Proxy data from a similar geographic area, company, facility and/or time can be used in place of data from the unit being studied if there are no resources for conducting a full study or if data gaps exist in actual data. For example, data from another company could be used to fill in gaps in an inventory, data from one factory could approximate the level of food loss and waste in another, or household data from another city could be used to assess household waste (either per person or in total). However, proxy data cannot be used to track progress over time.
A summary of the strengths and limitations of proxy data is shown in Table A14.
Table A14. Factors to Consider when Using Proxy Data to Quantify FLW
|Strengths||Limitations / Points to Consider|
How to Use Proxy Data to Quantify FLW
Step 1: Determine what data is needed
Proxy data is useful for filling identified gaps in an inventory. If a company wants to quantify its food loss and waste levels but cannot conduct its own measurements, it may use public data from another company in the same sector to approximate its own. Similarly, if a country is conducting a national food loss and waste assessment, it may look to a geographically similar country that has published data to estimate its own FLW levels.
Step 2: Determine available proxy data
Proxy data can be drawn from a range of sources. Databases such as the Food Waste Atlas and FAOSTAT compile data, allowing users to search to find the most useful proxy data for their needs. A simple Internet search should also help to identify potentially relevant sources of data.
Step 3: Select the data to use
Select the proxy data that is most similar to the inventory being approximated. Variations in geography, company, facility, timeframe and other factors can introduce uncertainty and result in a final number that is less accurate. If possible, inspect the methodology used to collect the proxy data to determine how the number was derived and how reliable it is.
Step 4: Prepare and Analyze the Data
The proxy data must be transformed into a factor that can be applied to the data gap in the quantification being undertaken. Depending on the sector, this factor could be something like FLW per employee or FLW per metric tonne of food processed by a facility. This factor can then be applied to the population or facility being studied to determine the approximated FLW level.
Common Data Challenges in Using Proxy Data
Inaccurate data. Although proxy data can help to estimate FLW levels, using data from other contexts will rarely be as accurate as performing a direct measurement study. For this reason, proxy data should be a last resort when a lack of resources or expertise prevents use of another method.
Lack of available data. Many public sources of FLW exist, but there may be instances where no similar data sources can be found for a given sector, geography or food type. In these cases, consider contacting companies or researchers in the sector or geography in question to see if they can share any nonpublic data.
Inability to track changes in FLW over time. Since proxy data approximates FLW in a different context than your own, it cannot be used to track FLW changes over time. This is because any change in FLW levels would be reflective of a change in the other context, not in the facility or geography being studied. For this reason, proxy data should be seen as a starting point before moving into more specific measurement methods as a company or facility becomes more active in reducing FLW.
Additional Resources for Using Proxy Data
FLW Protocol. 2016. Chapter 10. “Proxy Data.” In Guidance on FLW quantification methods. <http://flwprotocol.org/wp-content/uploads/2016/06/FLW_Guidance_Chapter10_Proxy_Data.pdf>.
WRAP and World Resourcs Institute. 2018 Food Loss and Waste Atlas. <https://thefoodwasteatlas.org/>.
FAOSTAT. “Food and agricultural data.” Database. Food and Agriculture Organizaiton of the United Nations. <www.fao.org/faostat/en/#home>.