Technical Platform on the Measurement and Reduction of Food Loss and Waste

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Food Loss and Waste Database - Background

Sustainable Development Goal Indicator 12.3 states “By 2030, to halve per capita global food waste at the retail and consumer levels and reduce food losses along production and supply chains, including post-harvest losses.”

To help monitor the state of food loss, FAO conducted an extensive review of literature in the public domain which gathered data and information from almost 500 publications, reports, and studies from various sources (including from organizations like the World Bank, GIZ, FAO, IFPRI, and more).

The data from this review is held within the interactive Food Loss and Waste Database which allows for micro and macro analysis of different sets of data.

The database allows users to filter food loss and waste information by:

  • Year
  • Geographical region
  • Country
  • Basket of items (commodity group)
  • Commodity
  • Stage of the value chain and activity
  • method of data collection

Moving from global food loss and waste estimates to the commodity, country, and stage level will vastly improve our knowledge of the food losses problem and present opportunities for intervention. In terms of working towards SDG Indicator 12.3.1, the improved food loss and waste estimates will allow us to improve technical assistance at country level.


Initial results and lessons from the Food Loss and Waste Database

The results displayed on the Food Loss and Waste Database will change over time as more information is incorporated into the dataset. The preliminary data led to the identification of several knowledge gaps and areas where capacity development is needed:

Often studies will sum the loss percentages across the supply chain. This tends to inflate the overall percentage losses and negate that the quantities of the commodities decrease through the supply chain with respect to losses and other utilizations.

The issue of food loss may not be fully captured within national estimates as they often present the lower bound of the problem. On the other hand, studies done with small sample sizes may not be fully representative, even though they represent the upper bound of the problem.

The figures show differences between estimates along different stages of the value chains and regions, making a one-size fits all estimate at the international level less practical when addressing losses. Considering how these estimates change over time by stage and ranges, it will be more effective to look at supply stages which fluctuate in their losses in order to focus on effective policy and investments.

Effective comparison of data is presently hindered by a lack of comparability on many levels: different measurement methodologies across space and commodities, and estimates that may not be repeated over time.

There may be underlying biases when it comes to the data that has been collected, both at the national level and within the individual studies and the focus will be put on problematic areas

In the case studies, the sample sizes may not be large enough to extrapolate causes and solutions to a general policy prescription.

The variability in the available data show the need to measure losses carefully and with reliable methods. More importantly, the gaps in the data by country, commodity, and for entire tracts of the supply chain call all stakeholders to act to increase the quality and quantity of available data. A sound evidence base is critically needed to support good investment and policy decisions.

The 2019 State of Food and Agriculture on Food Loss and Waste incorporates modelled loss estimates that stem from the first dataset batch from the database. The food loss estimation model was developed by FAO Statistics Division in 2016-2018 and serves as a basis for the SDG process in the absence of actual data.


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