There has been a growing focus on regeneration over the last few years. Mitigation is no longer enough.
In 2019, a survey illustrated that 80% of US consumers prefer “regenerative” over “sustainable” brands. Regenerative is synonymous with renewal – it goes beyond “doing no harm” to “harm reversal”,a critical theme when discussing the environment. While this is a multisectoral trend, regeneration is critical for agri-food systems. For instance, 34% of global agricultural land is degraded and will steadily become infertile to the point that we will not be able to grow food, fibre or feed on it.
The agriculture sector is also responsible for 72% of all freshwater withdrawals, a critical resource currently under threat. The industry contributes significantly to climate change: 21% to 37% of global anthropogenic emissions can be attributed to food systems. With such challenges on hand, sector actors need to focus on regenerating agriculture and food systems, especially to feed approximately 10 billion people by 2050.
Regenerative agriculture: for future food security and resilient food systems
Regenerative agriculture is centred on building resilient food systems by restoring soil health and enhancing natural resources such as water tables and on-farm biodiversity. Prioritizing soil regeneration ensures long-term sustainability and improves crop yields through healthier, more water-retentive soils. Additionally, regenerative agriculture can reduce agricultural emissions by optimizing input use. At the farm level, it strengthens resilience, making farms better equipped to withstand environmental challenges and, ultimately, leading to more stable incomes.
Understanding the role of digital and AI in agriculture
Slightly preceding the global momentum towards regenerative agriculture has been a focus on the digitalization of agriculture. It offers benefits like higher farm incomes, improved environmental outcomes, and better commercial viability when working with smallholder farmers. Research suggests that digital agriculture could boost the agricultural GDP of low- and middle-income countries by more than $450 billion, or 28% per annum. The increasing use of artificial intelligence (AI) in agriculture has further amplified these benefits for farmers. For example, leveraging AI and digital tech, the World Economic Forum’s AI for Agriculture Innovation initiative in collaboration with Government of Telangana, India supported chilli farmers achieve a 21% increase in yields, a 9% reduction in pesticide use, and an $800 income boost per acre per cycle.
Overlaying regenerative agriculture and AI: promising use cases
Many of the use cases of AI in agriculture have the potential to accelerate regenerative agriculture. We highlight five such promising use cases.
1. Geospatial imagery for landscape-level planning
Scaling regenerative agriculture often requires a landscape approach, focusing on the broader production area rather than individual farms. This allows for holistic management/regeneration of natural resources. AI models using geospatial data can analyze land-cover-land-use change, soil health, and water availability across large land patches, aiding the planning of regenerative landscapes. In Madhya Pradesh, the Forum’s Food Innovation Hub, in partnership with the state government, is working with Skymet Weather to integrate geospatial imagery into landscape planning. Data collected will further be linked with financial instruments to better support farmers in adopting sustainable practices.
2. AI enabled digital extension
Regenerative agriculture relies on tailored practices developed by research universities. Delivering these practices through extension agents is costly, and a low agent-to-farmer ratio leaves several farmers unserved. Technology advancements have improved the economics of disseminating such practices through digital channels. Additionally, large language models (LLMs) combined with Retrieval-Augmented Generation (RAG) models can make advice specific to farms based on localized data. Furthermore, AI-enabled language translations can enable delivery in local languages in a cost-effective manner, making them more accessible across regions.
3. Pest prediction for reduced pesticide use
The use of pesticides has been quoted as a “global human rights concern,” and regenerative agriculture programmes attempt to reduce pesticide use gradually. AI solutions based on image recognition and hyperspectral imagery can enable both predictive and preemptive pest detection, optimizing pesticide use.
4. AI-enabled financial incentives
A barrier to regenerative agriculture is the lack of financial incentives to make the shift. Financial incentives such as payment for carbon sequestered is complex owing to the high costs of monitoring and making payouts. However, recent pilots using sensors for soil health measurement and AI-enabled smart contracts have made payments faster, error-free, and cost-effective. Most carbon finance companies use geospatial data-enabled AI models to measure carbon sequestration remotely. Similar innovative models for incentivizing the transition are being used in the 100 Million Farmers Initiative. The initiative provides a mix of financial and non-financial support to transition towards regenerative agriculture. Through AI, it enables rewarding both farmers and early investors. Blueprints for replicating such financial models are available through the initiative.
Read More: Agrotech: Can It Serve Both Industrial Giants and Small Farmers Alike?
5. Rapid soil tests and programme monitoring
AI-enabled soil testing provides rapid assessments of soil health, enabling precise decisions on the effectiveness of regenerative practices. Additionally, geospatial AI models can be used to monitor practices like intercropping or cover cropping, which are generally difficult to monitor at scale. Such analysis can also enable farmer segmentation, enabling the delivery of customized support to farmers at different levels of adoption.
Scaling AI for regenerative agriculture
There are several challenges today that would need to be addressed to ensure that AI indeed facilitates climate action. Steps towards these include:
- Reducing AI’s carbon footprint: The growing demand for AI is driving up electricity use, leading to increased emissions from tech companies. Reducing these emissions is crucial, and options that support reduction, such as renewable energy and better data management, should be explored.
- Optimizing data infrastructure and framework: High-quality data is crucial for effective AI models, but agricultural data is often fragmented. Building digital public infrastructure for data sharing can help reduce costs by enabling organizations to reuse and recycle data. Harmonizing data collection via standards is essential for interoperability and making data use more efficient. Another aspect includes the collection of data on farmer’s practices and corroborate it with other data sets on soil, water etc., to build evidence on practices that work.
- Structuring a village-level service delivery network: Farmers may find it difficult to adopt AI-enabled technologies directly without any intermediation. Multistakeholder collaboration is needed to train and deploy village-level agents who can assist with the assisted delivery of AI-enabled services to farmers.
As more agricultural data is accumulated and farmers become conversant with technology, the role of AI in regenerative agriculture will increase. At the same time, with more data, the accuracy of existing solutions will improve. Therefore, to make the most of these advancements, planning for AI while designing regenerative agricultural programmes is critical.
For more information on how AI can help global agricultural development, please get in touch with the Forum’s AI4AI Team.
Contributing author: Abhay Pareek, Lead, AI for Agriculture Innovation, Centre for the Fourth Industrial Revolution.
