Technology Transfer and Adaptability in Smart Irrigation Systems: A Classification Based on Country-Specific Performance Indicators

<p><span>This study examines the relationships of smart agricultural irrigation systems under different conditions. The research statistically analyzes the adaptability and effectiveness of these systems according to country development level, climate type, technological approach, crop type, and implementing institution. A total of 64 independent studies were included. Using the meta-analysis research method, a detailed literature review was conducted. The correlation coefficients obtained were converted into the widely used Fisher’s Z values to allow for statistical comparisons and to prevent distortions in statistical results. Additionally, subgroup analyses were conducted to identify and reveal contextual differences. The findings indicate a high overall mean correlation (r = 0.853), demonstrating that these technologies have a strong impact on agricultural productivity. However, the high heterogeneity observed across all subgroups (I² > 75%) shows that success depends not only on technical performance but also on environmental, social, and institutional factors. Examination of developed countries, temperate climates, AI/ML-based systems, and academic implementations reveals that the highest adaptability was found in these categories. In contrast, developing countries, tropical climates, and farmer-led applications faced challenges due to infrastructure limitations, data accuracy issues, and operational constraints. The results highlight the importance of technology transfer strategies necessary to enhance the success of digital transformation in agriculture.</span></p> <p><strong><em><span>Keywords:</span></em></strong><em><span> Smart irrigation, meta-analysis, adaptability, digital agriculture, technology transfer, Fisher’s Z transformation, agri-tech.</span></em></p>