Impacts of food losses on the nutrition of the population in Bulgaria

Impacts of food losses on the nutrition of the population in Bulgaria | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impacts of food losses on the nutrition of the population in Bulgaria Stela Todorova, Kaloyan Haralampiev This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6311089/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Reducing food loss and waste is a key part of Sustainable Development Goal (SDG) 12 on Responsible Consumption and Production. Specifically, target 12.3 aims to "halve per capita global food waste at the retail and consumer levels by 2030, and reduce food losses along production and supply chains, including post-harvest losses.” Food loss refers to any food removed from the supply chain between maturity and sale, including inedible parts as these are integral to the marketed product. Our main hypothesis is that increased food loss results in a decreased food supply per person, subsequently reducing the ability to feed the population. We evaluated population nutrition using two indicators: average dietary energy supply adequacy and prevalence of undernourishment. The data comes from the Food and Agriculture Organization (FAO). To analyze these relationships and process the data, we used a simultaneous equations model (SEM). After evaluating the SEM, we obtained the following results: losses of vegetal products do not have a direct or indirect influence on the two dependent variables. However, losses of animal products directly and indirectly affect the prevalence of undernourishment. In conclusion, there is a positive relationship between the loss of animal products and mediator variables. Greater losses lead to greater per capita supply. Additionally, there is a negative relationship between per capita supply and the prevalence of undernourishment. A larger per capita supply leads to a smaller prevalence of undernourishment. Agricultural Economics & Policy Agroecology Environmental Economics Food losses FAO SEM Mediator variables Population nutrition Figures Figure 1 Figure 2 1. Introduction Food loss and waste (FLW) is recognized as a serious threat to food security, the economy, and the environment (Abiad and Meho, 2018). To understand food loss and waste, referring to the correct definition of "food" is important. This term refers to any substance or product, whether processed, partially processed, or unprocessed, intended to be or reasonably expected to be consumed by humans (Official Journal of EC, 2002). Food includes beverages, chewing gum, and any substance, including water, intentionally incorporated into food during its production, preparation, or processing (Patel et al., 2021). Therefore, a distinction must be made between food loss and food waste according to the stage at which they occur under the food supply chain (FSC). Several definitions of the food loss and waste phenomenon have been proposed in the literature. The products to be considered are only those agricultural products originally intended for human consumption, ready for harvest or post-harvest (FAO, 2014). Approximately one-third of all food produced for human consumption (1.3 billion tons of edible food) is lost and wasted across the entire supply chain every year (Gustavsson et al., 2011). The food supply chain includes a series of related activities used to produce, process, distribute, and consume food (FAO, 2019). Each stage of the food supply chain includes various agricultural and industrial operations, leading to different types of losses and waste. Understanding the causes and identifying why food is lost and wasted is a key step in improving resource efficiency in the long term (Luo et al., 2021). The amount of food loss and waste varies between countries, being influenced by the level of income, urbanization, and economic growth (Chalak et al., 2016). In less-developed countries, food loss and waste occur mainly in the post-harvest and processing stage (Gustavsson et al., 2011), which accounts for approximately 44% of global food loss and waste (HLPE, 2019). Understanding the causes and identifying why food is lost is a key step in improving resource efficiency in the long term (FAO , 2019). Losses along the food supply chain generally depend on socioeconomic, biological and microbiological, chemical or biochemical, mechanical, and environmental factors (FAO, 2015). What provoked us to investigate this problem is the lack of information and incentives for businesses on their optional practices and duties by law for the reduction of food waste and loss and for better food waste management. Sustainable Development Goal (SDG) 12.3 aims to halve food loss and waste by 2030. Food loss and waste is a global problem with major consequences at all levels (Fernandez-Zamudio et al., 2020). This challenge has been echoed by the scientific community, and there has been a large amount of research on food waste on the demand side (Vázquez-Rowe et al., 2020). On the other hand, sustainability of food security and containment of losses is a key priority in policy circles. In 2015, UN Member States adopted the Sustainable Development Goals (SDGs), where numbers #2 and #12 refer to “Zero Hunger” and “Responsible Consumption and Production,” respectively. Therefore, there is a need for analysis at other levels, and above all, to identify the main points of loss, possible causes, and their impact on the overall supply of food and nutrition of the population in general and in Bulgaria in particular. The world is facing population growth that could reach 9.7 billion by 2050 (FAO, IFAD, UNICEF,WFP and WHO, 2022). Global agriculture will be challenged to improve production by at least 50% to feed everyone and ensure global food security. Reducing FLW will help meet this growing demand and reduce pressure on production systems, especially in the context of dwindling natural resources and climate change. The paper aims to argue that the amount of food waste and losses after harvesting mostly depends on the supply chain strategies used in practice. It also aims to demonstrate, in a specific case, how these losses of plant and animal products impact the average adequacy of food energy supplies and the prevalence of undernourishment. The main hypothesis is that food losses affect the per capita supply, impacting the population's nutrition. The rationale for this hypothesis is as follows: • According to the Food balance Domestic Supply Total is the sum of Domestic Utilization as Food, Processing, Feed, Seed, Losses, Other uses (non-food), Tourist consumption, and Residuals. That means that, other things being equal, when food losses increase, other forms of domestic utilization are expected to decrease; • Per capita supply is obtained by dividing Domestic Utilization as Food by the number of the population. Thus, when Domestic Utilization of Food decreases, per capita supply will also decrease; • When per capita supply decreases, then the ability for population nutrition will decrease. 2. Materials and methods 2.1 Data The data source is the Food and Agriculture Organization (FAO). Data from Food Balances from 2010 until now (FAOSTAT, 2024), Food Balances until 2013 (FAOSTAT, 2024) and Food Security and Nutrition (FAOSTAT, 2024) were used. The data refer to the period 2000–2022. Food losses are “amount of the commodity in question lost through wastage (waste) during the year at all stages between the level at which production is recorded and the household, i.e. storage and transportation”(FAOSTAT, 2024). Food losses are calculated separately for vegetal and animal products. The first step is to calculate the total food losses for each vegetal and animal product in million metric tons. Then, these losses are divided by the total population. This calculation helps to determine the food losses per capita, making it easier to compare with per capita supply. For per capita supply, all four FAO indicators were used: Per Capita Supply Total (Kg/Year); Per Capita Supply Total (KCal/Day); Per Capita Supply Proteins (g/Day); Per Capita Supply Fat (g/Day). These indicators are available separately for vegetal and animal products. Per Capita Supply Total (Kg/Year) refers “to the total amount of the commodity available as human food during the reference period”; Per Capita Supply Total (KCal/Day) “refers to the total amount of food available for human consumption expressed in kilocalories”; Per Capita Supply Proteins (g/Day) “refers to the total amount of protein available for human consumption resulting from the multiplication of the quantity of food available”; Per Capita Supply Fat (g/Day) “refers to the total amount of fat available for human consumption resulting from the multiplication of the quantity of food available” (FAOSTAT, 2024). Two indicators were used to measure population nutrition: Average Dietary Energy Supply Adequacy and Prevalence of Undernourishment. Average Dietary Energy Supply Adequacy “expresses the Dietary Energy Supply as a percentage of the Average Dietary Energy Requirement”; Prevalence of Under-nourishment “expresses the probability that a randomly selected individual from the population consumes an amount of calories that is insufficient to cover her/his energy requirement for an active and healthy life” (FAOSTAT, 2024). The data for the last two indicators is based on 3-year averages, so all other indicators were also calculated as 3-year averages. To do this, we used a weighted arithmetic mean, taking into account the population and the number of days in the respective year as weights. As a result twenty, 3-year intervals were obtained, ranging from 2000–2002 to 2020–2022. Data used only for Bulgaria. 2.2 Methods The particular indicators we have used to measure food losses, per capita supply and nutrition of the population, lead to the concretization of the main hypothesis. In general, these concretizations are as follows: • The direct impact of Food Losses on Average Dietary Energy Supply Adequacy is negative – we expect an increase of Food Losses to lead to a decrease of Average Dietary Energy Supply Adequacy; • The direct impact of Food Losses on Prevalence of Undernourishment is positive – we expect an increase of Food Losses to lead to an increase of Prevalence of Undernourishment; • The impact of Food Losses on per capita supplies is negative – we expect an increase of Food Losses to lead to a decrease of per capita supplies; • The impact of per capita supplies on Average Dietary Energy Supply Adequacy is positive – we expect an increase of per capita supply to lead to an increase of Average Dietary Energy Supply Adequacy; • As a result, the indirect impact of Food Losses on Average Dietary Energy Supply Adequacy is negative, as is the direct impact; • The impact of per capita supplies on Prevalence of Undernourishment is negative – we expect an increase of per capita supplies to lead to an increase of Prevalence of Undernourishment; • As a result, the indirect impact of Food Losses on Prevalence of Undernourishment is positive, as is the direct impact; All hypotheses are presented on Fig. 1 . Expected positive relations are marked in green, while expected negative relations are marked in red. Simultaneous equations model (SEM) was used to model these relationships. Following Martin et al., 2013 simultaneous equations model is such a model where the dependent variable depends on a set of independent variables, but at the same time some of these independent variables depend on other independent variables. Following Lopez-Espin et al. (López-Espín, 2012), Chipeva and Boshnakov (2015), Dimitrov ( 1995 ) and Petkov ( 2017 ) a system is formed and solved to estimate the coefficients of equations. The equations in the system can be of two types – balance and regression. Only the coefficients of the regression equations are estimated. The variables in the equations are also of two types – endogenous and exogenous. Endogenous variables are those that depend on other variables in the model. Exogenous variables are those that do not depend on other variables in the model. The number of endogenous variables may not be greater than the number of equations in the system. The system of equations used to estimate the relationships between food losses, per capita supply and nutrition of the population is (Eq. 1): $$\:\left|\begin{array}{c}{\widehat{Y}}_{i}={a}_{i}+\sum\:_{k=1}^{2}{b}_{ik}{X}_{k}+\sum\:_{k=1}^{2}\sum\:_{j=1}^{4}{c}_{ikj}{M}_{kj}\:(i=\text{1,2})\\\:{\widehat{M}}_{1j}={a}_{1j}+{d}_{1j}{X}_{1}\:(j=\text{1,2},\text{3,4})\\\:{\widehat{M}}_{2j}={a}_{2j}+{d}_{2j}{X}_{2}\:(j=\text{1,2},\text{3,4})\end{array}\right.$$ Where endogenous variables are: \(\:{Y}_{1}\) is Average Dietary Energy Supply Adequacy; \(\:{Y}_{2}\) is the Prevalence of Undernourishment; \(\:{M}_{1j}\) are per capita supply vegetal products; \(\:{M}_{2j}\) are per capita supply animal products. Exogenous variables are: \(\:{X}_{1}\) are Losses Vegetal Products; \(\:{X}_{2}\) are Losses Animal Products. There are no balance equations in the model. All equations are regressions. The endogenous variables in the model are 10 as much as the number of equations in the system. This model allows the estimation of both the direct and indirect impacts of the independent variables on the dependent variables. 3. Results Before implementing Simultaneous equations model, we performed a time series stationarity test in Gretl using the Augmented Dickey-Fuller (ADF) test, the most widely used Unit Root test. Table 1 Augmented Dickey-Fuller test* Variable Level – intercept First differences – intercept Second differences – intercept Test statistics \(\:\varvec{p}\) -value Test statistics \(\:\varvec{p}\) -value Test statistics \(\:\varvec{p}\) -value Losses Vegetal Products Per Capita -2.58 0.116 -1.72 0.407 -13.09 0.000 Losses Animal Products Per Capita -1.56 0.479 -4.13 0.006 Per Capita Supply Total (Kg/Year) Vegetal Products -0.36 0.899 -3.37 0.028 Per Capita Supply Total (Kg/Year) Animal Products -1.15 0.675 -2.26 0.196 -6.69 0.000 Per Capita Supply Total (KCal/Day) Vegetal Products -1.32 0.601 -0.002 0.945 -5.60 0.000 Per Capita Supply Total (KCal/Day) Animal Products 1.89 1.000 -5.59 0.000 Per Capita Supply Proteins (g/Day) Vegetal Products -3.11 0.043 Per Capita Supply Proteins (g/Day) Animal Products 0.79 0.991 -3.35 0.028 Per Capita Supply Fat (g/Day) Vegetal Products -2.36 0.165 -1.75 0.393 -5.01 0.001 Per Capita Supply Fat (g/Day) Animal Products 2.24 1.000 -3.46 0.022 Average Dietary Energy Supply Adequacy 3.24 1.000 -2.39 0.157 -4.19 0.006 Prevalence of Undernourishment 0.26 0.970 -2.85 0.075 -6.04 0.000 * Null hypothesis is that time series have unit root, i.e., time series are non-stationary. The test results indicated: The time series of Per Capita Supply Proteins (g/Day) Vegetal Products is stationary; The time series of Losses Animal Products Per Capita, Per Capita Supply Total (Kg/Year) Vegetal Products, Per Capita Supply Total (KCal/Day) Animal Products, Per Capita Supply Proteins (g/Day) Animal Products, and Per Capita Supply Fat (g/Day) Animal Products are non-stationary, but the time series of the first differences are stationary. Therefore, the first differences are used in the analysis; The time series of Average Dietary Energy Supply Adequacy, Prevalence of Undernourishment, Losses Vegetal Products Per Capita, Per Capita Supply Total (Kg/Year) Animal Products, Per Capita Supply Total (KCal/Day) Vegetal Products, and Per Capita Supply Fat (g/Day) Vegetal Products are non-stationary and the time series of the first differences are also non-stationary. However, the time series of the second differences are stationary, so the second differences are used in the analysis. After evaluating the simultaneous equations model in JASP, we obtained specific results (Fig. 2 ). There are several conclusions that can be drawn from the relationships that have been discovered: Losses Vegetal Products Per Capita do not have a direct or indirect influence on the two dependent variables. Losses Animal Products Per Capita have both a direct and an indirect influence on the Average Dietary Energy Supply Adequacy. A direct relationship is observed: greater Losses Animal Products Per Capita lead to a higher Average Dietary Energy Supply Adequacy ( \(\:z=4.060,\:p=0.000\) ), which contradicts initial expectations; The indirect relationship is mediated by Per Capita Supply Total (Kg/Year) Animal Products and Per Capita Supply Proteins (g/Day) Animal Products: a. Per Capita Supply Total (Kg/Year) Animal Products: the relationship between Losses of Animal Products Per Capita and the mediator variable is positive. The data indicates that higher Losses of Animal Products Per Capita result in a higher Per Capita Supply Total (Kg/Year) Animal Products ( \(\:z=4.937,\:p=0.001\) ). This goes against our initial assumption that greater losses would lead to a smaller per capita supply. On the other hand, the inverse relationship between Per Capita Supply Total (Kg/Year) Animal Products and the Average Dietary Energy Supply Adequacy is negative ( \(\:z=-2.608,\:p=0.009\) ) which also contradicts our initial expectations. b. Per Capita Supply Proteins (g/Day) Animal Products: the relationship between Losses of Animal Products Per Capita and the mediator variable is positive. The data indicates that higher Losses of Animal Products Per Capita result in a higher Per Capita Supply Proteins (g/Day) Animal Products ( \(\:z=2.307,\:p=0.021\) ). This goes against our initial assumption that greater losses would lead to a smaller per capita supply. On the other hand, the inverse relationship between Per Capita Supply Proteins (g/Day) Animal Products and the Average Dietary Energy Supply Adequacy is negative ( \(\:z=-9.958,\:p=0.000\) ) which also contradicts our initial expectations. Losses Animal Products Per Capita have only an indirect influence on the Prevalence of Undernourishment. The indirect relationship is mediated by Per Capita Supply Proteins (g/Day) Animal Products: the relationship between Losses of Animal Products Per Capita and the mediator variable is positive. The data indicates that higher Losses of Animal Products Per Capita result in a higher Per Capita Supply Proteins (g/Day) Animal Products ( \(\:z=2.307,\:p=0.021\) ). This goes against our initial assumption that greater losses would lead to a smaller per capita supply. On the other hand, the inverse relationship between Per Capita Supply Proteins (g/Day) Animal Products and the Prevalence of Undernourishment is positive ( \(\:z=2.547,\:p=0.011\) ) which also contradicts our initial expectations. Some of the mediator variables have an independent influence on the dependent variables: Per Capita Supply Total (KCal/Day) Vegetal Products affects both Average Dietary Energy Supply Adequacy and Prevalence of Undernourishment: a. Greater Per Capita Supply Total (KCal/Day) Vegetal Products leads to greater Average Dietary Energy Supply Adequacy ( \(\:z=5.208,\:p=0.000\) ), which coincides with preliminary expectations; b. Larger Per Capita Supply Total (KCal/Day) Vegetal Products leads to lower Prevalence of Undernourishment ( \(\:z=-8.178,\:p=0.000\) ), which also coincides with preliminary expectations; Per Capita Supply Total (KCal/Day) Animal Products affects both Average Dietary Energy Supply Adequacy and Prevalence of Undernourishment: a. Greater Per Capita Supply Total (KCal/Day) Animal Products leads to greater Average Dietary Energy Supply Adequacy ( \(\:z=15.523,\:p=0.000\) ), which coincides with preliminary expectations; b. Larger Per Capita Supply Total (KCal/Day) Animal Products leads to lower Prevalence of Undernourishment ( \(\:z=-8.973,\:p=0.000\) ), which also coincides with preliminary expectations; Per Capita Supply Proteins (g/Day) Vegetal Products affects both Average Dietary Energy Supply Adequacy and Prevalence of Undernourishment: a. Higher Per Capita Supply Proteins (g/Day) Vegetal Products lead to higher Average Dietary Energy Supply Adequacy ( \(\:z=1.989,\:p=0.047\) ), which coincides with preliminary expectations; b. Larger Per Capita Supply Proteins (g/Day) Vegetal Products leads to lower Prevalence of Undernourishment ( \(\:z=-4.877,\:p=0.000\) ), which also coincides with preliminary expectations; Per Capita Supply Fat (g/Day) Vegetal Products affects both Average Dietary Energy Supply Adequacy and Prevalence of Undernourishment: a. Greater Per Capita Supply Fat (g/Day) Vegetal Products leads to greater Average Dietary Energy Supply Adequacy ( \(\:z=2.286,\:p=0.022\) ), which coincides with preliminary expectations; b. Higher Per Capita Supply Fat (g/Day) Vegetal Products leads to higher Prevalence of Undernourishment ( \(\:z=2.870,\:p=0.004\) ), which contradicts the initial expectation that a higher Per Capita Supply of Fat (g/Day) would result in a reduced Prevalence of Undernourishment; Per Capita Supply Fat (g/Day) Animal Products affects both Average Dietary Energy Supply Adequacy and Prevalence of Undernourishment: a. Greater Per Capita Supply Fat (g/Day) Animal Products leads to lower Average Dietary Energy Supply Adequacy ( \(\:z=-8.289,\:p=0.000\) ), which contradicts preliminary expectations; b. Higher Per Capita Supply Fat (g/Day) Animal Products leads to higher Prevalence of Undernourishment ( \(\:z=4.234,\:p=0.000\) ), which contradicts the initial expectation that a higher Per Capita Supply of Fat (g/Day) would result in a reduced Prevalence of Undernourishment; Per Capita Supply Total (Kg/Year) Vegetal Products does not influence both dependent variables. Losses Vegetal Products Per Capita influence positively Per Capita Supply Total (Kg/Year) Vegetal Products – higher Losses Vegetal Products Per Capita leads to higher Per Capita Supply Total (Kg/Year) Vegetal Products ( \(\:z=1.972,\:p=0.049\) ). This contradicts preliminary expectations that greater Losses Vegetal Products Per Capita will lead to a lower Per Capita Supply Total (Kg/Year) Vegetal Products. 4. Discussion There are three possible explanations for why when the Losses Per Capita increases, per capita supply also increases. To have a balance, Domestic supply = Domestic Utilization. In turn, Domestic Utilization = Food + Processing + Feed + Seed + Losses + Other uses (non-food) + Tourist consumption + Residuals. That means that if Domestic Utilization is constant then increasing of Losses will reduce all other collectibles. Therefore, increasing of Losses per capita will reduce all other collectibles per capita. However: Per capita supply is obtained as Food is divided by the total population in the corresponding year, and the population of Bulgaria decreases throughout the period, i.e., per capita supply can increase only because of the decreasing denominator. Food is one of these other collectibles, but it may also increase due to the decrease of the sum of all other collectibles. Domestic Utilization is not constant over time. It is possible that Domestic Utilization in the corresponding year is so large that both Losses and Food are large. So, contrary to preliminary expectations, it is possible that Losses is increasing, and at the same time the per capita supply is also increasing. A possible explanation for negative relationships between per capita supply and Average Dietary Energy Supply Adequacy and for positive relationships between per capita supply and Prevalence of Undernourishment is related not to the quantity, but to the quality of proteins and fats. It is possible that a higher amount of low-quality proteins and fats per capita does not lead to an increase in Average Dietary Energy Supply Adequacy and to a decrease in the Prevalence of Undernourishment, as does a lower amount of proteins and fats, but with higher quality. 5. Conclusions The study concludes that the loss of vegetal products per capita does not directly or indirectly affect the nutrition of the Bulgarian population. However, the loss of animal products per capita does impact the Average Dietary Energy Supply Adequacy, both directly and indirectly, and Prevalence of Undernourishment, only indirectly. There is a positive direct relationship, meaning that greater losses of animal products per capita lead to a higher Average Dietary Energy Supply Adequacy. The indirect relationship is mediated by the Per Capita Supply Total (Kg/Year) Animal Products and Per Capita Supply Proteins (g/Day) Animal Products. The relationships between Losses Animal Products Per Capita and the mediator variables are positive – greater losses per capita lead to greater per capita supply. The relationships between per capita supply and Average Dietary Energy Supply Adequacy are negative, and the relationship between per capita supply and Prevalence of Undernourishment is positive. Some of the mediation variables independently influence the dependent variables, as we have already discussed. Only one mediator variables do not influence the dependent variables. Most Bulgarian companies and households do not address FLW issues for many reasons, including cultural, economic, and political. Our study will help to increase the engagement of stakeholders and business enterprises in the actual implementation. It will also contribute to improving the efficiency of production, the sustainability of food security, and the limitation of food losses, which is a key priority of European society. Declarations Funding: This work was supported by the Bulgarian National Science Fund [grant number КП-06-КОСТ/15, 2024] References Abiad, M.G., Meho, L.I., 2018. Food loss and food waste research in the Arab world: A systematic review. Food Security, 10(3),1-12, https://doi.org/10.1007/s12571-018-0782-7. Chalak, A., Abou-Daher, C., Chaaban, J., Abiad, M.G., 2016. The global economic and regulatory determinants of household food waste generation: A cross-country analysis. 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Res., 39, 473–488. https://doi.org/10.1177/0734242X20983427. Petkov, P., 2017. Fundamentals of econometric modeling. Tsenov Academic Publishing House, Svishtov, pp. 30, ISBN: 978-954-23-1504-9, in Bulgarian Fernandez-Zamudio, M.A.; Barco, H.; Schneider, F., 2020. Direct measurement of mass and economic harvest and post-harvest losses in spanish persimmon primary production. Agriculture 10, 581. https://doi.org/10.3390/agriculture10120581. Vázquez-Rowe, I.; Laso, J.; Margallo, M.; Garcia-Herrero, I.; Hoehn, D.; Amo-Setién, F.; Bala, A.; Abjas, R.; Sarabia, C.; Dura, M.J.; et al., 2020. Food loss and waste metrics: A proposed nutritional cost footprint linking linear programming and life cycle assessment. Int. J. Life 25, 1197–1209. https://doi.org/10.1007/s11367-019-01655-1. Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6311089","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":434226942,"identity":"d906f995-d742-4fe1-9518-7dd81c0c9d6e","order_by":0,"name":"Stela Todorova","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-2440-1947","institution":"Agricultural university","correspondingAuthor":true,"prefix":"","firstName":"Stela","middleName":"","lastName":"Todorova","suffix":""},{"id":434226943,"identity":"1b27e40e-ba5b-4a10-91ba-cf2517b4df06","order_by":1,"name":"Kaloyan Haralampiev","email":"","orcid":"","institution":"Sofia University \"St. Kliment Ohridski\"","correspondingAuthor":false,"prefix":"","firstName":"Kaloyan","middleName":"","lastName":"Haralampiev","suffix":""}],"badges":[],"createdAt":"2025-03-26 09:55:45","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-6311089/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6311089/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79324181,"identity":"01aa534d-ec2a-4ee5-a774-d17acd472e7f","added_by":"auto","created_at":"2025-03-27 05:08:47","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":126697,"visible":true,"origin":"","legend":"\u003cp\u003eConcretization of the main hypothesis\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6311089/v1/42f7afc4c8d5ce07db37495b.jpg"},{"id":79324178,"identity":"43f73906-f370-42ca-9a1a-b930c902b56d","added_by":"auto","created_at":"2025-03-27 05:08:47","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":143619,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships identified through the Simultaneous equations model\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6311089/v1/58a5b44944e7f37eee051de8.jpg"},{"id":79326103,"identity":"bd9d296b-baca-41ee-830f-2ace00e40dfb","added_by":"auto","created_at":"2025-03-27 05:32:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":771177,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6311089/v1/36878c16-f2b3-477d-8b06-58df7c43541f.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eImpacts of food losses on the nutrition of the population in Bulgaria\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFood loss and waste (FLW) is recognized as a serious threat to food security, the economy, and the environment (Abiad and Meho, 2018). To understand food loss and waste, referring to the correct definition of \u0026quot;food\u0026quot; is important. This term refers to any substance or product, whether processed, partially processed, or unprocessed, intended to be or reasonably expected to be consumed by humans (Official Journal of EC, 2002). Food includes beverages, chewing gum, and any substance, including water, intentionally incorporated into food during its production, preparation, or processing (Patel et al., 2021). Therefore, a distinction must be made between food loss and food waste according to the stage at which they occur under the food supply chain (FSC). Several definitions of the food loss and waste phenomenon have been proposed in the literature. The products to be considered are only those agricultural products originally intended for human consumption, ready for harvest or post-harvest (FAO, 2014). Approximately one-third of all food produced for human consumption (1.3 billion tons of edible food) is lost and wasted across the entire supply chain every year (Gustavsson et al., 2011). The food supply chain includes a series of related activities used to produce, process, distribute, and consume food (FAO, 2019). Each stage of the food supply chain includes various agricultural and industrial operations, leading to different types of losses and waste. Understanding the causes and identifying why food is lost and wasted is a key step in improving resource efficiency in the long term (Luo et al., 2021). The amount of food loss and waste varies between countries, being influenced by the level of income, urbanization, and economic growth (Chalak et al., 2016). In less-developed countries, food loss and waste occur mainly in the post-harvest and processing stage (Gustavsson et al., 2011), which accounts for approximately 44% of global food loss and waste (HLPE, 2019). Understanding the causes and identifying why food is lost is a key step in improving resource efficiency in the long term (FAO , 2019). Losses along the food supply chain generally depend on socioeconomic, biological and microbiological, chemical or biochemical, mechanical, and environmental factors (FAO, 2015).\u003c/p\u003e\n\u003cp\u003eWhat provoked us to investigate this problem is the lack of information and incentives for businesses on their optional practices and duties by law for the reduction of food waste and loss and for better food waste management. Sustainable Development Goal (SDG) 12.3 aims to halve food loss and waste by 2030. \u0026nbsp;Food loss and waste is a global problem with major consequences at all levels (Fernandez-Zamudio et al., 2020). This challenge has been echoed by the scientific community, and there has been a large amount of research on food waste on the demand side (V\u0026aacute;zquez-Rowe et al., 2020). On the other hand, sustainability of food security and containment of losses is a key priority in policy circles. In 2015, UN Member States adopted the Sustainable Development Goals (SDGs), where numbers #2 and #12 refer to \u0026ldquo;Zero Hunger\u0026rdquo; and \u0026ldquo;Responsible Consumption and Production,\u0026rdquo; respectively. Therefore, there is a need for analysis at other levels, and above all, to identify the main points of loss, possible causes, and their impact on the overall supply of food and nutrition of the population in general and in Bulgaria in particular.\u003c/p\u003e\n\u003cp\u003eThe world is facing population growth that could reach 9.7 billion by 2050 (FAO, IFAD, UNICEF,WFP and WHO, 2022). Global agriculture will be challenged to improve production by at least 50% to feed everyone and ensure global food security. Reducing FLW will help meet this growing demand and reduce pressure on production systems, especially in the context of dwindling natural resources and climate change.\u003c/p\u003e\n\u003cp\u003eThe paper aims to argue that the amount of food waste and losses after harvesting mostly depends on the supply chain strategies used in practice. It also aims to demonstrate, in a specific case, how these losses of plant and animal products impact the average adequacy of food energy supplies and the prevalence of undernourishment. The main hypothesis is that food losses affect the per capita supply, impacting the population\u0026apos;s nutrition. The rationale for this hypothesis is as follows:\u003c/p\u003e\n\u003cp\u003e\u0026bull;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;According to the Food balance Domestic Supply Total is the sum of Domestic Utilization as Food, Processing, Feed, Seed, Losses, Other uses (non-food), Tourist consumption, and Residuals. That means that, other things being equal,\u0026nbsp;when food losses increase, other forms of domestic utilization are expected to decrease;\u003c/p\u003e\n\u003cp\u003e\u0026bull;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Per capita supply is obtained by dividing Domestic Utilization as Food by the number of the population. Thus, when Domestic Utilization of Food decreases, per capita supply will also decrease;\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u0026nbsp; \u0026nbsp; \u0026nbsp; When per capita supply decreases, then the ability for population nutrition will decrease.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 \u003cb\u003eData\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe data source is the Food and Agriculture Organization (FAO). Data from Food Balances from 2010 until now (FAOSTAT, 2024), Food Balances until 2013 (FAOSTAT, 2024) and Food Security and Nutrition (FAOSTAT, 2024) were used. The data refer to the period 2000\u0026ndash;2022.\u003c/p\u003e \u003cp\u003eFood losses are \u0026ldquo;amount of the commodity in question lost through wastage (waste) during the year at all stages between the level at which production is recorded and the household, i.e. storage and transportation\u0026rdquo;(FAOSTAT, 2024). Food losses are calculated separately for vegetal and animal products. The first step is to calculate the total food losses for each vegetal and animal product in million metric tons. Then, these losses are divided by the total population. This calculation helps to determine the food losses per capita, making it easier to compare with per capita supply.\u003c/p\u003e \u003cp\u003eFor per capita supply, all four FAO indicators were used: Per Capita Supply Total (Kg/Year); Per Capita Supply Total (KCal/Day); Per Capita Supply Proteins (g/Day); Per Capita Supply Fat (g/Day). These indicators are available separately for vegetal and animal products. Per Capita Supply Total (Kg/Year) refers \u0026ldquo;to the total amount of the commodity available as human food during the reference period\u0026rdquo;; Per Capita Supply Total (KCal/Day) \u0026ldquo;refers to the total amount of food available for human consumption expressed in kilocalories\u0026rdquo;; Per Capita Supply Proteins (g/Day) \u0026ldquo;refers to the total amount of protein available for human consumption resulting from the multiplication of the quantity of food available\u0026rdquo;; Per Capita Supply Fat (g/Day) \u0026ldquo;refers to the total amount of fat available for human consumption resulting from the multiplication of the quantity of food available\u0026rdquo; (FAOSTAT, 2024).\u003c/p\u003e \u003cp\u003eTwo indicators were used to measure population nutrition: Average Dietary Energy Supply Adequacy and Prevalence of Undernourishment. Average Dietary Energy Supply Adequacy \u0026ldquo;expresses the Dietary Energy Supply as a percentage of the Average Dietary Energy Requirement\u0026rdquo;; Prevalence of Under-nourishment \u0026ldquo;expresses the probability that a randomly selected individual from the population consumes an amount of calories that is insufficient to cover her/his energy requirement for an active and healthy life\u0026rdquo; (FAOSTAT, 2024).\u003c/p\u003e \u003cp\u003eThe data for the last two indicators is based on 3-year averages, so all other indicators were also calculated as 3-year averages. To do this, we used a weighted arithmetic mean, taking into account the population and the number of days in the respective year as weights. As a result twenty, 3-year intervals were obtained, ranging from 2000\u0026ndash;2002 to 2020\u0026ndash;2022. Data used only for Bulgaria.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 \u003cb\u003eMethods\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe particular indicators we have used to measure food losses, per capita supply and nutrition of the population, lead to the concretization of the main hypothesis. In general, these concretizations are as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e\u0026bull; The direct impact of Food Losses on Average Dietary Energy Supply Adequacy is negative \u0026ndash; we expect an increase of Food Losses to lead to a decrease of Average Dietary Energy Supply Adequacy;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e\u0026bull; The direct impact of Food Losses on Prevalence of Undernourishment is positive \u0026ndash; we expect an increase of Food Losses to lead to an increase of Prevalence of Undernourishment;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e\u0026bull; The impact of Food Losses on per capita supplies is negative \u0026ndash; we expect an increase of Food Losses to lead to a decrease of per capita supplies;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e\u0026bull; The impact of per capita supplies on Average Dietary Energy Supply Adequacy is positive \u0026ndash; we expect an increase of per capita supply to lead to an increase of Average Dietary Energy Supply Adequacy;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e\u0026bull; As a result, the indirect impact of Food Losses on Average Dietary Energy Supply Adequacy is negative, as is the direct impact;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e\u0026bull; The impact of per capita supplies on Prevalence of Undernourishment is negative \u0026ndash; we expect an increase of per capita supplies to lead to an increase of Prevalence of Undernourishment;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e\u0026bull; As a result, the indirect impact of Food Losses on Prevalence of Undernourishment is positive, as is the direct impact;\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAll hypotheses are presented on Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Expected positive relations are marked in green, while expected negative relations are marked in red.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSimultaneous equations model (SEM) was used to model these relationships. Following Martin et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e simultaneous equations model is such a model where the dependent variable depends on a set of independent variables, but at the same time some of these independent variables depend on other independent variables.\u003c/p\u003e \u003cp\u003eFollowing Lopez-Espin et al. (L\u0026oacute;pez-Esp\u0026iacute;n, 2012), Chipeva and Boshnakov (2015), Dimitrov (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) and Petkov (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) a system is formed and solved to estimate the coefficients of equations. The equations in the system can be of two types \u0026ndash; balance and regression. Only the coefficients of the regression equations are estimated. The variables in the equations are also of two types \u0026ndash; endogenous and exogenous. Endogenous variables are those that depend on other variables in the model. Exogenous variables are those that do not depend on other variables in the model. The number of endogenous variables may not be greater than the number of equations in the system.\u003c/p\u003e \u003cp\u003eThe system of equations used to estimate the relationships between food losses, per capita supply and nutrition of the population is (Eq.\u0026nbsp;1):\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\left|\\begin{array}{c}{\\widehat{Y}}_{i}={a}_{i}+\\sum\\:_{k=1}^{2}{b}_{ik}{X}_{k}+\\sum\\:_{k=1}^{2}\\sum\\:_{j=1}^{4}{c}_{ikj}{M}_{kj}\\:(i=\\text{1,2})\\\\\\:{\\widehat{M}}_{1j}={a}_{1j}+{d}_{1j}{X}_{1}\\:(j=\\text{1,2},\\text{3,4})\\\\\\:{\\widehat{M}}_{2j}={a}_{2j}+{d}_{2j}{X}_{2}\\:(j=\\text{1,2},\\text{3,4})\\end{array}\\right.$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere endogenous variables are:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{1}\\)\u003c/span\u003e \u003c/span\u003e is Average Dietary Energy Supply Adequacy;\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{2}\\)\u003c/span\u003e \u003c/span\u003e is the Prevalence of Undernourishment;\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{1j}\\)\u003c/span\u003e \u003c/span\u003e are per capita supply vegetal products;\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{2j}\\)\u003c/span\u003e \u003c/span\u003e are per capita supply animal products.\u003c/p\u003e \u003cp\u003eExogenous variables are:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{1}\\)\u003c/span\u003e \u003c/span\u003e are Losses Vegetal Products;\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{2}\\)\u003c/span\u003e \u003c/span\u003e are Losses Animal Products.\u003c/p\u003e \u003cp\u003eThere are no balance equations in the model. All equations are regressions. The endogenous variables in the model are 10 as much as the number of equations in the system. This model allows the estimation of both the direct and indirect impacts of the independent variables on the dependent variables.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eBefore implementing Simultaneous equations model, we performed a time series stationarity test in Gretl using the Augmented Dickey-Fuller (ADF) test, the most widely used Unit Root test.\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAugmented Dickey-Fuller test*\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLevel \u0026ndash; intercept\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFirst differences \u0026ndash; intercept\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSecond differences \u0026ndash; intercept\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTest statistics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{p}\\)\u003c/span\u003e\u003c/span\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTest statistics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{p}\\)\u003c/span\u003e\u003c/span\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTest statistics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{p}\\)\u003c/span\u003e\u003c/span\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLosses Vegetal Products Per Capita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-13.09\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLosses Animal Products Per Capita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-4.13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer Capita Supply Total (Kg/Year) Vegetal Products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-3.37\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer Capita Supply Total (Kg/Year) Animal Products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-6.69\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer Capita Supply Total (KCal/Day) Vegetal Products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-5.60\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer Capita Supply Total (KCal/Day) Animal Products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-5.59\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer Capita Supply Proteins (g/Day) Vegetal Products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-3.11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.043\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer Capita Supply Proteins (g/Day) Animal Products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-3.35\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer Capita Supply Fat (g/Day) Vegetal Products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-5.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer Capita Supply Fat (g/Day) Animal Products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-3.46\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage Dietary Energy Supply Adequacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-4.19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrevalence of Undernourishment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-6.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* Null hypothesis is that time series have unit root, i.e., time series are non-stationary.\u003c/p\u003e\n\u003cp\u003eThe test results indicated:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eThe time series of Per Capita Supply Proteins (g/Day) Vegetal Products is stationary;\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n\u003cp\u003eThe time series of Losses Animal Products Per Capita, Per Capita Supply Total (Kg/Year) Vegetal Products, Per Capita Supply Total (KCal/Day) Animal Products, Per Capita Supply Proteins (g/Day) Animal Products, and Per Capita Supply Fat (g/Day) Animal Products are non-stationary, but the time series of the first differences are stationary. Therefore, the first differences are used in the analysis;\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n\u003cp\u003eThe time series of Average Dietary Energy Supply Adequacy, Prevalence of Undernourishment, Losses Vegetal Products Per Capita, Per Capita Supply Total (Kg/Year) Animal Products, Per Capita Supply Total (KCal/Day) Vegetal Products, and Per Capita Supply Fat (g/Day) Vegetal Products are non-stationary and the time series of the first differences are also non-stationary. However, the time series of the second differences are stationary, so the second differences are used in the analysis.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAfter evaluating the simultaneous equations model in JASP, we obtained specific results (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThere are several conclusions that can be drawn from the relationships that have been discovered:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eLosses Vegetal Products Per Capita do not have a direct or indirect influence on the two dependent variables.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eLosses Animal Products Per Capita have both a direct and an indirect influence on the Average Dietary Energy Supply Adequacy.\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eA direct relationship is observed: greater Losses Animal Products Per Capita lead to a higher Average Dietary Energy Supply Adequacy (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:z=4.060,\\:p=0.000\\)\u003c/span\u003e\u003c/span\u003e), which contradicts initial expectations;\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe indirect relationship is mediated by Per Capita Supply Total (Kg/Year) Animal Products and Per Capita Supply Proteins (g/Day) Animal Products:\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e \n \u003cp\u003ea. Per Capita Supply Total (Kg/Year) Animal Products: the relationship between Losses of Animal Products Per Capita and the mediator variable is positive. The data indicates that higher Losses of Animal Products Per Capita result in a higher Per Capita Supply Total (Kg/Year) Animal Products (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:z=4.937,\\:p=0.001\\)\u003c/span\u003e\u003c/span\u003e). This goes against our initial assumption that greater losses would lead to a smaller per capita supply. On the other hand, the inverse relationship between Per Capita Supply Total (Kg/Year) Animal Products and the Average Dietary Energy Supply Adequacy is negative (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:z=-2.608,\\:p=0.009\\)\u003c/span\u003e\u003c/span\u003e) which also contradicts our initial expectations.\u003c/p\u003e \n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eb. Per Capita Supply Proteins (g/Day) Animal Products: the relationship between Losses of Animal Products Per Capita and the mediator variable is positive. The data indicates that higher Losses of Animal Products Per Capita result in a higher Per Capita Supply Proteins (g/Day) Animal Products (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:z=2.307,\\:p=0.021\\)\u003c/span\u003e\u003c/span\u003e). This goes against our initial assumption that greater losses would lead to a smaller per capita supply. On the other hand, the inverse relationship between Per Capita Supply Proteins (g/Day) Animal Products and the Average Dietary Energy Supply Adequacy is negative (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:z=-9.958,\\:p=0.000\\)\u003c/span\u003e\u003c/span\u003e) which also contradicts our initial expectations.\u003c/p\u003e\n\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eLosses Animal Products Per Capita have only an indirect influence on the Prevalence of Undernourishment. The indirect relationship is mediated by Per Capita Supply Proteins (g/Day) Animal Products: the relationship between Losses of Animal Products Per Capita and the mediator variable is positive. The data indicates that higher Losses of Animal Products Per Capita result in a higher Per Capita Supply Proteins (g/Day) Animal Products (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:z=2.307,\\:p=0.021\\)\u003c/span\u003e\u003c/span\u003e). This goes against our initial assumption that greater losses would lead to a smaller per capita supply. On the other hand, the inverse relationship between Per Capita Supply Proteins (g/Day) Animal Products and the Prevalence of Undernourishment is positive (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:z=2.547,\\:p=0.011\\)\u003c/span\u003e\u003c/span\u003e) which also contradicts our initial expectations.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eSome of the mediator variables have an independent influence on the dependent variables:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003ePer Capita Supply Total (KCal/Day) Vegetal Products affects both Average Dietary Energy Supply Adequacy and Prevalence of Undernourishment:\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e \n \u003cp\u003ea. Greater Per Capita Supply Total (KCal/Day) Vegetal Products leads to greater Average Dietary Energy Supply Adequacy (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:z=5.208,\\:p=0.000\\)\u003c/span\u003e\u003c/span\u003e), which coincides with preliminary expectations;\u003c/p\u003e \n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eb. Larger Per Capita Supply Total (KCal/Day) Vegetal Products leads to lower Prevalence of Undernourishment (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:z=-8.178,\\:p=0.000\\)\u003c/span\u003e\u003c/span\u003e), which also coincides with preliminary expectations;\u003c/p\u003e\n\n\u003cul style=\"list-style-type: circle;\"\u003e\n \u003cli\u003ePer Capita Supply Total (KCal/Day) Animal Products affects both Average Dietary Energy Supply Adequacy and Prevalence of Undernourishment:\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003cp\u003ea. Greater Per Capita Supply Total (KCal/Day) Animal Products leads to greater Average Dietary Energy Supply Adequacy (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:z=15.523,\\:p=0.000\\)\u003c/span\u003e\u003c/span\u003e), which coincides with preliminary expectations;\u003c/p\u003e\n\n\u003cp\u003eb. Larger Per Capita Supply Total (KCal/Day) Animal Products leads to lower Prevalence of Undernourishment (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:z=-8.973,\\:p=0.000\\)\u003c/span\u003e\u003c/span\u003e), which also coincides with preliminary expectations;\u003c/p\u003e\n\n\u003cul style=\"list-style-type: circle;\"\u003e\n \u003cli\u003ePer Capita Supply Proteins (g/Day) Vegetal Products affects both Average Dietary Energy Supply Adequacy and Prevalence of Undernourishment:\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003cp\u003ea. Higher Per Capita Supply Proteins (g/Day) Vegetal Products lead to higher Average Dietary Energy Supply Adequacy (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:z=1.989,\\:p=0.047\\)\u003c/span\u003e\u003c/span\u003e), which coincides with preliminary expectations;\u003c/p\u003e\n\n\u003cp\u003eb. Larger Per Capita Supply Proteins (g/Day) Vegetal Products leads to lower Prevalence of Undernourishment (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:z=-4.877,\\:p=0.000\\)\u003c/span\u003e\u003c/span\u003e), which also coincides with preliminary expectations;\u003c/p\u003e\n\n\u003cul style=\"list-style-type: circle;\"\u003e\n \u003cli\u003ePer Capita Supply Fat (g/Day) Vegetal Products affects both Average Dietary Energy Supply Adequacy and Prevalence of Undernourishment:\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003cp\u003ea. Greater Per Capita Supply Fat (g/Day) Vegetal Products leads to greater Average Dietary Energy Supply Adequacy (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:z=2.286,\\:p=0.022\\)\u003c/span\u003e\u003c/span\u003e), which coincides with preliminary expectations;\u003c/p\u003e\n\n\u003cp\u003eb. Higher Per Capita Supply Fat (g/Day) Vegetal Products leads to higher Prevalence of Undernourishment (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:z=2.870,\\:p=0.004\\)\u003c/span\u003e\u003c/span\u003e), which contradicts the initial expectation that a higher Per Capita Supply of Fat (g/Day) would result in a reduced Prevalence of Undernourishment;\u003c/p\u003e\n\n\u003cul style=\"list-style-type: circle;\"\u003e\n \u003cli\u003ePer Capita Supply Fat (g/Day) Animal Products affects both Average Dietary Energy Supply Adequacy and Prevalence of Undernourishment:\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003cp\u003ea. Greater Per Capita Supply Fat (g/Day) Animal Products leads to lower Average Dietary Energy Supply Adequacy (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:z=-8.289,\\:p=0.000\\)\u003c/span\u003e\u003c/span\u003e), which contradicts preliminary expectations;\u003c/p\u003e\n\n\u003cp\u003eb. Higher Per Capita Supply Fat (g/Day) Animal Products leads to higher Prevalence of Undernourishment (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:z=4.234,\\:p=0.000\\)\u003c/span\u003e\u003c/span\u003e), which contradicts the initial expectation that a higher Per Capita Supply of Fat (g/Day) would result in a reduced Prevalence of Undernourishment;\u003c/p\u003e\n\n\u003cul\u003e\n \u003cli\u003ePer Capita Supply Total (Kg/Year) Vegetal Products does not influence both dependent variables.\u003c/li\u003e\n \u003cli\u003eLosses Vegetal Products Per Capita influence positively Per Capita Supply Total (Kg/Year) Vegetal Products \u0026ndash; higher Losses Vegetal Products Per Capita leads to higher Per Capita Supply Total (Kg/Year) Vegetal Products (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:z=1.972,\\:p=0.049\\)\u003c/span\u003e\u003c/span\u003e). This contradicts preliminary expectations that greater Losses Vegetal Products Per Capita will lead to a lower Per Capita Supply Total (Kg/Year) Vegetal Products.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThere are three possible explanations for why when the Losses Per Capita increases, per capita supply also increases. To have a balance, Domestic supply\u0026thinsp;=\u0026thinsp;Domestic Utilization. In turn, Domestic Utilization\u0026thinsp;=\u0026thinsp;Food\u0026thinsp;+\u0026thinsp;Processing\u0026thinsp;+\u0026thinsp;Feed\u0026thinsp;+\u0026thinsp;Seed\u0026thinsp;+\u0026thinsp;Losses\u0026thinsp;+\u0026thinsp;Other uses (non-food)\u0026thinsp;+\u0026thinsp;Tourist consumption\u0026thinsp;+\u0026thinsp;Residuals. That means that if Domestic Utilization is constant then increasing of Losses will reduce all other collectibles. Therefore, increasing of Losses per capita will reduce all other collectibles per capita. However:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ePer capita supply is obtained as Food is divided by the total population in the corresponding year, and the population of Bulgaria decreases throughout the period, i.e., per capita supply can increase only because of the decreasing denominator.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFood is one of these other collectibles, but it may also increase due to the decrease of the sum of all other collectibles.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDomestic Utilization is not constant over time. It is possible that Domestic Utilization in the corresponding year is so large that both Losses and Food are large.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eSo, contrary to preliminary expectations, it is possible that Losses is increasing, and at the same time the per capita supply is also increasing.\u003c/p\u003e \u003cp\u003eA possible explanation for negative relationships between per capita supply and Average Dietary Energy Supply Adequacy and for positive relationships between per capita supply and Prevalence of Undernourishment is related not to the quantity, but to the quality of proteins and fats. It is possible that a higher amount of low-quality proteins and fats per capita does not lead to an increase in Average Dietary Energy Supply Adequacy and to a decrease in the Prevalence of Undernourishment, as does a lower amount of proteins and fats, but with higher quality.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe study concludes that the loss of vegetal products per capita does not directly or indirectly affect the nutrition of the Bulgarian population. However, the loss of animal products per capita does impact the Average Dietary Energy Supply Adequacy, both directly and indirectly, and Prevalence of Undernourishment, only indirectly. There is a positive direct relationship, meaning that greater losses of animal products per capita lead to a higher Average Dietary Energy Supply Adequacy. The indirect relationship is mediated by the Per Capita Supply Total (Kg/Year) Animal Products and Per Capita Supply Proteins (g/Day) Animal Products. The relationships between Losses Animal Products Per Capita and the mediator variables are positive \u0026ndash; greater losses per capita lead to greater per capita supply. The relationships between per capita supply and Average Dietary Energy Supply Adequacy are negative, and the relationship between per capita supply and Prevalence of Undernourishment is positive.\u003c/p\u003e \u003cp\u003eSome of the mediation variables independently influence the dependent variables, as we have already discussed. Only one mediator variables do not influence the dependent variables.\u003c/p\u003e \u003cp\u003eMost Bulgarian companies and households do not address FLW issues for many reasons, including cultural, economic, and political. Our study will help to increase the engagement of stakeholders and business enterprises in the actual implementation. It will also contribute to improving the efficiency of production, the sustainability of food security, and the limitation of food losses, which is a key priority of European society.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis work was supported by the Bulgarian National Science Fund [grant number КП-06-КОСТ/15, 2024]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbiad, M.G., Meho, L.I., 2018. Food loss and food waste research in the Arab world: A systematic review. Food Security, 10(3),1-12, https://doi.org/10.1007/s12571-018-0782-7.\u003c/li\u003e\n\u003cli\u003eChalak, A., Abou-Daher, C., Chaaban, J., Abiad, M.G., 2016. The global economic and regulatory determinants of household food waste generation: A cross-country analysis. Waste Management, 48, 418\u0026ndash;422, https://doi.org/10.1016/j.wasman.2015.11.040.\u003c/li\u003e\n\u003cli\u003eChipeva, S., Boshnakov. V., 2015.Introduction to Econometrics. UNWE Publishing Complex, Sofia, in Bulgarian\u003c/li\u003e\n\u003cli\u003eDimitrov, A., 1995. Introduction to Econometrics. ABAGAR, Veliko Tarnovo, in Bulgarian\u003c/li\u003e\n\u003cli\u003eEuropean Commission: Commission Regulation of the European Parliament and of the Council of 28 January 2002. Laying down the General Principles and Requirements of Food Law, Establishing the European Food Safety Authority and Laying down Procedures in Matters of Food Safety, 178/2002/EC; Official Journal, L 31, 1.2.2002; European Commission: Bruxelles, Belgium.\u003c/li\u003e\n\u003cli\u003eFAO., 2015. Global Strategy, Improving Methods for Estimating Post-Harvest Losses: A Review of Methods for Estimating Grain Post-Harvest Losses. Working Paper n. 2. \u003c/li\u003e\n\u003cli\u003eFAO., 2014. Definitional Framework of food loss. SAVE FOOD: Global Initiative on Food Loss and Waste Reduction.Working paper. Food and Agriculture Organization of the United Nations: Rome, Italy. http://www.fao.org/fileadmin/user_upload/save-food/PDF/FLW_Definition_and_Scope_2014.pdf (accessed 30 April, 2024).\u003c/li\u003e\n\u003cli\u003eFAO., 2019. The State of Food and Agriculture - Moving forward on Food Loss and Waste Reduction. FAO: Rome, Italy. \u003c/li\u003e\n\u003cli\u003eFAO., 2015. Global Strategy, Improving Methods for Estimating Post-Harvest Losses: A Review of Methods for Estimating Grain Post-Harvest Losses. Working Paper n. 2. \u003c/li\u003e\n\u003cli\u003eFAOSTAT Data page, https://www.fao.org/faostat/en/#data/FBS, (accessed 26 July 2024).\u003c/li\u003e\n\u003cli\u003eFAOSTAT Data page, https://www.fao.org/faostat/en/#data/FBSH, (accessed 26 July 2024).\u003c/li\u003e\n\u003cli\u003eFAOSTAT Data page, https://www.fao.org/faostat/en/#data/FS, (accessed 26 July 2024)\u003c/li\u003e\n\u003cli\u003eGustavsson, J., Cederberg, C., Sonesson, U., van Otterdijk, R., Meybeck, A., 2011. Global Food Losses and Food Waste, FAO: Rome, Italy.\u003c/li\u003e\n\u003cli\u003eHLPE., 2019. Food Losses and Waste in the Context of Sustainable Food Systems: A Report by the High-Level Panel of Experts on Food Security and Nutrition of the Committee on World Food Security. Rome, Italy. \u003c/li\u003e\n\u003cli\u003eL\u0026oacute;pez-Esp\u0026iacute;n, J.J., Vidal, A.M. Gim\u0026eacute;nez, D., 2012.Two-stage least squares and indirect least squares algorithms for simultaneous equations models.Journal of Computational and Applied Mathematics 236, 3676\u0026ndash;3684. https://doi.org/10.1016/j.cam.2011.07.005.\u003c/li\u003e\n\u003cli\u003eLuo, N., Olsen, T.L., Liu, Y., 2021. A Conceptual Framework to Analyze Food Loss and Waste within Food Supply Chains: An Operations Management Perspective. Sustainability. 13(2), 927. https://doi.org/10.3390/su13020927.\u003c/li\u003e\n\u003cli\u003eMartin, V., Hurn, S., Harris, D., 2013.Econometric Modelling with Time Series. Cambridge University Press. https://doi.org/10.1017/CBO9781139043205.\u003c/li\u003e\n\u003cli\u003ePatel, S.; Dora, M.; Hahladakis, J.N.; Iacovidou, E., 2021. Opportunities, challenges and trade-offs with decreasing avoidable food waste in the UK. Waste Manag. Res., 39, 473\u0026ndash;488. https://doi.org/10.1177/0734242X20983427.\u003c/li\u003e\n\u003cli\u003ePetkov, P., 2017. Fundamentals of econometric modeling. Tsenov Academic Publishing House, Svishtov, pp. 30, ISBN: 978-954-23-1504-9, in Bulgarian\u003c/li\u003e\n\u003cli\u003eFernandez-Zamudio, M.A.; Barco, H.; Schneider, F., 2020. Direct measurement of mass and economic harvest and post-harvest losses in spanish persimmon primary production. Agriculture 10, 581. https://doi.org/10.3390/agriculture10120581.\u003c/li\u003e\n\u003cli\u003eV\u0026aacute;zquez-Rowe, I.; Laso, J.; Margallo, M.; Garcia-Herrero, I.; Hoehn, D.; Amo-Seti\u0026eacute;n, F.; Bala, A.; Abjas, R.; Sarabia, C.; Dura, M.J.; et al., 2020. Food loss and waste metrics: A proposed nutritional cost footprint linking linear programming and life cycle assessment. Int. J. Life 25, 1197\u0026ndash;1209. https://doi.org/10.1007/s11367-019-01655-1.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"5882ad3a-445b-457d-8cfc-2d8881374a23","identifier":"10.13039/501100003336","name":"Bulgarian National Science Fund","awardNumber":"КП 06-КОСТ/15","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Agricultural University Plovdiv","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Food losses, FAO, SEM, Mediator variables, Population nutrition","lastPublishedDoi":"10.21203/rs.3.rs-6311089/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6311089/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eReducing food loss and waste is a key part of Sustainable Development Goal (SDG) 12 on Responsible Consumption and Production. Specifically, target 12.3 aims to \"halve per capita global food waste at the retail and consumer levels by 2030, and reduce food losses along production and supply chains, including post-harvest losses.” Food loss refers to any food removed from the supply chain between maturity and sale, including inedible parts as these are integral to the marketed product. Our main hypothesis is that increased food loss results in a decreased food supply per person, subsequently reducing the ability to feed the population. We evaluated population nutrition using two indicators: average dietary energy supply adequacy and prevalence of undernourishment. The data comes from the Food and Agriculture Organization (FAO). To analyze these relationships and process the data, we used a simultaneous equations model (SEM). After evaluating the SEM, we obtained the following results: losses of vegetal products do not have a direct or indirect influence on the two dependent variables. However, losses of animal products directly and indirectly affect the prevalence of undernourishment. In conclusion, there is a positive relationship between the loss of animal products and mediator variables. Greater losses lead to greater per capita supply. Additionally, there is a negative relationship between per capita supply and the prevalence of undernourishment. A larger per capita supply leads to a smaller prevalence of undernourishment.\u003c/p\u003e","manuscriptTitle":"Impacts of food losses on the nutrition of the population in Bulgaria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-27 05:08:37","doi":"10.21203/rs.3.rs-6311089/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5784d5c5-a3ee-46dd-8324-b8b23c675948","owner":[],"postedDate":"March 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":46237695,"name":"Agricultural Economics \u0026 Policy"},{"id":46237696,"name":"Agroecology"},{"id":46237697,"name":"Environmental Economics"}],"tags":[],"updatedAt":"2025-03-27T05:08:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-27 05:08:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6311089","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6311089","identity":"rs-6311089","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}