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All Journal Techno Agriculturae Studium of Research Scientechno: Journal of Science and Technology
Shahram Rahimov
Tajik National University
Agriculture, Biological Sciences & Forestry

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AGROECOLOGICAL STRATEGIES FOR BIODIVERSITY: PROMOTING SUSTAINABLE FARMING SYSTEMS THROUGH ECOLOGICAL INTERACTIONS Diana Sawen; Shahram Rahimov; Dilshod Tursunov
Techno Agriculturae Studium of Research Vol. 3 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v3i3.4076

Abstract

Intensive agricultural expansion has contributed to biodiversity loss, soil degradation, and reduced ecosystem resilience, raising urgent concerns for sustainable farming systems. Agroecological strategies offer an alternative approach by emphasizing ecological interactions and biodiversity-based farming practices. This study aims to analyze the role of agroecological strategies in promoting biodiversity and enhancing sustainability in agricultural systems. A qualitative-dominant mixed-methods design is employed using comparative ecological analysis, secondary biodiversity datasets, and field-based qualitative observations. Data are analyzed through comparative indicators of species richness, soil organic matter, pollinator abundance, and ecosystem resilience across conventional and agroecological farming systems. Results indicate that agroecological systems significantly outperform conventional systems, with higher species richness (8.4 vs 4.1), improved soil organic matter (5.7% vs 2.3%), greater pollinator abundance (92 vs 35 units/ha), and enhanced ecosystem resilience (0.88 vs 0.54). Findings also demonstrate improved yield stability and stronger ecological interactions in diversified farming landscapes. The study concludes that agroecological strategies play a critical role in enhancing biodiversity and promoting sustainable farming systems by strengthening ecological interactions and reducing dependency on external inputs, thereby supporting long-term agricultural resilience and environmental sustainability.
USING MACHINE LEARNING TO PREDICT DENGUE FEVER OUTBREAKS IN INDONESIAN URBAN CENTERS BASED ON CLIMATE AND MOBILITY DATA Som Chai; Shahram Rahimov; Dilshod Tursunuv; Gulbahor Alimova
Scientechno: Journal of Science and Technology Vol. 5 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientechno.v5i1.2641

Abstract

Dengue fever remains a critical public-health threat in Indonesia’s densely populated urban centers, where climatic fluctuations and human mobility accelerate transmission dynamics. This study aims to develop a predictive model for dengue outbreaks using machine-learning techniques that integrate multi-source climate indicators (temperature, rainfall, humidity) and population-mobility data. A quantitative research design employing supervised learning algorithms including Random Forest, Gradient Boosting, and Long Short-Term Memory (LSTM) networks—was applied to historical datasets from 2015–2023 across six major Indonesian cities. Model performance was evaluated using accuracy, precision, recall, and AUC metrics. Results indicate that the LSTM model achieved the highest predictive accuracy (92.3%) and superior temporal sensitivity to climatic shifts and mobility surges compared with traditional regression models. These findings demonstrate that machine-learning-based early-warning systems can identify outbreak hotspots up to four weeks in advance, providing actionable insights for urban health authorities. The study concludes that integrating climate and mobility analytics enhances the effectiveness of public-health surveillance and supports proactive dengue-control interventions in rapidly urbanizing environments.
Co-Authors Diana Sawen Dilshod Tursunov Dilshod Tursunuv Gulbahor Alimova Som Chai