Breakthrough science and innovation

N. Palmer (CIAT)
Outcomes & Impacts

Cracking patterns in big data saves Colombian rice farmers huge losses

Latin America
Climate-Smart Technologies and Practices

Sometimes the best climate advisories are the most counterintuitive. In 2014, 170 Colombian rice farmers avoided massive losses by taking the advice of their producers’ federation, FEDEARROZ, not to plant in the first of the two annual growing seasons. The farmers who took the advice avoided economic losses estimated at USD 3.6m. FEDEARROZ acted on a forecast by a team of young CCAFS scientists based at the International Center for Tropical Agriculture (CIAT). The scientists had mined 10 years of weather and crop data to understand how climatic variation impacts rice yields. The team then fed patterns in climate and yields into a computer model and predicted a drought in the Caribbean department of Córdoba, which led it to conclude that farmers in some regions could save themselves from crop failure by not planting at all.

In 2014 climate forecasts developed with big data techniques helped 170 rice farmers in Colombia avoid economic loss estimated at 3,600,000 USD.

This work is a great leap forward. The ability to analyse masses of crop and climate data to provide farmers with accurate, site-specific forecasts and advice has huge implications, not only for rice, but also for cassava, beans and potato, the main crops in Colombia, and other crops in other countries.

“Weather changes are strong and highly different in every rice region and season. …Your analysis was very helpful for discussing possible climate change impacts with the farmers.” Patricia Guzmán, FEDEARROZ Colombia Córdoba Department
Life changing predictions from weather and rice production data patterns won CIAT–CCAFS scientists the UN Big Data Climate Challenge

Life changing predictions from weather and rice production data patterns won CIAT–CCAFS scientists the UN Big Data Climate Challenge.

In Colombia, rice production has fallen from around 6 tonnes a hectare to 5 tonnes since 2007. Variable weather from season to season means harvests can fluctuate by 30–40%. Now, based on trends identified by the CIAT-CCAFS data team, FEDEARROZ and government extension services in three regions recommend the rice varieties that work best under specific weather conditions and the best date to plant. In Saldaña, for example, the computer model indicates the date farmers need to plant to take advantage of maximum solar energy during the ripening stage.

The CIAT-CCAFS Big Data Team (clockwise from bottom left): Hugo Andrés Dorado, María Camila Rebolledo, Daniel Jiménez, Víctor Hugo Patiño, Juan Felipe Rodríguez, Sylvain Delerce.
The CIAT-CCAFS Big Data Team (clockwise from bottom left): Hugo Andrés Dorado, María Camila Rebolledo, Daniel Jiménez, Víctor Hugo Patiño, Juan Felipe Rodríguez, Sylvain Delerce.
“…we observed that the big climate factor limiting yields is accumulated solar energy during the grain ripening phase. To ensure that crops get optimum radiation, farmers can just shift the sowing date, and to further reduce yield losses, they can adopt rice varieties that are less sensitive to the amount of radiation received.” Daniel Jimenez, CCAFS

In September, Global Pulse, a UN scheme to harness big data for sustainable development, named the climate-smart, site-specific agricultural decision-making tool one of two winners of the UN Big Data Climate Challenge. Two members of the CIAT-CCAFS team were invited to the UN Climate Summit, where their research was shared with heads of state as well as global business leaders and civil society leaders.

“[This is a] uniquely innovative project that uses big data to drive climate action.” UN Global Pulse Big Data Climate Challenge

This breakthrough has tangible benefits for farmers. By heeding forecasts and specific recommendations on what, when and how to plant rice in their area they can avoid losses of 1 to 2 tonnes/hectare. This matters because farmers already struggle to remain competitive in domestic and export markets. The price they get for their rice barely covers the costs of machinery, pesticides and fertilizers.

“As we get more and more data, we’ll soon be able to develop site-specific recommendations for every rice-producing area in Colombia.” Daniel Jimenez, award co-winner, International Center for Tropical Agriculture (CIAT)

The CCAFS team recognize that the more comprehensive the data they have the better forecasts they will be able to provide. To collect more data, the team developed a mobile phone app for farmers to capture and share information about their farms and their rice, maize and bean cultivation practices. This local knowledge and site-specific information, when fed into the computer model, enables scientists to refine the advice they give. The advice gives farmers an advantage: even as weather conditions become more variable; they can raise production and avoid catastrophic losses.