Muslimjon Isahanov

В статье на основе опыта США проанализированы механизмы экономического стимулирования сохранения и восстановления плодородия почв посредством выращивания покровных культур (cover crops). В исследовании рассчитана условная разница между размером стимулирующих выплат и затратами на применение данной технологии, а также обосновано, что получаемые выплаты не эквивалентны прибыли фермеров. Результаты исследования свидетельствуют о необходимости увязки стимулирующих выплат за выращивание покровных культур с первоначальными затратами на внедрение технологии, качеством выполнения агротехнических мероприятий и продолжительностью её применения.

This article examines the institutional foundations of economic incentives for agri-environmental activities aimed at preserving and improving soil fertility, using the European Union's Common Agricultural Policy (CAP) as a case study. The study demonstrates that financial allocations, program coverage, and actual changes in soil quality represent distinct dimensions of evaluation. Based on the findings, an economic incentive model is proposed that integrates base payments, targeted supplementary payments, monitoring mechanisms, and performance-based bonuses linked to measurable outcomes.

The adoption of digital agricultural technologies is becoming increasingly important for enhancing agricultural productivity, improving resource-use efficiency, and supporting the digital transformation of the agricultural sector. However, empirical evidence on the factors influencing farmers’ adoption of such technologies remains limited in Uzbekistan and other developing agricultural economies. This study examines the factors associated with the adoption of digital agricultural technologies among agricultural producers in Uzbekistan by extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework to include Trust and Perceived Risk. Primary data were collected from agricultural producers using a structured questionnaire. A total of 373 responses were obtained, of which 280 valid observations were retained after data screening. The proposed model was assessed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that Behavioral Intention is significantly associated with actual technology use, while Facilitating Conditions and Habit also contribute to Use Behavior. Among the factors explaining Behavioral Intention, Perceived Risk was the only statistically significant predictor; however, contrary to theoretical expectations, the relationship was positive rather than negative. The remaining UTAUT2 constructs and Trust did not have statistically significant independent effects on Behavioral Intention. These findings suggest that farmers’ decisions regarding the adoption of digital agricultural technologies in Uzbekistan may be influenced not only by perceived technological benefits but also by uncertainty, perceptions of risk, enabling conditions, and previous experience. The unexpected positive relationship between Perceived Risk and Behavioral Intention should, however, be interpreted with caution and warrants further investigation. The study provides empirical evidence from an underrepresented agricultural context and demonstrates the relevance of incorporating contextual factors such as Trust and Perceived Risk into technology acceptance models. The findings also highlight the importance of strengthening digital infrastructure, providing practical support, and reducing technology-related uncertainty to facilitate the digital transformation of agriculture in Uzbekistan.