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JOURNAL OF RESEARCH IN NATIONAL DEVELOPMENT VOLUME 8 NO 1, JUNE, 2010


PRODUCTIVITY OF WOMEN FARMERS IN THE DERIVED SAVANNAH ZONE OF NIGERIAPANACEA TO FOOD CRISIS
T.M. Yusuf
Department of Agricultural Education, Kwara State College of Education, Oro
E-mail: tmyusuf@yahoo.com
and
F.Y. Okunmadewa, K.O. Adenegan,and A.S.Oyekale
Department of Agricultural Economics, University of Ibadan, Ibadan

Abstract
In Nigeria,food supply can no longer meet up with food demand. Women  being the major food producers in the country implies that their productivity is  significantly low. This study therefore, explored the potential for improving productivity of women farmers in the Derived Savannah Zone of Nigeria.Multi-stage random sampling technique was used in collecting data. Kogi and Kwara states were randomly selected from the six states in the zone. Two Agricultural Development Programme (ADP) administrative zones were randomly selected from each state from which four Local Government Areas (LGAs) were then selected. Four villages were selected per LGA from which200 respondents were randomly selected on the basis of size. Descriptive Statistics, total factor productivity analysis, and ordinary least squares regression  analysis were used in the analysis. The  mean age and farming experience of the women farmers were 47.6 ± 9.5  and 20.4± 12.3years respectively. Their mean farm size and number of plots cultivated were 1.8 ± 1.18 acres and 2.0 ± 0.84 respectively. Total factor productivity  index ranged between 2.7 and 1,104.6  with a mean of 489.9 indicating low productvity level . Factors that contributed to low productivitywere land fragmentation and years of farming. A unit increase in these variables decrease productivity level by 0.1330 and 0.0069 respectively at (p<0.1) Low productivity of women food crop farmers resulted from land fragmentation and the conservative ideas of the old farmers. Technical training on land management would increase women food crop farmers’ productivity.

Keywords:  Women farmers,  total factor productivity , factor Share and, regression analysis


Introduction   
Agricultural productivity has been declining in Nigeria.  Its contribution to GDP declined from about 90% before independence to about 41% 2006 (CBN, 2003 and 2006). The low agricultural output has led to the poor performance of the food sector. Food production has not been able to keep pace with the demand in spite of the evidence that has shown that Nigeria is producing more food staples than it  was a decade ago (CBN,2005). Demand is high as a result of the rapid increase in population which is estimated to be growing at 3.2% per annum (NPC, 2006) while agricultural production is growing at 2.5% per annum (Ogundari and Ojo, 2007). This discrepancy has led to a food demand – supply deficit which has induced tremendous increase in the country’s import bills from N3.47 billion in 1990 to N113.63 billion in 2002 and then to N348 billion in 2007 (Okuneye, 2002 and Okunmadewa, 2003. This has subsequently increased the prices of major food staples over the years. Nigeria’s food crisis is compounded by the fact that more than 54 percent of Nigerian population is poor (NBS, 2007). This has left many Nigerians in the dilemma of having neither the means to produce food nor the money to buy food (Adeoti and Egwudike 2003; Ogundari and Ojo, 2007). As a result, 16 percent of Nigerians are currently severely undernourished while, about 41 percent are food insecure (FAO, 2005). This precarious situation calls for an urgent need to increase agricultural productivity. However, study carried out by the Food and Agricultural Organization (FAO, 2005) reveals that women farmers contribute 60 percent of the labour force and produce 80 percent of the food in Nigeria. If the productivity of these women could therefore be increased, then, food productivity will increase and Nigeria would be able to save some of its foreign exchange. Hence, this study focuses on how to improve food productivity through the improvement of women farmers’ productivity.

To achieve this aim, the study intends to find empirical solution to the following research questions. What is the productivity level of the women farmers in the study area?  What are the factors influencing productivity of these women? What is the possibility of increasing food production in the study area? The main objective of this paper therefore, is to analyze empirically, productivity of women farmers by determining the productivity level of women farmers in the derived savannah zone of Nigeria and identifying the factors influencing their productivity.

Conceptual framework and literature review
The concept of productivity as used in this study is  a relative measure of actual food output produced by the women farmers compared to the actual input of resources.  As output increases for a level of input or as the amount of input decreases for a constant level of output an increase in productivity occurs. Therefore, “productivity” in this sence describes how all available farm resources are being used to produce food output.

Productivity could be measured using partial factor productivity, multifactor productivity or total factor productivity. Total factor productivity is considered a conceptually superior way of measuring productivity (Rhaji 2006; Ashok and Balasubramaman, 2003). This is because Partial productivity measures could be misleading if considerable input substitution occurs as a result of widely differing input prices due to market imperfection. Total factor productivity measure was used is this paper because the study area involved different states and LGAS with wide differences in input prices.

Total factor productivity could be measured by two major approaches namely; linear aggregation (linear function) and geometric aggregation (Cobb-Douglas function)r approaches (Leman et al, 2008 The first is the linear aggregation of various inputs with market factor prices as weight. Traditionally, this method is known as accounting method. The second is the geometric aggregation with factor shares as weight. This is known as Total factor share.Evidence (Leman et al 2008) has it that   accounting method often results into valuation biases in situation where there is absence of market prices for valuing the cost of inputs, especially in agricultural production. The study therefore, used geometric aggregation with factor shares as weight.
This method involves the estimation of a production function. The estimated input coefficients (factor shares) are then used as weights to calculate the value of the bundle of inputs. The ratio of the observed output to the estimated bundle of input is the total factor productivity.

Methodology  
This study was conducted in the derived savannah zone of Nigeria. Two states i.e Kwara and Kogi states were used. The coordinates of Kwara State are 7o 45’N and 6o40’E while, Kogi State lies within 7o30N’ and 6o42’E. Kwara State has a total population of 2,701,056 with males being 1,550,548 and females 1,150,508. Total population in Kogi State is 3,278,495 comprising of 1,691,737 males and 1,586,758 females (NPC, 2006)     . The study area is basically agrarian. 80 percent of the population in Kwara State resides in the rural areas of which 90 percent are farmers. In Kogi State, 70 percent of the population resides in the rural areas with about 80 percent being farmers (NBS 2006). More than 50 percent of the farmers according to (NBS, 2006) are women in the two states. The region is blessed with  suitable ecological and climatic conditions                                                                                             

The selection of respondents was multi-stage and involved random sampling method, as well as purposive sampling. The first stage was a random selection of four ADP administrative zones from the eight ADP strata in the two states (i.e. two from each state). The second stage involved random selection of four Local Government Areas, one from each selected ADP stratum. The third stage of sampling was a purposive selection of four villages with a high concentration of women from each Local Government Area with the help of the list of women farmers provided by the states ADPs. The last stage, 10 to 15 women farmers were randomly selected from each village on the basis of probability proportionate to size to make up 200 women farmers in the sample.Structured questionnaires, personal interview and direct observation methods were used. Relevant secondary data were also used to supplement the primary data.

Combinations of methods were used to analyze the data collected.  These include descriptive statistics consisting of means and standard deviations used to examine the socio-economic characteristics of the women farmers, ordinary least squares regression analysis to obtain the regression coefficients that were used as weights to determine total factor productivity indices for the women farmers and to identify factors influencing women productivity. Total factor analysis was used to determine productivity levels of the farmers.

Models specification
Ordinary least squares regression analysis
Cobb-Douglas production function was estimated.The model is specified as;


LnQ = ao + a1lnxi + a2lnx2 + a3lnx3 + a4lnx4 + a5lnx5 + e               


where Q is aggregated output (kg grain equivalent), X1 is planting material (kg grain equivalent), X2 is Land (hectare), X3 is Family labour (days), X4 is Hired labour (days), X5 is Fertilizer, as are coefficients(factor shares) to be determined. ln stands for natural logarithm and e is error term
The estimated coefficients (factor shares) were then used to estimate total factor productivity index to determine the productivity of each woman farmer. The model for the estimation is as specified.


            
           


ai is the coefficient of input i used. NOTE: ai in Cobb-Douglas production function stands for marginal productivity of xi used  = (MPxi­)
Q = Output ( in grain equivalent)
Xi = Inputs used (as above)

In the second stage of the regression analysis, farmers’ specific characteristics were modeled as determinants of productivity to understand how these characteristics influence the level of productivity of the women farmers.  The Regression model used is as follows:-
PROD = f (Z1, Z2,   Z3, Z4, Z5, Z6, Z7).
Where
PROD  =          the estimated total factor productivity index
Z1,        =          Years of schooling
Z2,        =          Membership of organization
Z3,        =          Faming experience (Years)
Z4,        =          Access to credit (Dummy Variable Yes  = 1 otherwise  = 0)
Z5,        =          Contact with extension agent (Dummy variable Yes  = 1  Otherwise = 0)
Z6         =          Family size
Z7         =          Farm plots
Result and discussion
Socio-economic characteristics of women farmers

The characteristics considered were; Years of schooling, farming experience, family size, farm size, number of plots, membership of organization (MBO) and extension. The result is presented in Table 1.

The table reveals the average household size of 7 persons, comprising 4 adult members and 3 children. It further shows that women farmers in the study area are experienced farmers with an average farming experience of 20 years. However, analysis shows that they had an average of 4.5 years of  formal education, 1.87 acres of farmland and a number of farm plots raging between 1and 5.  They had mean extension visits of 14. This means that on the average each woman farmer had more than one contact with the extension agent in the study area.


Table 1: Descriptive Statistics for Selected Socio-economic characteristics of Women Farmers


Variable

Min

Max

Mean

Std. Dev.

Farming Experience (Years)

1

50

20.42

12.26

Years of schooling

0

14

3.72

1.58

Family Size (No)

2

20

7.80

4.72

Adult Members (No)

1

17

4.6

2.98

Children (≤ 15 years) (No)

0

10

3.06

2.43

Farm size(acres)

0.5

5

1.87

1.18

No of plot

1

5

2

0.84

Extension visit

0

48

14

12.23


Analysis of productivity of women farmers
The total factor productivity model derived earlier allows us to compute relative measure of productivity of the women farmers in the study area. The distribution of the productivity indices is as presented in Table 2


Table 2:           Distribution of Women Farmers Productivity Indices in the Derived  Savannah Zone of Nigeria


Productivity class index

Kwara state

Kogi state

overall

No. of farmers

 

% of
Farmers

No of farmers

% of farmers

No of farmers

% of farmers

< 50
50 – 99.99
100 – 149.99
150 – 199.99
200 – 249.99
250 – 299.99
300 – 349.99
350 – 399.99
400 – 449.99
450 – 500
> 500
Total

20
25
12
20
05
03
03
02
05
02
03
100

20
25
12
20
05
03
03
02
05
02
03

34
14
17
10
07
07
06
02
01
00
02
100

34
14
17
10
07
07
06
02
01
00
02

51
40
28
31
13
10
08
05
06
03
05
200

51
40
28
31
13
10
08
05
06
03
05

Lowest
Highest
Mean

6.5
1104.6
485.9

 

2.7
1097
139.6

 

2.7
1104.6
311.9

 

Source: computed from field survey data.


The result of the analysis reveals that all the female farmers in the study area are productive. The results of the ratios are positive and greater than one for all women farmers in the study area. However, there is a wide variation across the farms. The overall productivity indices range from 2.7 to 1104.6 with a mean productivity of 489.9.grain equivalent per factor share of the inputs use. The Implication of this finding is that majority of the women farmers did not manage the available resources very well to produce their output. There is some sort of waste in the use of resources. The gaps between the least productive farmer and the most productive shows that there are ample opportunities to tremendously increase food productivity in the study area. This will however depend on the adoption of the production principles of the most productive farmer.

 In Kwara state the productivity indices range from 6.5 to 1104.6 with a mean of 479.9. Kogi state has the least value of 2.7 productivity index and 1097.0 as the maximum value with the lowest mean of 139.6. The implication of this finding in relative terms is that Kogi state contributes more to low level of food productivity observed in the study area.

Determinants of productivity of women farmers
The result of the productivity model shows that four of the seven variables have significant impact on the farmers’ productivity. These include – family size (P<0.01), farming experience (P<0.05), membership of association (P<0.05) and number of plots (P<0.1). Total family size is significant and positively related to productivity at 1% level. This reveals that family size is an important determinant of women farmers productivity. It is an important source of family labour.

This finding is consistent with findings reported by Okoruwa et al.,(2006). Their study also showed a positive significant relationship between family size and farmers’ productivity in rice production.Number of plots  owned by an individual farmer has a negative but significant relationship with the farmers’ productivity. The significant relationship shows that farm plots are important perquisite of cop production. However, the negative sign indicates that, land fragmentation causes farmers’ productivity to decline. This is because in addition to not permting farm mechanization, the variale adds to the distance that will be covered by the farmers. This finding is in line with Kebede, (2001) and Adewuyi (2002). The coefficients of membership of farmers association and year of farming are significant and negatively related to productivity. These findings are contrary to a priori expectations of positive relationship between the two variables and productivity. The significant relationship between MBO (dummy variable) with productivity at 5% level shows  that membership of any agricultural association is an important determinant of productivity.The variable exposes farmers to productive information and sources of farm inputs.However, the negative sign   may probably be due to the fact that farmers  waste too much time on attending club functions rather than engaging in meaningful farm activities.
 
A possible explanation for the estimated negative effect of year of farming on farmers productivity is that new entrants are more knowledgeable about recent technological advances than their older counterparts. They are more likely to adopt new and better techniques than older farmers.This result is consistent with the findings of Mario (2006), Yusuf and Malomo (2007), Adewuyi (2002) They found out that increase in farming experience caused productivity of farmers to decline.


Table 3: Determinants of productivity of Women Farmers in Food Crop Production


Variable

Double log

t-value

Constant

-0.5360

-4.61***

Education (Z1)

0.8872

0.7605

MBO (Z2)

-0.1061

-2.5614**

Access to credit (Z3)

0.0492

0.46

Year of farming (Z4)

-.0069

-2.03**

Extension (Z5)

0.4092

0.7754

 Family size

0.7625

5.421399***

Number of plots

  • 0.13304

 

-1.948739*

*** Significant at 1%
** Significant at 5%
* Significant at 10%
           


The outcome of this analysis thus suggest that women farmers rely more on family labour for food production in the study area. As such labour substituted modern farm implements specifically designed for female farmers should be provided.
 
 Conclusion
 Women farmers in the study are predominately illiterates and small-scale food producers who cultivate on fragmented farm land and learn the art of farming from their experience and those of their forefathers. They are productive. The average ratio of output to inputs is positive but very low when compared to the most productive farmer in the sample.

 Their low productivity resulted from land fragmentation, year of farming and lack of adequate technical training as a result of wrong use of cooperative associations..Total family size contributed positively to women productivity.
  
Recommendations
The strong relationship existing between resource productivity and family size, an important source of family labour is a pointer to the fact that women farmers have to rely heavily on family labour to satisfy the household needs. This may have negative implication on the children within the study area who are supposed to be in schools considering their age, but been used to provide farm support. Therefore, for a better tomorrow of the rural children and economic development of the country, labour substituted modern farm implements specifically designed for women should be provided.

Also the positive and significant relationship between productivity and farming experience implies that majority of women farmers though young and active because  of their level of education have to learn the art of resource managament from experience. The negative but significant estimated coefficient of the variable in the productivity equation has shown that experience alone is not enough for the adoption and use of improved techniques. There is therefore, the need for the policy makers to look into the aspect of girl- child education for the purpose of increasing agricultural productivity in particular and economic development in general.
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