Supporting Brazilian smallholder farmers decision making in supplying institutional markets

Smallholder farmers are among the most vulnerable communities in developing countries due to poverty and social exclusion, relying on agriculture for subsistence and employment, while still lacking a stable income due to inconsistent access to markets. Aiming to tackle rural poverty, the Brazilian government established the PNAE program (Programa Nacional de Alimentação Escolar - National School Feeding Program), an institutional market for smallholder farmers to supply their produce to schools through a non-competitive bidding mechanism. Nevertheless, smallholder farmers still face challenges in the evaluation of the profitability of participating in each call of the bidding mechanism. As a result, their participation to PNAE is limited due to the challenging decision-making process. Aspiring to contribute towards increasing smallholder farmers’ participation, this study aims to support farmers into two key decisions they face during sequential stages of the bidding process, namely whether to bid for each available school and product combination and whether subsequently to accept the awarded bids once the bids’ outcome is known. A decision support system (DSS), based on two sequential mixed integer linear programming (MILP) optimisation models, was developed and applied to the case study of Canudos settlement, guiding farmers on the optimal bidding and contract acceptance strategy. The DSS models are theoretically relevant for the Operational Research (OR) community from an application and a methodological perspective. This work contributes to the decision support systems field by applying established OR methods, such as MILP, to a real-life problem within the new context of bid/no-bid DSS, being also the first DSS application for non-competitive bidding. It is also the first work in the bid/no-bid DSS literature, which defines an optimal bidding strategy for the users thanks to the use of optimisation methods. Finally, the work is also original within the bid/no-bid decision-making area by being the first DSS applied to the agricultural and institutional markets contexts, aiming to facilitate decision-making of smallholder farmers in a developing countries context. The DSS defines efficient school supply regimes for the farmers considering also transportation costs, thus allowing an increased share of the revenue to remain within the smallholder farmers’ communities instead of being wasted in unnecessary transport activities or in supplying non-profitable products. The DSS removes subjectivity and intuition from the decision-making process, leading to faster and more effective bidding strategies. Finally, it removes the obstacle of disagreements within the farmers’ cooperatives on where to bid and which successful bids to select, due to an objective strategy being defined. The work has a significant social impact for the common good, simultaneously contributing to the UN Sustainable Development Goals, SDG1 “No Poverty”, SDG2 “Zero Hunger” and SDG10 “Reduced Inequalities”. The additional funds available to smallholder farmers thanks to optimised bidding strategies allow to fulfil the intended social outcome of the PNAE program, by offering increased financial security to vulnerable rural communities, helping them to escape poverty. The real-life impact of this work is enhanced since the DSS models have been embedded into a Progressive Web App, accessible from mobile phones. This social technology, coupled with dedicated training sessions to farmers, aims to make the smallholder farmers independent throughout the PNAE bidding mechanism. After being successfully implemented through a single case study in the Brazilian smallholder farmers’ settlement of Canudos, the work could be further applied in additional farmers’ communities in the country, with a potential of 1.5 million users in Brazil, thus being highly relevant for rural poverty reduction efforts in the country. Inclusive rural growth in developing countries is a key objective globally, as it allows to concurrently address food security and economic growth as well as alleviating effects of climate change.