Artificial intelligence has been found in all sectors of agriculture and farming, becoming a great technological advancement and entity for this industry. Through four main sections—distributive justice, procedural justice, recognition justice, and restorative justice—AI has increased profitability, promoted sustainable practices, and reduced environmental impacts on large-scale farming, but it may have different effects on small-scale farming. Will they receive the same benefits and outcomes? Let's take a look—
Small-scale farming, or farms that are five acres or less, account for 35% of the world's total food production, and 83% of all farmers are considered smallholders. Since these numbers are higher than predicted, the advancements in AI should be distributed to these farmers for similar beneficial success. Many smallholder farmers do not receive the same technology or expertise as large operational farmers, for instance in regions of Africa, Asia, Latin America, and Oceania, many smallholders rely on traditional farming techniques, lacking the required access for AI applications. These regions are highly popular for food insecurity and face broader challenges for their growing populations. Many of these farmers also lack the technical skill or education for the comprehension and application of these AI operations, they also lack the economic stability to adopt these technologies.
"They are less willing to adopt AI because if they use it wrong or lose the benefits, it will be difficult for them to financially overcome the failure. "Surveys have demonstrated that larger farmers have more capital to invest in the technology and software, are more capable of taking risks due to the ability to absorb decreases in profit and can create specialized jobs to analyze and make decisions based on the collected data," stated South Dakota University graduate Kumar Saha.
The technological and economic gap is the main obstacle for these smallholders, the team developed a set of policy recommendations to combat these challenges. Looking at it through a social justice lens, the recommendations will require transparency standards for the models used by AI tools and applications. They also suggested keeping the AI market competitive through market regulation to limit the continuing power of an individual or group. Finally, recommending the extension of education services to farmers about what techniques and operations work best for them. The team believes by looking at how these smallholders will deal with AI in terms of speed and development of the specific technology, there could be potential success among the 35%.
