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

IFPRI food losses measurement methodology 

With support from the CGIAR’s Research Program on Policies, Institutions and Markets (PIM) and in collaboration with the International Potato Center (CIP),  the International Center for Agricultural Research in Dry Areas (ICARDA), and the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), The International Food Policy Research Institute (IFPRI) has developed a new methodology to characterize the nature of post-harvest losses across the value chain for different commodities in a wide array of countries. For this purpose, IFPRI has designed a set of surveys to measure the extent of food losses. While the surveys were tailored to specific countries, commodities, and varieties (e.g., varieties of maize in China and Guatemala have different characteristics), they provide a consistent measurement of food losses across different agents in the value chain (i.e., farmers, middlemen, and processors). The surveys capture detailed information about the different processes of each of these agents and quantify food losses in each production stage with four methods.

  • Disaggregated self-reported measures of losses: We collect self-reported measures of volumes and values of food losses incurred during different processes (harvesting, threshing, milling, shelling, winnowing, drying, packaging, transporting, sorting, picking, transforming, etc.).
  • Losses based on categories: We collect detailed data from farmers, middlemen, and processors on the quality – based on damage coefficients – of agricultural commodities. Damage coefficients allow us to determine broad categories (usually traded in the market) of crops and enable us to quantify the economic value of quality differentials based on these categories.
  • Losses based on commodity attributes: We capture information about different types of commodity attributes (e.g., size, impurities, broken grain, etc.) and ascertain the price penalty that each of these types of crop damage entails. In this line, we are able to identify particular factors that diminish commodities’ values and quantify food quality losses based on market conditions.

Surveys to estimate food losses across the four proposed methodologies have been collected in eleven countries for ten agricultural commodities (Table below). The coverage of the project spans across Africa, Asia, and Latin America, and encompasses some of the most important crops in developing countries.

Location

Commodity

Lead Institution

Ecuador

Potato

CIP/IFPRI

Peru

Potato

CIP/IFPRI

Honduras

Maize, Beans

IFPRI

Guatemala

Maize, Beans

IFPRI

Ethiopia

Teff

ICARDA/IFPRI

China

Wheat

IFPRI

Malawi

Maize, Groundnuts, Soya

IFPRI

Ghana

Groundnuts, Yams

IFPRI

Kenya

Potato, Tomato

IFPRI

Mozambique

Maize

FAO/IFPRI

Tanzania

Maize

FAO/IFPRI

These surveys allow to quantify the extent of food losses across the value chain using consistent approaches that are comparable across commodities and regions. They also enable to characterize the nature of food losses. In particular, it will be possible to ascertain the production stages across of the value chain and the particular processes in which losses are incurred. The results of these studies will inform about the particular areas that require investments to reduce food losses.

The following report compares the results of the extent and nature of food losses in the value chains of maize and beans (in Guatemala and Honduras), teff (in Ethiopia), and potatoes (in Ecuador and Peru). In general, the results of these surveys show that:

  • Food losses are important: The median of the extent of food losses we find is 17.7%, but there is wide variation between commodities, crops, and measurement methods.
  • Food losses primarily happen at the farm: Our results suggest that between 59% - 86% of food losses happen at the farm.
  • Quality matters: Food losses are especially large when we account for quality deterioration, regardless of the particular method: categories (16-21%), attributes (16%-26%) or prices (16-24%).