Measuring Food Loss and Waste: Post-harvest Loss Surveys
Reducing food loss along the entire value chain can play an important role in improving global and local food security. However, accurate, standardized definitions and measurements of food loss have proven elusive. Without being able to properly understand the scope of the problem, policymakers and researchers will find it difficult to enact effective policies to address it. Two recent studies from researchers at IFPRI, KU Leuven, and UN FAO aim to improve the way food loss are studied and measured in order to provide a clearer policy roadmap.
Food loss is important
Addressing food loss is important for several reasons. First, food security policies often focus on increasing yields and production, but policies aimed at reducing food loss are often less cost- and time-intensive. Second, reducing food loss can also improve the efficiency in the use of natural resources and help cut greenhouse gas emissions. Third, reducing losses along the agricultural value chain can boost producers’ incomes by getting a price premium for better quality produce and decrease consumers’ expenses by increasing available supply, with positive implications for poor populations
A 2021 paper published in Food Policy combines traditional methodology and three innovative methodologies to better quantify food loss and identify how and where food loss occurs for different commodities and value chain nodes. A second paper published in Applied Economics Perspectives and Policy uses econometric modeling to measure food losses along staple food value chains. The research informing both papers expands existing definitions of food loss to include pre-harvest losses and includes losses in quantity, quality and value. The work looks at food loss among producers, middlemen, and processors in staple value chains in six countries: potato in Peru and Ecuador, maize and beans in Honduras and Guatemala, teff in Ethiopia, Maize in Mozambique and wheat in China.
The new methodologies measure both quantity and quality losses and consist of the category method that classifies crops into quality categories; the attribute method that evaluates crops according to inferior visual, tactile, and olfactory product characteristics; and the price method that assumes that higher (or lower) prices reflect higher (or lower) quality. These three methodologies show larger food losses than the traditional aggregate self-reported methodology, because of the disaggregation of the elements behind the losses and because they capture both quantity and quality losses. They also allow for important distinctions to be made among producers, middlemen, and processors. These distinctions could allow for more accurately targeted policies to address food loss where it actually occurs.
Losses are consistently highest at the producer level
Losses are consistently highest at the producer level: 60-80 percent of total losses and can be mainly attributed to the pre harvest stage. Losses at the middleman level, on the other hand, account for only 7 percent. Across both studies, pests, crop diseases, unfavorable climate conditions (especially low rainfall), and lack of mechanized harvest practices and appropriate storage facilities appear to be important drivers of food loss at the producer level.
Beyond immediate micro-level causes of food loss
Both papers highlight the need to look beyond these immediate micro-level causes of food loss in order to effectively address the problem. More broadly, poor road infrastructure and lack of access to credit and financing also play an important role, particularly in rural areas and for small producers. In addition, demographics also appear to have a part in determining food losses. The research found that more educated and experienced producers appear to experience significantly less loss.
Targeted policies to address food loss
The extent of all of these factors varied by commodity, however. This highlights perhaps the most important finding from both papers: Policymakers and researchers need to continue to collect evidence-based information from specific value chains and at specific nodes in order to create targeted policies to address food loss.