Case study

SEEDS FOR NEEDS

What is the problem?

Climate change is already affecting food security as extreme weather events, changing patterns of rain and increasing temperatures mean some crops don’t grow well and farmers increasingly need to identify appropriate seeds for adaptation. Organizing large scale field trials (known as n-trials) that generate evidence about the efficacy of new crop varieties and fertilizers is a costly, resource and time intensive process. 

What is the collective intelligence solution?

Seeds for Needs is an initiative that works with smallholder farmers to identify the most climate resistant seeds for their local areas using citizen science. Farmers plant different varieties of seeds on their own farms and evaluate which ones grow best. Farmers report back their observations on their phones, using a free, open source software called Open Data Kit. The data from the farmers is aggregated and analyzed on the ClimMob platform, a free software that supports the design of large scale agricultural citizen science. Farmers access the platform to get the information about which seeds perform best in local conditions.

What was the benefit of using collective intelligence for this issue?

The combined knowledge that the farmers have generated has been proven to find seeds that are much better at surviving extreme weather conditions than those recommended on official government lists. To date, Seeds for Needs has engaged more than 50,000 citizen scientist farmers from 14 countries across Africa, Asia and Central America. Researchers have applied the methodology to help smallholders in Central America identify bean varieties that were most suitable under conditions of drought and water scarcity. Another example is a project in Nicaragua where field tests with seeds from the national seed bank help local farmers, many of whom are women, learn about crop-breeding techniques and how to adapt them to changes in the environment.

What does this experience tell us about collective intelligence and climate action?

Instead of a few researchers carrying out complicated field trials, large numbers of farmers or gardeners carry out small, simple trials on their land. Taken together, the many small trials can offer valuable information about the local suitability of agricultural technologies. The rapid analysis of results through the ClimMob software means that farmers get quick feedback about the efficacy of different crop varieties which means they can make timely decisions about which crops to grow the following season, resulting in overall improved cropland management. The ClimMob software design enhances accessibility via design features such as a simple ranking-based feedback format that allows even farmers with low literacy skills to contribute their evaluation data through various channels, including mobile telephones.

CLIMATE ACTION GAPS ADDRESSEDData Gap, Distance Gap, Doing Gap, Diversity Gap
COLLECTIVE INTELLIGENCE USE CASEDistributed Problem Solving
IPCC CATEGORYAdaptation, Land and Ocean Ecosystems, Improved Cropland Management
COUNTRIESMultiple -  14 countries across Africa, Asia, and Central and South America
COLLECTIVE INTELLIGENCE METHODSCitizen Science
PEOPLELocal Farmers, Scientists
DATAEnvironmental Samples, Citizen-generated Data
TECHNOLOGYOpen Data Kit (ODK) App, ClimMob Data Platform