Food Wastage Footprint, Food Security, Environment and Economic Growth Nexus in Developing Countries

Abstract

FAO estimates that each year, approximately one-third of all food produced for human consumption in the world is lost or wasted. This food wastage represents a missed opportunity to improve global food security, but also to mitigate environmental impacts and resource use from food chains. This chapter attempts to identify the impact of food wastage, food security, and environmental quality on economic growth of developing countries from 1990 to 2021. Method of Moments Quantile Regression (MMQR) has been used to estimate the results. The findings indicate that food wastage, food access, and environment have a negative effect while food availability and food security have a positive effect on economic growth. To the best of our knowledge, no study has yet analyzed the impacts of food wastage on economic growth from an environmental and food security perspective. This study provides a worldwide account of the environmental footprint of food wastage along the food security, focusing on impacts on environmental quality, as well as an economic quantification based on economic growth.

1. Introduction

Generally, food waste is considered as all the foodstuffs that have been discarded from the chain of food supply but still are fit for human consumption and perfectly edible. These goods are eventually eliminated and disposed of for some esthetic or economic reasons or may be due to the closeness to the “use by” or “best before” date. As a result, it creates negative externalities from the perspective of the environment and can raise economic costs along with revenue loss for the industries [1]. Food waste may occur at any stage such as processing, handling, packing, storing, or transportation. Many people are involved in different stages of production and consumption such as farmers, distributors, consumers, retailers, and wholesalers. Therefore, all these entities can be responsible for reducing the wastage of food due to their negligence or any other reason(s). Every year billions of tons of food usually are wasted globally [2]. In 2014, approximately, 6.68 billion tons of food were wasted; in 2015, 6.72 billion tons; and 6.92, 7.01, and 6.86 billion tons were wasted from 2016 to 2018 respectively [3]. The category of food wastage includes cereals, fruits, vegetables, spices, oilseeds, pulses, milk, roots as well as meat products [4].

Food waste asserts a negative effect on food security [5, 6]. In developing nations, a significant quantity of food is usually discarded by industries and households, and at a local level, it has been observed that better management and reduction of food wastage can lessen the food security of businesses and households [7, 8, 9, 10]. Food waste can reduce the availability of food and therefore, have an adverse effect on the environment, consumption chain, and the resources used for the production of food. About one-quarter of water, land, and fertilizer for the production of the crop, even though environmental and resource constraints are likely to limit the production of food universally [11]. In fact, at all stages of the food supply chain, the impact of food waste on the environment can occur, but the extent of its effect at different stages of the supply chain can vary according to the economic development and environmental dimensions of a country. In industrialized nations, most of the food is wasted at the end of the supply chain process, and it brings large food waste and environmental degradation. In developing nations, the reduction measures that target farm losses can be most operative in decreasing the environmental tracks of food waste. The trade-off between environmental sustainability and food security is likely to be intensified in future because of many universal transactions, dietary changes, wealthier and large populations, rising interdependence of energy and food as well as the competition between climate change, food, and bioenergy over the resources. Ensuring the accessibility of safe and high-quality food around the globe, specifically in face of climate change, needs adequate facilities of the cold chain [12]. According to an estimation by the International Institute of Refrigeration 2009, if developing nations attained the same capacities of cold chain same as developed nations, approximately 200 million tons of food can be saved annually.

2. Concepts and definitions

The concept and definitions of various terms are as follows:

2.1 Food waste

What is food waste? How can we define it? Although there are many definitions in the literature, no common definition of food waste exists. The existing definitions vary in different aspects such as: Which type of food would be considered? Only edible parts would be included? Food diverted to other usage can be considered food waste? How the food loss differs from food wastage?

Food wastage is a general term that encapsulates both food loss and food waste. Although the difference between food loss and food waste is relevant conceptually, it is beneficial from a policy point of view.¹ Food waste is a result of purchasing decisions by the consumers, or the decisions by food service providers and retailers that can affect the behavior of consumers. Various definitions of food waste can be stated as:

Surplus food that cannot be used for feeding humans or animals, or surplus food that would not be recovered or recycled in any form and is usually disposed of [13].

Food waste is a reduction in the quality or quantity of food that results from the actions as well as decisions by the consumers, retailers, or food service providers. Quantitative food waste is a physical reduction in the masses of food that results from the decisions taken by consumers, retailers, and food services while qualitative food waste is a decrease in food characteristics that reduced the food value in terms of its use.

2.2 Food security

Food security exists at all times, when all people, have economic, social, and physical access to safe, sufficient, and nutritious food to meet their nutritional requirements and preferences of food for a healthy and active life. This concept pinpoints four basic aspects of food security, i.e., food stability, food access, food availability, and food utilization [14, 15].

2.3 Availability

Availability reports whether or not the food is potentially, actually, or physically present and contains the features of food reserves, production, transportation, markets, and wild foods.

2.4 Access

If food is potentially, actually, or physically present, the next question is whether individuals or households have access to the food.

2.5 Utilization

The next issue after assuring that food is available and the households have suitable access to it, is whether or not the households are maximizing their intakes of sufficient energy and nutrition. Sufficient nutrients and energy by the individuals are a result of better feeding, care, dietary diversity as well as intra-household food distributions.

2.6 Stability

If all the above conditions are met, then stability is a situation in which the entire system is steady, hence ensuring that at all times households are food secure. Economic, social, climatic, and political factors can be the sources of instability.

2.7 Environmental sustainability

Environmental sustainability is associated with protecting the integrity of the ecosystem along with confirming that the activities of society remain within the carrying capacity of the ecological system of the earth [16, 17, 18]. In this concept, key metrics involved freshwater use, change in climate, change in land use, and the flows of nutrients via the ecosystem. The universal system of food uses a huge amount of resources to satisfy the demand of consumers for food, and the major contributors that influence the environment are biodiversity loss, phosphorus cycles, climate change, and disrupted nitrogen [19].

3. Review of assorted studies

This section reviews the various studies on food waste, economic growth, food security, and environmental quality.

3.1 Studies based on food waste and economic growth

In the twenty-first century, the management of food waste is one of the universal challenges due to its antagonistic effects on the economy and environment. A study investigated the effect of food wastage on the economic growth of 165 nations by using OLS and GLM techniques and found out the negative effect of food wastage as well as poverty on GDP growth [4]. Similarly, in Japan, it was observed that technology has needed in dropping and reutilizing food waste while studying the interrelationship between sustainable community and food waste [20]. A study done on Indonesia over the period 2000–2019 indicated that the combination of food loss reduction and yield improvement can raise the GDP of Indonesia by 0.2% approximately. In a comparison of increasing productivity and decreasing food loss, they prefer the reduction of food loss for increasing GDP [21]. Contrary to these studies, the social impact along with the economic impact of food wastage in the United States and South Korea was analyzed and concluded that in the United States, if a national strategy would not be adopted to remedy the waste, the food waste endures being a major issue [22]. However, South Korea embarked on an essential paradigm turning food wastage into an influential economic driver and leading to a fast decile in the waste of food [23]. Another study was carried on Sri Lanka’s hotel industry to examine the influence of food waste on the operations of sustainable business in 2009. This study indicated that food waste is common in the industry of hospitality since it usually has a great focus on the satisfaction of customers through the provision of high-quality foods, which in turn can depress their businesses [9].

3.2 Studies based on food waste and food security

Food security is a universal challenge. Current investments in genetic engineering and technologies of agriculture, which frequently focus on increasing productivity through crop intensification, expanded cropland, and higher yields, have contributed to the enhancement of crop efficiency and will contribute to meet the challenge of food production. Food wastage has a negative and significant influence on food security by considering urban women’s role in the management of households in Tehran [24]. Two factors that influence food waste are the number of family members and the education of farmers. So, it has been suggested that government would focus on farmers’ requirements to support food security [25]. Additionally, the significance of food waste and food loss inhibition as complementary solutions have been reported as a big challenge of environmental sustainability and food security and concluded that the reduction of food loss and food waste holds excessive potential for boosting food security [26]. Along with food security, the effect of reducing food loss and waste on greenhouse gas emissions, trade, and land use is also studied. Authors have concluded that there is substantial potential scope for reducing food waste and food loss on food security as well as greenhouse gas emissions [27]. On the other hand, it was also observed that lessening food waste could not only decrease the depletion and degradation burden of natural resources but can also raise food security by increasing the production of food by 60% [28].

3.3 Studies based on food wastage and environment

The environmental effect of food consumption and production is usually aggravated when food is wasted instead of consumed, and the environmental effect of food wastage covers all the emissions that have derived from various steps of the food supply chain. Many studies have evaluated the effect of food waste on the environment in different aspects. The effects on the environment due to food waste reduction were observed by using a systematic dynamic approach, and it was concluded that through a 1% reduction in the scenario of food waste, we can control the pollution emissions by 0.82% and save the energy demand by 0.96% [29]. Some studies also support the previous piece of literature that concluded that animal-based food wastage has a great environmental footprint in contrast to food wastage based on plants. This was analyzed during their assessment of the environmental influence of food wastage in Turkey [30]. The impact of global food waste and food surplus to tackle environmental and economic sustainability on seven nations, six organizations, and six continents was observed, which provides a framework to identify the crisis of environmental and economic sustainability. The authors find out that the selected organizations and regions were relevant to the fundamental problems of food waste, food surplus economic sustainability, and environmental sustainability. Further, through the globalization trend, this study pinpointed that the reduction of food wastage may help out for regulating environmental sustainability [7]. Alternative to these empirical studies, another study probed the environmental effects of technologies for food waste treatment in the United States. Findings revealed that overall burdens on the environment may be decreased through the source of separating food waste technologies [31]. Moreover, the authors recognized that climate change, resource depletion, environmental acidification and eutrophication, and ozone depletion are also influenced by food waste. Additionally, the environmental influence of food wastage in Europe was also analyzed. Results have confirmed that most of the environmental effects have been derived from the initial production step of the chain. Approximately three-quarters of food wasted is related to effects of the global warming initiates by greenhouse emissions during the steps of production. Emissions by the processing of food activities contribute 6%, distribution and retail 7%, consumption of food 8%, and food disposal 6% to the food wastage associated impacts [32]. It has also been exposed that the wastewater used for farming as well as irrigation of adjacent areas and the water drained from the kitchens of the restaurants were polluted considerably and were not suitable for irrigation, aquatic organisms, and agricultural purposes in India. This study has been carried out on five sampling points of canteens, restaurants, confectionaries, and famous hotels from January 2017 to December 2017 [33].

4. Model, data, and methodology

The general quantile conditional function for quantile $\tau$ is given as:

$$Q_{{\mathit{\text{GDPG}}}_{\mathit{it}}}\left(\tau |{\gamma }_i,{\delta }_t,X_{i,t}\right)=\begin{array}{l}{\gamma }_i+{\delta }_t+{\alpha }_{1,\tau }{\mathit{LAB}}_{i,t}+{\alpha }_{2,\tau }{\mathit{CAP}}_{\mathit{it}}+{\alpha }_{3,\tau }{\mathit{SSE}}_{i,t}\\ +{\alpha }_{4,\tau }\mathit{\text{FOOD}}\_W_{i,t}+{\alpha }_{5,\tau }\mathit{\text{FOOD}}\_{\mathit{AC}}_{i,t}+{\alpha }_{6,\tau }\mathit{\text{FOOD}}\_{\mathit{AV}}_{i,t}\\ +{\alpha }_{7,\tau }\mathit{\text{FOOD}}\_{\mathit{ST}}_{i,t}+{\alpha }_{8,\tau }\mathit{\text{FOOD}}\_{\mathit{UT}}_{i,t}+{\alpha }_{9,\tau }\mathit{CO}{2}_{i,t}+{\mu }_{\tau ,i,t}\end{array}$$E1

where $\tau$ show quantiles such as 25th, 50th 75th and 90th $i=1,\dots \dots \dots N$ is for cross-sections, and $t$ for the time-period starting from $t=1,\dots \dots T$, ${\mathit{\text{GDPG}}}_{\mathit{it}}$ is the dependent variable. Equations for each quantile are given as:

$$Q_{0.25}\left({\mathit{\text{GDPG}}}_{\mathit{it}}\right)=\begin{array}{l}{\gamma }_{0.25}+{\alpha }_{\mathrm{1,0.25}}{\mathit{LAB}}_{i,t}+{\alpha }_{\mathrm{2,0.25}}{\mathit{CAP}}_{\mathit{it}}+{\alpha }_{\mathrm{3,0.25}}{\mathit{SSE}}_{i,t}\\ +{\alpha }_{\mathrm{4,0.25}}\mathit{\text{FOOD}}\_W_{i,t}+{\alpha }_{\mathrm{5,0.25}}\mathit{\text{FOOD}}\_{\mathit{AC}}_{i,t}+{\alpha }_{\mathrm{6,0.25}}\mathit{\text{FOOD}}\_{\mathit{AV}}_{i,t}\\ +{\alpha }_{\mathrm{7,0.25}}\mathit{\text{FOOD}}\_{\mathit{ST}}_{i,t}+{\alpha }_{\mathrm{8,0.25}}\mathit{\text{FOOD}}\_{\mathit{UT}}_{i,t}+{\alpha }_{\mathrm{9,0.25}}C02+{\mu }_{0.25,i,t}\end{array}$$E2

$$Q_{0.50}\left({\mathit{\text{GDPG}}}_{\mathit{it}}\right)=\begin{array}{l}{\gamma }_{0.50}+{\alpha }_{\mathrm{1,0.50}}{\mathit{LAB}}_{i,t}+{\alpha }_{\mathrm{2,0.50}}{\mathit{CAP}}_{\mathit{it}}+{\alpha }_{\mathrm{3,0.50}}{\mathit{SSE}}_{i,t}\\ +{\alpha }_{\mathrm{4,0.50}}\mathit{\text{FOOD}}\_W_{i,t}+{\alpha }_{\mathrm{5,0.50}}\mathit{\text{FOOD}}\_{\mathit{AC}}_{i,t}+{\alpha }_{\mathrm{6,0.50}}\mathit{\text{FOOD}}\_{\mathit{AV}}_{i,t}\\ +{\alpha }_{\mathrm{7,0.50}}\mathit{\text{FOOD}}\_{\mathit{ST}}_{i,t}+{\alpha }_{\mathrm{8,0.50}}\mathit{\text{FOOD}}\_{\mathit{UT}}_{i,t}+{\alpha }_{\mathrm{9,0.50}}C02+{\mu }_{0.50,i,t}\end{array}$$E3

$$Q_{0.75}\left({\mathit{\text{GDPG}}}_{\mathit{it}}\right)=\begin{array}{l}{\gamma }_{0.75}+{\alpha }_{\mathrm{1,0.75}}{\mathit{LAB}}_{i,t}+{\alpha }_{\mathrm{2,0.75}}{\mathit{CAP}}_{\mathit{it}}+{\alpha }_{\mathrm{3,0.75}}{\mathit{SSE}}_{i,t}\\ +{\alpha }_{\mathrm{4,0.75}}\mathit{\text{FOOD}}\_W_{i,t}+{\alpha }_{\mathrm{5,0.75}}\mathit{\text{FOOD}}\_{\mathit{AC}}_{i,t}+{\alpha }_{\mathrm{6,0.75}}\mathit{\text{FOOD}}\_{\mathit{AV}}_{i,t}\\ +{\alpha }_{\mathrm{7,0.75}}\mathit{\text{FOOD}}\_{\mathit{ST}}_{i,t}+{\alpha }_{\mathrm{8,0.75}}\mathit{\text{FOOD}}\_{\mathit{UT}}_{i,t}+{\alpha }_{\mathrm{9,0.75}}C02+{\mu }_{0.75,i,t}\end{array}$$E4

$$Q_{0.90}\left({\mathit{\text{GDPG}}}_{\mathit{it}}\right)=\begin{array}{l}{\gamma }_{0.90}+{\alpha }_{\mathrm{1,0.90}}{\mathit{LAB}}_{i,t}+{\alpha }_{\mathrm{2,0.90}}{\mathit{CAP}}_{\mathit{it}}+{\alpha }_{\mathrm{3,0.90}}{\mathit{SSE}}_{i,t}\\ +{\alpha }_{\mathrm{4,0.90}}\mathit{\text{FOOD}}\_W_{i,t}+{\alpha }_{\mathrm{5,0.90}}\mathit{\text{FOOD}}\_{\mathit{AC}}_{i,t}+{\alpha }_{\mathrm{6,0.90}}\mathit{\text{FOOD}}\_{\mathit{AV}}_{i,t}\\ +{\alpha }_{\mathrm{7,0.90}}\mathit{\text{FOOD}}\_{\mathit{ST}}_{i,t}+{\alpha }_{\mathrm{8,0.90}}\mathit{\text{FOOD}}\_{\mathit{UT}}_{i,t}+{\alpha }_{\mathrm{9,0.90}}C02+{\mu }_{0.90i,t}\end{array}$$E5

Where:

LAB = Labor force growth rate(Annual growth rate, Percentage).

CAP = Gross Fixed Capital Formation (Annual growth rate, Percentage).

Food W = Waste (Solid Food Waste CH4 emission Gigagrams.

Food AC = Food Access (Prevalence of Undernourishment).

Food AV = Food Availability (Average Protein Supply).

Food ST = Food Stability (Per Capita Food Production Variability).

Food UT = Food Utilization (People using at least basic drinking water services).

CO2 = Carbon Dioxide Emission (Environmental Quality).

5. Data and methodology

To achieve the objectives of this study, we have obtained panel data for low-income, lower-middle-income, and upper-middle-income countries from World Development Indicators (WDI) and Food and Agriculture Organization (FAO) over the period 1990–2021. We have used the Methods of Moments Quantile Regression (MMQR) technique to probe the effect of food wastage, food security, and environment on the economic growth of developing countries.

6. Results and discussions

This section explains the results in detail. It includes summary statistics of key variables.

6.1 Descriptive statistics of key variables

Table 1 demonstrates the descriptive statistics of key variables for low-income, middle-income, and upper-middle-income countries. It can be observed from Table 1 that in lower-income countries, all variables exhibit positive skewness except GDPG while in lower-middle income and upper-middle income countries, variable i.e., GDPG and FOOD-UT show negative skewness, and all other variables indicate positive skewness. FOOD-AV, SSE, and FOOD-AV have high tails due to their very low kurtosis.

Var	Mean	Median	Max	Min	Std. Dev.	Skewness	Kurtosis	JB	Prob.
Low-income countries
GDPG	4.66	4.92	14.98	−6.12	4.35	−0.07	2.69	0.61	0.74
LAB	5,134,584	4,455,550	23,187,587	538,161	3,662,287	2.66	13.29	715.7	0.00
CAP	14.50	6.83	507.95	−36.12	49.33	7.98	79.65	32,689	0.00
SSE	17.10	12.52	51.03	5.29	11.16	1.27	3.89	38.67	0.00
FOOD_W	41.42	35.87	136.54	0.00	35.54	0.99	3.43	22.09	0.00
FOOD_AC	31.13	28.95	60.60	5.30	13.28	0.33	2.49	3.63	0.16
FOOD_AV	56.97	53.00	81.00	30.00	13.13	0.10	2.02	5.37	0.07
FOOD_ST	7.53	7.45	15.80	0.90	3.54	0.31	2.50	3.44	0.18
FOOD_UT	51.24	47.95	88.30	27.10	12.52	0.63	2.90	8.46	0.01
CO2	0.10	0.07	0.36	0.00	0.08	1.70	5.27	89.21	0.00
Lower middle-income countries
GDPG	4.10	4.42	15.33	−21.30	3.92	−1.71	11.37	1078.8	0.00
LAB	34,852,297	8,264,429	451,000,000	4.35	82,433,049	3.92	17.95	3763.7	0.00
CAP	7.235191	7.12	9.91	−14.51	556,642	17.72	315.00	13,023	0.00
SSE	49.47	46.41	92.88	5.28	21.19	0.09	2.08	11.73	0.00
FOOD_W	401.97	76.46	3200.32	0.00	762.13	2.51	8.19	687.47	0.00
FOOD_AC	20.26	18.80	54.40	5.10	10.78	0.49	2.57	15.15	0.00
FOOD_AV	62.19	59.00	94.00	44.00	11.75	0.85	2.66	40.05	0.00
FOOD_ST	6.99	5.60	38.90	1.10	5.24	2.26	10.26	966.87	0.00
FOOD_UT	72.13	76.00	95.60	29.10	15.18	−0.67	2.63	25.23	0.00
CO2	1.09	0.70	45.33	0.07	2.76	13.23	208.79	56,862	0.00
Upper middle-income countries
GDPG	3.92	4.04	14.35	−19.70	3.95	−1.43	11.14	673.10	0.00
LAB	6,924,844	2,708,509	83,821,485	60,325	11,442,809	3.64	20.67	3299.8	0.00
CAP	7.27	4.81	111.40	−38.68	18.18	1.19	8.13	289.56	0.00
SSE	66.18	66.07	109.99	21.98	18.26	−0.28	2.64	3.92	0.14
FOOD_W	160.63	45.33	1979.36	1.36	314.92	3.52	16.04	1985.3	0.00
FOOD_AC	15.88	14.40	39.90	5.00	9.46	0.74	2.57	21.43	0.00
FOOD_AV	67.36	67.00	88.00	47.00	9.13	0.03	2.55	1.86	0.40
FOOD_ST	14.93	11.30	60.20	2.10	10.75	1.38	4.93	102.79	0.00
FOOD_UT	84.73	86.60	99.60	53.00	10.19	−0.98	3.63	38.09	0.00
CO2	1.77	1.50	6.79	0.43	1.06	1.65	6.57	214.47	0.00

Table 1.

Summary statistics of key variables.

6.2 Diagnostic tests

Our estimation strategy follows three steps. Firstly, we have tested the slope homogeneity/heterogeneity test. In this regard, two tests are common: (i) the Delta test presented by Pesaran and Yamagata, 2008; (ii) the HAC Robust Delta test or Delta Adjusted test by Blomquist and Westerlund, 2013.

Table 2 presents the results of the homogeneity/heterogeneity test for low-income, middle-income, and upper middle-income countries. Findings indicate that the slope is heterogeneous for all countries in both tests.

Tests	Low income	Lower middle income	Upper middle income
Homogeneity/heterogeneity test
Status	Coefficients (p-value)
$∆$; Delta Test (Pesaran and Yamagata, 2008)	25.598***; (0.000)	9.554***; (0.000)	13.014***; (0.000)
HAC Robust Delta Test (Blomquist and Westerlund, 2013); ${\stackrel{\sim }{∆}}^{\mathit{\text{Adjusted}}}$	87.413***; (0.000)	42.435***; (0.000)	19.539***; (0.000)

Table 2.

Diagnostic tests.

In the residuals, models of panel data can also indicate the cross-section dependence that may occur due to the spatial spillover effects, common shocks, or general error interdependence. It is assumed that there is cross-sectional independence both in causality and cointegration analysis. So, in the second step, we tested the dependence/independence of cross sections. For this purpose, we have applied the Pesaran CD Test.

Results of dependence/independence of cross sections through the Pesaran CD test in Table 3 encapsulate that error terms of cross sections are corelated. It means that cross sections are not interdependent so there exists cross-section dependence in panel data of all countries.

Variables	Pesaran${\mathit{CD}}_{\mathit{\text{Statistics}}}$ $\left(\mathrm{p}-\text{value}\right)$
	Low income	Lower middle income	Upper middle income
GDPG	2.235***; (0.000)	1.803***; (0.000)	14.820***; (0.000)
LAB	9.00.8***; (0.000)	9.114***; (0.000)	4.253***; (0.000)
CAP	7.153***; (0.000)	4.825***; (0.000)	32.222***; (0.000)
SSE	8.721***; (0.000)	11.259***; (0.000)	12.893***; (0.000)
FOOD_W	3.322***; (0.000)	26.005***; (0.000)	16.803***; (0.000)
FOOD_AC	3.146***; (0.000)	29.193***; (0.000)	11.406***; (0.000)
FOOD_AV	4.850***; (0.000)	12.268***; (0.000)	92.823***; (0.000)
FOOD_ST	6.836***; (0.000)	19.223***; (0.000)	13.234***; (0.000)
FOOD_UT	8.126***; (0.000)	16.589***; (0.000)	18.685***; (0.000)
CO2	9.493***; (0.000)	14.286***; (0.000)	19.814***; (0.000)

Table 3.

Cross-section dependence/independence.

Note: ***, **, and * show significance level at 1%, 5%, and 10%

In the third step, we have adopted another test known as the Westerlund test that has more explanatory power in cross-section dependence, and it assumes two hypotheses: (i) no cointegration means no long-run relation exists; (ii) cointegration exists (long-run relation). Westerlund test is divided into two dimensions, namely group dimension and panel dimension.

Table 4 shows the results of the Westerlund test, which elaborate that a long-run relationship exists as we have rejected the null hypothesis of no-cointegration.

Statistic	Low income	Lower middle income	Upper middle income
$G_t$	−4.925***; (0.000)	−8.877***; (0.000)	−2.221***; (0.000)
$G_a$	−1.169**; (0.032)	−4.986**; (0.032)	−6.414**; (0.032)
$P_t$	−9.689***; (0.000)	−16.340***; (0.000)	−9.695***; (0.000)
$P_a$	−3.426***; (0.001)	−2.298***; (0.001)	−3.199***; (0.001)

Table 4.

Cointegration check Westerlund test.

Note: ***, **, and * show significance level at 1%, 5%, and 10%

6.3 MMQR regression results

Table 5 explains the estimated results for low-income, lower-middle, and upper-middle-income countries through four quantiles, i.e., 25th, 50th, 75th, and 90th. We elaborate on the findings with respect to the income levels of developing countries such as low-income, lower middle, and upper middle-income countries. Variables such as labor force, gross fixed capital formation, and secondary school enrollment have a positive and significant relationship with the economic growth of all developing countries but have a different magnitude in all quartiles such as: in low-income countries, labor force has high magnitude at 50th quartile, gross fixed capital formation at 50th, and SSE at 75th quartiles. In lower middle-income countries, fluctuations are high for labor force and gross fixed capital formation and have approximately the same effect of secondary school enrollment at all quartiles, and the same is true for upper middle-income countries. The positive effect of labor force and gross fixed capital formation inserts that the increasing labor force growth rate and fixed capital formation can regulate the production possibilities, which in turn raises economic growth. The positive impact of secondary school enrollment on economic growth may be a result of the enhancement of educational levels and technological know-how.

$\text{Variables}$	$\text{Location}$	$\text{Scale}$	$Q_{0.25}$; $\text{Coefficients}$; $\left[\mathrm{Std}.\text{Error}\right]$	$Q_{0.50}$; $\text{Coefficients}$; $\left[\mathrm{Std}.\text{Error}\right]$	$Q_{0.75}$; $\text{Coefficients}$; $\left[\mathrm{Std}.\text{Error}\right]$	$Q_{0.90}$; $\text{Coefficients}$; $\left[\mathrm{Std}.\text{Error}\right]$
Low-income countries
LAB	2.945	−0.052	1.258***; [0.000]	8.159***; [0.008]	3.169***; [0.000]	0.198***; [0.003]
CAP	−1.158	0.0445	0.361***; [0.005]	0.416***; [0.006]	0.985***; [0.007]	0.159***; [0.002]
SSE	−0.058	0.0152	0.085***; [0.024]	0.947**; [0.058]	0.982**; [0.034]	0.018*; [0.074]
FOOD_W	−0.147	0.0619	−0.982***; [0.005]	−0.882***; [0.021]	−0.158*; [0.107]	−0.369**; [0.055]
FOOD_AC	−8.179	0.028	−5.661***; [0.000]	−8.729***; [0.002]	−6.905**; [0.040]	−7.831**; [0.041]
FOOD_AV	−0.026	0.017	9.811***; [0.001]	10.294***; [0.002]	0.011**; [0.030]	0.050*; [0.070]
FOOD_ST	−0.076	0.037	0.674***; [0.025]	0.423**; [0.048]	0.027***; [0.014]	0.049*; [0.081]
FOOD_UT	−0.176	−0.009	0.131***; [0.000]	0.021***; [0.000]	0.052**8; [0.000]	0.019***; [0.000]
CO2	−0.202	0.085	−0.038***; [0.000]	−0.015***; [0.000]	−0.030***; [0.000]	−0.034***; [0.000]
C	0.153	−0.164	0.001***; [0.257]	0.003***; [0.398]	−0.561*; [0.147]	0.474**; [0.046]
Lower middle-income countries
LAB	4.050	−11.712	4.509*; [0.078]	3.371*; [0.089]	4.362***; [0.008]	9.743***; [0.002]
CAP	−0.180	1.140	0.507***; [0.009]	0.185*; [0.080]	0.596**; [0.030]	0.378*; [0.400]
SSE	0.016	0.061	0.036***; [0.025]	0.020***; [0.000]	0.031***; [0.008]	0.027**; [0.033]
FOOD_W	−0.098	−0.169	−0.031**; [0.036]	−0.012***; [0.000]	−0.030***; [0.000]	−0.021***; [0.025]
FOOD_AC	0.027	0.007	−0.089*; [0.080]	−4.045***; [0.000]	−7.369*; [0.069]	−0.001**; [0.036]
FOOD_AV	−0.136	−0.060	0.050*; [0.070]	0.030***; [0.000]	0.015**; [0.040]	0.045***; [0.000]
FOOD_ST	−0.105	−0.099	0.055**; [0.030]	0.024***; [0.000]	0.049*; [0.801]	0.067**; [0.030]
FOOD_UT	0.029	0.008	0.097*; [0.060]	0.041***; [0.000]	0.281*; [0.250]	0.055***; [0.025]
CO2	0.196	0.147	−0.085**; [0.050]	−0.020***; [0.000]	−0.103***; [0.025]	−0.020**; [0.039]
C	−2.419	14.293	0.102***; [0.020]	0.069***; [0.000]	0.331***; [0.000]	0.620*; [0.070]
Upper middle-income countries
LAB	−7.178	9.153	1.078***; [0.001]	6.490***; [0.000]	1.377***; [0.000]	6.387***; [0.000]
CAP	0.638	0.122	0.659***; [0.000]	0.287***; [0.025]	0.230***; [0.000]	0.241***; [0.000]
SSE	0.013	0.019	0.031***; [0.002]	0.025***; [0.022]	0.106***; [0.000]	0.020***; [0.000]
FOOD_W	−0.055	−0.214	−0.022***; [0.000]	−0.015***; [0.000]	−0.007***; [0.000]	−0.014***; [0.300]
FOOD_AC	0.013	0.009	−1.987***; [0.006]	−1.832***; [0.000]	−3.712*; [0.800]	−0.001***; [0.000]
FOOD_AV	−0.125	−0.288	0.029***; [0.000]	0.043**; [0.040]	0.005*; [0.060]	0.036***; [0.000]
FOOD_ST	−0.128	−0.224	0.031***; [0.009]	0.043***; [0.000]	0.037***; [0.000]	0.035*; [0.500]
FOOD_UT	0.113	0.027	0.026***; [0.001]	0.069*; [0.800]	0.021***; [0.020]	0.020***; [0.000]
CO2	0.198	0.267	−0.066***; [0.003]	−0.027***; [0.030]	−0.042***; [0.000]	−0.027***; [0.000]
C	−7.694	−4.908	0.137; [0.025]	0.137; [0.080]	−0.489; [0.030]	0.158; [0.000]

Table 5.

Method of moment quartile regression analysis (MMQR).

Note: ***, **, and * show significance level at 1%, 5%, and 10%

Food wastage shows a negative and significant relationship with economic growth in all quartiles and all countries, but the magnitude is high in the 25th quartile in low-income countries and approximately the same in the lower-middle-income and upper-middle-income countries. Food wastage may evolve poor regulatory capacity, which in turn reduces economic growth. We have estimated food security in four aspects, i.e., food access, food availability, food stability, and food utilization. Food access is measured in terms of the prevalence of undernourishment, which indicates a negative and significant impact on economic growth in all quartiles but in low income-countries magnitude is high in the 50th quartile and in lower-middle-income and upper-middle-income countries degree is high at 75th quartiles. This negative relationship may be because the undernourishment of food access reduces economic growth. The next feature of food security is food availability, and we have estimated it in terms of average protein supply. Food availability has a positive and significant association with economic growth in all three categories of countries, but the results show that in low-income countries the extent of its effect is high in the 50th quartile but in the lower middle and upper middle countries their degree of effectiveness is approximately the same in all quartiles elaborating that high supply of proteins can enhance the economic growth of developing countries. The other two aspects of food security are food stability and food utilization, which exert a positive and significant effect on economic growth in all developing countries. The intensity of the effects of food stability and food utilization is high in the 25th quartile in low-income countries and approximately the same in all other quartiles in lower-middle-income and upper-middle-income countries. The positive impact of food utilization endorses that the variability of per capita food production leads to enhancing economic growth while the positive influence of food utilization ratifies the development of economic growth due to the improvement in basic drinking water services. Carbon dioxide emission puts a negative influence on economic growth in all countries, and surprisingly, the fluctuation level is approximately the same in all quartiles in low-income, lower middle, and upper middle-income countries posting on the fact that environmental degradation reduces economic growth through poor health of labor, pollution, and deforestation.

7. Conclusions and policy recommendations

This study aimed to examine the effect of food wastage, food security, and the environment on the economic growth of developing countries over the period 1990–2021 by applying Methods of Moments Quantile Regression. Findings indicate that labor force, gross fixed capital formation, secondary school enrollment, food availability, food stability, and food utilization have a positive effect on the economic growth of developing countries, but the intensity of their effect differs from country to country. Further, the evidence indicates a negative association of food waste, food accessibility, and CO₂ emission with GDPG. Additionally, we have found that the strength of the effect of food waste on economic growth in low-income countries is high as compared with lower middle and upper middle-income countries. Nevertheless, while human and physical capital are efficient policy instruments for enhancing economic growth, they may not be sufficient for development. Other supportive policies such as reduction in food waste and food access are also needed. Most developing countries are blessed with natural resources, so there is also a need for the proper management of sustainable resources that may support economic growth. Moreover, to reinforce the behavioral changes, there would be given environmental and food protection awareness for all the citizens.

Acknowledgments

We would like to thank FAO for providing the data. We do not have any funding for this project.

Conflict of interest

We declare no conflict of interest.

A.

A.1 Food losses in low-income countries (2010–2021)

Year	Average	Maximum	Minimum	Countries	Food losses
2010	4.06	53	0.1	Burundi, Chad, Democratic Republic of the Congo, Benin, Ethiopia, Gambia, Guinea, Afghanistan, Liberia, Madagascar, Malawi, Mali, Mozambique, Nepal, Niger, Guinea-Bissau, Rwanda, Senegal, Sierra Leone, Somalia, Zimbabwe, Togo, Uganda, United Republic of Tanzania, Burkina Faso	Sorghum, Wheat, maize, rice, Tomatoes, fonio, millet
2011	3.25	50	0.02	Burundi, Chad, Democratic Republic of the Congo, Benin, Ethiopia, Gambia, Guinea, Afghanistan, Liberia, Madagascar, Malawi, Mali, Mozambique, Nepal, Niger, Guinea-Bissau, Rwanda, Senegal, Sierra Leone, Somalia, Zimbabwe, Togo, Uganda, United Republic of Tanzania, Burkina Faso	Cocoa beans, Onions and shallots, barley, bananas, pineapples, Tangerines, mandarins
2012	3.09	22	0.08	Burundi, Chad, Democratic Republic of the Congo, Benin, Ethiopia, Gambia, Guinea, Afghanistan, Liberia, Madagascar, Malawi, Mali, Mozambique, Nepal, Niger, Guinea-Bissau, Rwanda, Senegal, Sierra Leone, Somalia, Zimbabwe, Togo, Uganda, United Republic of Tanzania, Burkina Faso	Wheat, maize, rice, Sorghum, bananas, potatoes, millet
2013	3.72	63	0.08	Burundi, Chad, Democratic Republic of the Congo, Benin, Ethiopia, Gambia, Guinea, Afghanistan, Liberia, Madagascar, Malawi, Mali, Mozambique, Nepal, Niger, Guinea-Bissau, Rwanda, Senegal, Sierra Leone, Somalia, Zimbabwe, Togo, Uganda, United Republic of Tanzania, Burkina Faso	Plantains and cooking bananas, Groundnuts, excluding shelled, Tangerines, mandarins, Wheat, maize, rice, millet, Lentils, dry, Other beans, green, Bambara beans, dry, Sunflower seed, Sesame seed
2014	3.39	50	0.02	Burundi, Chad, Democratic Republic of the Congo, Benin, Ethiopia, Gambia, Guinea, Afghanistan, Liberia, Madagascar, Malawi, Mali, Mozambique, Nepal, Niger, Guinea-Bissau, Rwanda, Senegal, Sierra Leone, Somalia, Zimbabwe, Togo, Uganda, United Republic of Tanzania, Burkina Faso	Groundnuts, Sesame seed, Sweet potatoes, yams, Wheat, maize, rice, Other cereals, Sorghum, millet
2015	3.64	43	0.01	Burundi, Chad, Democratic Republic of the Congo, Benin, Ethiopia, Gambia, Guinea, Afghanistan, Liberia, Madagascar, Malawi, Mali, Mozambique, Nepal, Niger, Guinea-Bissau, Rwanda, Senegal, Sierra Leone, Somalia, Zimbabwe, Togo, Uganda, United Republic of Tanzania, Burkina Faso	Groundnuts, excluding shelled, Sesame seed, Cassava, fresh, Wheat, maize, rice, Sorghum
2016	3.21	29.96	0.01	Burundi, Chad, Democratic Republic of the Congo, Benin, Ethiopia, Gambia, Guinea, Afghanistan, Liberia, Madagascar, Malawi, Mali, Mozambique, Nepal, Niger, Guinea-Bissau, Rwanda, Senegal, Sierra Leone, Somalia, Zimbabwe, Togo, Uganda, United Republic of Tanzania, Burkina Faso	Groundnuts, Sesame seed, Sweet potatoes, yams, Wheat, maize, rice, Sorghum, millet, oats
2017	3.36	59.48	0.1	Burundi, Chad, Democratic Republic of the Congo, Benin, Ethiopia, Gambia, Guinea, Afghanistan, Liberia, Madagascar, Malawi, Mali, Mozambique, Nepal, Niger, Guinea-Bissau, Rwanda, Senegal, Sierra Leone, Somalia, Zimbabwe, Togo, Uganda, United Republic of Tanzania, Burkina Faso	Sunflower seed, Other cereals, fonio, Wheat, maize, rice, Sorghum
2018	3.13	49.7	0.1	Burundi, Chad, Democratic Republic of the Congo, Benin, Ethiopia, Gambia, Guinea, Afghanistan, Liberia, Madagascar, Malawi, Mali, Mozambique, Nepal, Niger, Guinea-Bissau, Rwanda, Senegal, Sierra Leone, Somalia, Zimbabwe, Togo, Uganda, United Republic of Tanzania, Burkina Faso	Cow peas, millet, Barley, fonio, Cow peas, oats.
2019	2.72	9.94	0.1	Burundi, Chad, Democratic Republic of the Congo, Benin, Ethiopia, Gambia, Guinea, Afghanistan, Liberia, Madagascar, Malawi, Mali, Mozambique, Nepal, Niger, Guinea-Bissau, Rwanda, Senegal, Sierra Leone, Somalia, Zimbabwe, Togo, Uganda, United Republic of Tanzania, Burkina Faso	Sorghum, Wheat, maize, rice, Barley, oats, fonio
2020	2.70	8.48	0.06	Burundi, Chad, Democratic Republic of the Congo, Benin, Ethiopia, Gambia, Guinea, Afghanistan, Liberia, Madagascar, Malawi, Mali, Mozambique, Nepal, Niger, Guinea-Bissau, Rwanda, Senegal, Sierra Leone, Somalia, Zimbabwe, Togo, Uganda, United Republic of Tanzania, Burkina Faso	Sorghum, Wheat, maize, rice, Barley, oats, fonio
2021	2.97	7.3	0.32	Nepal	Wheat, maize, rice

A.2 Food losses in lower middle-income countries (2010–2021)

Year	Average	Maximum	Minimum	Countries	Food losses
2010	7.78	62.5	0.07	Cameroon, Sri Lanka, Angola, Georgia, Ghana, Kenya, Kyrgyzstan, Lesotho, Mauritania, Nicaragua, Philippines, Ukraine, Moldova, Viet Nam, Nigeria, Tunisia, Sudan, Eswatini, Zambia, Ukraine, Pakistan, CÃ’te d’Ivoire, Bolivia, India, Bangladesh	Onions and shallots, green, Maize, Millet, Eggplants, Cassava, fresh, Potatoes, Sorghum, Soya beans, Groundnuts, Cassava, fresh, Sweet potatoes, Groundnuts, shelled, rice, wheat, Refined sugar, Sunflower seed, Raw milk of cattle, Pears, Meat of cattle, fresh or chilled, Okra, apples, yams, Eggplants, Plantains and cooking bananas, Strawberries, Other tropical and subtropical fruits, Chillies and peppers
2011	3.74	50	0.01	Cameroon, Angola, Georgia, Ghana, Kenya, Indonesia, Kyrgyzstan, Lesotho, Mauritania, Nicaragua, Philippines, Ukraine, Moldova, Viet Nam, Nigeria, Tunisia, Sudan, Eswatini, Zambia, Ukraine, Pakistan, CÃ’te d’Ivoire, Bolivia, India, Bangladesh	Lettuce and chicory, Maize, Millet, Cassava, fresh, Potatoes, Sorghum, Soya beans, Groundnuts, Cassava, fresh, Sweet potatoes, Groundnuts, shelled, rice, wheat, Refined sugar, Sunflower seed, Raw milk of cattle, Pears, Meat of cattle, fresh or chilled, Plantains and cooking bananas, Strawberries
2012	4.33	50	0.02	Cameroon, Angola, Georgia, Ghana, Kenya, Indonesia, Kyrgyzstan, Lesotho, Mauritania, Nicaragua, Philippines, Ukraine, Moldova, Viet Nam, Nigeria, Tunisia, Sudan, Eswatini, Zambia, Ukraine, Pakistan, CÃ’te d’Ivoire, Bolivia, India, Bangladesh	Mangoes, guavas and mangosteens, Tomatoes, Maize, Millet, Cassava, fresh, Potatoes, Sorghum, Soya beans, Groundnuts, Cassava, fresh, Sweet potatoes, Groundnuts, shelled, rice, wheat, Refined sugar, Sunflower seed, Raw milk of cattle, Pears, Meat of cattle, fresh or chilled, Plantains and cooking bananas
2013	5.31	60	0.01	Cameroon, El Salvador, Angola, Georgia, Ghana, Kenya, Indonesia, Kyrgyzstan, Lesotho, Mauritania, Nicaragua, Philippines, Ukraine, Moldova, Viet Nam, Nigeria, Tunisia, Sudan, Eswatini, Zambia, Ukraine, Pakistan, CÃ’te d’Ivoire, Bolivia, India, Bangladesh	Plantains and cooking bananas, Tomatoes, Rape or colza seed, Maize, Millet, Cassava, fresh, Potatoes, Sorghum, Soya beans, Groundnuts, Cassava, fresh, Sweet potatoes, Groundnuts, shelled, rice, wheat, Refined sugar, Sunflower seed, Raw milk of cattle, Pears, Meat of cattle, fresh or chilled, Plantains and cooking bananas, Sheep, Rape or colza seed
2014	2.58	35.7	0.01	Cameroon, Angola, Georgia, Ghana, Kenya, Indonesia, Kyrgyzstan, Lesotho, Mauritania, Nicaragua, Philippines, Ukraine, Moldova, Viet Nam, Nigeria, Tunisia, Sudan, Eswatini, Zambia, Ukraine, Pakistan, CÃ’te d’Ivoire, Bolivia, India, Bangladesh, Uzbekistan	Flour of cassava, Tomatoes, Rape or colza seed, Maize, Millet, Cassava, fresh, Potatoes, Sorghum, Soya beans, Groundnuts, Cassava, fresh, Sweet potatoes, Groundnuts, shelled, rice, wheat, Refined sugar, Sunflower seed, Raw milk of cattle, Pears, Meat of cattle, fresh or chilled, Plantains and cooking bananas, Sheep, Other tropical and subtropical fruit, Seed cotton, unginned, Plantains and cooking bananas, Broad beans and horse beans, dry, Buckwheat
2015	3.45	35	0.01	Cameroon, Angola, Georgia, Ghana, Indonesia, Kyrgyzstan, Lesotho, Mauritania, Nicaragua, Philippines, Ukraine, Moldova, Viet Nam, Nigeria, Tunisia, Sudan, Eswatini, Zambia, Ukraine, Pakistan, CÃ’te d’Ivoire, Bolivia, India, Bangladesh, Uzbekistan, Timor-Leste	Tomatoes, Rape or colza seed, Maize, Millet, Cassava, fresh, Potatoes, Sorghum, Soya beans, Groundnuts, Cassava, fresh, Sweet potatoes, Groundnuts, shelled, rice, wheat, Refined sugar, Sunflower seed, Raw milk of cattle, Pears, Meat of cattle, fresh or chilled, Plantains and cooking bananas, Sheep, Other tropical and subtropical fruit, Linseed, Tangerines, mandarins
2016	4.03	25	0.01	Cameroon, El Salvador, Angola, Georgia, Ghana, Indonesia, Kyrgyzstan, Lesotho, Mauritania, Honduras, Nicaragua, Philippines, Ukraine, Moldova, Viet Nam, Nigeria, Tunisia, Sudan, Eswatini, Zambia, Ukraine, Pakistan, CÃ’te d’Ivoire, Bolivia, India, Bangladesh, Uzbekistan	Mushrooms and truffles, Tomatoes, Rape or colza seed, Maize, Millet, Cassava, fresh, Potatoes, Sorghum, Soya beans, Groundnuts, Cassava, fresh, Sweet potatoes, Groundnuts, shelled, rice, wheat, Refined sugar, Sunflower seed, Raw milk of cattle, Pears, Meat of cattle, fresh or chilled, Plantains and cooking bananas, Sheep, Other tropical and subtropical fruit, Linseed, Tangerines, mandarins
2017	3.08	19.69	0.01	Cameroon, El Salvador, Angola, Georgia, Ghana, Indonesia, Kyrgyzstan, Lesotho, Mauritania, Honduras, Nicaragua, Philippines, Ukraine, Moldova, Viet Nam, Nigeria, Tunisia, Sudan, Eswatini, Zambia, Ukraine, Pakistan, CÃ’te d’Ivoire, Bolivia, India, Bangladesh, Uzbekistan	Seed cotton, unginned, Tomatoes, Rape or colza seed, Maize, Millet, Cassava, fresh, Potatoes, Sorghum, Soya beans, Groundnuts, Cassava, fresh, Sweet potatoes, Groundnuts, shelled, rice, wheat, Refined sugar, Sunflower seed, Raw milk of cattle, Pears, Meat of cattle, fresh or chilled, Plantains and cooking bananas, Sheep, Other tropical and subtropical fruit, Linseed, Tangerines, mandarins
2018	3.10	22	0.01	Cameroon, Angola, Georgia, Ghana, Indonesia, Kyrgyzstan, Lesotho, Mauritania, Honduras, Nicaragua, Philippines, Ukraine, Moldova, Viet Nam, Nigeria, Tunisia, Sudan, Eswatini, Zambia, Ukraine, Pakistan, CÃ’te d’Ivoire, Bolivia, India, Bangladesh, Uzbekistan	Sugar beet, unginned, Tomatoes, Rape or colza seed, Maize, Millet, Cassava, fresh, Potatoes, Sorghum, Soya beans, Groundnuts, Cassava, fresh, Sweet potatoes, Groundnuts, shelled, rice, wheat, Refined sugar, Sunflower seed, Raw milk of cattle, Pears, Meat of cattle, fresh or chilled, Plantains and cooking bananas, Sheep, Other tropical and subtropical fruit, Linseed, Tangerines, mandarins, Taro
2019	2.72	6.42	0.01	Cameroon, Angola, Ghana, Indonesia, Lesotho, Mauritania, Honduras, Nicaragua, Philippines, Ukraine, Moldova, Viet Nam, Nigeria, Tunisia, Sudan, Eswatini, Zambia, Ukraine, CÃ’te d’Ivoire, Bolivia	Broad beans and horse beans, Tomatoes, Rape or colza seed, Maize, Millet, Cassava, fresh, Potatoes, Sorghum, Soya beans, Groundnuts, Cassava, fresh, Sweet potatoes, Groundnuts, shelled, rice, wheat, Refined sugar, Sunflower seed, Raw milk of cattle, Pears, Meat of cattle, fresh or chilled, Plantains and cooking bananas, Sheep, Other tropical and subtropical fruit
2020	2.71	6.42	0.01	Cameroon, Angola, Ghana, Lesotho, Mauritania, Ukraine, Moldova, Viet Nam, Nigeria, Tunisia, Sudan, Eswatini, Zambia, Ukraine, CÃ’te d’Ivoire	Maize, rice, millet, wheat, sorghum, barley

A.3 Food losses in upper middle-income countries (2010–2021)

Year	Average	Maximum	Minimum	Countries	Food losses
2010	4.62	26.31	0.09	Maize, rice, Plantains and others, grapes, Strawberries, Other berries and fruits of the genus Vaccinium, Soya beans, Coffee, green, Beans, dry, lupins, sorghum, Meat of cattle boneless, fresh or chilled, Wheat and meslin flour, Rice, Milled, rye, quinoa, asparagus, cabbages, spinach, Cantaloupes and other melons, tomatoes, Pumpkins, squash and gourds, Carrots and turnips, Green garlic, bran of maize	Cuba, Azerbaijan, Kazakhstan, Fiji, Iran, Armenia, Guatemala, Mauritius, Peru, Romania, Russian Federation, Namibia, Russian Federation, South Africa, Botswana, Venezuela (Bolivarian Republic of), Serbia Botswana, South Africa, Malaysia, Jordan
2011	3.88	27.44	0.02	Maize, rice, Plantains and others, grapes, Strawberries, Other berries and fruits of the genus Vaccinium, Soya beans, Coffee, green, Beans, dry, lupins, sorghum, Meat of cattle boneless, fresh or chilled, Wheat and meslin flour, Rice, Milled, rye, quinoa, asparagus, cabbages, spinach, Cantaloupes and other melons, tomatoes, Pumpkins, squash and gourds, Carrots and turnips, Green garlic, bran of maize, Watermelons	Cuba, Azerbaijan, Kazakhstan, Fiji, Iran, Armenia, Guatemala, Mauritius, Peru, Romania, Russian Federation, Namibia, Russian Federation, South Africa, Botswana, Venezuela (Bolivarian Republic of), Serbia Botswana, South Africa, Malaysia, Jordan, Mexico
2012	4.49	50	0.09	Maize, rice, Plantains and others, grapes, Strawberries, Other berries and fruits of the genus Vaccinium, Soya beans, Coffee, green, Beans, dry, lupins, sorghum, Meat of cattle boneless, fresh or chilled, Wheat and meslin flour, Rice, Milled, rye, quinoa, asparagus, cabbages, spinach, Cantaloupes and other melons, tomatoes, Pumpkins, squash and gourds, Carrots and turnips, Green garlic, bran of maize, Watermelons, Oil palm fruit	Ecuador, Fiji, Azerbaijan, Kazakhstan, Fiji, Iran, Armenia, Guatemala, Mauritius, Peru, Romania, Russian Federation, Namibia, Russian Federation, South Africa, Botswana, Venezuela (Bolivarian Republic of), South Africa, Jordan
2013	11.32	49.07	0.11	Maize, quinoa, grapes, Strawberries, Other berries and fruits of the genus Vaccinium, Soya beans, Coffee, green, Beans, dry, lupins, sorghum, Meat of cattle boneless, fresh or chilled, Wheat and meslin flour, Rice, Milled, rye, quinoa, asparagus, cabbages, spinach, Cantaloupes and other melons, tomatoes, Pumpkins, squash and gourds, Carrots and turnips, Green garlic, bran of maize, Watermelons, Oil palm fruit, Other vegetables, fresh	Ecuador, Gabon, Azerbaijan, Kazakhstan, Armenia, Guatemala, Mauritius, Peru, Romania, Russian Federation, Namibia, Russian Federation, South Africa, Paraguay, Botswana, Venezuela (Bolivarian Republic of), South Africa, Jordan, Saint Lucia, Thailand, Turkey
2014	4.31	22.44	0.01	Wheat, Maize, rice, Soya beans, Coffee, green, Beans, dry, lupins, sorghum, Meat of cattle boneless, fresh or chilled, Wheat and meslin flour, Rice, Milled, rye, quinoa, asparagus, cabbages, spinach, Cantaloupes and other melons, tomatoes, Pumpkins, squash and gourds, Carrots and turnips, Green garlic, bran of maize, Watermelons, Oil palm fruit, Other vegetables, fresh, Natural honey	Azerbaijan, Kazakhstan, Armenia, Guatemala, Romania, Russian Federation, Namibia, Russian Federation, South, Serbia, Africa, Paraguay, Botswana, Venezuela (Bolivarian Republic of), South Africa, Jordan, Saint Lucia, Thailand, Turkey, The former Yugoslav Republic of Macedonia
2015	5.96	46.87	0.02	Tomatoes, Maize, rice, Soya beans, Coffee, green, Beans, dry, lupins, sorghum, Meat of cattle boneless, fresh or chilled, Wheat and meslin flour, Rice, Milled, rye, quinoa, asparagus, cabbages, spinach, Cantaloupes and other melons, Pumpkins, squash and gourds, Carrots and turnips, Green garlic, bran of maize, Watermelons, Oil palm fruit, Raw milk of goats	Costa Rica, Fiji, Azerbaijan, Guatemala, Guyana, Kazakhstan, Armenia, Jamaica, Guatemala, Romania, Russian Federation, Namibia, Russian Federation, South, Serbia, Africa, Paraguay, Botswana, Venezuela (Bolivarian Republic of), South Africa, Jordan, Saint Lucia, Turkey, The former Yugoslav Republic of Macedonia
2016	5.50	30.03	0.07	Papayas, Maize, rice, Soya beans, Coffee, green, Beans, dry, lupins, sorghum, Meat of cattle boneless, fresh or chilled, Wheat and meslin flour, Rice, Milled, rye, quinoa, asparagus, cabbages, spinach, Cantaloupes and other melons, Green garlic, bran of maize, Watermelons, Oil palm fruit, Raw milk of goats, pulses	Colombia, Ecuador, Fiji, Azerbaijan, Iran, Guatemala, Guyana, Kazakhstan, Armenia, Jamaica, Russian Federation, Namibia, Russian Federation, South, Serbia, Africa, Paraguay, Botswana, Venezuela (Bolivarian Republic of), South Africa, Jordan, Saint Lucia, Turkey, The former Yugoslav Republic of Macedonia
2017	5.51	45.8	0.06	Maize, rice, Soya beans, Coffee, green, Beans, dry, lupins, sorghum, Meat of cattle boneless, fresh or chilled, Wheat and meslin flour, Rice, Milled, rye, quinoa, Green garlic, bran of maize, Watermelons, Oil palm fruit, Raw milk of goats, pulses, taro	Fiji, Azerbaijan, Iran, Guatemala, Guyana, Kazakhstan, Armenia, Jamaica, Russian Federation, Namibia, Russian Federation, South, Serbia, Africa, Paraguay, Botswana, Venezuela (Bolivarian Republic of), South Africa
2018	4.23	30.03	0.22	Maize, rice, Soya beans, Coffee, green, Beans, dry, lupins, fresh or chilled, Wheat and meslin flour, Rice, Milled, rye, quinoa, Green garlic, bran of maize, Watermelons, Oil palm fruit, Raw milk of goats, pulses, taro, millet	Fiji, Azerbaijan, Iran, Guatemala, Guyana, Kazakhstan, Armenia, Jamaica, Russian Federation, Namibia, Russian Federation, South, Serbia, Africa, Paraguay, Botswana, South Africa, Armenia
2019	2.49	11.1	0.29	Wheat, maize, sorghum, millet, barley	Jordan, Namibia, South Africa, Botswana
2020	2.29	6.35	0.5	Mize, Wheat, maize, sorghum, millet, barley	Namibia, South Africa, Botswana
2021	17.2	31.6	5.5	Other fruits, n.e.c	South Africa