(b) Global trends that influence supply chain losses

Post-harvest losses are partly a function of the technology available in a country, as well as the extent to which markets have developed for agricultural produce. Three inter-related global drivers provide an overall structure for characterizing supply chains and future trends in developing and transitional countries.

• —Urbanization and the contraction of the agricultural sector. The proportion of the world's population employed in agriculture has declined in recent decades and 50 per cent of the world's population now lives in urban environments. This proportion is expected to rise to 70 per cent by 2050 (United Nations 2008). Rapid urbanization has created the need for extended FSCs to feed urban populations. For these to be efficient, countries need improvements in roads, transportation and marketing infrastructure to keep food affordable for lower income groups. How these extended supply chains develop has implications for food waste globally, now and in the future.

• —Dietary transition. Growth of household incomes, particularly in BRIC countries, is associated with a decline in consumption of starchy food staples and diversification of diet into FFVs, dairy, meat and fish. This transition conforms to Bennett's Law (Bennett 1941), where the food share of starchy staples declines as income increases. The shift towards vulnerable, shorter shelf-life items is associated with greater food waste and a greater draw on land and other resources (Lundqvist et al. 2008). The transition varies by country and culture, e.g. in India, there is less pressure on resources compared with China, where the demand for meat is increasing rapidly.

• —Increased globalization of trade. International trade in processed foods accounts for 10 per cent of total processed food sold (United Nations 2002). Globalization may open up opportunities for agricultural exports while representing a threat to development of internal markets through competition from inexpensive imports of higher quality than can be produced locally. Linked to trade liberalization, multi-national chains have become a driving force in the rapid growth of supermarkets in many transitional economies.

Industrialized countries are experiencing other drivers, the most significant being the ageing population profile and growth in single person households.

To reflect these important global drivers, post-harvest losses are considered along a technological/economic gradient: ‘developing’, ‘intermediate’ and ‘industrialized’ FSCs. Figure 1 provides an overview of the development of post-harvest infrastructure along this gradient, expanded in table 2.

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Figure 1.

Schematic development of FSCs in relation to post-harvest infrastructure.

Developing countries: The majority of the rural poor rely on short FSCs with limited post-harvest infrastructure and technologies. More extended FSCs feeding urban populations are likely to involve many intermediaries between growers and consumers, which may limit the potential for growers to receive higher prices for quality. Farming is mostly small scale with varying degrees of involvement in local markets and a rapidly diminishing proportion of subsistence farmers who neither buy nor sell food staples (Jayne et al. 2006). Interventions within these systems focus on training and upgrading technical capacity to reduce losses, increase efficiency and reduce labour intensity of the technologies employed. However, attempts to reduce post-harvest losses must take account of cultural implications. In years with food surpluses, the prices received for goods will be low. One option is to store surplus for lean years, but there may not be suitable storage facilities. To rectify this, investment and engineering skills are needed. There are many instances of relatively simple technologies providing effective solutions, such as an FAO project in Afghanistan and elsewhere that provided simple effective sealed storage drums for grain farmers, dramatically reducing post-harvest food losses (FAO 2008).

Transitional and industrialized post-harvest systems have a closer integration of producers, suppliers, processors, distribution systems and markets ensuring greater economies of scale, competitiveness and efficiency in the FSC. Supermarkets are the dominant intermediary between farmers and consumers. Even in poorer transitional economies, supermarkets are the main vehicle for delivering diversified diets: for the growing middle classes and the urban poor. This is almost entirely dependent on foreign direct investment, with high growth rates in Eastern Europe, Asia and Latin America (Reardon et al. 2007).

The sequence of transformation follows a different route in each country, particularly in the extent to which retailers bypass existing markets and traditional wholesalers to secure produce of the required standard and volume. There are often strong cross-links with export quality assurance, the quality standards set by supermarkets, and the procurement systems. Many of the issues identified are no different from supply chain issues in developed economies:

• —payment terms discouraging small growers;

• —retailer product quality standards deterring smallholders from supplying produce to the market;

• —high contractual penalties for partial or total non-delivery of orders by suppliers;

• —product take-back clauses in supplier contracts allowing retailers to return product to suppliers once a residual shelf-life has been reached;

• —often poor demand forecasting and replenishment systems and a lack of FSC transparency; and

• —difficulties inherent in transitioning from trading systems previously driven by spot market prices towards long-term contracts.

Literature on transitional economies lacks analysis of the relative resource efficiency of alternative models of retail development, although there are lessons that might be learnt from industrialized countries. For instance, in the UK (DEFRA 2007), estimated contractual penalties, product take-back clauses and poor demand forecasting had a combined influence that drove 10 per cent over-production and high levels of wastage in the UK FSC.

Accounts of supermarket expansion in some countries suggest there are instances of successful adaptation to traditional supply chains (Chen et al. 2005), particularly in regions that have not been so involved in export-orientated markets. Where central wholesale markets are used to source fresh produce, retailers may be reliant on wholesalers to perform the ‘out-grading’ that in developed countries is likely to occur on-farm or at front-end packing operations. In countries with traditional two-tier produce markets (higher quality export and lower quality domestic markets), local supermarkets have created a third market for intermediate to high-quality products. At the same time, retailers provide upward pressure to improve product quality and food safety in the domestic market.

Growth in FFV production has been particularly strong in the Asia-Pacific region (Rolle 2006), although the replacement of traditional markets with supermarkets has been slower in the fresh produce sector, compared with other food sectors. Within the region, FFV producers can be grouped into small farmers, groups of farmers, cooperatives, commercial farmers and foreign entities/multinationals. These producers target different markets, and show a gradient in their production capabilities, access to technologies, markets information and infrastructure. Production is dominated by small farmers with limited access to resources and technology. Growers generally focus on production activities, showing little interest in post-harvest and marketing, which are primarily undertaken by middlemen and traders. Their major markets include highly disorganized traditional wholesale and wet markets, though many supply the requirements of institutions, supermarkets and fast food chains. With limited access to financial resources and low returns from agricultural production, these farmers do not invest in new technologies or improve yields through increasing inputs to production (Mittal 2007).

Development of more industrialized FSCs can also result in growth in the food processing sector. In some BRIC countries, public sector investment is being considered to accelerate this process. In India, the government is discussing an ‘evergreen revolution’, which will involve the build-up of food processing units. While this is a sensitive issue because of concerns about the industrialized sector taking control over small farmers, the improved infrastructure has helped farmers branch out into new foods, diversifying their incomes.

Industrialized FSCs: medium-high income countries often argue that better resource efficiency and less waste are achieved through centrally processing food. Although more food wastage occurs at the factory, logic suggests less waste overall is generated as there is less ‘scratch-cooking’ at home. However, research on post-consumer food waste suggests that this is not the case, as consumers still waste significant quantities of food, thus potentially negating the benefits of centralized food processing.

(c) Estimates of FSC losses

(ii) Post-harvest loss estimates for non-perishable food crops

Losses in industrialized countries are not included as loss rates are generally considered to be low (e.g. barley losses can be as low as 0.07–2.81%; Smil 2004a) and are not considered significant under normal circumstances.

Grain losses occur in post-harvest systems owing to physical losses (spillage, consumed by pests) or loss in quality. Few datasets were found relating to loss of grain quality, owing to difficulty in measurement. As most of the global production of maize, wheat, rice, sorghum and millet must be held in storage for periods from one month to more than a year, many studies focus largely on storage losses.

Data available for rice post-harvest losses, based on field surveys and used here as an example, are quite extensive (table 4) and represent the ‘best case’ compared with data for other crops. More extensive studies suggest that about 15 per cent of grain may be lost in the post-harvest system (Liang et al. 1993), with higher storage losses associated with the 80 per cent of China's grain stored by peasants inside their houses or in poorly constructed granaries. The extent to which variations in data presented in table 4 might relate to different levels of post-harvest technology is unclear. For instance, data discussed by Grolleaud's review (2002) found the heaviest losses at the milling stage, perhaps attributable to case studies from more mechanized systems than Liang's data, where storage losses were predominant. This emphasizes the need for post-harvest loss data to be regularly updated and more fully described, particularly for transitioning economies.

Table 4.

Post-harvest loss estimates for rice.

geographical coverage	estimated % weight loss	comments	source
overall: Asia	13–15	quoted as 15% by Smil (2004b)	Grolleaud (2002)
country summaries
West Africa	6–24	drying 1–2%; on-farm storage 2–10%; parboiling 1–2%; milling 2–10%	FAO (2007)
Malaysia	17–25	central storage 6%; threshing 5–13%
	approx. 13	drying 2%; on-farm storage 5%; handling 6%
Philippines	9–34	drying 1–5%; unspecified storage 2–6%; threshing 2–6%
	up to 30	handling 3–10%
Thailand	8–14	on-farm storage 1.5–3.5%; central storage 1.5–3.5%
	12–25	on-farm storage 2–15%; handling 10%
Brazil	1–30	unspecified storage
Bolivia	16	on-farm 2%; drying 5%; unspecified storage 7%
India	3–5.5	improved traditional storage
	6	unspecified storage
Philippines	10–37
Vietnam	10–25	‘typical’ conditions	Phan & Nguyen (1995)
	40–80	‘extreme’ conditions
China (Zhejiang)	14.81	identified drying and storage phases as two main stages of loss	Grolleaud (2002)
China	5–23	excludes processing losses	Yong et al. (1997)

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Climatic conditions are also an important consideration in determining the wider applicability of data. In humid climates, rice losses are generally greater at the drying stage (Grolleaud 2002). Hodges (undated) reviewed grain losses in East and South Africa, attempting to compare loss rates in hot humid climates (where open storage structures were required to maintain airflow) and hot dry climates (favouring sealed storage designs). Hodges concluded that data on storage losses were too limited to permit reliable comparisons of loss rates under different climates. In common with other authors, Tyler (1982) suggested that the aggregated data reflecting losses on a worldwide basis are of little value. Long-term studies of post-harvest losses in Zambia and India were identified as using ‘reliable methodology’ and indicative of the fact that when post-harvest losses are determined by field survey, storage and related post-harvest losses are usually lower than previously reported (table 5; Tyler 1982).

Table 5.

Estimates of post-harvest grain losses in farm-level storage. From Tyler (1982).

estimated weight loss (%)	storage period (months)	cause of loss	grain	country
1.7	7	insects	maize	Zambia
4.3	7	insects, rodents and moulds	rice	India (Andhra Pradesh)
3.5	up to 9	insects and rodents	maize	Kenya
3.2	up to 9	insects	maize	Malawi
1.8	up to 9	insects	maize	Malawi
1.7	up to 9	insects	sorghum	Malawi
5.5	6	insects, rodents and moulds	maize	Nepal

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In summary, the main factors contributing to overestimation of grain losses were: (i) where extremes are taken rather than averages: ideally sample size and standard deviation should be quoted with the loss estimate to avoid this; (ii) removal from store over the season are not always accounted for and where they do occur, percentage losses calculated on the basis of grain remaining in store will be overestimates unless an inventory is kept; (iii) treating partial damage as a total loss, when the damaged grain would be used by farmers for home consumption or animal feed; and (iv) potential for double-counting losses at different stages in the post-harvest system.

(a) Types of study and data on post-consumer food waste

Methodologies for post-consumer waste analysis vary, from small numbers of households weighing food waste or using kitchen diaries to waste compositional and behavioural studies involving thousands of households (WRAP 2008, 2009a). Others have used contemporary archaeological excavations of landfill sites to determine historical levels of food waste (Jones 2006); estimated household food waste indirectly from loss coefficients based upon existing research (Sibrián et al. 2006); or estimated wastage using statistical models relating population metabolism and body weight (Hall et al. 2009).

Some studies have measured household food waste as a percentage of total consumed calories, others as a percentage of the total weight of consumed food or of the consumed food items. Some studies have sought to estimate the environmental impact of food waste, including the embodied greenhouse gas emissions (WRAP 2008, 2009a) or water (Lundqvist et al. 2008).

Most of the estimates relying on exogenous food loss coefficients come from studies dating back to the 1970s. Since then, technological progress resulting in fast changes in markets, distribution systems and household storage facilities have rendered these estimates outdated (Kantor 1998; Naska et al. 2001). Increased consumer choice and a decrease in the proportion of disposable income spent on food have tended to increase wasteful behaviour. As such, any study where waste was measured over time as a constant proportion of food consumed is in danger of being inaccurate (Sibrián et al. 2006).

In many studies, food scraps fed to domestic animals and sink disposals were not included, thus yielding inaccurate estimates for total food waste (Harrison et al. 1975; Wenlock & Buss 1977; T. Jones 2003, unpublished data). In some cases, the wastage owing to feeding to pets reached 30 per cent of the total food wastage in dietary energy terms (Mercado-Villavieja 1976; Wenlock et al. 1980; Osner 1982).

(b) Definition of household food waste

Sources of food and drinks that are consumed within the home include retail and contributions from home-grown food and takeaways. Figure 3 indicates which disposal routes are classified as household waste streams. In effect, this excludes significant quantities of food and drink eaten ‘on-the-go’, in the workplace or in catering establishments. Wherever possible, the distinction is made between three classifications of household food waste (figure 4): ‘avoidable’, ‘possibly avoidable’ and ‘unavoidable’.

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Figure 3.

Sources and disposal routes of household food and drink in UK homes. From WRAP (2009a) Household food and drink waste in the UK.

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Figure 4.

Definitions associated with household food and drink waste. From WRAP (2009a) Household food and drink waste in the UK.

(c) How much food is wasted in the home?

(d) What types of food are being wasted?

Most studies that have sought to identify the main food types wasted find that it is the most perishable food items that account for the highest proportion of food waste. FFVs are usually among the most-wasted items, followed by other perishables like bakery and dairy products, meat and fish (Pekcan et al. 2006; WRAP 2008; Morgan 2009; Thönissen 2009).

There is often a large variation in the wastage rates for different food types: WRAP (2009a) found that 7 per cent of milk purchases is wasted, 36 per cent of bakery and over 50 per cent of lettuce/leafy salads (by weight), while Jones et al. (T. Jones, A. Bockhorst, B. McKee & A. Ndiaye 2003, unpublished data) found similar variations in the average wastage rates for different food types.

Although food and drink categories are not fully consistent across studies, figure 5 serves to highlight variation in household food waste composition. Thönissen (2009) found an unusually high proportion of food waste consisted of dairy products, while in the Turkish data, wasted FFVs accounted for the highest proportion (Pekcan et al. 2006). The extent to which such differences relate to consumption patterns or different wastage rates cannot be divined from these data alone, although the Turkish study noted the importance of fruit in the diets of households studied. Nor do these compositional data distinguish between avoidable and unavoidable food waste, the exception being the UK data shown in figure 6.

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Figure 5.

Summary of household food waste composition across five countries.

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Figure 6.

Weight of food and drink waste by food group, split by ‘avoidability’. Brown bars, avoidable; yellow bars, possibly avoidable; dark blue bars, unavoidable. From WRAP (2009a) Household food and drink Waste in the UK. Brown bars, avoidable; yellow bars, possibly avoidable; dark blue bars, unavoidable.

(e) Who is wasting all of this food?

The following factors may help to explain variation in quantities of household food waste generated.

• —Household size and composition. Studies from the UK (Wenlock & Buss 1977; Osner 1982; WRAP 2009a) and the USA (Van Garde & Woodburn 1987) show that food wastage was significantly influenced by the composition of the family, with adults wasting more in absolute terms than children, and larger households wasting less per person than smaller households. Single-person householders tend to throw away more per capita, and households with children tend to waste more than households without children, although rates vary with the children's age.

• —Household income. The majority of studies suggest that there is lower food loss in low-income than in high-income households (Osner 1982; Brook Lyndhurst 2007); some studies (Dowler 1977; Wenlock et al. 1980; T. Jones 2003, unpublished data) found little or no correlation between income and food wastage.

• —Household demographics. Studies in the UK (Osner 1982; Brook Lyndhurst 2007) and Australia (Hamilton et al. 2005) suggest that young people waste more than older people, with pensioner households wasting the least (such households normally contain comparatively fewer people).

• —Household culture. There is some indication that culture partly determines food wastage. For example, Hispanic households in the USA have lower food loss rates (approx. 25% less) than non-Hispanics and Hispanic households consume more FFVs compared with non-Hispanic households. However, FFV consumption among Hispanic households has decreased over the last 20 years as they consume more prepared foods.

Food waste studies in the UK (Brook Lyndhurst 2007) and Australia (Hamilton et al. 2005) have sought to profile segments of the population according to their attitudes and behaviours with respect to food, drawn from household survey work and waste compositional data. Such studies are a useful tool in targeting particular elements of the population with waste reduction messages and information.

(a) Future trends

In the developing world, lack of infrastructure and associated technical and managerial skills in food production and post-harvest processing have been identified as key drivers in the creation of food waste, both now and over the near future (WFP 2009). This situation contrasts with that in developed countries where our interviewees forecast the majority of food waste continuing to be produced post-consumer, driven by the low price of food relative to disposable income, consumers' high expectations of food cosmetic standards and the increasing disconnection between consumers and how food is produced. Similarly, the increasing urbanization within transitioning countries will potentially disconnect those populations from how food is grown, which is likely to further increase food waste generation.

Across the globe, resource and commodity limitations, in part as a result of an increasing population but also owing to impacts of climate change, were viewed as being likely to increase the economic value of food, potentially driving more efficient processes that could lead to food waste reduction. Industrialized FSCs will continue to develop in response to these wider challenges by the development of shared logistics (e.g. collaborative warehousing), identification and labelling of products (use of barcodes and RFID tags) and better demand forecasting (Global Commerce Initiative 2008), and domestic kitchen technologies (smart fridges, cookers, online meal planning and recipe resources) may make it easier for consumers to manage their food better and waste less of it.

(b) The greatest potential for food waste reduction

Interviewees emphasized the importance of implementing sustainable solutions across the entire FSC to fully realize the potential for food waste reduction. In developing and emerging economies, this would require market-led large-scale investment in agricultural infrastructure, technological skills and knowledge, storage, transport and distribution. Such investments have been shown to stimulate rural economies (WFP 2009), e.g. the development of the Nile Perch Fishery in East Africa. In this case, and despite the unintended consequences of over-fishing and disruption of local communities, the international market for Nile perch stimulated infrastructure development and considerably reduced post-harvest losses.

Where international markets and local policies and investment are lacking, large-scale capital investment in infrastructure in developing countries has often failed (FAO 2003; Kader 2005). For long-term sustainability, development across the FSC in the developing world requires locally supported government policies and investment alongside any market-led private investment with reach through into developed world markets. Examples of integrated cross-FSC approaches to food waste reduction include various cooperative schemes, e.g. the Common Code for Coffee Community, and the Sustainable Agriculture Initiative.

Conversely, the greatest potential for the reduction of food waste in the developed world lies with retailers, food services and consumers. Cultural shifts in the ways consumers value food, stimulated via education, increased awareness of the FSC and food waste's impact on the environment have the potential to reduce waste production. Improved food labelling and better consumer understanding of labelling and food storage also have food waste reduction potential. WRAP's ongoing activities in this area, through programmes such as ‘Love Food Hate Waste’, are very recent and their impact is yet to be established. With food price recognized as the most important factor in determining consumer decisions, anecdotal evidence suggests that the economic crisis has stimulated a shift in consumer attitude to food waste.

Innovative technology throughout the FSC, in both developed and developing worlds, particularly in packaging, contributes to improving shelf life for perishable foods and semi-prepared meals. Continued developments in packaging, e.g. utilizing nanotechnology and materials science, have the potential to further increase shelf life.

The authors would like to acknowledge input to this review by those individuals interviewed during the course of the work. We are also grateful to the Foresight Team, David Lawrence, WRAP and Gaby Bloem of the Science and Innovation Network.