Agroforest Syst (2009) 75:175–187 DOI 10.1007/s10457-008-9180-z
Identifying important biophysical and social determinants of on-farm tree growing in subsistence-based traditional agroforestry systems Kamal Kishor Sood Æ C. Paul Mitchell
Received: 11 March 2008 / Accepted: 24 September 2008 / Published online: 18 October 2008 Ó Springer Science+Business Media B.V. 2008
Abstract Many expert-designed agroforestry projects enunciated in 1970s around the world, particularly in the developing countries, had uneven success due to inadequate adoption or abandonment after adoption. There are many empirical studies on factors affecting on-farm tree cultivation mainly where expert-designed agroforestry programmes were introduced but lacking in case of traditional agroforestry. Moreover, the concern to identify key factors influencing on-farm tree growing is gaining importance. The present study identifies key factors in on-farm tree growing based on investigation of traditional agroforestry using logistic regression approach. The study is based on household survey of 401 households located in Indian Western Himalaya. The factors affecting on-farm tree growing were grouped into: biophysical (included land use and infrastructural aspects) and social. Models predicting on-farm tree growing for each category were developed and key factors affecting on-farm tree growing in the respective category were identified. A
K. K. Sood (&) Division of Agroforestry, Sher-e-Kashmir University of Agricutural Sciences and Technology-Jammu, Faculty of Agriculture, Chatha, P.O. Bhour Camp, Jammu 180 009, J&K, India e-mail:
[email protected] C. P. Mitchell Kings College, College of Physical Sciences, University of Aberdeen, Aberdeen AB24 3FX, Scotland, UK
composite model was also developed by combining biophysical and social factors. In the present study, farm size, agroclimatic zone, soil fertility, mobility and importance of tree for future generations respectively were the key factors which influenced tree growing. In contrast to many previous studies which considered either biophysical or social factors, the composite model in the present study reveals that both biophysical and social factors are simultaneously important in motivating the farmers to grow trees on their farms in traditional agroforestry. Moreover, the present study open vistas for using farmers’ experience and knowledge of adoption of agroforestry to stimulate on-farm tree growing. The wider implication of the study is that biophysical as well as social variables should be considered together in designing suitable agroforestry systems in various parts of the world. Keywords Logistic regression India Himalaya Traditional Social Biophysical Composite model Growers Non-growers
Introduction The key determinant of any agroforestry programme’s success is its adoption by the intended beneficiaries. The adoption of agroforestry was promoted in 1970s in many parts of world by national and international organisations but interest was low in
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the case of smallholders and subsistence regions (Saxena and Ballabh 1995). This was mainly because planners did not appreciate that farmers are innovative and have experience of tree growing in traditional agroforestry systems and, planners had a stigma to learn from farmers’ experience and knowledge (Chambers et al. 1989). In most of the countries where agroforestry programmes were implemented, uniform models were recommended without any consideration of characteristics of farms and farmers, and farmers’ experience of adoption of traditional farm practises. Many programmes suffered from a lack of information about factors that influence tree growing by farmers (Saxena and Ballabh 1995). It is now well documented that farmers are knowledgeable and innovative (Chambers et al. 1989; Alexander and Kidd 2000; Mak 2001). Farmers’ innovations were, historically, the means through which technological advances in agriculture were made (Critchley 2000). The theory of induced innovation has focused on innovations as a consequence of changes in socio-economic conditions over time (like increased population pressure and market development) in relation to relative change in input and output prices (Ruthenberg 1980). The growing of trees and other woody perennials on farms by the farmers as a result of historical change in socioeconomic and ecological conditions has been explained (Raintree and Warner 1986; Scherr 1994). Traditional agroforestry systems are products of centuries’ experimentation by the farmers that have adapted them to consider ecological, socio-cultural and economic factors (Christanty and Iskandar 1985). Thus farmers themselves had knowledge and experience of integrating trees in their farming systems for centuries (de Foresta et al. 2000). It is also well known that the most effective approach to encourage farmers to grow trees is to study the adoption pattern of existing farm practises and build on them rather than changing instantly everything farmers do (Saxena and Ballabh 1995; Fisher and Bunch 1996). This approach allows planners to utilise farmers’ experience of adoption of tree growing to develop strategies that accelerate on-farm tree growing rather than taking outside recommendations with little or no local support. The complex management requirements of agroforestry may limit its adoption, and long period of testing and experimentation required compared to annual cropping
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technologies (Mercer 2004). The study of existing traditional agroforestry can help to design strategies for encouraging farmers to grow trees. Sinclair and Walker (1999) also indicated the lack of quantitative and predictive understanding about traditional agroforestry practises and its importance in making them more adoptable. Moreover, most past studies on onfarm tree growing were mainly limited to ex-ante appraisal of expert-designed trials and ex-post evaluation of expert induced agroforestry programmes (Mercer 2004). Developing new strategies for encouraging farmers to grow trees and improvements in existing systems can be designed if characteristics of the farms and farmers in relation to tree growing in existing agroforestry systems are studied (Nair and Dagar 1991). Pattanayak et al. (2003) reviewed 120 studies on onfarm tree growing (agroforestry adoption). This review revealed limited empirical attempts to study on-farm tree growing in relation to biophysical factors. The limited number of biophysical variables like slope, soil quality and irrigation were included in very few studies. Pattanayak et al. (2003) found that only 27% of the studies contained biophysical variables. Notwithstanding this, these variables were important predictors in a majority of studies (64%) where they were included. Thus they emphasised a need of comprehensive empirical studies on on-farm growing to incorporate biophysical factors. In most developing countries, rural communities practise agriculture. These communities are characterised by high socioeconomic inequalities. The level of participation in any agricultural innovation is influenced by the social aspects of these communities (Agarwal 1986). Agroforestry as a discipline of organised scientific inquiry is about two decades old. In the initial period of development of agroforestry, an increase in the agricultural and tree biomass production was prioritised to fulfil the agricultural and tree-based needs of the rising population. Therefore, the focus of research was on the technical aspects (increasing biological and economic productivity) rather than on social and desirability criteria (Mercer and Miller 1998; Nair 1998). However, tree growing is very rarely a pure technical problem as people in various parts of world have grown trees in one form or another without any technical assistance (FAO 1986). It was thus felt that tree growing is also a social problem as it may be related to various sociological issues (Burch 1986). A
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review by Mercer and Miller (1998) of agroforestry studies published in the journal ‘‘Agroforestry Systems (1982–1996)’’ revealed little attention to the social aspects of agroforestry. Moreover, many of the so-called social forestry programmes (agroforestry here considered as a type of social forestry) fail as they do not take into account social factors (Cernea 1992). This implies a need to study how the likelihood of tree growing varies with social aspects of the people. Sood (2003) reviewed 105 studies on agroforestry adoption (1987–2002) and found that only the limited studies (13) were concerned with tree growing in traditional agroforestry systems. Moreover, these studies were not comprehensive and concentrated on limited number of biophysical and socio-economic factors (aspect of the land, altitude, farm size, type of household, distance between house and farm, security of land tenure and type of farm family) of farms and farm households. This implies that the full range of potential biophysical and socio-economic factors have not been examined in previous empirical studies on tree growing, specifically in the traditional agroforestry systems. Therefore, this study was carried out in the traditional agroforestry systems to: (i) investigate the association between each individual biophysical and social factor, and tree growing and (ii) develop models to identify key factors and magnitude of their influence on tree growing. The study identifies key factors in each category (social and biophysical) and develop model of agroforestry tree growing for each category. Further, it is not known whether only biophysical factors pre-dominate the likelihood of tree growing when both the biophysical and social factors are considered together. To know this a composite model of biophysical–social factors was also developed.
Materials and methods The study area description, procedure of data collection, sampling procedure and, methods for data and logistic regression analysis are given below: Study area Mandi District, located in the Indian State of Himachal Pradesh, was the focus of the study due to the existence of traditional agroforestry systems in this district. Himachal Pradesh is situated in
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north-west India in the Himalayan ranges between latitude 30°220 –33°130 N and 75°460 –79°020 E longitude (Forest Survey of India (FSI) 1999). It is a mountainous State (province) with altitude ranging from 350 to 6,975 m above mean sea level. About 90% of the population of State inhabits villages and their economy is dependent on agriculture, horticulture, silviculture and animal husbandry. Agriculture is mainly subsistence. Agriculture is a means of livelihood for the vast majority of the State’s population and is a major source of employment (Sood and Sood 2000). The median farm holding in the current study area is very small (0.50 ha). About 70% of the farmers have very small farmholding (\0.50 ha) of which 34% are marginal (\0.25 ha). State forests in Himachal Pradesh provide fodder, fuelwood, timber, herbs, medicinal plants, small timber for agricultural implements, cattle sheds, huts and fencing to the local people (Sharma et al. 2000). Rural households obtained on average 830 kg of fodder, 444 kg of fuelwood and 0.3 m3 of timber per annum from the State forest (Singh 1999). All packing material for fruits came from State forests until 1990 and subsequently the State government started providing subsidised imported wooden and cardboard boxes to the farmers for apple packing (Singh 1999). The recorded forest area is 35,400 km2 which constitutes 63.4% of the geographical area of the State but the actual tree cover is only 13,082 km2, 23.5% of the geographical area (Forest Survey of India (FSI) 1999). Right holders exert a great pressure for extracting timber from the State forests. Livestock also put a great pressure on forests for grazing. Agrisilviculture (growing of trees with crops) and Silvopastoral (growing of trees on pastures) are the main types of on-farm tree growing in the form of traditional agroforestry systems in the State (Verma et al. 1989). The most frequent method of growing trees (except fruit trees) on the farms in the study area is through deliberate retention and management of naturally regenerating tree seedlings. The majority of fruit trees are grown by planting of the seedlings obtained from government and private nurseries. Data collection Since the household is the decision-making unit regarding growing (retaining natural growing trees
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and/or planting) trees on their own farmland, primary data were collected through a cross-sectional survey of the heads of household in the study area. This was supplemented by Rapid Rural Appraisal techniques (group discussion with a group of key informants and field observations) in each selected village. As a quarter of the population was illiterate, it was decided to collect data on a pre-structured schedule with faceto-face interview with the head of household. The questionnaires were edited immediately after completion of each interview so as to reduce errors in completing the questionnaires. The questionnaire was prepared after a literature review and on-going discussions with project supervisors. The questionnaire was translated into Hindi. Pre-testing of the questionnaire was done through a pilot survey in a village in the study area through face to face interview with the heads of the selected households. For this pilot survey, one-third (25 households) of the total households were selected using simple random sampling with replacement method. A few modifications and reduction in the number of questions were done taking cognisance of time constraints of the interview. It contained 66 questions (see Sood 2003 for details). The data were collected between August 2001 and May 2002 on pre-structured questionnaire through face-to-face interview with the household heads. Sampling procedure Households were selected using multistage sampling procedure. There are five forest divisions (Karsog, Mandi, Joginder Nagar, Nachan and Suket) in study district for forest administration and management. Hills have a set of constraints and opportunities that differ from foothills. Joginder Nagar and Suket were purposely selected as only these forest divisions represent hills as well as foothills. All other divisions had mainly the foothills. The villages in each of selected forest divisions were categorised into: hill and foothill villages. The hill and foothill categories were further stratified into: JFM villages (villages where Joint Forest Management Programme is in operation) and non-JFM villages. A random sample of two villages from each stratum was selected in each selected forest division with simple random sampling with replacement method. The households were divided into small farmers (land holdings
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B1 ha) and large farmers (land holding [1 ha) in each of the selected villages. One-third of households were selected for each category using simple random sampling in each selected village. The response rate was 95% and total sample size was 401 households. Data analysis Farmers with at least one tree (grown by natural or artificial regeneration) on their respective farms were categorised as tree growers and those farmers with no trees on their farms were categorised as non-growers. Most explanatory variables in the current study were categorical. Thus Chi-square test of association was established as the statistical technique to test association between independent variables and dependent variable (tree growing—growers vs. non-growers) to know the significance of association of each biophysical and social factors, individually, with tree growing (Table 1). The independent variables for the current study were: biophysical (agroclimatic zone, slope, aspect, soil texture, soil depth, soil fertility, extent of irrigation, level of mechanisation, total farm size and agricultural holding, per capita agricultural holding, pasture land, wasteland, land adjacency, extent of fencing, and distance of house from nearest road and nursery) and social (age of household head, number of old persons in household, caste, type of house, family type and size, number of non-resident members, education level of head of household, family literacy, primary occupation of head of household, government employment, land tenure, mobility, importance of tree growing for future generation and frequency of worship of holy trees). The description of these variables and associated hypotheses is given in Appendix. Logistic regression models The study confirms that a number of biophysical and socio-economic factors, each individually, influenced the on-farm tree growing (Table 1). The response of these variables in motivating on-farm tree growing while considering all the variables simultaneously is unknown. Effective strategies to encourage on-farm tree growing require knowledge of the important factors and magnitude of their influence on on-farm tree growing. Logistic
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Table 1 Magnitude and nature of association between agroforestry adoption, and biophysical and social factors Variable
Chi-square value
P value
Spearman’s rho (rs)
Coefficient direction
Remarks
Biophysical factors Agroclimatic zone
83.640
\0.0001
0.457
-
HS
Slope
58.476
\0.0001
0.368
-
HS
Aspect
52.482
\0.0001
NA
Nominal
HS
Soil texture
73.876
\0.0001
0.385
-
HS
Soil depth
69.905
\0.0001
0.415
?
HS
Soil fertility
66.343
\0.0001
0.404
?
HS
Irrigation
47.655
\0.0001
0.322
?
HS
Method of ploughing
15.875
\0.0001
0.199
?
HS
102.648
\0.0001
0.446
?
HS
Agricultural holding
93.023
\0.0001
0.435
?
HS
Per capita agricultural holding
89.228
\0.0001
0.418
?
HS
Pasture land
12.206
\0.002
0.160
?
S
3.973
\0.046
0.084
?
S
Land adjacency Fencing
28.523 78.978
\0.0001 \0.0001
0.258 0.405
? ?
HS HS
Distance to nearest road
24.447
\0.0001
0.214
-
HS
Distance to nearest nursery
29.449
\0.0001
0.262
-
HS
Age
1.509
\0.470
0.031
-
NS
Number of old persons
0.356
\0.837
0.027
-
NS
Caste
4.747
\0.029
0.109
?
S
Farm size
Wasteland
Social factors
46.259
\0.0001
NA
Nominal
HS
Family type
2.363
\0.124
0.077
-
NS
Family size
5.544
\0.063
0.058
?
NS
Non-residents
4.517
\0.034
0.106
?
S
Education level of head
20.549
\0.0001
0.224
?
HS
Family literacy
33.500
\0.0001
0.296
?
HS
5.000
\0.025
0.112
?
S
Government employment Land tenure
20.630 0.853
\0.0001 \0.356
0.227 0.046
? -
HS NS
Mobility
74.752
\0.0001
0.387
?
HS
Importance of tree growing for future generations
63.866
\0.0001
0.372
?
HS
Frequency of worship of holy tree
60.356
\0.0001
0.367
?
HS
Type of house
Primary occupation
NA not Applicable, HS significant at \0.001, S significant at \0.05, NS not significant
regression provides a statistical approach for such an investigation when dependent variable is binary (Tabachnick and Fidel 1996). The use of this technique in adoption of traditional agroforestry is gaining importance and would be useful to planners and policy makers in devising the proper extension strategies to encourage farmers to grow trees in socially designed and acceptable agroforestry
systems. Three logistic regression models—biophysical, social and composite (biophysical and social factors taken together) were developed using SPSS 11.0 software. The procedure followed is as: The independent variables which showed highly significant association with tree growing were initially chosen as independent variables for logistic regression analysis. Initially, the logistic regression
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analysis was done using the enter method which uses all respective independent variables simultaneously for each model. Some of the coefficients had high value but with very large standard errors. Some independent variables (which themselves were functions of other independent variables) which had highly significant correlations with other independent variables, were dropped to avoid multicollinearity (Table 2). If the regression coefficients are large and their standard errors have very high value, then this lowers the Wald statistics but increases the type II error (Tabachnick and Fidel 1996). Hence the Forward Likelihood criterion, instead of Wald statistics, was followed to select best predicting variables as the main aim was to select the best group of predictors. The Forward Likelihood ratio method selects the variables on the basis of improvement in the fit (based on significance of improvement in -2 Log likelihood i.e., -2LL) with the inclusion of the predictor in the model. Hence at each step the variable that resulted in significant change in -2LL was selected until there was no significant change with the further addition of the variables in the model. Forward selection starts with no variables in the model. At each step the predictor which contributes most to prediction is added. For the entry of the predictors in the model the default value of 0.05 significance level was adopted. Four outliers common to each model were dropped for further analysis for each model to get more reliable prediction of dependent variable. Therefore, the regression analysis was re-run for each model after dropping these outliers. Finally, the model with best prediction was selected. Model specification Logit is defined as natural log of odds and the model was specified as: Logit ¼ Ln ðP=1 PÞ ¼ b0 þ b1 X1 þ b2 X2 þ b3 X3 þ þ bk Xk ¼Z ð1Þ where P is the probability of tree growing (Y [ 0 trees); 1 - P is the probability of not growing (Y = 0 tree); b0 is the intercept term; b1, b2, b3 and bk are the coefficients associated with independent variables; X1, X2, X3 and Xk are the predictors in the equation.
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Table 2 shows the coding scheme for the independent variables used in regression analysis. To obtain standardised coefficients for each logistic regression model, the input data for the respective model were standardised and the regression was re-run with standardised score for independent variables. This procedure was followed separately for each model. Each model was validated using model Chi-square and Hosmer, Lameshow goodness-of-fit and cases correctly classified. The Nagelkerke’s R2 was used as measure of determination of variation caused by predictors. Standardised regression coefficients depicted the importance of the predictors in the model.
Results The Chi-square and Spearman’s correlation coefficient tests determined the magnitude and the direction of relationships of various factors, individually, with incidence of tree growing in traditional agroforestry (Table 1). Table 2 shows variables tested in biophysical and social models, their coding and descriptive statistics. The biophysical, social and composite models have been described separately in this section. On the basis of Model Chi-square, each model was significant in predicting the likelihood of on-farm tree growing or Logit (Y [ 0 trees) (Table 3). Hosmer and Lameshow Chi-square was non-significant for all the models (Table 3) which indicates that the difference between the predicted and observed frequencies for each dependent variable category (growers and nongrowers) was non-significant. Therefore each model provided a good fit. Biophysical model The estimated relationship for the likelihood of tree growing is given in Table 4. Each of variables which entered the model was significant in predicting incidence of on-farm tree growing (Table 4). Model shows that only four biophysical factors influenced tree growing when all the biophysical variables were considered together. FSIZE was the most important variable in estimating the likelihood of tree growing followed by the FENCI, AZONE and SFERT, respectively. With one
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Table 2 Variables in the models, their coding and descriptive statistics Variable name
Abbreviation and variable coding scheme
Units
Mean
SD
Biophysical model Farm size
FSIZE
Acre
1.93
1.70
Proportion of farm fenced (Traditional fencing)
FENCI (no fencing = 0, partial fencing = 1 and whole fencing = 2)
Score
0.9
0.80
Aspect
ASPEC (1) = 1 if southern otherwise 0, APPEC (2) = 1 if eastern otherwise 0, ASPEC (3) = 1 if no aspect otherwise 0, Western-ASPEC (0) = is 0 (reference)
Score
Western-16.7%, Southern20.4%, Eastern-31.2%, No aspect-31.2%
–
Agroclimatic zone (Altitude zone)
AZONE (midhill subhumid = 0, highhill temperate Score wet = 1 and Highhill temperate dry = 2)
1.58
0.75
Perceived soil fertility
SFERT (less fertile = 1, moderately fertile = 2, Highly fertile = 3)
Score
2.21
0.87
Slope
SLOPE (flat = 0, moderately steep = 1 and very steep = 2)
Score
0.96
0.76
Extent of irrigation
IRRIG (wholly rainfed/No irrigation = 1, Score partially irrigated = 2 and wholly irrigated = 3)
1.78
0.81
Distance between house and forest
DISFO
Km
4.85
4.30
Proportion of farm land adjacent to house
LDAJH (low = 1, medium = 2 and high = 3)
Score
1.65
0.80
Distance of house from nursery
DISNU
Km
2.95
2.40
Method of ploughing (Level of mechanisation)
MPLOG (manual = 1, draught = 2 and tractor = 3)
Score
2.14
0.47
Mobility of head (Mobility index)
MOBLT (0–9)
Score
4.23
2.98
Importance of on-farm tree growing for future generations
IMPOR (not important = 0, important = 1 and very important = 2)
Score
0.92
0.83
Frequency of worship of holy tree
WRSHP (do not worship = 0, occasionally = 1 and usually = 2)
Score
0.78
0.70
Type of house
HTYPE (1) = 1 if mud house otherwise 0, HTYPE (2) = 1 if mixed house otherwise 0, HTYPE (3) = 1 if wooden house otherwise 0, reference is Concrete = HTYPE (0) = 0
Score
Wooden-31.9%, Mud-23.4%, – Mixed-(Mud ? concrete)18.0%, Concrete-26.7%
Family literacy (literacy index)
LINDX (0–6)
Score
1.40
1.07
Number of government employees
NGEMP
Number 0.36
0.63
Education level of head of household
EDLHE
Years
5.09
Social model
unit increase in FSIZE, FENCI and SFERT, the odds of on-farm tree growing increased by a factor 9.05, 3.77 and 2.38, respectively, however, with the change of AZONE (with ascending altitudinal zone) the odds
4.88
in favour of on-farm tree growing decreased by a factor 0.32. The overall accuracy of prediction was 90.2% with correct predictions of 94.9% for grower and 76% for
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Table 3 Model summaries for biophysical, social and composite models Statistic
Value
df
P
Initial -2LL
447.553
–
Model -2LL
190.424
–
Model Chi-square
257.129
4
\0.0001
2.519
8
\0.961
0.705
–
–
–
–
Biophysical model
Hosmer and Lameshow Chi-square Nagelkerke R2 N
396
– –
Social model Initial -2LL
444.793
–
–
Final -2LL
243.053
–
–
Model Chi-square
201.740
4
\0.0001
4.081
8
\0.850
0.578
Hosmer and Lameshow Chi-square Nagelkerke R2
–
–
395
–
–
Initial -2LL
446.970
–
–
Model -2LL
288.985
–
–
Model Chi-square
217.985
5
\0.0001
1.877
8
\0.985
0.766
–
–
–
–
N Composite model
Hosmer and Lameshow Chi-square Nagelkerke R2 N
395
non-grower categories (Table 5). Nagelkerke R2 explained only 70.5% variation in tree growing (Table 3). The unexplained variation in the dependent variable could be due to other category of factors. The heterogeneity in social factors is considered to cause differential participation in production activities. Therefore, social factors were investigated to know their impact on variation in the dependent variable. Social model WRSHP was the most important variable followed by IMPOR, MOBLT and INDX, respectively (Table 4). With one unit increase in the value of WRSHP, IMPOR, MOBLT and LINDX, the odds of on-farm tree growing increased by a factor of 7.50, 3.90, 1.25 and 1.34, respectively.
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The overall success rate of social variables in prediction of tree growing was 83.8% with 86.5% for growing and 75.8% for not growing (Table 5). Based on Nagelkerke R2, social factors explained only 57.8% of the variation in the dependent variable (Table 3). The variation explained by social model is lower than that of biophysical. Hence the biophysical model as a whole is a better predictor of on-farm tree growing than the social model. Composite model The general notion is that only biophysical factors influence tree-growing. The composite model resulted in inclusion of both the biophysical and social factors (Table 4). This implies that both biophysical and social variables are important in motivating farmers to grow trees on their farms. On the basis of standardised logistic regression coefficients FSIZE, AZONE, SFERT, MOBLT and IMPOR, respectively were important factors affecting likelihood of tree growing. Based on Nagelkerke R2 (Table 3) the composite model explained the highest proportion (76.6%) of the variation in the dependent variable followed by the biophysical (70.5%) and social model (57.8%). The overall prediction rate was 92% in composite model compared to social (83.8%) and biophysical model (90.2%) (Table 5). All this shows that composite model is better predictor of on-farm tree growing than biophysical and social model.
Discussion and conclusions Many of the independent variables which individually showed a highly significant association with tree growing did not appear in the logistic regression models. This is probably due to the fact that much of the variation has been expressed by the variables in the model and partial contribution of the remaining variables is not strong enough to make further improvement in the models. The probability of on-farm tree growing increased with farm size due to availability of space and increase in the capability of the farmers to bear the risk of growing trees on their farms. The increase in likelihood of tree growing with an increase in the extent of traditional fencing might be attributed to a
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Table 4 Variables and their significance in biophysical, social and composite models Step
Variable in
Coefficients (b)
Improvement in -2LL (Chi-square)
df
Significance of change (P)
Standardised coefficients
Physical model FENCI
1.32
131.30
1
\0.0001
1.07
FSIZE
2.30
66.28
1
\0.0001
3.96
SFERT
0.87
47.64
1
\0.0001
0.75
AZONE
-1.14
11.91
1
\0.0001
-0.86
Constant
-1.68
1
\0.098
Social model IMPOR
1.36
140.63
1
\0.0001
1.15
WRSHP
2.11
40.92
1
\0.0001
1.58
MOBLT
0.22
12.46
1
\0.0001
0.67
LINDX Constant
0.29 -1.22
7.72
1 1
\0.005 \0.0001
0.31
Composite model IMPOR
0.90
130.08
1
\0.0001
0.75
FSIZE
3.45
73.21
1
\0.0001
6.20
SFERT
0.93
59.79
1
\0.0001
-1.46
16.96
1
\0.005
MOBLT
0.26
8.94
1
\0.0001
Constant
-2.77
1
\0.013
AZONE
0.80 -1.10 0.78
Table 5 Classification accuracy of different models Models
Biophysical Social
Type household
Predicted membership Grower
Non-grower
Grower
296
281 (94.9%)
15 (5.1%)
Non-grower
100
24 (24.0%)
76 (76.0%)
Grower
296
256 (86.5%)
40 (13.5%)
99
24 (24.2%)
75 (75.8%)
Grower
295
283 (96.0%)
12 (4.0%)
Non-grower
100
21 (21.0%)
79 (79.0%)
Non-grower Composite
Number of observations
restriction in the entry of the livestock in the farm and consequently protection of the seedlings or saplings from grazing. Moreover traditional fencing in the form of live bushes protects seedlings from frost and direct sun in the initial stages of their growth and development and consequently results in their better survival. There was a decrease in incidence of tree-growing with change in agroclimatic zone from midhill subhumid to highhill termperate wet and subsequently to highhill temperate dry (increase in altitudinal zone)
Overall prediction
90.2 83.8 92.0
because lower temperatures, lower sunlight intensity and a shorter growing season at higher altitude are limiting factors in integrating trees with crops in agroforestry systems. Food production is the primary livelihood objective of subsistence farming. So competition from perennials to annuals for solar radiations could have overridden other benefits of inclusion of trees as agroforestry at higher zones. Higher soil fertility would have a favourable effect on germination, growth, and survival of seedlings, have ability to provide sufficient nutrients to support
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trees and crops grown together and requires fewer efforts to establish seedlings. Thus chances of growing trees increased with soil fertility. Further, farmers with highly fertile soils would have higher agricultural production and consequently more food sufficient than those with lower fertility. Hence farmers with fertile soils have higher capability to bear the risk of growing trees on their farms and sustain their livelihoods. Traditional practise of holy tree worship influenced tree growing and it had a positive influence on agroforestry. There was also a positive and significant correlation between holy tree worship and farm size (rs = 0.415, P \ 0.0001). Therefore, it was the households with larger farms who devoted time to holy tree worship as they usually have higher agricultural production (other things remaining constant) and hence can also take the risk of growing trees on their land. Growing trees is an activity which is considered to increase the value of the onfarm assets to be inherited by posterity. Hence households which considered importance of trees for their coming generations were more likely to grow trees. The mobility of the respondent favoured tree growing because it exposed farmers to new farming practises, contacts with the outside world and awareness of procedures for felling, transport and sale, and the demand-supply relations of tree-products. The influence of education (literacy) on agroforestry innovativeness seems to be through social stigma attached to agricultural work compared to tree growing (latter is less labour intensive than the former) and higher incomes in the educated class due to more off-farm employment opportunities now available to educated people. The composite model contained both physical and social factors. The standardised coefficients of mobility, importance of tree growing for future generation were not considerably different from that of soil fertility in the composite model. Even the standardised coefficient of agroclimatic zone was only slightly higher than that of soil fertility, importance of tree growing for future generations. This implies that latter social factors were almost as important as biophysical factors: soil fertility and agroclimatic zone. Therefore, foresters should not only view onfarm tree growing in the context of physical but also in context of social environment of households.
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Literacy index of family had a lower bearing on tree growing in the social model (Table 4). Therefore, it was not retained in the composite model. The wider implications of use of logistic regression in agroforestry studies is that it would help in designing extension approaches based on key variables and magnitude of their influence on tree growing rather than concentrating simultaneously on many factors that individually affects tree growing. The present study implies that the extension agencies should concentrate only on five variables rather than on all the variables which individually influenced tree growing. This would help in designing effective agroforestry programmes and reducing costs of extension activities required to stimulate onfarm tree growing. The present study indicates that the extension agencies would need more efforts in higher altitude zone to encourage farmers to grow trees. The extension personnel and policy makers should devise strategies to increase soil fertility which would consequently motivate farmers to grow trees. The presence of trees would subsequently be expected to sustain the soil fertility. The extension agencies should also enhance the mobility of the farmers to places outside their villages through extension/demonstration visits to encourage farmers to become aware and grow trees. The study implies a need to consider traditional social practises or beliefs, like importance of trees for future generations, in designing socially acceptable agroforestry systems. Dynamics of changes over time as the result of implementation of agroforestry extension strategies should investigated. This would help adjusting the extension strategies overtime to motivate farmers to grow trees. Although the study implies a need to improve the soil fertility, yet, the effect of different organic and inorganic fertilisers on level of soil fertility and consequent tree growing still needs further investigation. Emphasis should be given to investigate how the proportion of different tree species varies with biophysical and socio-economic factors so that suitable tree species for different social and economic categories of households can be identified. This will help extension agencies to choose specific tree species for farmers with specific biophysical and socio-economic status. Further, there would be a need to investigate why farmers prefer natural regeneration as method of growing non-fruit trees than fruit trees.
Agroforest Syst (2009) 75:175–187 Acknowledgments The first author thanks Association of Commonwealth Universities for providing him Commonwealth Fellowship to carry out present study. The authors also thank University of Aberdeen for providing partial funding to carry field work in India. We also acknowledge the guidance of Prof K.J. Thomson and Dr. Tony Glendenning, University of Aberdeen in analysis of the data. The farmers of the study area deserve special mention for their keen participation in the study. The anonymous reviewer deserves special thanks for his keen and constructive criticism to improve the paper.
Appendix The description of variables and associated hypotheses is given below: Agroclimatic zone: Altitude influences not only the temperature but also the relief characteristics and consequently affects vegetation and farming systems. Hence altitude could be hypothesised to have effect on-farm tree growing. Agroclimatic zones in the study area based on altitude are: (1) 601–1,800 mmid-hill subhumid, (2) 1,801–2,200 m-high-hill temperate wet and (3) C2,201 m-high-hill temperate dry. Slope: Fields become narrower with increase in slope to the extent that the presence of trees may become a hindrance to ploughing. Steepness of the slope also affects the soil properties as well as retention of soil moisture. This consequently influences the growth, performance and type of vegetation which in turn is expected to affect cultivation of trees on the farms. The slope categories are: (1) flat (level fields), (2) moderately steep (up to 30°) and (3) very steep ([30°). Aspect: Aspect is the direction in which a slope faces. It is a topographic factor which influences the type of vegetation of a locality through its influence on micro-climatic conditions. Aspect was, therefore, hypothesised to have an influence on on-farm tree growing. Soil properties: Soil properties—soil texture, depth and fertility affect growth and development of plants. Therefore, decision to grow trees is expected to be influenced by these properties. Soil textures are: (1) retili domat (sandy loam), (2) chikney domat (clayey loam) and (3) pathrili (gravely). On the basis of depth soils are classified as: (1) shallow (up to 30 cm), (2) deep 31–45 cm and (3) very deep (46 cm and above). On the basis of perceived fertility the classification is: (1) less fertile, (2) moderately fertile and (3) highly fertile.
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Irrigation: Irrigation may enable farmers to establish and maintain trees on their land. Therefore, irrigation can be argued to have a positive effect on tree growing. The irrigation levels of farms are: (1) wholly irrigated, (2) partially irrigated and (3) wholly un-irrigated. Method (Mechanisation) of ploughing: Mechanisation of ploughing is assumed to discourage tree growing because it interferes with mechanisation. Manual, draught and tractor ploughing, respectively represent an ascending order of level of mechanisation. Farm size and agricultural holding: Tree growing is a land use activity and therefore farm size and agricultural holdings were hypothesised to have positive influence on growing trees on farms. Land Adjacency: Means the proportion of farmland immediately adjacent to the house. Increased visibility may help in the protection of agricultural crops and trees from livestock as well as from theft. Thus land adjacency could be hypothesised to motivate farmers to grow trees. The land adjacency is classified as: (1) low (0–25%), (2) medium (26– 50%) and (3) high (C51.0%). Traditional fencing: People in study area traditionally employ locally available material and resources like brushwood, live bushes most of which are thorny and stone pitching along the boundaries of their individual fields or farms to demarcate the holding and to protect their farmland. Proportion of fencing is categorised as: (1) no fencing: farms having no fencing at all, (2) partial fencing: where farm is fenced to any extent other than whole fencing. (3) Whole: farms where whole of the farm is fenced. Traditional fencing was hypothesised to encourage on-farm tree growing owing to protection of seedlings from grazing by livestock. Age of household head and number of old members in household: It is assumed that farmers may grow (plant or retain trees) as they grow older, to provide income in old age with low labour requirements. For similar reason the number of old persons was hypothesised to have positive influence on likelihood of on-farm tree growing. Caste: Caste means four scripturally sanctioned status groups of Hinduism namely Brahmins (priests), Kshatriyas (rulers or warriors), Vaishyas (traders or herdsmen) and Shudras (servants). This social stratification has lead to differentiation of farming activities in India. Therefore, caste was hypothesised
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to influence on-farm tree growing. The households were broadly divided into upper and lower castes for the present study. Type of house: There are four types of houses in the study area: mud, mixed (mud ? concrete), concrete, wooden and is a proxy for the socio-economic status of the household. The usual order of socialeconomic status associated with house type in the study area is concrete [ mixed [ mud & wood and was expected to influence tree growing. Family type: A family is considered joint where two or more married brothers with their parents and relatives live together. Families with an adult person, spouse and unmarried children or brothers/sisters and old parents was considered as nuclear families. It is presumed that in joint families, it may not be easy for the head of household to take a decision to adopt a technology or to grow trees due to the greater influence of other sub-units (sub-families) of the joint family than in nuclear families. Family size: A bigger family may mean greater availability of labour for growing woody perennials and more requirements of woody perennials for fuelwood, fodder, timber and fruits for household utilisation and to generate extra income to sustain their livelihoods. Hence, the size of the family can be hypothesised to have a positive association with tree growing. Non-residents: Non-residents are family members who live elsewhere. Non-residents would decrease the available of labour for working on the farm. Therefore, households with non-resident relatives may follow labour saving methods of farming including tree growing. So households with nonresident members can be hypothesised to have a positive impact on tree growing. Education level of head and family literacy: Educated farmers are considered to be innovative or opinion leaders and willing to take more risk than illiterates. Therefore, education level of family head as well as family literacy could be hypothesised to encourage on-farm growing of trees. Primary occupation of head of household: The households with a non-agricultural occupation would have less time or less labour available for agricultural activities. The hypothesis is based on the premise that those heads of household with agriculture as their main occupation would concentrate more on agricultural crops than tree growing and those heads of
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household with a non-agricultural occupation would devote less of his land, labour and capital to agricultural crops, and therefore, would opt tree growing in agroforestry systems. For similar reasons government employment was expected to have favourable influence on tree growing. Land tenure: Land tenure was categorised into sharecropping and self-cropping. Since there are chances of transfer of sharecropped land to sharecroppers due to land reforms in the study area, so it was hypothesised that the landlords might grow more trees to maintain the ownership of his land. Mobility: Mobility has been expressed as mobility index based on frequency of farmers visit to tehsil headquarters, district headquarters and other districts in the State. Mobility of farmer provides exposure to outside world which would create awareness of innovations and favourable attitude towards any innovation including on-farm tree growing. The farmers with mobility scores of 0, 1–3, 4–6 and 7–9 were categorised as: (1) immobile, (2) slightly mobile, (3) moderately mobile and (4) highly mobile, respectively. Importance of tree growing for future generation: Traditionally the head of household tends to retain or attain the best possible assets to be inherited by the children. On-farm tree growing is such an activity as it increases the value of the on-farm assets to be inherited. Farmers perceiving more importance of tree growing for future generations are expected to grow trees on their farms. Frequency of worship of holy trees: The tree worship may motivate farmers to grow trees on their farms. Households were categorised into three categories on the basis of worship of trees: (1) no: those households where nobody worshipped holy trees, (2) occasionally: those households where at least one member performed holy tree worship occasionally such as on certain social ceremonies and (3) usually: those households where at least one member frequently performed tree worship.
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