During the mid-1960s, India came to the brink of an acute food crisis in the midst of heavy
dependence on food imports. A period of rapid agricultural modernization that followed, known
as Green Revolution, transformed India from a net importer of food into an exporter. Although
an appropriate response for abating the impending starvation, the Green Revolution inflicted
several unintended consequences. For example, regulatory structure and fertilizer subsidies for
urea that were designed to stimulate growth instead resulted in a lock-in, which in turn
incentivized vast over-fertilization across the country. Today, this is a well-recognized problem,
and the Government of India has announced policies and schemes such as the National Soil
Health Card Scheme to increase knowledge of soil condition and curb fertilizer use. In reality,
however, the current need for information on soil health far exceeds the capacity for soil testing,
highlighting the need for a radical approach to meeting this policy objective.
This project, undertaken in collaboration with MIT Mechanical Engineering, takes a two-part
approach to addressing this problem, with the design of a point-of-use soil testing sensor and an
accompanying recommendation generation engine. This thesis presents the design of the latter
based upon the answer to the following question: what constitutes an actionable information for
resource constrained farmers? To answer it, we use a mixed methodology approach comprising
(i) a combination of stakeholder interviews and design workshops to elicit user needs, and (ii)
controlled experimentation with over 200 farmers covering an entire village to measure the
actionability of information in soil health recommendations. The results of the analysis of
experimental data reveal that the actionability of recommendations varies significantly within the
population of farmers tested, and can be attributed to the level of information provided, the
environment in which a farmer receives a recommendation, gender, and education level.
Consequently, an effective point-of-use diagnostic system must adjust for these factors in order
to maintain high actionability. To that end, we then use the experimental results to design a
recommendation generation engine, the core of which is a soil health database that maximizes
the actionability of information for a resource constrained farmer.