Title Page
 Characteristics of farmers in the...
 Division of labor within the farm...
 Off-farm income and the labor...
 Labor as a factor constraining...
 Summary and conclusions

Title: Labor use patterns for the production of maize in Southern Zaire
Full Citation
Permanent Link: http://ufdc.ufl.edu/UF00071907/00001
 Material Information
Title: Labor use patterns for the production of maize in Southern Zaire
Physical Description: 11 p. : ; 28 cm.
Language: English
Creator: Mwamufiya, Mbuki
Fitch, James B
International Maize and Wheat Improvement Center
United States -- Agency for International Development
Oregon State University -- Agricultural Experiment Station
Publisher: Centro Internacional de Mejoramiento de Maiz y Trigo
Place of Publication: Mexico D.F
Publication Date: 1977?
Subject: Corn -- Congo (Democratic Republic)   ( nal )
Agricultural laborers -- Congo (Democratic Republic)   ( nal )
Genre: bibliography   ( marcgt )
non-fiction   ( marcgt )
Spatial Coverage: Congo (Democratic Republic)
Bibliography: Includes bibliographical references (p. 11).
Statement of Responsibility: Mbuki Mwamufiya & James B. Fitch.
General Note: "Support for this research was received from CIMMYT, the U.S. Agency for International Development, and the Oregon Agricultural Experiment Station.
Funding: Electronic resources created as part of a prototype UF Institutional Repository and Faculty Papers project by the University of Florida.
 Record Information
Bibliographic ID: UF00071907
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: oclc - 09190087

Table of Contents
    Title Page
        Title Page
        Page 1
    Characteristics of farmers in the survey area
        Page 2
    Division of labor within the farm family
        Page 3
        Page 4
    Off-farm income and the labor market
        Page 5
    Labor as a factor constraining maize production
        Page 6
        Page 7
        Page 8
        Page 9
    Summary and conclusions
        Page 10
        Page 11
Full Text


3 .

/a ?7



Mbuki Mwamufiya
James B. Fitch ^I,


Support for this research was received from CIMMYT, the U.S. Agency
for International Development, and the Oregon Agricultural Experiment
Station. The authors are particularly indebted to Don Winkelmann for
his continued support and comments. Naturally, responsibility for any
flaws in the final product remains solely with the authors.


Mbuki Mwamufiya
James B. Fitch

This paper focuses on the use of labor in the production of maize
in South-Central Zaire. In this area, as in other parts of Zaire, addition-
al communal lands are available to many farmers, and land availability is
usually not viewfP as a constraint to expanding production. Farm size is
relatively small--, however, and in the absence of significant mechaniza-
tion2/, the quantity of labor available and how it is used become signifi-
cant factors in determining the amount of land farmed, the timing of
various cultural practices, and ultimately, how much maize and other
crops are produced.

Two considerations make this paper of interest. The first is the
data themselves which describe which family members engage in which
tasks related to maize production, use of hired labor, off-farm work,
periods of labor shortage, etc. The second is the question of the impor-
tance of labor constraints in limiting maize production.

Observations and data presented here are based on field research
undertaken by Mwamufiya in 1974 and 1975, in conjunction with Zaire's
national maize programV/. This research included a survey of 299 pro-
ducers from four adjacent districts. Three of these districts, Mwene-Ditu,
Gandajika, and Tshilenge, are in the region of Kasai Oriental, while
Kaniama, the fourth, is in the region of Shaba.

1/ Survey data shows average farm size to be 1.59 ha., over all districts
2/ For the entire survey area, 93 percent of the farmers sampled
indicated that they till corn primarily with a hoe. Less than 5 percent
reported any usage of the tractor for tilling maize.
3/ Mwamufiya's Ph.D. dissertation (2) provides a more complete descrip-
tion of the study and resultant findings. This is the second of three
short papers taken from the original research.

Characteristics of Farmers in the Survey Area

Tables 1-3 contain descriptive data on farmers of the study
area. These data require little explanation. Farm size is clearly the
largest in Kaniama, where settlement occurred later than in Kasai
Oriental and where population density and average village size are both
lower. The average age of male heads of family is in the mid-forties,
with a generally high proportion over 50 years old. Just over 40 percent
of the farmers in the survey were greater than 50 years in age compared
to a national average of slightly less than 30 percent in this age category
(4). Family size averaged 6.6 members over all of the districts surveyed,
which compares to a national average farm family size of 5.4 (4). A
final point relates to education, with over 60 percent of the farmers
interviewed having less than four years of formal schooling.


Mean values for age, family size, total area planted, and
percent of area planted to maize, with sample size, by

Kaniama Mwene-Ditu Gandajika Tshilenge

Age: Husband 44 44 43 48
Wife 32 28 28 28
Size of family 6.7 6.3 6.4 6.6
Total area planted (ha) 1.8 1.6 1.6 1.4
Percent of planted area
devoted to maize alone 23 21 26 16
Percent devoted to maize
interplanted with other
crops 19 21 20 24
Sample size 108 67 68 56

TABLE 2. Age Distribution of male farmers, classified by district.

Age group Kaniama Mwene-Ditu Gandajika Tshilenge

Under 30 25 21 24 17
30-49 31 46 46 29
50-69 42 25 22 47
Over 69 2 8 8 7

- 2 -

TABLE 3. Proportion of male farmers reporting years of formal

Years of Education Kaniama Mwene-Ditu Gandajika Tshilenge

0 33 34 35 32
1-3 26 27 27 30
4-6 31 28 22 21
Over 6 10 10 15 16

Division of Labor Within the Farm Family

Family labor availability depends not only on the size of the
family, but on the division of work responsibilities between males and
females, adults and children. In a few cases, all family members engage
in the four principal tasks associated with maize production -- clearing,
sowing, weeding, and harvesting. In most cases, however, tasks are
carried out more by one sex or by adults rather than children.

Tables 4 through 7 indicate the family members who participate
in each of the four tasks. Clearing land for maize tends to be an adult
male responsibility, although females frequently share this work with
males. Sowing and weeding are predominantly shared tasks, whereas
harvesting tends to be more a female activity4/. Of all the districts
surveyed, Kaniama, where average farm size-is larger, tends to have
higher joint participation of both male and female adults.

Notably few families report children participating in maize pro-
duction. Fewer than 10 percent of sampled households report children
engaging in any activity. They are said to be busy in school and unable
to help at the necessary times. However, the fact that all respondents
to the survey were adults may lend a downward bias to the reporting of
child participation.

Two questions were put to the farmers about decision making.
One dealt with decisions of which crops to plant and the area to be as-
signed to each. In less than 4 percent of the cases were women reported
to take these decisions alone. For the most part such decision making
is a male prerogative with over three quarters of the Kasai Oriental

4/ Marketing (not shown in tables) is even more dominated by females
than is harvesting. For the entire survey area, less than 10 percent
of the households reported marketing to be a males only activity,
while roughly 60 percent reported women only. Parenthetically, it is
noted that only 66 percent of the farm units surveyed sell a portion
of their crop. It is estimated that sales account for 19 percent of
overall production in the survey area.

- 3 -

TABLE 4. Percent of households reporting participation of various
family members in clearing maize plots.

Kaniama Mwene-Ditu Gandajika Tshilenge

Children only 2 2 10 2
Male adults only 33 72 45 48
Female adults only 1 5 13 11
Male and female adults 64 21 31 39
Children and adults 0 0 1 0

TABLE 5. Percent of households reporting participation of various
family members in sowing maize plots.

Kaniama Mwene-Ditu Gandajika Tshilenge

Children only 6 3 6 2
Male adults only .3 23 26 17
Female adults only 7 6 6 10
Male and female adults 83 68 62 70
Children and adults 1 0 0 0

TABLE 6. Percent of households reporting participation of various
family members in weeding maize

Kaniama Mwene-Ditu Gandajika Tshilenge

Children only 8 2 9 4
Male adults only 4 20 21 12
Female adults only 1 8 6 11
Male and female adults 86 70 64 73
Children and adults 1 0 0 0

- 4 -

TABLE 7. Percent of households reporting participation of various
family members in harvesting maize.

Kaniama Mwene-Ditu Gandajika Tshilenge
Children only 5 2 8 4
Male adults only 5 12 9 15
Female adults only 20 57 33 43
Male and female adults 69 26 43 32
Children and adults 1 3 7 6

farmers saying that those decisions are made by males. In Shaba (Kaniama),
48 percent of the respondents said that decision making, is a family matter,
while 45 percent claimed that these decisions are made entirely by males.

A second question dealt with the decision on how maize is to be
allocated among various uses -- consumption, sales, seed, animal feed,
or beverages. Women figure more prominently here, but even so, over
half of the households interviewed said that these decisions are taken by

Off-Farm Income and the Labor Market

The farm labor market is not highly developed in the survey
area. There is little reliance on outside workers to supplement the
family labor force during peak work periods, nor do farmers engage
much in outside work to augment their own incomes.

Table 8 illustrates the low rates of hired labor use. A some-
what larger proportion of families reported using hired labor for
harvesting (9 percent of all units sampled) than for weeding (8 percent)
or planting (6 percent), but still the difference was negligible. Tshilenge,
the district with the highest population density and largest average village
size of those areas surveyed, tended also to have the greatest use of
hired labor. It should be pointed out, however, that the hiring of labor
is widespread among the large commercial tobacco growers of the Kaniama
area. Many tobacco farmers also grow maize.

TABLE 8. Percent of families reporting use of hii ed labor in planting,
weeding, and harvesting of maize.

Kaniama Mwene-Ditu Gandajika Tshilenge
Planting/ 5.6 4.5 4.4 10.7
Weeding 5.6 7.5 10.3 10.7
Harvesting :6.6 9.0 11.8 12.5

a/ Includes both clearing and sowing.

- 5

While most of the farmers interviewed had some off-farm work
experience, few had income from off-farm activities in the year of the
survey (Table 9). It is notable that few of the farmers of the area had
off-farm work experience on commercial farms. The higher proportion
reported from Kaniama undoubtedly reflects work in that area's large
commercial farms. The contribution to family support of the farm
family by family members working off the farm is also notably low.


Percent of households reporting income or work
off the farm, by district.


Kaniama Mwene-Ditu Gandajika Tshilenge

Outside income by farmer
or spouse 8 15 22 16
Previously worked off
the farm 54 63 38 54
Worked off farm on
commercial farm 22 4 9 7
Have children with
off-farm jobs 5 12 12 23
Family members contributing
from off-farm jobs 3 2 6 7

Rather than a cash labor market, farmers in the study area
still tend to rely more on traditional work sharing. People from the
same village agree to join together for clearing, sowing, and harvesting,
in order to speed the completion of these tasks. In more densely
populated areas, around urban centers, job sharing at harvesting has
gradually evolved into more hiring of services for payment in maize

Labor as a Factor Constraining Maize Production

It can be asked if labor constraints presently limit maize pro-
duction. The data are inconclusive on this question. Hired labor is
little used in maize production and additional family labor is apparently
available: notably from adult women at planting and weeding time, and
from adult men at harvesting time (see Tables 4-7). Thus, cultural
dictates as to which activities are appropriate for men and which are
appropriate for women may impose an artificial labor availability con-

On the other hand, several activities compete with maize for
labor. Most Kasai Oriental farmers plant other food crops along with
maize in August and September, while Kaniama farmers do most such

- 6 -

planting in September and October. Planting and subsequent weeding might
exhaust all available labor. Still, Table 10 shows that most respondents
from Kasai Oriental dq not see August/September as the months in which
labor is heavily used./. However, those in Kaniama do report September/
October as among the critical months and also point to November, when
weeding of maize is required, as critical.


Percent of respondents reporting month for which labor is
most heavily used in agriculture.

Sep. Oct. Nov. Dec. Jan. Feb. Mar. Apr. May. Jun. Jul. Aug.

Orientala 2 4 13 9 8 5 4 19 25 12 -
Kaniama- 18 23 22 9 4 6 9 6 1 1 1 1

a/ All respondents females. Districts of Mwene-Ditu, Gandajika,
and Tshilenge combined.
b/ Most respondents male.

In responding to questions about what most limits maize produc-
tion, and what most limits agricultural income (Tables 11 and 12), labor
itself is given little emphasis. Even so, it might be argued that those
pointing to tractors were really signalling that labor is scarce during
critical periods.

TABLE 11. Percent of farmers reporting most important constraints
limiting maize production.

Kaniama Mwene-Ditu Gandajika Tshilenge

Land 2 19 19 13
Labor 12 8 4 14
Tractor 50 22 22 24
Good seed & fertilizer 4 19 18 22
Other 32 32 37 27

5/ It should be emphasized, however, that in Kasai Oriental, the
question about heavy labor use (Table 10) was asked of women in
in the marketplace. (It was included in the regular farm survey
in Kaniama.) Women participate less in planting and weeding in
the Kasai Oriental districts than in Kaniama.

- 7

TABLE 12. Percent of farmers reporting various alternative means of
increasing agricultural income.

Kaniama Mwene-Ditu Gandajika Tshilenge

Larger farms 79 73 61 72
Chemical inputs 7 19 27 22
Other/no answer 14 8 12 6

A linear regression model was employed to investigate the ef-
fects of some aspects of the labor factor and other explanatory variables
on the area planted in all crops and the area planted with maize. These
variables were chosen as proxies for total farm output and total maize
production, respectively, since actual output figures were not available.
Results of the regressions are presented in Table 13. Those explanatory
variables having a statistically significant impact on total area or area
in maize are specifically designated.

TABLE 13. Estimated interdistrict regression coefficients

Dependent Variable
Explanatory variable Total area Maize area

Constant 51.08 41.18
Size of the family 8.17* 1.96*
Level of education of head of household 3.84* 0.68
Age of head of household 0.53 0.03
Average age of the wife 1.96* 0.71*
Number of wives weighted by age 0.43* -
Number of wives 2.43
Size of the village 0.01* -
Distance to market 0.72+ 0.12
Participation in CAKO scheme- a/b/ 21.74 12.40
Participation in TABAZAIRE scheme- -52.25* -27.30*
Number of years under CAKO or
TABAZAIRE supervised production 8.55* 1.14
Ownership f, a bicycle or radio- 27.04* 11.89*
Mwene-DiY- -53.21* -21.66*
Tshilenge- -99.43* -30.71*
Gandajikaa/ -44.32* 5.91
Coefficient of multiple correlation (R) 0.54 0.41

a/ Indicates that a zero-one "dummy variable" was
this phenomenon.

used to classify

b/ CAKO and TABAZAIRE are government-supervised programs for
the production of cotton and tobacco, respectively.
-- Variable not used in a regression Significant at the 5% level
+ Significant at the 10% level

- 8

The size of the family has a significant impact on both dependent
variables, thus serving to underscore the importance of labor's influence
on the amount of land which can be cropped.

Several variables were included to measure the influence of
education and experience. The education of the head of household, in
most cases a male, is significant in determining the total area planted,
but not the area planted to maize.

The age of the head of household, a proxy for experience, is
not significant, whereas the age of the wife is statistically significant in
both regressions. Multicolonearity in the two age variables may explain
the lack of explanatory significance for the husband's age. Nevertheless,
these results serve to raise again the century-long. debate as to whether
it is the wife or husband who contributes more to production in the tra-
ditional African farm setting6. The lack of statistical significance of the
husband's age should not be interpreted to mean that he is unimportant
in the agricultural production of the study area. Tables 4-6 demonstrate
the substantial role that he plays in providing labor for clearing, sowing,
and weeding. Field observation suggests that the male role is even more
important in the production of such commercial (non-traditional) crops as
cotton and tobacco.

Many of the farmers in the survey area do participate in two
government programs designed to stimulate the production of tobacco
(the TABAZAIRE program) and cotton (CAKO). Participants receive
advice from extension agents, access to tractor plowing services for
land devoted to the specialty crop itself, and they can purchase certain
specialized inputs through the administrative agencies. Regression results
show the impacts of the programs.

Participation in TABAZAIRE has a negative impact on both total
area in crop and the area in maize. This can be understood in that
tobacco is a labor intensive crop -- devoting much effort to tobacco will
severely limit the time available to plant other crops. Furthermore, the
time period in which tobacco is produced (September through January)
coincides with the peak labor demand requirements of cassava, maize,
beans, and other traditional cash crops. In contrast, cotton production --
at least as organized through CAKO -- apparently does not conflict with
the production of other crops. While not statistically significant, the
regression coefficients for CAKO participation are I-oth positive.

The length of time of participation in the government programs
was viewed as another experience variable. While .th of participation
has a positive impact on total area cropped, it has no significant impact
on maize production per se. The interpretation of this result is that
participants in the two programs learn little which may be usefully car-
ried over to the production of maize.

6/ For discussion of this debate, see references (1, 3, 5).

- 9

The variable for ownership of a bicycle or radio was used to
signify modern orientation7. It does have a significant, positive as-
sociation with both total area and area in maize. More importantly,
perhaps, it serves as an indication that these modern artifacts are a
potential link between government programs and the better farmers. At
present, there are few, if any, farming information programs available
by radio in the study area.

The final three variables in the regressions are dummy variables
used to distinguish the districts of Mwene-Ditu, Gandajika, and Tshilenge,
from Kaniama, the district of reference. Their negative and (in most
cases) significant signs can be taken to mean that Kaniama has other-
wise unexplained advantages over the other three districts, in terms of
both maize and general farm production. The availability of more land
within walking distance, stemming from smaller villages and sparser
population in Kaniama, is certainly one factor which may explain this

Summary and Conclusions

This paper has explored the availability and use of farm labor
in one of the principal maize production areas of south-central Zaire.
Results indicate that farmers rely heavily on family labor for the pro-
duction of maize and other crops. Of the farmers surveyed, 12 percent
or less relied on outside hiring for principal maize production tasks.
Low reliance on outside work as an additional source of income to
farmers represents further evidence of the lack of an active labor
market in the survey area.

The availability of labor from within the family itself appears
to be constrained by the traditional division of tasks between males and
females. Males tend to take the major responsibility for clearing maize
land and, to a lesser extent, for sowing and weeding. Females, on the
other hand, do a heavy portion of the harvesting and marketing. This
is especially true in the Kasai Oriental districts of Mwene-Ditu, Gandajika,
and Tshilenge.

In the Kaniama district of Shaba, there is some tendency for
traditional roles to break down: both males and females participate
heavily in all maize production tasks. It is not clear whether this is

7/ In retrospect, one may wish to quarrel with this interpretation.
Cause and effect are at issue, i.e., Is a farmer more productive
because of "modern orientation" or is he able to afford the artifacts
of the modern world because he is more productive? Nevertheless,
the authors feel that the final interpretation of the results for this
variable is consistent with either case.

- 10

the cause or the effect of larger farm size. Population density and village
size are both lower in Kaniama than in other districts, and this would
presumably make available communal lands more readily accessible.
Farm size is indeed larger in Kaniama. This suggests that improving
accessibility to lands which lie more distant from larger villages may
be a key to increasing farm size and production in the study area.

In general, there is mixed evidence as to labor's role as a
constraining factor in production. When asked about limitations to increasing
output, most farmers identified the need for more tractor mechanization,
rather than labor per se. Yet perhaps this is nothing more than indirect
recognition of the scarcity of labor at critical periods. Results of a linear
regression clearly showed the importance of family size, an indicator of
labor availability, as a determinant of total area cropped and area planted
to maize.


1. Clark, B.A. 1975. The Work Done by Rural Women in Malawi.
Eastern African Journal of Rural Development. 8 (1 & 2):

2. Mwamufiya, M. 1976. Maize Production and Marketing in Four
Districts of Zaire: An Introductory Economic Analysis.
Corvallis, Oregon. A dissertation submitted to Oregon
State University for the degree of Doctor of Philosophy
in Agricultural and Resource Economics.

3. Okai, M. 1972. Some Aspects of Agricultural Labor Use in the
Main Short Grass Zone of Uganda. East African Journal
of Rural Development. 5 (1 & 2): 103

4. Republique du Zaire, Department de l'Agriculture. 1972(?).
Presentation de Quelques Resultats Preliminaires du
Recensement de 1'Agriculture. Unpublished report.

5. Vail, D.J. 1973. Induced Farm Innovation and Derived Scientific
Research Strategy: The Choice of Techniques in Developing
Smallholder Agriculture in Land Abudant Areas. Eastern
African Journal of Rural Development. 6 (1 & 2): 1 18.

- 11 -

University of Florida Home Page
© 2004 - 2010 University of Florida George A. Smathers Libraries.
All rights reserved.

Acceptable Use, Copyright, and Disclaimer Statement
Last updated October 10, 2010 - - mvs