By Anusuya Datta and Rachel Hor
As COP28 drew to a close, the spotlight expanded beyond the customary themes to encompass the food and agriculture sector. Significantly, the conference featured the inaugural Declaration on Food and Agriculture, garnering endorsement from over 150 nations and FAO support. This landmark declaration aims to address emissions originating from the food and agriculture industries.
As the significance of sustainable agriculture becomes paramount, the fusion of artificial intelligence (AI) and satellite-based Earth Observation within agriculture and agricultural insurance presents avenues to reduce greenhouse gas emissions and cultivate sustainable farming methods — key objectives of COP28. Climate-smart agriculture, crucial for mitigating climate change impacts, aligns closely with the integration of AI and Earth Observation. Climate-Smart Agriculture is also a key element in the World Bank’s Agriculture Action Plan 2023-2030 which envisions a $35 billion investment in climate-centric initiatives within the agricultural sector.
The ongoing evolution of advanced technologies foreshadows continued industry transformations, marking a progressive transition toward sustainable agricultural practices. This intricate connection between technologies and traditional farming practices isn’t merely revolutionizing the industry; it’s emerging as a guiding light in securing global food sustainability amid challenges such as Climate Change, resource constraints, population growth, and the pursuit of other sustainable development goals (SDGs).
The food system, from farm to fork, is responsible for about one-third of global greenhouse gas emissions, with the agriculture sector alone directly contributing to 14% of total greenhouse gas emissions. Broader rural land use decisions compound this impact, with deforestation accounting for an additional 18% of emissions. Yet, agriculture holds promise as a crucial player in climate mitigation, capable of reducing emissions and sequestering carbon dioxide. The Organisation for Economic Co-operation and Development (OECD) highlights that while agriculture lags behind other sectors in terms of climate commitments and actions, it can contribute to the overall mitigation solution by reducing GHG emissions and removing CO2 from the atmosphere by sequestering carbon.
The 2022 FAO report, the State of Food and Agriculture 2022, zeroes in on the integration of automation in the agricultural sector, highlighting two key challenges: the nascent stage of technologies and the lack of infrastructure that hinders the full adoption of these advancements.
Source: FAO
Satellites Revolutionizing the Agriculture Landscape
Yet, amidst the constraints lies a technology that has reached a mature stage for global implementation. Since the launch of the inaugural Landsat satellite in 1972 (then known as ERTS or Earth Resources Technology Satellite), remote sensing has continuously monitored Earth and its transformations.
It’s only recently that the widespread availability of satellite imagery and the amalgamation of AI elevated its capabilities to unprecedented heights. With AI-driven analysis, Earth Observation data becomes actionable intelligence, empowering the agriculture sector with data-driven decisions. Satellites meticulously track crop development, identify diseases, and predict yields. Simultaneously, AI scrutinizes this imagery, as advanced algorithms look for subtle shifts in land use, vegetation health, and environmental conditions, thus providing intricate insights into soil moisture, crop health, and various factors influencing crop growth.
FAO emphasizes that merging statistical and geospatial data is crucial for enhancing global development monitoring. In the past decade, FAO aided approximately 70 nations in creating national land cover databases and forecasting crops using Earth Observation data. Additionally, FAO is crafting a novel tool that connects satellite imagery to agricultural mapping and crop evaluation. This tool aims to generate precise estimates regarding agricultural production.
The fusion of AI and Earth Observation revolutionizes farming practices by offering precise and timely information crucial for optimizing agricultural processes.
Crop monitoring: By capturing detailed images of fields from above, satellites detect subtle variations in plant colors and growth patterns, indicating potential issues like nutrient deficiencies, pest infestations, or diseases. This early detection allows farmers to take prompt action, preventing widespread crop damage.
Weather prediction: Furthermore, satellites play a pivotal role in predicting weather patterns. This information is indispensable for farmers, enabling them to make informed decisions regarding planting, irrigation, and harvest schedules, ultimately optimizing crop yields and mitigating weather-related risks.
Identify irregularities: Detecting anomalies is another crucial function of Earth Observation in agriculture, which helps identify irregularities in crop growth or land conditions that may signify problems like water stress, soil degradation, or unauthorized land use. These anomalies act as early indicators, prompting farmers and agricultural authorities to investigate and address underlying issues promptly.
Precision farming: One of the most obvious processes where geospatial data is of most use is precision farming. Through high-resolution imaging and constant monitoring capabilities, satellites offer farmers an unparalleled perspective on their agricultural landscapes — whether it be soil moisture, temperature variations or vegetation indices — allowing growers to optimize planting patterns, monitor crop health, and manage irrigation with exceptional accuracy. This precision-driven approach not only boosts productivity but also leads to minimum wastage of resources, thus contributing to sustainable farming practices and ensuring a more resilient agricultural industry.
According to a study by the Association of Equipment Manufacturers (AEM), farmers embracing precision technology reaped significant benefits. This included a 4% crop production increase, 7% better fertilizer placement efficiency, 9% less herbicide and pesticide usage, and a 6% drop in fossil fuel consumption.
Transforming Agricultural Insurance
As the agricultural landscape undergoes metamorphosis, a parallel revolution unfolds in the insurance sector. Historical data, coupled with real-time geospatial information, empowers insurers to assess risks more accurately. When calamities strike, claims processing can be expedited with the swift assessment of crop damage and quicker payouts. This data-driven approach also enables the customization of insurance products tailored to individual farmer needs.
AI disrupts traditional risk management paradigms, injecting foresight into the sector. This predictive prowess not only fortifies the resilience of the agriculture sector but aligns seamlessly with the UN’s SDGs, including food security and climate resilience.
Assess damages: Leveraging historical data, climate models, and Earth Observation data, AI algorithms predict and assess potential risks in real time. This helps banks and insurance firms track crop yields, foresee issues like droughts or floods, and create better plans to reduce losses. The data also creates transparency to reduce fraud and speed up claims processing.
Smart lending: Structured data about farming methods helps finance and banking sectors gauge credit risks more precisely. With AI-driven analytics, readily accessible EO data about crop quality in specific fields becomes crucial for making wiser lending decisions, including cutting down the necessity for in-person farm visits. This also helps insurers design better products and services for farmers, such as index-based insurance and parametric insurance.
Compliance with environmental norms: Similarly, satellite-based Earth Observation, coupled with detailed analytics, offers a means to track the BFSI (Banking, Financial Services and Insurance) sector’s compliance with environmental standards by supplying information on diverse environmental aspects. Consequently, a lending strategy can promote sustainable and conscientious agricultural methods.
Managing climate risks: Beyond just assessing farming effectiveness, geospatial data can uncover how climate change impacts land and threatens agricultural output. Through timely insights from EO data, banks and insurers can spot and assess risks like flooding, drought, or other climate-related dangers, and help policymakers and researchers take steps to reduce these risks. They can also help identify and implement best practices and innovations for sustainable and resilient agriculture.
Amid rising occurrences of extreme weather events, more and more insurance companies are collaborating with agricultural tech firms to enhance efficiency and precision in underwriting, fund allocation, and claims evaluations.
This article from Accenture underscores the accessibility of EO data across various participants in the agricultural value chain, while emphasizing that digital technology is a transformative force in agricultural insurance, tackling critical issues like climate change, supply chain challenges, and inadequate coverage. While giving examples of successful case studies from across the world, Accenture argues that this data, coupled with analytics, provides profound insights, enabling informed decisions that mitigate risks and boost yields.
In essence, geospatial data analysis revolutionizes how banks and financial institutions gauge credit risks and establish trust in farm lending. Using this tech, they can track crop yields, assess risks, and meet environmental standards. This empowers them to make wiser lending choices, back sustainable farming methods, and foster economic growth in rural areas.
Nurturing Global Food Security
The integration of AI and Earth Observation data has reshaped agriculture and agricultural insurance, steering us toward a more sustainable and food-secure future. In a world grappling with the impacts of Climate Change and population expansion, these technologies fortify agricultural systems against unpredictability — empowering farmers to adapt to changing climatic patterns, minimize losses, and sustainably meet the burgeoning food demands.
This convergence of technologies is not only enhancing productivity and resilience but also laying the groundwork for a more harmonious relationship between agriculture and the environment. Caring about yield optimization and waste reduction help foster precision farming practices and allow the agriculture industry as well as financial institutions to contribute to other SDGs.
However, unlocking this potential requires collaborative efforts between governments, tech innovators, insurers, and farmers to democratize access to these technologies. Further, it is imperative to address ethical concerns, secure data, and ensure equitable access to these transformative technologies. Investments in infrastructure, education, and technological literacy are crucial in ensuring that these tools are accessible and beneficial to farmers worldwide.
The AI-powered future is not just a chapter in the book of progress; it is a narrative of resilience, sustainability, and collaboration between the human spirit and the genius of machines, echoing the urgency of securing a stable future for generations to come.
About the authors
Anusuya Datta is a seasoned writer/journalist with a special interest in Earth observation and sustainability issues. A published author on several international platforms, Anusuya is part of the EO4SDG board.
Rachel Hor is a prominent figure in the business and technology domains, also known for her achievements in the financial services sector. With expertise in navigating the intersection of technology and business, she is a dedicated advocate for climate sciences and sustainability, striving to foster a more environmentally conscious world.
