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Tracking Food Supply Chain Postharvest Losses on a Global Scale: The Development of the Postharvest Loss Information System

Author

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  • Thiago Guilherme Péra

    (Luiz de Queiroz College of Agriculture (ESALQ/USP), University of São Paulo, Piracicaba 13418-900, Brazil)

  • Fernando Vinícius da Rocha

    (Faculty of Animal Science and Food Engineering (FZEA/USP), University of São Paulo, Pirassununga 13635-900, Brazil)

  • José Vicente Caixeta Filho

    (Luiz de Queiroz College of Agriculture (ESALQ/USP), University of São Paulo, Piracicaba 13418-900, Brazil)

Abstract

Reducing food losses presents an opportunity to enhance food security, minimize waste, and improve profitability within the production sector. Creating awareness among various stakeholders in the value chain about the significance of reducing postharvest losses is a fundamental step in this discussion. This article addresses the Postharvest Loss Information System (SIPPOC) development and applicability. SIPPOC encompasses tools designed to facilitate understanding food loss occurrences across different supply chain segments. The article provides insights into the tools incorporated within the information system and describes its historical background and protocol for database updates. In essence, SIPPOC enables the analysis of food loss throughout diverse logistical stages, thereby aiding in identifying critical points and implementing targeted actions for loss reduction. Drawing on SIPPOC data, the article further examines losses within the logistics chain by comparing potato, tomato, and mango agricultural productions.

Suggested Citation

  • Thiago Guilherme Péra & Fernando Vinícius da Rocha & José Vicente Caixeta Filho, 2023. "Tracking Food Supply Chain Postharvest Losses on a Global Scale: The Development of the Postharvest Loss Information System," Agriculture, MDPI, vol. 13(10), pages 1-14, October.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:10:p:1990-:d:1258889
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    References listed on IDEAS

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    Cited by:

    1. Rui-Feng Wang & Wen-Hao Su, 2024. "The Application of Deep Learning in the Whole Potato Production Chain: A Comprehensive Review," Agriculture, MDPI, vol. 14(8), pages 1-30, July.

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