Smallholder farmers are amongst the most vulnerable communities in developing countries, lacking a stable income due to inconsistent access to markets. Aiming to tackle rural poverty, the Brazilian government established institutional markets for smallholder farmers to supply their produce to schools through a non-competitive bidding mechanism. However, participation of farmers is still limited due to the challenging decision-making process. Aspiring to contribute towards increasing their 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, based on two sequential MILP optimisation models, was developed and applied to the case study of Canudos settlement, guiding farmers on the optimal bidding and contract acceptance strategy. This study contributes to the decision support systems field by applying OR methods to a real-life problem within a new context. It is the first application of an OR-based decision support system in the non-competitive bid/no-bid literature, defining an optimal bidding strategy through the application of optimisation methods to maximise profitability while removing subjectivity from the decision-making process. Moreover, it is the first decision support system within the bid/no-bid decision-making field being applied to the agricultural and institutional market context. The proposed approach could have a significant social impact for smallholder farmers in Brazil, improving their living conditions by providing security of income and strengthening inclusive agricultural growth.

Supporting Brazilian smallholder farmers decision making in supplying institutional markets / Fuchigami, H. Y.; Tuni, A.; Barbosa, L. Q.; Severino, M. R.; Rentizelas, A.. - In: EUROPEAN JOURNAL OF OPERATIONAL RESEARCH. - ISSN 0377-2217. - 295:1(2021), pp. 321-335. [10.1016/j.ejor.2021.02.047]

Supporting Brazilian smallholder farmers decision making in supplying institutional markets

Tuni A.;
2021

Abstract

Smallholder farmers are amongst the most vulnerable communities in developing countries, lacking a stable income due to inconsistent access to markets. Aiming to tackle rural poverty, the Brazilian government established institutional markets for smallholder farmers to supply their produce to schools through a non-competitive bidding mechanism. However, participation of farmers is still limited due to the challenging decision-making process. Aspiring to contribute towards increasing their 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, based on two sequential MILP optimisation models, was developed and applied to the case study of Canudos settlement, guiding farmers on the optimal bidding and contract acceptance strategy. This study contributes to the decision support systems field by applying OR methods to a real-life problem within a new context. It is the first application of an OR-based decision support system in the non-competitive bid/no-bid literature, defining an optimal bidding strategy through the application of optimisation methods to maximise profitability while removing subjectivity from the decision-making process. Moreover, it is the first decision support system within the bid/no-bid decision-making field being applied to the agricultural and institutional market context. The proposed approach could have a significant social impact for smallholder farmers in Brazil, improving their living conditions by providing security of income and strengthening inclusive agricultural growth.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2970181