Consumption of fruit and vegetables

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As well as understanding how an existing market system operates and can be modified, it is also essential to quantify the present level of consumption of fruit and vegetables. There are two approaches to estimating consumption: a demand approach or a supply approach.

Demand approach. The basis for this method is the use of income elasticity coefficients, relating changes in income to spending behaviour. The concept is represented by a formula, relating population to income:

Qn = Qo (1 + p)(1 +ey) n

where: Qn = projected consumption at year n
Qo = consumption in the base year
p = annual rate of population growth
y = projected growth of per capita disposable income
e = projected income elasticity
n = number of years from base date

Although a number of countries, for example the Philippines and Sri Lanka, have rather complete data on elasticities this is not normally the case. Figures from other countries are not generally applicable, given different dietary and cultural habits. For example, the demand coefficient for eggs in the Philippines is 0.68, whilst in Sri Lanka it is 0.86 and more dramatically, the coefficient for maize in the Philippines is minus 0.61, whilst in Sri Lanka it is 0.2. There are other major difficulties in using demand elasticities as they assume that past consumption trends will continue. This might be reasonable if historic data is available to verify this, but generally it will not be. Another problem with the approach is in understanding the substitution effect between different vegetable and fruit crops, for which only limited data may be available.

Adequate demographic data often exists for estimating the population effect, but other factors in the equation are unlikely to be reliable without undertaking extensive surveys to establish income elasticities of demand, seasonal differences in trade and a profile of average disposable income.

Supply approach. Because of the problem of estimating consumption using a demand approach, reliance usually has to be placed on available data on per caput consumption of fruit and vegetables These are derived from estimates of present supply, making adjustments for imports, exporls and food processing. A typical example of such an estimate is shown in Table 12.5.

Table 12.5 National food balance sheer data for Nepal -1980/81

Commodity Production
Net Supply
Per caput*
kg. per annum
Potato 320 1 1 331 15.57
Vegetable# 511 6   517 31.35
Fruit 271 4 - 275 15.54
Irish 4 - - 4 0.27

Source: Marketing Services Division Ministry of Agriculture Nepal 1989. Notes: assuming allowances for seed production and waste.
#including leafy vegetables but excluding pulses and tubers.

Table 12.6 Typical per caput availability of major foods 1986 - 88 (kg)

Country Cereals Roots &
Pulses & beans Fruit & vegetables Animal products
Developing countries          
Bangladesh 1,582 29 44 22 50
China 1,867 159 29 68 239
India 1,310 38 126 67 117
Indonesia 1,789 187 18 58 71
Malaysia 1,224 75 29 105 380
Nepal 1,654 54 63 19 114
Pakistan 1,268 8 50 59 180
Philippines 1,390 109 8 104 224
Sri Lanka 1,340 73 50 103 98
Thailand 1,399 57 34 136 179
Tonga 501 1,146 - 127 179
Developed countries          
Australia 786 92 17 157 1,016
Japan 1,179 79 22 131 510
New Zealand 719 104 18 214 1,134

Source: Selected indicators of food and agricultural development in the Asia-Pacific Region 1979-89. 1990. Bangkok, FAO Regional Office for Asia and the Pacific RAPA Publication 1990/15.

FAO has undertaken a number of regional studies of nutrition, based on food balance sheets and taking into accounl availabilily of- foods and human energy requirements. From these studies estimates of per caput consumption can be derived. Data from one of these studies of selected countries in the Asia-Pacific Region is shown in Table 12.6. These type of data are likely to be the most easily available, but need to be used with some caution as the figures are national averages. They are likely to disguise substantial variations in consumption between different seasons, locations, income groups and between urban and rural areas, particularly if there is also a large tourist trade.

Per caput consumption data should ideally be derived from detailed local surveys. An example of such an approach is a study in Thailand which surveyed the daily intake of fruits and vegetables for a range of income classes, based on surveys in two villages over two seasons. The range of kg per caput values in Table 12.7 demonstrates the substantial variation that can occur. However, the overall average compares reasonably well to the FAO national figure of 136 kg per caput shown in Table 12.6. Results of a similar type of sample survey of consumption of major food items, for selected districts in Nepal, arc given in Table 12.8.

Table 12.7 Annual consumption of fruit & vegetables in rural Thailand

location year consumption (kg per caput):
minimum maximum
Surin (North Eastern Province) 1987 38.8 63.7
1988 88.2 131.9
Average 63.5 97.8
Nakorn Ratchasima (near Bangkok) 1987 38.8 96.9
1988 50.9 204.5
Average 44.9 150.7

Source: Konjing, C. Food security at household level in rural Thailand. Bangkok, Department of Agricultural Economics and Business Administration, Kasetsart University.

Table 12.8 Consumption in selected districts in Nepal (kg per caput)

Commodity Mustang Gorkha Jhapa Dhankuta Solokhumbu
Potato/Sweet Potato 61.83. 15.52 27.29 20.65 86.03
Vegetables (excl. legumes) 28.07 24.11 43.44 25.65 11.41
Fruit 7.67 7.60 7.08 8.70 3.15
Meat/Fish 10.18 7.87 8.12 7.98 4.44

Source: Food consumption survey, 1984/85. Nepal, Ministry of Agriculture.

Consumption estimates. The estimated consumption of fresh produce should be derived from the per caput data by relating it to estimates of the existing and future populations for the area served by the market. The following formula summarizes the calculation method:

Annual supply (tons) = total population served x per caput consumption x 0.001

The existing demand for fruit and vegetables in a typical city of around 300,000 people using this method and on the basis of an assumed range of kilogramme-per-caput consumption figures might be as follows:

Annual supply (tons) = 300, 000 x 150 kg. per caput x 0.001 = 45,000 tons.

Such an estimate needs to be checked against surveys of wholesale and retail markets in order to make an assessment of the quantities of produce that might be spoilt or be by-passing the formal marketing system. Particular care is needed in reviewing survey figures, as what may be reported as being sold at markets may also include a proportion of produce that either remains unsold at the end of the day or is sent on to other markets and perhaps sold twice in the same day. Another common distortion arises from some produce not having been purchased at the market and reaching the consumer through other channels. It may come from home-garden production (particularly fruits) or have been sold directly by farmers or traders to small corner stores, supermarkets, hotels and to institutions, such as hospitals, schools and army camps.

The values of daily throughput at a market may therefore need to be adjusted so that they can be matched with per caput consumption estimates. Table 12.9 gives an example from Northern Thailand of such a set of adjustments, comparing the trade in Muang Mai wholesale market to consumption in the Chiang Mai municipal area. It uses a range of values for the daily trade in Chiang Mai, taken from surveys (see case study in Chapter 11). The analysis assumes a constant per caput consumption and relates together possible low (dry season), medium and high (wet season) volumes-with variations in destination of produce. The analysis highlights the large volume (around 80 per cent of the total urban consumption) that by-passes the wholesale market, including a significant proportion coming from home-garden production.

Table 12.9 Daily consumption of produce, Chiang Mai, Thailand

Destination of Produce Volume Traded (tons):  
(Fruit and Vegetables) Low Medium High
Daily volume leaving Muang Mai wholesale market 210 250 300
Daily volume leaving Chiang Mai and      
going to other provinces and to Bangkok 190 230 270
Balance traded at retail markets in Chiang Mai 20 25 30
Other trade at retail markets in Chiang Mai 80 75 70
Volume by-passing Muang Mai wholesale market & retail markets: going to supermarkets, institutions and hotels (including home garden consumption) 40 40 40
Average daily urban consumption from all channels in Chiang Mai 140 140 140

Source: FAO Project - TCP/THA/8958

Estimating future demand

Future demand is always difficult to estimate as the marketing situation often alters rapidly in response to demographic and other changes. Other factors influencing demand may include a general increase in incomes, which is often attended by changes in dietary habits and substitution between different types of food stuffs. Frequently, such income changes lead to an increased consumption of meat, fish, speciality food and, sometimes, higher quality grades of rice (see Table 12.6). In making projections using the supply approach, however, the substitution effect between different produce has to be assumed to be zero.

The process by which the projections are made is to first make an assessment of how the existing pattern of trade might evolve (discussed in the first section of this chapter) and linen to estimate what proportion of this trade may be expected to pass through a new or improved market.

Market development policies. The first step in making the estimate of future demand is to try and set the projections in the context of a market development programme. Existing plans and programmes may already exist, either as public sector policies or as proposals for investment by the private sector. These will need to be reviewed to see whether they can form a realistic basis for an overall development programme. If not, it will be necessary to make some overall assumptions on the basis of the assessment made of existing trading patterns.

Projected demand In preparing estimates of the potential demand for produce (fruit, vegetables and fish) a number of assumptions will need to be made. The example shown in Table 12.10 from Kathmandu demonstrates the principles that might be followed in making an approximate estimate of future demand.

Other approaches to projecting demand levels for the urban consumption of fruit and vegetables are to use income elasticities (which is likely to pose the same problems as discussed previously) or to derive values from historical trends. Studies of per caput changes in consumption tend to suggest that., like increases in production levels, they will match fairly closely the rate of urban population increase.

Table 12.10 Demand assumption - Kahmandu, Nepal

The consumption of fruits, vegetables and fish for six districts in Nepal, based on consumption survey data was broadly matched with national food balance sheet data. For example, for vegetables, the per caput consumption in 1983/84 of 42.14 kg. could be equated with an average in six districts of 27.5 kg per capita, if leafy vegetables were excluded.
per caput consumption in the city of Kathmandu is substantially higher than the national average and an adjustment was derived by using unpublished data on average monthly household expenditure on goods and services. This enabled ratios to be calculated between the national average expenditure on fruits, vegetables and fish compared with that of urban Nepal and between urban Nepal and Kathmandu.
consumption of vegetables in Kathmandu was estimated as follows:
27.5 kg per caput x 1.43 (urban/rural ratio) x 1.05 (addition for Kathmandu) = 41.5 kg per caput
for fruits, however, using the same basis produced a figure of 33.14 kg per caput for Kathmandu, substantially above the national plan target. Therefore, the present consumption of 22.19 kg per caput was used.
for fish the per caput consumption was based on the existing estimated consumption of fish in Kathmandu i.e. 5.8 kg per caput.
all projections of future consumption were based on survey data or official published target levels of per caput consumption.

Source: FAO Project (GCP/NEP/043/SWI

Market throughput

After making projections of present and future demand, the next step in the preparation of an outline master plan is to ensure that there is sufficient space to accommodate the facilities required for the operational procedure envisaged (described in Chapter 13). As a basis for these calculations it is first necessary to make projections of the likely throughput of the market.

Design scenarios. A simple approach to projecting throughput is to develop scenarios for the peak monthly throughput of the market, using figures on per capita consumption and the likely population served, based on crude projections from previous population growth and migration trends (if more refined figures are not available). Possible design scenarios that often used are: a minimum size, corresponding to present immediate demand; a median size, corresponding with likely demand in the near future (say within the next 5 years); and an ultimate size, which would accommodate the growth in demand over the 20 -30 years of a project's life.

A typical example of a throughput calculation for Kathmandu, using the demand assumptions shown in Table 12.10 and taking into account production by-passing the wholesale market system, is shown in Table 12.11. The calculations use projected per caput consumption values for three types of produce at three design dates and assume an increasing share for the new market of the total wholesale trade in the city.

Design assumptions. The projections in Table 12.11 include estimates of the throughput at peak periods, taken as 2 - 2.5 times the annual monthly average production/sales, and arc based on the methods for assessing seasonal variations described earlier in this Chapter. It can be assumed, however, that the ratio will decrease over time. In estimating space requirements the extent to which these seasonal differences need to be considered is a matter of judgement. In principle, the estimating techniques described in Chapter 13 already take the peaks into account as they are based on average values. In some circumstances, for example, where there is a short duration peak caused by a particular crop, it may be better to calculate for the peak separately in order to make special provision for it.

Table 12.11 Kalimati wholesale market, Katmandu: design assumptions

  Per caput
Urban area consumption Traded at Kalimati:
(m/t per annum)
% of
Minimum size: (1988 Design Population - 411,000)      
Vegetables 41.50 17,060 3,410 13,650 30 340
Fruit 22.19 9,120 1,820 7,300 20 120
Fish 5.80 2,380 n/a 2,380 0 0
Total average monthly throughput (tons)           460
Peak monthly throughput, 2.5 x average month (tons)           1,150
Median size: (1990 Design Population - 442, 000)      
. Vegetables 50.90 22,500 4,500 18,000 30 450
Fruit 24.41 10,790 2,160 8,630 20 140
. Fish 8.00 3,540 n/a 3,540 0 0
Total average monthly throughput (tons)     590
Peak monthly throughput, 2.5 x average month (tons)           1,480
Ultimate size: (2000 Design Population - 700, 000)      
. Vegetables 65.00 45,600 9,120 36,480 60 1,820
. Fruit 35.20 24,640 4,930 19,710 60 990
Fish 13.34 9,340 n/a 9,340 50 390
Total average monthly throughput (tons)           3,200
Peak monthly throughput, 2.0 x average month (tons)           6,400

Source: FAO Project- GCP/NEP/043/SWI

Another key assumption which needs to be considered in estimating throughput is that the percentage of the wholesale trade going through the market will vary depending on the operation of existing marketing channels. Data from roadside and retail surveys may provide a basis for establishing how this might realistically change in the future. The figures should be treated with some caution, however, as they may not be representative of the whole year. Adjustments may therefore be needed, similar to those used when matching estimates of existing consumption to volumes recorded from roadside and other surveys (see estimated consumption in the previous section)

Design targets. A reasonable target for when the market is fully operational should also be projected but the extent to which trade would switch from present markets must be evaluated carefully, bearing in mind the degree to which some produce will by-pass the market system, particularly that from home gardens within the city. A likely eventuality is that a new market will gain the new trade and that the existing markets and other channels will broadly retain their present level of trade.

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