Improving data on food losses and waste: From theory to practice

Carola Fabi, Franck Cachia, Piero Conforti (), Alicia English and Jose Rosero

Food Policy, 2021, vol. 98, issue C

Abstract: Food Losses and Waste (FLW) have received increased attention in the past decade especially after the 2007–2008 food crisis, which has rekindled debate about the global availability of food. This has highlighted the need to reduce harvest and post-harvest losses in the most vulnerable low to middle-income countries. The research and international community have actively engaged on the conceptual frameworks underpinning FLW, on the measurement approaches and the policy interventions to reduce FLW. Critical information gaps and data needs are evident. This paper using as a base the main conceptual frameworks proposed for measurement of FLW and existing quantitative evidence, attempts to sketch out a number of steps for gathering internationally comparable policy-relevant information. It presents quantitative evidence from a meta-analysis based on a machine learning text-mining tool, which is used to reassess a global percentage of losses with an emphasis on low and middle-income countries. Findings show that: i) losses for perishable crops, such as fruits and vegetables, display a median of 6.4 percent compared to 2.7–3.8 percent for other crops; ii) losses are higher in low-middle income regions, with 10–15 percent median losses, for example, for fruits and vegetables, compared to 4–7 percent for Europe and North America; iii) significant information gaps remain and the comparison of results across countries or even between sectors within them remains challenging. Acknowledging that some of these gaps are the result of insufficient coordination between different initiatives, this paper proposes operational frameworks to improve synergies and coordination in the measurement of FLW, in support of stakeholders’ decision making for reducing FLW.

Keywords: Food losses and waste; Postharvest losses; Agricultural surveys; Food chain; SDGs; Meta-analysis; Natural Programming Language; SDG 12.3 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jfpoli:v:98:y:2021:i:c:s030691922030138x

DOI: 10.1016/j.foodpol.2020.101934

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