Abstract
Annually, the world is losing 1.3 billion tons of food, costing approximately $1 trillion, while emitting 8% of greenhouse gases, and consumes 25% of all agricultural water. More than 45% of the produced food is lost before consumption in Egypt, representing a major obstacle for achieving food security and sustainable agricultural development. Addressing this issue requires identifying the main causes for food losses and interpreting the interrelationships between them. In this study, a multi-stage sampling strategy is adopted to investigate 610 stakeholders including farmers, intermediates, and agro-processors. Twenty-two determinants of food losses and waste (FLW) mitigation are identified and categorized into four categories. The interrelationships between these categories are interpreted by the structural equations modeling (SEM). The results revealed that insufficient infrastructure and shortage of government legalizations are the main determinants for reducing FLW, followed by secondary causes, inadequate marketing systems, improper handling practices, and technological and environmental determinants. The direct and indirect interactions are identified and estimated. This study suggests interventions for reducing the FLW including a participatory, holistic, integrated, and multidimensional strategy considering the entire supply chain. Hence, these results could help policymakers, agro-investors, and international funding donors to design more sustainable intervention strategies for reducing FLW in developing countries, considering cost-effectiveness and simplicity of generalizing to achieve the environmental preservation, sustainable food supply chain, and the sustainable use of the limited resources at the local and global levels.
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Notes
“Ensure sustainable consumption and production patterns,” target 12.3 “by 2030, halve per capita global food waste at the retail and consumer levels and reduce food losses along production and supply chains, including post-harvest losses” (FAO et al. 2019).
Steve 2015, Mitigating Postharvest Loss: Addressing Causes Rather than Symptoms, ADM Institute for the Prevention of Postharvest Loss at the University of Illinois, USA. Accessed 20-12-2020. http://publish.illinois.edu/phlinstitute/2015/01/15/mitigating-postharvest-loss-addressing-causes-rather-than-symptoms/.
“Horticultural maturity: It is a developmental stage of the fruit on the tree, which will result in a satisfactory product after harvest.”
“Physiological maturity: It refers to the stage in the development of the fruits and vegetables when maximum growth and maturation have occurred. It is usually associated with full ripening in the fruits. The physiological mature stage is followed by senescence.” “Commercial maturity: It is the state of plant organ required by a market. It commonly bears little relation to physiological maturity and may occur at any stage during development stage.” “Harvest maturity: It may be defined in terms of physiological maturity and horticultural maturity; it is a stage, which will allow fruits/vegetables at its peak condition when it reaches to the consumers and develop acceptable flavor or appearance and having adequate shelf life.” http://www.eagri.org/eagri50/HORT381/pdf/lec02.pdf
Small Egyptian vegetable producers sometimes are forced when the product price is less than the production cost to leave the crop in the field or feed the animals because the harvesting operation cost is higher than the market price per kg. In this case, they are losing their all production because of the price fluctuation, which has an effect on the production in the next year. 16 = 1$ LE
“They mentioned the sample size should be >150 for conducting SEM.”
The cropping patterns of this area include filed crops (wheat, maize, clover, sugar beet, etc.), fruits (grapes, citrus, etc.), and vegetables (tomatoes, potatoes, onion, pepper, etc.).
Cronbach’s alpha to assess internal consistency, the minimum recommended reliability is .70, except for exploratory studies, where .60 is considered the minimum recommended reliability (Hair et al. 2018).
Kaiser-Meyer-Olkin (KMO) and Bartlett’s test used to check whether the sample size is adequate or not. Its value greater than 0.6 with the value for significance less than 0.005 indicates that data size is sufficient for grouping the various relevant factors (Shanker et al. 2019).
Discriminant validity coefficients are correlations between measures of different traits (constructs) and should be much lower than the convergent validity coefficients and/or the instrument reliability coefficients (Schumacker and Lomax 2004).
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Acknowledgements
The authors are thankful to anonymous reviewers for their valuable comments, suggestions, and proofreading of the manuscript; also they would like to thank the stakeholders and extension officers for their cooperation and assistance during field work.
Funding
This study was supported by the National Planning Office of Philosophy and Social Science of China (grant number 17BJY136).
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Ali, A., Xia, C., Ismaiel, M. et al. Analysis of determinants to mitigate food losses and waste in the developing countries: empirical evidence from Egypt. Mitig Adapt Strateg Glob Change 26, 23 (2021). https://doi.org/10.1007/s11027-021-09959-0
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DOI: https://doi.org/10.1007/s11027-021-09959-0