Multivariate models for analyzing the adoption of agricultural machinery and precision agriculture in banana production in the subtropical area of Ecuador

This research analyzes the determinants influencing the adoption of agricultural machinery and precision agriculture techniques in banana production in La Maná canton, Ecuador. A census of 45 producers spanning small, medium, and large farms—was surveyed using a 30-item structured questionnaire. A mixed descriptive–correlational approach was employed; data were processed with Principal Component Analysis, and K-means clustering. These multivariate methods revealed three distinct technological-adoption profiles: high, medium, and low. Producers in the high-adoption cluster have widely implemented automated irrigation systems, climate sensors, satellite navigation, and precision input applications, reporting tangible benefits such as lower operating costs, higher yields, improved water-use efficiency, and reduced environmental impact. Conversely, medium- and low-adoption groups face constraints related to financing, technical training, and resistance to innovation, which slow technological uptake. Findings show that basic mechanization is common, yet precision agriculture still encounters structural and cultural hurdles. Enhancing the competitiveness of the local banana sector therefore requires differentiated public policies that expand soft-credit access, strengthen continuous training programs, and deliver tailored extension services. Encouraging peer-to-peer knowledge exchange is also vital to accelerate technology diffusion. Collectively, these measures can foster a more efficient, profitable, and sustainable production model for banana growers in La Maná.