IJERD – International Journal of Environmental and Rural Development (2012) 3-2

Research articleerd

Determinants of Good Agricultural Practices (GAP)
Adoption in the Chili Production System in Northeastern
Thailand: A Case of Participatory Approach
THANAPORN ATHIPANYAKUL*
Faculty of Agriculture, Khon Kaen University, Khon Kaen, Thailand
Email: thanaporn_k@hotmail.com

WEERA PAK-UTHAI
Faculty of Agriculture, Khon Kaen University, Khon Kaen, Thailand
Received 15 December 2011

Accepted 18 April 2012(*: Corresponding Author)

Abstract A participatory research approach has been conceptually proposed as aneffective method for introducting knowledge-intensive technologies. This approach wasadopted for the promotion of Good Agricultural Practice (GAP) in the chili production
system in Thailand. However, some farmers still use conventional practices that often
result in an overuse of chemicals. This study projected the impact of participation andimproved knowledge of farmers, which can encourage the program adoption. Other
conventional factors such as farming characteristics were also considered. Research wascarried out involving 179 chili farmers both participants and non-participants in the GAP
program located in the Northeastern part of Thailand. A treatment effect model was usedin the identification of factors affecting the program adoption. Results showed asignificant  effect  on  program  participation  and  farmer’s  knowledge  that was the precursorof  adoption.  Farmers’  experience  in  chili  production  significantly  affected  adoption.  Age  was a negative determinant of adoption. The conclusion can be drawn that adoption ofknowledge-intensive technologies such as GAP needs an effective approach such as theparticipatory   research   program,   which   can   improve   farmers’   knowledge   and   encourage  them to adopt innovative technologies. This approach allows farmers to learn from theexperience of other farmers.
Keywords chili production, knowledge intensive technologies, treatment effect model

INTRODUCTION
The participatory research approach program has been developed to overcome the hierarchicalstructure of the training and visit system (Krasuaythong, 2008). Hussain, et al. (1993) had
confirmed that the adoption did not live up to its expectations seven years after the introduction of
the extension program using the training and visit approach. Therefore, the participatory research
approach was adopted as an effective way to transfer innovation, particularly for knowledgeintensive technologies (Feder, et al., 2003). It also has been adopted as the introduction of good
agricultural practices (GAP) for Thai farmers. The objective of the GAP program is to reduce theuse of pesticides, and increase the production and marketing standards of agricultural commodities.
It has been firstly applied in rice, vegetable, and fruit production systems; especially in the areas
where most farmers grow in commercial enterprises with high level usage of pesticides.
Chili is among the vegetable commodities, playing a major role as an important ingredient inThai cooking. Also, it is a high value crop providing a major source of income for small scale
farmers in Thailand. However, chili farmers in Thailand have overused pesticides both pre andpost-harvest, to control pests and diseases (Pak-Uthai, 2010). This still occurs even though several
organizations have been introducing practices that rely less on the use of pesticides and were morebenign for the environment and human health. Adoption of GAP seems to be at a very low scale.
ⒸISERD175

IJERD – International Journal of Environmental and Rural Development (2012) 3-2

Recently, chili exported from Thailand was banned from European countries, because it was foundto be contaminated with banned pesticides (Ariesen, 2011).
Adoption of innovation was a complex issue. Many factors were identified as determinants ofadoption   of   innovation.   Among   those   factors,   the   farmers’   knowledge   was   important   to   the  adoption of new technologies, particularly knowledge-intensive technologies (Krasuaythong, 2008).
The result of Krasuaythong (2008) was consistent with the suggestion of several studies, whichstated that producers with more knowledge will increase the probability of technology adoption(Praneetvatakul et al., 2007; Waibel and Zilberman, 2007). However, this was not clear for the caseof GAP adoption in chili production. The objective of this paper, therefore, was to determine thefactors that affect the adoption of GAP. The results of this research thus can guide policy makers indeveloping strategies suitable to achieve a more rapid and efficient introduction of such practices.
DATA COLLECTION
The Thailand Research Fund (TRF) is a non-government organization that provided the budget forKhon Kaen University to develop the GAP program in chili production system to farmers in theChaiyaphum province of Northern Thailand. This paper is a part of that research. Purposive
sampling was used for the selection of the study area. A multistate sampling technique was used fordata collection. The first stage consisted of the selection of four districts. In the second stage,
simple random sampling was used for the selection of a sub-district and, subsequently, a village.
Stratified random sampling was involved in the final stage. Farmers were divided into two stratums,
namely those participating in the GAP program, and those who were not participating in theprogram. Each farmer was interviewed to determine the socio-economic characteristics of theirhousehold by using a structured questionnaire. A total of 200 farmers were interviewed, but only
179 questionnaires were completed.
SPECIFICATION OF THE EMPIRICAL MODEL
A matrix z demonstrated  the  factors  affecting  the  farmers’  decision  to  participate  in  the  program,  and p was a vector of two parameters for participation and non-participation. The expected utilityof farmers was measured through their decision making, and was formulated as a dummy variable
P. It was identified as 1 if farmers participated in the program and 0 otherwise. The probability offarmers’  decision  for  participation  in  the  program  was  derived  as  Eq.  (1).

Prob(yi 1|z)

 Prob(U1  U0  e1  e0 )(1)

 F (pi  z)

Green (1997) stated that F (pi  z) was the cumulative distribution function for a random
disturbance term, or the error term ( ei ). The distribution of this term proposed the functional formof F (pi  z) , which can be of logistic distribution or a normal distribution. In this study, a probit
model was adopted, because this assumption was more efficient for the treatment effect model(Hechkman, 1979). Notation  is normally distributed with a mean and zero unit of variance, and(pi  z) was called as the probit score.
(2)
Prob(y |z) Φ(p  z)i 1i

Estimation of Eq. (2), a nonlinear maximum likelihood was used and the functional form wasderived as Eq. (3).
  ln 1 (pi  z )   ln(pi  z )(3)
yi 0yi 1
However, in practice; farmers who decided to participate in the GAP program are not randomly
selected.  It  implies  that  factors  affecting  the  farmers’  decision  to  participate  in  the  GAP  program alsoln LⒸISERD176

IJERD – International Journal of Environmental and Rural Development (2012) 3-2

determine the adoption of GAP, which means that the direction of causality obtained by simple
regression can be biased. To cope with these biases, the treatment effect model developed by Heckman(1979) was used. This model assumes that the unobserved variables (  ) of the utility function
parameter  of  adoption  are  correlated  with  the  unobserved  variables  affecting  the  farmer’s  decision  to  participate in the program ( e ), but not with the factors ( X ) determining the value of parameters(Heckman, 1979). Therefore the conditional expected value of the utility function observed when
individual farmers decide to adopt GAP can be represented as Eq. (4).
E(ADOPT | participat ion  1, X, z)  P  γ  X  β  ρ  σ parameter.[  Φ(z  p) /(1  Φ(z  p) )] (4) parameter is the standard deviation of the utility function, and  is the correlation between theunobservable independent variables ( , e) . z is   the   vector   determining   the   farmer’s   decision   to  participate in the program, p is an unknown parameter, and (.) is normally distributed with azero mean and unit variance. The final term in the brackets is the inverse mill ratio (IMR) thatrepresents a correlation between  and e . The Hypothesis for testing this correlation was carriedout using chi-square. If there was no correlation between those terms, the formulation was reduced
to a multiple linear regression model.
DESCRIPTION VARIABLES
The adoption variable was a dependent variable, and identified by the cumulative practices which
the farmers adopted. The GAP introduced by the researcher was a set of practices. It contained 17
practices in total. During the survey, chili farmers were asked whether they have knowledge, andin-depth understanding about these practices or not. Also, they were asked to identify many
practices they adopted, non-adopted, and dis-adopted. The independent variable used in thetreatment effect model contained five variables including participation, knowledge, asset, landlabor, and meeting (Table 1).
The participation variable was expressed as a dummy variable, and was used as a proxy of amajor source of knowledge for farmers who participated in the GAP program. It was expected tohave a positive and direct effect on practice scores. The meeting variable was the number of meetings with neighbors. The knowledge scores, measured from a number of in-depth practices, determined  the  farmers’  understanding.  This  factor  encouraged farmers to adopt new technologies.
Table 1 Description of dependent and independent variables
Variables
Dependent variable
AdoptionIndependent variable

Variable type

Expected sign

Description
Practice score that is the cumulative total of GAPpractices applied in chili production system.

Continuous

Participation

Dummy+

Knowledge

Continuous+

Meeting

Continuous+

Asset

Continuous+

Land-labor

Continuous-Age
Note: 1 ha = 6.25 rai

Continuous-

1=if farmers participated in GAP program;0=otherwise.
Farmers’  knowledge  scores.
Number of farmers meeting with neighbors anddiscussing chili production practices.
Value  of  farmers’  asset.
The ratio of household labor working full time on
chili production to total land area (manday/rai*).
Farmers’  age  (years).

The variable asset expressed the wealth of the farmers, and was expected to increase with thenumber of practices adopted by farmers. Age was expected to have a negative effect on adoption. In
this paper, the land-labor variable was the land to labor ratio. It was used as a farm resource, asⒸISERD177

IJERD – International Journal of Environmental and Rural Development (2012) 3-2

endowments use neither farm household labor nor land size. Gebresekassie and Sanders (2006)
pointed out that farmers with a larger farm size tend to be more likely to adopt new technologies,
but Feder et al. (2003) argued that small scale farmers were more likely to adopt labor intensive
technologies. In this study, the set of practices promoted by the GAP program were suggested aslabor-intensive technologies (Pak-Uthai, 2010).
RESULTS
Table 2 shows results obtained from the descriptive statistics of variables used in the model. Statistical analysis using the t-test showed significant differences between participants and nonparticipants, except asset, meeting and age. A correlation matrix was carried out between
knowledge and program participation. This may bring to light the problem of multi-co-linearity.
Table 2 Descriptive statistics of variables used in the model
Variable
Participants

Mean
Non-participants

All samples

3.99
Practices
12.12***
8.11
50.28%
Participation 149.72%
100.005.81
Knowledge
14.61***
10.23
5.97
Meeting
8.14ns
7.06
57,747.98
Asset (Thai Baht; 30 Thai Baht =$1)
14,026.50,674.17
7.92
Land-labor
5.29***
6.59
48.89
Age
50.00 ns
49.46
89
N90
179
Note: 1/ Percentage of total farmers
2/*Significant at the level 0.1 **Significant at the level 0.05; ***Significant at the level 0.04

Table 3 Correlation matrix of independent variables
VariablePart

knowledge

meeting

asset

land-labor

Age

Participation
Knowledge
Meeting
AssetLand-labor
Age

10.56
0.09-0.32-0.23
0.08

10.14
-0.15
-0.05
-0.11

1-0.010.0009
-0.06

10.17
0.05

10.09

1

Table 4 Determinants of GAP adoption estimated by treatment effect model
Variables

CoefficientStandard error

ZP-value

Constant
0.72388
1.35430
0.53
0.593
Participation
14.35064
1.0589713.55
0Knowledge
0.22935
0.05896
3.89
0Asset-0.01795
0.01390-1.29
0.196
Meeting
0.00001
0.00000
4.34
0Land to labor ratio-0.27156
0.41812-0.65
0.516
Age-0.04460
0.02114-2.11
0.035
Wald chi2
978.40***Log likelihood-526.963
Test of selection bias (IMR)
Chi2 = 14.56***
Note: 1/ *Significant at the level 0.1 **Significant at the level 0.05; ***Significant at the level 0.04

Table 3 shows a non-correlation between knowledge and program participation. Results obtained from the treatment effect model showed that the estimated model had selection biased probⒸISERD178

IJERD – International Journal of Environmental and Rural Development (2012) 3-2lems,  because  the  null  hypothesis  was  rejected  (IMR≠0).  Therefore,  they  were  appropriate  for  identification of factors determining adoption of GAP (Table 4).
The participation and knowledge variables have had a significant effect on the number ofpractices (measured by practice scores) with a high level of significance. However, the magnitudeof participation was greater than that of the knowledge variable. This suggests that participation in
the GAP program that applied the concept of a participatory research approach was more pronounced in adoption of knowledge intensive-technologies such as GAP. During participation,
farmers can gather information and exchange their experience with other farmers. This can affectthe   farmers’   decision   making   about   whether to adopt GAP practices. The meeting variable thatused as a proxy of social network increase the number of GAP practices adopted by farmers, asfarmers can learn from the experience of others (Foster and Rosenzweg, 1995). Therefore, farmers
who were non-participants were able to adopt some practices gained from those neighbors whoparticipated in GAP program. Richer farmers were more likely to adopt an increased number ofpractices, because they were more inclined to experiment with new technologies (Krasuaythong,2008). This was supported by the study of Jayasinghe-Mudalige and Weersink (2004). They pointed out that rich farmers tend to adopt as many environmental management practices as possible.
However, this study showed it significantly affected GAP adoption. The younger farmers tended toadopt more practice in the set of GAP. They opened their minds to innovation and tried to gather
more information as well as improving their knowledge.
Results of this paper support findings in several publications, which stated that program participation has a direct effect on adoption of GAP (Krasuaythong, 2008). Moreover, the process ofknowledge gathering was a major factor affecting adoption of technologies that related to natural
resource management (Waibel and Zilberman, 2007). However, adoption of knowledge-intensive
technologies   depended   upon   other  factors   as   well.   Farmers’   risk   attitude   and   their   behavior   were  also suggested from the study of Krasuaythong (2008).
CONCLUSION
This study aimed at identifying factor affecting adoption of GAP. The treatment effect model wasapplied. The results finding in this were similar to many  publications.  Farmers’  characteristics  such  as age and wealth play a major role in determining the adoption of technologies. An important finding
drawn from this paper was that using the participatory research approach can be an effective tool toenhance  farmers’  knowledge  and  increase  the  rate  of  adoption.  In  this  case,  it  was  measured  by  the  number of practices being adopted from the set of GAP. However, practices may have different levelsof complexity. This may suggest one reason for non-adoption or ceasing a practice. Additionally, other   factors   such   as   farmers’  risk   attitude   and   behavior   should   be   involved   in   the   adoption   model   as  they may affect adoption of innovation. This paper does not discuss these issues, and is therefore recommended for further study.
ACKNOWLEDGEMENTS
This paper is a part of the research funded by the Thailand Research Fund (TRF).
REFERENCES
Ariesen, M. 2011. Newsflash: Precautionary Ban on Thai veggies to the EU. http://www.cbi.eu/?pag=55&nid=992.
Defrancesco, E., Paola Gatto, P., Runge, F. and  Trestini,  S.  2008.  Factors  affecting  farmers  ‘participation  in  agri-environmental measures: A Northern Italian perspective. Journal of Agricultural Economics, 59,114-131.
Feder, G., Murgai, R. and Quizon, J.B. 2003. Sending farmers back to school: The impact of farmer fieldschools in Indonesia. Review of agricultural economics, 26, 45-62.
Foster, A.D. and Rosenzweg, M.R. 1995. Learning by doing and learning from others: Human capital andⒸISERD179IJERD – International Journal of Environmental and Rural Development (2012) 3-2

technical change in agriculture. The journal of political economy, 103, 1176-1209.
Gebreselassie, N. and Sanders, W.J.H. 2006. Farm-level adoption of sorghum technologies in Tigray, Ethiopia. Agricultural Systems, 91, 122-134.
Heckman, J.J. 1979. Sample selection bias as a specification error. Econometrica, 47, 153-161.
Hussain, S.S., Byerlee, D.and Heisey, P.W. 1993. Impacts of the training and visit extension system on farmers' knowledge and adoption of technology: Evidence from Pakistan. Agricultural Economics, 10, 39-47.
Jayasinghe-Mudalige, U. and Weersink, A. 2004. Factors Affecting the Adoption of Environmental Management Systems by Crop and Livestock Farms in Canada. Sri Lankan Journal of Agricultural Economics, 6, 25-36.
Krasuaythong, T. 2008. Technologies adoption in vegetable production in Northern Thailand. Ph.D. dissertation, Institute of Development and Agricultural Economics, University of Hannover, Germany.
Pak-uthai, W. 2010. The study chili supply chain management in the Kasetsomboon and Jaturas Districts ofChaiyaphum Province, Thailand. Research report, Thailand Research Fund.
Praneetvatakul, S., Waibel, H. and Meenakanit, L. 2007. Farmer field schools in Thailand: History, economics and policy. Leibniz University of Hannover, Germany.
Walbel, H. and Zilberman, D. 2007. Productivity enhancement and NRM, International research on natural
resource management: Advances in impact assessment, edited by H. Waibel and Zilberman, D. Oxford
university press, 43-81. USA.

ⒸISERD180