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Measuring food losses: a new methodology

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This webinar from the CGIAR Research Program on Policies, Institutions, and Markets (PIM) was held by Eduardo Nakasone (International Food Policy Research Institute (IFPRI)/Michigan State University) on May 8, 2018. The presentation also includes results of the application of the new method in the value chains of maize and beans (in Guatemala and Honduras), teff (in Ethiopia), wheat (in China), and potatoes (in Ecuador and Peru). We also discussed ongoing work to assess interventions to reduce loss. Full recording and more information available at http://bit.ly/FoodLossWebinar

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Measuring food losses: a new methodology

  1. 1. Outline 1. Development of a new methodology 2. Food loss measurement in selected countries / crops. 3. Testing mechanisms to reduce food losses. 4. Future activities
  2. 2. Methodology
  3. 3. FL estimation methodologies Macro approach
  4. 4. MacroApproach 14.3 - 15.8 % of the total production (APHLIS, 2014; SSA) 89 Mio tons or 179 KG/ capita (Monier et al., 2010; Eurostat data for EU 27) MicroApproach 32% of total production & 24% of total calories produced (FAO, 2011; Kummu et al., 2012; Lipinski et al., 2013 worldwide) 48% of total calories produced (Beretta et al., 2013; Switzerland) 53% of total production (Affognon et al., 2015; SSA) 1.4 – 5.9 % of total production (Kaminski & Christiaensen, 2014; SSA) 28.7% of harvested production – 180 KG/ capital (Venkrant, 2011; US) 32% of total production (Kader, 2009, worldwide) 18.4 Mio ton (WRAP, 2010; UK) 31% or 60 Mio ton of food supply at retail (Buzby et al. 2014, US) All loss and waste is reported per year 5.8 Mio ton (C-tech Innovations, 2004; UK) Different measurements (methods, stages, units, etc.) Wide variability in the results. Scarce evidence for developing countries.
  5. 5. Methodology to Measure Food Losses Improvements: • Consider the agricultural process as a whole (pre-harvest, harvest, and post-harvest losses). • Value chain approach (farmer, middlemen, processor) • Not only lost quantities, but also gauge economic value of quality deterioration along different nodes of the value chain • Identify most frequent reasons of losses
  6. 6. Losses along the production process Objective: Avoid scrapped food (discarded, poor quality). Recent emphasis on post-harvest losses (significant variability in the definition of PHL). However… food is also scrapped due to: • Management problems during pre-harvest (field fungal infections in beans / wheat, earworm affecting maize cobs, etc.) • At harvest (split potatoes due to poor harvesting techniques) • Unharvested production (low prices, high labor costs around harvest time)
  7. 7. Value Chain Analysis Production Post-harvest & Processing Distribution & Logistics Processing Consumption Food Losses: Losses in production (including PHL), distribution, and processing. - More prevalent in developing countries - Representative samples of farmers, middlemen and processors Food Waste - More prevalent in developed countries - Not covered
  8. 8. Assessing quality losses Food with quality deterioration might not be discarded. But there is ECONOMIC LOSS. Subjective: • Self-reported (aggregate) measurement. Categories: • Based on visual-scale methodology (by grades) Attributes: • Share of the crop affected by undesirable attributes • Example (beans): humidity, mold, broken grain, weevil perforations, premature grain, etc. Prices: • How much lower is the actual sales price compared to the “optimal”?
  9. 9. Where do loses occur? Photo credit: El Diario, Bolivia Photo credit: The Hindu, India Photo credit: Victor Agreda Photo credit: Eduardo Nakasone Photo credit: Eduardo Nakasone Photo credit: Eduardo Nakasone Photo credit: Eduardo Nakasone Photo credit: Eduardo Nakasone Photo credit: Eduardo Nakasone In the field? Post Harvest at farm? Transportation? Processing?
  10. 10. Food Loss Measurement
  11. 11. Measurement of Food Losses (Delgado et al. 2017) 1. Food losses are important (8-26%) 2. Much larger when we account for quality losses 3. Most food losses happen at the farm level (56-86%) 4. Main problems identified: weather related issues, lack of knowledge of available technologies (pests, plagues), mechanization and access to infrastructure, lack of price incentives. 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% 11% 16% 17% 16% 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 S C A P ECU, Potato PER, Potato GUA, Beans GUA, Maize HON, Beans HON, Maize ETH, Teff* CHN, Wheat Food Losses (% of value of total production) Farmer Middleman Processor Source: Delgado, L., M. Torero and M. Schuster (2017). The reality of food losses: A new measurement methodology. IFPRI Discussion Paper 01686. Washington, DC: International Food Policy Research Institute. Self reported Quality disaggregation
  12. 12. When do losses occur at the farm level? 0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20% S C A S C A S C A S C A S C A S C A S C A S C A ECU, Potato PER, Potato GUA, Beans GUA, Maize HON, Beans HON, Maize ETH, Teff CHN, Wheat Food Losses at the Farm Level (% of Total Value) Pre Harvest / Harvest Post - Harvest • Losses are important in post-harvest phases… but so are those in pre-harvest and harvest stages. Self reported Quality disaggregation
  13. 13. Dissemination Conferences Country Events G20 - FAO
  14. 14. Interventions to Reduce Food Losses
  15. 15. Ongoing Projects: From Measurement to Solutions Three ongoing projects: • [Market-Based Incentives] Input provision vs. Market-based contractual agreements (beans, Guatemala and Honduras) • [Technology Adoption] Hermetic storage bags (maize, Ethiopia - CIMMYT) • [Extension] Handheld Farmer Decision Tool (potatoes, Ecuador - CIP)
  16. 16. 21.59% 27.58% 48.89% Excessive rain Lack of rain Pest, disease, animals Excess of chemicals Wind Stolen Source:own data collection from 450 producers in 2016 Guatemala, Beans - Reason for Pre-Harvest Loss 81.45% 18.55% Poor harvest technique Lack of/costly labor Source:own data collection from 450 producers in 2016 Guatemala, Beans - Reason for product left in the field 22.56% 44.78% 18.86% 8.75% 5.05% Plagues, rodents, animals Laborer damages at selection Laborer damages at harvest Climate (too much sun or rain) Storage Source:own data collection from 450 producers in 2016 Guatemala, Beans - Reason for loss at Post-Harvest 14.83% 35.24% 48.75% Excessive rain Lack of rain Pest, disease, animals Excess of chemicals Wind Freeze Stolen Source:own data collection from 685 producers in 2016 Honduras, Beans - Reason for Pre-Harvest Loss 98.86% 1.14% Poor harvest technique Lack of/costly labor Source:own data collection from 685 producers in 2016 Honduras, beans - Reason for product left in the field 7.03% 65.69% 21.41% 2.93% 2.93% Plagues, rodents, animals Laborer damages at selection Laborer damages at harvest Climate (too much sun or rain) Storage Source:own data collection from 685 producers in 2016 Honduras, Beans - Reason for loss at Post-Harvest Reasons of losses among bean farmers in Guatemala and Honduras
  17. 17. Input provision vs. Market-based contractual agreements in Guatemala and Honduras Losses among bean farmers in Guatemala and Honduras: • Pests, diseases, lack of rain • Other reasons (inefficient harvesting, labor constraints) Potential policies: • Provide access to improved inputs (improved seeds, fertilizer) • Incentivize increases in quality through market-based contracts Test impact through field experiments Partnering with public and private sector
  18. 18. Honduras Bean producers in baseline Input provision Market-based contractual arrangements Control Department of Agriculture and Livestock (SAG) Improved seeds and fertilizer Experimental Arms Treatment Partner Quality- contingent price premium Honduran Institute of Agricultural Marketing
  19. 19. Guatemala Bean producers in baseline Input provision + Contractual Agreements Control ALBAY (Private Agro Industrial Company) Improved seeds and fertilizer Quality-contingent price premium Experimental Arms Treatment Partner Not possible to randomize across three groups Private sector company interested in beans from particular variety / seed.
  20. 20. Data collection – Physical Measures of Quality (ongoing) Systematic sampling of beans in storage Humidity levels Assessment of damages in bean sample
  21. 21. Data collection – Physical Measures of Quality (ongoing) Presence of insects Crop damage (attributes) Weevil perforations Fungus Broken grain Wrinkled grain
  22. 22. Data collection – Endline Household Survey Collect Household Survey in June / July • Similar approach as baseline to measure food losses. • Assess changes in food losses due to the interventions • Welfare implications of food losses: Estimate changes in income, expenditures, etc. • Complement with physical measures of food losses • Advance methodological tools: To what extent are survey-based measures comparable to physical measures?
  23. 23. Conclusions and Future Activities
  24. 24. Conclusions New methodology to measure food losses: (a) including the entire production process, (b) along the value chain, (c) accounting for reductions in quantity and quality, (d) identifying relevant processes. Food losses are important: especially important at the farm level and when accounting for quality. Need to account not only for post-harvest, but also for pre-harvest and harvest losses (important for quality, not only yields) Application to five commodities in six countries… need to expand to gain better understanding of FL
  25. 25. Dissemination of methodology to measure food losses • Consolidation of methodology as a public good, country workshops Test additional potential mechanisms to reduce food losses Expand applications: • Ghana (SNV-funded): Measurement of FL in the value chains of yam and groundnuts in three northern regions. Focus on yam and groundnuts. • Incorporate studies for perishable crops • T20 approved technical brief to scale up methodology (joint with WB). ▪ Methodology presented at G20 Agricultural Ministerial Meeting ▪ Pilots with WB. Links to loans / TA to solve problems identified through our methodology Future Activities
  26. 26. Thank you!

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