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Case Study: Climate

Enhancing Rice Productivity through Big Data Approaches

Farming First Farming First

International Center for Tropical Agriculture (CIAT)

Enhancing Rice Productivity through Big Data Approaches


In Colombia, the climate is becoming increasingly unpredictable, particularly with regard to rainfall and temperature extremes. This has resulted in average rice yields falling from six to five tonnes per hectare in less than five years.

It means traditional calendar-based decisions about when to plant are no longer reliable. CIAT scientists mined 10 years of weather and crop data to understand how climatic variation impacts rice yields in the country. In 2014, following roughly a year of data analysis, the team were able to predict a forthcoming drought in the country’s Caribbean department of Córdoba, a major rice-growing area. Using the information, the country’s rice growers’ association recommended that farmers could save themselves from crop failure by not planting rice at all. The drought came, and those who planted harvested nothing, but the 179 farmers who followed the advice, saved approximately USD 3.6 million in averted costs. The following season, again following advance warning – this time of delayed rains – the farmers postponed planting by two months. They were able to produce a good harvest, and take advantage of higher prices. “It turned out perfectly,” commented Oscar Lopez, a rice farmer who benefitted. “We got good production and good prices.”

The project is considered climate-smart because it has helped farmers both adapt to climate variability by helping them plant (or not plant) at the best possible time, and also improve the sustainability of production through “data-driven agronomy”.

This Big Data project has the potential to be scaled-up for application in other countries, thereby increasing agricultural resilience to climate change.

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Case study prepared by:

International Center for Tropical Agriculture

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