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Case study on the rice value chain in the Democratic Republic of Timor-Leste

Food loss analysis: causes and solutions











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    Book (stand-alone)
    Case study on the maize value chain in the Democratic Republic of Timor-Leste
    Food loss analysis: causes and solutions
    2018
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    In order to improve global, regional and local knowledge about the underlying reasons for food loss, as well as to assess where critical loss points occur, FAO undertook a series of case studies involving numerous food supply chains in developing countries. Utilizing a defined food loss and waste analysis framework, the Organization and its partners identified nationally-important food products, and commissioned local-level studies of the losses in these chains. The findings of the study will be used to develop technically, economically, environmentally and socially feasible solutions to reduce food losses and are published in this report on the maize value chain in Timor Leste.
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    Brochure, flyer, fact-sheet
    Rice supply chain in Timor-Leste
    Food loss analysis: causes and solutions
    2018
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    In order to improve global, regional and local knowledge about the underlying reasons for food loss, as well as to assess where critical loss points occur, FAO undertook a series of case studies involving numerous food supply chains in developing countries. Utilizing a defined food loss and waste analysis framework, the Organization and its partners identified nationally-important food products, and commissioned local-level studies of the losses in these chains. This brochure aims at disseminating the findings of the study to be used to develop technically, economically, environmentally and socially feasible solutions to reduce food losses along the rice value chain in Timor Leste.
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    Booklet
    Exploring the application of Artificial Intelligence for triggering drought anticipatory action: A Timor-Leste case study
    Technical Working paper
    2024
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    This research describes the process of developing an agricultural drought-triggering methodology for anticipatory action (AA) within the context of Timor-Leste, an Indo-Pacific nation grappling with limited observation data. Drought is a severe and recurring natural hazard in Timor-Leste, significantly impacting livelihoods and exacerbating food insecurity due to the compounding effects of the climate crisis. This study provides a comprehensive understanding of the methodology’s development, highlighting the collaborative establishment of an AA protocol with the government and the humanitarian community, spearheaded by the Food and Agriculture Organization of the United Nations and the Government of Timor-Leste. Overall, this study aims to facilitate a transition towards a preemptive approach for disaster risk management and highlight the advances of the introduction of Artificial Intelligence (AI) moving forward.

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