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African Journal of Agricultural Marketing ISSN 2375-1061 Vol. 5 (11), pp. 001-006, November, 2017. Available online 
at www.internationalscholarsjournals.org © International Scholars Journals 

 

Author(s)                                           Author(s) retain the copyright of this article. 
 
 

 

Full Length Research Paper 

 

Economic analysis of adoption of mulching 
technology in yam production in Osun State, Nigeria 

 
A. A. Akinola* and P. Owombo 

 
Department of Agricultural Economics, Faculty of Agriculture, Obafemi Awolowo University, Ile-Ife, Nigeria. 

 
Accepted 24 April, 2017 

 
In addressing the problem of low soil fertility and land degradation occasioned by increased population growth, 
erosion of soil nutrients and extreme exposure of land to harsh weather conditions resulting in reduced yam yield, 
mulching technology was adopted by the farmers in Osun State, Nigeria. This study used a multi-stage sampling 
technique to select 105 farmers involving adopters and non-adopters of mulching technology. Data were analyzed 
with the aid of descriptive statistics, budgetary techniques and probit model. The results of budgetary analysis 
showed that seed yam and labour costs constituted significant parts of the variable costs. The average revenue per 
hectare for adopters was N412, 971.69 while that of non-adopters was N346, 456.75. However, the average net 
incomes were N326, 865.02 and N236, 087.40 for the adopters and non-adopters, respectively. The benefit-cost 
ratios were 4.79 and 3.13 for adopters and non-adopters, respectively. The probit model revealed that household 
size and hired labour were significant factors determining the farmers’ adoption decisions. There is therefore the 
need to encourage farmers on the needs to adopt the land protecting technology and a policy thrust that make seed 
yam available and affordable as well as reducing the costs incurred on labour will be in the right direction. 

 

Key words: Adoption, mulching, yam, budgetary technique, probit. 
 
INTRODUCTION 

 
Yam (Dioscorea spp.) forms a basic staple food for 
millions of people in Nigeria, where it is eaten boiled, 
roasted, fried and also can be processed into various 
forms of flour and starchy paste. In the dominant yam 
production zone of West Africa, consumer demand for 
yam is generally very high and its cultivation is very 
profitable despite high production costs (IITA, 2009).  

Nigeria is the largest producer of yam (34 million 
tonnes) but Ghana which is the third largest producer 
exports the largest quantity of yam about 12000 tonnes 
(IITA, 2009). This is due to a number of reasons which 
include using of soils low in fertility and quality and hence 
quality of yam produced in Nigeria is very poor.  

Yams (Dioscorea species) are annual or perennial 
tuber-bearing and climbing plants. The genus Dioscorea 
has over 600 species but only a few are cultivated for 
food. The major edible species of African origin are white  
 
 
 
*Corresponding author. E-mail: Akinola@oauife.edu.ng. 

 
 
 
 

 
Guinea yam (D. rotundata Poir.), yellow Guinea yam (D. 
cayenensis Lam.),and trifoliate or bitter yam (D.  
dumetorum Kunth) (IITA, 2009).It is planted as sole crop 
but unusually intercropped with melon, pepper, okra and 
amaranthus. The most important part of the yam plant is 
the tuber. It can be grown in all tropical countries 
provided water is not a limiting factor. In Nigeria, it is 

grown within the coastal region up to latitude 12
o
N and 

corresponds to the rain forest, wood savannah and 
southern savannah belt. This is where the annual rainfall 
exceeds 800 mm in amount and 4 months in duration. 
Deep, fertile, friable, and well-drained soils are ideal for 
yam cultivation. Whole seed tubers or tuber portions are 
usually planted into mounds or ridges before or at the 
beginning of the rainy season. The sett sizes planted, 
sizes of mounds, interplant spacing and provision of 
stakes for the resultant plants depend on factors such as 
the yam species, agro ecology, and tuber sizes desired at 
harvest. Small-scale farmers in West and Central Africa 
often intercrop yams with cereals and vegetables.  

The labour requirements in yam cultivation for 

file:///C:\Users\user\Documents\REPUBLICATION\AGRICULTURAL%20SCIENCES\AppData\Local\Temp\www.internationalscholarsjournals.org


 
 
 

 

mounding, staking (especially in the forest zone), 
weeding, and harvesting exceed those for other starchy 
staples such as cassava (IITA, 2009). Yam matures 8-12 
months after planting (some are cut after 6 months are 
covered again to act as seed yam). However, increased 
population, erosion, and adverse weather conditions have 
reduced available land to yam production, thereby 
encouraging intensification of land leading to the 
degradation of land and depletion of soil nutrients that 
necessitate the use of land improving technologies such 
as mulching. Therefore, the main objective of the study 
was to compare the output and quality of yam produced 
by farmers as regards mulching in Osun State. The 
specific objectives were to examine the socio-economic 
characteristics of yam farmers; examine costs and 
returns to yam production; and determine the factors 
influencing the use of mulching in the study area. 
 
 
Mulching 
 

Mulching is a soil conservation practise. It involves the 
use of dry vegetable or grass materials to cover the 
surface of the soil. Mulch are substances spread on the 
ground to protect the roots and base of plants from 
extreme temperature, moisture changes and improve the 
quality of the soil and also stop the growth of weed. 
Mulching also enriches and protects soil and helps 
provide a better growing environment. Findings have 
shown that time of mulching can influence the growth 
environment and performance of yam (Dioscorea 
species).  

Cover cropping and residue mulching are good 
practices for low-intensity cultivation of marginal lands to 
achieve soil conservation effectiveness. The rising 
population density, especially in south eastern Nigeria 
and the declining land-to-man ratio, the consequent pre-
disposition of most agricultural lands to degradation, and 
the decline in fallow periods have driven most farmers 
into marginal states (Ruthernberg, 1977). These new 
lands have been unable to support the farmers' staple 
food crops. As these factors impact on land-use 
intensification (Turner et al., 1993; Tiffen et al., 1994; 
Scoones et al., 1996), farmers have increasingly invested 
in new or borrowed technologies, thereby conserving the 
resource base and increasing their production (Scoones 
et al., 1996). 
 
 
RESEARCH METHODOLOGY 
 
Area of study 
 
Osun State is located in the south-western part of Nigeria. It was 
created in 1991 and it covers an area of approximately 14, 875 
square kilometres. It shares common boundaries with Kwara, Ogun, 
Ekiti, Ondo and Oyo States. Osun State is an agrarian community. 
It has an estimated land area of 8, 822.55 square kilometres. The 
major crops grown here are maize, yam, cassava, cocoa, oil palm, 

  
  

 
 

 
timber and tomatoes among others. The climate of Osun State is 
favourable for yam production. The state experiences two major 
seasons, the dry season and the rainy season with August break 
during the rainy season; the dry season is from late October/early 
November to march. The mean annual temperature varies from 

21.1 to 31.1
o
C. Annual rainfall is within the range of 800 mm in the 

derived savannah agro-ecology to 1500 mm in the rain forest belt. 

 

Sampling technique and data 
 
A multi stage sampling was used to select respondents from the 
three agro-ecological zones in Osun State with the help of Osun 
State Agriculture Development Programme (OSSADEP) office in 
Ife/Ijesa zone, Osogbo zone and Iwo zones. The three agro-
ecological zones were chosen purposively for the study. The 
second stage of selection involves random selection of thirty five 
respondents in each of the zones consisting of adopters and non-
adopters of mulching technology giving a total of one hundred and 
five questionnaires administered through interviews. Data were 
collected through the use of structured questionnaire and personal 
interviews between the months of September and October, 2010. 
Data collected included input-output information, management and 
production practices, costs and returns associated with yam 
production. 

 

Analytical techniques 
 
Three main analytical methods were employed in this study: 
descriptive statistics, budgetary techniques and probit analysis. 
Descriptive statistics such as percentages, range and mean were 
used to describe the values of selected socio- economic variables 
such as age, farm size, level of education, etc. A total farm budget 
approach was undertaken to estimate costs and returns accruing to 
each of farmers. According to Alimi and Manyong (2002), a budget 
is the quantitative expression of total farm plan summarizing the 
income, cost and profit (a residue of total cost from total revenue). 
Gross margin which is the difference between total revenue and 
total variable cost were analysed. The total cost component is 
expressed as: 
 
TC=TFC+TVC 
 
Where: TC=Total cost; TFC=Total Fixed Cost; TVC=Total Variable 
Cost 
 
Gross margin= (TR)-(VC); TR=Total Revenue=Price x Quantity that 
is, PQ 
 
VC=Variable Cost; Profit=TR-TC 
 
The efficiency ratios that were analysed are fixed cost ratio, rate of 
returns, variable to cost ratio, labour intensity amongst others. 
These were computed to indicate the performance of farm 
enterprise.  

The probit model uses the cumulative distribution function (CDF) 
to explain the behaviour of a dichotomous dependent variable. 

Given the assumption of normality, the probability that I*i is less 

than or equal to Ii can be computed from the normal CDF as, 
 

Pi = P(Y=1/X) 
= P (Ii* < Ii ) 
=P (Zi < B1 + B2Xi) 
=F (B1 + B2Xi) 

 
Where I* = critical or threshold level of the index, such that if Ii 
exceeds I*, the family will adopt, otherwise it will not. P (Y=1/X) is 



 
 
 

 
the probability that an event occurs given the values of X, or 

explanatory variable(s) and where Zi is the normal variable, that is, 
Z~N(0, Q2).  

The term “probit” was coined in the 1930s by Chester Bliss and 
stands for probability unit. These two analyses, logit and probit are 
the same. As discussed previously, probit uses the cumulative 
normal distribution. The probit model is defined as: 
 
Pr(y =1/X) = Φ (xb) 
 
Where Φ is the standard cumulative normal probability distribution 
and xb is called the probit score or index.  

Since xb has a normal distribution, probit coefficient is interpreted 
in the Z (normal quartile) metric. The interpretation of a probit 
coefficient is that one-unit increase in the predictor leads to 
increasing the probit score by b standard deviations. Learning to 
think and communicate in the Z metric takes practice and can be 
confusing to others. We will make use of a number of tools 
developed by Long and Freese to aid in the interpretation of the 
results.  

The log- likelihood function for probit is: 
 

In L=∑WjInθ(xjb) + ∑wjIn(1-θ(xjb) 
 

Where wj denotes optional weights  
The model relating to the intensity of adoption is specified as 

follows: 
 
Pi = f (B0+B1X1+B2X2+B3X3+B4X4+B5X5+B6X6+B7X7+B8X8+B9X9) 
 
Where: Pi = adoption status measured as dummy (1= adopters, 0= 

non- adopters); X1 = sex of respondents (SEX); X2 = Age in years 

(AGE); X3 = Educational status (EDUCATION); X4 = Household 

size (HHSIZE); X5 = Farm size (FARMSIZE); X6 = Association 

(SOCKAP); X7 = Extension visits (EXTENTION); X8 = Off-farm 

income; X9 = Hired labour size (HRLABOUR) 

 

A priori expectation signs of the coefficients 
 
The multidisciplinary independent variables included farmer, farm 
and institutional factors postulated to influence technology adoption. 
These variables include age (AGE) of the household head in years, 
the number of people in the household (HHSIZE), measure of social 
interaction resulting from membership in a farmers’ organization 
(SOCKAP), off-farm income (OFFINCOME) measured in Nigerian 
naira (N), sex of the respondents (SEX), access to credit (CREDIT), 
education of household head (years of formal education), effective 
extension contacts (EXTENSION) measured by regularity of visits 
by extension agents, farm size, and asset. Off-farm income and 
assets were measured in natural logarithm. On the other hand, 
social capital, access to credit and extension were measured in 
dummies.   

The rationale for inclusion of these factors was based on a priori 
of agricultural technology adoption literature discussed in chapter 3. 
The effect of age (AGE) on a decision whether to adopt a 
technology may be negative or positive. Previous studies show that 
the age of individuals affects their mental attitude to new ideas and 
influences adoption in several ways. Younger farmers have been 
found to be more knowledgeable about new practices and may be 
more willing to bear risk and adopt new technology because of their 
longer planning horizons. The older the farmer, the less likely he is 
able to adopt new practices as he gains confidence in his old ways 
and methods. On the other hand, older farmers may have more 
experience, resources, or authority that may give them more 
possibilities for trying a new technology. Thus for this study, there is 
no agreement on the sign of this variable as the direction of the 

 
 
 
 

 
effect is location- or technology-specific (Feder et al., 1985; Nkonya 
et al., 1997; Oluoch-Kosura et al., 2001; Bekele and Drake, 2003). 
Education was hypothesized to positively influence the adoption of 
integrated soil fertility technologies since as farmers acquire more 
of this factor, their ability to obtain, process, and use new 
information improves and they are likely to adopt.  

Education increases the ability of farmers to use their resources 
efficiently and the allocative effect of education enhances the 
farmer’s ability to obtain, analyze and interpret information. Several 
studies reviewed by Feder et al. (1985) indicate the positive 
relationship between education and technological adoption. Alene 
et al. (2000), in the case of Ethiopia, reported that farmers with a 
higher level of education had a higher probability of adopting 
improved farming practices than those with lower level of education. 
Nkonya et al. (1997), in the case of Tanzania, and Oluoch-Kosura 
et al. (2001), in the case of Kenya, indicated that education is an 
important factor positively affecting the process of technical 
adoption.  

Institutional factors of social capital (SOCKAP), extension 
contacts (EXTENSION) and access to credit (CREDIT) were 
hypothesized to positively influence adoption as these support 
services facilitate the uptake of new technologies. SOCKAP such 
as cooperative societies has been found to enhance the interaction 
and cross-fertilization of ideas among farmers. This in effect will 
positively affect land-enhancing technologies (Bamire et al., 2002). 
Farmers who are non-members of associations are expected to 
have lower probabilities of adoption and lower level of use of ISFM 
technologies. The extension contact variable incorporates the 
information that the farmers obtain on their production activities on 
the importance and application of innovations through counseling 
and demonstrations by extension agents on regular bases. The 
effect of this information on adoption varies depending on channel, 
source, content, motivation, and frequency. It is hypothesized that 
the respondents who are not frequently visited by extension agents 
have lower possibilities of adoption than those frequently visited 
(Adesina and Zinnah, 1993; Shiferaw and Holden, 1998; Oluoch-
Kosura et al., 2001; Bamire et al., 2002). The variable is measured 
as dichotomous with respondents’contact during the period scoring 
one, and zero for non-extension contact on the use of ISFM.  

CREDIT takes cognizance of farmers’ access to sources of credit 
to finance the expenses relating to adoption of innovations. Access 
boosts farmers’ readiness to adopt technological innovations. It is 
hypothesized that the variable has a positive influence on the 
probability of the adoption and use of land enhancing technologies 
(Zeller et al., 1998; Oluoch-Kosura et al., 2001; Bekele and Drake, 
2003). It is measured as a dichotomous variable with access being 
one, and zero for no access.  

A measure of wealth OFFINCOME is hypothesized to positively 
influence adoption positively. They are generally considered to be 
capital that could be used either in the production process or be 
exchanged for cash or other productive assets. They are expected 
to influence the adoption of ISFM positively (Shiferaw and Holden, 
1998; Zeller et al., 1998; Negatu and Parikh, 1999). It increases the 
availability of capital which makes investment in land-enhancing 
technologies feasible. To the extent that liquidity is a constraint to 
adoption, OFFINCOME will have a positive effect on adoption by 
relaxing the constraint. The level of off-farm income, however, may 
not be exogenous but be affected by the profitability of the farming 
operation that in turn depends on technology adoption decisions. 
Thus, the adoption of technologies and the level of off-farm income 
may be determined simultaneously. The simultaneity arises due to 
the labor allocation decisions of the households about farm and 
non-farm activities. However, the off-farm income of the household 
surveyed is mostly derived from remittances of family members in 
non-farm business activities and from employment in non-farm 
sector. As the skill requirements for these jobs are likely to be 
different from that of farming, the farm and non-farm employment 



  
 
 

 
Table 1. Socio-economic, demographic and farm characteristics of respondents.  

 
 

Variable 
 Mean 

All sample  

 

Adopters Non-adopters 
 

   
 

 Age 56.03 56.87 56.45 
 

 Household size 8.96 8.79 8.88 
 

 Farm size 0.38 0.44 0.41 
 

 Non-farm income 117, 769.23 103, 439.72 110, 536.24 
 

 Size of hired labour 17.75 15.33 16.53 
 

 Level of experience 12.6 23.3 17.95 
 

 Sex: % % % 
 

 Male 92.5 90.4 91.4 
 

 Female 7.5 9.6 8.6 
 

 Marital status:    
 

 Married 81.1 84.7 82.9 
 

 Single 18.9 15.3 17.1 
 

 Education status    
 

 Formal education 26.4 36.5 31.4 
 

 No formal education 73.6 65.5 68.6 
 

 
Source: Field survey, 2010. 

 

 
may be considered as non-competitive activities. In this situation, 
the level of non-farm income would be largely exogenous to the 
adoption decision (Lapar and Pandy, 1999).  

Household size (HHSIZE) has been identified to have either 
positive or negative influence on adoption (Manyong and 
Houndekon, 1997, Zeller et al., 1998; Oluoch-Kosura et al., 2001; 
Bamire et al., 2002; Bekele and Drake, 2003). Larger family size is 
generally associated with a greater labor force being available to 
the household for the timely operation of farm activities including 
ISFM. More labor hours will be spent on the use of technologies 
during labor slack seasons because of the low opportunity cost of 
labor in rural areas. The negative relationship of the variable with 
adoption has been linked to increased consumption pressure 
associated with large family. It is therefore difficult to predict this 
variable a priori in this study.  

Previous studies have found a positive relationship between farm 
size (FARMSIZE) and technological adoption (Manyong and 
Houndekon, 1997; Negatu and Parikh, 1999; Oluoch-Kosura et al., 
2001; Bekele and Drake, 2003). Operators of large farms are likely 
to spend more on land-improving technologies. In many cases, 
large farm size is associated with increased availability of capital, 
which makes investment in innovations more feasible. For this 
analysis, farm size is included as the total cropland available to the 
farmer. A positive relationship is hypothesized with adoption of 
land-enhancing technologies. Sex is expected that male farmers will 
favour the adoption of improved mulching technology more than 
female counterparts. This is because men generally have high risk– 
bearing ability than their female counterparts (Akinola and 
Adeyemo, 2008). 
 
 
RESULTS AND DISCUSSION 
 
Socio-economic characteristics of respondents 
 
The analysis of the socio-economic characteristics of  the 

 
 

 

respondents (Table 1) showed that the mean age of the 
adopters was 56 years and that of non-adopters was 57 
years. The farm size ranges from 0.1 to 1.0 hectare with 
mean of 0.4 1 hectare. On the level of education, majority 
of the farmers had informal education. Most farmers 
practice mixed cropping. They intercrop maize and melon 
with yam. The result showed that about 36% of the 
respondents had adopted mulching technology. The 
mean household size for both adopters and non-adopters 
of mulching technology were 8.96 and 8.79 respectively. 
About 82% of the respondents were married, while others 
are single. The mean farming experience of the farmers 
was 18 years, which implies that most farmers in the area 
are experienced farmers. The average non-farm incomes 
for adopters and non-adopters were about N118, 000 and 
N103, 000, respectively. The farmers in the study area 
indulged in other activities like petty trading, carpentry 
and other artesian enterprises. It is noteworthy that 
farmers in the area used hired labour on their farms. The 
numbers of average hired labour used by an average 
farmer during a season were about 18 and 15 for 
adopters and non-adopters, respectively. This indicates 
that hired labour was readily available in the area for 
farming. Although, the labourers were non-native who 
came from the northern part of the country.  

Results of the budgetary analysis revealed that the 
proportion of labour (for adopters and non-adopters) 
varies (Table 2). It accounted for 36.3 and 25.8% of the 
total cost, respectively. This may be due to extra man 
power requirement for mulching activities. The results 
further revealed that the average total revenue for 



 
 
 

 
Table 2. Budgetary analysis per hectare of yam.  

 
 S/N Item  Adopters Non adopters Pooled 

 1 Seed (N)  37 086.00 46 456.73 41 726.78 

 2 Labour cost (N)  31 240.65 28 431.73 30 299.05 

 3 Total variable cost (N) 68 326.65 74 888.46 72 025.83 

 4 Total fixed cost (N) 17 780.02 35 480.87 26 546.15 

 5 Total cost (N)  86 106.67 110 369.33 98 600.00 

 6 Yield (kg)  8259.4 6929.13 7600.60 

 7 Revenue (N) 412 971.69 346 456.73 380 030. 27 

 8 Gross margin (N)  344 645.04 271 568.27 308 005.12 

 9 Net income  326865.02 236087.40 281000.00 

 10 Benefit-cost ratio  4.79 3.13 3.85 
 

N is Naira-Nigerian currency; N1 = US$0.0067.  
 
 
 

Table 3. Estimated probit regression.  
 

 Variables Coefficient/se T-ratio 

 Constant 8.227(2.694) 3.05 

 SEX -0.193(0.581) -0.33 

 AGE -0.249(0.347) -0.72 

 EDUCATION -0.841(0.565) -1.49 

 HHSIZE -1.221**(0.253) -4.83 
 FARMSIZE -0.369(0.311) -1.18 

 ASSOCIATION 0.077(0.289) 0.27 

 EXTENSION 0.083(0.697) 0.12 

 OFFARMINC -0.058(0.052) -1.11 

 HRLABOUR 0.105*(0.054) 1.93 
 

Source: Field survey, 2010; Log likelihood, -23.399; Chi-squared, 9.75; **significant at 
1%; *Significant at 10%. 

 
 

 

adopters was N412, 971.69 while that of the non-
adopters was found to be N346, 456.73. The average 
total variable cost for adopters and non-adopters were 
N68, 326.65 and N74, 888.46, respectively. Gross margin 
values were N334, 645.04 and N271, 568.27 for adopters 
and non-adopters. This implies a better performance of 
mulched yam than non-mulched yam. The benefit-cost 
ratios for the adopters and non-adopters of mulching 
were 4.79 and 3.13, respectively, implying a better return 
due to adoption of mulching. 
 

 

Budgetary analysis/hectare 
 

The probit regression result 

 

The result of probit regression in Table 3 showed that the 
log likelihood was -23,399 and the chi-squared value was 
98.75. This implies that the model as a whole is 
statistically significant at 1 percent level of significance.  

Household size was significant at 1% level while labour 

 
 
 
 

hired was at 10% level. This indicates that both 
household size and labour hired influenced the adoption 
of mulching as a soil conservation technique negatively 
and positively,respectively. This is right because both 
have been found to have either positive influence on 
adoption from previous studies (Feder et al., 1985; 
Nkonya et al., 1997; Oluoch-Kosura et al., 2001; Bekele 
and Drake, 2003). The negative coefficient of the 
household size implied that as the farmers household 
size increases there is increase in consumption pressure 
associated with large families and consequently 
decreased the respondents interests in adopting the 
technology.  

On the other hand, the positive coefficient of the hired 
labour implied that greater labour force is available for 
timely operation of farm activities and more labour hour 
was spent on the use of mulching technology during 
labour slack season because of low opportunity cost of 
labour in rural areas. As a result there is this variable 
increasing the probability of adopting mulching as a 
conservation technique. Age, sex, educational status, 



 
 
 

 

farm size, association to which farmers belonged, 
extension visit and off- farm income did not significantly 
influence adoption behaviour of farmers toward mulching 
technology. 
 

 

SUMMARY AND CONCLUSION 

 

Farmers in the study area were mostly married, middle-
aged with few having formal education. An average 
farmer cultivated about 0.4 ha of land with minimum land 
area being 0.1 ha and the maximum 1 ha.  

Budgetary analysis revealed higher values of gross 
margin and net income were recorded by the adopters of 
the mulching technology as compared to non- adopters. 
The average total revenue for adopters is about N412, 
971 while that of non- adopters was found to be around 
N344, 645. The average total cost for both adopters and 
non- adopters were N86, 106.67 and N110, 339.33, 
respectively. Again, the gross margin analysis showed 
that N344, 645.04 and N271, 568.27 were for adopters 
and non- adopters, respectively. The cost-benefit ratio for 
adopters was 4.79 while that of non-adopters is 3.13 
which implies a better return to adopters of mulching 
technology than for non- adopters. Farmers that adopted 
the mulching technology were more than non-adoptive 
farmers in the study area. There were 50.5% adopters 
and 49.5% non- adopters. The result of the probit model 
shows that the frequency of labour hired is highly 
significant at 10 percent. The adoption behaviour of 
farmers that determines the adoption of the mulching 
technology is highly dependent on the total number of 
labour hired and household size. The negative coefficient 
of the household size implied that as the farmers 
household size increases there is increase in 
consumption pressure associated with large families and 
consequently decreased the respondents interests in 
adopting the technology.  

Therefore, there is need to encourage farmers on the 
needs to adopt the land protecting technology and a 
policy thrust that make seed yam available and affordable 
as well as reducing the costs incurred on labour will be in 
the right direction. 
 

 
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