Climate Change and Agriculture: Can AI Help?
Climate change poses a significant threat to global food security, with agriculture both contributing to and being impacted by this crisis. Extreme weather events, shifting precipitation patterns, and rising temperatures disrupt crop yields and threaten livelihoods. Simultaneously, traditional agricultural practices contribute to greenhouse gas emissions, exacerbating climate change.
Artificial intelligence (AI) presents some interesting tools that allow us to address these interconnected challenges. By analyzing vast datasets and identifying patterns, AI can support more informed decision-making in agriculture. For example, AI-powered tools can help farmers optimize resource use, predict crop yields, and adapt to changing climate conditions.
However, realizing the full potential of AI requires careful consideration. Challenges such as data access, privacy, and ethical implications must be addressed. Additionally, ensuring that AI benefits are distributed equitably is crucial.
While AI is a promising tool, it is essential to remember that it is not a standalone solution. Human expertise, policy interventions, and sustainable practices remain indispensable for building resilient and sustainable food systems. By combining human ingenuity with AI's capabilities, we can make changes in order to mitigate and adapt to the increasing challenges of climate change.
This blog is inspired by a series of courses I have taken for Climate Change AI Summer School. I wanted to explore some of the ways that AI and machine learning (ML) are being used to reach emission reduction targets and make necessary changes without causing harm. AI should be carefully considered for each potential project, as simpler solutions are often more effective, cost-efficient, or have less detrimental environmental or social impact. AI is certainly not going to solve these problems on its own. There is much work to be done to understand and integrate these systems and apply them in appropriate ways, while addressing the sociotechnical as well as purely social issues along the way.
About Climate Change and AI
The climate crisis demands urgent and innovative solutions. The Intergovernmental Panel on Climate Change (IPCC) has made it clear that drastic reductions in greenhouse gas emissions (GHGs) are imperative to avert catastrophic global warming. While the challenge is immense, technology offers potential pathways forward.
Machine Learning (ML), which enables computers to learn from data without explicit programming, has far-reaching applications in climate science, mitigation, and adaptation. From predicting extreme weather events to optimizing renewable energy systems, ML can provide invaluable insights and support decision-making. However, it's crucial to recognize that ML is not a panacea. Challenges such as data bias, model interpretability, over-surveillance, and the potential for unintended consequences must be carefully considered. By understanding both the promise and limitations of ML, we can harness its power to create a more sustainable future.
In this series of posts, we will explore the potential of ML in addressing climate change, examining its applications across various sectors and discussing the critical factors for successful implementation.
This blog is the first of a series on the different courses that I attended over the summer. The courses started with an Introduction to Climate Change and AI, followed by AI for Agriculture, Forestry, and Other Land Use which will be the topic for this blog. Next they covered AI for Biodiversity and Ecosystems; AI for Social Sciences; Economics and Policy part 1 and part 2; AI for Climate Science; AI for Monitoring, Reporting, and Verification; Ethics, Impacts, and Regulation of AI; AI for Buildings and Cities; GHG Impact Assessment of AI; AI for Power and Energy Systems; AI for Transportation; and finally Climate, Health, and AI. All in all, there were 13 courses of lectures over 6 weeks from expert presenters on the cutting edge of these areas of AI for climate change. The links on each of these will take you to the actual courses if you are interested in learning for yourself. They are long and in-depth but totally worth the time! However, here I will be summarizing them for you and sharing my key takeaways.
Be on the lookout for upcoming blog posts covering useful information on each of these topics. For this blog, we will focus on AI for agriculture and climate change.
Agriculture and Climate Change: Where does AI come in?
You may have heard about robots being used on farms, but AI for agriculture has many more uses than that. When approaching this, it is first important to note that agriculture and climate change are a two-way street. Agriculture is both highly vulnerable to climate change (changing temperatures, extreme weather, reduced water) and a major contributor (methane emissions, fertilizers, land-use changes). As climate change intensifies, this creates more and more food insecurity. There is much work to be done to prevent this.
One way AI can interpret satellite imagery to give information on what is cropland and not cropland, image from Climate Change AI Summer School
Understanding ML for Climate Change and Agriculture
Many advancements in AI have useful applications for climate change and agriculture, such as machine learning. Techniques like remote sensing, coupled with ML, enable us to monitor vast areas, detect changes in land use, and assess crop health on a global scale. Precision agriculture, another application, leverages AI to optimize resource management at the farm level, increasing efficiency and reducing environmental impact. While some AI solutions are globally applicable, such as climate modeling and satellite imagery analysis, others are tailored to specific regions and crops, demonstrating the versatility of this technology in addressing diverse agricultural challenges. However, data is not equally available everywhere, and not as easily applied on the ground.
While satellite technology and AI offer immense potential for revolutionizing agriculture and ensuring food security, their impact is unevenly distributed. The availability of high-resolution satellite data is significantly higher in the Global North compared to the Global South, limiting the ability of farmers and policymakers in these regions to leverage these technologies to their full potential. Additionally, the specific agricultural challenges faced by different socioeconomic contexts vary widely. For instance, smallholder farmers in developing countries often prioritize short-term yields and subsistence, while large-scale commercial farms in developed countries may focus on maximizing profits, where everyone needs to be prioritizing environmental sustainability. These diverse needs require tailored AI solutions and data infrastructure to be truly effective.
Moreover, the digital divide further exacerbates these disparities. Access to technology and digital literacy are essential for farmers to utilize AI tools effectively. Bridging this gap is crucial for ensuring that the benefits of AI in agriculture are shared equitably across the globe.
AI for Informed Decisions: Who Benefits?
One way that AI can be helpful is in providing information and making decisions based on the data available, such as tracking satellite data or using a farm’s localized data. This holds different benefits for different stakeholders, such as for farmers and policymakers. Generally, farmers are concerned about their individual farms and keeping them running efficiently and producing crops, whereas policymakers are concerned about food security and climate impact for the masses.
Delivering Information to Decision Makers
To effectively address climate change, it's crucial to deliver timely and accurate information to those who can make a difference. Decision-makers such as farmers, policymakers, aid organizations, and industries require data-driven insights to inform their strategies. Farmers, for instance, grapple with questions about crop yield, weather patterns, soil moisture, and pest management. Climate change is altering traditional farming practices, making it increasingly difficult to predict optimal planting times and manage water resources effectively. By providing farmers with AI-powered tools to forecast weather, assess soil conditions, and predict crop performance, it can help them adapt to these challenges and ensure food security.
Policymakers, on the other hand, need a broader perspective. They require data on long-term climate impacts as well as data to track the resilience of agricultural systems and the interplay between agriculture and climate change. AI can help policymakers understand the data to address how climate change will affect livelihoods, water usage, and food production on a larger scale. By identifying regions vulnerable to climate shocks and analyzing the effectiveness of different policies, policymakers can make informed decisions to mitigate risks and build resilience.
Examples of AI in Action
Several initiatives have successfully harnessed AI to deliver valuable information to decision-makers. NASA Harvest, for example, provides global-scale agricultural data, enabling policymakers to track crop production and identify potential food shortages. Similarly, the Famine Early Warning Systems Network (FEWS NET) uses AI to forecast the impact of climate shocks on vulnerable populations, helping aid organizations allocate resources effectively. These examples demonstrate the potential of AI to inform decision-making and improve outcomes.
To fully realize the benefits of AI in agriculture, addressing challenges such as data availability, model generalizability, and effective communication with end-users is essential. By investing in research and development, building strong partnerships between scientists, farmers, and policymakers, and prioritizing the ethical use of AI, we can unlock its potential to create a more sustainable and resilient food system.
Many examples were given in the course of situations where AI is already being utilized to assist with climate change and agriculture. Some of the applications aim to make sure that farming is efficient and thus not adding unnecessarily to greenhouse gas emissions (GHGs), and some of the case studies provide ways to cope with the changing climate.
Harvest AI and Acres AI: Global and US Crop Monitoring
NASA Harvest and NASA Acres are pioneering initiatives leveraging satellite data and AI to monitor crop conditions on global and national scales, respectively.
Harvest AI takes a worldwide perspective, utilizing satellite imagery to track crop health, predict yields, and assess agricultural risks. By analyzing vast datasets, Harvest AI provides valuable insights to policymakers, aid organizations, and farmers in making informed decisions about food security and resource management.
On a more localized level, NASA Acres focuses specifically on the United States. By harnessing satellite data and advanced analytics, Acres AI supports American farmers in optimizing their agricultural practices. This initiative provides critical information on crop conditions, soil moisture, and weather patterns, enabling farmers to make data-driven decisions that improve yields, conserve resources, and mitigate the impacts of climate change.
Both Harvest AI and Acres AI exemplify the potential of satellite technology and AI to revolutionize agriculture and ensure food security. However, it's essential to consider the potential privacy implications of such large-scale data collection and analysis.
FEWS NET: Early Warning for Food Security
FEWS NET, the Famine Early Warning Systems Network, is a critical tool in the fight against food insecurity and humanitarian crises. By leveraging AI and advanced data analysis, FEWS NET is able to forecast the impact of climate and weather shocks on vulnerable populations. The network currently monitors food security conditions in over 35 countries across Africa, Central Asia, Central America, and Haiti. For example, the network can predict the potential consequences of flooding in Kenya, such as crop damage, displacement, and food shortages. The network accurately forecasted the devastating impact of desert locust swarms on crops and livelihoods in countries like Kenya, Ethiopia, and Somalia in 2019 and 2020. Additionally, FEWS NET has been instrumental in assessing the potential consequences of hurricanes in Central America, providing crucial early warnings to aid organizations and governments.
This early warning system allows humanitarian organizations, governments, and other stakeholders to anticipate crises, mobilize resources, and implement effective response plans. By understanding the potential impacts of climate shocks, FEWS NET helps to prevent food shortages from escalating into famines, saving lives and protecting livelihoods.
Maui Food Security Project: Leveraging Satellite Data for Sustainable Agriculture
The Maui Food Security Project is a prime example of how satellite data can be used to support sustainable agricultural practices. By extracting valuable information from satellite imagery, researchers and farmers can gain insights into crop health, soil conditions, and land use patterns. This data-driven approach enables more informed decision-making, leading to increased agricultural productivity and reduced environmental impact.
For instance, by analyzing satellite data, researchers can identify suitable areas for cultivation, optimize water usage, and detect potential pests or diseases early on. This information empowers farmers to make timely interventions, protecting their crops and maximizing yields. Moreover, satellite data can help monitor land use changes and identify opportunities for sustainable agriculture practices, such as agroforestry or organic farming.
Ultimately, the Maui Food Security Project demonstrates the potential of satellite technology to address food security challenges and promote sustainable agriculture on a local scale.
Challenges and Ethical Considerations in AI for Agriculture
The integration of AI into agriculture presents a complex landscape of opportunities and challenges. While technologies like satellite imagery and machine learning hold immense potential for enhancing crop yields, managing resources, and environmental sustainability, significant hurdles must be overcome.
One key challenge lies in the scarcity of high-quality, publicly accessible agricultural data, essential for training robust AI models. Overreliance on limited benchmark datasets can hinder model performance in real-world conditions. Additionally, the collection and use of data raise privacy concerns, particularly regarding satellite imagery. Safeguarding sensitive information while maximizing data-driven benefits is crucial.
Beyond data limitations, developing AI models that are reliable, interpretable, and explainable is essential for building trust among farmers and policymakers. Ensuring equitable access to AI technologies, especially in resource-constrained regions, is also imperative. Addressing technical, social, and ethical considerations is fundamental to unlocking AI's full potential for a sustainable and equitable food system.
The primary source of risks in crop monitoring lies in the utilization of satellite image data. Here is one site where satellite data that is publicly available: https://browser.dataspace.copernicus.eu/
The potential for misuse is significant, including economic espionage where competitors can gain a competitive edge by analyzing crop patterns and yields. Additionally, food security threats arise as malicious actors can exploit this data to manipulate markets or identify vulnerable regions. The combination of satellite data and advanced AI algorithms exacerbates these risks, as it allows for even more detailed and actionable insights to be extracted. To mitigate these risks, robust safeguards such as data anonymization, encryption, and strict access controls are necessary. This is an ongoing struggle with open source and publicly available data and AI. Is it worth the risks?
Conclusion
This blog has explored the potential of AI to address the complex challenges at the intersection of agriculture and climate change. By leveraging satellite data, machine learning, and other advanced technologies, we can gain valuable insights into crop health, weather patterns, and land use changes. These insights can inform decision-making for farmers, policymakers, and aid organizations, contributing to more resilient and sustainable food systems.
However, realizing the full potential of AI requires careful consideration of ethical, social, and environmental implications. Addressing challenges such as data privacy, model interpretability, and equitable access to technology is essential for building trust and ensuring that AI benefits all stakeholders.
As AI continues to evolve, ongoing research and development are crucial for addressing emerging challenges and unlocking new opportunities. By fostering collaboration between researchers, policymakers, farmers, and other stakeholders, we can harness the power of AI to create a more sustainable and equitable future for agriculture and the planet.