Published on International Journal of Forestry & Plantation
Publication Date: April
Fatoki O.A., Oguntoye T.O., Abiola O.M., Arowolo O.V. & Kolade R.I.
Department of Forest Economics and Extension Services, Forestry Research Institute of Nigeria Ibadan, Oyo State, Nigeria
Journal Full Text PDF: Positive Externalities of Agro-forestry Driven Green Growth and Implication on Poverty Profile.
Agroforestry-based green growth practices have the potential to achieve a green economy, ensure livelihood sustainability and improve farm productivity without having negative effects on the environment. This paper investigates the implications of the positive externalities of agroforestry driven green growth on poverty profile of rural farmers in Ogun State, Nigeria. Multi stage sampling technique was used to sample 155 farming households from Ijebu and Ilaro agroforestry taungya sites in the state. Data was collected with the aid of well structured questionnaire from adopters and non-adopters of taungya agroforestry system in the state. Analytical tools such as descriptive statistics and Foster, Greer and Thorbecke (FGT) poverty index measures were used for the study. The result showed that the average age of the respondents was 42.5. Majority (77.4%) were married with household size of 7 members. The study showed that (30.1) percent and (45.4) percent of the adopters and non-adopters of taungya farming respectively falls below the poverty line, whereas the poverty depth and severity of the adopters and non-adopters of taungya farming are (6.8) and (0.4) percent and (26.3) and (6.9) percent respectively. The study concluded that there is a remarkable difference in the poverty status of adopters and non-adopters of taungya agroforestry system because the poverty incidence in highest among non-adopters. Therefore, the study recommends that there is need for awareness creation on agroforestry driven green growth among the poor rural farmers to improve agricultural yields and initiate socioeconomic change.
Keywords: Agroforestry green growth practices, green economy, taungya agroforestry system, improve agricultural yields and initiate socioeconomic change.
Green growth can be defined as a growth that is efficient in its use of natural resources, minimizing pollution and environmental impacts and resilient in that it accounts for natural hazards (World Bank, 2012). Greening growth and moving towards a greener economy is complex and multidimensional in nature. It entails pricing externalities and valuing natural assets for the long-run services they provide (OECD, 2011). Agroforestry is a mjor component of green growth practices and the organic matter derivable from it is consistent as crop rotations help to minimize pollution through minimal fertilizer usage, while enhancing farm output (Okojie et al., 2017). Such green growth drivers of agro forestry – crop rotation, conservation tillage, raising animals on pasture and natural fertilization, raising agricultural crops with forest plantations, help to sustain farm productivity without having a negative effect on the environment. Agro-forestry is a concept of integrated land use that combines elements of agriculture and forestry in a sustainable production system (Noble and Dirzo, 1997; Okojie and Abiola M.O, 2014). The integration of trees, agricultural crops, and animals into an agroforestry system has the potential to enhance soil fertility and biodiversity conservation, reduce erosion, improve water quality, increase aesthetics, and sequester carbon (Garrett and McGraw, 2000).
The developing world is experiencing substantial environmental change and climate change is likely to accelerate these processes in coming decades (Dercon, 2011). But due to their initial poverty rate, and their relatively high dependence of environmental capital for their livelihoods, the poor are likely to suffer most due to their low resources for mitigation and for investment in adaptation. Furthermore, the use of environmental capital often involves externalities on others, from local level effects of local air or water pollution, soil degradation and increasingly adding up to global impacts, with impacts on production opportunities and welfare. These problems lead to some of the standard economic and welfare impacts of environmental pressures, with the emphasis on market failures. Agroforestry practices through the integration of annual agricultural crops with protected forest tree species can serve as an investment approach towards adaptation (Odurukwe, 2004). It will not only improve sources of livelihood to the farmers and reduce the poverty rate, but will also improve the environment through low input agriculture
Forty-eight percent of the African popu¬lation are desperately poor, the highest proportion in any region of the world (Garrity, 2004). Per capita food production is declining, and malnourishment and poverty continue to increase. The global community is staggering in its mobilization of its resources to address this in¬tolerable situation. Analyses of the farm¬ing systems in Africa (Dixon et al., 2001), Inter Academy Council of Scientific Societies (2004) and the UN Millennium Project (2005) have provided a thorough picture of the constraints and possibilities in attempting to alleviate hunger and rural poverty in Africa. These studies identify hotspots where focused efforts can en¬hance farm productivity, increase rural incomes and transform agriculture to be¬ a more dynamic driver of economic growth. In their recommendations on how to address the constraints, these studies highlight agroforestry practices as a crucial pathway towards greater prosperity. Agroforestry can help improve and protect farmland as well as promote ecosystem restoration that is vital for enhancing food security and improving farmers’ wellbeing. In view of the above, it is imperative to know how agroforestry practice through taungya farming has impacted on the poverty status of adopters and non-adopters of taungya farming system in the study area.
2.1 Study Area
The study area is Ogun State in South Western Nigeria. The state was created on February 3rd, 1976 from the old western region. It is neighbored by Oyo, Ondo, Lagos, Edo and Delta States. It is situated within the tropics and derives its name from the “River Ogun”. The state lies between longitude 202′ and 3055′ and latitude 700′ and 7018′. It is approximately 1.9 percent (16,762 km2) of Nigeria’s 923,219km2 land area of which over 70 percent is suitable for arable crop production. It is located in the moderately hot, humid tropical climatic zone of south western Nigeria. It has a tropical climate with two distinct seasons – the rainy and the dry season. The three main vegetation types in the study area are the tropical rainforest, guinea and derived savannah. It is made up of 20 Local Government Areas spread across the four main agricultural zones of the state – Abeokuta, Ijebu, Ikenne, and Ilaro. The overall population of the state is 3,728,098 (National Population Commission, 2006).
2.2 Data Sources and Collection
The research data were obtained through primary source with the aid of a well-structured questionnaire from the rural farmers. Data were collected on the socio-economic characteristics such as age, gender, marital status, educational status, and a host of other variables.
2.3 Sampling Procedure and Size
The sample size used for this study was 155 households. Multistage sampling technique was used for the selection. At stage one, two Agricultural Development Programme Zones – Ijebu and Ilaro agro forestry taungya sites were purposively selected out of the four ADP zones in Ogun State. At stage two, 50% of the blocks were selected from each zone i.e. three blocks from Ijebu and two blocks from Ilaro to give a total of five blocks. At stage three, five cells each per block were randomly selected to give twenty-five cells. At stage four, seven farming households were selected from each cell, which gave a total of one hundred and seventy five (175) farming households from the study area. Out of the 175 questionnaires administered in the study area, 155 contained detailed information that was used for the study.
2.4 Analytical Technique
A combination of analytical tools was employed during the course of this study. These include descriptive statistics and the Foster, Greer and Thorbecke (FGT) poverty index measures. The descriptive analysis was used to depict the socio-economic characteristics and it involved the use of tables, frequency and percentage proportions while the FGT poverty index developed by Foster et al.,(1984), which has found wider application in scholarly works (Appleton, 1996; Ayinde et al., 2002; Olorunsanya and Omotesho, 2012) was adopted. The model is a class of additively decomposable measure of poverty. The measure subsumes the headcount index and the poverty gap, and provides the distributional sensitive measure through the choice of a poverty aversion parameter “%”. The larger the value of the “%”, the greater the weight given by the index to the severity of poverty (Anyawu, 1997). The Foster, Greer and Thorbecke (FGT) poverty index measures was used to profile the poverty status of the adopters and non-adopters of taungya agroforestry practice using a seven days memory recall of the farmer expenditure on food and non-food expenses. The general specification of the model is given below:
The FGT measure for the subgroup ith Pαi is given as
Pαi= ———————————- (1)
Pαi = weighted poverty index for the ith subgroup;
n = total number of farmer household in the ith subgroup
yij = per capital expenditure of famer household j in sub-group ij;
z = the poverty line defined as 2/3 of mean consumption per adult equivalent of the sampled population (FOS, 1999);
z – y = is the proportionate shortfall below the poverty line
qi =the number of the sampled household heads below the poverty line;
α =the aversion to poverty or degree of concern.
When α is equal to zero, it implies no concern and the equation gives the head count ratio for the incidence of poverty (the proportion of the farming household heads that are poor).
The poverty line to be used for this study is defined as the two-thirds of mean household per capital expenditure.
Pαi= = ———————- (2)
When α is equal to 1, it shows uniform concern and equation becomes:
P1i= ———————- (3)
This measures the depth of poverty (the proportion of expenditure shortfall from the poverty line) according to Hall et al., (2005), it is otherwise called the poverty gap-the average difference between the income of the poor and the poverty line. When α is equal to 2, distinction is made between the poor and the poorest (Assadzadeh et al., 2003). The equation becomes:
P2i= ———————- (4)
The equation gives a distribution sensitive FGT index called the severity of poverty. It tells us the extent of the distribution of expenditure among the poor.
The FGT measure for the whole group or population was obtained using:
Pα= ———————– (5)
Where Pα = the weighted poverty index for the whole group
m = the number of subgroups;
n = total number of household heads in the whole group
ni = total number of household heads in the ith sub-group
The contribution (Ci) of each sub-group’s weighted poverty measure to the whole group’s weighted poverty measure was determined using:
Ci = ———————— (6)
3. RESULTS AND DISCUSSION
3.1 Socio-economic Distribution of the Respondents
Socio-economic characteristics of respondents considered were gender, age, income, marital status, household size and educational level of adopters and non-adopters of taungya agroforestry system. Majority of the farmer respondents in the study are males. The distribution was (71.6%) males while (28.4%) are females, which imply that more males are engaged in taungya farming in the study area. As shown in table 1, the proportion of farmers between the ages of 50-59 years was 42.6% which constituted the largest percentage. A greater percentage (38.1%) of the respondents are earning between N10,000 – N30,000 per month followed by those in the income group of 31,000 – 50,000 per month which was 26.5% . The adopters and non-adopters of taungya farming that were sampled consisted of 32.3% proportion that had primary education, 33.5% secondary education, 11.6% tertiary level of education and Farmers without formal education occupied a proportion of 22.6%. Furthermore, Majority (77.4%) of the respondents are married and the mean household size is 7 members.
Table 1: Socio-Economic Characteristics of the Respondents.
Variables Frequency Percentage
Male 111 71.6
Female 44 28.4
Total 155 100
Less than 30 20 12.9
30 – 39 38 24.5
40 – 49 21 13.5
50 – 59 66 42.6
60 and above 10 6.5
Total 155 100
Less than 10,000 25 16.1
10,000 – 30,000 59 38.1
31,000 – 50,000 41 26.5
51,000 – 70,000 21 13.5
71,000 and above 9 5.8
Total 155 100
No formal education 30 19.4
Primary 47 30.3
Secondary 49 31.6
Tertiary 29 18.7
Total 155 100
Single 30 19.4
Married 120 77.4
Divorce 5 3.2
Total 155 100
1 – 3 32 20.6
4 – 6 40 25.8
7 – 9 83 53.6
Total 155 100
Source: Computed from Field Survey Data, 2018
3.2 The effect of demographic characteristics on the poverty level based on adoption of
agroforestry driven green growth
The Foster Greer Thorbecke (FGT) analysis was used in determining the poverty profile of the adopters and non-adopters of taungya agroforestry system, bearing in mind the socio-economic characteristics of the sampled farm households. Table 2 showed that the poverty incidence was most noticed among the non-adopters of taungya agro-forestry at (45.4) percent. Furthermore, this study showed that (30.1) percent and (45.4) percent of the adopters and non-adopters of taungya farming respectively falls below the poverty line, whereas the poverty depth and severity of the adopters and non-adopters of taungya farming are (6.8) and (0.4) percent and (26.3) and (6.9) percent respectively. Which implies that (6.8) and (26.3) percent of the adopters and non-adopters falls short of the poverty line by (6.8) and (26.3) percent respectively. It also implies that (6.8) and (26.3) percent of the poverty line that is required to get the adopters and non-adopters out of poverty is N21.44 and N82.94 respectively. The poverty severity of the adopters and non-adopters was calculated as (0.4) and (6.9) percent respectively, which means the percentage of agro-forestry adopters and non-adopters that are core poor.
Table 2: Poverty profile of agro-forestry adopters and non-adopters
Categories P0 P1 P2 Q N
Adopters 0.301 0.0688 0.0047 30 100
Non-adopters 0.4545 0.263 0.069 25 55
Pooled 0.354 0.135 0.018 55 155
Source: Computed from Field Survey Data, 2018
Table 3: Test of Difference of mean between the poverty incidence, depth and severity of the Adopters and Non-adopters of Agro forestry
Categories Standard error N T-ratio
Adopters (Incidence) 118957.026 100 3.271*
Non-adopters(Incidence) 86245.953 55
Adopters (Gap) 1.743876 100 2.47**
Non-adopters(Gap) 0.670056 55
Adopters (Severity) 2.76491 100 -4.78*
Non-adopters(Severity) 1.54900 55
Source: Computed from Field Survey Data, 2018
3.3 Decomposition of Poverty profile based on socio-economic sub-groups of the Respondent
The decomposition of the poverty status according to sub-groups was shown on Table 4; the adopter’s age sub-group showed that the poverty incidence was highest among the 70 and above age group at 57.1 percent, which implies that 57.1 percent of the adopters within this age group fall below the poverty line. Furthermore, the poverty depth was 1.3 percent which means 1.3 percent of the adopters fall short of the poverty line, also 1.7 percent of them were core poor. Also, the age-group less than 30 had the lowest incidence rate of poverty at 16.6 percent, which implies that 16.6 percent of the adopters that fall within this age group are below the poverty line. Whereas the poverty depth and severity of this age group is 0.1 and 0.00000289 percent respectively.
For the non-adopters age sub-group, it was shown that the 50 – 59 age groups had the highest poverty incidence at 0.8 percent, and a poverty depth and severity of 5.1 and 0.00260 percent. This implies that 0.80 percent of the non-adopters within this age group fall below the poverty line. Also the poverty depth which is 5.1 percent means 5.1 percent of the non-adopters fall short of the poverty line and 0.00260 percent of them were core poor. Also, the age-group 60 – 69 had the lowest incidence rate of poverty at 28.5 percent, which implies that 28.5 percent of the non- adopters that fall within this age group are below the poverty line. Whereas the poverty depth and severity of this age group is 2.4 and 0.000605 percent respectively.
The decomposition with regards to income of the adopters sub-group revealed that the 10000 – 30000 earners had the highest poverty incidence of 36.8 percent, which implies that 36.8 percent of this group falls below the poverty line. Furthermore, the poverty depth and severity of this group was 0.2 and 0.00000441 percent. Also 21.0 percent of the 31000 – 50000 income earners had the lowest poverty incidence, which means 21.0 percent of this group fall below the poverty line. With respect to the non-adopters income sub-group, 21.0 percent of the 71000 and above income earners had the lowest poverty incidence, which implies that 21.0 percent fall below the poverty line.
The decomposition with respect to educational status sub-group revealed that among the adopters, those with no formal education have the highest poverty incidence of 33.3 percent, which implies that 33.3 percent of this group fall short the poverty line, with a poverty depth and severity of 4.3 and 0.00186 percent respectively, which implies that 4.3 percent of this group fall short the poverty line and 0.00186 percent are core poor. Also 25.0 percent of the adopters that posses tertiary education has the lowest poverty incidence rate, which implies that 25.0 percent of this group fall below the poverty line, with a depth and severity of 0.0000907 and 8.22E-09 percent respectively. With respect to the non-adopters educational subgroup, only 1.0 percent of those with tertiary education has the highest poverty incidence, which implies that 1.0 percent of this group fall short the poverty line and also with a depth and severity of 1.1 and 0.000132 percent respectively. Furthermore, 36.0 percent of the non-adopters educational sub-group had the lowest poverty incidence with a depth and severity of 1.0 and 1.1 percent respectively.
Table 4: Poverty profile of sub-group based on socio-economic characteristics of adopters and non-adopters of agro-forestry driven green growth.
Categories P0 P1 P2 Q N
Less than 30 0.16 0.0017 0.00000289 1 6
30-39 0.29 0.0096 0.0000222 5 17
40-49 0.21 0.0221 0.0001 7 32
50-59 0.29 0.0117 0.000136 7 24
60-69 0.42 0.0117 0.000136 6 14
70 and above 0.571 0.0133 0.00017 4 7
Less than 30 0.6 0.011 0.00012 6 10
30-39 0.538 0.0573 0.00328 7 13
40-49 0.33 0.063 0.00396 6 18
50-59 0.8 0.051 0.0026 4 5
60-69 0.285 0.0246 0.000605 2 7
70 and above 0 0 0 0 2
Age (Pooled Sampled Farmers)
Less than 30 0.437 0.179 0.032 7 16
30-39 0.4 0.048 0.0023 12 30
40-49 0.26 0.86 0.739 13 50
50-59 0.37 0.73 0.532 11 29
60-69 0.38 0.009 0.00008 8 21
70 and above 0.44 0.006 0.444 4 9
Less than 10000 0 0 0 0 4
10000-30000 0.368 0.0021 0.00000441 7 19
31000-50000 0.21 0.0126 0.000158 4 19
51000-70000 0.333 0.0095 0.0009 3 9
71000 and above 0.2653 0.0188 0.00035 13 49
Less than 10000 1 0.0712 0.00506 6 6
10000-30000 0.66 0.0828 0.00685 8 12
31000-50000 0.5 0.0126 0.00015 2 4
51000-70000 0.6 0.0398 0.00158 3 5
71000 and above 0.21 0.056 0.00313 6 28
Income(Pooled sampled farmers)
Less than 10000 0.6 0.113 0.0127 6 10
10000-30000 0.48 0.044 0.0019 15 31
31000-50000 0.26 0.179 0.032 6 23
51000-70000 0.42 0.032 0.0012 6 14
71000 and above 0.24 0.086 0.00739 19 77
Educational status (Adopters)
No formal 0.333 0.0432 0.00186 15 45
Primary 0.28 0.016 0.00025 7 25
Secondary 0.26 0.0094 0.00008 7 26
Tertiary 0.25 9.07E-05 8.22E-09 1 4
Educational status (Non-Adopters)
No formal 0.36 0.105 0.011 9 25
Primary 0.46 0.056 0.00313 6 13
Secondary 0.56 0.0896 0.00802 9 16
Tertiary 1 0.0115 0.000132 1 1
Educational status (Pooled sampled farmers)
No formal 0.342 0.084 0.007 24 70
Primary 0.342 0.022 0.0004 13 38
Secondary 0.38 0.044 0.0019 16 42
Tertiary 0.4 0.023 0.00052 2 5
Source: Computed from Field Survey Data, 2018
This study revealed a marked difference in the poverty status of both adopters and non-adopters of taungya farming system; it was showed that the poverty incidence was highest among the non-adopters of taungya farming, which implies that farmers adopting agro forestry are better off in terms of welfare than farmers that are not adopting. This is possible because farmers adopting taungya farming system has the potential of generating higher yield which could have emanated from the inorganic matter from decomposed tree trunks, and litter fall. The differential in yields is likely to encourage non-adopters farmers to adopt agro forestry driven green growth so as to realize the benefit of increased productivity to offset the high cost of food production, as well as enjoy higher standards of living resulting from increased farm income.
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