The expression nutritional requirements refers to the quantity of energy and of nutrients, expressed on a daily basis, necessary for a given category of individuals that will allow these individuals, when in good health, to develop and lead a normal life.

Nutritional requirements have been established on the basis of physiological studies (metabolic balances) and field epidemiological studies. Requirements vary according to age, sex, body weight, level of activity and physiological status (for example, pregnancy and lactation). They are expressed in the form of averages, taking into account individual variation.

Reference nutrient intakes (RNIs) are used as a standard to assess the adequacy of intake of various nutrients. They are evaluated by national or international committees of experts, on the basis of clinical, epidemiological and experimental data, for physiological requirements for energy and other nutrients (the most recent of these recommendations are contained in the report FAO/WHO/UNU, 1985 - Energy and Protein Requirements, 1986 - Requirements of Vitamin A, Iron, Folate and B12).

RNIs are calculated in such a way as to ensure the best possible nutritional status (according to the current state of knowledge) for the particular population at which the recommendations are aimed. For all nutrients, with the exception of energy, they include a safety margin to take account of individual variation, taking the form of the mean plus two standard deviations, thus ensuring the levels will be adequate for 97.5% of the population. The RNI for energy is fixed at the mean requirement, as excess energy intake can be an indication of malnutrition, in the form of obesity.

Figure 2.2 shows the relationship between requirements and various reference values for all nutrients with the exception of energy.

Figure 2.2: Relationship between various reference values

The diagram shows the estimated average requirement, which is the mean of the range of individual requirements. It is assumed that individual requirements are normally distributed around that mean. The RNI is set at the mean plus two standard deviations, which means that that amount of the nutrient is enough for almost every individual, even those with high needs. Thus this minimises the chances of assessing an individual as having an adequate intake of the nutrient when in fact they are deficient. It also shows the lower reference nutrient intake at the mean minus two standard deviations. This is a yardstick which is increasingly being used as indicating an amount of the nutrient which would be enough only for a very small amount of the population. Thus if people are normally eating less than the LRNI, they will almost certainly be deficient in that nutrient. Because it is impossible to tell what an individual's requirements are, with out undertaking expensive laboratory work, it is difficult to say with any accuracy whether an individual has adequate intake of any specific nutrient if their intake lies between the LRNI and the RNI. However, if we are looking at group data, then the larger the group, the more likely that their intake should be at or above the EAR if they are, in fact, adequately nourished.

This gives some idea of the complexity of using measurements of nutrient intake to assess the adequacy of diet, whether at the individual or population level. It is difficult to use intake data to assess nutritional status.

Nutritional status is the term used to indicate the net outcome of individual food usage (ingestion, absorption and utilisation), disease status and work demand. It is the outcome of previous nutrition, and indicates the presence or absence of deficiency signs, the failure of growth or some other aspect of functional capacity. It is a rather broad, multi-faceted concept which is difficult to measure directly. Nonetheless, certain anthropometric measurements are generally considered reliable indicators of nutritional status, particularly for young children. The most frequently used measurements are weight, height and arm circumference. Measurements for an individual child are compared to reference values for the appropriate age and gender and are used to assess the status of a population of children.

1.3 Factors affecting individual nutritional requirements

In order to survive, work and reproduce, the human being must find in his diet energy and the necessary nutrients in adequate amounts. A balanced diet is that which provides, in the correct proportions all the essential nutrients to the body.

Several factors influence nutritional requirements.

1.3.1 Growth

During the first five years of life, nutritional requirements, both of energy and other nutrients, are relatively higher than for the adult. A child doubles its weight in the first six months of its life. Table 2.1 shows child energy requirements in Kcal per Kg of body weight per day.

Table 2.1: Energy Needs of Children 0-10 years, Kcal/kg bodyweight/day

Because of their relatively high energy requirements, young children are much more vulnerable to energy deficiencies than adults. At this period of their lives, malnutrition can affect both physical, and eventually mental development.

1.3.2 Pregnancy

Pregnancy increases the energy requirements of the mother by about 15%. For most of the other nutrients, requirements are also higher. If the mother is malnourished before or during pregnancy, she may give birth to a low-birth weight baby, whose risk of premature death will be greater.

1.3.3 Lactation

In order to produce enough milk without lowering her own body reserves, the lactating woman must increase her energy intake by an additional 10% over and above the increase recommended during pregnancy.

1.3.4 Physical activity

The more active an individual is, the higher his energy requirement. In rural areas, requirements vary according to the agricultural seasons (land clearance, sowing, weeding and harvest). In general, individuals are normally classified as engaging in light, moderate or heavy physical activity, according to lifestyle.

1.3.5 Body weight

Along with physical activity, body weight constitutes the major item of expenditure of an adult. In practice, body weight determines the basal metabolic rate (BMR), that is the minimum quantity of energy necessary to support the body at rest. A method for calculating BMR will be discussed in section 1.4.

1.3.6 Infection and rehabilitation

Infections can increase the loss of nitrogen and of certain vitamins and minerals from the body, and energy expenditure. This means that requirements for energy, protein and other nutrients will be increased during infection and recovery. Appetite is often reduced during infections. Repeated episodes of diarrhoea can lead to dehydration and severe malnutrition in an already vulnerable child. In Africa, measles can often be a precursor of severe protein-energy malnutrition in children.

1.4 Reference nutrient intakes (RNI)

As discussed above, RNIs are used to assess the adequacy of diet for both individuals, but more importantly as far as food security is concerned, the adequacy of the supply of nutrients, in particular energy and protein, available to the population of a country, and to which they have access.

1.4.1 Reference nutrient intakes for energy

RNIs for energy reflect the average requirement for the relevant population group, to avoid recommending energy intakes which could lead to obesity. RNIs are given by gender and by age group, which can then be aggregated, according to the proportions of the different categories present in the population, to give national figures.

Energy intake can be measured in calories (Kcals.) or joules (Kjs.). 1 Kcal. is equivalent to 4.2 Kjs. In this chapter kilocalories will be used as the unit of measurement.

The energy content of food depends on its composition. One gram of carbohydrate contains 4 Kcal, as does one gram of protein, whereas one gram of fat contains 9 Kcal. Energy is used by the human body for the following purposes:

i) To maintain body functions, breathing, circulation, temperature maintenance, while the body is at rest. The rate at which the body uses energy while it is at complete rest is defined as the Basal Metabolic Rate (BMR). BMR varies according to body weight.

ii) To sustain the chosen level of physical activity (PAL). In general, adjustment for the PAL will vary according to gender and as to whether lifestyle involves light, moderate or high levels of activity. In most developed countries, a PAL of 1.55 is applicable to most people, i.e. 1.55 times the BMR. This represents a sedentary lifestyle with desirable activity. A high level of physical activity at work and leisure would be represented by a PAL of 1.8 for women and 2.1 for men.

iii) To cover the energy costs of metabolising foods after meals. This is usually included in calculations of the BMR.

EAR = BMR * PAL

To calculate the EAR or RNI (which are the same for energy intake) for a group of the population the analyst must know the average weight of the population under consideration, by age group. Based on this, the appropriate BMRs can be calculated, using the formulae (according to Schofield's equation) given in Table 2.2.

Table 2.2: Basal metabolic rate (BMR) for adolescents and adults (Kcal/day)

After the relevant BMRs have been calculated, then appropriate figures for the PAL should be applied to the BMRs. This will vary according to the normal levels of physical activity undertaken in the society concerned. Table 2.3 shows this as estimated by FAO for Africa, on the assumption that 20% of the population is urbanised.

Table 2.3: Appropriate physical activity levels for Africa

These figures can be aggregated, and the needs of pregnant and lactating women taken into account as shown in Table 2.4. Population figures by age group are filled in to column B, estimates of average body weight by age group into column C, then the formulae given in Table 2.2 can be used to calculate column D. Columns G and H can then be calculated, and the average individual energy allowance can be derived from dividing the sum of column H by the sum of column B. This figure can then be used to make comparisons at a national level, for example in a food balance sheet, as will be discussed in section 2.1. Care has to be taken not to use these kinds of figures inappropriately by, for example, comparing them to measures taken at the population level, without taking into consideration the inequality in the distribution of food at the inter and intra- household level.

Table 2.4: Calculation of total energy requirements for a country

The ESN Division of FAO can provide software (DOS compatible) which allows automatic calculation and/or adaptation of the spreadsheet shown in Table 2.4.

1.4.2 RNIs for protein

Food proteins provide amino acids for the synthesis of body proteins and nitrogen for the synthesis of many other tissue constituents. The body is in a dynamic state, with proteins being broken down and released on a daily basis. The body needs to replace these proteins, even after growth has stopped, and the body has reached its adult size.

Protein can be broken down into twenty amino acids, of which ten cannot be synthesised by the human body but are necessary for growth and maintenance. These are known as essential amino acids and they have to be provided from the regular diet. The other ten can be synthesised by the body and therefore do not need to be ingested in the diet.

RNIs for protein have been falling over time, as understanding of the use of protein in the body has improved. Previously recommendations were based on patterns of protein intake in healthy populations, but now they are mainly based on nitrogen balance studies, which look at the turnover of nitrogen, resulting from the breakdown of protein in the body. Thus the RNI is now based on measures of need.

The protein RNI has been set for all adults aged 19 years and over at 0.75g/kg/day. In other words, it is set in relationship to body weight. This assumes that the protein is of high quality, i.e. that the essential amino acid composition in the food protein is close to the human body's need. This will be the case in a mixed diet, composed of a mixture of animal and vegetable protein, particularly containing a reasonable proportion of milk, eggs, meat and fish. When a high proportion of protein ingested comes from a few vegetable sources, such as sorghum, millet or legumes, then a higher intake will be necessary to ensure an adequate intake of all the essential amino acids. It has been suggested that for the typical African diet that the RNI should be closer to 1g/kg/day.

RNIs for children and pregnant and lactating women are higher than the standard adult figure, to allow for growth in children, the growth of foetal and maternal tissue in pregnant women and breast milk production in lactating women. It is recommended that pregnant women add an additional 6g/day to the RNI for protein and that lactating women add 11g/day.

The intake of protein recommended will only be effective in preventing signs of protein deficiency if the needs for energy are met. If energy needs are not met, dietary protein will be used as a source of energy, rather than going towards tissue growth and repair. Thus protein adequacy should be assessed in conjunction with energy adequacy. A child could be living off a diet which was primarily of meat and milk, but could show signs of protein deficiency because of a low level of overall energy intake.

1.4.3 Other nutrient RNIs

Recommended nutrient intakes have also been developed for other essential nutrients such as the major vitamins, A, the five B vitamins, C, D, E and K, plus the important dietary minerals. These can be as important as protein and energy in ensuring a healthy and productive life for a country's population. Deficiencies can cause blindness, anaemia and other debilitating conditions and clearly have to be included in a broad definition of food security. However, many of the methods described in this chapter are not sufficiently detailed or precise to enable a proper assessment of adequacy. Where a diet is adequate in energy, and is composed of a reasonable variety of foods, the needs for these other nutrients will often be met. However, nutrition surveys may show specific nutrient deficiencies, or health records may identify cause for concern. The principles here are the same as for protein RNIs. A level of intake is identified which should cover the needs of 97.5% of the population, without encouraging high levels of intake which could, in some cases such as vitamin A, cause negative effects through excess ingestion.

2. Food Consumption

Once the reference values for different nutrients have been identified at different levels of aggregation, the next step is to measure actual levels of nutrient intake and assess their adequacy. In this section various methods of measuring both the food available for human consumption, and more importantly the actual amounts of food consumed are discussed.

2.1 Methods of measuring food consumption

2.1.1 Food balance sheet (FBS)

Food balance sheets, which are collated in most countries now, show the quantities of food commodities available for human consumption at the national level. By building up a picture of production, imports and exports, the FBS shows the average level of food supply in a country over a given period of time. Table 2.5 shows a condensed FBS for Indonesia for 1976. It can be seen that the necessary information to compile a FBS is as follows:

• domestic production figures by commodity and by food group;
• changes in stocks over the period concerned (an addition to stocks reduces the amount available for human consumption);
• imports and exports;
• amounts going to animal feed, seed, food and non-food manufacture and waste.

Table 2.5: Summary Food Balance Sheet, Indonesia, 1976 (thousands of tons)

This total can be compared with the estimates of requirements as discussed in section 1.4 to assess overall adequacy of the food supply in the country. The FBS can also provide a useful overview of the degree of dependency of the diet on any particular commodity or on foreign trade. For example, in the case of Indonesia as shown, it can be calculated that under 10% of the total availability in the country is imported. However, over 50% of calories come from milled rice. Thus examining the health of the rice economy in this country will tell the analyst a great deal about overall food security.

Care must be taken in the use of food balance sheets. The figures themselves can be quite crude approximations. The information necessary about rates of wastage, processing coefficients and nutrient content can only be estimates. It would be too costly and time consuming to re-estimate these every year and in some countries these figures are best described as guesstimates. They do not include any measure of domestic food waste, i.e. that which occurs in the household. If FBSs are compiled each year, then they can give a reasonable picture of trends in food availability.

FBSs are only as good as the data which are entered into them. Thus they will reflect existing problems with agricultural production data and even population numbers. In particular, subsistence production data are likely to be significantly under-represented, if they are included at all. It is fair to say that the more developed and monetised an economy is, the better the figures in the FBS (though this may be offset somewhat by complexity in the food processing sector).

The FBS gives no information about food supply at the regional, local or household level, in other words it gives no disaggregated information. It is perfectly possible for a country to have an adequate supply of food as shown by the FBS, at the national level, and yet for there to be regions or groups of households which have severe problems of food security. The more unequal income or asset distribution is in a country the less useful is the FBS as a tool for investigating food security problems. Other methods of assessing food consumption levels must be used.

2.1.2 Household food consumption surveys

To get a better picture of the variety of food consumption levels and patterns, the best approach may be to undertake food consumption surveys. These are usually carried out at the household level. This allows an examination of food consumption patterns within geographical regions, by income strata and by social class. This information allows a much more precise formulation of food security problems and, as a result, of appropriate policy responses.

The comprehensiveness of the information is dependent on how the survey is undertaken. Surveys may be conducted in such a way as to allow comparisons between food consumption patterns at different times of the year, to enable the identification of problems of seasonal food insecurity or they may have a very narrow time frame. If there is prior information to suggest that food security problems are concentrated in certain regions or amongst certain social groups, then it may be decided for reasons of cost to concentrate on those specific issues. For the most part, food consumption surveys measure consumption at the household, rather than individual level. Individual dietary surveys will be discussed in the next section.

There are four main types of data collection procedure used for household consumption surveys.

• the inventory method involves measuring household stocks at the beginning and end of the survey period, plus recording all the foods brought into the household during this period whether purchases, gifts or from home production. The usual period of time covered is between three and seven days.
• the recall procedure is used when the interviewer uses a list of major food items in a structured questionnaire, to help the respondent recall the amounts and prices of all foods used in the household, again usually over three to seven days.
• the food accounts method is mostly used in industrialised countries, where households do not generally keep large stores of food. The homemaker keeps records of the amount of foods purchased during the period of the survey. This is most likely to be satisfactory where there is limited use of freezers and shopping occurs three or four times a week.
• weighed records of the foods consumed in the house each day may be appropriate for small-scale surveys. It requires the investigator to visit the home on a daily basis, and is recommended by FAO particularly in rural areas where food is simple, home production is important and measures are not standardised.

Information can also be gathered from household budget surveys. These are often undertaken for reasons other than collecting information on food consumption, for example to update cost of living figures. The emphasis is on income and expenditure figures, rather than on consumption. However, if information is available on prices, preferably collected in the survey itself, inferences can be made about quantities of food coming in to the household. How good an approximation this is to overall household food consumption depends on the importance of purchased food in the overall diet. Box 2.1 above illustrates the differences that can arise when different survey methods are used.

2.1.3 Individual dietary surveys

Similar methods can be used for collecting food consumption data on an individual basis. Interview methods based on 24-hour recall, or taking a dietary history can obtain a picture of the food consumption patterns of individuals in specific groups, and usually have a high co-operation rate, because they tend not to be a great burden for the participant. They are limited by the accuracy of the recall and the skills of the interviewer. Recording current food intake can be done by weighing methods and recording methods. These can be more accurate but can only be carried out for a short period of time and requires a high level of co-operation from the participants.

2.2: Patterns of food consumption

As the example given in Chapter 1.2, of changing food demand in China, shows, underlying many of the changes in trading patterns seen recently in the world are changes in the demand for food at the national, and household level. These patterns are often systematically linked with economic variables such as income and may be common to most countries. Figure 2.3 below was prepared from a review of food consumption patterns, as shown by Food Balance Sheets, for countries at different levels of GNP.

At low levels of income, almost 75% of calories come from starchy staple carbohydrates such as maize, rice, wheat and tubers. As income rises, the diet becomes more complex. Intake of fats increases, particularly from animal products, and the proportion of calories coming from the starchy staple falls to 30%. The proportion of calories coming from sugars increases, but the relative protein contribution remains relatively constant, though there is a switch from vegetable to animal sources of protein. Total protein in the diet does increase, as the total availability of calories increases with a rise in GNP. These figures look at national changes in food availability, but within countries too there is evidence that people consume a more varied diet as there income rises and as they become more urbanised.

This is interesting in itself, and also gives a basis on which to judge the reliability of the results of different types of food consumption survey. However, it is not a useful way of predicting food consumption and expenditure behaviour. People buy and consume specific foods which contain nutrients, not the nutrients as such. Thus to explain individual food consumption behaviour, analysis must be in terms of commodities.

Figure 1.2: Percentage distribution of calorie source by level of per caput GNP

Source: Perisse et al., The Effect of Income on the Structure of the Diet, Nutrition Newsletter, 1969.

There have been a number of attempts to categorise food behaviour as income rises. Engel's Law (derived from studies undertaken on in Belgian mining communities by an applied economist called Engel) states that as income rises, the proportion of income spent on food falls (total food expenditure may rise but it decreases in relative importance). Analysis done recently by Lipton suggests that there are some groups of the population for whom this may not apply. The ultra-poor, as he refers to them, spend about 80% of their income on food, and cannot spend any more than this, because they also need to buy fuel, clothes and other basic needs. When their income rises, they will continue to spend a similar proportion on food until they come close to satisfying their nutritional needs. He suggests that if household expenditure data show that 80% of a family's income is insufficient to provide 80% of their calorie requirements, then the family should be classified as ultra-poor with a considerable food security problem.

Figure 2.3 showed how starchy staples as a whole declined in relative importance as income increased. This is a generally observed pattern, often referred to as Bennett's Law. Figure 2.4 shows how complex that process can be in any given country. Data for Peru show calories from starchy staples increasing from about 600 Kcal at low income levels to just over 1100 Kcal at high income levels. Consumption of maize and barley fell as income rose, potato consumption rose and stabilised and the consumption of rice and white bread increased steadily with income. This is typical of many countries with a number of starchy staples commonly consumed. There will be evidence of a clear hierarchy in preference amongst the different staples.

Figure 2.4: Consumption of starchy staples by income group in Peru

These kinds of patterns can be quantified for use in demand forecasting. The income elasticity of demand for a commodity is defined as follows:

i.e. the percentage change in demand for food, divided by the percentage change in income.

The demand for a commodity will also change as its price changes. The price elasticity of demand for a commodity is given by dividing the percentage change in demand by the percentage change in price.

These parameters can be used to predict changes in the market demand for food commodities over time. (For greater detail, see Annex 1).

Although there are many factors which can affect the demand for a commodity over time, particularly at national level, such as changing tastes, urbanisation and changes in a country's internal income distribution, there are three major factors which are included in most estimations of future demand for a commodity. These are:

the rate of growth of the population (n)
the rate of growth of real per capita income (y)
the rate of change of real prices (p)

In other words, the rate of growth of demand for a commodity can be decomposed into the rate of growth of population plus the rate of growth per capita demand. If real prices are stable, then the equation simplifies to:

Thus, if a government planner wished to know what increase would be necessary in maize production to meet demand in five years time, he would have to know the predicted rate of growth of the population, say 3% per annum, the rate of growth of per capita income, say 2%, and the income elasticity of demand for maize, say 0.15. Then the rate of growth of demand for maize in one year would be:

Over five years the increase in demand, using the compound growth rate, would be 17.6%. Of course it is unlikely that the price of maize would remain constant in real terms over five years. This could vary according to changes in world prices, in price policy and in the prices of other food commodities which were regarded as substitutes for maize. The more complex model could be used to assess what an appropriate price policy would be, with regard to concerns for food security.

The same model can be used to predict the impact of price and income changes on specific groups of the population, rather than the level of national demand. Once the impact of population growth and income changes has identified the overall change in demand, and any likely resulting changes in price, this can be translated into the impact on food consumption of groups who were perceived to be particularly vulnerable to changes in market prices.

This section has examined recent trends in the world food economy, and ways in which food consumption in particular shows systematic patterns and variation which can be used to predict likely changes in demand. These economic forces do not work themselves out in a vacuum. The next section looks at the policy objectives which may determine the social, institutional and regulatory environment within which food security has to be achieved.

3. Direct Measures of Nutritional Status

The measurements which have been discussed so far are measurements of intake and availability. They are useful in trying to describe the economic and social factors which lead to food insecurity. However, to identify the most vulnerable members of society measurements of outcome have to be employed. The most common of these are direct measures of physical size and well-being.

3.1 Anthropometric measures

Anthropometric indicators, which are usually based on estimates of the total mass of body tissues, such as weight or arm circumference, are commonly accepted measures of the nutritional well-being of an individual. Unlike other measurements which have been discussed, these can only be measured at the individual level, not at household level. They can however be aggregated to give information about a specific population.

In one sense, they are the ultimate outcome indicators of food system of a country, but as was shown in Figure 1 they are affected by both food and non-food elements such as illness and parasitic disease, and have to be interpreted with care. Their significance will be discussed in the next section.

Anthropometric measurements are most commonly taken on young children. This is for a number of reasons. They are often seen to be amongst the most vulnerable members of society and therefore their nutritional status is a more sensitive indicator of well-being than that of adults. Certainly very young children who have relatively large energy requirements for growth, show the effects of low energy intakes more clearly than adults, who may adapt to low intakes by reducing activity levels. Until recently, there has been more information to help the interpretation of anthropometric data in children, in terms being able to associate low measurements with an increased risk of morbidity and death. However, there are indicators of adult nutrition which are quite sensitive to nutritional deprivation. The incidence of low-birth weight babies is clearly linked to maternal malnutrition. The Body Mass Index (BMI) is increasingly used as an indicator of adult nutrition in developing countries as well as industrialised nations. (This is described in more detail later in this section).

The most commonly used anthropometric indicators in children are weight-for-age, height-for-age, weight-for-height and mid-upper arm circumference. In all cases, measurements are taken and compared to reference standards, the mean or median values of body dimensions in a well fed and cared for population. As with nutrient intakes, there is a certain distribution of weights in a well-fed population for genetic reasons, and cut-off points are determined on the basis of this distribution. Thus if a child is less than 2 standard deviations (z-score) of the mean weight-for-height of children of the same age in a well-fed population, the chances of this being the result of their genetic make-up is very slight indeed. The child is almost certainly malnourished. Table 2.6 is an example of the kind of standard reference table that would be used when assessing weight-for height. The child's weight would be measured, and then the weight of the child assessed in relationship to this. Similar tables are available for weight-for-age and height-for-age.

Table 2.6: Weight for height for boys between 106 and 120 cms (in Kg)

Table 2.7 shows some of the characteristics of commonly used anthropometric indicators for children, with their respective cut-off points, and the terms normally used to refer to the different categories of malnutrition. They have different degrees of ease in use. The first two require knowledge of the child's age, which may be difficult to get accurately in some communities. However, weight-for-age is perhaps the most commonly used indicator, and reflects present and past history of malnutrition. Height-for-age is generally interpreted as reflecting the effects of chronic malnutrition, leading to growth retardation or stunting. Weight-for-height does not require knowledge of the child's age and is sensitive to recent episodes of malnutrition which have resulted in wasting. Thus a child who has suffered severe episodes of malnutrition in the past, but is now well-fed and healthy might be identified as malnourished by the first two indicators but not the third. Arm circumference measurements also reflect current episodes of malnutrition. This can be one of the cheapest methods of measuring children, though it does, like the other methods, require proper training of the investigators.

Table 2.7: Characteristics of Commonly Used Anthropometric Indicators

Body Mass Index (BMI) is becoming more accepted as the best anthropometric way of identifying changes in the wellbeing of adults. This index is calculated by dividing weight (W), measured in kilos, by height (H), squared, measured in metres:

Depending on the degree of undernutrition or obesity of the subject, the BMI can take values between 15 and 40. The following classification has been proposed:

below 16 severe chronic malnutrition
16-17.5 chronic malnutrition with wasting
17.5-18.5 chronic malnutrition, underweight
18.5-25 normal
25-30 overweight
over 30 obese

The choice of anthropometric indicators depends on a number of factors, including cost and ease of collection, but also the purpose for which they will be used. Anthropometric indicators are often used for screening children in emergencies, to establish eligibility for relief programmes. The indicator of choice here, arm circumference or weight for height, will be different to that appropriate for assessment of long-term food security problems at a national level.

3.2 Interpretation of nutritional indicators in food policy analysis

There are three major problems in the use of anthropometric indicators for food policy analysts: to determine whether the problem indicated is actually a food security problem, as opposed to, for example, a public health problem; to determine how significant the problem indicated by nutritional information is; and to determine what an appropriate policy response might be.

As has been mentioned before, a nutrition survey can indicate severe nutrition problems which do not arise from inadequate availability or access to food. Rather the problem may be one of chronic gastro-intestinal disease, resulting from inadequate sanitation, or a problem of a disease such as malaria interacting with mild malnutrition to result in severe nutritional problems. One way of pinning down the problem further is to examine a combination of nutritional and food consumption data, particularly food expenditure rather than intake, to identify whether a problem of access to food exists. Health indicators may also clarify the likely causes of poor nutritional status. No one data source will give a clear picture of the sources of the problem. A picture has to be built up using whatever information exists.

There is also the question of interpreting the nutritional statistics. How serious a situation is it if, say, a survey determines that 5% of the nation's children suffer from third degree malnutrition? There are two ways of interpreting the information, in relationship to figures prevailing in other countries and in terms of the trend existing in one's own country. The World Health Organisation suggested in the 1980s that 40-45% of children in developing countries suffered from mild or 1st degree malnutrition, 25% from 2nd degree or moderate malnutrition and 3% from severe or third degree malnutrition. However, these numbers are heavily weighted by relatively high levels of malnutrition in the populous Asian countries. Should a country like Costa Rica be concerned over the existence of 0.5% severe malnutrition in 1975? In terms of other countries in Central America this is a low figure, and it had fallen to this level from 1.5% a decade earlier. At this level, the issue is one of social and political priorities, as well as the economic cost of tackling a problem of this magnitude.

The appropriate response will depend on the relative size and concentration of the nutrition problem and whether it can be shown to be primarily a food security problem. If 40% of the population appears to suffer from a nutrition problem which is primarily food related, then the policy response has to be a broad one, probably affecting major macro-economic parameters such as price levels and the overall level of economic activity. A problem which affects 5% of the population at a serious level may be approached from a tightly targeted perspective. The extent and pattern of the problem, as shown by nutritional indicators, will determine the nature of the policy response.

4. The Aggregate Household Food Security Index

In the last few years, the Committee on Food Security in FAO has been supporting efforts to develop and index for food security that incorporates all three elements of FAO's concept of food security, namely availability and stability of food supplies and access to food. The Aggregate Household Food Security Index which has been developed combines an indicator of per caput food availability for human consumption (Dietary energy supplies in kilocalories) with information on the distribution of available food, and takes the following form:

AHFSI = 100-[H{G+(1-G)IP} + 0.5 {1-H[G-(1-G)IP]}]100

where

H is a head-count of the proportion of the total population undernourished;
G is a measure of the extent of the food gap of the average undernourished shortfall in dietary energy supplies from national average requirements for dietary energy;
Ip is a measure of inequality in the distribution of the individual food gaps of the undernourished, based on the Gini coefficient;
is the coefficient of variation in dietary energy supplies, which gives the probability of facing temporary food shortage.

These cut-off points are fairly arbitrary, but the index does give an overall measurement which can be used to compare countries, and monitor the progress of an individual country over time. Much, of course, depends on the accuracy of the data available and there is considerable extrapolation from income statistics.

Table 2.8 shows the AHFSI for a selection of countries whose AHFSI was originally medium or below and dropped during the period 1988-90 to 1991-93.

Table 2.8: AHFSI values for selected countries

There is not, as yet, a great deal of experience in the use of these indicators, and some of the values shown in Table 2.8, for example for Haiti, would seem to indicate a considerable variation in value. However, the AHFSI goes a long way to allowing a preliminary identification of a country with considerable food security problems, which need a closer analysis for policy assistance. The index is likely to prove particularly useful in assessing requests for food and other emergency assistance.

5. Assessing the Nutrition Situation at Different Levels of Aggregation

This chapter has discussed a number of different indicators at different levels of aggregation. They each can give useful information on different aspects of the food system and potential food security problems, but the analyst has to be careful to interpret the results sensibly and not to make unreasonable inferences from the data available.

Food Balance Sheet information gives a highly aggregated picture of food flows through the system, culminating in an estimate of average calorie and protein availability at a national level. This can be compared with estimates of average RNIs (reference nutrient intakes) for the population to assess the adequacy of the food supply. However, although RNIs give an estimate of the minimum food supply necessary if food were distributed according to need, it does not give much information about the adequacy of the food supply to meet needs under the existing income and asset distribution. In other words it does not give information on actual access.

Food Consumption Data taken from expenditure or budget data are a better measure of potential access to food. However, they often are weak in their coverage of non-monetary access to food, through social institutions and obligations or through subsistence production. They may only cover some of the sources of food entitlement. The resulting figures have to be compared to RNIs to assess adequacy. This may be a reasonable procedure if the food consumption data are aggregated over the whole survey, or a significant subgroup of the survey, but the more disaggregated the data, the more dubious a comparison with an RNI figure based on average requirements. In other words, an individual household may consume less food than average because it has more small children or elderly people, or the family is genetically small in body weight, or it has lower requirements for other reasons. Equally a household which appears to meet the average RNI for the country may be deficit because of higher physical activity or the age composition of the household. Adjustments can be made to take some of these factors into account, but potential error can still be high. If Lower RNIs have been calculated (see section 1.4) then they can be used to identify with reasonable certainty a minimum of families which are deficient in nutrient intake, but this will only identify the worst off households. There will certainly be other deficient households which will be above the minimum threshold but still heavily deficient.

Food Intake estimates, whether through recall or weighed intake, usually give a more accurate estimate of actual food intake than food consumption data. However, they are subject to the same problem of comparison with average standards or RNIs. Unless intake figures are collected along with other socio-economic information, it can be difficult to identify the sources of entitlement which determine the household's food intake. An informed policy response has to be able to identify the underlying cause of any food security problem.

Anthropometric Data give a clearer indication of problems of poor nutritional status. The data are somewhat easier to interpret as to the existence of a problem, but do not indicate whether nutrient or food deficiencies are the major cause. Unless the survey has been carefully planned to assist policy formulation, there may be insufficient socio-economic data collected along with the nutritional data to clarify the links with the food system.

The task of food policy analyst is not an easy one when it comes to identifying relevant data and interpreting it correctly to enable policy formation. Box 2.2 describes how one researcher used multiple sources of food related data to clarify the food security problem in southern Mali. His results are fairly typical of the complexity of food systems and the difficulty in describing them with only one or two data sources.

Compiling a description of food security problems in a given country is rather like trying to solve a jigsaw puzzle, with a number of the pieces missing, or following a map which has become torn and illegible in places. The analyst has to use what data are available. If specific policies are being contemplated, then there may be a need for very focused survey work to elaborate on the appropriateness and likely impact of that policy. There will be a place for a broad range of socio-economic data which have not been discussed in this chapter to identify problems in availability, access and stability of food supplies for specific groups of the population. Qualitative information can be useful as well as the quantitative sources discussed above.

However, the analyst or decision-maker should not become too focused on the problem of inadequate data. Data collection can be extremely expensive, and a balance has to be struck between spending scarce resources on collecting more information and spending those resources on tackling problems. This is not an easy balance to strike, but it is central to tackling food security problems successfully.

Activities related to Chapter 2

Introduction

1. Activities 1, 2 and 3 proposed below should refer to a specific country case. Depending on the available data, this could be the country where the course is held or the country of origin of the participants.

Activity 1: Calculation of energy requirements

Participants will be asked to calculate the energy requirements for their own country using Table 2.4. Where available and appropriate, the software program EPINUT can be used.

Activity 2: Typical patterns of food consumption

Participants should make notes on what they regard as a typical day's food consumption for:

• an urban family


• poor


• middle income


• a rural agricultural worker's family


• a rich landowner

What are the major staples consumed? How often and when do different family members eat? What foods are people likely to increase their consumption of when their income rises? Are these foods grown in the country or imported?

Activity 3: Measuring food availability

Participants will be given a number of different data sets for the country being used in this training course. These will include, as and when available:

Using this data, analyse the food security situation for the region and for the country.

If data are not available, the following data for Mideastia can be used.

Table 1: Consumption and food security data for Mideastia, 1988-1990

Table 2: Calorie consumption patterns by season in four districts of Mideastia

Table 3: Sources of calories for four districts in Mideastia

Table 4: Nutritional status of pre-school children

Table 5: Mean body mass index (BMI) of adults 20 years old or more, by income group