained capacity building, complementary infrastructure investments, and equitable governance frameworks for successful technology adoption in developing agricultural contexts.

Page 2 of 2 | Total Record : 15

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