Soil Health Cards Made Farmer Friendly

State and Development Partner

Uttar Pradesh,

IDinsight

Problem

Close to 30% of Indian farmland faces desertification, largely due to fertiliser misuse. If farmers had enouph information on the type and quantity of fertiliser to use, this could be potentially reversed. However, an overwhelming majority of farmers were unable to understand their Soil Health Cards due to their poor design. The cards used small font sizes, scientific terminology, unfamiliar land units and illegible formatting, making it difficult to understand even for literate farmers.

Conventional Solution

Soil Health Scheme was introduced with the best intentions under which more than 170 million cards were distributed. This scheme targeted 12O million farmers throughout India since February 2015. Over a two-yea r cycle, soil samples from farmers’ plots were tested across 12 parameters to identify nutrient deficiencies and crop-specific fertiliser dosage were recommended. The expectation was that by providing farmers this information, the scheme will encourage judicious use of fertilisers which will improve soil health and ultimately boost stagnating agricultural productivity. But despite all the work, the cards themselves were not designed keeping the end user in mind which eventually led to farmers’ not using them.

Behavioural Insights Based Solution

Idinsight redesigned the Soil Health Cards employing elements of Human-centred Design (HCD). Redesigned samples were tested on farmers and feedback was collected on five factors- usefulness, attractiveness, comprehensibility, relevance and persuasiveness. This iterative process where the end user was included in the feedback process finally led to a successful design that made the formatting clearer by introducing tables and colours to denote sufficiency and deficiency, gave clear instructions on how to interpret data, and contact details of extension services for further help. Additionally, scientific terms of fertilisers and crops were replaced with local names and local land units were used. By being mindful of the language and the literacy levels of the farmers while designing the new cards, cognitive overload of how to interpret the data on the card was reduced. It made it easier for the farmers to understand and act on because tables and colours worked as visual cues.

Impact

It was found that around 33% of farmers could accurately calculate the recommended quantity of fertilisers usinp the new card in their first attempt, while only around 0.5% of farmers who could do so using the old card.

Source : Stories of Change from India's Aspirational districts - NITI Aayog publication