Farm forestry has proved to be an important enterprise for small- and large-scale farmers worldwide. It has the potential of improving forest/tree cover across the globe. In Kenya, the forest cover is less than 2%. The country envisions achieving 10% forest cover over the next decade through promotion of farm forestry. However, the decision to plant trees on farmers’ land could be difficult. The study aimed to analyze the determinants of tree retention on farm for improvement of forest cover. Stratified and simple random sampling techniques were used in selecting 209 farmers. The results showed that sites, land size, age, education level, monthly income, tree management, extension services, availability of markets, harvesting regulation, and aesthetic and environmental motivation were significant determinants of tree retention. In particular, the chances of farmers who had gained technical skills in tree management were about 2.2 times higher to retain trees as compared to those who had not acquired such skills. Similarly, chances of farmers motivated to plant trees for environmental conservation were about 3.5 times higher to retain trees as compared to the group of farmers planting trees as a source of livelihood. These determinants would be instrumental in strengthening the current policies and reforms in forestry and agricultural sectors to enable Kenya to achieve 10% of forest cover.
Farm forestry in the context of evergreen agriculture is emerging as an affordable and accessible science-based solution to caring better for the land and increasing small-scale food production [
The success of farm forestry may be assessed in terms of effects of various determinants such as advanced use of farm labour, positive environmental changes, increased financial returns among others [
The developing countries in sub-Saharan Africa have continued to experience forest destruction as a result of uncontrolled timber harvesting, conversion of forests to farm and pasture lands, increased needs of human population, migration, education, energy prices, road construction, fire outbreaks, and other related mortality factors [
As a result, the Government of Kenya has developed various strategies to counter the decrease of forest cover in the country. The first one was ban of tree harvesting in the state forests in 1999. Since then, the country has been sourcing domestic and industrial wood from farms, supplemented with timber imported from neighbouring countries. This strategy has seen the country over depending on farms for forest products which, has led to depletion of farmland wood stock [
Since inception of these strategies in Kenya, limited studies have been carried out to assess factors associated with tree planting and retention by farmers in order to realize the expected output of improving tree/forest cover. The decision by farmers to plant trees may be difficult due to many land use needs especially agriculture in enhancing food security of about 40 million Kenyans. Subsequently, land size for farm forestry has continued to shrink as a result of high land fragmentation and settlement, unsupportive land tenure arrangements whereby women, married sons and other landless have limited access to land for either tree planting or management of naturally growing woodlands [
Kenya is divided into 47 Counties [
Nyeri North comprises the most western part of the moist windward side of Mt. Kenya, the drier western leeward side of the extinct volcano and the borders of the semi-arid Laikipia Plateau. Nyeri South comprises the moist windward eastern slope of the Aberdare Range (Figure
Map showing Nyeri South and Nyeri North districts in Nyeri County.
Kiambu’s agroecological zone (AEZ) extends in a typical pattern along the eastern slopes of the Aberdare Range. It is the most densely populated area with 640 persons per km2 in 2009 compared to 562 persons per km2 in 1999 and 280 persons per km2 in 1979. It occupied 1323.9 km2 as compared to 2448 km2 in 1979 implying a great decrease of agricultural land size holding per person. Also due to its combination of good soils, climate and proximity to Nairobi, the country’s main market, makes Kiambu the most economic farming region in the country [
Lari district lies on the upper highland AEZ one (UH 1). It is classified as sheep and dairy zone with permanent cropping possibilities, dividable in a long cropping season followed by medium one. It ranges from 2415 to 2591 m a.s.l and receives 1150 to 1276 mm mean annual rainfall. Kikuyu district lies on the lower highland AEZ two (LH 2) Figure
Map showing Lari and Kikuyu districts in Kiambu County.
A sampling frame was designed in all study areas where a list of farmers who planted over one hundred trees or at least a quarter an acre under woodlot or plantations was drawn. Farmers were then stratified according to land sizes, tree planting densities, and species diversity varying from intense boundary planting, woodlots to plantation. To quantify the area under trees for cases of boundary planting conversions were done to assume uniform area under trees. This was equated as either a woodlot or a plantation of 0.5 ha. In each stratum, a list of farmers was drawn and each individual farmer was assigned random number. The random numbers were then ranked and targeted numbers of farmers were selected using the simple random sampling. Questionnaires were then allocated proportionately in each of the stratified category resulting to selection 209 respondents. A total of 48, 79, 48, and 34 questionnaires were allocated to Nyeri South, Nyeri North, Lari and Kikuyu districts, respectively. The data collected were mainly on household and farm characteristics. Data collectors were trained before carrying out the survey. Pre-testing was done to ensure consistency, reliability, and validity of the instrument.
The selections of determinants used in this study were based from government of Kenya blue prints and authors conceptualization from cited literature. These were broadly classified as: demographic characteristics, land ownership and land use, tree planting on farm and use, problems of tree planting, tree management, social function of farm forestry, and economic benefits of trees on farm. In each of these classes, specific variables were assessed in relation to tree planting and retention. It was hypothesized that the likelihood of the farmer willing to retain trees on farm would be influenced by a number of determinants in each of the seven broad categories. Tree retention was assessed on the basis of farmers’ frequency of tree planting in at least 10 years where a sample of data of tree planting was taken from 2004 to 2010 based on the respondent’s recall. The other attributes considered for tree retention included; availability of different tree sizes on farms, species diversity of different growth rates, available plans of land use management, and willingness to invest in tree farming. In this case a group of farmers were considered to most likely or likely to retain trees on their farms if it was found that they were planting trees yearly on their farms, there were different trees on their farm of difference sizes (small diameters <10 cm, medium diameter 10–20 cm, and large ones >20 cm) some ready for harvesting but farmer not willing to cut because of some reasons. The other measure of retention especially for large scale planting was indirect benefits of tree planting such as carbon credits. In this regard group farmers had anticipated of accessing carbon market with the understanding that they were not likely to harvest their trees after some period of time where they would be compensated with payment of carbon credits. This was observed in farmers’ field where some had started shifting to planting of indigenous trees which have slow growth rate and likely to be in a long period of time on their farms. The other characteristics of tree retention were based on the fuelwood demand and regulation mechanisms by Kenya Forestry Service (KFS) where farmers ensured that any given time, they had trees on their farms to meet the increasing needs of various household demands. This was evidenced from the pattern of planting. It is in this respect that retention was measured as a latent variable but categorized into three or two levels farmers who were most likely, less likely and not likely; likely and not likely to plant and retain trees on farm as the dependent variable. This classification lead to the use of multinomial and binary logistic regression models, respectively. A criterion based on land size, age of the farmers, species diversity on farm, last period/frequency of tree planting, land use management priorities, hindrances of tree planting, and tree harvesting regimes was used to group farmers as “not likely”, “likely”, and “most likely” to retain trees on farm. Table
Definitions and description of modelling variables.
Variables | Description | Dummy description |
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Dependent variable: | ||
Farmer’s tree retention on farm | Farmer classified as most likely, less likely and not likely to retain trees on farm or likely and not likely to retain trees on farm | 0 = not likely; 1 = less likely; 3 = most likely or 0 = not likely, and 1 = likely. |
Explanatory variables: | ||
Occupation ( |
Main occupation of the household head classified as either formal employment or non-formal employment | 1 = Formal; 2 = non-formal; 0 = otherwise |
Age ( |
Age of the household head | |
Education ( |
Education level of the household head | 1 = Formal learning; 0 = otherwise |
Marital status ( |
Marital status of the head of household | 1 = Married; 0 = Single |
Number household ( |
Number of members of the household | |
Income ( |
Income of the household head | |
Land ( |
Land ownership of the household head | 1 = Household that owns land; 0 = otherwise |
Land size ( |
Land size owned by household members | |
Landuse ( |
Area of land under trees | 1 = Area under trees; 0 = otherwise |
Tree use ( |
Purpose of trees planted on farm | 1 = Fuelwood; 2 = Aesthetic |
Technical skills ( |
Provision of technical skills on tree management | 1 = Received technical skills; 0 = otherwise |
Labour ( |
Labour involvement on tree management | 1 = intensive; 0 = Not intensive |
Extension services (X13) | Accessing regular extension services | 1 = Accessed extension services; 0 = otherwise |
Regulation ( |
Regulation by KFS on tree harvesting | 1 = Supports KFS regulation; 0 = otherwise |
Forest association ( |
Existence and participation on forest organization | 1 = Existence of forest association; 0 = otherwise |
Marketability ( |
Knowledge and access to markets and policies | 1 = Ready market; 0 = otherwise |
Economic motivation ( |
Economic returns from tree growing | 1 = High returns; 0 = otherwise |
Chi-square statistical test and percentage frequencies were used to explore the association between the likelihood of tree retention and selected determinants. In order to control multi-collinearity among determinants, correlation analysis was performed in order to identify correlated variables before fitting the models.
In order to examine the probability and extent at which the farmers were willing to retain trees on farm, multinomial and binary logistic regression models were used. In particular, when the dependent variable was in three categories, “not likely”, “likely”, and “most likely,” multinomial logistic regression model of the form below was used:
When
Both multinomial and binary logistic regression models assumed that there exists an index/a desire or intent by the farmer to retain trees on farm which was a linear function of the vector of predictors expressed as
If this index exceeds the individual threshold, retention of trees occurs.
Similarly, extend of planting trees was also a function of the determinants, through the index. The greater desire to plant trees on farm, the greater the extent of retaining:
Essentially, each farmer may have a different value. For instance, if extension services are a significant determinant (predictor variable), then more of the extension services may be required to push one farmer over threshold than that required to induce another farmer’s retaining ability. Since individual threshold differ, at any given index value, there will be both a concentration of zeros (for non-retaining) and a distribution of positive extents of retaining (for those who would retain). Therefore the probability of planting and retaining trees, given a particular index value was given by
Expected extend of the planting and retaining, given a particular index value was given by
Estimation of
The results showed study sites were significantly associated (
Study sites, household, and land ownership determinants associated with likelihood of farmers’ tree retention in Central Kenya.
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Determinants | Categories | Not likely | Less likely | Most likely | Total ( |
Not likely | Likely | Total ( |
% | % |
% | % | % | ||||
Site | Kiambu-Lari | 19 | 35 | 46 | 48 | 46 | 54 | 48 |
Kiambu-Kikuyu | 38 | 21 | 41 | 34 | 59 | 41 | 34 | |
Nyeri-South | 32 | 27 | 40 | 47 | 60 | 40 | 47 | |
Nyeri North | 15 | 23 | 62 | 79 | 38 | 62 | 79 | |
Gender | Male | 19 | 28 | 53 | 161 | 47 | 53 | 161 |
Female | 39 | 21 | 40 | 199 | 61 | 40 | 38 | |
Main occupation | Full time farmer | 25 | 28 | 47 | 174 | 53 | 47 | 174 |
Formal job | 15 | 19 | 68 | 14 | 33 | 68 | 27 | |
Education level | None | 30 | 35 | 35 | 23 | 65 | 35 | 23 |
Primary | 22 | 24 | 54 | 79 | 46 | 54 | 79 | |
Secondary | 22 | 32 | 46 | 69 | 54 | 46 | 69 | |
Post secondary | 18 | 11 | 71 | 28 | 29 | 71 | 28 | |
Marital status | Married | 22 | 26 | 52 | 187 | 48 | 52 | 187 |
Not married | 40 | 20 | 40 | 15 | 61 | 40 | 15 | |
Inherited | 21 | 30 | 49 | 120 | 51 | 49 | 120 | |
Land ownership | Bought | 28 | 17 | 55 | 60 | 45 | 55 | 60 |
Donated | 30 | 20 | 50 | 20 | 50 | 50 | 20 |
Similarly, there were significant association (
Consequently, of the sampled farmers, 59, 31, and 10 per cent owned their land through inheritance from their parents, purchase, and given by the community/government, respectively. However, no significant associations were found between type of land ownership and farmers’ decision to retain trees (Table
The study revealed 84 per cent of farmers interviewed lacked any technical skills in tree management as compared to 16 per cent who had acquired such skills. The specific skills were nursery establishment, thinning, pollarding, short rotation coppice, fertilizer application, tree harvesting, forest economics, and management of tree competition with agricultural crops among others. The acquisition of the technical skills was significantly associated (
Tree management and marketability determinants of influencing farmer’s tree retention on farm in Central Kenya.
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Determinants | Categories | Not likely | Less likely | Most likely | Total ( |
Not likely | Likely | Total ( |
% | % | % | % | % | ||||
Technical skills | Yes | 14 | 21 | 66 | 29 | 34 | 66 | 29 |
No | 26 | 28 | 46 | 163 | 54 | 46 | 163 | |
Use of skills | Yes | 14 | 7 | 79 | 28 | 21 | 79 | 28 |
No | 26 | 19 | 55 | 31 | 45 | 55 | 31 | |
Labour and cost | Yes | 9 | 28 | 63 | 43 | 37 | 63 | 43 |
No | 30 | 24 | 46 | 127 | 54 | 46 | 127 | |
Extension services | Yes | 0 | 22 | 79 | 9 | 22 | 78 | 9 |
No | 25 | 27 | 48 | 183 | 52 | 48 | 183 | |
Harvesting permission | Yes | 18 | 26 | 57 | 97 | 43 | 57 | 97 |
No | 32 | 27 | 41 | 71 | 59 | 41 | 71 | |
Harvesting regulation | Yes | 16 | 29 | 55 | 76 | 45 | 55 | 76 |
No | 35 | 20 | 45 | 75 | 55 | 45 | 75 | |
Village forest associations | Yes | 16 | 35 | 49 | 57 | 51 | 49 | 57 |
No | 27 | 22 | 51 | 112 | 49 | 51 | 112 | |
Membership | Yes | 23 | 31 | 46 | 35 | 54 | 46 | 35 |
No | 25 | 26 | 49 | 148 | 51 | 49 | 148 | |
Ready market | Yes | 21 | 25 | 54 | 109 | 46 | 54 | 109 |
No | 36 | 21 | 42 | 33 | 58 | 42 | 33 | |
Marketing problems | Yes | 10 | 29 | 61 | 31 | 39 | 61 | 31 |
No | 28 | 25 | 47 | 117 | 53 | 47 | 117 |
Farmers who sought authority from Kenya Forestry Service (KFS) to harvest their trees were significantly associated (
The results also showed a positive significant correlation (
Correlations among various demographic variables of tree retention.
Demographic variables | Gender of household |
Marital status | Major |
Household size | Monthly |
Age (yrs) | Education |
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Gender of household head | 1.000 | 0.247 | −0.125 | −0.038 | −0.048 | 0.063 | −0.098 |
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Marital status | 1.000 | −0.032 | −0.165 | −0.041 | −0.227 | 0.070 | |
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Major occupation | 1.000 | −0.108 | 0.240 | −0.029 | 0.374 | ||
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Number of members household | 1.000 | 0.035 | 0.063 | −0.169 | |||
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Monthly income | 1.000 | 0.011 | 0.179 | ||||
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Age in years | 1.000 | −0.452 | |||||
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Education | 1.000 |
Consequently, there was a positive significant correlation (
Correlation matrix among the farm determinants of tree retention.
Technical skills | Use of skills | Labour cost | Extension |
Harvesting permission | Harvesting regulation | |
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Technical skills | 1.000 | 0.819 | −0.024 | 0.275 | 0.152 | 0.483 |
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Use of skills | 1.00 | 0.013 | 0.204 | 0.145 | 0.372 | |
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Labour and cost | 1.000 | −0.081 | −0.063 | −0.188 | ||
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Extension services | 1.000 | 0.130 | 0.231 | |||
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Harvesting permission | 1.000 | 0.244 | ||||
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Harvesting regulation and tree farming | 1.000 |
There was also a significant positive correlation (
Both binary and multinomial logistic regression following stepwise method of fitting variables showed gender, age, major occupation, education level, monthly income, land size, extension services, site, motivational reasons of tree planting, labour, acquisition of technical skills, cost involved in tree management, harvesting permission from KFS and existence of forest associations as significant determinants influenced the likelihood of the farmer willing to plant and retain trees on farm (Table
Likelihood ratio tests and model classification of tree retention determinants using binary and multinomial logistic regression.
Determinants | −2log likelihood | d.f |
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% Model classification | ||||
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*Logt | *Mult | Logistic | Multinomial | Logistic | Multinomial | Logistic | Multinomial | |
Site | 281 | 45.1 | 3 | 6 | 0.05 | 0.04 | 59 | 50 |
Gender HH* | 273 | 24.8 | 1 | 2 | 0.12 | 0.03 | 55 | 51 |
Occupation | 275 | 20.6 | 1 | 2 | 0.06 | 0.16 | 55 | 50 |
Age | 262 | 238 | 1 | 126 | 0.93 | 0.06 | 52 | 67 |
Education | 267 | 40.0 | 3 | 6 | 0.04 | 0.14 | 58 | 52 |
Marital status | 279 | 18.7 | 1 | 2 | 0.38 | 0.32 | 53 | 51 |
NMH* | 275 | 85 | 1 | 2 | 0.47 | 0.47 | 53 | 52 |
Income | 220 | 168 | 1 | 80 | 0.00 | 0.00 | 60 | 53 |
Land tenure | 276 | 43.9 | 2 | 10 | 0.76 | 0.15 | 52 | 51 |
Land size | 231 | 130 | 1 | 6 | 0.00 | 0.00 | 68 | 68 |
Tree use | 240 | 27.3 | 2 | 4 | 0.35 | 0.53 | 55 | 48 |
Motivation | 225 | 27.5 | 1 | 2 | 0.20 | 0.01 | 56 | 52 |
Technical skills | 262 | 21.1 | 1 | 2 | 0.05 | 0.13 | 56 | 49 |
Skill effect | 71.8 | 16.6 | 1 | 2 | 0.05 | 0.14 | 66 | 66 |
Labour and cost | 232 | 25.8 | 1 | 2 | 0.05 | 0.01 | 57 | 50 |
Extension services | 263 | 18.8 | 1 | 2 | 0.07 | 0.06 | 53 | 50 |
Harvest permission | 229 | 23.9 | 1 | 2 | 0.04 | 0.05 | 58 | 50 |
Harvesting regulation | 208 | 25 | 1 | 2 | 0.22 | 0.02 | 55 | 50 |
Forest associations | 234 | 22.1 | 1 | 2 | 0.83 | 0.11 | 51 | 50 |
Membership | 253 | 18.1 | 1 | 2 | 0.70 | 0.79 | 51 | 49 |
Ready market | 195 | 19.8 | 1 | 2 | 0.24 | 0.22 | 55 | 51 |
Marketing problems | 203 | 21.7 | 1 | 2 | 0.16 | 0.07 | 55 | 50 |
Farmers with higher monthly income and large land size had high chance of planting and retaining trees on farm. Specifically, increase of income had a unit increase in tree planting and retention. Similarly, unit increase of land resulted to about 1.4 times chances higher of farmers’ decision to plant and retains trees. The computed predicted probabilities on monthly income and land size of the farmers showed that as monthly income increased and land size, there was corresponding increase of probability of the farmers planting and retaining trees on farm with 100% model prediction (Figures
Monthly income (Kenya Shilling) and predicted probability of the farmers tree planting and retention on farm in Central Kenya.
Land size and predicted probability of the farmers’ tree planting and retention on farm in Central Kenya.
The odds of farmers from Nyeri North district likely to retain trees on farm as compared to those from Nyeri South, Kikuyu, and Lari were 60, 60, and 50 per cent higher, respectively (Table
Binary logistic regression model on fitting determinants of farmer’s tree retention on farm in Central Kenya.
Determinants | Categories | Reference category |
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S.e ( |
Odds ratio |
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Kiambu-Lari | Nyeri North | −0.66 | 0.37 | 0.5 | 0.076 | |
Site | Kiambu-Kikuyu | −0.85 | 0.42 | 0.4 | 0.043 | |
Nyeri South | −0.88 | 0.38 | 0.4 | 0.020 | ||
Gender | Male | Female | 0.56 | 0.37 | 1. 8 | 0.125 |
Household members | — | — | 0.04 | 0.05 | 1.8 | 0.477 |
Monthly income | — | — | 0.00 | 0.00 | 1 | 0.004 |
Land size | — | — | 0.36 | 0.07 | 1.4 | 0.000 |
Marital status | Single | Married | 0.48 | 0.55 | 1.6 | 0.380 |
Major occupation | Formal job | Full time farmer | −0.81 | 0.44 | 0.5 | 0.064 |
None | Post secondary | −1.55 | 0.61 | 0.2 | 0.011 | |
Education | Primary | −0.74 | 0.48 | 0.5 | 0.120 | |
Secondary | −1.06 | 0.48 | 0.3 | 0.028 | ||
Age | — | — | 0.001 | 0.01 | 1.0 | 0.927 |
Tree use | Fuel wood | Aesthetic | −0.55 | 0.47 | 0.6 | 0.243 |
Timber | −0.16 | 0.49 | 0.8 | 0.533 | ||
Tree Motivation | Conserve environment | Source of livelihood | 0.420 | 0.330 | 1.5 | 0.203 |
Technical skills | Obtained technical skills | Did not obtain skills | 0.802 | 0.421 | 2.2 | 0.057 |
Effect of skills | Skills were useful | Skills not useful | 1.105 | 0.585 | 3.0 | 0.059 |
Labour and cost | Intensive | Not intensive | 0.697 | 0.362 | 2.0 | 0.054 |
Extension services | Received extension services | Did not receive extension services | 1.329 | 0.815 | 3.8 | 0.103 |
Harvesting permission | Sought harvesting permission | Did not seek permission | 0.640 | 0.317 | 1.9 | 0.043 |
Harvesting regulation | Harvesting regulation important | Harvesting regulation not important | 0.399 | 0.327 | 1.5 | 0.223 |
Forest associations | Existence of forest associations | No existence of forest association | −0.071 | 0.325 | 0.9 | 0.828 |
Membership | Member of forest association | Not a member | −0.145 | 0.377 | 0.9 | 0.701 |
Ready market | Ready market access | No ready market access | 0.471 | 0.401 | 1.6 | 0.241 |
Marketing problems | Existence of marketing problems | No marketing problems | 0.579 | 0.413 | 1.8 | 0.160 |
However, there were no significant differences (
Farmers on full time formal employment had 50 per cent higher logits of tree planting and retention as compared to full time farmers. In addition, farmers with postsecondary education had about 80, 50, and 70 per cent chances higher of planting and retaining trees on farm as compared to those with formal primary and secondary education, respectively. Furthermore, the logits of the male-headed households most likely and less likely to retain trees on farm were about 2.9 and 2.8 times significantly higher as compared to female-headed households not likely to plant and retain trees (Table
Multinomial logistic regression model on fitting determinants of farmer’s tree retention on farm in Central Kenya.
Explanatory variable | Most likely/not likely | Less likely/not likely | ||||||
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S.e ( |
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Odds ratio |
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S.e ( |
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Odds ratio | |
Intercept | 1.41 | 0.32 | — | — | 0.41 | 0.37 | — | — |
Site | ||||||||
Lari | −0.51 | 0.51 | 0.315 | 0.6 | 0.23 | 0.56 | 0.678 | 1.3 |
Kikuyu | −1.33 | 0.50 | 0.008 | 0.3 | −1.03 | 0.60 | 0.087 | 0.4 |
Nyeri South | −1.17 | 0.47 | 0.013 | 0.3 | −0.55 | 0.53 | 0.302 | 0.6 |
Gender | 1.05 | 0.42 | 0.013 | 2.9 | 1.03 | 0.50 | 0.038 | 2.8 |
Intercept | 0.00 | 0.37 | −0.63 | 0.44 | ||||
Education | ||||||||
None | −1.25 | 0.72 | 0.082 | 0.3 | 0.64 | 0.90 | 0.472 | 1.9 |
Primary | −0.46 | 0.58 | 0.426 | 0.6 | 0.62 | 0.80 | 0.439 | 1.9 |
Secondary | −0.63 | 0.59 | 0.287 | 0.5 | 0.89 | 0.80 | 0.266 | 2.4 |
Intercept | 1.39 | 0.50 | −0.511 | 0.73 | ||||
Motivation | 1.24 | 0.44 | 0.005 | 3.5 | 1.40 | 0.49 | 0.005 | 4.0 |
Intercept | 0.37 | 0.31 | ||||||
Labour and cost | 1.49 | 0.58 | 0.010 | 4.4 | 1.30 | 0.63 | 0.038 | 3.7 |
Intercept | 0.42 | 0.21 | −0.20 | 0.24 | ||||
Harvesting permission | 0.94 | 0.39 | 0.017 | 2.6 | 0.58 | 0.44 | 0.191 | 1.8 |
Intercept | 0.23 | 0.28 | −0.19 | 0.31 | ||||
Harvesting regulation | 0.98 | 0.42 | 0.019 | 2.7 | 1.16 | 0.48 | 0.017 | 3.2 |
Intercept | 0.27 | 0.26 | −0.55 | 0.32 | ||||
Marketing problem | 1.33 | 0.66 | 0.043 | 3.8 | 1.23 | 0.71 | 0.085 | 3.4 |
Intercept | 0.51 | 0.22 | −0.13 | 0.26 | ||||
Forest association | 0.49 | 0.445 | 0.267 | 1.6 | 0.98 | 0.48 | 0.043 | 2.7 |
Intercept | 0.64 | 0.27 | −0.18 | 0.27 |
Farmers with technical skills had about 2.2 times chances higher of planting and retaining trees than those who had not acquired similar skills. Moreover, farmers who did not view labour involved in tree management as intense and costly had about two times chances higher of tree planting and retention as compared to who viewed tree farming as labour intense and costly. The chances of farmers who sought permission from KFS to harvest their trees were about 2.6 times higher to plant and retain trees (Table
The significant effect of site in influencing farmers’ decision to plant and retain trees on farm underscored the importance of taking into account the uniqueness of each geographical location. This is because sites vary in climatic conditions and other characteristics that are likely to have a great influence in tree planting and retention. In this study the four selected sites were very distinct in amount of rainfall, soils, population density, and proximity to other geographical features/major towns among others. For instance, Nyeri North district had large land sizes, which may explain high level of tree planting and retention as compared to other three districts. This corroborates with other studies that have shown land is a significant factor influencing community’s decision to plant trees on large scale. One percent increase in land under outright ownership, there was a 7.6 per cent in the probability that farmers will establish forest plantations. Farmers with small-scale land holdings opted for agricultural rather than forest plantations, as they needed immediate cash flow hence shorter rotations of crops cultivated [
The low levels of likelihood of farmers to plant and retain trees at Nyeri South and Kikuyu districts may be attributed to small land holdings due to high population. The nature of their farming activities were dairy, tea growing and subsistence crops. This may have delineated them from active participation in tree farming as most of the land was needed for pasture and food crops. This was in contrast with Nyeri North district where the concept of planting alone was not adequate to advance forest cover but the interest to keep trees on farm will significantly contribute to forest cover. For instance, the discussions held with farmers during data collection in this region pointed out that majority of them viewed tree growing as a long-term investment with no immediate cash to offset household needs, hence lowly prioritized. Therefore, chances of finding more trees on farm of varied sizes were small reflecting less retention. In cases where farmers had trees on their farms, the site was less productive and sometimes with deep gradient which was not suitable for agricultural farming. This demonstrated that such group of farmers least valued trees in their fertile lands as compared to food crops hence a motivation of continuous tree planting and retention so long as the site of the land remained unproductive. This was further evidenced by farmers in the drier area of Kikuyu district engaged more in tree planting due to less land productivity and less rainfall for agricultural crops. In this case there was high likelihood that such farmers would continue planting and keeping trees on their farm to meet various household needs unless better alternatives of land uses for drier areas were availed. This corroborates with findings of other studies where farmer-owned woodlands were reported to generally occur along rivers and streams which were too hilly or rocky for row crops [
The significant contribution of male headed household in the likelihood of tree planting and retention as compared to female headed households may be explained by cultural setting of the community members of the study sites. Women might have limited access to landownership, participation in community groups as a result of household duties among others resulting to less interest in tree farming. Similar differentials were reported by [
The major occupation of farmers particularly, those in formal employment were leveraged in tree planting and retention. These groups of farmers in formal employment were not necessarily depending on their land for their livelihoods hence may give environmental, aesthetic and recreational factors more weight than financial ones when making land use decisions. This resulted to high likelihood of tree planting and retention corroborating well with the findings of [
Education plays a significant role in understanding the need to conserve the environment through various practices. During the learning period individuals acquire relevant knowledge, skills, and values appropriate for sustainable farm forestry. This was evident on this study where farmers with secondary school and post secondary education qualifications were most likely to plant and retain trees. Knowledge in agroforestry has been found to significantly correlate with level of education where college graduates tended to be more interested in agroforestry than their counterparts with less academic qualifications. Every additional year of education decreased the probability that the household exploited forest/tree products and less conversion of woodlands to arable land [
Middle aged and young farmers were planting trees on their farm with an aim of generating income and household needs like supply of fuelwood, timber, construction poles, and boundary marking of their lands from neighbours. This tended to influence the probability of likelihood of tree planting and retention. For instance, with population increase, there will be always demand for wood products indicating ready market that is likely to stimulate tree planting and retention for future direct and indirect needs. This concurs with a study carried on farmer participatory evaluation of agroforestry trees in eastern Zambia which showed that fuelwood and construction materials were most important and second most important by-products among the group of farmers likely to have influenced tree growing [
The significant contribution of village forest association on the likelihood of tree planting and retention implied that the roles undertaken by these groups such as: supply of seeds, seedling production, tree planting, thinning, pruning, bee keeping, environmental conservation, and marketing had a positive effect on farm forestry. Studies have shown that becoming a member of a farmers’ group increased knowledge and farmers’ participation in forestry activities [
The reinforcement of forest regulation on tree harvesting by Government of Republic of Kenya through KFS significantly influenced the farmers to retain trees on farm and participate in tree growing. This was in contrast with what has been reported in the past where seeking permission to harvest trees, which the farmers had planted, would discourage them from continuous tree planting [
The household, farm attributes, tree management and marketability characteristics were instrumental determinants that influenced the farmer’s decision on tree planting and retention. The findings of this study therefore would be crucial in assisting the government of Kenya to effectively address forestry and agriculture sector policy reforms geared towards improving forest cover to 10% in the next decade. One objective of such policy reforms would perhaps be enhancing capacity to monitor the land use management systems across the country. This would lead to identification and mapping of specific sites that would be useful in tree planting such as top hills, degraded areas not suitable for agricultural crops and fallow lands among others. Consequently, the forest policy would provide direction on evaluation of farm forestry needs in each of the specific locations of the country in strengthening the culture of tree planting. This is likely to direct the allocation of resources while greening the country. The role played by gender, age, and major occupation in tree planting and retention would guide activities of promoting specific forestry programmes in Kenya. For instance, it is important to match the type of tree to the needs of men and women and farmers of different ages.
The need for extension services requires more investment in human capital and facilities. This would dictate for allocation of more funds to recruit and equip the personnel with appropriate facilities to enable them undertake their extension services well. The revision of Sessional Paper No. 1 on Forest Policy which is currently underway and new farm forestry guidelines in the Agricultural Act (Cap 318) would need to take into account of these needs if 10 per cent forest cover in Kenya would become a reality. For example, it has been currently diagnosed that Kenya has a shortfall of over 3000 agricultural and forestry extension officers. This would significantly hinder evergreen agricultural services to the general public. The significant contribution of household income in tree planting and retention would as well shift the government policies on employment opportunities in order to reduce pressure on land and enhance tree planting. Equally, enhancing the level of literacy in the country would strengthen efforts of environmental conservation measures. Overall, the significant determinants found in this study influencing tree planting and retention would significantly help the country to develop appropriate technical working papers that will spearhead the success of farm forestry with an aim of improving forest cover in Kenya.
In conclusion, the approaches used in undertaking this study especially the application of binary and multinomial logistic regression model explicitly revealed the key determinants influenced farmers’ decision to plant and retain trees on farm. Ideally tree planting alone is not sufficient to indicate that tree cover will be improved but an assessment of retention forms a critical point that needs to be assessed because in farm forestry farmers have out right ownership of land and management. They are likely to shift to different land uses other than tree growing. Accounting the contribution of various determinants to tree growing would lead to set of realistic objectives and strategies that would enable the country realize the dream of increasing tree/forest cover to 10 per cent.
The authors thank Kenya Forestry Research Institute Board of Managament and Director for allocating financial resources to undertake this study. They are especially grateful to farmers who were resourceful in providing useful data for this study. They are indebted to the enumerators James Mwaura, Monica Ndegwa, Ulysses Gitonga, George Okwaro, and Kimani Samuel for their effective data collection.