  Procuring agricultural inputs (agri-inputs for short) such as seeds,
fertilizers, and pesticides, at desired quality levels and at affordable cost,
forms a critical component of agricultural input operations. This is a
particularly challenging problem being faced by small and marginal farmers in
any emerging economy. Farmer collectives (FCs), which are cooperative societies
of farmers, offer an excellent prospect for enabling cost-effective procurement
of inputs with assured quality to the farmers. In this paper, our objective is
to design sound, explainable mechanisms by which an FC will be able to procure
agri-inputs in bulk and distribute the inputs procured to the individual
farmers who are members of the FC. In the methodology proposed here, an FC
engages qualified suppliers in a competitive, volume discount procurement
auction in which the suppliers specify price discounts based on volumes
supplied. The desiderata of properties for such an auction include:
minimization of the total cost of procurement; incentive compatibility;
individual rationality; fairness; and other business constraints. An auction
satisfying all these properties is analytically infeasible and a key
contribution of this paper is to develop a deep learning based approach to
design such an auction. We use two realistic, stylized case studies from chili
seeds procurement and a popular pesticide procurement to demonstrate the
efficacy of these auctions.
