Food loss and waste in Sub-Saharan Africa: A critical review

Megan Sheahana* and Christopher B. Barrettb

a Charles H. Dyson School of Applied Economics and Management, Cornell University, 340J Warren Hall, Ithaca, NY 14853, USA; email: megan.sheahan@gmail.com

b Charles H. Dyson School of Applied Economics and Management, Cornell University, 210B Warren Hall, Ithaca, NY 14853, USA; email: cbb2@cornell.edu

*Corresponding author

February 2016 version

Abstract: The research, development practitioner, and donor community has begun to focus on food loss and waste – often referred to as post-harvest losses (PHL) – in Sub-Saharan Africa. This article reviews the current state of the literature on PHL mitigation. First, we identify explicitly the varied objectives underlying efforts to reduce PHL levels. Second, we summarize the estimated magnitudes of losses, evaluate the methodologies used to generate those estimates, and explore the dearth of thoughtful assessment around “optimal” PHL levels. Third, we synthesize and critique the impact evaluation literature around on-farm and off-farm interventions expected to deliver PHL reduction. Fourth, we suggest a suite of other approaches to advancing these same objectives, some of which may prove more cost-effective. Finally, we conclude with a summary of main points. JEL codes: O13, O19, O20, Q18, Q19

Keywords: post-harvest losses, food loss, food waste, Sub-Saharan Africa, review Acknowledgements: An earlier version of this paper was prepared at the request and with the support of the Bill and Melinda Gates Foundation. The authors thank Corinne Alexander, Sara Boettiger, Luc Christiaensen, Karin Östergren, Per Pinstrup-Andersen, and Jacob Ricker-Gilbert for their useful comments and insights. Any remaining errors are solely our responsibility.

1. Introduction

In September 2015, the United Nations (UN) ambitiously announced a goal of halving worldwide

food waste and substantially reducing global food loss by 2030 as part of its Sustainable Development

Goals (SDG) agenda. This pledge codifies the huge amount of renewed international attention around

reducing the edible losses and waste incurred between farm and fork in the global food system. In Sub-

Saharan Africa (SSA), where rural populations depend heavily on food production for their income and

food purchases make up a large portion of expenditures in both rural and urban areas, the dialogue is most

focused around the theme of “post-harvest losses” (PHL), those that reflect potential consumables that

leave farmers’ fields but never make their way into consumers’ mouths. With so much interest and the

prospect for significant resource mobilization aimed at reducing PHL, perhaps especially in SSA, it is

important to establish what we already know about PHL and interventions aimed at their reduction.

Despite plenty of enthusiasm in the development community, holes in critically synthesizing the abundant

research still remain.

This review aims to guide development practitioners, donors, and the research community

struggling to identify how to devote their time and resources to PHL mitigation. In so doing, we suggest

that this community refocus its approach by first returning to the objectives underlying the desire to

reduce PHL levels, of which we offer four (Section 2). Then, since most research and interventions to

date are merely guided by the estimated magnitude of PHL, we summarize the current state of our

understanding of loss levels, critique the methodologies and gaps therein, and explore the lack of

thoughtful assessment around the “optimal” PHL levels for which we should be striving (Section 3). We

continue by synthesizing the impact evaluation literature around five on-farm PHL reduction

interventions, establishing the many gaps that remain in this evidence base, and detailing some of the

unique adoption (and dis-adoption) challenges in this arena (Section 4). Because the impact evaluation

literature for most existing PHL technologies remains meager and given substantial work specifically on

storage technologies, we also discuss four off-farm interventions that may deliver significant PHL

reduction alongside broader benefits (also Section 4). We then return to the four objectives we see as

guiding the renewed interest in PHL reduction to suggest a suite of other approaches to advancing these

same goals, likely with greater cost-effectiveness (Section 5). Finally, we conclude with a summary of

main points (Section 6).

We do not attempt to reconcile the competing definitions of food waste, food loss, and PHL or

the distinctions between surplus versus loss as proposed by Papargyropoulou et al. (2014). In most work

on the topic, food loss refers to anything lost by producers or in distribution while food waste refers to

anything lost at the consumer level (de Gorter 2014). These lines are drawn mostly for convenience

reasons and are clearly blurred for rural SSA households who often function as both producers and

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consumers, rendering the categorization essentially meaningless in this context. We prefer to adopt the

approach that refers generically to food loss and waste (FLW), a term we use interchangeably with PHL.

2. Objectives underpinning investments in reducing PHL

The reasons for the renewed interest in PHL reduction in SSA are best understood by

enumerating the multifaceted objectives underpinning the goal recently announced by the UN. One main

finding of our review is the lack of a well-defined objective guiding most research and interventions to

date, as echoed by the High Level Panel of Experts on Food Security and Nutrition (2014). Most research

is motivated only by invocation of the estimated magnitude of PHL and not by what the magnitude means

nor by its consequences. Moreover, even when an objective is identified – for example, expanding the

supply of grain available to consumers – the full set of subpopulations that might gain or lose from

reducing PHL – farmers, middlemen, consumers, etc. – are rarely considered in full. We start by

articulating the four objectives that we see as most important in guiding research and interventions on

PHL in SSA.

The first objective is to improve food security via all four internationally recognized pillars:

availability, access, utilization, and stability (FAO 2008). By definition, reducing food loss increases the

quantity of food available which can reduce the need to supplement availability through transfer programs

(at household level) or via commercial imports or food aid donations (at national level). An increased

food supply, under normal circumstances, should also translate into a reduction in prices for consumers,

improving overall access. It is no accident that the surge of interest in PHL reduction emerged with the

2007 and 2011 global food price spikes. Retention of inferior quality products, those most likely to be lost

currently, could disproportionately benefit the poor where there are price discounts associated with lower

quality food (Kadjo, Ricker-Gilbert, Alexander 2016). PHL reduction in the form of food quality, for

example due to vitamin or protein decay, can improve food utilization (nutrition) among consumers. An

increase in retained food can be especially important seasonally in places where the prices of storable

staples commonly increase sharply several months after the harvest period, by improving access precisely

when seasonally food insecure households most need it and by providing stability.

The second objective is to improve food safety, as distinct from food security. Plenty of food is

lost in our system because the quality deteriorates beyond what is acceptable for human consumption. But

sometimes spoilage or contamination is not perceptible to the human senses and goes undetected, leading

to adverse health effects when food is consumed. Several well-publicized outbreaks of acute aflatoxicosis

in SSA – including the death of 125 Kenyans in 2004 – suggest undetected food spoilage with very severe

human health implications. Mycotoxins, in the forms of fumonisin and aflatoxins, can lead to slow-

developing esophageal and liver cancers (respectively) and are growth-retarding and immunosuppressive

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even in doses well-short of the more sensational, and often deadly, acute aflatoxicosis. These food safety

concerns, arising from fungal or pest infestations, have major disease and global health implications.

The third objective is to reduce unnecessary resource use. These resources come in the form of

on-farm inputs that pose sustainability challenges, including water, chemical fertilizer, agro-chemicals,

labor, and land. Anticipated PHL by farmers may mean that more of these resources are used than is

necessary to meet production or consumption targets. Reducing PHL and, thereby, creating a longer term

incentive for farmers to use complementary resources more effectively and efficiently, this line of

thinking goes, could ultimately lead to a reduction in the use of scarce resources. Where there may be

adverse environmental or human health consequences to use or overuse of inorganic fertilizer (Ayoub

1999) or agro-chemicals (Sheahan, Barrett, Goldvale 2015), minimizing unnecessary applications via the

reduction of expected PHL could be particularly advantageous. Similar arguments apply to the post-

harvest value chain, where reduced PHL could, in principle, reduce fuel costs, transport-related pollution,

energy consumption in processing, etc. Not only might limiting input use result in environmental or

human health benefits, but it should also reduce costs for farmers, traders, processors, and other actors in

food value chains, potentially leading to an increase in profits and/or a decrease in consumer food prices.

Elaborating on this point, the fourth objective is to increase profits for food value chain actors.

The private sector, including smallholder farmers, plays an undeniably central role in making food

available to consumers and, at levels above the farmer, establishing a supply chain for producers to

utilize. The vast majority of food flows through commercial, not government or non-profit, channels in

SSA. Insofar as profit is a natural objective of commercial entities, reducing waste and thereby cost holds

natural appeal to private actors in food value chains. In SSA, PHL reduction could mean improving the

livelihoods of both smallholder farmers and large-scale agribusinesses. Indeed, recognizing this natural

profit motive to reduce PHL that is intrinsic to virtually all actors in food systems in SSA (and elsewhere)

is essential to a clear-eyed understanding of the likely benefits to direct interventions that reduce PHL.

For the most part, with the partial exception of food safety considerations, private sector actors have a

strong material incentive to reduce PHL for their own revenue and profit maximization goals.

3. Estimated magnitudes of PHL

PHL can occur anywhere between farmers’ fields and consumers’ plates: at harvest, drying,

winnowing, cleaning, on-farm storage, handling, milling, processing, transport, larger-scale mixed

storage, retailing, and consumers’ home storage, meal preparation, and consumption. We provide a

generalized picture of the post-harvest environment in SSA in the Appendix, detailing where and how

losses occur in each of these nodes. It should be acknowledged, however, that the PHL environment

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varies significantly by crop, country, and even region within country, necessitating a careful review of

specific contexts before investing or offering a one-size-fits-all approach.

The urgency of the need to reduce PHL in SSA depends largely on the magnitude of such losses

relative to some estimated optimum PHL levels. Similarly, the ability to assess the impact of PHL

remediation strategies also hinges on the ability to accurately measure losses before and after an

intervention. The sheer number of existing PHL estimates is dizzying, but one of the important takeaways

from our review is the lack of consensus on loss size, both in physical mass and value. In this section, we

review some of the most recent estimates from SSA, offer explanations for the discrepancies in magnitude

estimates, point out the major gaps that remain in our PHL magnitude understanding, and discuss

important nuance often overlooked in the establishment of “optimal” loss levels.

3.1. Review of estimated magnitudes

Losses and waste can be measured in quantity and/or quality terms, with important distinctions.

Quantity losses occur when the actual amount of food, often measured in either kilograms or calories,

reduces over time and space. This is generally the focus on PHL magnitude estimation (and reduction

strategies) to date. Quality losses occur via the loss of important nutrients and/or through contamination

of food. These loss types can be more obscure, more difficult to detect, but potentially more important

given that micronutrient deficiencies are far more prevalent around the world than is undernourishment

due to insufficient dietary energy intake (Barrett and Bevis 2015) and food-borne health hazards with

direct, sometimes catastrophic, effects on human health. When quality losses are sensorily apparent, poor

quality may also lead to economic (and thereby quantity) losses when consumers pay a reduced price for

inferior quality product (Hodges, Buzby, Bennett 2011; Kadjo et al. 2016).

The FAO (2011) estimates that roughly one-third of the physical mass of all food is lost or wasted

around the world. In SSA, the estimate is roughly 37 percent or 120-170 kg/year per capita. These

estimates are derived from a simplified mass flow model by food group and region using estimates and

assumptions from the literature and available macro-level data, such as that published in FAO’s food

balance sheets. Lipinski et al. (2013) translate the weight volumes reported by the FAO into calorie terms,

reducing the level to a loss of about 23 percent worldwide. World Bank et al. (2011) estimates the value

of all grain PHL in SSA to be around USD 4 billion per year. Value and volume estimates like these set

off alarm bells for development practitioners and the donor community when global food prices spiked a

few years ago and motivate ongoing interest in the topic among both researchers and practitioners.

But there are many methodological reasons to remain skeptical of the FAO and other highly

aggregate numbers popularized today. For these reasons, investments have been made in better data

collection systems. Most notably in the SSA context, in 2009, coming on the heels of the global food

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price crisis, the European Commission funded the creation of the African Postharvest Losses Information

System (APHLIS), a network of cereal grain experts in eastern and southern Africa charged with

accurately estimating PHL for grains across the region. On average across all grains in APHLIS’s current

profile, quantity losses range from 14-18 percent between 2003 and 2014 (exclusive of consumer level

losses). The FAO (2011) reports a total quantity loss of about 20 percent for all cereals across SSA. The

APHLIS numbers, therefore, represent slightly more conservative estimates, either for reasons of

methodology, regional specificity, or included crops.

Hodges et al. (2010) and Rembold et al. (2011) describe the database, underlying process, and

methodology of APHLIS. In general, estimated losses are pulled from the literature and from data

reported by scientists in the region. The value added provided by APHLIS is the interpolation of available

data among provinces clustered by climate and crop so as to estimate losses where data are missing.

Regional experts alter and update the values over time and space using local knowledge, new data, and

year-specific information on production and weather. While a hugely valuable integrative exercise, the

main two drawbacks to relying exclusively on this methodology and the loss magnitudes derived from it

include (1) the serious oversimplification of a complex PHL environment and (2) the reliance on existing

data as an input into their model without being able to scrutinize its quality (Affognon et al. 2015). In

other words, APHLIS estimates are only as good as the weakest data input series used.

At the other end of the methodological spectrum one finds PHL estimation methods that rely on

highly standardized and carefully supervised household surveys. Kaminski and Christiaensen (2014)

estimate the PHL incurred by maize farmers in Malawi, Tanzania, and Uganda using the nationally

representative, recently collected, and cross country comparable Living Standards Measurement Study

Integrated Surveys on Agriculture (LSMS-ISA). Using responses from farmers to standardized

questionnaires, they estimate farmer losses to be only 1.4-5.9 percent of total production. Because these

values represent losses on-farm only, they are best compared with the farm-level post-harvest handling

and storage loss estimate of 8 percent by the FAO (2011). Household survey responses, therefore,

represent a sharp reduction in estimated PHL at that stage in the food supply chain.

Using household survey data as part of the Purdue Improved Crop Storage Project (PICS)1 from

Benin, Nigeria, Ethiopia, Uganda, and Tanzania, Abdoulaye et al. (in progress) also calculate farmer-

reported losses of maize, but only specific to the amount lost in storage. Similar to Kaminski and

Christiaensen (2014), farmer-reported loss rates are quite low, ranging from 3.7 percent in Uganda to 6.9

percent in Tanzania. While their sample size is large, the underlying surveys were not designed to be

nationally representative, precluding their extrapolation to more aggregate levels. But the near match with

1 The PICS program was originally calls the Purdue Improved Cowpea Storage Program, but the mandate has since expanded to other crops.

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other estimates derived from nationally representative household survey data points to either an inflation

of values from other methodologies or to the fact that farmers do not perceive their losses to be very high,

potentially due to the concurrent use of a range of loss-mediating technologies or early sales.

One might reasonably ask whether household survey data are likely to generate reliable estimates

of PHL at farm level. The honest answer is that no one really knows. They obviously rely on a farmer’s

ability to perfectly recall cumulative losses between harvest and the current point in time and are,

therefore, subject to measurement error. But there is little reason to believe that farmers’ measurement

error is appreciably greater than that of other sources of loss reporting. The timing of survey

implementation may also matter, especially if it does not capture the entire duration of the post-harvest

period, so that responses reflect only a fraction of the time crops might be stored. Kaminski and

Christiaensen (2014) attempt to correct for this prospective source of bias by estimating full season losses

conditional on the timing of questionnaire completion. Likewise, if losses are standard in a community,

then farmers might subconsciously report only relative loss rates, thereby underestimating the true PHL

magnitude. In the absence of any experimental evidence to compare self-reporting versus directly

measured loss rates, it is reasonable to believe that household self-reports based on carefully designed

questionnaires implemented using standardized survey protocols to be the best available current data

source for farm-level PHL estimates.

The most recent and comprehensive review of work estimating the magnitude of PHL in SSA is

Affognon et al. (2015)’s meta-analysis of six SSA countries (Benin, Ghana, Kenya, Malawi,

Mozambique, Tanzania) for a variety of crops, not specific to grains. One important and illustrative case

is their findings on maize loss. Unlike the common claim that between 40-50 percent of grains are lost in

SSA, the literature they summarize yields estimates ranging from 20 percent, where no loss controls are

used, to as low as 4 percent, where some loss prevention mechanism is employed. Rosegrant et al. (2015)

also estimate the percent of food lost on-farm, up the value chain, by consumers, and in total across SSA

by compiling various sources, with findings qualitatively similar to those of Affognon et al. (2015).

Like Rosegrant et al. (2015), many studies attempt to estimate the distribution of losses at

particular points along the farm-to-fork chain. The consensus across sources, despite variation in

magnitude, is that most grains and cereals are lost during post-harvest handling and storage on-farm,

while loss of fresh produce, meat, and seafood is concentrated in processing, packaging, and distribution

(FAO 2011, Hodges et al. 2011, World Bank et al. 2011). In no instance do losses at the consumer level

appear significant in SSA (perhaps in many cases because the producer and consumer are the same). As

the meta-analysis by Affognon et al. (2015) reveals, over 80 percent of all studies in their sample focused

specifically on losses in on-farm storage, so the evidence has been self-reinforcing on this point. Of

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course, we should expect the distribution to change as supply chains elongate with urbanization and as

consumer incomes and the consumption of food away from home grow.

3.2. Methodological critiques and gaps

The main takeaway from Affognon et al. (2015) is the inadequacies of loss assessment

methodologies across the existing literature – published and unpublished – used to date. Not only is it

difficult to compare values due to differences in included crop, levels of the value chain, scale, agro-

ecologies, seasonality, and geography, but the methodologies employed are often unsatisfactory too.

Methodologies range from modeling/simulation to direct observation to residual methods, all of which

have their place when used appropriately, but can be readily misused and misinterpreted. Affognon et al.

(2015) call for further research to establish methodologies aimed at accurately measuring losses at levels

of the value chain beyond the farm. While most of the existing studies of losses in value chains rely

exclusively on case study approaches that are not statistically representative, Minten et al. (2016)

pioneered a method of fielding surveys at each level of the value chain to capture wastage figures

incrementally, piloting their approach in three countries in Asia. This method offers far more conservative

magnitudes of losses than other estimates, underscoring the need to critically examine methodology

before relying on estimates and invest in similarly rigorous methods elsewhere, particularly in SSA.

Furthermore, despite a widespread and systematic search of published and unpublished literature,

Affognon et al. (2015) – like us – find no conclusive evidence on the magnitude of quality losses

stemming from diminished nutritional value or food safety concerns. The only studies we found point to

the percent of sampled crops over acceptable mycotoxin levels and in very narrow geographic regions.

While most research has focused on mycotoxin contamination in high outbreak areas, Mutiga et al.

(2015) find aflatoxin contamination above the regulated limit even in areas with no reports of human

fatalities, such as western Kenya. This is likely due to lack of comprehensive surveillance and undetected,

sub-clinical human health effects. These findings, among others, raise new concerns about how

widespread quality losses, particularly those with the most adverse human health effects, might be in

SSA. Other efforts, such as the IFPRI-led Aflacontrol project in Mali and Kenya, aimed to estimate the

economic impact of mycotoxins in the SSA food system but was unable “due to a lack of good data” (Wu

et al. 2011, p.9). Estimating the losses derived from degraded nutrient quality may be hugely difficult –

especially because food of lower-nutrient quality is likely to still be consumed – but incredibly important

to our understanding and ability to address.

3.3. Establishing an “optimal” PHL magnitude

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The raw estimated magnitude of PHL is often, but inappropriately, used to motivate research and

investments in the area. The research and development practitioner community to date lacks a serious

assessment of what “optimal” PHL levels may be for meeting their underlying objectives. As de Gorter

(2014) lays out cogently, some positive level of PHL is almost surely optimal because eliminating all

PHL is prohibitively expensive even with the best imaginable technologies and institutional

arrangements. Moreover, because some contamination or spoilage is unavoidable, improving the quality

of the food supply sometimes requires incurring some PHL in quantity terms. The appropriate way to

frame the question is whether estimated PHL magnitudes exceed optimal PHL rates, but one scarcely, if

ever, finds discussions of PHL framed this way.

It is important to understand the microeconomic rationale for PHL before tackling strategies to

alleviate the result of those decisions (Barrett 2015; de Gorter 2014; Goldsmith, Martins, Moura 2015;

Waterfield and Zilberman 2012). Some positive level of PHL is economically rational, reflecting a

sensible choice by private individuals not to invest in loss reduction. There are plenty of instances where

waste may be intentional and optimal, at least from the perspective of a private actor whose objective is

profit – not production – maximization. As but one example, soybean farmers in Brazil willingly increase

PHL at harvest (through increases in combine speeds) in order to plant earlier in the first season and make

time for a second cropping season and maximize profits (Goldsmith et al. 2015). This is a poignant

reminder that increasing farmer profitability may mean greater levels of food loss, especially where

currently available technologies force this tradeoff. Private and social optima may diverge, of course,

particularly where externalities are not considered in private decision-making. For whom a given

magnitude is optimal is an important question worth exploring when suggesting interventions with a goal

of approaching a given PHL magnitude.

Optimal levels, however, may deviate for PHL emanating from quantity and quality reasons.

While optimal levels of quantity losses require economic, financial, or accounting-type analysis to

establish, removing contaminated and toxic food from the system is crucial and overdue, suggesting that

we may need to encourage more loss of unsafe food in so much as we know that a potentially substantial

amount of food is of problematic quality for human consumption, and even more so for children and

infants (Magoha et al. 2014). We offer that in some cases greater PHL levels – that which removes unsafe

product from the food supply – can be desirable, while in other cases lower PHL – resulting from less

degraded product in the food supply – is the objective. The lack of critical distinction between the

magnitude of these losses and the absence of discussion on what should be our targets in each arena

suggests the need for more rigor and dialogue in this area.

Another important thread in the discussion of “optimal” is related to the drivers of current loss

levels, particularly given evidence derived from household surveys suggesting that farmer-perceived

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losses are lower than macro-level statistics suggest. Low loss levels do not necessarily imply effective

storage or management technologies but may be the result of releasing harvest onto the market early in

order to limit expected losses due to poor storage facilities. While physical quantity losses, therefore, may

appear low, the value and revenue losses associated with quickly depleting stocks, generally when market

prices are lowest, may be massive. If farmers were able to confidently store a larger portion of their

harvest for longer, revenues may increase with the seasonal increase in selling prices. Kadjo et al. (2015),

for example, find that farmers in Benin who believe they will lose more in storage are less likely to hold

grain, suggesting revenue losses inversely related to amount stored. By reframing PHL discussions

around what is most profit-enhancing for smallholder farmers, related to our fourth objective in Section 2,

the underlying “issue” at hand begins to take on a new light.

4. PHL mitigation strategies and impacts

To date, significant resources have been leveraged for reducing FLW/PHL around the world.

Most interventions in SSA have focused directly on farm-level losses, under the assumption that most

losses occur or start on-farm. Few interventions focus on downstream links in the food supply chain, at

least not with specific attention to the PHL implications. In the following sub-sections, we review the

literature on PHL abatement interventions derived from “modern” or “improved” technologies and any

currently available impact analysis.2 Impact may come in the form of PHL reduction, farmer or

household-level welfare or behavioral impacts (profitability, income levels, food security, savings

behavior, nutrient intake, consumption smoothing, expenditure changes, market participation), and higher

level general equilibrium impacts (overall food availability and prices that affect individuals beyond just

adopters of a new technology). We follow with mention of the major gaps in and challenges for impact

analysis approaches to date, thoughts on adoption and dis-adoption patterns, and a review of other off-

farm investment opportunities with potentially more broad-based effects.

4.1. On-farm interventions

While other technologies aimed at preventing losses may exist or are in experimentation stage, in

this sub-section we focus on five PHL reduction techniques used on-farm that have at least some

accumulated literature base.3 The discussion is largely skewed towards hermetic storage technologies, the

PHL reduction strategy that enjoys the most attention in the literature. We focus on effectiveness and

2 We do not study traditional approaches via botanicals, vegetable oils, dusts, solar, and underground pit storage. 3 For example, one idea in experimental stage is the use of acoustics to detect pest outbreaks in storage. Kiobia (2015) provides the engineering details on design and functionality, with specific consideration of the constraints faced by maize farmers in Tanzania. Little is known about the viability, profitability, and impacts of this technology, but the fact that this research exists suggests that further innovations for PHL reduction are currently underway.

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impact with respect to staple grains, and refer interested readers to Saran et al. (2012) and Mohapatra,

Kar, and Giri (2015) for more on PHL mitigating technologies in horticulture and pulses, respectively.

Improved varietals for pre- and post-harvest loss reduction

Because of the compounding effects of pests and deterioration accumulated before harvest,

interventions that aim to reduce PHL while crops are still in the field is arguably more effective than

deploying strategies that only start after harvest (Ippolito and Nigro 2000). John (2014) contends that the

focus on PHL exclusively is ill-placed and challenges the research and practitioner community to think

more critically about pre-harvest losses as well. One of the most important means of mitigating losses in

the field is cultivar selection and development. A major challenge on this front, however, is that hybrid

and other higher-yielding varieties are more susceptible to quicker losses post-harvest, suggesting an

unfortunate tradeoff for farmers. Indeed, the non-adoption or dis-adoption of yield-enhancing varietals

may be a rational choice where the gains are simply lost in storage. Much investment and research is

needed to produce high-yielding varieties with long post-harvest lives. Genetics play a major role not only

in quantity losses, but also in preventing or reducing susceptibility to myocotoxin contamination.

Affognon et al. (2015) note that very few studies link new varietal development to PHL reduction

and impacts, for either quantity or quality reasons, and that those few existing studies only focus on

effectiveness in a laboratory setting. Individuals from the PICS project noted that breeding cowpea

varieties with longer storage lives was initially unsuccessful, although little is known about the challenges

and lessons-learned from this exercise. Otherwise, we are unaware of other interventions in this area.

Research and development activities around improved cultivar production that achieves simultaneous

goals of increasing productivity, longer shelf life, and reduced likelihood of mycotoxin contamination

would be highly advantageous to a multitude of goals.

Education on best practices in harvest and post-harvest handling

Interventions also occur around education for best practices in harvest and post-harvest handling,

generally in the form of extension messaging. A manual by Golob (2009) describes the FAO’s approach

to extension advice on these two related areas. The most obvious techniques include better sorting:

throwing out problematic grains and retaining higher quality for storage. The World Food Programme

(WFP) incorporates training on these topics into its multi-faceted PHL reduction strategy with Purchase

for Progress (P4P) farmers. USAID invested in post-harvest training of trainer (TOT) activities in SSA

related to horticulture, activities described by Kitinoja et al. (2011) as “too short term in nature, too

expensive, and too oriented to large-scale commercial horticultural businesses and export crops” (p.600).

Little is known about the tangible impact of these activities as separate from other interventions.

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Chemical sprays in storage

It is well-documented, in data from specific regions (e.g., Ngamo et al. 2007) and from

nationally-representative household survey data (Kaminski and Christiaensen 2014), that many farmers

use some form of chemical or natural spray during home-based storage as a means of keeping pests and

insects away from food. Chemical sprays either take the form of fumigants – applied to deal with already

burgeoning infestations – or contact insecticides (or “protectants”) – used to prevent infestations. There

are many types of spray to counter particular types of deterioration; the effectiveness of chemical spray

use on reducing PHL depends on the type, application techniques, etc. Even when farmers use chemical

sprays on stored maize in Benin, Kadjo et al. (2015) do not find that farmers store more maize as a result,

suggesting that they do not have full confidence that these sprays decrease losses substantially.

The largest known promotion scheme of chemical protectants in storage is via Malawi’s input

subsidy program, which subsidized maize storage chemicals between 2009 and 2012 alongside inorganic

fertilizer and improved seed varieties. Ricker-Gilbert and Jones (2015) find farm-household level impacts

include an increase in the probability of adopting improved maize varieties and increases in total area and

share of area planted to improved maize. The only other known (to us) major international push to

increase the use of chemical sprays in storage came from the FAO in the early 1990s. In southern Benin, a

new chemical called Sofagrain was promoted alongside use of improved wooden granaries. The

socioeconomic impact of this intervention is detailed in Adegbola (2010). The one major welfare impact

mentioned in this study is the induced increase of schooling expenditures of adopters.

No more specific welfare impact analysis of chemical spray use appears available. This may be

because chemical sprays used in storage (and elsewhere on-farm), particularly in SSA, are known to be of

dubious quality (Williamson, Ball, Pretty 2008; Williamson 2003, 2011) and can lead to either short-term

(acute) or long-term (accumulating) negative human health effects (Hayes 1991). Where chemical sprays

are used to reduce PHL but inadvertently lead to poor health outcomes, then an unfortunate trade-off

exists between PHL reduction and meeting the broader objectives from Section 2. Moreover,

indiscriminate chemical application is known to lead to resistant pests (Boyer, Zhang, Lempérière 2012),

making it only a short-term viable strategy before harsher chemicals are necessary. Arthur (1996)

explains the expected decline in the use of these “grain protectants” over time. For all of these reasons,

promoting more chemical use in storage may not have the greatest positive impact.

Improved storage of grains through hermetic technologies: bags and silos

By far the most widespread intervention strategy, at least judging from the size of the literature, is

the use of improved storage devices, especially but not limited to “hermetic” technologies, those that

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stabilize oxygen levels and offer tight seals to limit the reproduction and life cycle of insects and other

pests or pathogens that destroy stored food, especially grains (Murdock et al. 2012). These technologies

appear to work well without the coupled application of storage chemicals, another reason for their

attractiveness. The two types of hermetic technologies we review are bags and silos (metal or plastic).

Various institutions have developed and promoted the use of specialty hermetic technologies, including

PICS, the WFP’s P4P, the Effective Grain Storage Project (EGSP), and the FAO. Private-sector led

options also exist, including GrainPro, Inc. which offers hermetic and polypropylene bags in addition to

other bag and non-bag storage options for commercial purchase.

A recent (2014) special issue of the Journal of Stored Products Research was dedicated to

synthesizing the available research on PICS, one of the most heavily funded and widely celebrated

interventions in on-farm storage improvements for mitigating PHL. Most of the issue focuses on the

ability of the bags to reduce losses relative to baseline levels. The effectiveness of hermetic bags for

storing maize in Kenya was also studied by De Groote et al. (2013) and cowpea in Niger by Baoua et al.

(2012), both of whom also find considerable loss prevention, like many others. The main critique of this

literature, as described in more detail by Affognon et al. (2015), is that many – although not all – of these

studies draw conclusions about the effectiveness of their hermetic technologies based on controlled

laboratory settings, not actual and often imperfect use by farmers under their varied constraints and

operating environments. The WFP (2014) conducted its own study of the effectiveness of its pilot PHL

interventions using farmer responses. Their analysis, which reported that farmers retained 98 percent of

their harvest via these new methods over 100 days, was not specific to hermetic bags or silo use, but

rather reflects the suite of PHL interventions they provided farmers, including extension services for

harvesting, handling, drying, threshing, etc. We are unaware of any external evidence from independent

researchers on the effectiveness of the intervention and/or a careful breakdown by technology employed.

It is not enough to show that these hermetic technologies work to prevent losses; the investment

must also imply higher profits for farmers or others undertaking storage. Jones, Alexander, and

Lowenberg-DeBoer (2014) show that, on average, PICS bags offer higher levels of profitability for

farmers than government-subsidized chemical storage protectants in Malawi. Kimenju and De Groote

(2010) study the economics of the “super” GrainPro bag in Kenya and find that these bags were not

economically attractive because the loss abatement was not substantial enough to make the necessary

annual purchase profitable. Ndegwa et al. (2015) use randomized control trial (RCT) methods to study the

profitability of hermetic bag technology in Kenya, bags that do not need to be replaced repeatedly,

estimating a benefit-cost ratio of 1.6 for one season and 4.8 for three seasons, signally a sound investment

for farmers. Kimenju and De Groote (2010) find that the returns to metal silos are high and increase with

silo size, but only in the long run after benefits accrue.

12

But to what extent have hermetic bags or silos helped to achieve any or all of the underlying

objectives of PHL reduction, as enumerated in Section 2? Only two rigorous impact studies exist (to our

knowledge). Gitonga et al. (2013) use propensity score matching (PSM) techniques to analyze the

difference in adopters and non-adopters of metal silos in Kenya, finding that adopters experience almost

the complete elimination of losses due to insects, an increase in 150-198 kg/household of available maize

grain, a reduction in expenditures on chemical storage sprays, an increase in home-based maize

consumption by 1.8-2.4 months (a decrease in market reliance), an increase in wait time before selling

grains on market (higher prices received for sales), and a reduction of time associated with food insecurity

by one month. On the contrary, Cunguara and Darnhofer (2011) find no significant impact on household

income levels in nationally-representative data from rural Mozambique through the adoption and use of

improved granaries, although provide no details on the specific types of granaries under investigation.

There are no similarly rigorous impact analysis studies for hermetic bags, to our knowledge. The

WFP (2014) report makes big claims about increased food security and other impact measures, but

provides no actual data or description of methodology used to arrive at these conclusions. We understand

from private communication that welfare analysis is planned on the PICS project, but only after another

round of data is collected. We are also unaware of any evidence linking the use hermetic or any other

improved storage option with mycotoxin levels or other food quality outcomes, including nutrition.4

Integrated pest management in storage (IPM)

The integrated pest management (IPM) paradigm is generally discussed with respect to the

prevalence of pests pre-harvest, but IPM can be just as useful during storage. With the outbreak of grain

borers, in particular, and given concerns about indiscriminant use of potentially toxic chemicals in

storage, biological control – the release of natural biological predators into a system to control unwanted

and destructive pests – has been suggested as an alternative. Indeed post-harvest IPM control may be

easier and more effective than pre-harvest IPM control due to the enclosed nature of storage devices

within which biological control agents would be released. For a more extensive overview of how IPM

works in storage, with a developed country perspective, see Scholler et al. (1997).

The economics literature has not considered the effectiveness, profitability, and impact

dimensions of IPM in storage. The only known paper to model any element of farmer behavior linked to

IPM adoption in storage is Waterfield and Zilberman (2012), with a focus on optimal pest management

decisions from both private and social perspectives. Adda et al. (2002) study an IPM experiment

4 Another complementary goal of promoting hermetic bag and silo use is the opportunity to simultaneously develop a local supply chain for the production and distribution of these products. We refer interested readers to Gitonga et al. (2013), Ndegwa, De Groote, and Gitonga (2015), and Brennan and Gooding (2015) for more on this topic.

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conducted on stored maize in Togo by the International Institute of Tropical Agriculture (IITA). These

researchers found the IPM-based storage methods, as trialed on smallholder farms under normal

conditions, were just as effective as chemical-based methods in reducing grain infestation and losses and

significantly better than longstanding and rudimentary storage methods. The IPM strategy also involved

grain sorting before storage, linking back to pre-storage handling interventions, so as to dispose of

potentially problematic grains before larger contamination could take hold.

Haines (2000) discusses the many challenges to a more robust use of IPM for storage in

developing countries, not the least of which is the lack of research to help better advance the technologies

and adoption. Until physical and social scientists are able to tackle these known challenges, IPM will

likely remain a marginal contributor to PHL reduction strategies.

4.2. Impact evaluation critiques and gaps

Our review demonstrates that the literature offering rigorous estimates of the on-farm impacts of

PHL reduction technologies is thin at best. The evidence that exists largely relates to changes in physical

losses. There is negligible systematic evidence of quality premiums paid to staple crop producers, which

might help encourage investment in PHL remediation and quality control processes. Very little is known

about actual welfare impacts at the household level on a range of indicators – income levels, food

security, savings behavior, consumption smoothing, expenditure patterns, investments, market

participation, etc. – apart from the few studies highlighted. There is also a serious lack of linkages with

mycotoxin contamination levels or other food quality or safety metrics, including nutrition indicators.

As best as we can tell, nothing is known about general equilibrium impacts of PHL reduction

efforts specifically, i.e., of the ways in which on-farm PHL interventions affect non-adopting farm

households or actors further down the value chain, including consumers. Many questions remain. For

example, how do even modest rates of on-farm PHL technology adoption affect local level market supply

conditions, especially as related to seasonal availability? Do these potential changes in local supply affect

prices? Are they able to stabilize prices interseasonally, reducing the magnitude of the difference between

low post-harvest and high lean season prices? Do on-farm PHL interventions simply displace losses until

further down the value chain, especially when other intermediaries do not have the capacity to absorb the

supply? How do greater levels of food supply affect incentives and behavior of transporters, wholesalers,

retailers, etc.? Tomlins et al. (2007) expose that sweet potato traders in Tanzania are not interested in

buying/selling stored roots. Do preferences like these create disincentives for farmers to want to invest in

on-farm PHL reduction? Is there a knowledge gap at higher levels of the supply chain that will offset any

loss remediation on-farm? In the end, do the abated losses actually reach consumers?

14

Femenia (2015) studies the world-wide implications of public storage subsidies in an

industrialized country context and finds that these policies can have destabilizing effects on world

agricultural markets. While not entirely relevant in the SSA context, the study does provide a useful

caution about the broader range of unintended or unaccounted for impacts of PHL reduction strategies.

Tracing the impacts of one intervention through the supply chain is an incredibly difficult task, especially

when pursuing causal impacts, but an important one if we are to understand how PHL investments stack

up against the underlying objectives from Section 2.

4.3. Adoption barriers and dis-adoption patterns

It is difficult to pull much from the small literature on adoption patterns since most underlying

data sets are not representative of any meaningful population. Affognon et al. (2015) finds that studies of

adoption levels show percentages ranging from 12 to 74 across technologies, crops, and countries

covered. They only note one study where dis-adoption was recorded: 56 to 73 percent of initial adopters

in the study on Sofagrain and improved granary structures in southern Benin (Adegbola 2010).

Many studies delve into the drivers of adoption, offering similar stories to barriers found in other

parts of the agricultural technology adoption literature: income and credit access constraints, education or

other knowledge barriers, gender-sensitivity issues, labor availability bottlenecks, etc. In their impact

analysis study, Gitonga et al. (2013) found major differences in socio-economic and other baseline

characteristics across adopters and non-adopters of metal silos in Kenya, signaling that these technologies

are still only within reach of the relatively more affluent or productive farming households.

Other studies point to adoption constraints that are more specific to these technologies. For

example, Kadjo et al. (2015) study the determinants of storage decisions among maize farmers in Benin.

In their sample, farmers make decisions about PHL technology use based on their expectations about loss

size; those that expect higher losses are more likely to release their grains onto the market more quickly

and not use an improved storage technique. Matita and Dambolachepa (2015) study adoption of post-

harvest management technologies in Malawi over the period where chemical insecticides used in storage

were subsidized by the government. In addition to other adoption challenges, they also found the fear of

theft was a major deterrent to farmers using improved storage options external to the household dwelling.

Low adoption rates of on-farm PHL prevention may also be an expression of low expected

returns to investment in potentially very useful storage devices. While several papers found favorable

benefit-cost ratios for PHL interventions, particularly hermetic storage, these profitability dimensions

may not be internalized by farmers. One of the findings from the various benefit-cost calculations is the

known level of production and swings in prices after harvest necessary for a given storage device and size

to be profitable. These conditions should be explained to farmers fully. For other technologies, only the

15

efficacy of the PHL intervention is known (and sometimes only in a controlled laboratory setting), not the

profitability dimensions given actual resource-constrained farmer conditions.

Affognon et al. (2015) provides several instances of well-intentioned technologies not performing

optimally when used on-farmers’ fields due to the many constraints not considered or accounted for in

design or failures in extension messaging on proper use. It is well-noted that farmers have many questions

about these technologies, some of which extension agents are not equipped to answer. One of the most

costly components of some interventions is the training and extension programming included to help

farmers understand how to use the technologies effectively; a significant part of the PICS project is

devoted to providing village-level demonstrations. Baoua, Amadou, and Murdock (2013) list and answer

the set of questions they most commonly heard from farmers using or considering hermetic bags in rural

Niger. Baributsa et al. (2010) offer innovative suggestions on how to communicate knowledge about best

practices surrounding hermetic storage by coupling purchase with cell phone-based extension outreach.

4.4. Off-farm interventions with potentially more broad-based benefits

In their meta-analysis, Affognon et al. (2015) found that only 19 percent of the papers they

surveyed mentioned a technology for mitigating PHL beyond the farm-level. And within this small set of

papers, they often focused on non-staple grain foods, like highly perishable fish or fresh fruits and

vegetables. But, given we know so little about the true and diffuse impacts of PHL reduction strategies

on-farm, it may also be appropriate to consider off-farm interventions with more broad-based benefits.

The type of investments that we survey in this sub-section may not be specifically under the guise of PHL

reduction, but benefits are expected to accrue in PHL reduction given the interconnected nature of food

systems and the endogeneity of PHL rates to food system agents’ behaviors. Several of the strategies

mentioned below also appear in Hodges et al. (2011).

Infrastructure improvements

Investments in food marketing infrastructure (roads, market facilities, electricity, etc.) are known

to induce behavior changes along agricultural supply chains, with potentially major direct reductions in

PHL as well as indirect reductions via complementary private investments. For instance, Minten et al.

(2014) study the rapid rise in the number and capacity of cold storage facilities in a potato-producing

region in India. Through key informant interviews, they find that the private sector was willing to make

major investments following policy changes and investments made by the government, including

widespread infrastructure improvements.

Rosegrant et al. (2015) econometrically test the relationship between investment across a suite of

infrastructure variables and PHL levels, finding that the following are the largest contributors to PHL

16

reduction: more extensive paved road networks, higher usage of railways, and higher consumption of

electricity. They also estimate the average size of investment necessary and the food security implications

through a reduction in global prices. An invited response to these findings suggests that the estimated

benefits to food security that occur through PHL reduction directly may be exaggerated because some of

the decrease may come indirectly through other benefits to infrastructure advances, such as increased

uptake of improved cultivars, reduced transport costs that affect prices, etc. (Barrett 2015).

Warehouse receipt systems

Another avenue for simultaneously reducing PHL while tending to other important rural

objectives in SSA is investment in warehouse receipt systems (WRS). Coulter and Onumah (2002)

explain the role of WRS in the region. The quick removal of food from smallholder farms to more

modern and well-regulated centralized storage areas can decrease storage losses significantly, especially

when using improved shelf-life technologies (e.g., first-expired-first-out) as described in Maarten et al.

(2014). We, however, find no specific evidence linking WRS to PHL abatement in SSA.

WRSs work to “facilitate market exchange by reducing transaction costs and imperfect

information [that] will benefit the agricultural trade in Africa” (Coulter and Onumah 2002, p. 322) and

may also help to improve the service of SSA’s commodity exchange systems, several of which are

currently under-performing (Sitko and Jayne 2012). WRSs can serve as a means of collateralizing

producer credit, addressing financial market failures that commonly discourage storage by liquidity-

constrained farmers (Stephens and Barrett 2011; Burke 2014). Like infrastructure improvements, the

many indirect benefits of WRSs could also help to tackle the objectives underlying PHL reduction work.

Rural financial market development

Given the challenges to adoption of established PHL technologies, in some part related to income

and credit constraints, one intervention with both direct and indirect pathways to PHL reduction, as well

as to the objectives that motivate PHL reduction, is rural financial market development. When they work

effectively, financial markets provide smallholders with access to credit, savings, and insurance that help

facilitate investments in PHL remediation. Kadjo et al. (2015) suggest pairing access to credit with access

to hermetic storage in order to promote increased farmer uptake alongside other benefits. Implicitly, this

recognizes that farmers who cannot readily access financial services may currently store their grains as a

form of in-kind savings, or sell their grains only to predictably buy back later in the season as a form of

in-kind credit (Stephens and Barrett 2011; Burke 2014).

Farmer choices with respect to storage and storage technologies are thus intimately bound up with

farmers’ access to modern financial services. A significant development economics literature details the

17

many ways in which financial markets failures manifest in seemingly-inefficient behaviors in commodity

markets, technology uptake, etc., but that the best approach to addressing these ‘displaced distortions’ is

commonly to address the root underlying financial market failure rather than to treat the behavior – such

as inefficient storage with a high PHL rate – that is merely its symptom (Barrett 2007). Credit, insurance,

and other financial product market development are widely expected to trigger household and firm

investment in a larger suite of on-farm and post-harvest technologies and perhaps in collective marketing

strategies that help to tackle some of the underlying objectives that motivate PHL reduction.

More efficient food value chains

One of the major developments of the past two decades in developing country agriculture has

been the rapid rise of modern food value chains (Reardon et al. 2003, 2009; Gomez et al. 2011). Vertical

integration and expanded contracting from food retailers, wholesalers, and restaurants has typically led to

significant improvements in logistics broadly, including reduced PHL rates. A potentially interesting

option related to transport is the use of computer-based modeling and monitoring systems that optimize

and adapt the route and scheduling of transport via a method piloted by Venus et al. (2013) for the

movement of tomatoes between producing regions in Burkina Faso and consuming markets in Ghana. In

other regions of the world, researchers note the role of uneven power dynamics between actors in value

chains in determining the size of PHL, mainly due to the inability to transmit information that could be

potentially useful to all parties (Xhoxhi et al. 2014). Funding innovative methods of lessening

information constraints and bottlenecks within the rapidly evolving value chain may serve as a way to

create more efficient markets with the added benefit of reducing PHL.

5. Alternative approaches to achieve the same objectives

de Gorter (2014) makes the case that while simple arithmetic implies that reducing PHL would

feed more people – helping to tackle the first objective described in Section 2 – the cost of doing so is

hugely prohibitive, suggesting that other approaches to achieving the same objectives are worth

exploring. In this section, we consider some (of many) alternate approaches to achieving the four noble

and important objectives enumerated in Section 2. Rosegrant et al. (2015) and Barrett (2015) both argue

that while PHL reduction investments might pass the most basic benefit/cost > 1 test, and in some cases

significantly greater than one (e.g., Ndegwa et al. 2015), multiple other candidate interventions seem to

dominate PHL investments in terms of delivering on one or more of the objectives used to motivate PHL

remediation strategies. We do not concern ourselves with ranking the benefit/cost ratios but rather suggest

these other and likely more effective means of achieving the same objectives.

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Objective 1: Improve food security

Barrett (2015) stresses that poverty, not PHL levels, is the main driver of food insecurity. And no

one seriously argues that PHL is a significant driver of poverty. It follows that other ways of reducing

poverty may offer more direct, lower cost means of advancing food security objectives than PHL

reduction investments. We note the more fundamental importance of poverty reduction to improving food

security because as incomes inevitably rise, PHL volumes may increase for various reasons, including (i)

as food appears cheaper relative to income, households may over-purchase in order to avoid stock-outs,

resulting in greater PHL, and (ii) as diets diversify towards more perishable meat and fruits and

vegetables, higher PHL is a natural result of shifting composition of consumers’ food basket. PHL and

food insecurity rates do not necessarily move in the same direction.

In order to reduce poverty, we must identify strategies that raise income levels of the poor.

Because the bulk of today’s poor in SSA still rely on agriculture-related livelihoods, a focus on poverty

alleviation will necessitate agricultural productivity growth in most parts of SSA, returning emphasis to

pre-harvest interventions that bolster productivity growth, especially labor productivity growth that is the

main driver of poverty reduction (Christiaensen and Demery 2007). Focusing on pre-harvest losses in

SSA is critically important given the size of the “yield gap” relative to nearly all other regions of the

world. This approach may involve reducing widespread uncertainties in agricultural production through

the use of better varietals, on-field pest control, and crop disease prevention (de Gorter 2014). It may also

involve the continued promotion of appropriate modern agricultural inputs beyond what is already being

used on-farmers’ fields (Sheahan and Barrett 2014).

Objective 2: Improve food safety

Mutiga et al. (2015), among others, suggest that fumonisin, aflatoxin, and other mycotoxins

contamination are more widespread than previously appreciated. Instead of focusing purely on PHL

reduction, a potentially more impactful approach might involve investing in greater food safety

surveillance. Mutiga et al. (2015) advocate that local grain mills may be the most appropriate venue for

the detection of mycotoxins in local food supplies since rural households often bring their grains to mills

for grinding. Testing is necessary for many mycotoxins because visible inspection only identifies severe

cases and quite imperfectly. Investing in low-cost testing and analysis is suggested by Wagacha and

Muthomi (2008). Harvey et al. (2013) offer a review of simple diagnostic tests for aflatoxin specifically.

Again, detecting and removing unsafe food from the system before it reaches consumers may actually

increase PHL levels if it leads to disposal or destruction of unsafe commodities.

Investments in food safety in SSA may consider established approaches within food value chains

globally, such as the systemic Hazard Analysis Critical Control Point (HACCP) process mandatory for

19

many food sub-sectors in high income countries (Pierson 2012; Mortimore and Wallace 2013).

Systematic processes to detect biological, chemical, and physical contaminants become essential to

ensuring food safety, especially for food that travels through the hands of many intermediaries, which is

increasingly the case as SSA food value chains elongate with urbanization. FAO has actively promoted

HACCP and similar food quality assurance systems and procedures in the marketing of fish in developing

countries, but these have not extended across sub-sectors or countries. A further benefit of investment in

food safety comes from signaling higher product standards which may lead to increased opportunities for

exports and access into international markets for SSA producers and processors (Swinnen et al. 2015).

Objective 3: Reduce wasted resources

Reducing PHL towards the objective of reducing wasted resources and expenditures, especially

on-farm, can also be achieved through agricultural productivity growth and efficiency gains. A significant

share of innovations aimed at agricultural production and processing promote cost-reducing gains through

more efficient conversion of inputs into product. Particularly given evidence of low levels of paired inputs

with known complementarity gains in SSA farming (Sheahan and Barrett 2014), reallocation of resources

already on-farm could lead to productivity gains without the use of additional inputs. There is no evidence

that PHL remediation outperforms more conventional technological change or extension efforts to

improve the efficient use of new technologies in terms of resource conservation. Investments in this area

could also involve simple soil testing to the aim of only applying the fertilizers that will be taken up by

plants and result in yield gains.

One could also envision several investments that seek to reduce the “resources” embodied in the

wasted food instead of the food itself. This might involve creating or bolstering secondary markets for

imperfectly appearing edible food (de Gorter 2014); differentiating products and creating parallel supply

chains for new or alternative products also suggest growing profits along the value chain (see objective

4). It could also involve diverting inedible food towards safe composting or energy-creation, for example,

by turning aflatoxin contamination grains and groundnuts into charcoal briquettes (Filbert and Brown

2012). At the same time, it should also be noted that currently “lost” food left on-farm may deliver eco-

system services benefits, including delivering nutrients to the soil or carbon sequestration.

Objective 4: Increase profit for food industry firms and farmers

Traditional food value chains in developing countries, as defined by Gómez and Ricketts (2013),

exhibit the highest PHL rates but also deliver nutrients to consumers most cheaply. As value chains

morph with the rise of a middle class and changing preferences, PHL remediation strategies will be forced

to shift from a focus on PHL alleviation on-farm towards more downstream levels of the value chain

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(Affognon et al. 2015). Minten et al. (2012) observe exactly this in private investment in cold storage in

Indian crop value chains. The task then will be to create an environment that induces substantial private

investments as opposed to undertaking those investments directly. Such investments include the physical

and institutional infrastructure on which private intermediaries depend, as well as other public goods and

services such as research and extension throughout the value chain, police protection, contract

enforcement and dispute resolution services, etc.

6. Conclusions

The resurgence of activity and interest in reducing post-harvest losses in Sub-Saharan Africa –

largely thought of as the counter-strategy to increasing productivity – encourages a thoughtful synthesis

and critical review of the available literature. This article helps to establish several important points about

the current state of our understanding and reveals the even larger set of unanswered questions that will

help to guide more appropriate research and investments. We summarize nine key points:

1. There exist four distinct, underlying objectives of PHL reduction work that most interest the

development community: (1) improve food security, (2) improve food safety, (4) reduce wasted

resources, and (4) increase profits along the food supply chain.

2. There remains lack of consensus on the estimated magnitude of PHL in SSA, and even the “best

guess” estimates are problematic for a variety of reasons. Furthermore, little reliable information

about losses incurred beyond the farm level is currently available. We highlight a new survey method

developed and employed in Asia as one for potential replication in SSA.

3. Food quality losses, as separate from food quantity losses, are likely more widespread in SSA than is

commonly acknowledged. Unlike efforts to better establish the magnitude of and find innovative

ways to curtail or detect quantity losses, we find no similar effort related to the pervasiveness of food

of substandard quality in SSA.

4. The development community lacks a serious assessment of what “optimal” PHL levels are for

meeting any of the four underlying objectives identified in point 1. Given that remediation is costly,

optimal PHL rates are surely positive. Yet much of the discourse and literature seems to assume that

zero PHL is both feasible and optimal. Optimal levels of PHL may diverge, however, for PHL

emanating from quantity versus quality/safety reasons, especially conditional on current technologies.

5. Current estimates of the magnitudes and distribution of losses suggests that most PHL in SSA occur

on-farm. As a result, most interventions aimed at reducing PHL have also been directed on-farm,

almost entirely around hermetic storage (bags and silos). There is a swell of literature about these

21

products and their efficacy in reducing PHL, although more often than not from controlled settings.

There are few other evaluations of innovations related to on- or beyond-farm technologies.

6. Despite many organizations working on improved storage options, there is a very thin literature on

the human welfare impacts or on the impacts with respect to any of the other underlying objectives of

PHL (from point 1) related to these technologies. We identify only two studies that employ rigorous

evaluation methods that uncover interesting (although contradictory) household level impacts. The

literature is also mostly silent on the food quality and safety impacts of these technologies.

7. Little is known about broader, general equilibrium impacts, the ways in which PHL interventions

affect non-adopting farm households as well as actors further up or down the value chain, including

consumers. We offer a range of important research questions that have yet to be answered, those most

important for understanding the cumulative impact of PHL reduction strategies and the distributional

consequences among different sub-populations.

8. Because the impact evaluation evidence for most existing PHL technologies remains meager and

given a crowd of organizations working on storage issues, there may be good reason to consider off-

farm interventions that deliver PHL reduction alongside other more broad-based benefits. While other

strategies exist, we highlight four: (1) infrastructure improvements, (2) warehouse receipt systems, (3)

rural financial market development, and (4) more efficient markets and value chains.

9. The cost of PHL reduction is likely massive, and the full benefit/cost of many PHL interventions

remains largely unknown. The limited, imperfect efforts made to date to estimate relative benefit/cost

suggest that PHL reduction is not the most cost-effective means of achieving our objectives (from

point 1). We offer a limited set of non-PHL approaches to tackling these same goals.

The many unanswered questions emanating from these points suggest that investing in a better

understanding is crucial before funneling more funds and attention to PHL reduction technologies or

processes that may have little payoff. Indeed, the most important place to start might be asking a series of

even more foundational questions: if PHL rates appear high, why? Is that perhaps a conditional optimum

or do we think agents are systematically making mistakes? If they are making mistakes, is it due to

information gaps that can be remedied? Or is the problem that the conditional optimum is constrained by

insufficient technological options that can be remedied through product innovation or diffusion? Or is a

seemingly high PHL rate merely a symptom of broader systemic problems – e.g., rural financial market

failures, poor institutional and physical marketing infrastructure – that merit attention irrespective of PHL

considerations? Or is a high PHL rate the byproduct of a highly efficient system that results in food cheap

enough that people can afford to lose some?

22

Any researcher or organization committed to advancing the four objectives we outline needs to

pay attention to PHL in SSA given widespread concerns that such rates are, in some vague sense, “too

high.” Yet there is a striking absence of evidence that the rapid increase in attention to and investments in

PHL remediation for SSA is paying significant dividends. In part, we suspect this has to do with the

remarkable lack of attention to date in attempting to identify what optimal PHL rates might be for

different actors in food value chains, given the broader array of (often non-agricultural) constraints and

incentives they face, and thus whether PHL remediation efforts are likely to induce uptake and impact.

The lack of impact evaluation evidence likely results from a near-exclusive focus on reducing PHL

quantities and insufficient emphasis on food safety and quality assurance. A narrow emphasis on reducing

food volumes lost post-harvest might even inadvertently aggravate and already serious issue of high rates

of food contamination in SSA. With so many important gaps in current knowledge, more emphasis needs

to be placed on coordinated learning, especially assessment of whether PHL remediation investments are

relatively cost-effective in advancing the four core objectives that motivate such initiatives: improved

food security, food safety, and profitability, as well as reduced resource use.

23

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Appendix

In this appendix, we provide a short overview of where and how losses – both in quantity and

quality terms – occur between farm and fork in SSA.

Pre-harvest and harvest

Pre-harvest, farmers in SSA fully expect some element of loss before harvest on account of pests,

rodents, weeds, poor or erratic rainfall, storm damage, flooding, and sub-optimal management. At harvest

time, low labor availability, time constraints, or lack of adequate storage or market opportunities may

make it economically rational for farmers to leave some of their crop in their fields, unharvested.

Imperfect human handling of crops at harvest, widespread in traditional SSA agriculture, often results in

on-site losses, but also transfers oils from human hands to crops which may result in deterioration later in

storage. Further, pre-harvest management decisions (including cultivar selection and pest management

while in the field) significantly influence PHL outcomes (e.g., Ippolito and Nigro 2000). These instances

reflect losses in quantity even before post-harvest processes kick in.

Quality losses are just as important as quantity losses prior to and during harvest. Crops can be

contaminated with naturally-occurring toxic fungi during cultivation due to stress conditions (from

drought, pests, soil degradation, etc.), management decisions (e.g., planting date), genetic factors of crops,

and the preferences of local pathogens (Hell et al. 2008; Wiatrak et al. 2005). The continuing degradation

of soils, including through depletion of minerals essential for human health, represents another avenue for

pre-harvest quality loss, especially important since most of the minerals ingested by humans come from

either soil or water (Allaway 1986).

Drying, winnowing, cleaning

After harvest, many grains and some vegetables may require or benefit from drying prior to

consumption or storage. Heavy reliance on sun drying techniques, with exposure to elements, is one

important on-farm space where rapid biodeterioration starts. While all crops deteriorate, the pace of

deterioration can be altered by maintaining an environment conducive to long “shelf” life. Climate change

introduces more variability and less predictability in weather and moisture patterns, potentially

exacerbating the difficulty of properly drying grains without machinery (Stathers, Lamboll, Mvumi

2013). Quality losses in the form of nutrient degradation can occur during drying, as well. The technique

used to dry orange-fleshed sweet potatoes on-farm, for example, is highly related to the amount of

carotenoids retained (Bechoff et al. 2011). Some grain crops, including millets and sorghum, also require

threshing while others, like maize and rice, may require winnowing or cleaning before storage. Manual

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and rudimentary processes, also widespread throughout SSA, commonly lead to a higher loss of grain

than do mechanized methods.

On-farm storage

Most farmers store agricultural products for at least some time before consuming at home or

selling at market. Suboptimal pre-storage drying practices are common preconditions for the

accumulation of mycotoxins during storage (Hodges et al. 2011).Traditional storage shelters made out of

natural materials often harbor or fail to keep out produce-eating pests and/or are unable to protect food

from humidity, temperature variability, etc. that result in the vitamin or protein breakdown or food

spoilage. Traditional storage methods, including botanicals and ash, in addition to the use of insecticides

and fumigants are often used in storage to keep infestations to a minimum. Even where storage structures

are modern, loss at this juncture may be stochastic or unavoidable for a host of other reasons, including

electricity interruptions, storm damage, etc.

Handling, milling, and processing

Between storage and consumption, several other post-harvest processes may occur that result in

losses. Over the past three decades, there has been a significant move from home-based milling of grains

(through laborious pounding) to small-scale milling operations throughout SSA (World Bank et al. 2011).

It remains unknown how this new institutional arrangement has changed the magnitude of quantity loss at

this stage. Rough handling and processing is known to be responsible for losses of fresh fruits and

vegetables, in part due to loss of aesthetic appeal to consumers.

Milling and processing commonly leads to quality degradation, especially in the form

micronutrient loss (Welch 2001; Barrett and Bevis 2015). For example, modern polishing of rice removes

bran and germ of the grain that contain much of the iron, zinc, calcium, vitamins, phytate, and some of the

protein in rice (Lauren et al. 2001). As with rice, modern milling of wheat typically removes both the

bran and the germ of the wheat grain, sheering away many of the vitamins as well as most of the minerals

and healthy oils carried in wheat grain (Welch 2001; Pollan 2013). Welch and Graham (1999) show that

rice and wheat lose 69 and 67 percent of their iron contents to milling, respectively, as well as 39 and 73

percent of their respective zinc contents. Processed cereals of all types have lower levels of minerals and

vitamins in the absence of a concerted (and costly) effort to fortify the food later.

Transport

With the lowest road network densities in the world (World Bank 2009) and inadequately

surfaced roads where they exist, poor transportation infrastructure throughout SSA naturally results in

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PHL for various reasons. First, poor rural market development, high cost of transport, few traders, and

other marketing intermediaries operating in rural areas may mean that grains sit in storage longer than

necessary, repeating or exacerbating the storage losses described above. Second, because intermediate

storage facilities often do not exist in rural areas, food may need to be transported farther than necessary

on first haul in order to sell before spoilage, increasing overall transport costs (Barrett 1996). Third, even

when food does move between the farm-gate and market or higher order storage facilities, the lack of cold

storage or climate control during transport as a result of poor technology and logistics leads to further

PHL. Sub-optimal humidity conditions may encourage spoilage and other food quality loss where mold or

fungus takes hold. Fourth, suboptimal road surface condition and packaging can also mean that food is

damaged or lost during transport, particularly when not kept in containers that safeguard losses. Finally,

because transportation costs are high, vehicles are often over-packed to limit redundant travel which

compresses (and possibly ruins) food and can lead to higher accident rates, which exacerbate PHL.

Large-scale/mixed storage

Food may also sit in larger-scale and mixed storage facilities for some time before consumption,

either at private silos or warehouses or in government or multinational agency depots. Several SSA

governments maintain heavy control over grain reserves in large-scale storage facilities. While facilities

are generally far more modern than on-farm structures, losses appear high for a range of other reasons,

including corruption, indiscriminate applications of insecticides and pesticides (leading to resistance and

increased pest pressure over time), as well as poor timing/planning on when to release grain reserves into

the market. For example, Zambia’s Food Reserve Agency, a government-run parastatal food reserve and

maize marketing board, sometimes struggles to balance pledges made to farmers about the amount of

maize they will buy and the excessive amount they hold in storage (Mason and Myers 2013). In the

2012/13 season, it is estimated that 32 percent of the reserves were lost in government storage as a result

of excessive volumes and the inability to release onto the market without causing significant price

distortions (Sitko and Kuteya 2013). Similar stories, related to policy choices and food security strategies

on the part of numerous SSA governments, exist elsewhere in the region.

Retailing

PHL incurred during retailing are often related to overstocking food in piles to create attractive

displays for consumers, a problem more likely in developed country contexts than in SSA (Hodges et al.

2011). Losses also occur at the retail level when backstream storage facilities are inadequate, forcing food

to reach retail in volumes mismatched with consumer demand. Retailers may also dispose of food that

does not have the appearance of being high quality in order for their selection to be more attractive (e.g.,

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blemish free, appropriately sized, not misshapen) to discerning buyers or those concerned about the

safety/quality of their purchases. Finally, if they face a significant consumer penalty from stock-outs,

retailers and restaurants may optimally overstock many perishables, causing an increase in PHL. Since

food-away-from-home is the food expenditure category with the highest income elasticity of demand and

the most rapid growth due to urbanization, economic growth and demographic change in SSA will

inexorably lead to expansion in this element of PHL. In urban areas and growing towns, the inevitable

increase in PHL at this level may create compounding issues for waste management systems.

Consumers

After consumers procure food from markets, PHL occurs within the household, often labelled

“consumer waste.” This happens where refrigeration and other food storage capacities are low, when

consumers are unable or unwilling to smooth purchases over time and need to buy in significant bulk, etc.

The rapidly changing nature of food consumption behaviors due to increases in incomes and urbanization

typically lead to higher rates of consumer-level waste for the same reasons it does at retail level (Kearney

2010). These trends may also lead more individuals to purchase more food for home use than necessary,

with full knowledge that some of it will be lost, purely as a matter of convenience. Rising incomes also

imply shifting preference towards higher quality foods that demand more sorting, grading, processing,

and general attention to detail throughout value chains (Parfitt et al. 2010).

A troubling consumer-level trend in rural SSA is that food that is thought or known to be

contaminated or of poor quality may be diverted to feeding livestock instead. While this may represent a

“second best” use of unsafe food, research reveals that animals fed contaminated food often pass the

pathogens or toxins through to their milk, which humans often consume, with adverse health

consequences (ILRI 2013).

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