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Food marketing margins during the COVID-19 pandemic: Evidence from vegetables in Ethiopia
Photo credit: World Bank

It is widely feared that the COVID-19 pandemic will lead to a significant worsening of the food security situation in low and middle-income countries. One reason for this is the disruption of food marketing systems and subsequent changes in farm and consumer prices.

Food safety, modernization, and food prices: Evidence from milk in Ethiopia
Photo credit: FAO

Modern marketing arrangements are increasingly being implemented to assure improved food quality and safety. However, it is not well known how these modern marketing arrangements perform in early stages of roll-out. We study this issue in the case of rural-urban milk value chains in Ethiopia, where modern processing companies – selling branded pasteurized milk – and modern retail have expanded rapidly in recent years.

Impacts of the COVID-19 crisis on vegetable value chains in Ethiopia
The COVID-19 pandemic is beginning to disrupt food value chains in Ethiopia and elsewhere, impacting the livelihoods of farmers and the diets of rural and urban households. These effects are likely to hit the poorest and most vulnerable farmers and consumers the hardest, but they are not yet well understood. More evidence is needed to guide the government and other organizations in devising responses.
COVID-19 is shifting consumption and disrupting dairy value chains in Ethiopia
The COVID-19 pandemic is beginning to disrupt food value chains in Ethiopia and elsewhere, impacting the livelihoods of farmers and the diets of rural and urban households. These effects are likely to hit the poorest and most vulnerable farmers and consumers the hardest, but they are not yet well understood. More evidence is needed to guide the government and other organizations in devising responses.

By Luciana Delgado and Alan de Brauw

This post originally appeared on

Photo by Devrig Velly EU/ECHO


The SDG 12.3 target calls 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” by 2030. It is already 2020, and the rapid spread of COVID-19 has raised – along with concerns about general food shortages - the threat of renewed food loss as the world’s food systems are being affected by quickly changing demand patterns.

To create better strategies to reduce food loss and waste, it is important to first measure them accurately and consistently. Measurement, however, is a tricky business. As discussed in the 2019 Report on the State of Food and Agriculture, assessing food loss and waste is not straightforward. It has been done using various methodologies, making it difficult to even compare measurements. Oftentimes, differences in results stem from how the loss is measured rather than from differences in harvest practices, storage, or other factors.

To help make estimates of food loss along the value chain clearer and more consistent over time, researchers supported by the CGIAR Research Program on Policies, Institutions, and Markets (PIM) developed detailed survey modules that can be applied to different components of the food value chain and adjusted to specific commodities. The methodology not only incorporates components of the Food Loss Index for SDG 12.3.1, i.e. on farm post-harvest, transport, storage and distribution, processing and packaging; but it also includes losses identified at the pre-harvest and harvest level, thus capturing the entire value chain up to the consumer.

PIM methodology

This methodology allows a quantification of the extent of food loss across the value chain using consistent approaches, comparable across commodities and regions. We can characterize the nature of food loss, specifically the production stages and processes at which the loss happens. Furthermore, it is possible to provide estimates of food losses in different ways: subjective estimates (based on self-reported data); quality attributes lost (inferior visual, tactile, and olfactory product characteristics); and reductions in value, or the price received per unit output (based on the reasoning that price differences of a product in the same locations and in the same time period reflect quality differences.

The methodology was piloted in Ecuador, Peru, Honduras, Guatemala, Ethiopia, China, and Malawi in 2016, followed by Ghana, Kenya, Mozambique, and Tanzania in 2019-2020. For each country and crop combination, the specification of the survey was slightly adjusted to make it more relevant to the context. For example, in Guatemala, the instrument, adjusted for beans, included five quality categories (ranging from 1-2% to over 25% of damaged grain) and thirteen quality attributes (dirty/not clean, chopped, size (small), presence of weevils, acid odor, wrinkled grain, swollen from moisture, rotted by moisture, humid, damage by fungus, different colors, broken grain, other odors).

Results and conclusion

Across all value chains for which post-farm measures are available, we estimate food losses at between 6 and 28 percent of total production and total produced value. Loss figures are consistently largest at the producer level, which means that much less value is lost within the value chain between producer and consumer. Loss at the producer level tends to represent between 60 and 80 percent of the total value chain loss, while the average loss at the middleman and processor levels lies between 7 and 19 percent. figures are larger than those recently obtained by Kaminski and Christiansen (2014) and Minten et al. (2016a and b) due to the inclusion of qualitative loss, but generally much smaller than the oft-cited statistic that one-third of food is lost or wasted.

These results suggest that interventions to reduce food loss should concentrate on the farm link of the value chain, or close to it. It is important to target smallholders with techniques that help them grow food with more marketable attributes (to reduce value loss) and improve on- or near-farm storage capabilities. As Brenda Mareri, SNV, noted, “Understanding the extent of food losses at each stage of the value chain, and its causes, helps SNV and the civil society groups we work with better target our advocacy for solutions.”

In the time of crisis such as the world is living through now, it is critical to use available resources as efficiently as possible. Consistently measuring post-harvest loss can help prioritize the right types of interventions to make sure that more nutritious food gets to consumers who need it.

Related publications

Luciana Delgado is research analyst and Alan De Brauw is senior research fellow at the Markets, Trade, and Institutions division of IFPRI. Alan De Brauw co-leads PIM's cluster of research on Interventions to Strengthen Value Chains and leads the cluster on Value Chains Research: Outreach and Scaling within Flagship 3: Inclusive and Efficient Value Chains.

Cities, value chains, and dairy production in Ethiopia

This paper explores the spatial heterogeneity in dairy production in the highland production area around the capital of Ethiopia, Addis Ababa. We look at how urban proximity – defined as the travel time from the farm to the central market of Addis Ababa – affects the production decisions of Ethiopian dairy farmers. We sampled 870 households from the major rural production zones around Addis Ababa, where villages were stratified according to their distance to Addis Ababa.

Offering Rainfall Insurance to Informal Insurance Groups: Evidence from a Field Experiment in Ethiopia

We show theoretically that the presence of basis risk in index insurance makes it a complement to informal risk sharing, implying that index insurance crowds-in risk sharing and leading to a prediction that demand will be higher among groups of individuals that can share risk. We report results from rural Ethiopia from a first attempt to market weather insurance products to existing informal risk-sharing groups. The groups were offered training on risk management and the possible benefits of holding insurance.

Adoption of Weather-Index Insurance: Learning from Willingness to Pay Among a Panel of Households in Rural Ethiopia

In this paper we examine which farmers would be early entrants into weather index insurance markets in Ethiopia, were such markets to develop on a large scale. We do this by examining the determinants of willingness to pay for weather insurance among 1,400 Ethiopian households that have been tracked for 15 years as part of the Ethiopia Rural Household Survey. This provides both historical and current information with which to assess the determinants of demand. We find that educated, rich, and proactive individuals were more likely to purchase insurance.

An Experiment on the Impact of Weather Shocks and Insurance on Risky Investment

We conduct a framed field experiment in rural Ethiopia to test the seminal hypothesis that insurance provision induces farmers to take greater, yet profitable, risks. Farmers participated in a game protocol in which they were asked to make a simple decision: whether to purchase fertilizer, and if so, how many bags. The return to fertilizer was dependent on a stochastic weather draw made in each round of the game protocol. In later rounds of the game protocol, a random selection of farmers made this decision in the presence of a stylized weather-index insurance contract.

Flexible Insurance for Heterogeneous Farmers: Results from a Small Scale Pilot in Ethiopia

We analyze the effectiveness of a new approach in providing weather index-based insurance products to low-income populations. The approach is based on the concept of providing multiple weather securities that pay a fixed amount if the event written on the security (that monthly rainfall at a nearby weather station falls below a stated cutoff) comes true. A theoretical model is developed to outline the conditions in which weather securities could outperform crop-specific weather index-based insurance policies.

The wheat supply chain in Ethiopia: Patterns, trends, and policy options

Wheat is one of the four most important food grains in Ethiopia. As a source of calories in the diet, wheat is second to maize.  In terms of the area of production, wheat is fourth, after teff, maize, and sorghum.  In terms of the value of production, it is 4th or 5th, after teff, enset, and maize, and approximately tied with sorghum. 

Wheat production has expanded rapidly in the past decade.  According to the CSA, wheat production has grown at 7.5% per year since 1995-96 and at 9.3% over the past decade.

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