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close this book Measuring drought and drought impacts in Red Sea Province
close this folder 2. Measuring drought and food insecurity in Red Sea province: in 1987 and 1988: a technique for Pthe rapid assessment of large areas. Roy Cole
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View the document Limitations of the study and comments on the research method
View the document An alternative method
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2. Measuring drought and food insecurity in Red Sea province: in 1987 and 1988: a technique for Pthe rapid assessment of large areas. Roy Cole

 

Summary

A system of rapid areal assessment of drought impacts and food insecurity was developed in Oxfam Port Sudan for use in 1987 and 1988. The system was designed to be used in the absence of sophisticated technology such as satellite imagery and extensive quantitative data. Experienced field workers used six variables related to drought and economy to assess the impact of drought and food insecurity in the province. Results indicate that the greatest drought impacts and food insecurity in 1987 were in the southwestern interior of Red Sea Province and in the South Tokar coast. The former area is one where the floods failed for two consecutive years, 1983 and 1984, and where destitute people moved to the roadside in 1985 to beg for food. The latter area is a destination of war refugees from Eritrea and people trapped by the war in Red Sea Province and prevented from pursuing their normal patterns of transhumance from the Red Sea lowlands to the Eritrean highlands. Low drought impacts and food insecurity scores appear to be linked with relatively reliable agriculture such as that practiced in the Tokar Delta or linkage to an urban area. The unprecedented rainfall of 1988 was felt principally in the southern and central parts of the province according to the evaluation. The northern areas stayed the same or declined.

The results of this study indicate that drought and drought risk must be reexamined in Red Sea Province. Two things must be looked at: the potential for change in an area (interannual variability) and the existence of intervening, exogenous variables (for example, war) that affect the ability of people to respond to drought and economic adversity. Where environmental variability has historically been greatest we found the worst and the best results from year to year. However, for those areas that remained the same during both periods, we found a relative lack of potential for improvement. Locusts had an important impact on the scores only in the southwest of the province and southwest of Suakin in 1988.

The technique could be improved if the following conditions are met:

1. The unit of analysis should be smaller.

2. More variables should be included.

3. To minimise differences in observers only one person should do the classification.

 

Introduction

In early 1988 the Research Section at Oxfam Port Sudan developed a system for rapid areal assessment for relief food allocations for the Oxfam Food Monitors. The assessment system was part of some early explorations into changes of the Food Monitoring procedures and was also intended to provide information about drought impacts and food insecurity for 1987. The intention was to provide a spatial unit and structure for the Oxfam Food Monitoring Team's evaluations of areas for relief food allocations as well as criteria for a more systematic and accountable evaluation than had been done previously. The work was repeated at the end of 1988 to provide additional data for comparison.

 

Methods

Experienced field workers were asked to rank on six variables each of the subecozones ("strata") defined by Watson (1976) in his study of livestock and human population in Red Sea Province. Some of the field workers had been involved for the three previous years in touring the districts of the province making relief food allocation assessments and others touring for nutritional assessment of children under five years of age. Assessments of the zones for the purposes of the present study were done while assessments for relief food or nutritional surveillance were being made. The assessments were not done at one moment in time but reflect repeated visits and are a composite of the conditions prevailing in or around each zone throughout each year of study.

Watson's subecozones are a subset of his ecozones ("ecotypes") which were defined from satellite imagery and ground observation using variables relating to vegetation, geomorphology, soils, drainage, and topography and fall into the classes presented in the following table.

Table 2.1. Ecozones used In Watson (1976).

Ecozone

Name

Area

Number

(km2)

 

1

Coast

20896

2

Mountain

66276

3

Interior

112412

4

Khor 'Arab watershed

12611

5

Khor Baraka and Osir watersheds

4624

6

Tokar Delta

1136

 

TOTAL AREA

217955

Watson's 25 subecozones were used as the basis for the present study. They were slightly altered for the 1987 data and changed considerably for the 1988 work This was done because Watson based his classification on physical features of Red Sea Province. Our purposes involved people in addition to the environment. Zones were altered, for example, to reflect tribal territory, migration patterns, or the presence of significant towns. Watson's strata may be grouped into the following classes.

1. Coastal strip units.

2. Contiguous mountain units.

3. Watershed units.

The map on the next page presents the ecozones and subecozones of Watson.


Map 2.1. Ecozones and subecozones used by Watson (1976).

The six variables to classify each of the subecozones thereafter called "zones") far the present study are as follows:

1. Rainfall quantity and distribution.

2. Abundance and condition of useful vegetation.

3. The cash crop harvest.

4. The food crop harvest.

5. Livestock numbers, condition and recovery from drought.

6. The availability and use by zone residents of economic opportunities in or outside their zone.

The first two of these variables were used to measure drought impacts and the four remaining variables to measure food (and economic) insecurity. The scores for each variable were scaled from 0 to 3: None, Poor, Medium, and Good. The responses for each group of variables were summed (1 and 2 equals "Drought Impacts", 3-6 equals "Food Insecurity") and were plotted with Food Insecurity on the Y axis and Drought Impacts on the X axis.

The data were grouped into four classes according to the deviation of each zone from the mean for each year. Group 1 is equivalent to low drought impacts and food insecurity. Groups 2 and 3 are equivalent to moderate drought impacts and food insecurity; group 2 being moderately above and group 3 being moderately below the mean. Group four is equivalent to high drought impacts and food insecurity. These classes break down in standard deviations from the mean as follows (see Maps 2.2 and 2.3 also).

1. Greater than 1 standard deviation above the mean.

2. Zero to 1 standard deviation above the mean.

3. Less than 0 to 1 standard deviation below the mean.

4. Greater than 1 standard deviation below the mean.

Although it is useful to use means and standard deviations in interpreting the within years scores, it is more informative to use raw scores to compare the change from one year to another. This method gives results for each zone uninfluenced by the scores of the other zones for that year. What is of interest in this case is the performance of the zone from time 1 to time 2 not the relation of the zone to the other zones at time n. A technique for the standardization of unlike spatial units was employed to accomplish the comparison of each zone with itself over the two time periods. A grid composed of cells sized one-half the area of the smallest zone on the maps of Drought Impacts and Food Insecurity was superimposed over each map. For all areas that had a value for both years a difference was calculated and placed in the common grid cell. These cells were then grouped and mapped. The range of classes used in the grouping and mapping are as detailed below. The values reflect the number of positive or negative points change for the cell from 1987 to 1988.

1. >= 5 points change.

2. 2 - 4 points change.

3. 1 to -1 points change.

4. -2 to -4 points change.

5. <= -5 points change.

 

Results

Average scores and standard deviations for 1987 and 1988 are presented in the table below. The scores for each assessment zone for 1987 and 1988 are presented in the two tables that follow Table 2.2.

Table 2.2. Average scores and standard deviations on two variables for Red Sea Province, 1987 and 1988.

 

Drought

Impacts

Food

Insecurity

Total

 

1987

1988

1987

1988

1987

1988

Mean

3.25

5.10

6.10

6.89

9.35

12.00

SD

0.76

1.05

1.72

2.03

2.06

2.72

Table 2.3. Drought Impacts and food insecurity scores, Red Sea Province, 1987.

DROUGHT IMPACTS FOOD INSECURITY

Zone

Rain

Veg

Sub

Cash

Food

Stock

Econ

Sub

TOTAL

Deviation

     

Total

Crop

Crop

Cond

Acts

Total

 

from Mean

1a Port Sudan-Tokar Coast

2

1

3

2

2

2

2

8

11

1.65

1b S. Tokar Coast

1

2

3

1

3

2

2

8

11

1.65

2 S. Tokar Central

2

1

3

1

1

2

0

4

7

-2.35

3 S. Tokar Mountain

2

2

4

1

1

2

0

4

8

-1.35

4 Khor baraka-langeb

2

2

4

3

3

2

2

10

14

4.65

6 Derudeb Khor langeb

1

1

2

0

0

2

2

4

6

-3.35

7a RPS-Halaib Central Mountain

1

2

3

1

1

3

1

6

9

-0.35

7b Sinkat/Erkowit Mountain

1

1

2

1

1

2

2

6

8

-1.35

8a Tahamyam East

2

1

3

1

1

2

2

6

9

-0.35

8b Haya,Tahamyam

1

1

2

1

1

2

2

6

8

-1.35

8c Haya, Khor 'Arab Basin

2

2

4

1

2

2

1

6

10

0.65

12 Haya North

2

2

4

1

2

2

1

6

10

0.65

16 Halaib Central Mountain

2

2

4

1

2

3

1

7

11

1.65

17 Halaib Coast South

2

1

3

1

1

3

2

7

10

0.65

18 Halaib Mountain Central

2

2

4

1

2

3

1

7

11

1.65

19 Halaib Mountain North

1

1

2

1

2

2

1

6

8

-1.35

20 Halaib Coast North

2

2

4

1

2

3

2

8

12

2.65

24 RPS-Halaib Central Mountain

2

2

4

1

2

2

1

6

10

0.65

25a Derudeb Central

2

1

3

0

0

2

0

2

5

-4.35

25b Derudeb West

2

2

4

1

2

2

0

5

9

-0.35


Map 2.2. Drought impacts and food Insecurity, Red Sea Province, 1987.

Table 2.4. Drought Impacts and food insecurity scores, Red Sea Province, 1988.

 

DROUGHT IMPACTS

FOOD INSECURITY

Zone

Rain

Veg

Sub

Cash

Food

Stock

Econ

Sub

TOTAL

Deviation

     

Total

Crop

Crop

Cond

Acts

Total

 

from Mean

1 Sarara A

1

3

4

0

0

3

0

3

7

-5

2 Halaib west

no data

3 Khor 'alaagi

2

3

5

0

0

2

2

4

9

-3

4 Sarara B

3

2

5

0

0

2

2

4

9

-3

5 Halaib town

1

3

4

0

0

3

3

6

10

-2

6 Muhammed qul A

1

1

2

0

0

2

3

5

7

-5

7 Gebeit al-ma'adiin

1

3

4

0

0

3

3

6

10

-2

8 Khor oko

3

3

6

0

3

3

3

9

15

3

9 Muhammed qul B

2

3

5

0

0

3

3

6

11

-1

10 Khor arba'at A

2

3

5

0

0

2

3

5

10

-2

11 Khor tumaala/Oko

3

3

6

3

3

3

3

12

18

6

12 Khor 'amuur

3

3

6

0

3

3

2

8

14

2

13 North haya

3

2

5

1

1

2

1

5

10

-2

14 Khor agwampt

3

3

6

0

2

2

2

6

12

0

15 Khor 'udrus B

3

3

6

0

1

2

3

6

12

0

16 Khor 'udrus A

3

3

6

0

3

3

3

9

15

3

17 Khor arba'at B

2

1

3

0

0

2

3

5

8

-4

18 Port Sudan

2

2

4

1

1

3

3

8

12

0

19 Suakin

3

3

6

0

0

3

3

6

12

0

20 Khor akwaat/sallum

3

3

6

0

3

3

3

9

15

3

21 Gebeit/'agaba

3

3

6

0

2

3

3

8

14

2

22 Ayshaf A

3

3

6

0

2

2

2

6

12

0

23 Ayshaf B

3

1

4

0

1

3

3

7

11

-1

24 Erkowit/hadirbab B

3

1

4

0

1

2

1

4

8

-4

25 Hadirbab A

3

3

6

0

3

3

2

8

14

2

26 Khor asot

3

2

5

1

2

2

3

8

13

1

27 Wahribab

3

3

6

0

2

3

3

8

14

2

28 Khor osir

3

3

6

0

0

3

3

6

12

0

29 Suakin/Tokar road area

2

3

5

2

3

2

3

10

15

3

31 Coastal S. Tokar

1

2

3

1

1

2

2

6

9

-3

32 Central S. Tokar

2

2

4

0

2

2

3

7

11

-1

33 Mountain S. Tokar

2

3

5

0

0

2

3

5

10

-2

34 Khor baraka

3

3

6

3

3

2

3

11

17

5

35 Hamashkoreb

Grouped with zone 34

36 Khor langeb

3

3

6

2

2

3

3

10

16

4

37 Derudeb east

3

3

6

0

2

2

3

7

13

1

38 Khor langeb

3

2

5

0

2

2

2

6

11

-1

39 East haya

3

3

6

1

1

3

2

7

13

1

40 Derudeb west

3

3

6

2

3

2

2

9

15

3

 


Map 2.3. Drought impacts and food insecurity, Red Sea Province, 1988.

Two areas experienced low drought impacts and food insecurity in 1987 in Red Sea Province: the Khor Baraka watershed (Zone 4) and the coastal strip from Halaib Town to Egypt (Zone 20). The rest of the coastal strip (Zones 1A, 1B, and 17), the central highlands in Halaib District (Zones 16, 18), and the west central portions of Red Sea Province (Zones 12, 24, and 8C) experienced moderately low drought impacts and food insecurity in 1987. Those areas that experienced moderately high drought impacts and food insecurity in 1987 were west of Halaib (Zone 19), the central and southern Red Sea Hills Zones 7A, 7B, 8A, 8B, and 3), and the Braytek basin in the southwest (Zone 25B). Those areas with the worst scores were two: southcentral Red Sea Province (Zones 25A and 6) and the foothills of the South Tokar mountains (Zone 2) (see Map 2.2).

In 1988 there was a dramatic reversal of position; in general those zones that scored highest in 1987 scored lowest in 1988. Lowest drought impacts and food insecurity was found in the south of the province in Zones 29, 30, 34, 35, 36 and 40), in the centre in the 'Udrus and Akwaat valleys, and in the north centred on Khors Oko and Tumaala in Zones 8 and 11. Moderately low positive scores were registered along the Port Sudan to Suakin coast (Zones 18, 19), in the Khor 'Amour (Zone 12), Agwamt and western 'Udrus watersheds (Zones 15, 14), the Tahamyam-Haya area (Zones 22, 25, 26, 27, 39) into Khor Osir (Zone 28) and south into eastern Derudeb (Zone 37). Zones scoring moderately low below the mean (negative scores) were scattered around the province. This group is composed of Zones 5, 7, 9 and 10 along the north coast, Zones 13 and 28 in the western and southern interior, and Zones 32 and 33 in the South Tokar mountains. The Zones scoring highest in Drought Impacts and Food Insecurity were, for the most part, located in the north of the province. These Zones are 1, 2, 4 and 8. The three other areas scoring the lowest were scattered around the province with no apparent pattern. These zones are 17, 24, and 31.

The figure below presents a view of the differences between the two years. Variables measuring rainfall or a rainfall related product such as pasture, vegetation, agriculture, or agricultural employment all scored higher in 1988. The scores for 1988 were on average 1.85 points higher than 1987 for the Drought Impacts variable and 0.79 points higher for the Food Insecurity variable and 2.65 points higher on both variables. The difference in percent is 57, 13 and 28 percent respectively.


Figure 2.1. Drought Impacts and food Insecurity, Red Sea Province, 1987 and 1988 zone scores.

Map 2.4 below presents change from 1987 to 1988- in the Drought Impacts and Food Insecurity scores. The scores are not marked in standard deviations as in Maps 2.2 and 2.3 but in points change.


Map 2.4. Change in Drought Impacts and Food Insecurity, Red Sea Province, 1987 to 1988.

 

Conclusion

Considerable change in drought impacts and food insecurity has occurred from 1987 to 1988 in Red Sea Province as measured by the method described in the present paper. This change was due to the excellent rains of 1988 which fell generally over southern and central Red Sea Province. The rains were so good that pasture was abundantly available in and outside of khors and areas were cultivated that had never been cultivated before. There were areas where rainfall was good but vegetation and other variables scored low.

Our method appears to be a useful technique for the rapid assessment of large areas, however, there may have been some problem with measuring the influence of intervening variables, for example, we were not able to assess the economic significance of camel and sheep smuggling in the northern areas of the province. Had we been able to do so the scores for some of these areas might have been higher. Specific issues relating to the method and results will be addressed in the Discussion and Limitations of the Study sections below.

 

Discussion

Although the method for the rapid assessment of large areas used in the present study appears useful, there are some problems in the interpretation of Maps 2.2 and 2.3 that need to be addressed. The maps themselves are not directly comparable because each zone's value in the map is influenced by the scores of the other zones. While this is a useful method to understand the relationship between the zones in one year it is less useful when comparing zones between years. For example, a change from best to worst for a zone between the two time periods does not necessarily mean any great change in the drought impacts or food insecurity in that zone. The change may be related, instead, to change within other zones that alter the position of the mean value and the position of all other values to the mean. This is what happened between 1987 and 1988 and it reflects the extreme annual variability of the environment of the southern and central portions of Red Sea Province. From 1987 to 1988 the southern part of the province, with the exception of the perennially well-off Khor Baraka basin and the lagging South Tokar District, leapt from last place to first place, principally because of the unprecedented rainfall. This caused the position of the northern zones, ordinarily receiving an unpredictable scattering of from 25 to 50 mm of rainfall annually, to apparently decline. These northern areas are in fact relatively immune from drought because they exist is a state of perpetual aridity.

In order to address this problem (and also to standardise the units of comparison) Map 2.4 was made. Map 2.4 presents a direct comparison of the raw scores for each zone on the map that had observations for both years. Indeed, the greatest changes took place where there was the greatest potential for environmental variability - in the southern and central parts of the province. The relatively low change in one area that received much rainfall, Zone 13 (1988), in the southwest of the province, may reflect the infestation of locusts in that area and also to its east toward Suakin. The extreme difference from 1987 to 1988 at the northern tip of the province was exactly -5.

Poor rain in 1988 was the reason why area was classed so low in 1988 but as it is a borderline case perhaps it would best be classed with the next group up, the -2 to -4 class. Relative lack of change in the northern areas as mentioned above reflects the perennial state of affairs in this area. The northern exception is the Khor Oko area which scored above the mean in 1987 and well above the mean in 1988. The good rains of 1988 were responsible for this as well as the nationally and internationally connected social support network of Shariif Aderob of the Mosque at Tumaala. The same observation holds for the Mosque of Shaykh Sulaymaan at Hamashkoreb in the extreme south of the province. The two religious settlements provided assistance to destitute people during and after the drought. The population of children in the rural khalwas, religious training centres associated with the mosques, rose dramatically during the drought as did the founding of khalwas.

Zone 24, the Erkowit area, registered a 4 point decline from 1987 to 1988. Although rainfall was good, vegetation, cash crops, food crops and employment opportunities were not. There is evidence to indicate that there have been significant changes in vegetation in the Erkowit area (Vetaas 1989). These changes may have had impacts on livestock production and recovery. It is known from reports that planting in the Erkowit area had to be done several times because of excessive flooding and crop damage. This may have been responsible for the food crop problems.

The only area that had a relatively high decline in points from 1987 to 1988 in the south of the province was the coastal strip from 'Agig almost to Garora The poor rain along this portion of the coast plus the high numbers of refugees from Eritrea living in poor conditions resulted in a low score

Based on our study what can we say about the economic recovery from drought? Quite simply we may state that at present those areas that scored poorly in 1988 did so for reasons different from those relating simply to drought or the general economic climate. In South Tokar, for example, the principal reason for poor scores was war in Eritrea and the influx of refugees. It should be mentioned that the majority of these refugees are indistinguishable from the people living in South Tokar District. The only difference is that, unlike their Sudanese cousins, they have no land or cultivation rights in Red Sea Province. They used to seasonally migrate with the rains from Eritrea to Red Sea Province in search of grazing and employment. When the Sudanese-Eritrean border was closed and mined by the Ethiopian government many of these people were trapped on the Sudan side and lost their livestock in the dry season. When the border was reopened after the Eritreans took the border areas in the mid-1980s it was too late for these people; they were already destitute.

The environment of northern Red Sea Province is an example of low potential for change. The rainfall is scattered and low. The economy in northern Red Sea Province, however, is one of the strongest regional economies in the Sudan. This economy has two linked components: a pastoral economy centred on camel and sheep raising for sale to Saudia Arabia and Egypt and a settled economy which is involved in the smuggling of other goods. The variables used in the present study may not have measured the strength of the northern economy as well as expected.

Lack of recovery in certain areas of Red Sea Province today is less related to environmental or economic factors than to political instability and the specific social conditions of individuals and should be addressed in a more appropriate manner than that done presently by international donors. A specific programme to address the needs of people marginalised by the Eritrean conflict who live outside the camps in South Tokar should be undertaken. Efforts should be undertaken to target vulnerable groups: divorced or widowed women with dependent children and limited family support, pregnant and lactating women and children of weaning age.

 

Limitations of the study and comments on the research method

Problems we encountered during the course of the work are listed below.

1. The units of analysis used in the study in 1987 were too big and did not reflect human land use. Smaller units. perhaps clusters of villages, would have been more appropriate. The costs of data collection are a consideration, however. For 1988, several of the zones were still inordinately large. With more information (see below) they could be trimmed into coherent subunits.

2. The concept of the uniform spatial unit was difficult for some of the field workers to grasp. They invariably attempted to classify subareas of the subecozones rather than synthesise and generalise over the entire area. Perhaps this problem would be alleviated by the use of smaller, easier to handle, spatial units.

 

An alternative method

Although expanding the complexity of the work goes counter to our intention to explore alternative, low-technology methods of areal assessment, this type of research would benefit from the high technology approach. The use of the Normalised Difference Vegetation Index (NDVI) maps rather than subjective assessments of rainfall and vegetation quality would have gone far to reduce error in classification. The NDVI is prepared on a monthly basis for the governments of most drought-prone African countries by the United States Geological Survey, National Mapping Division at the EROS Data Center in the United States. It is funded by the Africa Bureau of the United States Agency for International Development (USAID). NDVI images are produced from the United States National Oceanographic and Atmospheric Administration's Advanced Very High Resolution Radiometer (AVHRR) satellite data. The scale of the imagery is 1:2500000 and the resolution is 1 km2. Reflectance values of vegetation are on a greenness scale from -1.0 to 1.0, the lower limit being equivalent to bare rock and the upper to green fields of wheat.

The study would have benefited from the use of more variables, for example, rainfall in millimetres, cropped and harvested areas, human and livestock populations, number of migrant workers, wage rates and destinations, security, number of refugees, malnutrition rates, and locust damage to crops. These types of data together would go well in a principal components or factor analysis. Principal components and factor analysis are used to bring together variables and different types of data and create new variables that express the relationships between the original variables. These new variables can be mapped in meaningful ways. There are two interrelated reasons to use principal components or factor analysis in a study such as the present one4:

1. To identify groups of intercorrelated variables.

2. To create new variables through combination of old variables.

Intercorrelated variables are independent variables that measure the same thing in different ways. In combining the variables we define the "thing" we are measuring. Sometimes this is called "fishing around in the data." One variable, for example, human population, may not explain the distribution of livestock in Red Sea Province at a given time during the year. The aggregation of more than one variable will do so. For example, what is the relationship between the variables, rainfall, tribal affiliation, human population, livestock populations and the Braytek basin? It is a good guess that these variables may explain it. The factor analysis combines such variables and creates factors instead. Out of 25 variables, for example, the factors produced by factor analysis would be about 4. Each factor expresses a characteristic or a concept. For example, a factor composed of the variables, human population, livestock population, rainfall, and location possesses the characteristics of rainfall, rural, areas of good pasture production, people, and location. It may be called "areas of good grazing and human and livestock concentration". In Red Sea Province, where this variable is found to be high one can expect all that the variable measures to exist there. Where it is low, the opposite is expected. Other groups of variables may be so grouped. This reduces many of the headaches of determining significant variables and also creates important variables from variable that alone would not be significant.

The use of a Geographic Information System would also be a great help for similar studies that use more variables. A Geographic Information System (GIS) is a computer programme that stores, analyzes and makes maps of data such as those collected for the present study. A GIS has the power to manipulate masses of data, do much of the statistical work done by expensive statistics programmes (which, these days, may include the creation of factors from groups of variable), and automate the process from data to maps (see Clarke (1986) and Lillesand and Kiefer (1987) for further information). The investment necessary for the high technology approach would be more appropriate for large and long term programmes with generous budgets and secure financing. A prograrnme such as this is more appropriately conducted on the national or international level rather than at the provincial level. A system such as or similar to that described in the present paper would go a long way in providing useful data for drought impacts and food insecurity assessments on the regional or national level. The most important consideration in setting up such a system is to keep the unit of analysis, the cell or zone, as small as possible. If the unit of analysis is too big it becomes insensitive or masks the differences within the unit. Ideally, differences within a unit should be minimised. Good criteria in constructing a unit would be uniform economic activities, land use, land cover, ethnic, cultural or familial uniformity, and urbanisation or linkage to urban areas. Although the District Councils in the Sudan may be of an appropriate size and homogeneity for zoning in a meaningful way, there may be traditional systems of areal zoning that are more useful, for example, lineage or ethnic areas. In the case of the Sudan, it may be more convenient to use the District Councils because they are a unit of data collection on a variety of topics. For example, data are collected at the District Council headquarters on cultivated and harvested areas within the District Councils, rainfall and market prices for cereals and livestock. It is here that data for the Islamic social security system (from the zakaat) are collected. It should be noted that the District Council can be broken down into any number of units because of the way the data are collected. For example, data on cultivated and harvested areas are collected by khor. If a particular khor fits better into some other spatial grouping than the District Council, then it can be regrouped. Also available are data on rainfall (Meterological Department in Khartoum) and wadi flooding (National Water Corporation in Khartoum). Rainfall data are available on a daily, monthly, or annual basis. Available flood data, collected from permanent gauging stations around the country, concern the number of floods, the duration of each flood, the maximum discharge in cubic metres and the total flow per flood in cubic metres. In Red Sea Province there are seven such gauging stations The agricultural schemes throughout the Sudan collect their own data on rainfall and flooding as well. NDVI maps of vegetation on a monthly basis are available, as mentioned above, but in deeply incised areas of the Red Sea Hills their usefulness is limited because the large unit of analysis (1 km2) mixes the reflectance values for the khors with the mountains. Landsat Thematic Mapper data would be more appropriate but are more expensive and take longer to process than the NDVI data.

 

References

Clarke, R. (1986) The handbook of ecological monitoring. Clarendon Press, Oxford

Environmental Research Group Oxford (1989) Personal communication.

Johnston, R.J. (1980) Multivariate statistical analysis in Geography. Longman, Essex.

Lillesand, T.M. and Kiefer, R.W. (1987) Remote sensing and image interpretation. Second edition. John Wiley and Sons, New York.

Rummel, R.J. (1967) Understanding factor analysis. Journal of Conflict Resolution, 11: 444-480.

Watson, R.M., Tippett, C.I. and Rizk, F. (1975) Sudan national livestock census and resource inventory. Volume 15, the results of an aerial census of resources in Red Sea Province in December 1975. Sudan Veterinary Research Administration, Ministry of Agriculture, Food and Natural Resources, Khartoum.