The focus on regeneration has intensified in recent years, as mitigation alone no longer meets the growing environmental needs. In 2019, a survey highlighted that 80% of US consumers prefer “regenerative” brands over “sustainable” ones. Regenerative practices emphasize renewal, shifting from simply “doing no harm” to actively reversing harm. This approach is particularly crucial in agriculture, where 34% of global agricultural land is degraded and becoming increasingly infertile.
The agricultural sector also accounts for 72% of all freshwater withdrawals, a critical resource that is currently under threat. Additionally, the industry contributes significantly to climate change, with food systems responsible for 21% to 37% of global anthropogenic emissions. To address these challenges, it is vital for the agriculture sector to focus on regenerative practices, especially given the need to feed approximately 10 billion people by 2050.
Advancing Regenerative Agriculture for Future Food Security
Regenerative agriculture is focused on developing resilient food systems by restoring soil health and enhancing natural resources like water tables and on-farm biodiversity. By prioritizing soil regeneration, we ensure long-term sustainability and improve crop yields through healthier, more water-retentive soils. This approach also reduces agricultural emissions by optimizing input use, strengthens farm resilience to environmental challenges, and leads to more stable incomes.
The Intersection of Digital Innovation and Agriculture
Before the global momentum towards regenerative agriculture, there was already a growing emphasis on digitalizing agriculture. Digital tools offer benefits such as higher farm incomes, improved environmental outcomes, and enhanced commercial viability for smallholder farmers. Research indicates that digital agriculture could boost the agricultural GDP of low- and middle-income countries by over $450 billion annually, representing a 28% increase. The integration of artificial intelligence (AI) in agriculture further enhances these benefits. For example, the World Economic Forum’s AI for Agriculture Innovation initiative, in collaboration with the Government of Telangana, India, helped chili farmers achieve a 21% increase in yields, a 9% reduction in pesticide use, and an $800 income boost per acre per cycle.
Promising AI Applications in Regenerative Agriculture
AI has several promising applications that can accelerate regenerative agriculture:
- Geospatial Imagery for Landscape-Level Planning: Scaling regenerative agriculture often requires a landscape-level approach, focusing on larger production areas rather than individual farms. AI models utilizing geospatial data can analyze land-cover changes, soil health, and water availability across extensive areas. This analysis aids in planning regenerative landscapes. For example, in Madhya Pradesh, the Forum’s Food Innovation Hub, in partnership with the state government, is integrating geospatial imagery into landscape planning with Skymet Weather. The collected data will be linked with financial instruments to support farmers in adopting sustainable practices.
- AI-Enabled Digital Extension Services: Regenerative agriculture depends on research-based practices delivered through extension agents, which can be costly. Advances in technology have improved the economics of disseminating these practices through digital channels. Large language models (LLMs) combined with Retrieval-Augmented Generation (RAG) models can provide farm-specific advice based on localized data. Moreover, AI-enabled language translation can deliver information in local languages, making it more accessible to farmers across different regions.
- Pest Prediction to Reduce Pesticide Use: The use of pesticides is a significant concern, and regenerative agriculture aims to reduce their use gradually. AI solutions that utilize image recognition and hyperspectral imagery can predict and preemptively detect pests, optimizing pesticide application and minimizing environmental impact.
- AI-Enabled Financial Incentives: Financial incentives are crucial for encouraging the shift to regenerative agriculture. However, complexities in monitoring and payments have been barriers. Recent innovations, such as using sensors for soil health measurement and AI-enabled smart contracts, have made these processes faster, more accurate, and cost-effective. Most carbon finance companies now use geospatial data-enabled AI models for remote measurement of carbon sequestration. The 100 Million Farmers Initiative exemplifies innovative financial models, providing both financial and non-financial support to transition towards regenerative agriculture. AI facilitates rewards for farmers and early investors, with blueprints for replicating these models available through the initiative.
- Rapid Soil Testing and Program Monitoring: AI-enabled soil testing allows for rapid assessments of soil health, facilitating precise decisions on the effectiveness of regenerative practices. Geospatial AI models can monitor practices like intercropping or cover cropping, which are challenging to track at scale. This analysis also supports farmer segmentation, enabling the delivery of customized support based on different levels of adoption.
Scaling AI for Regenerative Agriculture
To ensure AI effectively supports climate action, several challenges must be addressed:
- Reducing AI’s Carbon Footprint: The rising demand for AI increases electricity consumption, leading to higher emissions from tech companies. To mitigate this, it is crucial to explore options such as renewable energy and improved data management practices.
- Optimizing Data Infrastructure: Effective AI models require high-quality data, but agricultural data is often fragmented. Building digital public infrastructure for data sharing can lower costs by enabling data reuse and recycling. Standardizing data collection and integrating it with other datasets on soil and water can provide evidence of effective practices.
- Developing Village-Level Service Delivery Networks: Farmers may struggle to adopt AI technologies without proper support. Multistakeholder collaboration is necessary to train and deploy village-level agents who can assist in delivering AI-enabled services to farmers.
As agricultural data accumulates and farmers become more familiar with technology, AI’s role in regenerative agriculture will grow. With increased data, the accuracy of AI solutions will improve. Therefore, integrating AI into regenerative agricultural programs is essential for maximizing advancements in the field.
