UnEarth Magazine.

_{Writing by Ellie Thorson – Art by Emily Warden}

While artificial intelligence (AI) is often criticized for its environmental impact and ethical concerns, dismissing its potential to drive progress toward the Sustainable Development Goals (SDGs) would be shortsighted. By 2050, our food systems must support a population of 10 billion, yet under current agricultural practices, 34% of global farmland is already degraded and on track to becoming infertile, threatening both SDG 2 (Zero Hunger) and SDG 12 (Responsible Consumption and Production). To meet future demands, we must urgently adopt new ways of producing food that not only prevent further environmental harm but actively regenerate our natural resources. 

To address the challenges of food production, we must rethink how our food is grown. Regenerative agriculture offers a holistic approach that contrasts with traditional farming by restoring soil health, sequestering carbon, and rejuvenating the land—acting as a solution to the resource-depleting practices of industrial agriculture. Key principles of regenerative agriculture include minimizing soil disturbance through reduced tillage, maintaining year-round soil coverage with crops or residues, and promoting crop diversity through practices like rotation and cover cropping. Cover crops, catch crops, and green manures are non-cash crops that can offer valuable benefits within a crop rotation. These methods also progressively reduce the reliance on synthetic inputs. Notably, a 2019 survey found that 80% of Americans preferred “regenerative” over “sustainable” brands, highlighting a consumer preference for brands that actively restore and improve ecosystems rather than simply maintaining them. To meet the demands of a growing population, it’s crucial that food systems transition to regenerative practices that enhance and preserve the vitality of the land. 

As AI continues to expand across industrial sectors, it is becoming an essential “silent partner” in the regenerative agriculture movement, empowering farmers to make more efficient, sustainable, and informed decisions.  Frank Terhorst, Head of Strategy and Sustainability at Bayer’s Crop Science division, highlights that “a regenerative agricultural system that produces more with less is only possible with advanced digital technologies, including artificial intelligence and data science, integrated into every phase of the farming cycle.”  

So, how does AI work in regenerative agriculture? AI-enabled soil testing provides precise assessments of soil health, optimizing the evaluation of regenerative practices. Additionally, AI-driven technologies, such as advanced imaging for early pest detection, allow farmers to reduce pesticide use, applying them only when necessary. These innovations not only boost farm profits but also enhance environmental outcomes, build resilience against climate change, and foster greater commercial success for smallholder farmers. By 2028, the AI market in agriculture is expected to grow from being worth $1.7 billion in 2023 to $4.7 billion. 

While new technologies offer promising solutions to scale regenerative agriculture, it is crucial that we do not overlook the deep connection to the land, as embodied by Indigenous knowledge. Many Indigenous cultures view humans and nature as interconnected, where both are essential parts of a larger whole that must coexist and support one another to thrive. Regenerative agriculture itself is rooted in traditional practices, such as intercropping—growing multiple crops together to create beneficial synergies—and permaculture, which aligns farming with the natural environment. These methods have been practiced by Indigenous cultures long before the term “regenerative agriculture” was even coined. Therefore, it is vital that Indigenous and traditional knowledge continues to be recognized and integrated into regenerative practices, particularly as new AI technologies are incorporated into modern farming systems. 

However, the integration of AI into agriculture also raises several concerns. AI itself has sustainability challenges, as the growing demand for these technologies leads to increased energy consumption, resulting in a significant carbon footprint. To ensure that AI is used sustainably in agricultural production, it is essential to explore alternatives that rely on renewable energy sources. Moreover, farmers may struggle to adopt AI technologies independently due to a lack of technical expertise or resources, making multi-stakeholder collaboration essential. This approach involves cooperation between farmers, technology providers, researchers, and policymakers to ensure effective training, knowledge-sharing, and the successful deployment of AI-enabled services tailored to agricultural needs. On a broader scale, for digital technologies and AI to reach their full potential in regenerative agriculture, it is crucial to implement supportive policies at local, national, and international levels, while also recognizing and incorporating Indigenous and traditional knowledge into these systems. 

The future of food security lies in the fusion of advanced technology and existing knowledge. By integrating AI with regenerative practices, we can heal our soils, empower farmers, and move closer to a more equitable and resilient food system. Only through this holistic approach can we regenerate our agricultural landscapes and meet the growing demands of a changing world.