Satellite Earth observations for food security
Increasing food production sustainably under increasingly variable weather, a warming climate and population growth is one of the most pressing challenges of today. Big-dollar food security decisions would benefit enormously from timelier, more accurate crop information particularly in the context of smallholder farming. However, huge uncertainties remain regarding where and when food is grown as well as how much will be produced, early warning of crop shortfalls, where (and why) there are yield gaps and how conflicts around land cover will impact production. Recent advances in satellite technology – and the integration of such technology into existing monitoring systems and agricultural decision-making processes across sectors – has the potential to address most of these uncertainties.
This Swiss Re Foundation-funded project will start with implementation of a prototype in Kenya intended to enhance the cost-effectiveness and empirical basis of the Kenya Crop Insurance Programme, which supports 300,000 smallholder farmers. To test the generalisability of the approach to a very different context, it will then be adapted and evaluated in a pilot study in Mexico in collaboration with the International Maize and Wheat Improvement Center (CIMMYT).
A key outcome of the project will be the development of an open-source, portable and cloud agnostic (containerized) machine learning-based tool - EO-FARM - for deriving key agricultural variables from satellite imagery, including cultivated area, type, condition, and yield. This tool could be used off-the-shelf for future growing seasons or fine-tuned with user-specific training data or crop types and will be refined and modified throughout the project and after.
To prepare for scaling and deployment of the approach elsewhere, the University of Maryland NASA Harvest project team will create a business case with the support of policymakers in Kenya and Mexico, which will include the:
1) Investment required for deployment
2) Estimated cost and time savings, compared to traditional methods, for assessing crop conditions and yields
3) Benefits of improved accuracy and timeliness for policymaking and policy instruments such as crop insurance programmes
4) Benefits for insurance and agricultural tech development
GOALS AND EXPECTED IMPACT
The University of Maryland NASA Harvest strives to shift the fundamental approach to agricultural mapping and monitoring by developing scalable, transferable methodologies and approaches that leverage satellite observation data to assess maize and wheat cultivation and yield.
The project will address major gaps in data quality, availability, accessibility and cost for farming in Kenya and Mexico with an aim to enable replication in many more countries.
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