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Kinyili et al./ Journal of Tropical Forestry and Environment Vol. 10, No. 01 (2020) 87-100

Influence of Agroforestry on Rural Income and Livelihood of

Smallholder Farmers in the Semi-Arid Region of Sub Saharan Africa

B.M. Kinyili
1
*, E. Ndunda

2
and E. Kitur

1
Kenya Forest Services, Eldoret, Kenya

2
Department of Environmental Science, Kenyatta University, Nairobi, Kenya

Date Received: 23-06-2019 Date Accepted: 30-04-2020

Abstract

Semi-arid lands typically suffer from sustainable land use challenges including climate

variability, declining agricultural productivity, low economic prowess and poor livelihood conditions. In

order to sustainably address these challenges, agroforestry has been fronted as a critical entry point

allowing for the integration of trees on farms and diversification of production in agricultural

landscapes. Nevertheless, the contribution of agroforestry to socio-economic and rural livelihood in

several developing countries remains debatable. This study determined the influence of agroforestry on

rural income and livelihood of smallholder farmers in Machakos county (Kenya). The study was

conducted using survey research design from a sample of 248 smallholder farmers, who were selected

using stratified, random sampling. Data were collected using questionnaires and interviews. Results

showed that agroforestry was adopted by 82% of the smallholder farmers as a strategy for livelihood

improvement in the region. Total income was higher among adopters from timber, fuel wood,

posts/poles and fodder. Adopters also had more money to spend on food, clothing, education, medicine

and basic needs as a result of revenues from agroforestry. The overall gross revenue, net returns above

variable costs and total costs were also higher among adopters compared to the non adopters due to sales

of agroforestry products. The study recommends adoption of agroforestry as a strategy to boost rural

income and livelihood.

Keywords: agroforestry, socio-economic, rural income and livelihood, machakos, sub Saharan Africa

1. Introduction

Globally, dryland areas characterised by low moisture content due to low rainfall and high rates of

evaporation, and a gradient of low agricultural productivity, comprise of approximately 100 countries

and cover 42% of the global surface landmass (6.4 billion ha) (Prăvălie 2016; Bastin et al., 2017;

Prăvălie et al., 2019). Despite the wide coverage, concern have been raised on human conditions in

dryland environments in Africa, calling for significant development assistance and frequent

humanitarian aid (De Leeuw et al., 2014). The gravity of the situation in drylands of Africa is clearer

since it account for nearly 400 million people who live and derive their livelihood in these areas

(Aleman et al., 2018; Gaur and Squires, 2018). The situations within the dryland areas are being

orchestrated by innumerable challenges such as climate variability, frequent drought, natural resources

degradation, declining agricultural productivity and high population increment (Syano et al., 2016).

Therefore, there is a consensus that most of the agro-based activities within these landscapes must be

geared towards solving foreseeable challenges (Krishnamurthy et al., 2019). Agroforestry as a dynamic,

ecologically based natural resources management system, integrates trees on farms and in agricultural

landscapes has been under consideration as an integral component of dryland regions(Ceperley et al.,

2016).

__________________________________
*Correspondence: bmkinyili@yahoo.com

Tel: +25 4723393737

©University of Sri Jayewardenepura

DOI: https://doi.org/10.31357/jtfe.v10i1.4691

The multiple perceived benefits and merits of agroforestry for providing environmental benefits,

economic products and social goods are well known and widely recognised (Franzel, 2004; Jose, 2009;

Fanish and Priya, 2013; Gao et al., 2014). In rural households, trees can be used as sources of food, fuel,

fodder, construction materials, medicine, to meet subsistence needs (Adekunle and Bakare, 2004; Kumar

and Thakur, 2017; Jemal et al., 2018). Historically, agroforestry was narrowly defined in terms of their

subsistence production (Somarriba, 1992) but currently seen in light of economic terms stressing the

enhancement of the economic return of the system (Kareem et al., 2016; Mercer et al., 2017; Paul et al.,

2017; Bruck et al., 2019). In light of recurring food shortages, and rising prices of fossil fuel-based

agricultural inputs, economic benefits of agroforestry has recently experienced a surge in interest from

the research communities, especially in developing countries (Amejo et al., 2018).

In africa, agroforestry is currently practiced by many smallholder farmers (Mbow et al., 2014)

where there has been increasing adoption by farmers particularly in the sub Saharan Africa (Franzel et

al., 2001; Leakey et al., 2005; Meijer et al., 2015; Beyene et al., 2019). Adoption of agroforestry is still

rampant despite the persistent attempts at introducing monoculture crop production (Djurfeldt et al.,

2005; Altieri et al., 2012). Many of the residents view the option of integrating and managing trees with

crops and livestock on the same landscape as an opportunity cost representing a conscious investment

due to goods and services derived from the practice (Amare et al., 2019). The suits of goods and services

derived from the practice of agroforestry include firewood, building materials (posts and timber), food

such as fruits, medicine and invaluable environmental services (Wulan et al., 2008; Kimaro et al., 2019).

In rural areas, other additional non-timber products include beeswax, honey, edible fruits, edible insects,

wild vegetable, game meat, traditional medicines and fibres, estimated to boost annual income of

households (Leakey et al., 2005; Kalaba et al., 2010). Consequently, the insight that trees on farms

improve the socio-economic prospects and provide livelihood benefits is increasingly being recognised

in the sub Saharan African region (Kalaba et al., 2010; Quandt et al., 2018).

Profitability of the various agroforestry practices has been analysed by various workers and the

results show large degree of variation among research as to the overall socio-economic and livelihood

impacts (Kang and Akinnifesi, 2000; Roshetko et al., 2007; Steffan-Dewenter et al., 2007; Akinnifesi et

al., 2008). Nevertheless, in several drylands of developing countries especially in the Sub Saharan

Africa, studies addressing contribution of agroforestry to socio-economic status and rural livelihood are

limited (Jama et al., 2006; Iiyama et al., 2014) and therefore may be inconclusive. Therefore more

studies on agroforestry adoption and socio-economic conditions are needed. The aim of this study was to

determine the influence of agroforestry adoption on the rural income and livelihood in Machakos county

in Kenya within the tropical region.

2. Methodology

2.1 The study area

The study was conducted in Machakos county (Figure 1) which covers an area of 5,953 km². It

lies between latitudes 0
o
45

/
South and 1

o
31

/
South and longitudes 36

o
45

/
East and 37

o
45

/
East. Most of

the land is semi-arid with population of 1,098,584 as per the 2009 Kenya National census (Kenya

National Bureau of Statistics, 2010). Administratively the county is divided into 11 divisions: Kalama,

Kangundo, Kathiani, Machakos Central, Masinga, Matungulu, Mavoko, Mwala, Ndithini, Yathui and

Yatta. In terms of political structure, the county has eight constituencies including: Kangundo, Kathiani,

Machakos Town, Masinga, Matungulu, Mavoko, Mwala and Yatta. There are overlaps between

divisions and constituencies were they are in most cases referred to as Sub-Counties. Among the

division and constituencies, Kathiani, Mavoko and Machakos Town practice agrofostry. Four siteswhere

Kinyili et al./ Journal of Tropical Forestry and Environment Vol. 10, No. 01 (2020) 87-100

agroforestry are practiced included: Mua (Mavoko, Machakos Town and Kathiani) and Iveti Hills

(Machakos Central and Kathiani), Kima-Kimwe and Kalama in Machakos Constituency.

Figure 1. Map of Machakos county showing the study area, Kenya.

The local climate is semi-arid with hilly terrain and an altitude of 1,000 to 2,100 m above sea

level. The area is composed of hilltops rising to 1,594-2,100 m above sea level. The annual average

rainfall is 1,000 mm (range, 500 to 1,300 mm), and is bimodal; short rains occur in October to December

and long rains in March to May. Temperatures range between 18.7
o
C and 29.7

o
C. The soils are well

drained shallow dark red volcanic on hilltops and clay soils in the plains. Irrigation farming is practiced

utilising the permanent rivers and streams that flow from the hilltop catchment areas towards South

Eastern to join Athi river. Crop such as maize, beans, pigeon peas, vegetables are dominant. Dairy and

beef cattle, sheep, and goats are the major livestock kept.

2.1 Data collection

This study was conducted through an exploratory survey design. Surveys are normally used to

systematically gather factual quantifiable information necessary for decision-making (Nardi, 2018).

Surveys are efficient methods of collecting descriptive data regarding the characteristics of populations,

current practices and conditions or needs. They also help gather information from large populations by

employing use of samples hence cutting down on costs. Survey study research design was adopted in

this study in order to capture descriptive data from selected samples and generalise the findings to the

populations from which the sample was drawn.

The study targeted household heads from Mua Hills (Mavoko, Machakos Town and Kathiani),

Iveti Hills (Machakos Central and Kathiani), Kima-Kimwe and Kalama Hills in Machakos Constituency.

The sample size of the households adopting agroforestry as earlier established in the region was used.

According to (Nzilu, 2015), 80% of the households had adopted agroforestry in Mwala (Machakos

county). The appropriate sample size was computed using the formula described in Mugenda and

Mugenda (Mugenda and Mugenda, 2003) (equation 1).

where: n=the desired sample size

z=the z score at the required confidence level α=0.05 (1.96)

p=the proportion in the target population assumed to be adopter
d=permissible marginal error (the level of statistical significance, set at α=0.05).

Using the values of z, p and d, the value of n was computed as follows (equation 2).

The sample size was 246 but the two research assistants who hail from the area also provided

additional information resulting in a total of 248 respondents. Adopters who were included in the study

were households practicing any form of agroforestry while non adopters were those who had no form of

agroforestry or tree growing in their farms. Samples were selected through stratified, random sampling

at each of the selected spatial units and used to identify the adopters and non adopters.

This study relied on primary type of data. Primary data on income, expenditure and rural

livelihood among the respondents was collected using structured researcher administered questionnaires.

The designing of the instruments were such that they ensured an in-depth exploration of personal views,

feelings and opinions on agroforestry and benefits accrued. Before data collection, the respondents were

contacted in advance and asked to organize their time for the research. Two research assistants were

recruited and trained to aid in data collection. The questionnaires were administered by physical drop

and pick by the researcher and two research assistants. The researcher personally administered the

instrument and made prior visits to assist in defining timings and distribution of research instruments.

Research instruments were developed by examining the aim of the research. The validity of the

instruments was sought through expert judgment who examined the face, content and construct validities

in order to determine whether items measured what they were supposed to determine. They established

whether the numbers of items are adequate for the purpose intended research and thus their expert

judgments ensured validity of the instruments.

The reliability of the instruments was established through a pilot study of 12 household members

from the study area who did not participate in this study. The results of the study were used to compute

the reliability of the instruments through Cronbach’s coefficient alpha (Bonett and Wright, 2015). The

study considered the instrument as reliable and acceptable if the computation yielded a reliability

coefficient of 0.7 and above. For this study, the reliability coefficient was 0.83 which was suitable for

research.

All questionnaire data were coded into Statistical Package for Social Sciences (SPSS 23) for

analysis. Differences in rural income, expenditure and livelihood were evaluated using chi-square

analysis and ANOVA. All analyses were declared significant at p<0.05.

3. Results

The socio-economic profile of the respondents in Machakos county during the study is shown in

Table 1. Majority of the respondents were aged 36-55, most being females with primary and secondary

2
)1(

ppz
n




24686.245
05.0

)8.01(8.096.1
2




n

(1)

(2)

Kinyili et al./ Journal of Tropical Forestry and Environment Vol. 10, No. 01 (2020) 87-100

levels of education. Household size for the majority was 6-10. The land size ranged between 0.4 to 24

acres with majority having land size ranging between 2-5 acres followed by those with less than 2 acres.

Table 1: Socio-economic profiles of the respondents.

Agroforestry adopters Agroforestry non adopters

Variable
Response

category

Frequency

(n=204)
Percent (%)

Frequency

(n=44)
Percent (%)

Age (years)

18-25 11 5.4 6 6.9

26-35 28 13.7 8 18.2

36-55 84 41.2 14 31.8

>55 81 39.1 16 36.4

Gender
Female 116 56.9 26 59.1

Male 88 43.1 18 40.9

Marital status
Single 12 5.9 1 2.3

Married 192 94.1 43 97.7

Level of

education

None 5 2.5 7 15.9

Primary 112 54.9 18 40.9

Secondary 73 35.8 14 31.8

Tertiary 14 6.8 5 11.4

Household size

<3 3 1.5 0 0.0

3-5 75 36.8 27 61.4

6-10 105 51.5 17 38.6

>10 21 10.3 0 0.0

Land size

<2 acre 72 35.3 14 31.8

2-5 acres 106 52.0 26 59.1

5.1-10 acres 26 12.7 4 9.1

The computed average income from crops, livestock and total income from the adopters and non

adopters of agroforestry in Machakos are provided in Table 2. The income derived from crop, livestock,

tree seedlings and tree products as well as the farm and total income of the farmers were all significantly

higher for the adopters than non adopters (p<0.05).

Table 2: Average income from crops, livestock and total income computed between adopters and non

adopters of agroforestry in Machakos (Values are in US $).

Income Adopters Non adopters t value p value

Average annual farm income from crop

proceeds
*

278.39 154.16 30.1281 0.0000

Average annual farm income from livestock
*
228.38 156.05 9.531 0.0021

Average annual income from tree seedlings
*
205.18 109.83 17.391 0.0001

Average income from wood/wood products
*
271.34 142.91 16.680 0.0001

Average farm income per annum
*
253.44 195.61 5.985 0.0056

Total income from agroforestry
*
1236.73 758.56 60.104 0.0000

*
Differences are significant at p<0.05

NS denotes not significantly different

The average income wood and wood products from the adopters and non adopters of agroforestry

in Machakos are provided in Table 3. The income derived from timber and fuel wood as well as the total

income derived from wood/wood products was significantly higher for the adopters than non adopters

(p<0.05). However, the income derived from posts/poles and from fodder was similar for the adopters

and non adopters.

Table 3: Income derived from wood and wood products between the adopters and non adopters in

Machakos county (Values are in US $).

Wood income Adopters Non adopters t value p value

Income realised annually from timber
*
162.00 77.73 14.088 0.0000

Income realised annually from fuelwood
*
96.06 67.15 3.184 0.0413

Income realised annually from post/poles
NS

60.16 53.61 0.248 0.6193

Income realised annually from fodder
NS

63.00 64.72 0.005 0.9546

Total annual income from wood/wood products
*
271.34 142.91 16.680 0.0001

*
Differences are significant at p<0.05

NS denotes not significantly different

Annual expenditure on basic needs adopters and non adopters of agroforestry in Machakos

County are shown in Table 4. The annual expenditure on food, clothing, education, medicine and total

household expenditure on basic needs were all significantly higher for the adopters than non adopters

(p<0.05).

Table 4: Annual expenditure on basic needs between adopters and non adopters of agroforestry in

Machakos county (Values are in US $).

Expenditure on basic needs Adopters Non adopters t value p value

Annual household expenditure on food
*
222.27 86.82 74.954 0.0000

Annual household expenditure on clothing
*
157.02 69.89 62.944 0.0000

Annual household expenditure on education
*
206.28 151.09 11.389 0.0014

Annual household expenditure on medicine
*
92.42 57.55 9.304 0.0034

Annual household expenditure on basic needs
*
646.55 329.52 111.851 0.0000

*
Differences are significant at p<0.05

NS denotes not significantly different

The annual expenditure budget for wood and wood products between adopters and non adopters

are shown in Table 5. The household annual expenditure on timber, poles as well as the total expenditure

on wood and wood products was significantly higher for the non adopters than adopters (p<0.05).

Table 5: Annual expenditure budget for wood and wood products between adopters and non adopters.

(Values are in US $).

Wood/wood product expenditure category Adopters Non adopters t value P value

Household annual expenditure on timber
*
71.00 164.62 4.276 0.0225

Household annual expenditure on fuel wood
*
45.81 49.91 0.4254 0.0432

Household annual expenditure on poles/posts
NS

50.09 52.30 0.0452 0.8323

Household annual expenditure on fodder
NS

31.03 37.51 2.796 0.3422

Total expenditure on wood/wood products
*
199.93 302.34 10.672 0.0001

*
Differences are significant at p<0.05

NS denotes not significantly different

The enterprise budget for adopter and non adopters of agroforestry practices in Machakos county

are shown in Table 6. Based on the table, gross revenue for the adopters (US $ 1,236.73) was higher

than the non adopters (US $ 758.56). Also the overall expenditure on variable cost by the adopters (US $

890.16) was consistently higher than the non adopters (US $ 663.86). The total fixed cost of the

Kinyili et al./ Journal of Tropical Forestry and Environment Vol. 10, No. 01 (2020) 87-100

agroforestry adopters was nevertheless similar to the non adopters (US $ 70.80). As a consequence, there

were higher net returns above Total Variable Costs (TVC) for the adopters (US $ 346.57) compared to

the non adopters (US $ 94.70), which resulted in positive higher net returns above Total Cost (TC) for

the adopters (US $ 275.77) compared to the non adopters (US $ 23.90). The computed margins above

TVC (%) was therefore higher for the agroforestry adopters (28.02%) than the non adopters (12.48%)

and margins above the total cost for the adopters was 22.30% and 3.15% for the non adopters.

Table 6: Computed enterprise budget for adopter and non adopters of agroforestry practices in Machakos

county (Values are in US $).

Parameters Adopters Non adopters

Revenues

Average annual farm income from crop proceeds 278.39 154.16

Average annual farm income from livestock 228.38 156.05

Average annual income from tree seedlings 205.18 109.83

Average annual income from wood/wood products 271.34 142.91

Average annual farm income per annum 253.44 195.61

Total income from agroforestry 1236.73 758.56

Variable costs

Household expenditure on food per year 222.27 86.82

Annual household expenditure on clothing 157.02 69.89

Annual household expenditure on education 206.28 151.09

Annual household expenditure on medicine 92.42 57.55

Total Annual household expenditure on basic needs 646.55 329.52

Household annual expenditure on timber 71.00 164.62

Household annual expenditure on fuel wood 45.81 49.91

Household annual expenditure on poles/posts 52.09 50.30

Household annual expenditure on fodder 31.03 37.51

Total expenditure on wood/wood products 199.93 302.34

Miscellaneous 43.68 32.00

Total variable cost (TVC) 890.16 663.86

Fixed costs

Amortisation 60.00 60.00

Interest on fixed cost 10.80 10.80

Total fixed cost 70.80 70.80

Total cost (TC) 960.96 734.66

Net returns above TVC 346.57 94.70

Net returns above TC 275.77 23.90

Margins above TVC (%) 28.02 12.48

Margins above TC (%) 22.30 3.15

The indicators of improved livelihood among the adopters and non adopters of agroforestry were

also determined (Table 7). There were significant differences in the responses to the contribution of

agroforestry to livelihood between the adopters and non adopters (
2
=45.2312, df=8, p<0.001). Among

the adopters of agroforestry, majority attested that indeed there was increased food supply, improved

educational attendance and increased energy in the household.

Table 7: Indicators of improved livelihood among adopters of agroforestry.

Livelihood indicators
Adopters Non adopters

Frequency Percent (%) Frequency Percent (%)

Reduced use of fertilizers 168 82.4 15 34.1

Increased energy in the household 174 85.3 17 38.6

Increased food supply 178 87.3 11 25.0

Increased household income 124 60.8 15 34.1

Improved educational outcomes 101 49.5 15 34.1

Improved medical attendance 78 38.2 12 27.3

Improvement in employment 122 59.8 8 18.2

Improved educational attendance 177 86.8 11 25.0

Increase in land sizes 106 52.0 7 15.9

The scores of the indicators of household livelihoods was also determined among the adopters

and compared with the non adopters. The results are as shown in Figure 2. Based on the scores from the

figure, there were consistently higher rank scores for all the livelihood indicators among adopters

compared to the non adopters except for improved educational outcomes, improved medical attendance,

and increased land sizes.

Figure 2. Scores of the indicators of household livelihoods among adopters and non adopters in

Machakos county.

P
e
rc

e
n
t

ra
n
k
s

c
o
re

s
o
f

th
e
r

e
sp

o
n
se

Indicator of livelihood

Adopters

Non adopters

Kinyili et al./ Journal of Tropical Forestry and Environment Vol. 10, No. 01 (2020) 87-100

4. Discussion

During the study, the income derived from crop, livestock, tree seedlings and tree products as

well as the farm and total income of the farmers were higher for the adopters than non adopters. This

concurs with other studies which indicated that earning from crops, livestock and trees among

agroforestry adopters is often higher owing to the income earned from sales of the crops, livestock and

trees from the agroforestry (Neupane and Thapa, 2001; Franzel, 2004; Namwata et al., 2012; Kareem et

al., 2016; Kassie, 2018). Indeed agroforestry increase livelihood benefits for people such as food

security, employment, income generation among others. Meanwhile the average annual farm income

from livestock proceeds displayed significant differences since it was established that agroforestry

adopters did keep higher number of animals than those not practicing agroforestry and therefore the

earnings from livestock were similar. The study also established a higher income from timber, fuel wood

and wood/wood products due to agroforestry adoption which concurs with several other studies (Scherr,

2004; Bertomeu, 2006). Apart for domestic use of the timber and fuel wood, there are instances where

farmers with larger scale practice of agroforestry can sell some of their products and earn income higher

than those without any form of agroforestry. Nevertheless the income derived from posts/poles and from

fodder were similar for the adopters and non adopters which may be attributed to low production of

these wood products among farmers and the fact that they do not sell posts/poles and fodder.

The annual expenditure on food, clothing, education, medicine and total household expenditure

on basic needs were all significantly higher for the adopters than non adopters due to the higher disposal

income from agroforestry that enabled them spend more on food, clothing, education and medicine.

Given one of the largest costs of most rural areas is on fuel wood as a source of energy (Sharma et al.,

2016; Waldron et al., 2017), most of the farmers with trees in their farms will save the income and use it

to purchase food, built better houses and spend more on quality education as well as search for better

healthcare (Borish et al., 2017).

The household annual expenditure on timber, poles as well as the total expenditure on wood and

wood products was significantly higher for the non adopters than adopters which concurs with other

studies (Leakey et al., 2005) due to the fact that most of the adopters have these products in their farms

and therefore they don’t need to buy these products from outside their farms. During adoption of

agroforestry, farmers have access to wood and wood products and therefore the amount of money going

towards purchase of such are expected to be lower than those who have no wood from any agroforestry

practice. However, expenditure on fodder was not different between the adopters and the non adopters

mainly because most of the agroforestry practices were not planting fodders in their farms.

Analysis of enterprise budget yielded several observations. First the gross revenue for the

adopters (US $ 1,236.73) was higher than the non adopters (US $ 758.56) indicating higher income

derived from agroforestry practices. Similarly the overall expenditure on variable cost by the adopters

(US $ 890.16) was consistently higher than the non adopters (US $ 663.86) which was attributed to the

adopters having higher disposal incomes. The total fixed cost of the agroforestry adopters was

nevertheless similar to the non adopters (US $ 70.80) suggesting that fixed cost for the adopters and non

adopters tend to be somewhat similar. As a consequence, there were higher net returns above TVC for

the adopters (US $ 346.57) compared to the non adopters (US $ 94.70), which resulted in positive higher

net returns above TC for the adopters (US $ 275.77) compared to the non adopters (US $ 23.90). Based

on the above statistics, the computed margins above TVC (%) was therefore higher for the agroforestry

adopters (28.02%) than the non adopters (12.48%) and margins above the total cost for the adopters was

22.30% and 3.15% for the non adopters. These results suggest that income was higher for the adopters

resulting in overall profitable operational margins that render adoption as a good enterprise.

This study also determined the influence of adoption of agroforestry practices on rural livelihood

of smallholder farmers and found that adopters of agroforestry had increased food supply, improved

educational attendance and increased energy in the household, which concurs with several studies

among agroforestry adopters (Quandt and McCabe 2017; Quandt et al., 2018). The diversification of

crops, keeping of livestock and trees in the same farm which can be sold by the farmers is expected to

create opportunities for achieving a steady and sometimes higher rural income through the more efficient

use of resources and the exploitation of comparative advantages (Kassie 2018). Agroforestry systems

have also been determined to combine short-term and long-term benefits for the farm households with

the aim of livelihood protection and sustainability in the use of resources in semi arid areas (Quandt et

al., 2017). The mango-based alley cropping that was practiced by majority of the farmers played a vital

role in rural livelihood strategies.

The scores of the indicators of household livelihoods were consistently higher rank scores for all

the livelihood indicators among adopters compared to the non adopters except for improved educational

outcomes, improved medical attendance, and increased land sizes. Improvement of livelihood among

agroforestry adopters have been identified in several studies. The land use systems in the study area are

generally agro-crop production along with timber and fruit tree species and livestock production

systems. Here, the farmers practice agroforestry include woodlot, alley cropping,

windbreaks/shelterbelts and intercrop. Most of the farmers intercropped grain, vegetables and tree crops.

The grain crops cultivated in the land use system included maize, bean, millet, sorghum, pigeon peas,

peas, green chili, etc. with horticultural produce such as avocado, carrot, kales, oranges, mangoes,

pawpaw, onions, tomatoes, cabbages, gourd, bitter gourd, pumpkin, and pineapple, which are often sold

to increase livelihood indices. Nevertheless it was found that income generating activities in the study

area were not diversified as compared to other regions of the world (Burgess et al., 2017; Mosquera-

Losada et al., 2018). From above results, it is clear that agroforestry plays a major role in supporting the

socio-economic needs and improving the livelihood conditions of the people in Machakos, Kenya.

5. Conclusion

The study shows that in the dryland area of Machakos in Kenya, adoption of agroforestry

improved the socio-economic and livelihood indicators of the local communities by enhancing income

and expenditure. Agroforestry adoption generated more money to adopters to send their children to

schools, buy medicine, buy clothes and other necessities that eventually improved the livelihood. It can

be concluded that agroforestry adoption had a significant impact on the livelihood of most agroforestry

adopters and their households

Acknowledgment

The Kenya Organization for Environmental Education (KOEE) through Faith Based Climate

Change Education for Sustainable Development (FBCCESD) financed this study while Kenya

Agricultural and Livestock Research Organization (KALRO) helped in providing information on areas

with previous agroforestry projects. The Kenya Forest Service (KFS) assisted in identification of active

agroforestry adopters who participated in this study. We thank the local community members who

agreed to sacrifice their time to respond to the questionnaires.

References

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agroforestry system in Ondo State of Nigeria. Small-scale Forest Economics, Management and

Policy, 3:131-138.

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