Relating household characteristics to urban
sheep keeping in West Africa
M. Siegmund-Schultze *, B. Rischkowsky
University of GoÈttingen, Tropical Animal Production, Kellnerweg 6, 37077 GoÈttingen, Germany
Received 25 April 2000; received in revised form 21 July 2000; accepted 19 October 2000
Abstract
Urban sheep production is widespread in Bobo-Dioulasso despite its formal illegality. This study was aimed at the identi®cation of socioeconomic characteristics in¯uencing the deci-sions of households to take up this activity. One hundred and thirty-six households (half of them keeping sheep, half not keeping small ruminants) were interviewed to collect data on their socioeconomic situation. Three techniques of multivariate analyses were compared. Cluster analysis and logistic regression revealed the following socioeconomic dierences between the two groups: the probability of keeping sheep increases with the size of the household and the rate of illiteracy. Households are also more likely to keep sheep if urban cattle husbandry is practised, if there is only one household in the compound and if the keeper has already changed his/her trade at least once. Correspondence analysis provided visual con®rmation of these results. Cluster analysis allowed a more profound understanding of the situation by drawing attention to a `transitional dierentiation': non-keepers in a group of keepers and vice versa tell us something about potential future keepers or non-keepers.
#2001 Elsevier Science Ltd. All rights reserved.
Keywords:Household characteristics; Urban sheep keeping; West Africa
1. Introduction
The spectacular growth rates in the largest African cities of the 1960s and 1970s due to rural±urban migration have declined since the 1980s to that associated with a natural increase. Nevertheless, the urban population on the African continent has continued to grow in the 1990s by approximately 4.5% per year. Where national
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economics have stagnated, the continuous urban growth has been associated with deterioration of services and infrastructure leading to drastic changes in the labour market. There has been a decline in well-paid secure employment and a shift to the burgeoning informal or small scale, unregulated sector comprising a wide variety of activities in response to the needs and ®nancial capacity of the poor (UNCHS, 1996).
This change has led to a rapid expansion of urban farming during the 1980s, e.g. in Dar Es Salaam an increase from 18% of families engaged in agriculture in 1967 to 67% in 1991 (UNDP, 1996) of which 74% kept livestock. A widespread system of on-plot keeping of livestock is practised in West and North African cities, where stallkept and tethered sheep are fattened for Muslim ceremonies (Waters-Bayer, 1995).
This development of urban small scale livestock production is unplanned and in the densely populated neighbourhoods gives growing concern that it is creating health and environmental hazards (UNDP, 1996). Consequently, urban livestock keeping is often illegal, e.g. in our study area in Burkina Faso (Front Populaire, 1991). Nevertheless, the practice persists because it oers food and jobs which would not otherwise be available to the community. Therefore, this dilemma has to be addressed.
To do so, city planners need to understand the underlying reasons and the char-acteristics that predispose to urban livestock keeping. It may be a consequence of poverty regardless of the origin and cultural background of the people. However, it is suggested that rural immigrants are more likely to practice it than their urban neighbours, because it is the job they are best equipped to do. This study hypothe-sises that a decision in favour of urban sheep keeping can be related to the socio-economic pro®le of households. Thus, it compares sociosocio-economic characteristics of urban sheep keepers with their non-keeping neighbours.
To test the hypothesis a multivariate method is needed which allows the formation of socioeconomic groups with comparable behaviour. Dierent analytical techni-ques are available including cluster analysis, which is a common method and was used to group e.g. farming systems (Hardiman et al., 1990; Williams, 1994), grazing patterns of pastoralists (Artz and Jamtgaard, 1988) or years with similar patterns of ¯ock productivity (Rey and Das, 1997).
Logistic regression has also been applied mainly to analyse veterinary aspects, e.g. modelling dichotomous response of infected/not infected or similar questions (Uhaa et al., 1990; Jensen and Hùier, 1993; Curnow and Hau, 1996; Jamaluddin et al., 1996; Martin et al., 1997). Application has been less frequent in the analysis of reproduction (Gates, 1993; Nash et al., 1996), production aspects (Smuts et al., 1995) and systems (Roth, 1990; Jolly and Gadbois, 1996). A similar categorical modelling technique, the loglinear model, was used by Richardson and Whitney (1995) to predict the occurrence of households keeping goats in Khartoum.
These three multivariate analyses pursue quite dierent methods to arrive at a classi®cation: cluster analysis is used in a case-driven and logistic regression in a variable-driven way, while correspondence analysis leads to relative answers by comparing proximity between points in an Euclidean space. So there is a need to check if the characteristics which emerge are dependant on the method used. Therefore, the aim of this paper is twofold: to identify the driving forces in the decision for sheep husbandry and to test the consistency of results obtained with dierent analytical techniques.
2. Material and methods
The study area chosen was Bobo-Dioulasso, the second largest town of Burkina Faso with approximately 400,000 inhabitants (estimation of MinisteÁre de l'EÂquipe-ment, 1990), located in the sub-humid zone.
In Bobo approximately one sixth of the households kept small ruminants (Cen-treÁs, 1991). A survey in 1480 urban households keeping sheep organized by CIRDES in 1995 showed that on average four hair sheep with ¯ock sizes ranging from one to 60 animals are kept. Sheep production is nearly always a secondary activity of the households, their main activities covering all sorts of trade. Ten percent of the owners are women. Sheep are of special importance in Muslim ceremonies and the majority of keepers are Muslim, but the proportion corresponds to their overall predominance in Bobo-Dioulasso (Kocty-Thiombiano, 1995).
It was planned to interview a subsample of households from the CIRDES survey which proved impossible because those households could not be retraced due to missing street names and housenumbers and non reliable documentation of names. Therefore, 72 sheep keepers in a central and a peripheral district of Bobo-Dioulasso were interviewed assuring that their characteristics represented the main sample. Similar data were collected in 64 neighbouring households not keeping small ruminants.
The statistical analyses were performed with socioeconomic variables only, i.e. no characteristics of sheep husbandry were entered. Table 1 presents the variables selected for the analysis with their codes and short descriptions. The variables explain life circumstances and origin of the actors (age, religion, ethnic group, activity of parents, time spent in the city, changes of activity, possession of compound), others describe the household (composition, number of households on the compound), the education (literacy rate, duration and type of school attendance) and the agri-cultural activities (land, cattle).
Hierarchical agglomerate clustering (proc cluster) was applied in order to classify interview partners in two or more non overlapping groups. The variables included in cluster analysis were chosen with the aim to give a large enough, but not too detailed, overview of important socioeconomic characteristics. The cluster procedure was per-formed in subsequent steps, ®rst computing distance matrix using Jaccard coecient. As the variables did not show equal numbers of categories, it was necessary to per-form separate steps for each variable to prevent overvaluing of the more detailed ones. Then, single distance matrices were added to complete the matrix which entered in the proper clustering process using Ward's minimum-variance method as algorithm. The resulting cluster formation on dierent hierarchical levels was then
Table 1
Description of variable codes
Variable code Description
Numerical variables
AGE Age of keeper or non-keeper at testing
CATTLE Number of cattle reared in town by the household
FRLITRAT French literacy rate of household (household members of all ages, who declared to be literate in French divided by members above 7 years old assumed to be potentially literate)
LAND Ha cultivated by the household
MODMAX Maximal duration of modern education of one of the household members NBHH Number of households in compound
SINCE Since...years in town
SUMRED Potential manpower of household (sum of persons in household reduced by infants and children attending school)
Categorical variables and their levels
ACTCHANG Changed Ð not changed main activity (trade)
ACTPAR Activity of parents: livestock farmer Ð crop farmer Ð crop farmer+other non-livestock activity Ð other
BORNIN Keeper or non-keeper born in Bobo Ð born anywhere else
EDUCAT Education categories of keeper or non-keeper: only Koran school Ð Koran and modern school Ð modern up to 6 years Ð modern more than 6 years Ð not frequented school
ETHNIC Ethnic groups: Mossi Ð Peulh Ð other groups Ð foreigner, not from Burkina Faso LOCPROP Status of habitat: tenant Ð proprietor
RELIGION Muslim Ð Christian Ð animist SECTOR Peripheral Ð central sector of town CATTLE Keeping Ð not keeping cattle in town
Signi®cant numerical variables transformed into categories (for cluster analysis) FRLITRAT Four quartiles (0±0.357; 0.358±0.625; 0.626±0.810; 0.811±1.00) NBHH One Ð more households in the compound
SUMRED Three terciles (1±3; 4±7; 8±34)
Reduced categories of variables as included in logistic regression
ACTPAR Three categories: crop farmer Ð livestock farmer Ð other FRLITRAT Two categories: index <0.63 Ð index50.63
evaluated by elbow diagram, dendrogram and R-squared statistics to arrive at a justi®ed number of clusters (hierarchical level) and their respective characterisation. Categorical modelling (proc catmod) is a generalised form of analysis of variance. In this study logistic regression was adopted to model probabilities of response variable keeping/not keeping. Qualitative explanatory variables are used to explain the variation among response probabilities. The objective is to derive a simple model from empirical data, i.e. the smallest possible set of explanatory variables. The variables proven signi®cant in the univariate statistical comparison of keepers and non-keepers were included in the logistic regression process. To simplify the model, the number of categories in the variables SUMRED, FRLTRAT and ACTPAR were reduced (Table 1). In a backward stepwise procedure, the full model was compared with reduced models estimating maximum likelihood for the model parameters. The best model was selected by goodness of ®t comparing the calculated likelihood ratios of the dierent test models.
Multiple correspondence analysis (proc corresp) is a weighted principal compo-nent analysis of a contingency table, it gives a graphical representation of the asso-ciation between rows and columns of a table (SAS Institute Inc., 1990). Only the relative description of the proximity of points is possible. Points close to one another show similarities. The same variables as in logistic regression were used. As supple-mentary variable keeping/not keeping sheep is projected onto the map to illustrate and to help to interpret the output ®gure. This illustrator does not contribute to the determination of the axes.
3. Results
The univariate data analysis (Tables 2±4) shows a highly signi®cant dierence in the size of households keeping and not keeping sheep in town, with sheep keepers often belonging to bigger households. Likewise they are more often the owner of the plot they live on, have changed their activity branch at least once and keep cattle more frequently than those without small ruminants. Also there is a signi®cant dif-ference in literacy rate, namely a lower literacy rate among the sheep keeper. A greater number of households on the compound as well as a shorter modern edu-cation of respondents are related with low signi®cance to keeping sheep in town. Likewise the proportion of keepers whose parents were livestock farmers and who belong to a pastoral ethnic group is higher than of non-keepers.
Table 2
Descriptive statistics and signi®cance of numerical explanatory variables used in the comparison of sheep-keepers with non-sheep-keepers in Bobo-Dioulasso
Variable code Sheep keepers Non-keepers of small ruminants Signi®cance (t-test or Kruskal±Wallis-test)a Range n Mean S.D. Range n Mean S.D.
AGE 17±79 68 50.4 12.7 20±73 59 47.6 14.0 n.s.
CATTLE 1±20 20 3.6 4.2 2 2 2.0 0 n.s.
FRLITRAT 0±1 72 0.53 0.29 0±1 64 0.65 0.29 **
LAND 0.25±25 26 4.8 6.3 0.5±10 20 3.0 2.4 n.s.
MODMAX 1±18 66 8.5 3.1 0±15 62 9.0 4.2 n.s.
NBHH 1±8 72 1.5 1.4 1±10 63 2.2 2.1 *
SINCE 10±68 70 31.6 12.6 0.3±70 62 29.1 18.1 n.s.
SUMRED 2±34 70 7.5 6.2 1±14 64 4.4 2.7 ***
a n.s. not signi®cant (P50.05), *P<0.05, **P<0.01, ***P<0.001.
Table 3
Signi®cance of categorical explanatory variables in the comparison of sheep-keepers with non-keepers in Bobo-Dioulasso
Variable code Categories n Signi®cance (w2-test)a
ACTCHANG 2 categories 136 ***
ACTPAR 4 categories 135 *
BORNIN 2 categories 136 n.s.
EDUCAT 5 categories 136 *
ETHNIC 3 categories 132 *b
LOCPROP 2 categories 132 ***
RELIGION 3 categories 135 n.s.c
CATTLE 2 categories 136 ***
Signi®cant numerical variables transformed into categories
FRLITRAT 4 quartiles 136 *
NBHH 2 categories 136 **
SUMRED 3 terciles 136 **
Reduced categories of variables as included in logistic regression
ACTPAR 3 categories 136 *
FRLITRAT 2 categories 136 *
SUMRED 2 categories 136 **
a n.s. not signi®cant (P50.05), *P<0.05, **P<0.01, ***P<0.001 b
w2-test without category `foreigner'. c
kept sheep in the past. (Various reasons for giving it up were stated: theft, health problems, need of cash, problems with neighbours, etc.) Relating this information to the groups formed by cluster analysis, showed that they are mostly found in the mixed clusters, most of all in the mixed keeper cluster. Likewise some respondents, who do not keep sheep at present, said that they would like to do so. Those house-holds are numerous in all clusters except the two non-keeper clusters. This supports the suggestion to regard the mixed clusters as transition groups.
In the logistic regression procedure the likelihood ratio of the full model including all variables was compared with reduced models subsequently eliminating one vari-able (Tvari-able 6). In the ®rst step the best ®t was achieved for the model excluding FRLITRAT. In the next step ETHNIC was removed and than LOCPROP. No better ®t (expressed by likelihood ratio) could be achieved by further test runs. The probability of likelihood ratio of full logistic regression model isPf=0.74 (d.f.=82,
w2=73.31). The reduced model with the best ®t, Pr3=0.97 (d.f.=43, w2=27.04),
includes the following variables: ACTCHANG, ACTPAR, NBHH, SUMRED, CATTLE, EDUCAT.
The result of logistic regression corresponds to a great extent to that of cluster analysis showing that the probability or decision of keeping or not keeping sheep seems to be in¯uenced namely by the following variables: ACTCHANG, CATTLE, EDUCAT, NBHH, SUMRED (Table 6). The variables AGE and SINCE/BORNIN, which are of relative importance in cluster analysis, were not included in the logistic regression, as they did not show signi®cant dierences in the preceding descriptive statistical analysis. ACTPAR is important according to logistic regression, but cluster analysis did not support this.
Table 4
Frequencies (%) of signi®cant variables (w2-test) in the comparison of sheep-keepers (S) with non-keepers (N) in Bobo-Dioulasso
Variable code Category 1 Category 2 Category 3 Category 4 Category 5
ACTCHANG Changed Not changed
S 40 60
N 9 91
ACTPAR Livestock farmer Crop farmer Crops+non livesta Other
S 25 58 3 14
N 11 72 12 5
EDUCAT No school Koran Modern46 years Modern>6 years Koran+modern
S 36 27 22 8 7
N 27 19 31 20 3
ETHNIC Mossi Peulh Otherb Not from Burkina
S 42 12 46 0
N 27 5 62 6
LOCPROP Tenant Proprietor
S 97 3
N 77 23
Fig
.
1.
Dendro
gramm
e
of
136
househ
olds
resulting
from
clust
er
The ®rst four dimensions (the axes) in correspondence analysis explain more than half of the variance. Fig. 2 shows proximity of categories relative to the two ®rst axes, explaining 31% of variance. Axis 1 provides a good separation of the illus-trator variable, with urban sheep keepers on the right positive half. The two cate-gories of household size (SUMRED) are also well separated, bigger household near
Table 5
Synthesis of dierences between the keeper (S) and the non-keeper clusters (Na and Nb)
Group Characteristics Variable code
Sheep-keepers (cluster S) Older people who are in town since a long time AGE, SINCE Lower educational level of sheep-keeper EDUCAT Sheep-keeper has already changed activity branch ACTCHANG Cattle rearing is relatively common CATTLE
More potential manpower SUMRED
Non-keepers More younger people AGE
(clusters Na and Nb) Non-keepers with a higher education level EDUCAT Households chairing the compound with other
households
NBHH
Households with less manpower SUMRED
Non keepers (Na) High proportion of natives of Bobo-Dioulasso BORNIN
Non keepers (Nb) Have recently settled in Bobo as tenants SINCE, LOCPROP
Table 6
Most important variables to predict urban sheep keeping in comparison of results from cluster analysis and logistic regression
Variable codea Variable entered in Explanatory variables according to
Cluster analysis Logistic regression Cluster analysis Logistic regression
ACTCHANG + + + +
ACTPAR + + +
AGE + +
CATTLE + + + +
EDUCAT + + + +
ETHNIC + +
FRLITRAT + +
LAND +
LOCPROP + +
MODMAX +
NBHH + + + +
RELIGION +
SECTOR +
SINCE/BORNIN + +
SUMRED + + + +
Fig. 2. Plot of correspondence analysis, showing proximity of variable categories to sheep keeper/non-keeper marker (illustrator variable).
M.
Siegmund-Schul
tze,
B.
Rischkowsky
/
Agricultura
l
Systems
67
(2001)
sheep keeper illustrator and vice versa. Near the marker for sheep keeping are also placed, but in increasing distance: Koran education, living on own plot with no other household sharing it, additional cattle rearing, lower French literacy rate and parents being livestock farmers. Close to the non-keeper illustrator are found small households, no cattle rearing, no typical or big ethnic group, no change in activity branch, parents who were crop farmers and higher educational level. Thus, the gra-phical presentation of correspondence analysis con®rms the importance of the vari-ables also identi®ed by the other techniques. Furthermore, it clearly shows that particular categories of variables explain the occurrence of keeping or not keeping sheep. Only in the case of household size are both categories likewise important. Regarding the other variables often only one of their respective categories was strongly related as indicated by proximity to either keeping or not keeping sheep.
The hypothesis that a certain socioeconomic pro®le predisposes to urban sheep keeping can be accepted. The pro®le of a likely sheep keeper in Bobo-Dioulasso can be described as follows: being in an unstable job situation (ACTCHANG) with limited chances to ®nd an ocial job because of restricted education (EDUCAT), the decision-maker of the household can make use of its high manpower potential (SUMRED), use the space and take the liberty of decision on the compound as there are no other families (NBHH) to keep sheep and often cattle (CATTLE). While according to logistic regression the parents' activity has to be added: the sheep keepers' parents often also kept livestock (ACTPAR); cluster analysis suggested to include the age (AGE) and the time spent in the town or the fact to be born in Bobo-Dioulasso (SINCE/BORNIN) with a trend to older decision makers, who spent a long time in town. The more frequent this combination of characteristics will occur, the more sheep keepers will be found in the respective town.
4. Discussion
A crucial issue in multivariate analysis is the selection of the entering variables. The good ®t of the logistic regression model proved that the variables tested in this study were valid to explain the occurrence of sheep keeping in town. A study con-cerned with the prediction of goat keeping in Khartoum revealed a correlation between keeping goats and place of birth, number of neighbours keeping animals, and number of children (Richardson and Whitney, 1995). For the case of Bobo-Dioulasso this could not be completely con®rmed. Place of birth showed a relative importance according to cluster analysis, but was not included in the other analyses because of non signi®cance of the dierence between keepers and non-keepers in the univariate statistical comparison. The number of neighbours keeping animals is a parameter which would have been hard to assess in Bobo as respondents often were very hesitant to give any information beyond themselves. The importance of number of children corresponds well with the results from Bobo-Dioulasso: the SUMRED variable is highly correlated with number of children (R=0.94).
tool for a quick overview of most important categories. Logistic regression is also a relatively simple way to assess important driving factors behind keeping/not keeping. This method revealed enough information to test the hypothesis, but the outcome is rather limited.
In this study cluster analysis turned out a rather time-consuming technique due to non-homogeneity of variables. If the number of categories were the same, this would not have been the case. However, cluster analysis provided additional information by drawing attention to the `transitional dierentiation' of keepers and non-keepers. It is not surprising that keepers as non-keepers did not form homogeneous groups, particularly in a highly dierentiated urban society. Non-keepers in a group of kee-pers and vice versa tell us something about potential future keekee-pers or non-keekee-pers. Thus, the outcome of cluster analysis was more detailed and supports a more pro-found understanding of the situation and ongoing processes than that of categorical modelling. This additional information is useful as urban planners have to keep in mind the existing dierences between actors and the possible pathways between keeping and not keeping. To this end, cluster analysis is the most appropriate method. Both, logistic regression and correspondence analysis could be used to prepare the data set for a subsequent cluster analysis by indicating the most impor-tant driving factors. While cluster analysis has been frequently used in systems ana-lysis, correspondence analysis seems to be limited to francophone researchers indicating that knowledge exchange between linguistic zones might be de®cient. Likewise this seems to apply to exchange between disciplines: categorical modelling has been frequently used in veterinary research, but widely overlooked as a tool for agricultural systems research though it appears to be very suitable.
Acknowledgements
This paper results from the collaboration between CIRDES (International Centre for Animal Husbandry Research and Development in the sub-humid zone) in Bur-kina Faso and the Department of Tropical Animal Production, University of GoÈt-tingen in Germany. The study was part of the EU-funded project SECOVILLE (Socio-eÂconomie de l'eÂlevage ovin peÂri-urbain), a collaborative project between ®ve African institutions in Togo, Guinea, Ivory Coast, Cameroon and Burkina Faso and four European partners in France, Greece, Belgium and Germany. We are grateful to the above named institutions and especially to Mrs. Diara Kocty-Thiombiano from CIRDES for supporting this work.
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