IJERD – International Journal of Environmental and Rural Development (2011) 2–1

Research article
erd

Awareness of Organic Vegetable Production Practices inWest Java and Bali, Indonesia
CHIFUMI TAKAGIInternational Rice Research Institute, Makassar, Sulawesi Selatan, 90213, Indonesia
Email: c.takagi@irri.org, takagich@msu.edu

MURARI SUVEDI
Michigan State University, Michigan, USA
Received 16 December 2010

Accepted 21 February 2011

Abstract One of the challenges in developing organic agricultural production in Indonesia
is  increasing  farmers’  awareness  of  organic  farming methods to grow high quality organic
products. Recognizing the need for research and development of organic agriculture, theDevelopment of New Bio-Agents for Alternative Farming (DNBAF) project establishedpilot farms in West Java and Bali in 2005 in order to field test the use of bio-pesticides andcompost. Since lack of extension is a constraint in developing organic agriculture inIndonesia,   it   is   important   to   understand   what   factors   determine   farmers’   knowledge   of  organic farming methods. The goal of this study was to determine the factors associatedwith awareness of organic vegetable production practices including bio-pesticides andcompost in West Java and Bali, Indonesia. The study tested the hypothesis that socioeconomic variables affect the farmers’   familiarity   with   bio-pesticides and compost. The
socio-economic variables examined were: respondent's location, gender, age, educationlevel, household size, number of family laborers, farming experience, farm size, distanceto the pilot farm, irrigation sources, land tenure status, net revenue of cabbage, tomato,
carrot production, exposure to the pilot farm and any information source groups (media,
extension, farmer and commercial groups). Data were collected using a face-to-face survey.
In the survey, a total of 627 households of vegetable farmers surrounding the pilot farms inWest Java and Bali constituted the population, and 210 farmers selected by a systematic
random sampling method were interviewed. The study hypothesis was tested by a binary
logit analysis. The binary logit analysis showed that the factors associated with awarenessof the practices included: location, gender, educational level, distance to the pilot farms,
exposure to the pilot farms, and information sources.
Keywords organic agriculture, bio-pesticide, compost, socio-economic, binary logit analysis

INTRODUCTION
Vegetable production is an important agricultural sub-sector in Indonesia. In recent decades, due toa rapid increase and diversification in demand for fresh vegetables, production of temperate
vegetables has drastically increased in the upland areas of the major islands including Java andSumatera. For farmers, growing temperate vegetables has great potential for increasing theirincome (Fujimoto and Miyaura, 1997).
Historically, the golden era of agricultural extension in Indonesia was the Green Revolution
program, and at this time agricultural extension played a pivotal role in increasing production ofrice (Mundy, 1992). Although the agricultural extension system played an important role in thedevelopment of agriculture, lack of sufficient management and the huge institutional inertia of alarge extension bureaucracy considerably weakened the system. Because of weakening financial
and technical support, extension services virtually collapsed (Sulaiman and Hall, 2004). In the
1990s, the Ministry of Agriculture moved toward decentralization to ensure effectiveness, increaseaccountability to farmers, and reduce costs to the central government. Many districts dissolved their© ISERD

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old extension systems and set up new extension structures based on the needs of regional farming
conditions (Zakaria, 2003). One result of this decentralization was the elimination of governmental
subsidies for chemical fertilizers and pesticides, which increased their cost to farmers, andconsequently decreased their use. This increased cost of chemical fertilizers and pesticides boosted
interest in integrated pest management and non-chemical alternative technologies, such as organic
agriculture* (Johnson, et al., 2008).
In 1997 the soaring cost of agricultural inputs precipitated by the monetary crisis induced
many farmers to switch to organic methods (Prawoto and Suyono, 2005). Following this organic
agriculture movement, the government issued the National Standard for Organic Food (SNI 016729-2002) adopted from the Codex CAC-GL 32/1999 in 2002. However, research anddevelopment activities related to organic agriculture in the country have been largely lacking(Prawoto and Suyono, 2005).
Recognizing the need for research and development of organic vegetable production, theDevelopment of New Bio-Agents for Alternative Farming (DNBAF) project established pilot farms
in West Java and Bali in 2005 in order to field test the application of biological insecticides (biopesticides) and the use of organic fertilizers (compost).
One   of   the   challenges   in   developing   organic   farming   in   Indonesia   is   increasing   farmers’  knowledge of organic farming methods to grow high quality organic products (Surono, 2007). As aresult of an agricultural extension process, farmers may adopt a new technology. The farmers willobtain enough knowledge of the technology through communication and education processes.
Through these processes, farmers will form an attitude toward the technology, and can decide
whether they will adopt the technology or not. If the farmers decide to adopt the technology, it will
be implemented (Rogers, 1995). Since lack of extension is a constraint in organic agriculture inIndonesia, at the first step, it is   very   important   to   understand   what   factors   determine   farmers’  knowledge of organic farming methods. However, until 2007, no systematic technology adoption
study of the organic conversion process had been conducted in Indonesia.
Since the DNBAF project is an example of organic conversion, this study investigatedfarmers’   knowledge  of   organic   farming   methods,  including   use   of   bio-pesticides and compost on
three vegetable crops – cabbage, tomato, and carrot. The three crops were the major vegetables
grown in the project sites. The study was conducted in two communities surrounding the pilot
farms: Sukagalih village, sub-district of Megamedung, district of Bogor in West Java and Bangli
village, sub-district of Baturiti, district of Tabanang in Bali.
METHODOLOGY
Data were collected using a face-to-face survey from July to October 2007. In the survey, a total of
627 households of vegetable farmers surrounding the pilot farms in West Java and Bali constituted
the population, and 210 farmers selected by a systematic random sampling method wereinterviewed. This study tested the hypothesis that socio-economic   variables   affect   the   farmers’  familiarity with bio-pesticides and compost. The socio-economic variables examined were:
respondent's location, gender, age, education level, household size, number of family laborers,
farming experience, farm size, distance to the pilot farm, irrigation sources, land tenure status, net
revenue of cabbage, tomato, carrot production, exposure to the pilot farm and any information
source groups (media, extension, farmer and commercial groups). These variables were identified
based   on   Padel’s   (2001)   organic   conversion   model   and   a   literature   review   of   organic   vegetable  production in Indonesia (Sudana, et al., 2003; Syaukat, 2003; Dadang, et al., 2005; Sumiartha, et al.,2005; Dadang, et al., 2006; Syaukat, 2006) . The study hypothesis was tested by a binary logit*

Organic agriculture is defined by the Secretariat of the Joint FAO/WHO Food Standards Program (Codex CAC-GL
32/1999) as “holistic   production   management   systems which promote and enhance agro-ecosystem health, including
biodiversity, biological cycles, and soil biological activity. It emphasizes the use of management practices in preference
to the use of off-farm inputs, taking into account that regional conditions require locally adapted systems. This isaccomplished by using, where possible, cultural, biological and mechanical methods, as opposed to using synthetic
materials, to fulfill any specific function  within  the  system”  (p.3).

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analysis, which is commonly used to analyze the factors influencing decision making in the field ofagriculture, such as adoption of new technologies (Cavane, 2007; Banerjee, et al., 2008; Zhou, etal., 2008). The statistical software SPSS was used for the analysis. Definitions of variables used in
the logit model are listed in Table 1. Two dichotomous dependent variables were:(1) familiarity
with bio-pesticides is a dependent variable if respondent is familiar with bio-pesticides = 1 and if
not familiar = 0 (F_BIO); (2) familiarity with compost is a dependent variable if respondent isfamiliar with compost = 1 and if not familiar = 0 (F_COMP).
Table 1 Variables definition in the binary logit model
Variables
Dependent Variables
F_BIO
F_COMP
Explanatory Variables
LOCA
GEN
AGE
EDU2
EDU3
EDU4
EDU5
HS
FL
EXPERIENCE
FARM
DIS
DIRR
DLAND
CNETREVTNETREVWNETREVEXPO
IFG_MEDIA
IFG_EXTENIFG_FARMER
IFG_COMMER

Definition
Familiarity with bio-pesticides (familiar = 1, not familiar = 0)
Familiarity with compost (familiar = 1, not familiar = 0)
Location of respondent (West Java = 1, Bali = 0)
Gender of respondent (male = 1, female = 0)
Age of respondent (year)
Education level of respondent (some primary school = 1, other = 0)
Education level of respondent (completed primary school = 1, other = 0)
Education level of respondent (completed junior high school = 1, other = 0)
Education level of respondent (completed high school = 1, other = 0)
Household size of respondent
Number of farming labor force in a household
Farming experience of respondent (year)
Area of the farm (unit: are; 1 ha = 100 a)
Distance  from  respondent’s  house  to  the  pilot  farm  (km)Irrigation dummy (have irrigation = 1, no irrigation = 0)
Land tenure dummy (secure land tenure = 1, unscure land tenure = 0)
Net revenue of cabbage production (thousand rupia / 0.5 ha)
Net revenue of tomato production (thousand rupia / 0.5 ha)
Net revenue of carrot production (thousand rupia / 0.5 ha)
Total  number  of  “exposure  question”  checks
Total  number  of  “information  source  question”  checks:  TV,  radio,  magazine/journal, internet (media information group)
Total  number  of  “information  source  question”  checks:  extension  agent,  NGO, university (extension information group)
Total  number  of  “information  source  question”  checks:  farmers’  group,  organic farmers, other farmers, family member, self-study (farmer
information group)
Total  number  of  “information  source”  checks:  market  people,  commercial  company/agricultural retail store (commercial information group)

Note: EDU variables compare familiarity with bio-pesticides relative to vegetable farmers with educational levelof no schooling (EDU1).

Explanatory variables include location of respondent (LOCA), gender of respondent (GEN),age of respondent (AGE), five dummies for education level (EDU), household size of respondent(HS), number of farming family labor (FL), farming experience of respondent (EXPERIENCE),
area   of   the   farm   (FARM),   distance   from   respondent’s   house   to   the   pilot   farm   (DIS),   irrigation  dummy variable if respondent has an irrigation = 1 and if no irrigation = 0 (DIRR ), land tenure
status dummy if respondent has secure land tenure = 1 and if unsecure land tenure = 0 (DLAND ),net revenue of cabbage production in Indonesian rupiah per 0.5 hectare (CNETREV), net revenueof tomato production in Indonesian rupiah per 0.5 hectare (TNETREV), net revenue of carrot
production in Indonesian rupiah per  0.5  hectare  (WNETREV),  total  number  of  “information  source  question”   checks   for   media   information   source   group   including   TV,   radio,   magazine/journal   and  internet   (IFG_MEDIA),   total   number   of   “information   source   question” checks for extension
information group including extension agent, NGO, university (IFG_EXTEN), total number of© ISERD

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IJERD – International Journal of Environmental and Rural Development (2011) 2–1“information   source   question”   checks   for   farmer   information   source   group   including   farmers’  group, organic farmers, other farmers, family member and self-study (IFG_FARMER) and total
number  of  “information  source”  checks  for  commercial  information  source  group  including  market  people, commercial company/agricultural retail store (IFG_COMMER). In addition, variable of
exposure to the pilot farm was identified  as  total  number  of  “exposure  question”  checks  (EXPO).  
RESULTSThe coefficients, their standard errors, significance levels and odds ratio for bio-pesticides are listed
in Table 2. The likelihood ratio test suggests the estimated model had a good fit with a statisticallysignificant score of 192.15 at the 1 percent level. The McFadden R2, a pseudo R-square, was 0.294,
which  falls  in  the  range  0.2  to  0.4  that  is  considered  an  “extremely  good  fit”  (Hensher  and  Johnson,  1981). Prediction success statistics indicated that the model correctly predicted about 76.6 percentof the responses.
The results of Table 2 identified no significant influences of any variables except exposure to
the pilot farm (EXPO), educational level equaling completed primary school (EDU3), educational
level  equaling  completed  junior  high  school  (EDU4)  and  distance  from  respondent’s  house  to  the  pilot farm (DIS) for the probability of familiarity with bio-pesticides.
Tables 2 Estimated coefficients of the binary logit model for familiarity of bio-pesticide
Variables
LOCA
GEN
AGE
EDU2
EDU3**
EDU4**EDU5
HS
FL
EXPERIENCE
FARM
DIS*
DIRR
DLAND
CNETREVTNETREVWNETREVEXPO***
IFG_MEDIA
IFG_EXTENIFG_FARMER
IFG_COMMER
Constant

B
1.204
20.194
0.005
1.239
1.729
2.392
1.495
0.167
-0.1110.009
0.001-0.433-0.660-0.210
0.000
0.000
0.000
0.551
-0.029
0.003
0.191
0.244
-23.155

S.E.
0.733
11838.243
0.021
0.759
0.872
1.087
1.209
0.146
0.310
0.023
0.003
0.253
0.574
0.488
0.000
0.000
0.000
0.183
0.267
0.403
0.333
0.386
11838.243

Wald
2.697
0.000
0.054
2.666
3.930
4.846
1.531
1.301
0.128
0.163
0.244
2.938
1.322
0.186
0.332
0.127
0.918
9.026
0.011
0.000
0.328
0.400
0.000

Sig.
0.101
0.999
0.816
0.103
0.047
0.028
0.216
0.254
0.720
0.686
0.621
0.087
0.250
0.667
0.564
0.722
0.338
0.003
0.915
0.993
0.567
0.527
0.998

Exp(B)
3.332
589042554.824
1.005
3.453
5.637
10.938
4.461
1.182
0.895
1.009
1.001
0.648
0.517
0.810
1.000
1.000
1.000
1.734
0.972
1.003
1.210
1.277
0.000

Note:  Likelihood  ratio  test:  χ2 =  192.152  (d.f.  =  22);;  critical  χ2 = 33.92; p >  χ2: < 0.0001. McFadden R2 = 0.294.
The percent correct classification is 76.6 %. Number of observations = 210. ***, ** and * indicate statisticalsignificant P<0.01, P<0.05, and P<0.10, respectively. EDU variables compare familiarity with bio-pesticides
relative to vegetable farmers with an educational level of no school (EDU1). A positive sign on any of theeducational dummies would mean that farmers in that particular educational category had a higher probability of
being familiar with bio-pesticides than farmers with an educational level of no school.

Exposure to the pilot farm (EXPO) had a significant positive effect at the 1 percent level in themodel, indicating that vegetable farmers who knew about the pilot farm had higher probabilities ofbeing familiar with bio-pesticides than farmers who did not know about the pilot farm. In addition,© ISERD

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the positive coefficients of the educational level of completed primary school (EDU3) and theeducational level of completed junior high school (EDU4) were significantly different from zero atthe 5 percent level. The odds ratio of DEU3 indicated that farmers who had completed primaryschool had about six times higher probability of being familiar with bio-pesticides, compared to thefarmers who never went to school. Also, the odds ratio of DEU4 indicated that farmers who hadcompleted junior high school had about eleven times higher probability of being familiar with biopesticides, compared to the farmers who never went to school. Moreover, the negative coefficientof distance  from  respondent’s  house  to  the  pilot  farm  (DIS)  was  significantly  different  from  zero  at  the 10 percent level. This was interpreted to indicate that farmers who lived closer to the pilot farm
had a higher probability of being familiar with bio-pesticides rather than those who lived far fromthe pilot farm.
Table 3 shows the coefficients, their standard errors, significance levels and odds ratio forcompost. The likelihood ratio test suggests the estimated model had a good fit with a statisticallysignificant score of 209.116 at the 1 percent level. The McFadden R2, a pseudo R-square, was
0.218, which falls  in  the  range  0.2  to  0.4  that  is  considered  an  “extremely  good  fit”  (Hensher  and  Johnson, 1981). Prediction success statistics indicated that the model correctly predicted 72.6
percent of the responses.
Table 3 Estimated coefficients of the binary logit model for familiarity of compost
Variables
LOCA*
GEN*
AGE
EDU2
EDU3
EDU4
EDU5
HS
FL
EXPERIENCE
FARM
DIS
DIRR
DLAND
CNETREV*TNETREVWNETREVEXPO**
IFG_MEDIA
IFG_EXTENIFG_FARMER
IFG_COMMER*
Constant*

B
1.329
1.730
0.013
-0.317-0.263
0.853
1.598
0.010
0.090
-0.0160.001-0.029-0.316
0.213
0.000
0.000
0.000
0.464
0.163
0.430
0.143
0.644
-3.113

S.E.
0.710
0.898
0.020
0.664
0.731
0.920
1.197
0.143
0.284
0.021
0.003
0.224
0.584
0.482
0.000
0.000
0.000
0.216
0.284
0.411
0.340
0.359
1.640

Wald
3.509
3.716
0.399
0.228
0.129
0.860
1.782
0.005
0.100
0.552
0.043
0.016
0.293
0.195
3.514
0.843
2.604
4.616
0.332
1.092
0.178
3.206
3.603

Sig.
0.061
0.054
0.527
0.633
0.719
0.354
0.182
0.946
0.752
0.457
0.836
0.899
0.588
0.659
0.061
0.359
0.107
0.032
0.564
0.296
0.673
0.073
0.058

Exp(B)
3.779
5.643
1.013
0.728
0.769
2.347
4.944
1.010
1.094
0.984
1.001
0.972
0.729
1.237
1.000
1.000
1.000
1.591
1.178
1.537
1.154
1.903
0.044

Note:  Likelihood  ratio  test:  χ2 =  209.116  (d.f.  =  22);;  critical  χ2 = 33.92; p >  χ2: < 0.0001.
McFadden R2 = 0.218.
The percent correct classification is 72.6 %. Number of observations = 210. ***, ** and * indicate statisticalsignificant P<0.01, P<0.05, and P<0.10, respectively. EDU variables compare familiarity with bio-pesticides
relative to vegetable farmers with an educational level of no school (EDU1). A positive sign on any of theeducational dummies would mean that farmers in that particular educational category had a higher
probability of being familiar with compost than farmers with an educational level of no school.

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The results of Table 3 identified no significant influences of any variables except exposure to
the pilot farm (EXPO), location of respondent (LOCA), gender of respondent (GEN), net revenueof cabbage production (CNETREV), total   number   of   “information   source”   checks   for   the  commercial information source group (IFG_COMMER), and the constant on the probability of
being familiar with compost.
Exposure to the pilot farm (EXPO) had a significant positive effect at the 5 percent level in themodel, indicating that vegetable farmers who were exposed to the pilot farm had higher
probabilities of being familiar with compost than farmers who were not exposed to the pilot farm.
In addition, the positive coefficients of the location of respondent (LOCA) and gender of
respondent (GEN) were significantly different from zero at the 10 percent level. According to theodds ratio of LOCA, farmers in West Java had about a four times higher probability of being
familiar with compost than farmers in Bali. According to the odds ratio of GEN, male farmers had
about a six times higher probability of being familiar with compost than female farmers. Moreover,the positive coefficient of net revenue of cabbage production (CNETREV) was significantly
different from zero at the 10 percent level. However, the coefficients of CNETREV were zero
because their frequencies were small; thus there was no influence on the familiarity with compost.
The positive coefficient of total   number   of   “information   source”   checks   for   commercial  information source group (IFG_COMMER) was significantly different from zero at the 10 percent
level. This was interpreted to indicate that farmers who obtained information for their vegetableproduction from market people and commercial company/agricultural retail stores (commercial
information group) had higher probability of being familiar with compost than those who did not
obtain information from the commercial information group. Finally, the negative coefficient ofconstant was significantly different from zero at the 10 percent level, but this interpretation of theintercept might not have any real meaning (Gujarati, 1995).
DISCUSSION
The analysis showed that an educational level of at least primary school (EDU3 and EDU4) anddistance  from  respondent’s  house  to  the  pilot  farm  (DIS)  were  associated  with  increasing  farmers’  awareness of bio-pesticide (Table 2) but there was no significant influence of educational level onthe  farmers’  awareness  of  compost  (Table  3).  One  of  the  possible  reasons  for  this  disparity  is  that  use of compost might be recognized as a traditional technology and use of bio-pesticide might berecognized as a modern-new technology of organic farming by the vegetable farmers. For the
farmers, they might be able to learn a new technology easily from their experiences despite the lackof a higher educational background if it was based on a traditional technology because they might
have experience in using a similar form technology. However, if the new technology is a modernnew technology, it might be more difficult to learn because of lack of experience. To understandthe modern-new technology, farmers may need guidance by agricultural specialists, observableconcrete examples and basic knowledge of the technology.
Location of respondent (LOCA) and gender of respondent (GEN) were associated withincreasing  farmers’  awareness  of  compost  (Table  3)  while  there  was  no  significant  influence  of  the  location  on  the  farmers’  awareness  of  bio-pesticide (Table 2). One of the possible reasons for thisresult is that the uneven gender balance in the respondents between West Java and Bali. All
respondents in West Java were male. Most respondents in Bali (89.3 percent) were also male. Oneof the main reasons for the predominance of male respondents was that the study sites were in rural
Indonesia, where the societies are male-centered, conservative, and have heavy religious influencesfrom both Islam and Hindu faiths. When the researcher developed a list of respondents, all weremale heads of households in West Java, and male heads of households also predominated in Bali.
The  researcher  explored  who  was  really  involved  in  vegetable  farming  in  a  respondent’s  household  during a short conversation before the actual interviews, and this was what the actual collected data
showed.

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CONCLUSION
The results indicated no significant influence of any of the variables, except exposure to the pilot
farm (EXPO), educational level of completed primary school (EDU3), educational level ofcompleted  junior  high  school  (EDU4)  and  distance  from  respondent’s house to the pilot farm (DIS)
on the probability of being familiar with bio-pesticides; no significant influences of any variables
except exposure to the pilot farm (EXPO), location of respondent (LOCA), gender of respondent(GEN), and commercial information source groups (IFG_COMMER) on the probability of being
familiar with compost.
These findings imply that an educational level of at least primary school, distance to the pilot
farms, and exposure to the pilot farms would be the key factors in increasing  farmers’  awareness  of  bio-pesticides. Gender, exposure to the pilot farms, and commercial information source groups
would   be   the   key   factors   to   increase   farmers’   awareness   of   compost.   Especially,   exposure   to   the  pilot farms would be the most important factor  to  increase  farmers’  awareness  of  the  target  organic  vegetable production practices.
ACKNOWLEDGEMENT
We would like to express our appreciation to Dr. Akimi Fujimoto, who was a leader of DNBAF projectat Tokyo University of Agriculture in Japan. He provided the research opportunity for this study, andfinancial support for fieldwork in Indonesia.

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