Earth observation and AI-based Impact Assessment

Have you wondered how earth observations can be used for impact assessment?🤔

Can we automate 🔧🔨 the process of monitoring a farm and comparing the farm’s performance with others using earth observations?

What if all this (monitoring, verification, reporting, and benchmarking) of a farm is already built in a tool and you need limited knowledge to use it? 🤩

Earlier this year a large agricultural foundation approached us with a similar problem. The foundation’s primary goal is to help farmers increase their resilience to climate change while increasing their farm income. They provide multiple services to the farmers through village-level agri-entrepreneurs. These services range from advisory on climate-smart agriculture (CSA) practices to market and credit linkages.

A major challenge for the foundation is the tracking of these objectives, especially the impact of CSA advisory. Because of the project’s size, the foundation was looking for more efficient ways to benchmark and monitor their farms. It is practically impossible for them to visit all of the farms manually throughout the season. Even if they are capable of doing so, keeping track of all the variables (such as soil, farming practices, weather conditions) and reaching a conclusion is difficult.

The Agtuall team partnered with the foundation and created an MRV system (monitoring, reporting & verification) for the CSA-related activities. The system was deployed in Thane and Palghar districts of Maharashtra in India. Some of the parameters that the system tracks are:

  • Weather conditions in a certain area and risks due to natural calamities
  • Vegetation health, crop diversity, and cropping intensity
  • Water resources availability and usage

An “Impact Assessment” score was developed through the system. The score gives a sense of how effective the interventions have been in increasing the resilience of the farmer. The score is generated by considering a variety of data points such as irrigation, soil condition, crop diversification, subsequently giving weightage to each of them based on CSA goals. This score is used to track aspects of the farm that may be improved, as well as alert the agri-entrepreneur (AE) in that region to take action. For example, suppose an AE notices that a specific farm has less soil moisture or a low NPK content. He could also devise the best agricultural practices and machinery to assist farmers in dealing with these issues effectively.

We have encountered some limitations of the MRV system during its deployment. The insights are still dependent on the initial ground-level data that can only be collected by visiting the farm. Estimations regarding parameters such as soil type and health are far from reliable at this moment. However, the benefits of the system outweigh these limitations. Parameters such as water usage, cropping pattern changes are invaluable for CSA advisory and can be measured with high accuracy. Using earth observation data provides much-needed scale and MRV capabilities to foundations and other entities that provide CSA advisory. Want to know more about this system and explore collaboration opportunities? Head to this link.



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