Research on Quantitative Evaluation Methods for Grassroots Agricultural Technology Extension within the Structure-Conduct Performance Frameworks

Abstract

The quantitative evaluation of agricultural technology extension is critical for ensuring the effective diffusion of innovations and best practices among grassroots practitioners. The complexity of this task arises from the diverse socio-economic conditions and regional disparities encountered within the sector. Current quantitative evaluation methodologies often fall short in addressing this complexity, due to their inability to fully capture the multi-dimensional nature of agricultural extension activities. This paper proposes a novel evaluation method set within the Structure-Conduct-Performance (SCP) framework, which offers a more granular perspective on the effectiveness of technology dissemination strategies at the grassroots level. We construct a comprehensive indicator system reflecting key aspects of technology extension and preprocess the collected data utilizing advanced techniques such as feature scaling, encoding, normalization, and transformation to enhance model performance. By employing an ensemble learning approach that synergistically combines Random Forest, Gradient Boosting Machine, and Support Vector Machine, our method leverages the strengths of each model through voting or stacking to increase predictive accuracy and robustness. Comparative experiments demonstrate significant superiority over existing quantitative evaluation methods, suggesting the potential of our SCP-based approach in guiding policy-making and resource allocation within agricultural extension programs.