As part of 2017 IFPRI Egypt Seminar

1. REALITY OF FOOD LOSSES:
A NEW MEASURMENT
METHODOLOGY
Eduardo Nakasone (IFPRI)
Based on: “Reality of Food Losses: A New
Measurement Methodology” (Delgado,
Schuster and Torero 2017).
https://www.ifpri.org/publication/reality-food-losses-new-measurement-methodology

2. Key Facts
• Reducing food loss and waste can contribute to food
security and sustainability
• Our lack of clear knowledge about the real magnitude of
food loss and waste is a major barrier to addressing the
problem
• Estimates of global magnitude varies from 27% (1 Billion
Tons) to 32% (1.3 Billion Tons) of all food produced in the
world
• There are significant differences across studies at the
global, regional and country level and at the commodity
group and commodity level
• Policy Priority

3. In this presentation…
• We discuss existing methods to measure FOOD
LOSSES.
• Propose a new analytical measurement framework:
• Across the value chain
• Accounting for quantity, quality and value losses
• Identify underlying causes
• Empirical application: Four commodities and five
countries.

4. What is the problem?

5. What are we measuring?
Confusion in the definition
quantityversus quality Weight, caloric, nutritionaland/or economic loss
Inclusion/ exclusion of different
loss dimensions
naturalversus
unnatural
In percentage of total, harvested or potential
production
edible versus inedible
Avoidable,possibilyavoidableand unavoidable
real loss versus re-use

6. How are we measuring:
estimation methodologies
Macro
approach
Literature using these methods: Gustavsson et al. (FAO, 2011), Kummu et al (2012) and Lipinski et
al. (2013), Beretta et al. 2013, and Buzby et al. 2014. Stuart, 2009 looks at major disadvantages.

7. How are we measuring:
estimation methodologies
Literature using these methods: APHLIS, 2014, Monier et al. (2010), WRAP (2009, and 2010),
Kaminski and Christiansen, 2014; Minten et al., 2016a; Minten et al., 2016b

8. Considerable Differences in
Available Estimates
• Differences within aggregate measures
• Micro measurements:
• Case studies: 6% - 50% of production
• Even within similar crops
• Even within same crops in same countries

9. Challenges
1. No accurate information about the extent of the problem
(especially in developing countries)
2. Scarce evidence regarding the source of food loss
3. Little evidence regarding how to successfully reduce food loss
across the value chain
Goal:
• In this stage, we aim to contribute to the first two challenges
• Objective:
• Improve quantification of food loss
• Characterize the nature of food loss across the value chain for different
commodities in a wide array of countries.

10. Proposed Methodology

11. What we do?
• Value chain concept
• FLW occurs at different stages of the food VC: production, post-
production procedures, processing, distribution, and consumption
(FAO, 2011; HLPE, 2014; Lipinski et al., 2013)
• We collect information through representative surveys among
farmers, middlemen, and processors (identify specific nodes).
• What we measure
• We distinguish Food LOSSES: physical quantities / quality and
value.
• Compare Alternative Methodologies
• 4 methods: 1 traditional method and 3 new methods

12. Three micro approach methods in
addition to traditional method
• Self-reported method (traditional) - for example used by
Kaminski and Christiansen, 2014; Minten et al., 2016a; Minten et
al., 2016b
• Category method - based on the evaluation of a crop and the
classification of that crop into quality categories.
• Attribute method - based on the evaluation of a crop according
to inferior visual, tactile, and olfactory product characteristics.
• Price Method: - based on the reasoning that higher (lower)
values of a commodity reflect higher (lower) quality.

13. Category Method

14. Attribute Method

15. Price Method

16. Data
• For selected commodities we collect random samples of three
different agents in the VC: producer, middleman and processor.
• We developed specialized questionnaires for the three different
agents of the value chain and with the specificities of the
commodities.
• Methodology consistent and comparable across commodities and
countries
• The questionnaires enable us to characterize the nature of food
loss, specifically the production stages and the particular
processes at which loss is incurred.

17. Data
Sample
Ecuador
Potatoes
Peru
Potatoes
Honduras
Beans and maize
Guatemala
Beans and maize
Ethiopia
Teff
Producer 302 411 1209 1155 1203
Middlemen 182 85 325 365 ---
Processor 147 139 224 245 ---
Total 631 594 1758 1765 1203

18. Self-reported (S)
losses -Traditional
method
Category classification
(C), Attribute
measurement (A) and
price (P) methods
* Ethiopia: Losses assessed at the farmer level only
Losses are significant,
but vary depending
on the method (8-
26%).
The aggregate self-
reported method
yields systematically
lower magnitudes of
losses.
Food losses are larger
at the farmer level
(between 60-80%)
10%
18%
14%
16%
10%
22%
22%
26%
12%
17%
22%
21%
12%
19%
17%
19%
8%
18%
21% 21%
13%
21%
19%
22%
6%
9% 9%
9%
0%
5%
10%
15%
20%
25%
30%
S C A P S C A P S C A P S C A P S C A P S C A P S C A P
ECU,
Potato
PER,
Potato
GUA,
Beans
GUA,
Maize
HON,
Beans
HON,
Maize
ETH,
Teff *
Food Losses (% of value of total production)
Farmer Middleman Wholesaler

19. Self-reported (S)
losses -Traditional
method
Category classification
(C), Attribute
measurement (A) and
price (P) methods
* Ethiopia: Losses assessed at the farmer level only
Farmer losses: 5-20%
Close magnitudes in
the three new
proposed methods
(quality matters!).
6%
14%
10%
12%
6%
17%
16%
20%
8%
13%
18%
17%
8%
15%
13%
15%
5%
15%
18%
17%
9%
17%
15%
17%
6%
9%
9% 9%
0%
5%
10%
15%
20%
25%
S C A P S C A P S C A P S C A P S C A P S C A P S C A P
ECU,
Potato
PER,
Potato
GUA,
Beans
GUA,
Maize
HON,
Beans
HON,
Maize
ETH,
Teff *
Farmer Food Losses (% of value of total production)

20. Results: major problems identified
• Weather related issues
• Lack of knowledge of available technology
• Pests
• Plagues
• Mechanization and access to infrastructure
• Lack of price incentives because of non-existence of standards

21. Reasons for loss at different levels
of the value chain
59.36%
18.33%
6.773%
15.54%
other pest; disease; animals little rain
freeze lack or excess of inputs
Source:own data collection from 302 producers in 2016
Ecuador, potato - Reason for Pre-Harvest Loss
15.15%
36.36%30.3%
18.18%
bad harvest technique small or bad quality potato
lack or costly labor low price
Source:own data collection from 302 producers in 2016
Ecuador, potato - Reason for product left in the field
63.97%
16.54%
11.03%
8.456%
laborer damages at harvest
laborer damages at selection/cla
climate, too much sun or rain
transport
Source:own data collection from 302 producers in 2016
Ecuador, potato - Reason for loss at Post-Harvest
34.82%
32.09%
23.02%
10.07%
other pest; disease; animals little rain
freeze lack or excess of inputs
Source:own data collection from 411 producers in 2016
Peru, potato - Reason for Pre-Harvest Loss
43.41%
10.73%
35.12%
10.24%.4878%
bad harvest technique small or bad quality potato
lack or costly labor low price
no transport
Source:own data collection from 411 producers in 2016
Peru, potato - Reason for product left in the field
39.68%
15.23%
15.43%
29.66%
laborer damages at harvest
laborer damages at selection/cla
climate, too much sun or rain
transport
Source:own data collection from 411 producers in 2016
Peru, potato - Reason for loss at Post-Harvest

22. Next Steps
1. Expand the application of the methodology
 Other countries / commodities
 Partnerships with other institutions
 Materials will be publicly available at the TPFLW
2. Test tools to reduce the extent of food losses
 Ecuador / Peru: Handheld Decision Support Tools (HHDST)
for late potato blight.
 Ethiopia: Maize storage in Ethiopia
 Guatemala / Honduras: Market-based approaches to
incentivize quality improvement among beans / maize
farmers.

23. For more details on the
methodology, please go to…
https://www.ifpri.org/publication/reality-
food-losses-new-measurement-
methodology

24. Appendix

25. Differences in aggregate measures

26. Differences in aggregate measures

27. Literature review shows wide
variation by commodity group
0
204060
Cereals Roots Oilseeds Fruit&Veg Animal
Total
Source: Rosegrant et al., 2015. Returns to Investment in Reducing Postharvest Food Losses and Increasing Agricultural
Productivity Growth. Food security and nutrition assessment paper. Copenhagen Consensus Center.

28. Literature review shows wide
variation by commodity
Commodity Country Author % PHL -
Maximum
(no
interventio
n in place)
Weights (wi) % PHL -
Minimum (with
interventions in
place)
Maize Benin Borgemeister et al. (1998) 16.40 0.09 5.50
Benin Meikle et al. (1998) 41.30 0.10 15.80
Benin Schneider et al. (2004) 18.70 0.18 3.00
Benin Meikle et al. (2002) 23.00 0.08 7.00
Benin Affognon et al. (2000) 33.50 0.04 2.10
Benin Adda, Borgemeister, Biliwa, and Aboe (1997) 12.00 0.44 7.00
Ghana Compton & Sherrington (1999) 21.50 0.05 4.80
Ghana Ofosu (1987) 35.90 0.06 11.70
Kenya Mutambuki and Ngatia (2012) 20.60 0.02 9.70
Kenya Komen, Mutoko, Wanyama, Rono, and Mose (2006) 7.60 0.01 3.90
Kenya Mutambuki and Ngatia (2006) 29.10 0.41 19.30
Tanzania Makundi et al. (2010) 16.00 0.44 1.00
Tanzania Golob and Hodges (1982) 11.10 0.01 5.20
Tanzania Golob and Boag (1985) 26.40 0.00 2.50
Mango Benin Vayssie`res, Korie, and Ayegnon (2009) 75.40 0.01 17.60
Benin Vayssie`res, Korie, Coulibaly, Temple, and Boueyi (2008) 70.00 0.00 17.00
Dried cassava chipscGhana Chijindu, Boateng, Ayertey, Cudjoe, and Okonkwo (2008) 75.50 0.19 20.90
Ghana Isah, Ayertey, Ukeh, and Umoetok (2012) 75.50 0.03 68.50
Tanzania Hodges, Meik, & Denton 1985 73.60 0.00 52.30
Sweet potatoeTanzania Rees et al. (2003) 35.80 0.01 32.50
Tanzania Tomlins et al. (2007) 66.90 0.00 23.70
Source: Affognon et.al. (2014).

29. 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00
Borgemeister etal. (1998)
Meikle et al. (1998)
Schneider et al. (2004)
Meikle et al. (2002)
Affognon et al. (2000)
Adda, Borgemeister, Biliwa, and Aboe (1997)
Compton & Sherrington (1999)
Ofosu (1987)
Mutambuki and Ngatia (2012)
Komen, Mutoko, Wanyama, Rono, and Mose (2006)
Mutambuki and Ngatia (2006)
Makundi et al. (2010)
Golob and Hodges (1982)
Golob and Boag (1985)
Vayssie`res, Korie, and Ayegnon (2009)
Vayssie`res, Korie, Coulibaly, Temple, and Boueyi (2008)
Chijindu, Boateng, Ayertey, Cudjoe, and Okonkwo (2008)
Isah, Ayertey, Ukeh, and Umoetok (2012)
Hodges, Meik, & Denton 1985
Rees et al. (2003)
Tomlins et al. (2007)
% PHL - Maximum
Benin
Benin
Ghana
Ghana
Tanzania
Tanzania
Tanzania
Sweet
Potatoe
Dried
Cassava
Mangoes
Maizea
Benin
Benin
Benin
Benin
Benin
Benin
Ghana
Ghana
Kenya
Kenya
Kenya
Tanzania
Tanzania
Tanzania
Literature review shows wide
variation by commodity