Food balance sheets provide information on food security,indicators of the prevalence of undernourishment and losses in the cases of Benin,Guinea and Mali

Abstract

This paper was done within the framework of the Monitoring of the SDGs in Africa (SODDA) project which supported the analysis of the food balance sheets drawn up through the letter of agreement between FAO/GS ${}^{1}$ and AFRISTAT. Analysis of the self-sufficiency rate over the 2010–2015 period shows that Mali has higher food self-sufficiency than Benin and Guinea. In Guinea, overall, 43.2% of domestic product supplies are on average imports. Plant products are the most dependent on imports with an average annual IDR of 48.2% compared to 12.5% for animal products. In the three countries, plant products are the most dependent on imports. The use of FAO methodologies for calculating the prevalence of undernourishment under SDG 2 and the food loss index under SDG 12 made it possible to estimate these two indicators using BAs and other related indicators. The results in 2015 show that Benin and Guinea with respectively 14.2% and 15.6% of the population with a prevalence of undernourishment are ahead of Mali with 5%. In terms of individuals, estimates give 1.5 million Beninese, 1.2 million Guineans and 0.9 million Malians who were undernourished in 2015.

Keywords

Food balance sheet food security SDGs undernourished export import self-sufficiency food losses

1. Introduction

While agriculture is the most important sector, particularly in developing countries, agricultural and rural statistics remain the poor relatives of national statistical systems. The initiative to develop the Global Strategy to improve agricultural and rural statistics is a response to the lack of capacity of developing countries in collecting reliable statistical data on agriculture and food and in developing a framework benchmark for sustainable and long-term agricultural statistics systems (see reference [1] World Bank, FAO and United Nations, 2010). To be able to respond to these challenges in developing countries, a Global Strategy (FAO/Global Strategy) was initiated for the improvement of agricultural and rural statistics and adopted by the United Nations Statistical Commission in February 2010. The Global Strategy aims to strengthen the statistical capacities of developing countries to enable them to provide reliable statistics on agriculture, food and rural development, necessary for the formulation, monitoring and evaluation of development policies.

Table 1
Self-Sufficiency Ratio (%) 2010–2015

Benin Guinea Mali

Year Animal products Vegetable products Animal products Vegetable products Animal products Vegetable products

2010 41.1 84.1 87.9 6.9 97.8 94.2

2011 44.2 76.2 86.9 33.4 98.1 93.5

2012 41.3 86.3 87.5 75.2 96.6 89.4

2013 40.6 80.6 86.7 92.7 98.3 92.8

2014 51.8 82.3 88.8 76.8 97.9 91.4

2015 53.4 85.9 87.8 48.1 98.7 90.3

	Benin	Guinea	Mali
Year	Animal products	Vegetable products	Animal products	Vegetable products	Animal products	Vegetable products
2010	41.1	84.1	87.9	6.9	97.8	94.2
2011	44.2	76.2	86.9	33.4	98.1	93.5
2012	41.3	86.3	87.5	75.2	96.6	89.4
2013	40.6	80.6	86.7	92.7	98.3	92.8
2014	51.8	82.3	88.8	76.8	97.9	91.4
2015	53.4	85.9	87.8	48.1	98.7	90.3

Source: INSAE, INS, INSTAT and our calculations.

It is in this context, through an accelerated technical assistance plan, the Food and Agriculture Organization of the United Nations (FAO) and the Economic and Statistical Observatory of Sub-Saharan Africa (AFRISTAT) have signed in December 2016, a letter of Agreement with a view to contributing to the implementation of certain actions of the Global Strategy for the improvement of agricultural and rural statistics in terms of training and technical assistance. This protocol has two components:

(1) The creation and use of main sampling frames in agricultural surveys such as those provided for in the integrated agricultural survey (AGRIS). (2) The process of compiling food balance sheets using the new methodology developed by the FAO.

More precisely, four countries (Benin, Guinea, Madagascar and Mali) benefited from support for the compilation of a food balance sheet for the reference year 2015. Following this exercise, the SODDA project (Support project Monitoring of Sustainable Development Goals in Africa)2 in accordance with its objective has enabled in-depth analysis of food balance sheets (see reference [7]). Indeed, these assessments were used, among other things, to inform two indicators of the Sustainable Development Goals (SDGs): Indicator 2.1.1 on the prevalence of undernourishment (PoU) Target 1 of SDG 2 “From here by 2030, end hunger and ensure that everyone, especially the poor and vulnerable, including infants, has access to healthy, nutritious and sufficient food throughout the year “and the indicator 12.3.1: Global food loss index for target 3 of SDG 12 “By 2030, halve the volume of food waste per capita globally in terms of both distribution and consumption and reduce food losses along the production and supply chains, including post-harvest losses”.

2. Methodology and results

Four indicators, according to the methodology recommended by the FAO, were estimated for the three countries of Benin, Guinea and Mali: (i) the self-sufficiency ratio; (ii) the import dependency ratio; (iii) prevalence of undernourishment and (iv) food losses.

2.1 Self-Sufficiency Ratio (SSR)

The Self-Sufficiency Ratio expresses the importance of domestic production in relation to domestic consumption. It is given by the equation:

$\displaystyle\text{SSR}=\frac{\text{Production}}{\text{Production}+\text{% Imports}-\text{Exports}+\Delta\text{Stocks}}\times 100$

In the context of food security, the SSR is often used to show the extent to which a country is self-sufficient in its own productive resources: the higher the SSR, the closer the country becomes to self-sufficiency. It can often be very high when the country is highly dependent on imports to feed its population. This occurs when a significant amount of domestic production is exported.

It appears from the table above that, overall, Mali tends to achieve food self-sufficiency since the SSR for the two types of products (plants and animals) close to 100.

On the other hand, in the case of Guinea, 60% of the domestic availability3 of products comes from national production. It should be noted that in 2015 self-sufficiency was higher with products of animal origin (87.8%) than those of plant origin (48.1%).

2.2 Import Dependency Ratio (IDR)

The Import Dependency Ratio expresses the share of available domestic supplies that come from imports. Its formula is:

$\displaystyle\text{IDR}=\frac{\text{Imports}}{\text{Production}+\text{Imports}% -\text{Exports}+\Delta\text{Stocks}}\times 100$

This rate only makes sense if the imports are used exclusively for domestic consumption and are not re-exported.

Table 2
Import Dependency Ratio (IDR) from 2010 to 2015

Benin Guinea Mali

Year Animal Vegetable Overall Animal Vegetable Overall Animal Vegetable Overall

products products ratio products products ratio products products ratio

2010 64 13 46 12 45 40 2 6 24

2011 56 19 43 13 53 48 2 6 22

2012 59 12 31 13 48 42 3 11 25

2013 59 15 30 13 46 41 2 7 27

2014 48 17 28 11 49 44 2 9 22

2015 47 15 29 12 48 43 1 10 27

Average 56 15 35 12 48 43 2 9 24

Sources: INSAE, INS, INSTAT and our calculations.

In Guinea, overall, 43% of domestic product supplies are on average imports. Plant products are the most dependent on imports with an average annual IDR of 48% compared to 13% for animal products.

Mali is more dependent on imports of plant products than products of animal origin. However, it should be noted that fishery products (fish and seafood) have not yet been integrated into the new approach to drawing up food balance sheets.

2.3 Indicator of the prevalence of undernourishment (PoU)

The prevalence of undernourishment is an indicator of access to food and an indicator of the Sustainable Development Goals (SDGs). It measures the achievement of target 1 of SDG 2 which states: “By 2030, eliminate hunger and ensure that everyone, especially the poor and people in vulnerable situations, including infants, have access throughout the year to a healthy, nutritious and sufficient diet”.

FAO defines undernourishment as “a situation in which an individual’s usual food consumption is insufficient to provide the dietary energy consumption (Dietary Energy Consumption, DEC) necessary for a normal, healthy and active life”. The corresponding indicator is the prevalence of undernourishment (PoU), which is an estimate of the percentage of people in the total population who are undernourished.

The methodology is exposed in Appendix. For Benin and Guinea, which have household consumption surveys, the coefficients of variation have been estimated.

Table 3a
Food loss percentages and food loss index for Benin

Products % of loss 2010 % of loss 2015 Loss index 2015/2010

Maize 25.83% 15.03% 58.19

Rice (Blanched Equivalent) 8.01% 6.95% 86.81

Wheat 1.52% 6.19% 408.25

Sorghum 11.42% 8.93% 78.18

Dry beans 31.38% 0.10% 0.32

Pineapple 9.99% 3.11% 31.13

Coconut 10.05% 10.05% 100.00

Tomato 4.54% 4.54% 100.00

Yams 10.00% 8.75% 87.45

Cassava 13.00% 14.63% 112.53

Soybean 4.00% 32.17% 804.15

Other Legumes 10.05% 3.55% 35.35

Groundnut 17.01% 17.01% 100.00

Products	% of loss 2010	% of loss 2015	Loss index 2015/2010
Maize	25.83%	15.03%	58.19
Rice (Blanched Equivalent)	8.01%	6.95%	86.81
Wheat	1.52%	6.19%	408.25
Sorghum	11.42%	8.93%	78.18
Dry beans	31.38%	0.10%	0.32
Pineapple	9.99%	3.11%	31.13
Coconut	10.05%	10.05%	100.00
Tomato	4.54%	4.54%	100.00
Yams	10.00%	8.75%	87.45
Cassava	13.00%	14.63%	112.53
Soybean	4.00%	32.17%	804.15
Other Legumes	10.05%	3.55%	35.35
Groundnut	17.01%	17.01%	100.00

Sources: National Institute of Statistics and Economic Analysis (INSAE) and our calculations.

Table 3b1

Evolution of food losses in MT by type of product from 2010 to 2015 in Guinea

Products	2010	2011	2012	2013	2014	2015
Animal products	10.7	11.2	11.8	12.4	12.2	12.9
Vegetable products	994	1027.3	1097.7	1217.5	1152.6	1297.7
Total of losses (MT)	1004.7	1038.5	1109.5	1229.9	1164.8	1310.6

Sources: National Institute of Guinea (INS) and our Calculations.

Table 3b2

Evolution of food losses in MT by product group from 2010 to 2015 in Guinea

Products	2010	2011	2012	2013	2014	2015	Weight (%)
	Volume of food losses (in MT)
Cereals and products	573.7	602.2	653.1	692.1	626.4	663.8	55.57
Starchy roots and products	163.9	167	182.9	191.6	240.6	281	17.89
Fruits and products (Wine Excluded.)	179.4	181.3	185.2	257.4	211.1	211.3	17.87
Vegetables and products	41.3	40	39.3	38.7	40.3	33.7	3.40
Oilseeds (excl. Prod.)	17.2	18.3	17.9	18.2	13.7	87.2	2.52
Sugar crops (Excl. Prod.)	14	14	14.7	15	15.7	15.4	1.29
Milk and products (Butter excluded)	7.1	7.5	7.9	8.3	7	7.8	0.66
Legumes and products	4.5	4.5	4.6	4.5	4.7	4.1	0.39
Eggs and products	3.6	3.7	3.9	4.1	0	0	0.22
Meats (slaughter) and products	0	0	0	0	4.7	4.6	0.14
Stimulants	0	0	0	0	0	1.1	0.02
Edible offal	0	0	0	0	0.3	0.3	0.01
Animal fats and products	0	0	0	0	0.2	0.2	0.01
Sweeteners	0	0	0	0	0.1	0.1	0.00
Alcohol (Beer and Wine included)	0	0	0	0	0	0	0.00
Spices	0	0	0	0	0	0	0.00

Sources: National Institute of Guinea (INS) and our Calculations.

In the case of Mali, there is no data on household food consumption that can be used to estimate the calculation parameters for the period 2010–2015. Coefficients of variation are provided by FAO and a normal logarithmic probability density function has been assumed to characterize the distribution of DEC. The minimum food energy requirements (MDER) are determined using the standards established by the FAO/WHO4 expert group on energy needs. The distribution of the population by age group and sex is given by the United Nations population outlook (2017 estimates). Data on the size of individuals are obtained from WHO and birth rates are those of the National Statistical Offices (INSAE5 for Benin, INS6 for Guinea and INSTAT7 for Mali). The MDER has been estimated by combining all of this information using the EXCEL model put online by the FAO for this purpose the food energy availability per person per day of the FBS serves as a proxy for the average food energy intake (DEC).

According to this methodology, the results in 2015 show that Benin and Guinea with respectively 14.23% and 15.6% of the population with a prevalence of undernourishment are ahead of Mali with 5%. In terms of individuals. estimates give 1.5 million Beninese. 1.91 million Guineans. and 0.9 million Malians who were undernourished in 2015.

Table 3c

Distribution of food losses by product for Mali (%)

Products	2010	2011	2012	2013	2014	2015	Weights
Wheat	5.2	5.4	5.4	5.6	5.8	5.6	0.3
Maize	15.0	15.0	15.0	15.0	15.0	15.0	20.7
Rice (Blanched Equivalent)	11.0	10.6	9.7	10.3	10.6	10.6	29.4
Millet	16.4	16.4	16.3	16.6	16.4	16.4	19.0
Sorghum	16.4	16.4	16.6	16.4	16.5	16.5	17.1
Cassava	15.0	15.0	15.0	15.0	15.0	8.8	0.2
Potatoes	32.9	34.7	34.8	35.0	34.8	34.6	0.1
Sweet potatoes	15.0	15.0	15.0	15.0	15.0	15.0	1.0
Yam	5.0	5.0	5.0	5.0	5.0	5.0	0.4
Peanuts	16.3	16.1	15.7	16.1	15.9	16.6	6.6
Tomatoes	14.3	14.4	12.9	14.1	13.0	13.6	0.2
Onions	10.0	10.0	10.0	10.0	10.0	10.0	0.1
Oranges and Mandarins	9.9	9.9	9.9	9.7	8.0	8.1	0.6
Bananas	20.0	20.0	20.0	20.0	20.0	20.0	0.5
Milk (excl. Butter)	4.5	4.2	4.4	3.8	3.8	3.7	3.8
Eggs	25.0	25.0	24.9	25.0	24.9	24.9	0.1
Total percentage of food losses	14.0	13.8	13.6	13.8	13.8	13.9

Sources: National Institute of Mali (INSTAT) and our Calculations.

Table 4

Evolution and loss index

	Benin		Guinea		Mali
	Percentage	Index	Percentage	Index	Percentage	Index
2010	12.45	100	12.5	100	14	100
2015	10.58	85.0	11.3	90.4	13.9	99.1

Sources: INSAE.INS.INSTAT and our calculations

2.4 World food loss index (WFLI)

SDG 12 which aims to “guarantee sustainable consumption and production methods” with in particular its Target 3 which stipulates that “By 2030, halve the volume of food waste per capita globally to the level distribution as well as consumption and reduce food losses along the production and supply chains, including post-harvest losses.” To achieve this objective, it is necessary to be able to assess food losses before any intervention aimed at reducing them. The United Nations agencies responsible for the evaluation of food losses (reference [6] Global Strategy Research Program, 2018) have proposed to split them into two parts: a part concerning food losses measured by the Global Food Loss Index and another part relating to food waste, the indicator of which is the Food Waste Index.

The selection of products is done taking into account national objectives. Indeed, it is difficult to find loss estimates for all products consumed in all countries to estimate the overall index and facilitate international comparisons. Given that dietary diversity and the achievement of food security are the main priorities targeted through the calculation of the WFLI.

For the three countries, the selection of the basket of goods was made according to the caloric intake of the products. The two products with the highest caloric intake by section in the base year (2010 as part of this exercise) are selected. Given the importance of certain products in the diet of each country, certain adjustments were made to the basket of products.

The results below were obtained for the three countries.

In the case of Benin, for the year 2010, the percentage of food loss is 12.45%, meaning that 12.45% of the food produced is lost during production, storage and processing. In 2015, this percentage rose to 10.58% equivalent to an index of loss this year compared to 2010 of 85.03%. The percentages of losses thus decreased by 14.97% on average over the period 2010–2015.8 Corn and beans were the most contributing to this decrease in the loss rate with food loss indices of 58.19% and 0.32% respectively, while cassava contributed in the opposite direction with an index food loss of around 112.53%.

For Guinea, cereal products are the most exposed to food loss since this group of products alone represents half (55.6%) of the average volumes of losses over the six years. Then come the starchy roots and the fruits which each represent 17.9% in the average of the losses expressed in in metric tons (MT).

More specifically for Guinea, food losses particularly concern rice and its derivatives. Indeed, the average lost volume of this product alone represents more than a third (35.7%) of food losses over 6 years. Cassava is the second product affected by losses (12%), then corn (11%). Bananas and plantains recorded 10.3 losses on average over the period considered.

In Mali, the percentages of food losses have evolved around 13.8% over the period 2010–2015. The highest percentage of food loss was observed in 2010 with 14%. In addition, a fall was observed between 2011 and 2012 when the index fell from 13.8% to 13.6% before rising to 13.8% in 2013 and 2014, this fall is mainly explained by the fall in the percentage loss of rice which fell from 10.6% to 9.7% between 2011 and 2012, a decrease of 8.7 percentage points.

The Tables 3a, 3b1 3b2 and 3c below summarize some results obtained for the three countries.

The Table 4 presents the estimates obtained for the percentages of losses in 2010 and 2015 as well as the loss index for 2015 compared to 2010.

3. Caveat

The use of the food balance sheet to estimate undernourishment is an interesting remedy for consumption surveys which measure this estimate more precisely. Indeed. consumer surveys in southern countries are not too frequent because they are too expensive. The advantage of food balance sheets lies in the fact that they must be periodic. which allows for an evolving vision of undernourishment and post-harvest losses. As the compilation of food balance sheets requires the mobilization of a lot of data and therefore of many actors of the statistical system. depending on the country this can prove to be difficult. And one of the main limitations lies in the fact that for many products we have recourse to estimates or expert opinions.

4. Conclusion and recommendations

Analysis of the self-sufficiency ratio over the 2010–2015 period shows that Mali has higher food self-sufficiency than Benin and Guinea.

In Guinea. overall. 43.2% of domestic product supplies are on average imports. Plant products are the most dependent on imports with an average annual IDR of 48.2% compared to 12.5% for animal products.

In the three countries. plant products are the most dependent on imports less more for Benin.

The use of FAO methodologies for calculating the prevalence of undernourishment under SDG 2 and the food loss index under SDG 12 made it possible to estimate these two indicators using FBS and other related indicators.

Also. most of the information relating to industrial uses is not available at the national level. However. to have a complete FBS. it is recommended that the Technical Working Group (TWG) in Benin continue for seeking information on the uses of broken rice. in particular the quantity allocated to food. The Benin TWG started the FBS development activity for 2016. 2017 and 2018 which will allow monitoring of the SDG indicators.

In general. a certain number of aspects must be considered for monitoring these indicators:

Improve the production of statistics for the sectors concerned by the preparation of the food balance sheet to obtain quality data;

Continue the training and assistance of technical working groups in order to perpetuate the monitoring of aggregates useful for formulating policies in the context of the fight against food insecurity and undernourishment;

Put in place food preservation systems to reduce losses of products in general. rice and cassava in particular;

Support the agriculture sector at the national level by investing in research and development. manpower training and equipment in order to increase the productivity of the sector;

Support policies to eradicate the undernourishment by promoting a resilient socio-political. economic and health environment.

Footnotes

Only few pilot countries were concerned and the objective was to support them for the monitoring of the SDGs.

Domestic availability $=$ Production $+$ Import $-$ Export $-$ $\Delta$ Stocks.

WHO: World Health Organization.

INSAE: National Institute of Statistics and Economic Analysis (Benin).

INS: National Statistics Office (Guinea).

INSTAT: National Statistics Office (Mali).

Note: This variation may be due to the fact that the years 2010 to 2014 have more estimates than 2015.

Acknowledgments

I thank Paul-Henri NGUEMA MEYE, Pieter Everaers, James Whitworth and two anonymous referees for helpful comments. I was working at AFRISTAT when this research was initiated. Usual disclaimers apply.

Appendix

Food balance sheet (see reference [ 2 ])

A food balance sheet (FBS) can be defined as an aggregated and analytical dataset that “presents a comprehensive picture of the pattern of a country’s food supply during a specified reference period.”9

⁹

For this definition and a more extended description of the motivation behind the development of food balance sheets, see the 2001 FAO food balance sheets handbook: “Food Balance Sheets: A Handbook,” http://www.fao.org/docrep/003/X9892E/X9892E00.HTM.

This is achieved in an accounting framework. wherein all potential sources of both supply and utilization of a given food product are specified. The quantities allocated to all the sources of total supply – amount of the food item produced. the amount of the food item that is imported. and the amount of the item that is either added to or taken from stocks – must be equal to the quantities allocated to all the sources of utilization. which can include exports. losses along the supply chain. livestock feed. seed use. tourist food. industrial uses. other uses. and food available for consumption by a country’s residents. This balance is compiled for every food item (estimated on a primary commodity equivalent basis) consumed within a country. and then all of the primary commodity equivalent balances are combined into one overall food balance sheet.

This definition can be formalized by the following equation: SUPPLY $=$ UTILIZATION

P $+$ I $-$ dSt $=$ X $+$ Fo $+$ Fe $+$ Se $+$ T $\displaystyle\quad\text{$+$ IU $+$ Lo $+$ ROU $+$ food processing}$

where P $=$ production; $I=$ imports; dSt $=\Delta$ stocks; Fo $=$ food; Fe $=$ feed; Se $=$ seed; $T=$ tourist food; IU $=$ industrial Use; Lo $=$ Loss: Rou $=$ Residual or other uses.

Methodology for calculating the Prevalence of undernourishment (PoU) (see reference [ 4 ] and [ 5 ] Wanner and al. (2014). Naiken. L. (2003))

The PoU indicator is defined as the probability that an individual’s daily dietary energy intake (x). taken randomly from the reference population. will be less than the minimum dietary energy requirement (MDER) to lead a normal. healthy and active life (Wanner and al. 2014). Hence the formula:

$\displaystyle PoU=\int_{x<\textit{MDER}}f(x|\theta){\rm d}x;$

where $f(x)$ is the probability density function of daily calorie consumption per individual (Dietary Energy Consumption. DEC) and $\theta$ a vector of parameters of this function. The number of parameters depends on the law followed by the function $f$ . In most cases. the distribution is considered to follow a log-normal distribution. in which case it is determined by two parameters. namely the mean DEC and the coefficient of variation (CV). In other cases. an asymmetric normal or asymmetric log-normal distribution is considered. so in addition to the two previous parameters. the asymmetry coefficient (Skewness. SK) is required (FAO and al.. 2018).

So to calculate the PoU. you must first choose a functional form of the distribution of food consumption $f(x)$ and have the following parameters: the daily food energy intake (DEC); the threshold for minimum food energy requirements (MDER); the coefficient of variation which accounts for the inequality in the consumption of food products between the different layers of the reference population and the coefficient of asymmetry (in the case where the distribution of daily consumption is not symmetrical to a transformation-close).

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Available from: http://www.afristat.org/contenu/pdf/publications/Rapport_analyse_bilans_alimentaires_Benin.pdf. http://www.afristat.org/contenu/pdf/publications/Rapport_analyse_bilans_alimentaires_Guinee.pdf. http://www.afristat.org/contenu/pdf/publications/Rapport_analyse_bilans_alimentaires_Mali.pdf.