Application of machine learning in establishing determinants of growth in the horticultural export sub-sector in Kenya

Abstract

The export industry is key in any country’s economic growth (Furuoka, Harvey & Munir, 2019) of any country. The export industry plays a crucial role in a country’s economic growth, yet factors influencing its stability and contribution to economic development remain areas of concern. In Kenya, the horticultural sub-sector has experienced slow growth over the past decade, prompting questions about the key challenges affecting its performance. This study aimed to identify factors influencing the growth of horticultural exports and explore ways to enhance the industry’s contribution to employment, foreign exchange, and overall economic stability. Using an augmented gravity model, the study analyzed variables such as exchange rates, agricultural GDP, interest rates, climate, trade distance, and preferential trade policies to assess their impact on Kenya’s horticultural trade. The findings underscore the importance of monitoring climatic conditions, as they significantly affect production, labor force participation, and economic stability. The results highlight the predictive model’s economic significance in shaping GDP, employment, trade balance, and market growth. These insights can guide stakeholders including Policymakers and farmers in making strategic decisions regarding high-value horticultural products, market investments, and effective marketing strategies to drive revenue growth

Key Terms: Gravity Model, Horticultural Product Code, predictive modelling