Corinna Mueller_Application of technologies acquired in a Farmer Field School
Philipps University Marburg
Adapting farming practices to climate change:
Understanding farmers' behavior in applying farming
technologies acquired in a Farmer Field School
International Development Studies
At the faculties of
Human Sciences and Philosophy and Economics
at Philipps University Marburg
Prof. Dr. Michael Kirk
Dr. Thomas Dufhues
Corinna Mareike Müller
from Frankfurt am Main
I would like to thank all the people who contributed to the work described in this thesis.
First and foremost, I would like to express my sincere gratitude to my academic advisor, Professor
Michael Kirk, for his continuous support throughout my study, his knowledge, motivation and the
patience needed as the data gathered for this study took place on another continent.
In addition, I am also grateful to the Philippine governmental bodies for their assistance in
organizing the required meetings, mainly the Agricultural Trainings Institute (ATI), the Bureau of
Soil and Water Management (BSWM), the Philippine Crop Insurance Corporation (PCIC) and the
Philippine Climate Change Adaptation Project (PhilCCAP). In addition, I would like to give my
special thanks to Norman Cajucom, Senior Vice-President at PCIC, for his great support in
specifying the topic and coordinating with other parties involved. I would further like to express
my appreciation to Wilbur G. Dee, Project Manager of PhilCCAP, for his support and care, as well
as Dr. Gina P. Nilo, Karlene G.Zuniga, Sarah Buarao and Vivien Medidas for providing help and
encouragement in some of my specific questions.
I would also like to give a very special thanks to Claris M. Alaska and George M. Soriano, as well as
Janene Belamino and Jerry Guanco, whom assisted me during the data collection, guided me to
the right places and kindly supported me whenever a translation was required. In addition, I am
grateful to Nestor Jemoga-on, Eva Flores and Ricky Dador for their companionship and
background information. I would also like to extend thanks to every single farmer for taking the
time to answering my questions and for their hospitality and welcome. Without their
contribution, this research would not have been possible.
Last, but not least, I would like to give thanks to my family who supported me throughout my
whole study program whether I was in Germany, Mexico or the Philippines.
Many Philippine farmers struggle with a high number of natural catastrophes and the adverse
effects of climate change. In order to help them adapt their farming practices, the Philippine
government has implemented the Enhanced Climate Smart Farmers Field School (ECSFFS).
While studies about these Farmer Field Schools generally show a positive impact, none of these
scientific works examines the driving forces of farmers’ behavior. This thesis aims at filling this gap
by analyzing the factors influencing farmers in applying the technologies acquired at the ECSFFS.
To do so, the thesis uses the framework of the Theory of Planned Behavior complemented by
aspects of the Diffusion of Innovation Theory. In a first step, qualitative group interviews among
24 ECSFFS participants were conducted to find out what factors play a role in the application
process. In a second step, the data of 106 Philippine farmers was collected by means of a
questionnaire to check the correlation between their application behavior and certain factors.
Results have shown that attitude and social appreciation were significantly positively correlated
with the application behavior. The better a farmer’s opinion on the technologies was and the
more family and friends appreciated what he or she was doing, the more likely the farmer applied
the technology. Perceived obstacles such as the lack of money, inputs or time only played a role
for the application of specific technologies. While farmers stated to benefit from personal and
second-hand experiences in the interviews, this was not correlated with their application
behavior. In addition, it was found that some farmers were reluctant to attend the classes
because of a lack of time due to another livelihood or small children.
Considering the results, it is recommendable to extend the project by involving farmers into the
teaching process, to approach structural obstacles for example by promoting microfinance and to
introduce a child care facility during classes. The results and policy recommendations of this thesis
are intended to increase the success of the Farmer Field School by conceptualizing the trainings
Table of Contents
Table of Contents
List of Abbreviations ..................................................................................................................................V
List of Figures............................................................................................................................................VI
List of Tables............................................................................................................................................VII
1 Introduction........................................................................................................................... 1
2 Theories................................................................................................................................. 5
2.1 Theory of Planned Behavior ................................................................................................... 5
2.2 Diffusion of Innovations Theory ........................................................................................... 10
2.3 A Comparison........................................................................................................................ 12
3 Methodology........................................................................................................................ 13
3.1 Case Study............................................................................................................................. 13
3.1.1 PhilCCAP and the Farmer Field School .................................................................... 13
3.1.2 Study area................................................................................................................ 16
3.2 Methods of data collection................................................................................................... 19
3.2.1 Mixed Methods ....................................................................................................... 20
3.2.2 Qualitative Part of Data Collection.......................................................................... 21
3.2.3 Deriving the framework used for the quantitative research .................................. 22
3.2.4 Quantitative Part of Data Collection ....................................................................... 24
3.3 Methods used in data analysis ............................................................................................. 27
3.4 Limitations of methodology.................................................................................................. 30
4 Presentation and Analysis of Results ..................................................................................... 32
4.1 Description of the sample..................................................................................................... 32
4.2 Factors influencing farmers’ application of new farming technologies ............................... 35
4.2.1 Perceived Capability................................................................................................ 36
4.2.2 Attitude and Opinion............................................................................................... 42
4.2.3 Social Appreciation.................................................................................................. 32
4.2.4 Experiences ............................................................................................................. 49
4.2.5 Reluctance to new techniques................................................................................ 52
4.3 Factors influencing the schools’ attendance ........................................................................ 52
5 Policy Recommendations...................................................................................................... 54
6 Conclusion............................................................................................................................ 58
Appendix 1: Table of Interviews................................................................................................................ XI
Appendix 2: Interview Guideline.............................................................................................................. XII
Appendix 3: Questionnaire ..................................................................................................................... XIII
Appendix 4: Questions and codes..........................................................................................................XVII
Appendix 5: Further Tables..................................................................................................................XVIIX
List of Abbreviations
List of Abbreviations
ATI Agricultural Training Institute
DJF December, January, February
DOI Diffusion of Innovation Theory
ECSFFS Enhanced Climate Smart Farmers Field School
e.g. exempli gratia (lat.), for example
ibid ibidem (lat.), same source as cited before
IPM Integrated Pest Management
JJA June, July, August
MAM March, April, May
PhilCCAP Philippine Climate Change Adaptation Project
PTD Participatory Technology Demonstration
SON September, October, November
TPB Theory of Planned Behavior
TRA Theory of Reasoned Action
List of Figures
List of Figures
Figure 1 Observed annual mean temperature anomalies (1951-2010) in the
Philippines based on 1971-2000 normal values …………………………….………. 1
Figure 2 Theory of Planned Behavior. Adapted from Ajzen (1991: 182) …..……..….. 7
Figure 3 Research areas in Cagayan Valley and Western Visayas …………………….… 18
Figure 4 Conceptual framework of the study ………………………………..…………………… 19
Figure 5 Deriving the framework used in this thesis ………………………………………….. 23
Figure 6 Vermi-compost on a farm which is used for demonstration purposes .... 24
Figure 7 Age distribution among the respondents ………………………………..…………… 33
Figure 8 Educational level of the respondents ………………………………..…………….…… 33
Figure 9 Area of Land cultivated by the respondents …………………………………………. 34
Figure 10 Technologies applied by farmers ……………….……………………………………..…. 35
Figure 11 Mean values of sub-factor “Perceived Capability” ……………….………………. 37
Figure 12 Mean values of sub-factor “Attitude and Opinion” …………………..……….… 42
Figure 13 Importance of all sub-factors for “Attitude and Opinion“ .……………..……. 45
Figure 14 Mean values of sub-factor “Social Appreciation” ………..…………..………….. 46
Figure 15 Importance of all sub-factors for ”Social Appreciation“ …..…………..……… 47
Figure 16 Mean values of sub-factor “Experiences” ………..…………..…………..…………. 49
Figure 17 Importance of all sub-factors for “Experiences“ ..……..…………..………….…. 51
List of Tables
List of Tables
Table 1 Comparison of the two study areas ……………………………………………………… 17
Table 2 Residential area and year of graduation of survey respondents .…………. 32
Table 3 Spearman rank coefficient for the correlation between application and
gender, age, education and household size ……………………………….………… 35
Table 4 Spearman rank coefficient for the correlation between actual application
of farming technologies and the four pre-defined factors ……...……………. 36
Table 5 Spearman rank coefficient for the correlation between socioeconomic
factors and sub-factors of “Perceived Capability”…………………...……………. 40
Table 6 Spearman rank coefficient for the correlation between the single
technologies and all sub-factors of “Perceived Capability” ……..…………… 42
Table 7 Timely ordered overview of all qualitative (white) and quantitative (grey)
interviews …………………………………………………………….……………………….……… XI
Table 8 Coding of all relevant questions of the survey, possible answers and
related values that were used for the analysis ………………………………..…. XVII
Table 9 Spearman rank coefficient for the correlation between the application
value, the single technologies and “ProbPast” ………………………………..….. XIX
According to the World Risk Index, the Philippines ranks second among the countries that are
“most at risk” worldwide (Alliance Development Works, 2013: 44). The country severely suffers
from natural catastrophes such as typhoons, droughts, floods, but also from earthquakes and
volcanic eruptions. The most prominent example is the typhoon Yolanda/Haiyan which hit the
country in November 2013. It affected around 12.2 million people and led to economic losses of
USD 12.9 billion (World Bank, 2014: xiii). The effects of the world-wide climate change exacerbate
this situation further. As trends are showing, the Philippines experienced an increase by 0.64% of
its mean temperature between 1951 and 2010 (Pagasa, 2011: 16; see figure 1) as well as an
increase of the occurrence of tropical cyclones. Climate projections predict that all parts of the
Philippines are becoming warmer and that there will be a reduction of rainfall in most parts in
summer (March to May) while rainfall will increase in the monsoon season (June to August) in
most areas of Luzon and Visayas (Climate Change Commission, 2011: 2). The high impact of
climate change on the Philippines is also highlighted by the Global Change Vulnerability Index. It
identified 32 countries at “extreme risk” due to climate change with the Philippines ranking 8th
Natural catastrophes disproportionally affect the poor (World Bank, 2014: xiii). They especially
impose a great pressure on farmers relying on climatic conditions. Strong droughts, storms or
excessive rainfall threaten their harvest and their livelihood. Philippine farmers have already been
experiencing the effects caused by climate change leading to higher temperatures and to a shift in
seasons (Climate Change Commission, 2011: 2). This makes it more difficult for them to decide
what kind of crop to plant and at what time to plant and to harvest.
2010) in the
In order to mitigate the consequences of natural disasters and climate change for farmers,
different coping mechanisms are possible. The main project contributing to climate change
adaptation in the Philippines is the five-year Philippine Climate Change Adaptation Project
(PhilCCAP), which develops approaches to strengthen the country’s resilience to climate change
(AIDSI, 2015: 2). One part of the PhilCCAP is the Enhanced Climate Smart Farmers Field School
(ECSFFS) under the implementation of the Bureau of Soils and Water Management as well as the
Agricultural Training Institute (ATI). The basic idea of a Farmer Field School is to strengthen
farmers’ confidence and to teach them agricultural principles through discovery learning (Tripp et
al., 2005: 1706-1707). In the ECSFFS, the farmers, most of them smallholders, are educated about
topics such as the consequences of climate change and how to adapt their farming technology1
the current circumstances, but also about the principles of organic farming (PhilCCAP, 2014: 1).
If the project turns out to be successful, it may have a great potential to adapt farming to the
current tendencies of climate change. As with most development projects, the effect and success
highly relies on the behavior of the recipient, which is the individual farmer. However, it is not yet
clear how and by what factors this behavior is influenced. Many studies were conducted in the
areas of Agricultural and Developing Economics dealing with the impact of Farmer Field Schools.
Most of them ascertain a significant positive influence that Farmer Field Schools have on
productivity knowledge and change of farming practices; yet some results suggest a more critical
Bunyatta et al. (2006), for instance, try to assess the effectiveness of knowledge acquisition,
adoption as well as dissemination of soil and crop management technologies among small-scale
farmers in Kenya. Their method is to compare participants with non-participants using structured
interviews. By doing so, they observe both a significant higher knowledge and higher adoption
rate among participants. Ortiz et al. (2004) use a similar approach to evaluate knowledge and
productivity of participants of a Farmer Field School in Peru teaching them how to manage a
potato disease. According to their findings, students do not only have more knowledge, but they
also show a higher productivity. Other studies, such as Godtland et al. (2004) or Guo et al. (2015),
exclusively deal with the acquisition of this knowledge of farmers while omitting its application.
Whereas Godtland et al. (2004) find a high increase of productivity for potato farmers in the
Andes after having attended a school, Guo et al. (2015) do not concur entirely. Using a
Within the scope of this study, the term “farming technology” will refer to those techniques and practices
that support and control seeding, controlling and harvesting of crops as well as livestock production.
randomized controlled trial evaluation design, they conduct a research on knowledge acquisition
in rice production in Eastern China. They observe an improved knowledge on pest management
and agro-environment, but cannot identify any effect on participants’ knowledge of nutrient
management and cultivation. In addition, there is an even smaller effect on both women and the
elderly population (ibid).
A wide range of studies looks into the impact of Farmer Field Schools that specialize in Integrated
Pest Management (IPM) only. The analyzed schools teach cacao farmers in Cameroon (David,
2007), rice growers in Indonesia (Feder et al., 2004), urban and peri-urban vegetable producers in
Benin (Lund et al. 2010), as well as cotton farmers in Zimbabwe (Mutandwa and Mpanga, 2004)
and in Pakistan (Siddiqui et al., 2012). Feder et al. (2004) use panel data from Indonesia that lead
to the conclusion that the school program has no significant effect on the performance of the
graduates. In contrast, all the other studies listed above suggest that Farmer Field Schools
increase the knowledge of their participants. According to Mutandwa and Mpanga (2004), the
participants also achieve a higher crop yield than the comparative group. David (2007) adds that
the knowledge is diffused to other farmers. Farmer Field Schools are therefore considered as a
possible starting point for farmer empowerment (ibid).
However, this last conclusion regarding knowledge diffusion is not shared by everybody. Tripp et
al. (2005) conduct a study among rice growers in Sri Lanka. Their interest is in finding out how
many farmers can be reached by the Farmer Field School that intended to contribute to a lower
pesticide use for rice. Comparing participants with non-participants they found out that despite
an increase of the students' knowledge, it did not spread to other farmers of that area. A similar
problem is revealed by Rola et al. (2002). Their study explores the long-term effects of a Farmer
Field School in Iloilo in the Philippines which is one of the project sites of PhilCCAP and this study
as well. Their household survey compares participants with farmers who are not participants but
live in the same barangay2
as participants, and non-participants living in different barangays.
Similar to other studies they conclude that the school has a measurable positive impact on its
participants’ knowledge. People who had been participating years before possessed a similar level
of knowledge compared to people who graduated recently. This leads to the assertion that the
acquired knowledge does not fade significantly over time. However, Rola et al. (2002) find no
evidence of a diffusion of this knowledge to their fellow farmers. Yamazaki and Resodudarmo
(2008) come up with the question of sustainability of knowledge as well. Using regression analysis
Filipino term for the lowest administrative level
on previous panel data sets of Indonesia they conclude that the positive impact of the schools is
ceasing over time. Their result, thus, opposes the findings by Rola et al. (2002).
In order to investigate the performance of Farmer Field Schools against other approaches, Bentley
et al. (2004) compare them with community workshops and education through radio broadcasts.
The study finds out that Farmer Field Schools are more expensive but also more effective than the
other methods. Especially when it comes to more complicated technologies, they prove to be the
best way of teaching among the three options (ibid). Another study that compares Farmer Field
Schools with the alternative method of classroom trainings was conducted by Young et al. (2008).
They conclude that Farmer Field Schools result in a high knowledge gain, which is not true for
Summing up, the overall impact of Farmer Field Schools is a very positive one as in almost all
cases the knowledge of farmers improved. Studies that additionally deal with actual application
practices suggest that a big part of the knowledge is put to practice leading to an increase of
productivity. In regard to the diffusion of knowledge and long-term consequences the results are
more diverse. However, while all of the studies deal with the schools’ impact, none of them
explores the factors that promote or impede the application of acquired knowledge by the
participants. There are studies on the farmers’ reasons for applying new technologies (Borges et
al., 2014; Läpple and Kelley, 2010; Lynne et al., 1995; Price and Leviston, 2014; Yazdanpanah et
al., 2014), but those do not cover farmers participating in a Farmer Field School.
In other words, the effects of the schools have been dealt with, but not the reasons behind these
effects. This is what this thesis will contribute to. This study’s aim is to understand the behavior of
the individual farmer with respect to the knowledge he or she acquired at school. Thus, the
central question to be dealt with is: What factors influence the farmer’s behavior in applying the
new technologies learned at the Enhanced Climate Smart Farmers Field School? In addition, it is
asked what policy recommendations may be derived by these findings. The clarification of both
questions contributes to the current debate on Farmer Field Schools and will help designing
future training programs more efficiently.
The thesis is based on an extended version of the Theory of Planned Behavior (TPB) by Ajzen
(Ajzen, 1985, 1991) that is accompanied by aspects of the Diffusion of Innovation Theory (DOI) by
Rogers (Rogers, 2003). The chosen methodology is a mixed methods approach relying both on
qualitative group interviews as well as on a quantitative questionnaire.
The study is structured as follows. Chapter 2 introduces both theories and shows how they have
been used in the past within the background of agricultural behavior. The methodology used is
explained in chapter 3. After giving an overview over the project and the two research areas (3.1),
the chapter presents the idea of mixed methods with its two components, the qualitative and the
quantitative part (3.2). It combines the two theories with the results from the qualitative
interviews to derive a framework for the application behavior of ECSFFS participants which was
then used for the quantitative data collection. In addition, the chosen approach to analyze the
data is explained (3.3). The chapter ends by discussing the limitations of the methodological
approach (3.4). Chapter 4 represents the core of the thesis. After giving a brief overview of the
structure of the sample (4.1), it uses both quantitative and qualitative results to go through the
factors of the framework analyzing the aspects that influence farmers’ application behavior (4.2).
Additionally, it highlights the obstacles for people not even attending the school (4.3). The results
from chapter 4 are used to derive policy recommendations in chapter 5 that are followed by an
overall conclusion in chapter 6.
As indicated above, the study is based on two theoretical constructs, firstly and primarily the
Theory of Planned Behavior (TPB) by Ajzen and secondly the Diffusion of Innovation Theory (DOI)
by Rogers. The following part explains the basic principles of both theories.
2.1 Theory of Planned Behavior
As this study aims to analyze the underlying drivers for the behavior of farmers, a behavioral
approach will represent the theoretical basis of the analysis. Behavioral approaches aim to
understand why people (do not) behave in a certain way and to predict their behavior. They go
back to the economic models of the 1950s that tried to explain behavior purely based on
economic reasons neglecting all other possible causes (Burton, 2004: 36). One of the first
approaches going beyond the economic rational of this time was Simon’s (1957) ‘satisficing
concept’ acknowledging that decisions are not only made in accordance to economic
optimization, but may also pursue other goals which might be of social, intrinsic or expressive
order (Burton, 2004: 360). Simon’s ideas were extended in the 1960s and 1970s when an
increasing number of researchers realized the importance of non-economic goals in decision
making. However, these goals were only considered as an addition to the traditional approaches
In 1975, the publication of the Theory of Reasoned Action (TRA) by Ajzen and Fishbein shifted the
focus of scientific interests in favor of the behavioral approaches. According to the TRA, a person’s
intention to perform a certain behavior is influenced by the person’s attitude towards the
behavior as well as the subjective norm, which is the pressure exerted by society. During the
1980s and 1990s, this theory was applied in many scientific papers. While some researchers
directly adopted it, others combined the TRA with aspects such as farm size, education and family
structure (ibid: 361-363). Ajzen later discovered that the two factors are insufficient to predict a
person’s intention to perform a behavior. In addition, the person’s perceived prospect of success
plays a role for his or her effort. If a person perceives him or herself as incapable of performing
the action anyway, he or she will not provide the same effort as someone who is strongly
convinced to succeed (Ajzen, 1991: 184). Due to this finding, Ajzen added the perceived
behavioral control to the TRA. The result was the Theory of Planned Behavior (TPB).
Similar to the TRA, the TPB focuses on intentions because they “will often be better predictors of
attempted than actual behavior” as shown by empirical findings (Ajzen, 1985: 30). According to
Ajzen, “intentions are assumed to capture the motivational factors that influence a behavior; they
are indicators on how hard people are willing to try, of how much effort they are planning to
exert, in order to perform a behavior” (Ajzen, 1991: 181). The stronger the intention for a
behavior of an individual is, the higher is the likeliness for the actual performance of this certain
behavior. As already indicated above, the TPB identifies three factors, the attitude towards
behavior, the subjective norm and the perceived behavioral control, that influence this intention.
Their relationship is depicted in figure 2.
The first one is the attitude towards behavior, which is “the degree to which a person has a
favorable or unfavorable evaluation of the behavior in question” (ibid: 188). This attitude results
from various believes towards the behavior which are linked to certain outcomes (ibid: 191). To
give an example, a person’s attitude towards performing a diet might be formed by this person’s
believes whether the diet really leads to a loss of weight, whether it results in high costs and
whether it leads to better health conditions or not. If the person believes that the weight could be
reduced at low cost leading to improved health conditions and if these outcomes all matter to
that person, he or she has a positive belief index and most likely a positive attitude towards
performing the diet. Within this study, a positive attitude towards the application of the
technologies could be to ‘improve the harvest’. A farmer who cares about having a better harvest
and who believes that implementing new practices would actually improve it, will more likely
comply than a farmer who has a negative attitude.
In order to mathematically derive a belief index, Ajzen proposes to multiply each single evaluation
(e) with the strength of each belief (b) and to sum up all n products (see equation 1). The resulting
belief index which is directly proportional () to a person’s attitude (A) can be used as an
estimated value for the attitude itself (ibid):
1i iiebA (1)
The subjective norm captures the social pressure a person perceives towards (not) performing a
behavior (ibid: 188). It is derived from normative beliefs, in other words the perceived behavioral
expectations of referents (ibid: 195). Those referents are people who are important for the
individual such as the partner, relatives, friends or teachers. Keeping with the diet example used
before, a normative belief might be the approval or disapproval of performing the diet by the
partner or the best friend. The more the partner approves the diet and the more the opinion of
the partner matters to the individual, the higher is the belief index of the subjective norm. Within
the scope of this study, the participating farmers might be exposed to approving or disapproving
opinions from their relatives influencing the application decision.
In order to derive the corresponding number, “the strength of each normative belief (n) is
multiplied by the person’s motivation to comply (m) with the referent in question” (ibid) as shown
in equation (2).
Figure 2: Theory of
Adapted from Ajzen
1i iimnSN (2)
The third and final factor that sets the TPB apart from the TRA is the perceived behavioral control.
It refers to the perception of an action's performance as either simple or rather difficult,
especially due to the presence or absence of required resources. This evaluation may be derived
from own experiences or second-hand information (ibid: 184, 197). “The more resources and
opportunities individuals believe they possess, and the fewer obstacles or impediments they
anticipate, the greater should be their perceived control over the behavior” (ibid: 197). For
example, if two people have equally strong intentions to learn to ski, the person who is more
confident to succeed is more likely to do so than the one doubting his or her abilities (ibid: 184).
Alternatively, a farmer who does not believe that he has enough skills, time or resources will be
less likely to apply new technologies than a farmer who perceives himself as capable.
For the calculation, as equation (3) shows, each control belief (c) is weighted with the power of
the control factor in facilitating or complicating the performance of the behavior (p) and summed
up over n control beliefs. The perceived behavioral control (PBC) is correlated with this sum which
can be used as an approximate value.
1i ii pcPBC (3)
It should be noted that the PBC has to be differentiated from actual circumstances for actions. It
does not measure which one of the two ski novices is actually more capable of performing the
activity. Instead, it just deals with the way the individual evaluates his or her resources and
opportunities. Ajzen expects the expended effort to increase, the more capable the individual
considers himself to be (ibid: 183). For this reason, the perceived behavioral control can increase
or decrease the intention of an individual (represented by the solid line arrow in figure 2).
However, in addition to this relation, the perceived behavioral control can further be used as a
substitute for measuring the actual behavior control, especially when the perceptions are
accurate (ibid: 184). If one of the ski partners perceives him or herself as rather unexercised and
this perception is right, it will not only influence the intention, but also the actual success.
Likewise, the farmer that feels to have a lack of skills or resources is probably right and this deficit
might be an actual obstacle. This influence is represented by the dotted line in figure 2. These
“actual” factors which are identified within the perceived behavioral control, but also others that
go beyond can be influential when it comes to transmitting the intention into actual behavior.
Ajzen distinguishes between internal and external factors that either allow or prevent the
performance of an action. The internal factors comprise individual differences such as the physical
constitution, skills, abilities, emotions and the “power of will” as well as the availability of
information (Ajzen, 1985: 25-27). In the category of external factors falls the time availability,
opportunity and the dependence on other people that are required to perform the action (ibid:
While Ajzen’s theory has never been used for analyzing the behavior of Farmer Field School
participants, it got already applied to a number of researches about agricultural behavior. One
example is provided by Borges et al. (2014) who examine the intention of cattle farmers in Brazil
of using improved grassland as well as the factors that determine their intention. By using
correlation analysis with data derived from a quantitative survey, Borges et al. (2014) are able to
confirm the TPB. The cattle farmers' disposition towards the application of the new technology is
determined by their behavioral, normative and control believes. Similar studies analyze farmers in
countries of the global north. Among those are studies about the acceptance of practices for
animal welfare in Holland (Lauwere et al., 2012), organic farming in Ireland (Läpple and Kelley,
2010), water conservation technologies in Florida (Lynne et al., 1995), land use practices in South
Korea (Poppenborg and Koellner, 2013) and pro-environmental practices in Australia (Price and
Leviston, 2014). They are in line with the theoretical presumptions of the TPB.
Yazdanpanah et al. (2014) use a revised and expanded version of the TPB to analyze Iranian
farmers' water conservation practices. According to their study, the Theory of Reasoned Action
(TRA), the precedent approach of the TPB, is more capable to explain what is happening. In
addition, other factors that are not covered by both theories such as the perceived risk, self-
identity and the moral norm play a role as well.
To sum up, in most cases, the studies on farmers’ behavior confirm the hypotheses deriving from
the theory. However, it sometimes turns out to be necessary to strongly adapt the approach to
the specific conditions of the research area and topic or to expand it as experienced by
Yazdanpanah et al. (2014). The necessity of extending the theory is something which is
acknowledged by Ajzen himself writing that the TPB is “in principle, open to the inclusion of
additional predictors” (Ajzen, 1991: 199) if these predictors can explain some of the variance of
the data. To get additional ideas of factors that could be included, another theory that has already
been applied to agricultural studies and that focuses on the diffusion of new ideas as well will be
taken into account.
2.2 The Diffusion of Innovations Theory
Ajzen’s theory will be complemented by the theoretical thoughts of Everett M. Rogers. His
Diffusion of Innovations (DOI) theory was originally published in 1962 and got widely applied
during the decades that followed (Rogers, 2003: xv). According to the DOI, an innovation is “an
idea, practice or object which is perceived as new by an individual” (ibid: 12) or a group of
individuals. As such, it does not matter whether the innovation is objectively new as long as it is
perceived as new by the individual (ibid). In accordance to this definition, most of the farming
practices taught at the ECSFFS are new from the perspective of a farmer and can therefore be
considered as innovations.
The DOI has amongst others become famous for the description of the individual process leading
to the decision to adapt an innovation. According to Rogers, this takes place in five phases: In the
knowledge stage the individual learns about the existence as well as about the characteristics of
an innovation (ibid: 21). In the second phase, the persuasion stage, the individual forms a positive
or negative attitude towards the innovation. This attitude is influenced by the five perceived
factors relative advantage, compatibility, complexity, trialability and observability that will be
discussed in detail below. At the decision stage, the individual decides whether to fully or partially
accept or to reject the innovation. The implementation itself then takes place in the
implementation stage. During the confirmation stage, the (non-)adopter aims to receive support
for the decision. Depending on the feedback received, he or she will decide to either continue or
stop if the innovation had already been implemented or either to adopt or not to adopt in case
the innovation had not been implemented before (ibid: 176-189). As the ECSFFS attendance of
most of the interviewees was currently taking place during the research or did not lie very far in
the past, the focus was laid on the persuasion stage. As it describes the factors forming people’s
attitude towards an innovation, it can be expected to be a good complementation of the TPB.
As mentioned above, in the persuasion stage the individual develops an either positive or
negative attitude towards the innovation being influenced by the five perceived factors: relative
advantage, compatibility, complexity, trialability and observability. The higher (or as in the case of
complexity the lower) the strength of each of these factors is, the more likely the members of the
community will adopt the innovation. In order to not exceed the scope of this study, only these
five factors will be used for the analysis while other aspects of the DOI such as the five stages of
the adoption process cannot be applied.
The first one of these factors is the relative advantage. It measures the degree to which the
innovation is perceived as better than its alternatives (ibid: 15). To give an example for this
particular research, the relative advantage asks whether the new technologies are better when
compared to traditional farming practices. If the farmers perceive the ECSFFS technologies to be
advantageous, they will more likely implement them on their own farm.
The second factor is the degree of compatibility with regards to needs, values and past
experiences of the adopters. If what is taught at the Farmer Field School is not consistent with the
participants' needs, its implementation is rather unlikely even if there are relative advantages
compared to traditional methods. In addition, compatibility captures the fact that “an idea that is
incompatible with the values and norms of a social system will not be adopted as rapidly as an
innovation that is compatible” (ibid). Its introduction might first require the introduction of a new
value system. To support his statement, Rogers highlights the example of the introduction to
contraceptive methods to Muslim and Catholic nations where ways of family planning are
undesired. Although the implementers perceive these methods as advantageous, the religious
values might hinder their diffusion. Finally, the innovation has to be in line with past experiences
(ibid). For instance, if farmers already made bad experiences with parts of the technologies, they
might be less willing to adopt others.
Furthermore, the complexity defines the severity of understanding or employing new practices.
According to the theory, a higher complexity, in this particular case of the technologies, will
ceteris paribus lead to a lower adoption rate (ibid: 16). The fourth attribute is trialability, the
degree to which it is possible to experiment with and to try out the new practices, for example
during classes on a farm for demonstration purposes. By trying out an innovation before finally
deciding whether to apply it, the uncertainty is decreased (ibid).
Finally, observability is about whether the results of these practices are visible to others. Hence, if
it is easy for individuals to see the consequence of the innovation, they are more likely to adopt it.
The underlying reason is that, “such visibility stimulates peer discussion of a new idea, as the
friends and neighbors of an adopter often request innovation evaluation information about it”
(ibid). Applying the theory, it can be expected that neighbors of ECSFFS participants who can
observe what their fellow farmers are doing, are likely to discuss about it. By doing so, they would
distribute the innovation.
Most studies using the DOI in agriculture, do only consider it as one approach among others.
According to them, it is only partially applicable to explain the adoption of innovations in
agriculture while other factors are found to be equally or even more important than those
identified by Rogers.
Padel (2001), for instance, examines a large number of studies of organic farmer concluding that
the model “should not be applied to the process of conversion to organic farming without
considering some of the main points of criticism” (ibid: 54). Apart from others, the model is
perceived to focus too much on the individual farmer without considering the general economic,
structural and institutional environment (ibid: 55). Simin and Jankovic (2014), in contrast, perceive
organic farming to be a social innovation in the sense of Rogers (ibid: 522).
Peshin et al. (2009) analyzes the use of the DOI for Integrated Pest Management (IPM). According
to them, the DOI is not adequate to explain the diffusion of IPM. This is explained by the fact that
the DOI pays to much attention on the individual’s socio-economic factors and on “ex-post-facto”
research. Instead, researchers are proposed to conduct a more practical oriented “action
research” (ibid: 1). Hårsmar (2011) discusses the capability of the DOI to explain technological
change in agriculture in sub-Saharan countries in comparison to other technologies. He states that
the DOI is relevant, but does still not entirely capture the process (ibid: 20)3
Again, the theory proposes some aspects that are important for the adaptation of an innovation
by farmers, but is not considered as sufficient. While some aspects are overemphasized, others
get to be neglected.
2.3 A Comparison
Both the DOI and the TPB got widely applied in agriculture, but approach the topic from different
perspectives. While the DOI focuses on innovations, the TPB tries to understand a behavior.
However, comparing them, there are some overlaps showing once more the relevance of these
factors in questions. Ajzen’s attitude towards behavior resembles Rogers’ relative advantage and
compatibility because a perceived relative advantage and the fulfillment of needs lead to a
positive attitude. However, the elements of the two technologies are not identical. While Rogers’
complexity represents one aspect of Ajzen’s perceived behavioral control, it is not equivalent as
the latter covers more aspects such as the availability of time and inputs. At the same time, the
DOI offers aspects which are not considered within the behavioral approach at all. Neither the
aspect trialability nor observability is part of the TPB, but might play a role in the adaptation
process of new farming technologies. The TPB and the DOI are both recognized theories that got
widely applied. They are characterized by particular strengths to include specific elements and by
Other critical points relate to the five phases of diffusion (e.g. Padel, 2001: 55) which are not relevant here as this
study does not include this aspect.
particular downsides to neglect others. Therefore, it is worth taking both into further
Other studies imply that further factors that are not part of both DOI and TPB play a role. In their
study on the adoption of modern agricultural production practices in Ghana, Akudugu Abunga et
al. (2012) identify factors such as the farm size, the age, the level of education and the gender as
important for the acceptance of new technologies (ibid: 3). The consideration of these factors is in
line with Padel (2001). In addition, the public policy and administrative factors can be influential
for the adoption of a new technology by farmers (Ndah, 2014: 56).
To understand the methodological approach of this thesis, the following part first outlines the
case study with the project and the research area in 3.1. This background information is followed
by a description of the mixed methods approach and its application to the study. Both qualitative
and quantitative data collection that were used as well as the underlying reasons for the chosen
method are explained. 3.3 then depicts how the data was analyzed. The chapter closes with the
limitations of the chosen methodology.
3.1 Case Study
The Farmer Field School which is described in this study is part of a project of the Philippine
government to adapt the country to climate change. The project is mainly located in three
geographically dispersed areas of the Philippines of which two were taken into consideration.
3.1.1 PhilCCAP and the Farmer Field School
As the Philippines belong to the countries that are most vulnerable to climate change worldwide
(Maplecroft, 2014), the Philippine government is working on strategies to mitigate its
consequences. To do so, the Philippine Climate Change Adaptation Project (PhilCCAP) was
implemented. This five-year pilot project from 2011 to 2015 relies on a grant from the GEF-Special
Climate Change Fund and the World Bank. It is executed by various government bodies, that is to
say the Department of Environment and Natural Resources, the Department of Agriculture, the
Department of Science and Technology, the Climate Change Commission and other agencies. The
declared goal of the PhilCCAP is to develop coping mechanisms aiming to increase the Philippines’
resilience when it comes to climate change (AIDSI, 2015: 2).
The proposed approach of the PhilCCAP is to increase the adaptation capacity of farming
communities by improving farming strategies under conditions of climate change, by providing
access to weather forecasting and weather index based insurance4
and by strengthening
ecosystems. To achieve these goals, the PhilCCAP consists of four main components: Component
one works on strengthening relevant government agencies to include the topic of climate change
adaptation in their agenda. The second component seeks to help rural communities adapting to
the consequences of climate change. Component three aims to improve the access to scientific
information related to climate, in particular for those people working in agriculture. The fourth
component supports coordination functions (ibid).
One of the tasks performed under component two is the implementation of the Enhanced Climate
Smart Farmer’s Field School (ECSFFS) which is an extension of the concept of Farmer Field
Schools. These schools were originally promoted by the Food and Agriculture Organization (FAO)
as a way to teach farmers about Integrated Pest Management (IPM) concepts in East Asia, but are
increasingly used for other topics and regions (Feder et al., 2003). In the special case of the
Philippines, the ECSFFS was developed by the Agricultural Trainings Institute (ATI) as a non-formal
education extension. It relies on participatory training methods to help farmers in decision making
and to teach them new farming practices. Moreover, farmers are familiarized with the effects of
climate change (Zuniga, n.y.). In addition to farmers, the target group of the ECSFFS comprises
Agricultural Extension Workers or representatives from concerned agencies (PhilCCAP, 2014: 5).
Since 2012, the ECSFFS is conducted every season (Alaska, 2015) with classes lasting for four
months (Interview 9: 133). During this time, the students meet once a week usually at the same
place, which is either on a spacious farm of one of the participants or at a public meeting place5
The classes take place in the morning and all students receive a Merienda, a light meal (Group
: 125). Each class comprises 30 to 40 participants with a target number of 35 (Group
A weather index based microinsurance is an insurance for people with low-income, mainly in the
agricultural sector. The determination of the payout does not depend on the actual damage, but on the
value of an index, such as the amount of rainfall. If the observed rainfall exceeds or undergoes the trigger
values defined in the contract, a payment is triggered (Hochrainer et al. 2008: 235).
Some of the background information about the school was gathered through informal talks and personal
An overview of all interviews can be found in Appendix 1.
Interview 6: 159). The instructors all use the same manual which consists of more than 200 pages
explaining each technology and the way it is supposed to be taught during the lessons (PhilCCAP,
2014). The first weeks of classes deal with the climate of the Philippines, as well as the analysis of
the soil. In the second part of the course, farmers are educated about climate change adaptation
technologies. They learn about weather forecasting and new farming practices. The latter includes
basics such as land preparation and water management, but also more advanced topics such as
the adjustment of the cropping calendar to changing climatic conditions. The third part deals with
climate change and related risk management. They learn about storm warning signals, to identify
risk-prone areas and the possibilities of financial risk transfer as provided by the PCIC (ibid).
To help participants implementing the technologies, they are provided with various subsidies,
which differ across the schools. They are given inputs such as mongo or vegetable seeds and
fertilizer. Other subsidies comprise the worms which are necessary for vermi-composting7
pair of chicken to build up livestock. In some cases, water pumps are installed to improve the
irrigation possibilities (Expert Interview 1: 7, 17, Group Interview 3: 51; 7: 128-133; 8: 7f). Not all
participants stated that they had received a subsidy. In Dumangas only the first batch received
this kind of support, which is not handed out anymore (Group Interview 9: 46-48, Expert Interview
2: 23-25). For those who receive a support, it ends with the completion of the program. However,
there are still other subsidies provided by the department of agriculture that benefit for every
farmer (Short statement 2: 11).
In order to give farmers the opportunity to deepen their knowledge by “Learning by doing”, a
Participatory Technology Demonstration (PTD) is implemented which “serves as a showcase of
climate change adaptation strategies or good practices suited to the location” (ibid: 175). The
PTD has a minimum size of 1 ha and receives its required inputs from the ATI and the DA of region
VI (ibid: 177). Even if the school only takes place once a week, the participants have the possibility
go to this “demo farm” every day (Group Interview 9: 133). At the end of the four months, the
farmers are invited to participate at a mass graduation party which is attended by representatives
from involved governmental bodies. Each class prepares a small show in which it shows the
learnings of the past four months with the best shows being rewarded. Those farmers, who stood
out in a positive way, for example by being most punctual, are honored and receive additional
For a description of the technology see chapter 3.2.4
The success of the project is monitored. The Midterm Report on PhilCCAP outcome indicators, for
instance, surveyed 2,386 farmer households8
in the pilot area finding out that “18.21 percent of
[those households] were practicing or intending to practice PhilCCAP adaptation technologies9
(AIDSI, 2015: 1) and therefore concluding that the project “has been successful in developing and
demonstrating approaches that would enable targeted communities to adapt to the potential
impacts of climate variability and change” (ibid).
PhilCCAP with ECSFFS as one component is implemented in seven municipalities within the three
Philippine regions: Cagayan Province in Region II which is located in the northern part of Luzon,
Iloilo Province in Region VI which is located in Visayas and finally Surigao Province in CARAGA
Region on the southern island Mindanao (AIDSI, 2015: 2).
3.1.2 Study area
Due to both capacity constraints as well as security issues concerning Mindanao, this study only
focuses on farmers in two regions of the project. The first one is Cagayan Valley, the other place
can be found close to Iloilo (see figure 3). As the two regions are 770 km apart from each other,
their climatic and therefore agricultural conditions differ in some aspects.
Cagayan province with the two municipalities Tuguegarao and Peñablanca is part of Cagayan
Valley (region II) in the north-eastern part of Luzon. The area is famous for being the hottest
region of the Philippines with its maximum in JJA10
of 28.9°C (see table 1) on average and lowest
temperatures of 24.5°C in DJF (PAGASA 2011: 30). However, according to a climatic scenario11
In contrast to this thesis, the study conducted a stratified sampling by preselecting farms according to
elevation and farm size, but did not differentiate between participants and non-participants (AIDSI, 2015:
10). The sample therefore includes both ECSFFS participants and non-participants.
The evaluated adaptation strategies were the Palay Check system, the Integrated Farming System, Climate
Change Adaptation Practices, both introduced by the ECSFFS, as well as Weather Index Based Crop
Insurance, Small Automated Weather Stations, Decisions Support System and Retrofitting Irrigation
Systems, all introduced by other actors (AIDSI, 2015: 10). The focus of the mid-term report therefore clearly
differs from this study.
DJF: December, January, February, MAM: March, April, May, JJA: June, July, August and SON: September,
The predictions were presented by the Philippine Atmospheric, Geophysical and Astronomical Services
Administration and are based on a regional climate model developed at the UK Met Office Hadley Centre
for Climate Prediction and Research. It uses observations from 1971 to 2000 and predicts for two scenarios
centered on 2020 (2006-2035) and 2050 (2036-2065). The values used for this thesis rely on a medium-
range emission scenario based on a future world with high economic growth rates and a population peak in
temperatures will still rise by around 2°C until 2050. At the same time, the seasonal rainfall is
expected to increase from 284 mm to 325 mm in DJF and to decrease from 208 mm to 160 mm in
MAM. It will stay constantly high for the rest of the year with approximately 590 mm in JJA and
832 mm in SON. Both the number of days with extreme temperatures (>35°C) as well as the
number of days with extreme amounts of rainfall (>200m) will increase in frequency. The climate
conditions will become even more extreme than in the past (ibid).
Taking a closer look to the population, Cagayan province is dominated by the ethnic group of
Ilocanos (69%) (Philippine Statistics Authority, 2002). The total population of 1.12 Mio is growing
with a rate of 1.5% which is below the average growth rate of the PH of 1.9% (Philippine Statistic
Authority, 2012). More than half of the population is employed in agriculture. In 2002, the
province counted around 120,000 farms with an average farm size of 1.5 ha. They mainly produce
rice (42% of total agricultural output of the region) and corn (30%), followed by sugarcane (8%)
and banana (6%). Cagayan Valley is the Philippines’ most important corn producer. The yield per
hectare is 4.14 metric tons per hectare for rice and 4.11 for corn. Considering the prices, this leads
to a net return per hectare of 16,640 pesos for rice and 18,580 pesos for corn (Philippine Statistic
Authority 2013, n.y.a).
Table 1: Comparison of both study areas. Own illustration with data from PAGASA, 2011 and Philippine
Statistic Authority, 2012.
Cagayan Province Iloilo Province
Location Northern Luzon Western Visayas
Season DJF MAM JJA SON DJF MMA JJA SON
1971-2000 24.5 28.1 28.9 27.1 26.4 28.2 27.9 27.6
(change in °C)
+2.0 +2.2 +2.0 +1.8 +1.9 +2.4 +2.1 +1.9
1971-2000 284.4 207.7 538.4 832.1 324.8 290.6 932.8 828.3
(change in %)
+14.6 -23.3 +0.9 -1.0 +20.4 -13.3 +3.8 +3.9
Agriculture 42% Rice
the middle of the century. In addition, the model expects a fast introduction of more efficient technologies
(PAGASA 2011: 7).
Prediction, see footnote 9.
The other two municipalities, in which the research was conducted, are Pototan and Dumangas,
both in Iloilo province close to Iloilo City. Iloilo province is located in Western Visayas (region VI),
right in the center of the Philippine islands. During past decades, temperatures in this area used
to range between 26.4°C in DJF and 28.2°C in MAM. Within the next 50 years they are expected to
rise around 2°C, especially in the hottest season. Iloilo’s driest season is MAM with 290 mm while
the wettest season is JJA with 933 mm of rainfall. Similar to Cagayan Valley, the scenario predicts
a strong increase of rainfall of 20.4% in DJF and a strong decrease of 13.1% in MAM. While the
number of days with heavy rainfall used to be quite low and will stay alike, the number of days
with extreme temperature (>35°C) is expected to increase from 460 days in 30 years to more than
3000 days in 30 years. In other words, there used to be an average of 15 days per year with
Figure 3: Research areas in Cagayan Valley and Western Visayas. Own illustration.
temperatures exceeding 35°C from 1971 to 2000. For the period 2036 to 2065, models predict an
average of 100 days with extreme temperatures (ibid: 37). Iloilo province has 1.8 Mio inhabitants
and a growth rate of 1.5% as well (Philippine Statistic Authority 2012). The 133,000 farms (in
2002) have an average farm size of 1.4 ha which is similar to Cagayan. When it comes to the share
of total agricultural output of Western Visayas, the most important crop is again rice (25%) while
corn does not play a very important role (3%). The second crop in importance is instead sugarcane
(15%), in which Iloilo is leading the country ranking, closely followed by hog (12%) and chicken
(7%). The relative output is much higher than Cagayan Valley and amounts to 3.4 metric tons per
hectare for rice and 2.8 for corn. In 2012, the net income per hectare was 16,840 pesos for rice
and 10,820 pesos for corn (Philippine Statistic Authority 2013, n.y.b).
Comparing both provinces, Cagayan Valley has the more extreme temperatures while Iloilo has
more rainfall. They both focus on rice production resulting in almost the same net return. After
rice, the second most important product of Cagayan province is corn, of Iloilo it is sugar cane.
3.2 Methods of data collection
The scientific approach of this thesis is represented in figure 4. It is based on the application of the
two theories that were introduced in chapter 2. Combining both, an interview guideline was
developed and interviews with farmers were conducted. A quantitative survey based on the
combination of the theories and adjusted by the interview outcomes was then filled out by
current and former participants of the Farmer Field Schools. After the completion of the survey,
several farmers were interviewed once more in order to clarify some questions arising from the
results of the questionnaire.
Theory Qualitative Research Quantitative & QualitativeAdjusted Framework
Theory of Planned Behavior:
- Attitude towards behavior
- Social norms
- Perceived capability
Diffusion of Innovation:
- relative advantage
Four of main factors
(money, inputs, time etc.)
Attitude towards behavior
(consequence for output,
(Opinion of referents)
(Role of observation and
106 farmers in
Cagayan Valley and
interviews with six
farmers in Iloilo region
and one expert
Figure 4: Conceptual framework of the study. Own illustration.
3.2.1 Mixed Methods
In order to apply the introduced theories to the case study, the thesis avails itself of the mixed
methods approach being a combination of qualitative and quantitative research methods. It
“involves the collection or analysis of both quantitative and/or qualitative data in a single study in
which the data are collected concurrently or sequentially, are given a priority, and involve the
integration of the data at one or more stages in the process of research” (Creswell et al., 2003:
212). While having been considered as competing research methods for a long time, an increasing
number of researchers recognize the benefits of a combination as the methods can support each
other which results in a broader picture of the research subject. On the one hand, quantitative
research can solve the problem of generalizability qualitative research has. On the other hand,
qualitative methods can help explaining and interpreting the result of a quantitative study (Flick,
2011: 76). The mixed methods are said to end the paradigm wars and are considered as the “Third
methodological movement” with the quantitative methods being the first and the qualitative
methods being the second movement (ibid: 77). However, the discussion on the “right”
approaches is still ongoing and various researchers are working on the development of suitable
Miles and Hubermann (1994: 41) propose four different basis designs. For the first design, both
quantitative and qualitative data is collected in a parallel manner. In the second approach, a
continuously conducted field study is complemented by several sequences of quantitative data
collection based on a survey. The third approach starts with an explorative qualitative research
used as a basis for the conduct of a quantitative research session. This is again followed by a
qualitative method to deepen and check the results. Within the fourth design, which is described
by Miles and Hubermann (ibid), the results of a survey are complemented and deepened by a
field study followed by an experiment.
The third design is the one applied for this thesis. Here, guided interviews enable a first
explorative analysis of the situations. As the theory is very broad, it is necessary to determine
what kind of factors might have an influence on farmers’ behavior. For example, it is unclear what
aspects shape the attitude of the farmers. Without conducting the interviews, one might just
guess that they perceive a high output and low costs as important, but are there other factors
that matter? And what might be the problem that lowers their perceived behavioral control?
These are the points that can only be identified with a first explorative part. In a second research
section, the results of the interviews are used for the development of a questionnaire. The survey
is capable of revealing the strength of the influence the identified factors have on farmers’
behavior. However, pure numbers barely give interpretations for the revealed correlations.
Therefore another part of qualitative analysis then contributes to giving an explanation of the
results of the survey.
3.2.2 Qualitative Part of Data Collection
The first qualitative interviews were conducted only in the two municipalities Peñablanca and
Tuguegarao in Cagayan Valley in May 2015. In both cases, the farmers were invited by the actors
from the Philippine government on a voluntary basis.
In Peñablanca, the interviewees were met on a former participant's farm, whereas in Tuguegarao
the interviews took place directly at the Farmer Field School. Both places enabled the farmers to
be in a familiar environment. This diminished potential feelings of restraint and uncertainty when
being interviewed by a foreigner. With regard to getting as many statements as possible in the
available time, seven group interviews were conducted with two to four participants so that a
total number of 24 farmers could be interviewed. In addition, a complementary interview with
the city agriculturalist of Tuguegarao provided information from another point of view. Prior to
the interviews with the farmers, a guideline was developed (see Appendix 2) using the factors
derived from the two theories with the TPB as guideline and the questions related to the DOI
being fit in between resulting in a reasonable order.
At the beginning, the farmers were invited to tell something about themselves, their family and
their farm. Although this introductory part was not directly intended to be used in the analysis, it
served as an ice-breaker and gave a first impression about the interview partners’ language
abilities, eloquence and motivation to talk so. Consequently, the interview style could be
The first question was followed by a second section of questions on the school and the topics the
participants remembered most. They were asked whether the topics being taught at the ECSFFS
made sense according to their experiences as farmers. Another question was related to the
compatibility aspect derived from the DOI.
The structure of the third and most important part of the questionnaire mainly followed the TPB
by exploring the intention, the attitude, the social norms and the perceived behavioral control. Its
goal was to identify positive or negative effects of applying the new practices, people who might
play a role for social norms as well as factors that facilitated or prevented the adoption of the
technologies. First, the interview partners were asked whether they were already applying the
technologies or planning to apply them, probably after having tried them out. To understand
people’s attitude, they were asked what they considered as advantages or disadvantages when
thinking about the technologies taught at the ECSFFS. In order to explore the role of social norms,
the farmers were asked how other people behaved when they observed them attending the
school and especially what groups of people (dis)approved it. Another question was related to the
observability of the DOI and dealt with the farmers being influenced by other people who had
attended the school before. The last part finally investigated the perceived barriers of applying
the knowledge that was acquired in the school which also included the complexity aspect of the
Although these questions were mainly followed as explained, the order sometimes changed due
to the flow of the conversation. When necessary and perceived as important, further questions
were asked. All interviews were recorded with the consent of the participants so that they could
be transcribed for a more detailed analysis13
3.2.3 Deriving the framework used for the quantitative research
In order to understand the following, a small part of the analysis has to be anticipated at this
point. The analysis of these interviews in connection with the theories led to the determination of
four factors expected to play a role in the application process and worth being looked at with the
help of a survey (see figure 5).
The first factor is “Perceived Capability” which is closely related to Ajzen’s “perceived behavioral
control”. It comprises the sub-factors financial aspects, time and input availability as well as the
own knowledge and thus includes the aspect of “complexity” of the DOI. It will be shown later
that all factors had been named by the interviewees as barriers for the application of the
technologies (see 4.2.1).
The second factor “Attitude and Opinion” combines Ajzen’s “attitude towards behavior” with
Rogers’ “relative advantage”. The determinants of the opinion being identified in the interviews
(see 4.2.2) and thus being included in the questionnaire are an increase in output and a decrease
in costs, the preservation of natural resources, the production of healthy food, a safe output as
well as the disadvantage of more work.
The transcripts and further information (codes, quantitative data set and syntax) are provided on the
Figure 5: Deriving the framework used in this thesis. Own illustration.
The third factor “Social Appreciation” is primarily derived from Ajzen’s “Social Norms”. The
influential groups of people identified were the friends and the family of the farmer. Another sub-
factor is the aspect of sharing knowledge in order to influence other people in following the own
example (see 4.2.3). This aspect is neither part of the TPB nor of the DOI, but was included
because it was identified as critically important in the course of the interviews.
Finally, a fourth factor was added which did not ostensibly play a role in the TPB as well, but
turned out to be important for the farmers: The factor “Experiences” combines Rogers
“observability” and “trialability” of the new technologies. Its three sub-factors are to hear about
the results with the new technologies from others (‘Hear’), to passively observe what others are
doing (‘Observe’), and to actively try out the technologies, for example on the school’s training
The aspect of “compatibility” which is part of the DOI was perceived to be irrelevant after
conducting the interviews as no interview declared any conflict with needs, values or past
experience. It is therefore not included in the framework.
Theory of Planned Behavior Diffusion of Innovation
trialability observabilitycompatibility complexity
Perceived Capability Attitude and Opinion Social Appreciation Experiences
- money availability
- input availability
- time availability
- own knowledge
- increase in output
- decrease of costs
- preserve nat. resources
- healthy food
- safe output
- work more
- role of sharing (added!)
- try out/ “hands-on”
- hear what others told
3.2.4 Quantitative Part of Data Collection
The factors derived in the previous section and illustrated in figure 5 were used for the
development of the questionnaire (see Appendix 3). As the Philippines is a country with several
languages, it was written in the two national languages, Filipino/Tagalog and English (Auswärtiges
The questionnaire started with an explanation of what the questionnaire was about assuring the
farmers that their participation would be voluntary and anonymous. The first question then asked
in which year the school had been attended as the idea was to find out whether there is a
difference between people who had graduated earlier and those who were still attending. After
this opening question, the participants’ application success was assessed by asking which of five
central technologies they had already (partially) applied or which they were going to apply in the
future. The five chosen technologies Integrated Farming, Effective Microorganism, Organic
Fertilizer, Agro-Ecosystem Analysis and Cropping Parallelogram shall be briefly presented at this
The first technology that was inquired in the questionnaire is Integrated Farming. According to
the trainer’s manual of the ECSFFS, this term “refers to agricultural systems that integrate
livestock and crop production so that the ‘waste’ from one component becomes an input for
another part of the system, which reduces costs and improves production and income of farmers”
(PhilCCAP, 2014: 169). The school educates farmers so that they understand the advantages of
combining different crops or crops and livestock. In addition, it helps them formulating a farm
plan design (ibid). Effective Microorganism is a culture of beneficial microorganisms that naturally
occur mostly in foods. They reinforce indigenous
species such as fungi, bacteria and microorganisms and
thus facilitate decomposition, suppress diseases and
reduce toxicants. The Effective Microorganism is one
way to produce liquid organic fertilizer and is the
method which was primarily mentioned during the
interviews (ibid: 102).
After inquiring into this specific technology, the
technology of using Organic Fertilizer in general was
next to be mentioned in the survey. This is “a product
of biological decomposition or processing of organic
Figure 6: Vermi-compost on a farm which
is used for demonstration purposes. Own
materials from animals and/or plants, which can supply one or more essential nutrients for plant
growth and development” (ibid: 101). Apart from the Effective Microorganism, an important
method is the vermi-composting (see figure 6) which is to breakdown the organic by using an
earthworm (ibid: 106). The fourth technology is the Agro-Ecosystem Analysis which helps to
understand the interactions of the different factors in the field and how they affect the growth of
the rice crops. It comprises a weekly monitoring of weather, natural enemies, disease incidence,
soil moisture and other factors by observing and gathering data (ibid.: 97). Finally, the Cropping
Parallelogram is a tool which is used for testing a potential cropping pattern. The latter describes
the sequence of crops that are planted in a given area and period in relation to the amount of
available water. Thus, the Cropping Parallelogram helps to analyze the rainfall data to determine
the right time to start the cropping (ibid: 161, 163). The five technologies had been chosen
according to the technologies the farmers had primarily mentioned during the previous interviews
as well as in alignment with an expert from the ATI.
The next part of the questionnaire was based on the four factors identified before (see figure 5).
Firstly, the “Perceived Capability” was inquired. The respondents had to decide whether they are
undecided, (dis)agree, rather (dis)agree with statements such as “I have not enough money” or “I
have not enough inputs”. All points were directly derived from problems the interview partners
had identified. In addition, the possibility was given to add other barriers which had not been
named before. The following section covered the second factor, “Attitude and Opinion”. Here, the
main elements the interview partners had named when being asked about advantages and
disadvantages were listed. This comprised the statement “My output will increase” as well as
“The food I produce is healthier” and others. Again, the respondents could mark their answer on a
five-point scale. The tables for “Social Appreciation” and “Experiences” were structured alike.
Although farmers have a certain view on these statements, it is still possible that some of the
factors are not of great importance for them and thus do not influence their application behavior.
For example, if a farmer perceives the technologies to contribute to the conservation of natural
resources, but does not consider it as very important, the opinion would not influence his or her
behavior. This is why the introduction of a importance ranking was necessary. During the pretest,
a five-point scale from “Very important” to “Not important” had been tried out. However, it had
turned out that the respondents tended to consider everything as important so that all crosses
were made in the first column, probably without any deep reflection. In order to initiate a
reflection process, another method was chosen in the final questionnaire. Here, the respondents
had to assign numbers to the single statements starting from one for most important. They could
still rate every statement with a one, but the likeliness for a more reflected response was
perceived to be higher using this approach.
In the next section, the farmers were asked about their future plans concerning the application of
the techniques or what their barriers might be. Due to strong constraints in space, this question
was not as elaborate and comprehensive as it should have been and was therefore not used for
the analysis. The last part covered questions on socio-demographic data. Its aim was to give a
descriptive picture of the interrogated people. Referring to Akudugu Abunga et al. (2012), the
person’s background such as the gender the education, the level of education and the age size
might influence the application behavior or the attitude. By inquiring this information, it was
possible to check their finding for the Philippine farmers.
In general, the questionnaire was intended to be kept as simple as possible because the target
group of respondents was expected not to be familiar with this kind of standardized inquiry. For
this reason, no filter questions or indirectly formulated questions were included. The previous
usage of the qualitative interviews enabled the almost exclusive application of closed questions,
in which the respondents had to choose between given options. The closed questions implied a
better compatibility of responses and eased the completion (Raithel, 2006: 67). The five-point
scale was used to give the respondents the opportunity to be undecided and to not enforce the
positioning on one side or the other (ibid: 69). Five categories were perceived to be enough to
enable the farmers to give precise feedback on the question as they could for example choose
between “agree” and “rather agree”. More categories might have led to unnecessary confusions.
The questionnaire was completed by 106 farmers. The majority of them were interviewed after
the Mass Graduation Ceremony in the gymnasium of the Department of Agriculture in
Tuguegarao City on May 29. This brought along the advantage, that farmers from different
barangays and schools participated painting a broad picture of opinions. The 202 graduates came
from schools in Peñablanca and Tuguegarao. From Peñablanca, 37 farmers participated from the
municipality of Manga, 38 from Minanga, 35 from Aggugaddan and 32 from Dodan. From
Tuguegarao City, 29 farmers attended from Namabbalan and 31 from Capatan (Alaska, 2015). The
rest of the data was collected in the two municipalities Pototan and Dumangas in Iloilo province in
the first week of June. As there was no comparable event that could be used at that time, farmers
had to be specially invited by the governmental bodies. After viewing the data from the
questionnaires, two detailed and several short interviews with farmers were conducted to shed
light on some of the questions that consequently emerged.
3.3 Methods used in data analysis
The recorded interviews were transcribed and coded to make the data more accessible. Reflecting
the theories on which this thesis is based, the codes comprise the attitude towards behavior,
subjective, perceived behavioral control (TPB) as well as relative advantages, compatibility with
people’s needs, complexity, trialability and observability (DOI). In addition, it was crucial to
highlight statements about which technologies had already been applied. Further codes gained
importance during the coding process.
The quantitative data was analyzed with statistical software. A first descriptive analysis provided
an overview over the data collected and enabled the creation of diagrams. However, in order to
be able to answer the research question, a more elaborated statistical method was required. This
was necessary to detect correlations between the adaptation rate of the new technologies and
the four factors Perceived Capability, Attitude and Opinion, Social Appreciation and Experiences.
For a start, the gathered data had to be transformed to derive mean values. The first required
value was the application value (a) of the five chosen technologies (t)14
with at representing the
specific application value of technology t. All possible responses were assigned to numbers
starting from 1 (“I apply”) and ending with 6 (“I used to apply, but stopped”)15
, that can be found
in table 8 (see Appendix 4). For example, if a farmer declared not to apply “Integrated farming”
(t=1), this was captured as a1=5. The average application value a of a single farmer was then
calculated as the arithmetic mean of the five values at (see equation 4).
The higher a of a single farmer, the less technologies he or she (fully) applies and plans to apply.
For the four factors (i) which are expected to be influential16
, xi represents the farmer’s evaluation
of factor i. The first step worked accordingly to equation 4. All sub-factors (j) that were part of the
values for Perceived Capability (x1), Attitude and Opinion (x2), Social Appreciation (x3) and
Experiences (x4), the respondents had to rate on a scale between “I agree” and “I don’t agree”.
t=1: Integrated Farming; t=2: Effective Microorganism; t=3: Organic Fertilizer; t=4: Agro-Ecosystem
Analysis; t=5: Cropping Parallelogram
Starting a technology, but then stopping it, is perceived as a sign for a failure of the technology taught.
For this reason, it is ranked with 6, the “worst” value. In contrast, a technology that a farmer never applied
is ranked with 5.
i=1: Perceived Capability; i=2: Attitude and Opinion; i=3: Social Appreciation; i=4: Experiences.
For analytical purposes, these values were again assigned to numbers with “I agree” becoming 1
(xij=1), “I rather agree” becoming 2 (xij=2) and so on up to “I don’t agree” which was assigned to 5
(xij=5). Again, the arithmetic mean x of all xi over m sub-factors was calculated:
To give an example, the formula used to calculate the mean of “Experiences” (x4) consisted of the
three sub-factors (m=3), that is to say ‘Observe’, ‘Hear’ and ‘Hands-on’ which were summed up
and divided by three:
For Perceived Capability, a lower value 1x indicates less perceived problems when it comes to
applying what was learnt at school17
. For the mean of “Attitude and Opinion”, a low number
points to a positive attitude towards the new technologies. The lower the values for “Social
Appreciation”, the more the application of the technologies is perceived to be in line with the
opinion of other people. Finally, lower means calculated for “Experiences” indicate that farmers
gained a lot of experience from other farmers by observing, trying out or hearing from others
about what was taught at the ECSFFS. In all cases, low numbers have a rather positive implication.
Although the means calculated with equation 5 already provide an overview about the factors
influencing farmers’ decisions, not all sub-factors j are equally important for every farmer. For this
reason, an adjusted mean yi was calculated which included the rankings the farmers made for all
m sub-factors. For each xij, they had decided about its importance Imp(xij) with 1 indicating that
the sub-factor is “most important”. As some farmers had rated several sub-factors with the same
number, equation (7) was able to capture every possible situation:
The numerator is the sum of all weighted sub-factors. In order to make sure that the sub-factor
ranked with the highest importance (rank 1) is most strongly integrated, this sub-factor is
multiplied by (m+1- Imp(xij)). This results in mxij for the most important sub-factor, 1 mxij
For Perceived Capability, all sub-factors were formulated in a negative way and thus had to be reversed. 1
became 5, 2 became 4 and so on (x’1j= 6 – x1j). The same was true for the sub-factor “I have to work more or
harder” (Attitude and Opinion) which was perceived as negative during the interviews.
for the second in importance and so on. The sub-factor with the lowest ranking is only multiplied
by 1 and thus has the lowest weight. In case a farmer rated all sub-factors with 1, all of them are
equally integrated. The denominator is the sum of all weightings. The whole fraction thus
represents a mean of all sub-factors of one factor being corrected by the importance the
individual farmer attaches to each sub-factor.
To give an example how this calculation was done, the shortest one is again best to be used. The
factor “Experiences_adjusted” (y4) consists of three sub-factors (j) with three rated values (y4j) and
the importance ranking Imp(y4j). There are three sub-factors, so m=3 and (m+1)=4. Inserting all
these values in (7) leads to (8):
Some of the farmers had not completed the ranking. In these cases, the usual arithmetic mean
without weighting as presented in equation (5) was used instead.
After calculating the adjusted means of each factor, the next step was to find out if there was a
relationship between the application rate a and the four factors yi, or whether their variation was
independent from each other. To do so, a correlation analysis was carried out. The Null-
hypothesis was defined as follows:
Ho: The application rate is independent from (a) the perceived capability of farmers to implement
the technologies, (b) the farmers’ attitude and opinion, (c) the appreciation of others and (d) the
direct and indirect experiences the farmers could make with the technologies.
As a Kolmogorov-Smirnov- Test concluded that “Application”, “Perceived Capability” and
“Attitude and Opinion”, are not normally distributed, the Spearman-rank correlation was chosen
(Raithel, 2006: 153). It is calculated according to the following equation (6):
Here, dh is the difference in ranks of the h-th pair of data in the sample while n is the total number
of pairs. For example, for the third farmer in the sample (h=3) the application value is 1.8 while
the mean value for perceived capability is 2.6 resulting in d3
=0.64. To receive the
correlation coefficient ρ, these differences were summed up for all n respondents of the
questionnaire, multiplied by 6 and divided by n(n2
-1) (Nicholson, 2014).
The computation results in a correlation coefficient which may have values between -1 for an
extremely negative correlation and +1 for an extremely positive correlation. If the correlation
coefficient equals 0, there is no dependence between the two variables (ibid: 152). For every
correlation, the statistical significance has to be checked to find out whether the correlation
coefficient significantly differs from 0. In other words, the significance decides whether the null
hypothesis that there is no dependence can be rejected. If a correlation is not significant, it is
possible that it only occurred by chance, not because of a reason. The lower the significance, the
higher is the likeliness that the observed correlation is no coincidence. To give an example, a
significance of 5% implies that the null hypothesis should be rejected with a probability of 95%.
Dependence between the two variables should be assumed. Usually significant levels of 5% or 1%
are used for hypothesis testing (Nicholson, 2014).
3.4 Limitations of methodology
Although the research was conducted with great care, the methodology of the study and
especially the data collection was subject to some limitations that should be mentioned at this
point. One of the limitations is the choice of the area for the data collection. While PhilCCAP is
conducted in three regions of the Philippines, only two of them (region II and VI) could be
integrated in the sample due to time limitations and security concerns. Within the regions that
were included, only farmers of few areas were interrogated so that some ECSFFS classes are over-
represented while other classes are not represented in the sample at all. The questionnaire itself
was only based on the results of interviews carried out in Peñablanca and Tuguegarao (region II)
and could unfortunately not include statements from farmers living in Iloilo province such as
Dumangas or Pototan (region VI) which were conducted afterwards. Although both regions are
heterogeneous and a long distance apart from each other, the farmers were expected to be
comparable which is not necessarily true (see 3.1.2).
Another challenge was the way the sampling was conducted within the chosen areas.
Representatives of the ATI had invited a lot of farmers to the interview meetings, but the
participation was of course voluntary. Therefore the group who attended the meetings can be
expected to have an above-average motivation to participate in events related to the ECSFFS most
likely due to a high opinion on the program. At the same time, farmers with less motivation due to
a lower opinion regarding the school did probably not join. This might have led to a bias in favor
of the school, which is unfortunately not verifiable. However, this is not the case for the farmers
who completed the questionnaire after the graduation party as this was attended by every
participant of the season.
During the qualitative interviews, the language barrier turned out to be a problem. Although most
Filipinos understand and speak English, their language abilities vary considerably. While some
interviews could be directly conducted in English, poor language skills and reluctance of some
interview partners required an interpreter. The translation was carried out by a representative
from one of the governmental bodies involved. Whether the presence of the interpreter or
probably imprecise translations affected the interview results, is unclear. In addition, it is possible
that the interviewees influenced each other as all interrogations were group interviews. However,
without forming groups, a far lower number of people could have been interviewed due to time
limitations. For this reason, the group interviews still represented the better option.
The greatest limitation of the quantitative part was the length of the questionnaire which was
supposed not to exceed a certain limit. This is why some questions could not be asked, even if
they would have been of interest. One example is how intense a farmer makes use of the
technologies. Whether the tool is used on a regular basis on the whole farm or just once for a
small part of the farm was not inquired in detail. To get a better understanding of the specific
challenges and motivating factors for each technology, the optimal solution would have been to
ask each question for each of the technologies, which is naturally utopic as this would have gone
beyond the scope by far. Furthermore, the question of the respondents’ perception of the risks
due to climate change would have been another topic of interest. These missing topics are
unfortunate but might represent areas for further research. However, they do not severely
influence the findings of the thesis.
Another limitation related to the questionnaire is the interviewees’ comprehension of the
questions. Although all farmers were able to read without larger difficulties and although
questions were often additionally read out and explained, it is possible that not all parts were fully
understood. This might especially be true for the questions about perceived capability, but also
Presentation and Analysis of Results
for other sections. For instance, the respondents’ answer to “I have not enough money” might
refer to a general lack of financial resources and not, as it had been demanded above, to the
specific limitation for the application of a technology.
An unfortunate circumstance was the different
sample conditions in Iloilo and Tuguegarao. While
all farmers in region II received the survey after
the graduation ceremony and therefore, apart
from two exemptions, graduated in 2015, the
respondents of region VI had already graduated
either in 2013 or 2014 (see table 2). Therefore,
neither a comparison of regions nor a comparison of the graduation year and thus the length of
possible application is feasible. Without any further information, it would be impossible to find
out where any variation derives from.
None of the limitations mentioned here represents a fundamental problem for the validity of the
study. However, they should be kept in mind to better understand and evaluate the analysis.
4 Presentation and Analysis of Results
After explaining the underlying theories and methodology that were used to gather and analyze
the data, the following part will present the findings. The first section (4.1) will purely describe the
sample paying attention to socio-economic factors of the group of respondents. The second part
(4.2) then forms the core of this thesis as it will analyze how the four factors influence the
application of the technologies taught at school. It only deals with those people who completed
the school, but does not consider the group of people that started or were invited to the ECSFFFS
but then decided not to attend. With one exception, it was not possible to talk to people in this
group directly. Nevertheless, 4.3 will give an overview of the underlying reasons for not attending
as derived from interviews with attendees and experts.
4.1 Description of the sample
Table 7 provides an overview of the data collection process (see Appendix 1). The qualitative
sample consisted of 28 farmers who were interviewed in groups as well as two single expert
interviews and three very short statements from farmers. Among the 28 farmers, 18 farmers were
Year of Graduation
2012 2013 2014 2015
II 2 0 0 66
VI 0 13 22 0
Table 2: Residential area and year of
graduation of survey respondent. Own data.
Presentation and Analysis of Results
women and 10 men representing well the gender distribution among ECSFFS participants18
majority of group interviews were conducted in region II (Cagayan Valley) where 24 farmers were
interviewed whereas in region VI (Western Visayas) only two interviews with two farmers each
The quantitative sample included 106 questionnaires in total with 64%19
being filled out in region
II and 36% being filled out in region VI. Among the valid answers, 29% of the respondents were
men and 71% women. The age distribution clearly demonstrates that the school is mainly
attended by farmers that are older than 40 years with the peak being in the 50 to 59 years age
bracket (see figure 7). Just one single person was younger than 20.
The educational level of the respondents is quite high as their highest degree is distributed among
Elementary School (29%), High School (41%) and University (30%) (see figure 8). The high share of
the last group and the high age of the participants can be partially explained by a number of
retired professionals who used to work for example as school teachers and started to work on
their farm after their retirement20
. A younger man is getting a degree in electronics, but
consciously decides to work as farmer (Group Interview 3: 14).
According to own observations during the graduation ceremony.
Values are rounded to the nearest whole number.
A group of these retired professionals was met and talked to in Pototan, Iloilo province.
Figure 8: Educational level of the respondentsFigure 7: Age distribution among the respondents
Presentation and Analysis of Results
Just one person is living in a single-person household while most respondents were living in a
household with two to four members (38%) or with five to seven members (48%). Fourteen
percent of the households consist of eight or more members. The area of land the respondents
and their household members are cultivating ranges from 0.1 ha to 10 ha with a mean of 2.4 ha.
Approximately half of the answers were between 1 ha and 1.5 ha. Only seven farms have a size of
8 to 10 ha (see figure 9).
Almost two-thirds of the students who filled out the questionnaire graduated from the ECSFFS in
2015, while others graduated in 2014 (21%) or 2013 (13%). Only two respondents had already
graduated in 2012. Here it should be remarked, that this distribution is not representative of the
attendance of the ECSFFS within the certain years. It rather stems from the fact that a large
number of farmers were interviewed after the graduation party, 2015.
When looking at the application of the technologies taught at school, Integrated Farming is the
one which is mostly applied. Ninety-four respondents (87%) already at least partially apply this
technology in which the farmers combine different crops or crops and livestock to make use of
their complementarity. The second most commonly applied technology is the Cropping
Parallelogram which is at least partially applied by 78% followed by the usage of Organic Fertilizer
with 61% and the Agro-Ecosystem Analysis with 60%. The technology which seems to be the
hardest to follow is the Effective Microorganism. Only one in two farmers are making use of this
technology (52%), while one in four farmers are not yet applying it, but are planning to. Both the
Effective Microorganism and the Organic Fertilizer were only applied partially by many
respondents (see figure 10).
Area of Land