2018 Winner

Using Commercial Microwave Links (CML) 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 winner, with the team receiving 100,000 USD to put their ideas into practice.

Using Commercial Microwave Links (CML) to estimate rainfalls

The lack of accurate, real-time rainfall measurements in Africa leads to imprecision in 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 (CML) technology to estimate rainfalls in crop production monitoring and help design better rainfall-based index insurance in Kenya.

Commercial Microwave Links 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 Africa; satellites lack accuracy near the ground surface – the proposed method can provide highly accurate rain estimates with high spatio-temporal resolution (measurements are taken typically every 15 mins). The implementation cost is also minimal, since the data is already collected and logged by the cellular operators. In addition, many of these links are installed in areas where access is difficult such as orographic terrain and complex topography. As such, this method enables measurements in places that have been hard to access in the past or where rainfall has never been measured before.

The project plans to leverage two ongoing initiatives. The first one will estimate rainfall using CMLs existing data from Safaricom in Kenya. The other is the piloting of a bundle of credit with rainfall insurance in Machakos county in Kenya, which relies on rainfall measurements from weather stations plus household surveys of 1150 farms from 65 villages in 2017, 18 and 19. The survey provides information on agricultural land, production and inputs, livestock, etc. Rain gauges will be installed in some sample villages.

By combining data from the household survey, rain gauges, CML, weather stations, and satellite, the team aims to explore the value-added of using CML rainfall in crop production monitoring and quantify the reduction of basis risk in real-world index insurance using CML rainfall measurements 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.

Team

Yanyan Liu | Email
Senior Research Fellow, IFPRI

Oliver Gao | Email
Professor, School of Civil and Environmental Engineering, Cornell University

Noam David | Email
Postdoctoral Research Associate, School of Civil and Environmental Engineering, Cornell University

Partners

Step by step

October 2018

US$100K Grant

The project was one of five winners of the Inspire Challenge 2018 and was awarded US$100K at the second annual convention of the CGIAR Platform for Big Data in Agriculture, 3-5 October 2018.

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March 2019

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 will provide CML and rain gauge data from Kericho, Kenya.

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June 2019

Accepted conference paper and presentation

The team authored a paper that was accepted by the 16th International Conference on Environmental Science and Technology.

The paper will be presented at the Conference in Rhodes, Greece, 4-7 September 2019.

August 2019

Guatemala field visit

The team travelled to Guatemala to discuss this project with potential partners including Tigo Guatemala, the Famine Early Warning Systems Network, Guate Invierte, Microrisk, and USAID.

The team obtained CML data from Chiquimula Department, and data from the western highlands region is to come.

August 2019

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 will be compared with rain gauge data to show that CML estimation is more reliable.

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September 2019

Simulation of basis risks

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.

Additionally, the team will present at the 16th International Conference on Environmental Science and Technology in Rhodes, Greece, 4-7 September 2019.

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October 2019

Presentation of results

The team plans to publish two papers, including one on how the project’s results can be applied in agricultural insurance.

Additionally, the team will present results to Tigo Guatemala and write a joint brief with the company.