2018 & 2019 WinnerUsing commercial microwave links (CMLs) to estimate rainfalls
The Inspire Challenge is an initiative to challenge partners, universities, and others to use CGIAR data to create innovative pilot projects that will scale. We look for novel approaches that democratize data-driven insights to inform local, national, regional, and global policies and applications in agriculture and food security in real time; helping people–especially smallholder farmers and producers–to lead happier and healthier lives.
This proposal was selected as a 2018 pilot project and 2019 scale-up runner up project, with the team receiving a total of 225,000 USD to put their ideas into practice.
Using commercial microwave links (CMLs) to estimate rainfalls
A lack of accurate, real-time rainfall measurements in regions where the data is not collected or difficult to obtain leads to imprecise crop yield monitoring, which in turn leads to high basis risks of rainfall-based index insurance.
This project proposes to demonstrate the potential of using recent advances in commercial microwave links (CMLs) technology to estimate rainfalls in crop production monitoring and help design better rainfall-based index insurance in developing economies.
CMLs are operated as backhaul on a cellular phone network. Rainfall causes attenuation to the radio signals between transmitter and receiver stations in the network, which means that precise rainfall estimates can be drawn based on changes observed in the quality of the signal.
The wireless microwave links used in these networks are widely deployed by mobile phone service providers and stand just a few tens of meters above ground level. While conventional rainfall monitoring techniques (rain gauge, satellites, radars) have shortcomings: weather stations are costly and sparsely deployed in developing economies and satellites lack accuracy near the ground surface.
CMLs can provide highly accurate rain estimates with high spatio-temporal resolution; measurements are taken typically every 15 minutes. The implementation cost is also minimal, since the data is already collected and logged by the cellular operators. Additionally, many of these links are installed in areas where access is difficult such as orographic terrain and complex topography. Therefore, the CML method enables measurements in places that have been hard to access in the past or where rainfall has never been measured before.
By combining data from the household survey, rain gauges, CMLs, weather stations, and satellite, the team aims to explore the value-add of using CML rainfall estimates in crop production monitoring and quantify the reduction of basis risk in real-world index insurance compared to the alternative methods: weather stations and satellites.
Farmers will benefit from better design of rainfall-based index insurance and poor households will benefit from more accurate crop yield monitoring by policymakers.
This is the first intended assessment of application of the CML technology in crop production monitoring and index insurance. It is a sustainable solution on the long-term as it relies on existing infrastructure and data collection by a telecommunication companies.
Step by step
Partnership for Kenyan data
The team partnered with the Department of Geo-Information Science and Earth Observation (ITC) at the University of Twente, Enschede, Netherlands.
The University of Twente provided CML and rain gauge data from Kericho, Kenya.
Acquisition of satellite data and comparison with rain gauge data
The team obtained satellite data from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), an open source, 30+ year quasi-global rainfall dataset.
The satellite data and Kenyan weather station data has been compared with rain gauge data to show that CML estimation is more reliable.
Conference paper and presentation
The team authored a paper on the analysis of the CML and rain gauge data from Kericho that was accepted by the 16th International Conference on Environmental Science and Technology. The paper was presented at the Conference in Rhodes, Greece, 4-7 September 2019.
US$125K scale-up grant
The project was awarded a 2019 Inspire Challenge Scale-up grant of US$125K at the third annual convention of the CGIAR Platform for Big Data in Agriculture, 16-18 October 2019.
The team will submit a paper to Water.
Collaboration with insurance providers
Using existing insurance policies, the team will simulate the reduction of basis risks using CML rainfall information under different scenarios. These scenarios will then be compared to basis risks that use satellite and weather station rainfall.
The team hopes to set up a partnership with crop insurance providers in Guatemala.