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Eduardo Nakasone • 2017 IFPRI Egypt Seminar Series: Food Loss and Waste in Egypt

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Eduardo Nakasone • 2017 IFPRI Egypt Seminar Series: Food Loss and Waste in Egypt

  1. 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. 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. 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. 4. What is the problem?
  5. 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. 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. 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. 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. 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. 10. Proposed Methodology
  11. 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. 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. 13. Category Method
  14. 14. Attribute Method
  15. 15. Price Method
  16. 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. 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. 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. 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. 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. 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. 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. 23. For more details on the methodology, please go to… https://www.ifpri.org/publication/reality- food-losses-new-measurement- methodology
  24. 24. Appendix
  25. 25. Differences in aggregate measures
  26. 26. Differences in aggregate measures
  27. 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. 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. 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

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