=Paper=
{{Paper
|id=Vol-1498/HAICTA_2015_paper52
|storemode=property
|title=Diversification Factors of Cultivators/Investors of Robinia pseudoacacia (Black locust)
|pdfUrl=https://ceur-ws.org/Vol-1498/HAICTA_2015_paper52.pdf
|volume=Vol-1498
|dblpUrl=https://dblp.org/rec/conf/haicta/TsiantikoudisG15
}}
==Diversification Factors of Cultivators/Investors of Robinia pseudoacacia (Black locust)==
https://ceur-ws.org/Vol-1498/HAICTA_2015_paper52.pdf
Diversification Factors of Cultivators/Investors of
Robinia pseudoacacia (Black locust)
Stavros Ch. Tsiantikoudis1, Spyros Goumas2
1
Department of Forestry and Management of the Environment and Natural Resources,
Democritus University of Thrace, Orestiada, Greece, e-mail: stsianti@fmenr.duth.gr
2
Department of Accounting and Finance, Piraeus University of Applied Sciences, Greece,
e-mail: sgoum@teipir.gr
Abstract. In an effort to minimize environmental degradation caused by the
intensive use of agrochemicals, European Union adopted regulations relevant
to afforestation and set aside of agricultural land. The multifunctional role of
agriculture enhanced through these regulations and provided adequate motives
for a change in the conventional land uses. Cultivators/investors that are
interested in afforested agricultural land find them quite attractive and revealed
an interest in implement them. In order to investigate the factors that influence
the probability of adopting the above mentioned regulations we organize a
research to the related cultivators/investors. For the collection of data we used
questionnaire and the lists of approved for aid from the Regulations 2080/92
and 1257/99. Finally we collected 205 valid questionnaires from a) farmers by
main occupation and b) other owners of agricultural land who are not farmers
by main occupation. In this study we used a linear regression and a logistic
regression model.
Keywords: Regulations, cultivators/investors, Black locust, Evros regional
unit
1 Introduction
In the last decades we notice a change in the consideration of rural development due
to the successive revisions of the Common Agricultural Policy (CAP). Intensive
agriculture is no more the leading developmental sector of rural areas. The
multifunctional role of agriculture, that enhances the natural environment, gradually
is been taking place in these areas: environmental, cultural, nutritional, social and
developmental (Arabatzis et al., 2006a; Arabatzis et al., 2006b; Arabatzis 2008;
Chalikias et.al 2010 (4)). Developmental initiatives of the European Union aim to the
enhancement of the multifunctional role of rural areas, as the LEADER programme
(Andreopoulou et al., 2008; Arabatzis et al., 2010; Arabatzis et al., 2011). In the CAP
framework, EU instituted regulations EC2080/92 and EC1257/99 providing
incentives for the reforestation of agricultural land boosting forestry (Arabatzis,
2010). The above mentioned regulations introduced the necessity of producing
energy through forest plantations and the role of related farms (Chalikias et.al 2010,
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Chalikias 2010, Chalikias and Christopoulou 2011, Kyriakopoulos et.al 2010a,
2010b, 2010c, Kolovos et.al 2011). Also, afforestation of agricultural land play
significant role in the enhancement of local quality of life (Chalikias 2013, Chalikias
and Kolovos 2013).
2 Research Methodology
The survey was conducted in Evros regional unit. Evros area is characterized by
rich natural resources that contribute mainly in development of primary and tourism
sector (Arabatzis and Grigoroudis, 2010; Grigoroudis et al., 2012, Chalikias 2012).
Also, there are significant afforested, with Black locust, areas of agricultural land in
Evros comparatively with other areas.
The questionnaire included questions mostly closed type with predetermined
answers. The questionnaire investigated the individual and social characteristics of
cultivators/investors farmers, the structural characteristics of their land and their
attitudes to forest plantations. The questionnaires were collected in the period April-
May 2012 by personal interviews at cultivators’/investors’ residence or place of
work. Finally we collected 205 valid questionnaires that represented a) farmers and
b) other owners of agricultural land who were not farmers by main occupation.
The purpose of this study is to explore the factors that differentiate
cultivators/investors, and the factors that influence their income. The data processing
was done with STATA statistical package and methods of descriptive statistics. A
linear regression model and a generalized linear model (a logistic model) have been
applied.
The variables used in the models are given below:
Education: What education level has the cultivator/investor (1=none, 2 =primary
education, 3 = obligatory education, 4 = secondary education, 5 = tertiary education).
Age: Age of cultivator/investor (in years).
Gender (1 = male, 2 = female).
Family size: Number of members in the family (1-10).
Income: Annual income in euros
Cultivator/Investor: (1 = main occupation is farming, 2 = secondary occupation is
farming).
Associations: (1= Yes, 2 = No)
Seminars: (1 = Yes, 0 = No).
Educational tv, radio broadcasts: (0 = never, 1 = rarely, 2 = occasionally, 3 =
often, 4 = very often).
Experience: Number of years of farming as main occupation.
Heredity: (1 = father was a farmer, 0 = father was not a farmer).
Area: in ha.
Agricultural holdings: No of agricultural holdings
Type: Type of species plantations (1 = Poplar, 2 = Pine, 3 = Black locust, 4 =
Walnut) (1=Poplar, 2= Black locust, 3= Mulberry, 4= Walnut).
Reforestation: Worked in reforestation (1 = Yes, 2 = No).
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Value: (0 = no increase in the value of agricultural land in the region over the past
5 years, 1 = increased, 2 = remained stable).
Profit and loss account: (0 = no records, 1 = yes).
Laws: Aware of others regulations for the agriculture (1 = Yes, 2 = No).
Forest: There is a municipal/community forest near the plantation (1 = Yes, 2 =
No).
Market: Purchased of land for productive purposes before installing forest
plantations (1 = Yes, 2 = No).
Lease: Land hiring before installing forest plantations (1 = Yes, 2 = No).
3 Results
We used a linear and a generalized linear (logistic) model. Applying the logistic
model we found and compared the characteristics and factors that differentiate those
who have agriculture as their main occupation to those who have it as secondary. The
choice of the most appropriate model was stepwise regression with pe (0.1) and pr
(0.2), the dependent variable (the farmer) is categorical. Table 1 shows the
coefficients of the model.
Table 1. Coefficients of the logistic model
Variable Β S.e (β) P value
Education -0.45 0.08 0.000
Articles -0.73 0.33 0.026
Associations -0.99 0.76 0.195
The model was tested for its validity with Hosmer Lemeshow test (Hosmer and
Lemeshow, 1978) that explores the good adaptability. The null hypothesis was found
not rejected and the model has good adaptability (P-value = 0.121). Also, to test good
adaptability was used the figure 1 below by showing the deviance of the model
(corresponding measure to residuals in linear regression). The model has good
adaptability, since most estimated values (predicted values) have deviance below
0.05 which appears on the shaft of Cook's Distance (Cook and Weisberg 1982).
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Fig. 1. Cook's Distance
Thus, we conclude that the factors that differentiate the cultivators/investors
whose main occupation is agriculture to other farmers are the educational level, the
fact that they have read articles with rural content and their participation in
associations.
We examined the factors that shape the agricultural income for farmers by main
occupation. So by linear regression with the dependent variable, the income of the
cultivator/investor and independent almost all variables in the table mentioned
previously, with Collet's approach we selected the following model (Table 2):
Table 2. Linear model’s coefficients
Variables B S.e (β) P value
Lease 14,178.408 4,712 0.003
Gender -10,270.92 3,012 0.001
Family size 3,916.11 116 0.000
Associations -253.42 153 0.101
For the validity of the model we examined the regularity of residuals with
appropriate statistical tests (Sh.Wilk, S.Francia) and diagrams (histogram, Q-Q plot).
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4 Discussion - Conclusions
From the linear model we find that the factors influencing farm income are
gender, leasing and the number of family members, etc. From the logistic model by
examining how the statistically significant independent variables affect the
dependent (income) we found that the vast majority of farmers by main occupation
have not finished secondary education, while the figures for the remaining are
significantly smaller. From the interpretation of the corresponding coefficient of the
model we conclude that for each increase in educational level of a unit (as defined
earlier) the probability that the farmer has agriculture as main occupation rather than
secondary reduces by 1- exp (-0.45) = 20.1%.
Moreover we noticed that by main occupation farmers read fewer articles than
others. Interesting is that, there is no statistically significant difference in income and
loans to cultivators/investors, and there is a difference in income from agriculture.
Moreover statistically significant difference has the cultivator/investor located
near community forest in relation to the others (the corresponding averages are 11.19
thousands euro versus 17.50).
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