Using machine learning to evaluate 1.2 million studies on small-scale farming and post-production food systems in low- and middle-income countries

Abstract

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Recent developments have emphasized the need for agrifood systems to move beyond a production-oriented approach to recognize agriculture as part of a broader agrifood system that prioritizes livelihoods, social equity, diets, and climate and environmental outcomes. At the same time, the knowledge base for agriculture is growing exponentially. Using artificial intelligence and machine learning approaches, we reviewed more than 1.2 million publications from the past 20 years to assess the current landscape of agricultural research taking place in low- and middle-income countries. The result is a clearer picture of what research has been conducted on small-scale farming and post-production systems from 2000 to the present, and where persistent evidence gaps exist. We found that the greatest focus of the literature is on economic outcomes, such as productivity, yield, and incomes. There is also some emphasis on identifying and measuring environmental outcomes. However, noticeable data gaps exist for agricultural research focused on nutrition and diet, and gender and inclusivity.

Keywords

• agrifood systems
• small scale farmers
• evidence gaps
• low-and middle-income countries
• machine-learning
• data science