university of hohenheim · 2008. 5. 5. · crops, livestock, technologies, and methods. the...

University of Hohenheim

Institute for Agricultural Economics and Social

Sciences in the Tropics and Subtropics

Josef G. Knoll Professorship for Development Research

Master Thesis

The Diffusion of Innovations among Farm Households

in Northwest Vietnam – a Case Study

Submitted by: Sonja Hähnke

Date of submission: 04/07/2007

1st supervising professor: Prof. Dr. Thomas Berger

2nd supervising professor: Prof. Dr. Matin Qaim

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Table of Content

Summary.......................................................................................................................... v

1 Introduction............................................................................................................ 1 1.1 Relevance of the Topic ................................................................................... 1 1.2 Objectives ....................................................................................................... 3 1.3 Organization of the Thesis .............................................................................. 3

2 Theoretical Background........................................................................................ 4 2.1 Diffusion of Innovations ................................................................................. 4 2.2 Hypotheses...................................................................................................... 8

3 Methodology and Data ........................................................................................ 10 3.1 Changes in Vietnamese Agriculture ............................................................. 10 3.2 Research Area ............................................................................................... 12 3.3 Data Collection ............................................................................................. 13 3.4 Description of the Two Research Villages ................................................... 16 3.5 Data Analyses ............................................................................................... 21 3.6 Linear Regression Model of Innovativeness................................................. 22

3.6.1 Econometric Model of Innovativeness ............................................. 23 3.6.2 Empirical Specification..................................................................... 23

3.7 Binary Regression Models of Technology Adoption ................................... 25 3.7.1 Econometric Models of Adoption Decisions.................................... 25 3.7.2 Empirical Specification..................................................................... 26

3.8 Ordered Regression Models of Technology Adoption ................................. 27 3.8.1 Econometric Models of Ordered Adoption Decsions....................... 27 3.8.2 Empirical Specification..................................................................... 28

3.9 Model of Household Networks..................................................................... 28

4 Results ................................................................................................................... 30 4.1 Innovations in the Research Area ................................................................. 30 4.2 Diffusion of Selected Innovations in the Research Villages ........................ 32 4.3 Reasons for Non-adoption, Discontinuation, and Financing Sources .......... 35 4.4 Objectives of the Farm Households.............................................................. 36 4.5 Characteristics of Innovative Farmers .......................................................... 38 4.6 Binary Choice Model to Explain Adoption / Non-adoption......................... 40 4.7 Ordinal Choice Model to Analyze Adoption over Time .............................. 44 4.8 Information Sources for Three Innovations.................................................. 47 4.9 Network Analysis of Survey Households ..................................................... 49

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5 Discussion.............................................................................................................. 52 5.1 Innovation Diffusion over Time ................................................................... 52 5.2 Diffusion through Certain Communication Channels .................................. 53 5.3 Diffusion among the Members of a Social System ...................................... 55

6 Concluding Remarks ........................................................................................... 59

7 References............................................................................................................. 61

List of Annexes Annex 1: Descriptive statistics of variables used in regression analyses ..................... 65 Annex 2: List of innovations by year of introduction (baseline 1955)......................... 66 Annex 3: List of innovations by year of introduction (baseline 1955), continued ....... 67 Annex 4: Reasons for Non-adoption of Innovations .................................................... 68 Annex 5: Reasons for Discontinuation of Innovations................................................. 69 Annex 6: Sources of Finance for Innovations .............................................................. 70 Electronic Annex ...........................................................................................................CD

List of Tables Table 1: Household size, sex and labour endowment of sample households ............. 16 Table 2: Summary of household head characteristics................................................. 17 Table 3: Overview of land and pond endowment in sample households.................... 18 Table 4: Short description of variables used in the analysis of innovativeness .......... 23 Table 5: Short description of selected innovations ..................................................... 26 Table 6: Categories and ranks assigned to dependent variables ................................. 28 Table 7: List of innovations by year of introduction................................................... 31 Table 8: Adoption rates of observed innovations in Ban Me and Ban Tum............... 32 Table 9: Most and least important household objectives ............................................ 36 Table 10: The top three household objectives by adopter categories ........................... 37 Table 11: Results of linear regression model ................................................................ 39 Table 12: Logit model fits and parameter estimates across selected innovations......... 43 Table 13: Ordered logistic models and parameter estimates ........................................ 45 Table 14: Correct predictions of adopter categories ..................................................... 46 Table 15: Relationship (correlation coefficient) between time of adoption and

personal network........................................................................................... 49 Table 16: Statistically significant relationships between personal network

characteristics and the adoption/non-adoption of innovations...................... 51

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List of Figures Figure 1: Innovation diffusion over time........................................................................ 4 Figure 2: A framework of five stages in the innovation-decision process ..................... 5 Figure 3: Factors influencing on the adoption decision of a farm household ................ 9 Figure 4: Political map of Vietnam and map of the research area ............................... 13 Figure 5: Education level by sex and age..................................................................... 17 Figure 6: Average proportion of operated land allocated to crops in the rainy season 19 Figure 7: Yield in t/ha for main crops .......................................................................... 20 Figure 8: Adoption curves for 18 innovations diffusing in Ban Me/ Ban Tum ........... 33 Figure 9: Box-Whisker-Plots of revenues from agriculture by adopter category ........ 38 Figure 10: Sources of information about three different innovations ............................ 48 Figure 11: Personal network thresholds and system innovativeness.............................. 50 Figure 12: Sociogram of household-to-household contacts in the study villages .......... 51

Abbreviations ANOVA Analysis of Variance corr. coeff. correlation coefficient GSO General Statistics Office of Vietnam GTZ Gesellschaft für Technische Zusammenarbeit ha hectare m metre max. maximum min. minimum mm millimetre # obs. number of observations OLS Ordinary Least Squares pers. comm. personal communication r Pearson product-moment correlation coefficient SFB Sonderforschungsbereich sqm square metre Std. Dev. Standard Deviation t ton

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Acknowledgements I would like to thank Prof. Dr. Thomas Berger for giving me the opportunity to collect primary data in Vietnam in the framework of the Uplands Program (SFB 564) of the University of Hohenheim. My deepest thanks go to Dr. Pepijn Schreinemachers for his great encouragement, patience, and advice in the course of this work. He and his wife Paan supported the survey with their valuable knowledge and experience. I enjoyed their company during the start of the survey and we shared both the hospitality of the survey participants as well as exciting restaurant visits in Yen Chau town.

I am very thankful to Gerhard Clemens and Nguyen Thi Nam Hong from the Uplands Program office in Vietnam for their valuable support and for facilitating the cooperation with the local authorities. The local authorities in the research villages and the interviewed farmers were very welcoming and invited us to share their experiences and knowledge with them. I would like to thank them cordially for their patience and openness to our questions. This case study would not have been possible without the great help of my two local assistants who carefully interviewed all sample households. They also helped me to understand more of the local culture. Sang and Tho, sự cảm ơn!

For their useful tips, discussions, and moral support, I want to thank my family as well as James Rao and Marco Huigen. Stephanie Tripp did an excellent job in proofreading my thesis.

I gratefully acknowledge that this survey received financial support from the G1.2 subproject ‘Innovation and sustainability strategies‘ of the Uplands Program of the University of Hohenheim. The program is funded by the Deutsche Forschungs-gemeinschaft, Germany (DFG) and co-funded by the National Research Council of Thailand (NRCT) and Ministry of Science, Technology and Environment (MOST), Vietnam.

http://www.dfg.de/

http://www.dfg.de/

http://www.nrct.net/

http://www.nrct.net/

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Summary The present survey studied the diffusion of innovations among 80 randomly selected farm households in a northern mountainous region in Vietnam. Efficient introduction of suitable innovations might contribute to improve livelihoods of farmers in this remote region.

The case study examined two adjacent villages about current innovations and analyzed diffusion of 18 innovations over time selected from a large variety of new crops, livestock, technologies, and methods. The cumulated adoption curves were tailed to the left indicating slow start of diffusion except for innovations that were actively promoted by extension bodies or companies. The reasons for non-adoption were manifold and varied for each innovation. The main constraints as perceived by farmers were lack of land and cash, followed by lack of experience and labour. Often farmers stated that an innovation was not suitable for their farm. Although more than 50% of the sample households had taken official credit in the past, only in few cases innovations were financed with official credit. Semi-formal credits from local associations and credits from shopkeepers were more frequently used, and the main source of finance was own savings.

A linear regression model has been used to estimate the relation of socioeconomic and communication variables to innovativeness. Age and education of the household head, farm and pond size as well as contacts with extension bodies appeared to be significant independent variables. Additionally, innovative farmers had different objectives than less innovative farmers. However, the differences were not statistically significant. Between higher agricultural revenues in 2005 and higher innovativeness, a significant correlation was found.

Furthermore, binary and ordered regression models were employed to model technology adoption. Results showed that the models for the selected innovations were significant and that different variables appeared to be statistically significant depending on the innovation. Variables like age, education, and contacts with extension bodies were positively correlated especially in the linear and ordered logistic models where the time dimension of adoption was captured.

Finally, this study examined the relation between different information channels and adoption decisions. Mass media, external local sources, and internal village sources were distinguished. Early adopters did not receive information about innovations from substantially different channels than later adopters. The analyses of household networks complement this. For most innovations, the centrality degree and the density of the household networks were not significantly different between adopters and non-adopters. However, measuring the personal threshold value of a household in its network over three different innovations was significantly correlated with innovativeness.

Introduction

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1 Introduction

1.1 Relevance of the Topic Innovation is “broadly defined as the implementation of ideas, goods, practices, processes, or services that are new or significantly improved within a particular environment and are intended to be beneficial” (ROGERS 1995). Innovation is a driving force of progress and development. Farmers are continuously adapting their agricultural practices to new ecological, economic or social circumstances. Amongst others, changing prices, increasing land scarcity, and new technological possibilities lead them to find and adopt new production methods in order to secure their livelihoods. However, farmers do not adopt innovations simultaneously, some adopting more rapidly than others, while some never adopt certain innovations.

A vast body of scientific literature on technology adoption exists. Already before 1970, at least 708 empirical studies were published by different disciplines (ROGERS and STANFIELD 1968). Most of these studies examined the adoption of an individual technology in a specific geographical area, making it difficult to draw general conclusions. Also the results of these studies vary as they used different methods, data, and explanatory variables. Even for the same type of technology, results are often conflicting. Reviewing literature on technology adoption in agriculture, RUBAS (2004) observed that in the case of soil conservation technologies, some studies found education to be positively related to adoption, some studies found education to be unrelated to adoption, and others found education to be negatively related to the adoption. These conflicting results were also observed for other variables like outreach effort, farm size, and age of the farmer.

If we consider innovation diffusion, we have to acknowledge that factors influencing the decision to adopt are usually manifold and intertwined. Furthermore, their influence does usually not only depend on the innovations’ and adopters’ characteristics, but also on the overall cultural and political background, the social structure in which the diffusion takes place, and of course the specific ecological and economic circumstances. It is important to understand the main mechanisms that drive innovations in order to get a better picture of factors which are important for innovation diffusion. By understanding the diffusion mechanisms and patterns of past innovations, we can compare them with present or future innovations. If we have a clearer understanding about which factors make an innovation likely to be adopted, or a farmer more likely to be an adopter, this might help to guide future diffusion processes. Extension services could address farmers which are likely to adopt and therefore create a larger base of early adopters who might further spread the innovation. Also, innovations could be designed in a way that makes adoption easier for potential adopters. Furthermore, if certain characteristics of the adopters would appear to favour the adoption of an innovation, why not create this characteristic, e.g.

Introduction

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education or market access, among non-adopters, or even before dissemination of a future innovation?

Innovations are understood to be beneficial, which in developing countries often means securing and/or improving the adopters’ livelihoods. The ‘Uplands Program’ of the University of Hohenheim, Germany, aims to contribute scientifically to the maintenance of natural resources and improvement of livelihoods of smallholder farmers in mountainous regions in South East Asia. The present study has been conducted in Son La Province in Northwest Vietnam, which is one of the research regions of the Uplands Program. Identifying this region’s mechanisms of agricultural innovation diffusion in this region might contribute to the future dissemination of innovations developed by the Uplands Program.

Nowadays, agricultural practices in North Vietnam are quite diverse. During the collectivist period, farmers’ possibilities to make own decisions on agricultural practices and livelihood systems were heavily restricted. Farmers have in the meanwhile changed their farming systems in order to cope with different situations. Even in remote areas new technologies have been introduced. However, their success and adoption by farmers differ considerably. The Yen Chau district in Son La province is a poor and remote agricultural area and efficiently introducing innovations and making them accessible to farmers could help to improve livelihoods and food security of farm households in the district.

An innovation is an investment for the farmer, and as such the investment costs are an important consideration for a farmer in his decision to adopt. If an innovation could considerably improve the farmer’s income but the farmer cannot afford it, then extension services or others need to address the cash constraint together with promoting the innovation. So far, little is known on the influence of credit access on adoption decisions. A farmer who could borrow a so-called micro-credit might be more willing to adopt than a farmer who has no access to credit. If we can understand why a farmer has implemented an innovation, or has not yet done so, we can possibly draw conclusions for the diffusion of future technologies. This can help to assess their potential impact and make it easier to pursue agricultural development policies. If we understand the factors which make a technology likely to be adopted and who could be a potential adopter this can help extension services to target the farmers in a more efficient way and speed up diffusion process.

Introduction

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1.2 Objectives The objective of this research project is to understand how innovations diffuse in the selected area. This might enable to researchers and extension workers to better predict adoption rates of future innovations and find ways of how to speed up the diffusion process. In the course of this work, several questions were investigated.

Which innovations are present in the selected area and how did they diffuse over time? What are their characteristics and how might these relate to adoption decisions?

What are the objectives of the farm households? Is there a pattern between objectives and innovativeness? Which farmers are generally innovative?

What are reasons for non-adoption and discontinuance of innovations? Which constraints do farmers perceive? How do farmers finance innovations? What are important information channels for the farmers?

Which factors influence adoption decisions and are they comparable across different innovations? Can social networks help explain diffusion?

1.3 Organization of the Thesis This thesis is organized as follows. Section 1 introduces the topic of the present study and stated the objectives of this research. Section 2 portrays the theoretical background of innovation adoption and diffusion, and concludes with hypotheses. In section 3, the research area is described and methods of data collection and analyses are explained. Section 4 and section 5 present results and discussion of the research, while section 6 finally draws conclusions and identifies limitations of the research project.

Theoretical Background

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2 Theoretical Background

2.1 Diffusion of Innovations Research on the adoption and diffusion of agricultural technologies has a long history in social sciences. “Diffusion is the process by which an innovation is communicated through certain channels over time among the members of a social system” (ROGERS

1995), where time is involved in the innovation-diffusion process, innovativeness, and an innovation’s rate of adoption. Thus, diffusion depends on four elements: the innovation itself, communication channels, time, and a social system in which diffusion takes place (ROGERS 1995). Under innovation, we understand any new idea, practice or object that is intended to be beneficial for the adopter. Although the explanations for adoption seem to vary between studies, many studies have confirmed that innovation diffusion follows a sigmoid diffusion path over time (see Figure 1). However, there is some debate for the reasons behind the shape or the most appropriate functional form (RUBAS 2004). Additionally, if new technologies, methods or ideas are related, occur at the same time or require the same resources then the diffusion paths of innovations affect one another. For a given innovation in general, initially there are only a few adopters, called innovators. Others learn from the innovation from various sources, including from the innovators, and occasionally adopt. The number of adopters per time unit increases and eventually reaches a maximum, and subsequently decreases as fewer non-adopters are left. The red line in Figure 1 shows the relative amount of adopters per year, and the blue line depicts the sigmoid cumulated adoption curve over time, eventually reaching adoption by all members of the social system. Full adoption however, does not always occur and hence, saturation can be reached at a lower level of diffusion (ROGERS 1995).

Figure 1: Innovation diffusion over time

(adapted from ROGERS 1995)

0102030405060708090

100

time

% o

f ado

ptio

n

Adopters per year Cumulated adoption

years


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Diffusion processes can be examined considering adoption throughout the social system as well as the decision process for each individual in the social system. In the innovation-diffusion framework (see Figure 2), a change agent (e.g. rural extension services) introduces a new technology or idea to the final users, thus creating awareness of the innovation (ROGERS 1995). The potential users seek knowledge about the innovation in order to decrease uncertainty and consequently form an attitude towards it. The information acquired in the persuasion stage of the process will finally lead to the decision to adopt or reject the innovation. After implementing the decision to adopt or reject, the individual continues gathering information concerning the innovation either in order to review the decision or to confirm it. Figure 2 depicts that in every stage of the innovation-decision process, the individual acquires information from various communication channels. The information channels accessed by the individual and the behaviour throughout the process are greatly influenced by the decision maker’s personal characteristics, the individual’s innovativeness, and the prior conditions of both the decision maker’s situation and the social system. ROGERS (1995) defined personal innovativeness as “the degree to which an individual or other unit of adoption is relatively earlier in adopting new ideas than other members of a system”. A widely used approach ROGERS (1995) mentioned is based on the S-shaped curve, which results when the cumulative adoption of an innovation within a social system is plotted over time. When plotted over time on a frequency basis, the distribution of adopters usually follows a normal, bell-shaped curve. He pointed out that also the degree of innovativeness as a human trait is expected to be normally distributed. Additionally, the innovation-decision process is different for innovations, depending on the distinct innovation’s characteristics. ROGERS (1995) found five main attributes of innovations that influence adoption decisions: “relative advantage, compatibility, complexity, trial-ability, and observability.”

Figure 2: A framework of five stages in the innovation-decision process

(adapted from ROGERS 1995)

Innovation-decision process:

1. Knowledge 2. Persuasion 3. Decision

(Adoption / Rejection) 4. Implementation 5. Confirmation

Information / communication channels

Change agent

NEW !!

Decision maker’s characteristics Innovation’s characteristics Prior conditions: - previous practice - felt needs / problems - innovativeness - norms of

the social system


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In order to answer the question of who adopts, when and why, several models can be identified in literature. The first model is based on the assumption that people adopt an innovation when sufficient information has reached them. It is called information-contagion model, epidemic model, network model, or threshold model. Extended models include that information about the innovation is assessed towards an individual threshold that can change over time and depending on the adoption rate within the personal network of the decision-maker. GRANOVETTER (1978) postulated that individuals were heterogeneous in the extent to which their social system influences on them. The “degree an individual is influenced by others in his or her social system is an individual’s threshold” (VALENTE 1995). Thus, individuals have varying thresholds for adoption of an innovation. According to threshold models, individuals make decisions based on the proportion of others that have already done so (GRANOVETTER 1978). Theory on innovation adoption suggests that diffusion occurs among the members of a social system, and hence the adoption behaviour of one member influences the adoption decision of another member (ROGERS 1995; VALENTE 1995). Some individuals are more willing to adopt than others who only adopt when most members of the system already have adopted. In this regard, an individual can be innovative with respect to his/her own network, and/or innovative with respect to the whole system. VALENTE (1995) found that later adopters generally also have higher exposure levels of peers in their personal network before they adopt. Furthermore, an individual’s position and connectedness within a social system is important for adoption behaviour (VALENTE 1995). Individuals who have more direct ties to other actors are more innovative, receive more information, and are less dependent on other individuals (FREEMAN 1979; WASSERMAN and FAUST 1994; VALENTE 1995; HANNEMANN and RIDDLE 2005). Furthermore, individuals who have dense networks are considered not to receive much information from outside. In a dense personal network, most members are connected to each other and are thought to hear of an innovation later (VALENTE 1995).

The theory of innovation diffusion identifies the spread of information as an essential aspect of the diffusion process. This information diffuses through certain communication channels. Mass media is considered to be more effective in creating initial knowledge of innovations, whereas the adoption decision is influenced more by interpersonal contacts. Likewise, earlier adopters are thought to obtain information from outside and pass information about an innovation to other farmers in their social system. The later adopters would base their adoption decision on the evaluation of their near peers and thus rely stronger on internal sources (ROGERS 1995). Thus, earlier adopters receive information from different communication channels than later adopters.

However, these information-based models do not take into account economic evaluation of an innovation and other constraints to adoption. An individual might have sufficient information about an innovation and still not adopt. This can be due to constraints or requirements of the innovation such as required labour or capital input,


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access to production inputs or minimum farm size. This model is called the economic constraints model or factor endowment model. Because of heterogeneity in factor endowments, the benefits of an innovation differ among potential users, and thus the households with the highest perceived benefit being the first ones to adopt (BLACKMAN 1999). However, the net return on adoption is assumed to increase over time because of such factors as external economies of scale, learning by doing and thereby perfection of the innovation by early adopters, falling search costs for information, and depreciation of existing capital and assets (BLACKMAN 1999). This leads to adoption by other households for which net return on adoption was not high enough in the early introduction phase of the innovation.

Another model, the technology characteristics-user’s context model, focuses on the characteristics of a technology in a certain agro-ecological, socioeconomic and institutional context of the potential user. In this model, the characteristics underlying the context and especially the perceptions of potential adopters are decisive for adoption and the diffusion process. Including the perceptions of the farmers in the model implies their possible involvement in the technology development process (GOULD et al. 1989; BIGGS 1990; SCOONES and THOMSON 1994). Modelling the perceptions of farmers with respect to an innovation’s characteristics in a specific context as determinants of adoption and involving farmers in technology development can be very useful to explain diffusion on a local level but makes it problematic to compare different contexts.

Two further models on technology diffusion are order models and stock models (KARSHENAS and STONEMAN 1993), both based on the differences in net returns among potential adopters. In order models, the net return is determined by the order of adopters, and is higher for earlier adopters. This order effects arise from limited supply of a critical input, such as skilled labour. Even if firms are identical, it will only be profitable for a few to adopt early because of this order effect. Over time, net returns increase from external economies of scale lowering the cost of adoption, the perfection of the technology, falling search costs, and depreciation of existing assets. As net returns from adoption increase over time, more and more firms adopt the innovation (BLACKMAN 1999).

Stock models assume that the net return on adoption for any additional adopter decreases as the total number of adopters increases. The innovation is assumed to decrease production costs for adopters. Hence, the output increases and affects the market, decreasing prices due to higher supply. Thus, the net return decreases for later adopters. The more firms initially adopt a new technology, the lower the output prices, and the lower the net returns. Therefore, only a few firms will be early adopters. However, for the same reasons as in order models, costs of adoption decrease over time due to economies of scale, the perfection of the technology, falling search costs, and depreciation of existing assets. This in turn increases net returns for later adopters, making it more profitable for other firms to adopt the technology (KARSHENAS and STONEMAN 1993; BLACKMAN 1999).


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All of the above models can be used to explain innovation diffusion. Especially the information-contagion model and the economic constraints model are widely used in the agricultural context. However, a study of MBEYO’O (2001) in Cameroon on local agricultural practices and knowledge change showed that reality lies somewhere in between different theoretical models. MBEYO’O (2001) found that the “farmers’ propensity to test new ideas is impeded by their inadequate access to the needed inputs, due to their low purchasing power or inadequate access to technical information, perception of some technologies as inadapted to their environment, reliance on other people’s decisions or actions, and their perception of some imported technologies as less productive than local alternatives.” ZELLER et al. (1997) found in a study in Malawi, that risk exposure of the rural household, the capacity to bear risk and credit constraints are main factors that influence the adoption decision.

2.2 Hypotheses From the theoretical background, we can identify many factors that influence the adoption decision of an individual and hence, the diffusion of an innovation. We can classify these factors into innovation characteristics, household or socioeconomic characteristics, access to information, and the general conditions of the environment where the innovation has been introduced. Figure 3 gives a schematic overview about the four main factors influencing adoption decisions. For each main factor, sub determinants are listed such as household and farm size under the general point ‘household characteristics’.

It is assumed that innovative farmers use many different new crops, livestock, and technologies. Furthermore, they use these innovations before most other farmers do. Additionally, it is assumed that farmers who have strong economic goals would be more open towards new higher yielding crop varieties or new profitable production methods. Socioeconomic factors are supposed to influence adoption decisions. Education, age, productive assets are assumed to be positively related to adoption decisions and general innovativeness of a farm household. If a farm household has more resources it is easier to cope with drawbacks from unsuccessful innovations.

Literature suggests that a farmer is more innovative if he/she is well-connected to information from outside his social system. According to the innovation-decision process mentioned earlier, a farmer gathers information about the innovation before he/she decides to use it. Different farmers inform themselves in different ways and the source of information depends much on the type of innovation as well as the stage in the diffusion process. According to epidemic models of innovation diffusion, a few farmers initially learn about new technologies from outside their own village and then start to use it. Other farmers learn from these early users and then start using it as well. The individual has a threshold to adoption, and this is influenced by his/her


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connectedness within the social system, and the adoption behaviour of the near peers in the network.

This study assumes that socioeconomic factors, as well as access to information and social interaction play a crucial role for specific adoption decisions, the timing of adoption, and the general innovativeness of a farm household. Furthermore, the general conditions of the environment and the innovation’s characteristics are important factors determining adoption decisions and diffusion of innovations (see Figure 3). In the present study, the general conditions like institutional framework or access to innovations are assumed the same or at least very similar for all sample households and therefore not differentiated.

Figure 3: Factors influencing on the adoption decision of a farm household

(adapted from ROGERS 1995; BLACKMAN 1999; RUBAS 2004; SCHREINEMACHERS et al. 2006)

Adoption decision

General conditions

Household characteristics

Innovation characteristics Information

Permanence/ reversibility

Econom.+social benefits/costs

Fits in farming system

Complexity of innovation

Trialability

Observability

Age structure of household

Farm size, farm assets

Household (hh) size

Income

Education

Dependency on farming

Social status of farmer

Social /profess. networks

Agricultural training

Connectivity to outside

Repair facility, spare parts

Physical access to innovation

Access to credits

Institutional framework

Methodology and Data

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3 Methodology and Data The fieldwork for the presented study was conducted between August and November 2006. It was associated to a larger research project on ‘Innovation and sustainability strategies’, which is one project within the interdisciplinary ‘Uplands Program’ or ‘Sonderforschungsbereich’ (SFB) 564 ‘Research for Sustainable Land Use and Rural Development in Mountainous Regions of Southeast Asia’ of the University of Hohenheim, Germany. This chapter first gives general background information on the research area. Second, it describes the methodology used for data collection followed by a detailed description of the survey villages. Subsequently, handling of data as well as the methods used for analyzing adoption decisions is explained.

3.1 Changes in Vietnamese Agriculture Vietnam has undergone great changes in its agricultural development during the last 5 decades. In 1953, during the Indochinese War (1946 – 1954), the North Vietnamese government decreed a land reform to consolidate the Communist Party’s power in rural areas of Vietnam. It was an attempt to reduce the power of rural elites and large-scale landowners by redistributing land and assets such as animals and tools to smallholder farmers and landless people. The implementation of the land reform caused significant turmoil and violence in rural areas. This was especially due to land reform teams, who often arbitrarily classified people as ‘despots’, ‘despot-leaders’, ‘traitors’ or ‘reactionaries’, resulting in frequent dispossessions, imprisonment and executions (PHAM QUANG MINH cited in FRIEDERICHSEN 2006). However, the tenth party plenary decided a ‘correction campaign and self criticism’ in the late 1950ies. The party admitted mistakes in the implementation of the land reform. Around 23,000 formerly persecuted ‘class enemies’ were rehabilitated and part of the land reform was undone by giving back land and assets to the former owners. Nevertheless, following the land reform, the North Vietnamese government initiated the collectivization of production. While the South Vietnamese government had also decreed a land reform in 1955, the transition to agricultural production in cooperatives was more successful in the North than in the South of the country (FRIEDERICHSEN 2006).

From agricultural collectivization with no individual ownership of land in the North and subsequent Five-Year Plans, Vietnam went into a crisis phase with severe production drops in the end of the 1970ies and early 1980ies, mainly due to adverse weather conditions and shortages in supply of agricultural inputs. Landownership and control over agricultural production was gradually returned in two successive reforms. The first reform was based on decree 100 (1981), which allocated land-use rights of fields to households. However, households still had to contribute a rice quota to the cooperatives. A major change consisted in that they could keep the surplus. The second shift was initiated by resolution 10, issued by the Vietnamese Communist


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Party’s Political Bureau in 1988. With latter resolution, households received longer-term use rights to land. At the same time, the role of the cooperatives in the production process was reduced to service provision (BERESFORD 1990; DAO 1997; PHAM QUANG MINH cited in FRIEDERICHSEN 2006).

Since 1986, a reform process called ‘Doi Moi’ (all-round renovation process) started. This reform consisted of six major economic policy changes, comprising decentralization of state economic management; replacement of administrative measures by economic ones, including a market orientated monetary policy; adoption of an outward orientated policy in external economic relations; agricultural policies that allowed for long term land use rights and greater freedom to buy inputs and market products; reliance on the private sector as an engine of economic growth; and letting state and privately owned industries deal directly with the foreign market for both import and export purposes (VIETNAM MINISTRY OF FOREIGN AFFAIRS 2004). These changes also strongly affected farmers’ livelihoods. While agricultural practices and livelihood systems in Northern Vietnam were fairly homogeneous during the collectivist period, this changed dramatically after the reforms (FFORDE

1987; DAO 1997).

The 1993 Land Law is the most recent change in the land allocation to farmers. It regulates the allocation of land use rights to individuals, households, and state or private organizations. The land allocation process comprises cadastral surveys, mapping, title registration, and the issuance of land use right certificates, while the law distinguishes between different land types such as agricultural and forestry land, residential land, land for specialized use, and unused land. Under the new Land Law farmers can “exchange, lease, inherit and pawn land use rights” (FRIEDERICHSEN 2006).

Since 1994, government extension workers have been present in Yen Chau district to support agriculture (MINOT et al. 2006). Yet their involvement mostly concentrates along major roads and the more accessible communes (FRIEDERICHSEN

2006). In Yen Chau district, the main offices to support the agricultural sector are the Veterinary Office, Plant Protection Office, and the Agricultural Extension Office. The district Agricultural Extension Office in Yen Chau cooperates with the World Bank, GTZ, and other international organizations. It is, however, poorly staffed with 19 extension workers covering 14 communes comprising around 60,000 people (SOCIAL

FORESTRY DEVELOPMENT PROJECT (GTZ) 2004 cited in FRIEDERICHSEN 2006). While direct assistance to farmers is limited, most of the extension agents work only with the head of the commune or village (MINOT et al. 2006). It is still unclear how much actual influence the extension service has on farmers’ decisions, but the extension service’s financial and human resources are scarce. The extension service mainly organizes trainings for farmers and sets up “demonstration plots in order to disseminate agricultural techniques related to newly introduced and improved crop varieties or crops”, as well as to test and introduce new pesticides or fertilizers (FRIEDERICHSEN 2006; HONG and THAI 2006, personal communication).


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3.2 Research Area The research was conducted in Son La Province, which is located in the mountainous region of Northwest Vietnam, bordering Laos to the south (see Figure 4 for a political map of Vietnam). Apart from two large plateaus, the province consists of valleys, high hills and mountains. Climate in Son La Province is subtropical, with mean monthly temperatures varying between 15°C at the coldest time of the year from December to January and 26°C during the hottest time of the year around June/July. From November until March, there is the dry northeast monsoon while the southwest monsoon between April and October delivers three quarters of the annual rainfall. In Son La Province, the annual rainfall fluctuates between 1200 – 1700 mm. Average sunshine per month is around 155 hours and average humidity ranges within 70 – 90% during the year (GSO 2005a; The Uplands Program 2007a).

Around 89% of the population in Son La Province lives in rural areas, and hence most livelihoods are based on a broad spectrum of agricultural and forestry activities (GSO 2005b). Farmers intensively grow rice on irrigated paddy fields and produce one to two harvests per year. Upland areas are mainly under maize and to a smaller degree under cassava. Sometimes maize is intercropped while upland rice has decreased. Recently, farmers have engaged in industrial crop production such as sugarcane and cotton. Furthermore, many households cultivate fruit such as banana, mango, lychee, longan, and plums for home-consumption or market them on a small scale. The same applies to vegetable production. Large ruminants serve as a source of cash income (predominantly cattle) or as draught animals (mostly buffaloes). Farmers raise other animals such as pigs, goats, chicken, ducks, and fish both for home consumption and for sale (FRIEDERICHSEN 2006; pre-survey group interviews 2006, pers. comm.).

Within Son La province, Yen Chau district was selected. The altitude in this area varies from 200 m to 1420 m above sea level. The total area of Yen Chau is 84,000 ha of which around 14,000 ha is declared as agricultural land (NEEF et al. 2006). The population in this region is quite diverse. Vietnam counts 54 different ethnic groups with the majority (86%) being Kinh or ethnic Vietnamese (CIA 2007). According to NEEF et al. (2006), the population in Yen Chau in 2005 was 63,191 persons in 13,597 households. These belonged to 12 different ethnic groups, dominated by the Black Thai (53.5%), followed by Kinh (21.1%), Hmong (12.8%), Xinh Mun (11.9%), and Kho-mu (0.4%).


13

Figure 4: Political map of Vietnam and map of the research area

(adapted from CIA 2007; The Uplands Program 2007b)

Two Black Thai villages were chosen for data collection. The selection of villages in Yen Chau district was based on an existing research project of the ‘Uplands Program’. Within Yen Chau district, the two villages are located in the Chieng Khoi commune (Figure 4). Before 1995, there was only one single village. Because of the village size, the officials decided to split the village into two. The main road roughly divides the households to the two villages, and contacts between the villagers are frequent due to relational matters and sharing the same access road (HONG and THAI, village headmen of Ban Me and Ban Tum, 2006, personal communication).

3.3 Data Collection In mid-October 2006, the village headmen and the headmen of the Farmers’ Union in the two selected villages were asked to identify four to five innovative farmers for a group interview in each village. The interviews were conducted with the help of a male interpreter, translating from English into Vietnamese. Therefore, the respondents

Research villages: Ban Me and Ban Tum


14

from the Black Thai ethnic minority could not use their mother language but answered the questions in Vietnamese. The interpreter helped researchers in this region before, and even specifically in these two villages. Although he belonged to the Kinh or ethnic Vietnamese majority, he had considerable knowledge about customs and people in this area. The group interviews were conducted in order to get a general overview over village life and the agricultural innovations during the last fifty years. They were semi-structured and should allow for discussions among the farmers, too. Leading questions were about general welfare status of the village (access to electricity, drinking water, etc.) and the time sequence of innovations for crops, production technologies, livestock, and conservation measures.

The information from these two group sessions was used to refine the structured and coded questionnaire for a survey of 80 households. In Vietnam and also within the ethnic minorities of the research region, the head of a household is typically a male person. Hence, the respondents were usually the male household heads with their wives sometimes helping to answer the questions. Only in cases where the male household head was not present or too old, the spouse or their adult child provided the information. These datasets were treated the same way as the others.

A random sample of 80 households was drawn from the household lists provided by the two village headmen. The household interviews were conducted in October and November 2006. The questionnaire was based on a study conducted by SCHREINEMACHERS et al. (2006) in a mountainous region in Northern Thailand and was adjusted to the present research area. Additional questions were included to find financing sources and credit access, and then refined with the help of the group discussions and two trial interviews. Originally, it was planned to include in-depth questions on credit and the perception of innovations through farmers. However, this had to be dropped due to time limits. The questionnaire was organized in sections, which are listed below:

Section A is for general information. This page identifies the household, respondent, interviewer, date and time of the interview.

Section B identifies the household objectives through ranking of different objectives by the farmer. It is assumed that farmers who strive stronger to increase their income would be more open towards new higher yielding crop varieties or new production methods.

Section C asks the farmer about his/her connectedness and access to agricultural extension services, markets and other information channels. Literature suggests that a farmer is more innovative if he/she is well-connected to information from outside his social system.

Section D explores for a determined list of innovations whether the farmer adopted it. It asks for the year of adoption, financing sources, and reasons for non-adoption and discontinuance. The objective of this section is to identify innovative farmers. It is assumed that innovative farmers use many different new crops and


15

technologies. Furthermore, they use these new crops and technologies before most other farmers do.

Section E asks the farmer to identify sources from which he heard about the innovation before adoption. According to diffusion theory and the innovation-decision process mentioned earlier, a farmer gathers information about the innovation before he/she decides to use it. Different farmers inform themselves in different ways and the source of information depends much on the type of innovation as well as the stage in the diffusion process. Section E asks for three different innovations, and if the farmer ever used the innovation he/she is asked to identify those sources which provided information before adoption, and to rank them in order of importance.

Section F attempts to analyze the farmer’s network of friends, and their adoption behaviour of three selected innovations with regard to him. As provided in the theoretical background information about diffusion theory, farmers influence on each other. The individual has a threshold to adoption, and this is influenced by his/her status and connectedness within the social system, and the adoption behaviour of the near peers in the network. The objective of this section is to test the importance of friendship networks on adoption behaviour of the interviewed farmer.

Section G is to see whether a farmer ever obtained official credit and his attitude towards this. Official credit was identified as an innovation in itself.

Section H asks for household characteristics and is split into two parts due to organizational matters. The first part deals with land endowment, crop cultivation, revenue, and self-consumption of crops. The second part asks for livestock production (revenue and self-consumption from these activities), household equipment, demographic data as well as data on human capital. According to the theory of innovation diffusion, a few farmers initially learn about new technologies from outside their own village and then start to use it. Other farmers learn from these early users and then start using it as well. This study tries to relate the time of adoption to characteristics of farm households. Literature suggests that certain household characteristics possibly influence the use of new crops or technologies. As elaborated earlier, the household size, the age structure (young vs. old household head; ratio of people in working age) and education would be such indicators. Another influencing factor of learning about innovations could be the farmer’s possibilities to connect to the world outside of the village (motorcycles, phones, TVs) and if the farmer received agricultural training.


16

3.4 Description of the Two Research Villages Based on the data gathered via the survey for the present study, the following section provides more detailed information to describe the households in the two research villages.

Household Size and Labour Endowment

In total, the study surveyed 80 households with an overall sex ratio of 0.86 (180 male/ 208 female). This is low compared to a countrywide sex ratio of 0.98 (CIA 2007). Household sizes vary between 2 and 8 household members, with 4.85 as the overall mean and on average 2.25 men and 2.6 women respectively (see Table 1).

Table 1: Household size, sex and labour endowment of sample households Mean Std. Dev. Minimum Maximum No. of men in household 2.25 0.85 1 4 No. of women in hh 2.60 1.07 1 6 Men 15-64 years 1.68 0.81 0 4 Women 15-64 years 1.73 0.69 1 4 Full-time labourers 2.85 1.10 1 6.1

Around 9% and 21% of the sample population were aged below 15 and above 64 respectively. From the total 388 members of the sample households, 70% were in working age between 15 and 64, comprising 134 men and 138 women. This is in line with a countrywide average of 67.9% of the population aged 15 – 64 years (CIA 2007). Respondents were asked to indicate the labour availability in terms of full- and part-time work on the farm. Households in the sample had 1 to 6.1 full-time labourers available, with 2.85 labourers on average (Std. Dev. 1.10). The labour endowment of households correlates significantly with the household sizes (r = 0.73).

Education and Household Head Characteristics

Overall literacy in Vietnam is 90.3%, defined as people aged 15 and above who can, with understanding, read, and write a short, simple statement on their everyday life (CIA 2007; World Bank 2007). Male literacy is generally higher than female literacy, with 93.9% and 86.9% respectively (CIA 2007). In the surveyed households, literacy in Vietnamese language was somewhat lower, with an overall 85% for people aged 15 and above. Men had a considerably higher literacy rate (90%) than women (79%). Figure 5 shows that particularly the age group of people above 64 received no education at all. Within the total population, men predominantly received secondary or higher education (60% of male population) while most women (41%) received basic education, which means being able to read and write Vietnamese or having attended primary school. However, more than half of the females between 15 and 40 received secondary or higher education, thus indicating that in the younger generation education is also more accessible to females.


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Figure 5: Education level by sex and age

Almost 60% (n = 47) of the household heads in the surveyed sample attended secondary schools or received other higher education (see Table 2). 35% attended primary school or could at least read and write Vietnamese, and only 5 household heads (6.25%) did not receive any education. Out of the 80 households, only three were headed by a female, where the male household head had deceased.

Table 2: Summary of household head characteristics # observations Mean Std. Dev. Min. Max. Age 80 44.91 12.24 23 84 Farm management experience 80 18.41 10.93 1 56 % of time working on farm 80 74.82 33.83 0 100 Education

No education 5 Basic education 28 Secondary education 47

Main occupation Farming 64 Shopkeeper, self-employed 5 Employee for government 7 Other employment, trader 4

The average age of the household head was almost 45 years (Std. Dev. 12.24) while average experience in farm management was much lower at 18.41 years (Std. Dev. 10.93). Around 25% of the farmers in the sample had 10 years or less of experience in farm management. Mean working time on the farm was around 75% part-time (Std. Dev. 33.83).


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Main occupation of most household heads was farming (n = 64), and the remaining 16 household heads were government employees, self-employed shopkeepers, or others (see Table 2)

Land Endowment

Households in the sample had an average of 1.17 ha of agricultural land under cultivation (Std. Dev. 0.48). Renting area in and out to other farmers did not seem to be widely practised. Only one household rented out some farming land, while as many as 24 households rented in area, though never more than 0.6 ha at once. If intercropping during the rainy season is taken into account, the total operated land per household slightly differs from actual land size, with minimum and maximum values staying the same. Table 3 gives a brief overview of land endowment, which varied over a wide range from 0.14 to 2.42 ha available per household. In addition, pond sizes have been included since they constitute a major productive factor for cash revenues and self-consumption. Out of 80 households, 10 did not possess a pond. The mean among the other 70 households was quite small, with 0.14 ha per household and a non-normal distribution ranging from 0.06 to 1.05 ha.

Table 3: Overview of land and pond endowment in sample households Percentiles # obs. Mean

(ha) Std. Dev.

Min. (ha)

Max. (ha) 25% 50% 75%

Actual land size 80 1.17 0.48 0.14 2.42 0.82 1.18 2.06 Area rented out 1 0.24 Area rented in 24 0.24 0.15 0.012 0.60 Operated land 80 1.24 0.49 0.14 2.42 0.93 1.21 2.21 Pond size 70 0.14 0.15 0.014 1.05 0.06 0.11 0.33

During the interviews, farmers were often not sure about the size of their land. Hence, farmers estimated the land size and thus, the values were sometimes not exact. A list of land ownership was not available from the village headmen. In informal talks, more information about land distribution during the land reforms was obtained (HONG and THAI, village headmen of Ban Me and Ban Tum, 2006, pers. comm.). In 1997, the Vietnamese government collected all lowland paddy fields and upland fields. Orchard land, ponds and resident land remained in possession of the private households. After confiscation, the land was redistributed per head regardless of the areas each household contributed before. In both villages, each person received 2000 sqm regardless of age (280 sqm [290 sqm] of paddy field + 1720 sqm [1710 sqm] of upland field in Ban Me [in Ban Tum]).

Agricultural Production

Farmers in the area usually practice intercropping, especially with maize and cassava in the upland fields. While maize is only cultivated during the rainy season, cassava is grown during the whole year with no distinct seasons. As mentioned above, the total operated land amounts to a bigger number than if only the actual land size is taken


19

into account. Cassava and maize are important cash crops and some varieties are used for self-consumption. Figure 6 depicts the relative importance of these two crops. Farmers allocated in average more than 70% of their operated land for cassava and maize production.

Figure 6: Average proportion of operated land allocated to crops in the rainy season

Rice is a staple crop and households in the sample allocated in average 12.9% (Std. Dev. 5.48) of their operated land to paddy production. Farmers grow two paddy rice harvests per year. This is possible in the dry season due to a nearby hydropower dam, the Chieng Khoi lake. The two research villages are located downstream and are connected to the dam via a system of irrigation channels. However, during the dry season, not all paddy fields are suitable for a second rice crop and thus the cultivated rice area is smaller. Figure 6 above represents the mean percentage of total operated land per household allocated to rice and other crops during the rainy season, either in monoculture or intercropped.

Figure 7 shows the median, 25th, and 75th percentile of yield levels observed in the present study.

Since farmers grow a second rice harvest only where water is not scarce, the yield levels in both the rainy and dry season were the same in 2005 (mean 5.50 and 5.45 t/ha respectively). Maize and cassava yields varied widely while cotton yields were very similar over all observations. The large differences in cassava cultivation were due to different varieties, ranging from 3-month to 3-year cultivars. Average maize yields were 5.49 t/ha in 2005, and those of cassava and cotton were 12.58 and 1.26 t/ha in average. Paddy rice and cotton yields were in line with national averages from 2005 (4.9 and 1.3 t/ha respectively). In contrast, nationwide maize yields were in average much lower (3.6 t/ha), while mean cassava yields were higher at 15.7 t/ha (GSO 2005b).


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Figure 7: Yield in t/ha for main crops

Farm Equipment

Farm machinery in the sample households was limited to water pumps, hoes and ploughs. Almost 40% of the households did not possess an electric water pump. The respondents reported that they could use the pump of their neighbours or relatives. As a possible means of transport, more than 80% of the households possessed one or more motorcycles. Phones were not used for agricultural purposes such as information gathering. Only 13 households (16.25%) had either a fixed or a mobile phone or both. Land preparation and harvesting was done manually, and buffalos (and sometimes cattle) delivered animal traction.

Livestock Ownership

Poultry and fish were the most important animals for the sample households. 90% of the households kept poultry while half of the farmers had more than 30 chicken or ducks. Like poultry, fish is an important source of protein and income. 50% of the sample households had more than 75 kg of fish in their pond, while 12.5% did not have a pond. As mentioned above, buffalos are especially important for land preparation and transport. Likewise cattle, buffalos are kind of a savings account for the family and can be sold if immediate cash is needed. 60% of the households owned in average 1.85 buffalo each (Std. Dev. 0.92), and half of the families possessed in average 2.73 cows each (Std. Dev. 1.63). Goats were kept by around 50% of the households for meat production. The households kept from 1 – 60 goats (mean 4.63 and Std. Dev. 9.37). Only 15 households (18.75%) had 1 – 3 pigs and thus, pig production was of minor importance in the sample households.

Information

Knowledge and information are important factors for undertaking, improving or developing agricultural activities. Visits to markets in the nearby cities are important


21

for sales and exchanging information with people from outside the own village. While two households never went to a market, 50% visited the city market once per month or more (mean 26.9 times/year, Std. Dev. 47.4). More than 80% of the sample households were members of the local Farmers’ Union and could receive agricultural information through this channel. However, only 40% of the members had at least one contact with an extension agent in the previous year. From the total sample, slightly more than half of the farmers have ever received agricultural training. 37.5% of the households read newspapers and only 27.5% read a farming journal regularly. The overlap of households between both reading or both not reading a newspaper and a farming journal was 82.5%. Concerning the connectedness within the village, 5% of the sample households were neither member of the Farmers’ Union, the People’s Party, or any other association in the village, nor did they attend village meetings while 6.25% of the surveyed farmers participated in all these activities.

3.5 Data Analyses Four main groups of factors were identified from the theoretical background that could have an influence on adoption: a decision maker’s perception of an innovation, household characteristics, connectedness within and outside the village, and the general environment into which an innovation has been introduced. Due to time constraints available for the interviews, the first main factor, namely the perception of the innovation by farmers, could not be included into the survey. All survey participants lived in two villages, which ten years ago was only one. It was therefore assumed that roughly the same general environment applies for each household, thus with household characteristics and connectedness of the households being the main source of variation. The two villages were jointly analyzed and no distinction was made.

An Ordinary Least Square regression was conducted to test variables with a potential to explain innovativeness. In order to determine the influence of explanatory variables on the adoption decision for different innovations and on the specific time of adoption, logistic regression and ordered logistic regression were used. The models are explained in the following sections. The explanatory variables employed were the same for all models. For a detailed explanation of the variables, refer to the empirical specification section and to Annex 1. Furthermore, descriptive statistics were used to examine innovation diffusion over time, reasons for non-adoption or discontinuation, financing sources, objectives of households related to innovativeness, and sources of information prior to adoption of three specific innovations. The peer networks of the sample households were studied with respect to adoption decisions, and a network analysis based on the household networks was conducted.

Data was entered into Microsoft Excel 2003 spreadsheets and statistical analyses were conducted using the statistical software package StataSE Version 9. For the


22

analysis of household networks, UCINET software and the graphical network program NetDRAW were used.

3.6 Linear Regression Model of Innovativeness ROGERS (1995) suggested measuring the degree of innovativeness according to the relative time the individual adopted an innovation within the social system. Based on the mean and standard deviation of the normal distribution, adopters are classified within five adopter categories. However, this method gives no innovativeness score to non-adopters and hence is incomplete if used with only one single innovation. DEUTSCHMANN and BORDA (1962) combined six innovations into a composite measure of innovativeness, based on the respective time of adoption and giving a greater weight for relatively earlier adoption. In the present study, a similar innovativeness index of the sample farm households proposed by SCHREINEMACHERS (2007, personal communication) is used. It takes into account both the adoption decision and the relative time of adoption and is calculated as follows. (1) A score of 1 is given for each innovation a household adopted. (2) This score is then reduced by the proportion of households in the sample who adopted earlier than the household did. (3) For each household, the scores are summed over all innovations, divided by the total number of innovations and multiplied by 100 percent.

Mathematically, the innovativeness index for household i and for n innovations is expressed as:

100)111(1

∗=−∗= ∑=

n

jijiji )|αp(

nindex (1)

in which j indicates the innovation with j =1,2,…n. The parameter aj is the adoption decision of household i on innovation j and pj is the proportion of households in the population (n = 80) that have adopted innovation j before respondent i did. Non-adopters of an innovation receive the score 0. The innovativeness index is expressed as a percentage by dividing it with the total number of innovations n and multiplying it by 100 percent (SCHREINEMACHERS 2007, personal communication).

The separate scores for each individual innovation were tested for correlation. A negative relation could indicate that the diffusion of one innovation inhibits the diffusion of another. However, only few scores had a negative correlation, and none of them was significant at the 5% level. Significant positive correlations appeared for the adoption of crops and the respective fertilizers (r > 0.75), other significant correlations did not exceed r = 0.50. The sum of the individual scores per innovation results in the variable ‘index’ and approaches normality with mean 38.35 and standard deviation 15.92 (minimum = 2.64; maximum = 74.38).


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3.6.1 Econometric Model of Innovativeness

Innovativeness can be expressed as a linear combination of explanatory variables. In a multiple regression model, the dependent variable index is related to a number of explanatory variables through a linear equation that can be written as

∑+= ijj1i xββindex (2)

where indexi denotes the innovativeness of the ith household depending on j explanatory variables x.

3.6.2 Empirical Specification

From a large number of studies, ROGERS (1995) identified key components of innovativeness. These comprise socioeconomic variables, personality variables, and the communication behaviour of adopters. Personality variables would be empathy or attitude towards change and were not included in the present study due to measuring limitations. However, socioeconomic variables and communication behaviour could be evaluated. After accounting for collinearity, several variables were selected for the multiple regression model (see Table 4 and Annex 1 for descriptive statistics).

Table 4: Short description of variables used in the analysis of innovativeness Socioeconomic variables Age_hhead Age of household head Edu_hhead Education of household head

0 = not educated or primary school; 1 = secondary or higher education

Dependency_ratio The ratio of persons in a household aged below 15 or above 64, who depend on the persons in working age (HAUPT and KANE 2004)

Area_cult1000 Area cultivated by the household per 1000 sqm Area_pond1000 Pond area of the household per 1000 sqm Buffalo Number of buffalos owned by the household Communication behaviour Membership The household participates in village meetings, local Unions or

cooperatives, and is a member of the People’s Party 0 = participation below average; 1 = participation above average

Extension The household is member of the Farmer’s Union, contacted the extension agent during the previous year, and at least one member has received agricultural training 0 = below average; 1 = above average

Farmjournal One member of the household regularly reads a farming journal 0 = no; 1 = yes

City_bin Frequency of visits to two important cities/markets 0 = below average; 1 = above average

Hhead2market The household head goes to the market him-/herself 0 = no; 1 = yes


24

It is expected the age of the farmer to be positively correlated with innovativeness, though literature did not find a clear association between age and innovativeness yet. Results from other studies give no clear evidence on the influence of age on adoption behaviour (ROGERS 1995; RUBAS 2004) while higher education is commonly associated with a higher ability to understand and use innovations. Labour availability might be an important factor in innovativeness of a household, and is significantly correlated with the number of household members (r = 0.7294). It is expected that higher labour availability is positively correlated with innovativeness since a new technique would not compete for labour. The dependency ratio reflects the ratio between persons below 15 and above 64 as compared to 15 – 64 year olds, which is the most productive and most educated generation. The smaller the ratio is the more persons between 15 – 64 years live in the household. The dependency ratio is assumed to be negatively correlated with innovativeness. This is because innovativeness is associated to increase with productive assets and higher education. Because people aged between 15 – 64 are generally most productive, the dependency ratio is employed to estimate the innovativeness index, even when people above or below this age are working on the farm. Cultivated area, pond size and the number of buffalos (draft power) represent productive assets which are often positively correlated with innovative behaviour. One reason is that a farmer with more assets can take higher risks and uncertainty in order to try an innovation.

Concerning communication behaviour more social participation, cosmo-politeness, change agent contact, and greater exposure to mass media are some of the factors that are associated with greater innovativeness. In the present study, the behaviour of the household head but to an extent also the connectedness of the household have been measured. The variables ‘membership’ and ‘extension’ each comprise three activities described in Table 4 that represent social participation within the village and contact with change agents respectively. Involvement in two or more such activities is above average.

If the household head himself does not go to a market in the city, s/he might miss the news in agriculture. Therefore, ‘Hhead2market’ is included as a way to gather information from outside the own village. Similarly, a household where someone regularly reads a farming journal is assumed to be better informed than others. Therefore, active information seeking is expected to positively correlate with innovative behaviour. Finally, the frequency of market visits by a household also determines its openness. ‘City_bin’ is a dummy variable that specifies whether the frequency a household visits the market is above or below average. More openness is assumed to correlate with more innovative behaviour.


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3.7 Binary Regression Models of Technology Adoption

3.7.1 Econometric Models of Adoption Decisions

Whether or not a household has adopted a certain technology can be represented as a binary response variable. Many adoption studies these days that investigate the effect of explanatory variables on dichotomous dependent variables use either the logit or the probit model (FEDER et al. 1985). Mathematically, a binary choice model (WOOLDRIDGE 2006) can be expressed as:

)()...()|1( 010 βββββ xGxxGxyP kk +=+++== (3)

where G is a function taking on values strictly between zero and one. The two most commonly used distributions for the G function are the cumulative distribution function for a standard logistic random variable in the logit model and the standard normal cumulative distribution function in the probit model. The coefficients of the two models are not the same, but the resulting marginal effects and the predicted probabilities will be almost identical. For the present study, the logit model was chosen. In the logit model, G is the cumulative distribution function for a standard logistic random variable,

[ ] )()exp(1/)exp()( zzzzG Λ=+= (4)

which is between zero and one for all real numbers z. Such a binary response model can be derived from an underlying latent variable y*,

[ ]0y* 1,* 0 >=++= yexy iiiββ (5)

where xβ = β1x1 + …+ βkxk, and xi are variables determining adoption decision, ei is the error term, assumed to be independent of x, has a standard logistic distribution and is symmetrically distributed about zero, βi are the parameters to be estimated.

Since the interest of the present thesis is on adoption decisions, we want to study the effects of the explanatory variables on the response probability P(y = 1|x), that is, the probability of adoption,

[ ][ ] )()(1

|)()|0*()|1(

00

0

ββββββ

xGxGxxePxyPxyP

+=+−−=+−>=>==

(6)

To obtain probability statements for every observation in the dataset, for 1 = being adopter and 0 = non-adopter, the log likelihood function of a given observation i has to be calculated. This is done by taking the log of the density of yi given xi:

[ ] [ ])(1log)1()(log)( βββ iiiii xGyxGyl −−+= (7)

For n observations, li has to be summed to obtain the log-likelihood. Maximizing the log-likelihood function with respect to each β will yield the maximum likelihood estimators.


26


For the specification of the binary choice model, the same variables are used as in the linear regression of the innovativeness index (see chapter 3.6.2 above). Expectations of the coefficients are similar, with differences regarding specific innovations.

Seven innovations have been chosen for binary regression. The selection is based on relatively high adoption rates and possibly different innovations. Table 5 lists the selected innovations and gives a short overview of their attributes as perceived by the headmen of the Farmers’ Unions in the two villages.

Table 5: Short description of selected innovations Selected innovation (adoption rate)

Some features of the selected innovations

Improved cotton (55%)

- higher requirements to soil, water, fertilizer than traditional variety

- promoted by Hanoi Cotton Comp. & extension services - cash crop

Improved longan (57.5%)

- fruit tree, perennial - better marketability

Improved pig (43.75%)

- higher selling prices - higher labour input

Motorcycle (88.75%)

- means of transport - symbol of wealth - increases mobility

Stable outside of village (33.75%)

- reduces time needed for grazing - stables near ponds manure as fish feed - reduces disease due to high density in village

Tree fences on slopes (33.75%)

- reduces erosion - competes for space with crops - labour necessary to plant

Official credit (58.75%)

- possibility to finance investments - collateral needed - inflexible terms of payment

(adapted from HA and LE, headmen of Farmers’ Union in Ban Me and Ban Tum, 2006, personal communication)


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3.8 Ordered Regression Models of Technology Adoption If we use logistic regression, only adopters and non-adopters of an innovation can be tested on the explanatory variables. However, a binary adoption model does not explain differences between adopters that may trigger earlier or later adoption decisions. The adoption decisions in subsequent years can be represented as choices of adoption time. Since time is one-directional, an ordered logistic regression model can be used to express subsequent choices of adoption times.

Examples from literature concerning the use of ordered regression models to explain the timing of adoption are scarce. Usually, opinions or attitudes which can be ranked in a Likert-scale are assessed against factors that influence the dependent variable. The outcome variable has ranked categories, but the real distances between different categories are unknown. Ordinal numbers indicate the ranking, but the actual values are assigned arbitrarily. Higher numbers only indicate that one alternative is ranked higher than another. KENNEDY and FISS (2006) estimated the timing of hospital adoption of Total Quality Management depending on motivation variables and found results that suggested that hospitals do not only adopt because of legal institutionalization but might be early or late adopters due to managers’ motivations.

3.8.1 Econometric Models of Ordered Adoption Decsions

The ordered logistic model is based on the binary logistic regression model. When the parameter estimates for the binary logit model are represented by

ββ xPP +=− 0)1/ln( , (8)

then the ordered logit model has the form (STAT/MATHCENTER 2007):

1... and)...1/...ln()...ln(

)1/ln()ln()1/ln()ln(

121

212121

2212121

1111

=++++=−−−−+++≡+++

+=−−+≡++=−≡

+k

kkkk

PPPxPPPPPPPPP

xPPPPPPxPPP

ββ

ββββ

M (9)

As can be seen from the equation above, the ordered logistic regression models the cumulative probability where it simultaneously estimates multiple equations depending on the number of categories in the dependent variable (number of categories minus one to avoid overspecification). The resulting log odds is independent of the category, and the odds ratio is assumed to be constant for all categories (proportional odds or parallel regression assumption). This means that the model assumes that the coefficients that describe the relationship between any group of categories are the same. If the dependent variable has four categories, then 3 equations are estimated.


28


Using the specific year of adoption to form the categories of the dependent variable implies that the model estimation does not run stable or not at all, because the number of observations per category would be too small. Hence, for a given innovation, adopters were classified in four groups as early adopters (first 16%), early majority (16 – 50%), late majority (50 – 84%) and laggards (84 – 100%). Classification was done using the methods mentioned by ROGERS (1995) which uses standard deviations and means to assign adopter categories. However, due to small sample size, the innovators and early adopters were combined as early adopters. Sometimes the classes did not exactly correspond to the percentages suggested. This is because adopters of the same year were not arbitrarily assigned to two different classes. Additionally, non-adopters were assigned to a fifth category. Because higher ranks are associated with higher outcomes, for the purpose of easier interpretation the ranks were chosen as in Table 6. According to this, positive log odds or odds ratios > 1 increase the odds of earlier adoption. The same explanatory variables were used for the ordered logistic regression as in chapter 3.7.2. It was intended to study the regression model for the same innovations as selected for the binary logit model. However, because of low adoption rates and therefore resulting small groups per adopter category, three innovations were dropped: improved pigs, keeping animals in a stable outside the village, and planting tree fences on the slopes of upland fields. Table 6 shows adopter categories with respective ranking and number of observations per category for each innovation.

Table 6: Categories and ranks assigned to dependent variables

Category Early

adopters Early

majorityLate

majority Laggards Non-

adoptersRank 4 3 2 1 0

# obs.: Cotton 7 11 16 10 36

Longan 7 14 17 8 34Motorcycle 13 22 23 13 9

Credit 8 16 15 8 33

3.9 Model of Household Networks Respondents in the present study were asked to name friends within the two villages’ boundaries, and to indicate the peers’ adoption decisions relative to their own time of adoption for three different innovations (hybrid maize, motorcycles, and stable outside the village). The number of peers who adopted an innovation earlier than the respondent divided by the total number of peers in the respondent’s network is the threshold value or exposure level of the respondent. Consequently, the threshold value for each innovation was tested against the time of adoption in the whole sample. In a further step, the average of the three threshold values for each sample household was


29

calculated in order to derive a combined threshold value, which was then examined for correlation with the innovativeness index derived earlier.

From the personal network of friends for each household, a sociomatrix including all households of the two research villages was produced. Based on graph theory described by WASSERMAN and FAUST (1994), relationships are seen as nodes and ties. Nodes are the actors and ties represent the relation between them. Ties may be directional or undirectional.

For further analyses, symmetric or undirected ties were used, since friendship is usually reciprocated or will otherwise not last long. The overall network’s characteristics are not examined in this analysis since the survey collected information from only one third of the total households in the two villages, and only asked for so-called ego-centric networks and did not consider the relations between the friends of a respondent. Because this study considered the households as entities in the network, the network of households was analysed. The following attributes of the household networks were examined:

1. Centrality: A simple measure of centrality is the degree CD, which counts of the number of nodes adjacent to a given node pk in the network (NIEMINEN 1974),

∑=

=n

ikikD ppapC

1

),()( , (10)

where pk is the individual and pi are nodes adjacent to it, and a(pi, pk) = 1 only if pk and pi are connected by a tie and zero otherwise. The degree is large if the node pk has many contacts and is zero if the node is totally isolated (FREEMAN

1979). The FreemanDegree routine in UCINET (BORGATTI et al. 2002) was used to compute the degrees for each household.

2. Density: The personal network density is “the degree an individual’s personal network is interconnected” (VALENTE 1995). A personal household network is dense if the friends of a household are connected among each other. The personal network density is calculated by the actual number of ties divided by the maximum possible number of ties among the friends in a personal household network.

For each innovation examined in the present survey, the two measures were used to test statistically significant differences between adopters and non-adopters.

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4 Results

4.1 Innovations in the Research Area Group interviews conducted before the individual household survey showed about 50 innovations that have diffused since 1955. Table 7 gives a condensed overview of the innovations and more detailed information is given in Annex 2 and Annex 3.

The farmers in the group interviews recalled agricultural crops, livestock, technologies, and subsequent innovations starting from year 1955. Half a decade ago, agriculture in the research villages was for home consumption and farmers did hardly sell their products. During the early communist era however, farmers were obliged to sell a certain amount of maize to the cooperatives, followed by abolishment of private ownership and collectivized production. Marketing of crop, fruit and livestock products therefore started only in the late 1980s, with the exception of rice, which in 2005 was self-consumed by almost all sample households. While the range of crops and livestock in the village was already diverse in 1955, not many entirely new crops or animals have since then been introduced. Most traditional crops have been replaced by improved or hybrid varieties since 1955, while in the livestock sector major changes concerned the start of aquaculture and subsequent introductions of new fish varieties.

The majority of innovations concerned either new crop varieties or agricultural technologies like manure, mineral fertilizer, and pesticide application. Especially varieties of staple crops such as rice and maize have been important. As a result of ‘Doi Moi’, the all-round renovation program launched by the government in 1986, new crop and animal varieties, and agricultural techniques were developed. Ten years after the start of Doi Moi, companies and government agencies started to promote the improved varieties and techniques in the research villages, which explains the high density of innovations in 1995/96.

Many innovations are interrelated. In 1955, farmers irrigated paddy rice manually and therefore cultivated it only near rivers. As the irrigation system further developed, the paddy rice area also expanded. New commercial varieties of cassava and maize were introduced, and the area of upland rice decreased. Sample households did not grow upland rice in 2005 while this was the main upland crop in 1955. Upland ploughing with buffalos started in 1995, and made it possible to cultivate hybrid maize in the upland fields. While local traditional crops were grown without additional inputs, manure, mineral fertilizers, and pesticides were implemented simultaneously or subsequently with improved varieties or hybrid crops (see Table 7).

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Table 7: List of innovations by year of introduction 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

Improved rice, manure Pump sprayer Hybrid rice Paddy

rice Chemical pesticides Mineral fertilizer Upland rice Local maize

Sell to cooperative Improved maize Own marketing Hybrid maize, mineral fertilizer, chemical pesticides, private trader

Local cassava Improved cassava Local cotton Improved cotton, mineral

fertilizer, chemical pesticides Local vegetables Chemical

pesticides Improved varieties, marketing, manure, mineral fertilizer

Local mango, banana, jackfruit, sugarcane

Improved mango, pineapple, lychee, longan, pomelo, manure, mineral fertilizer

Fishery, wild fish in ponds Raise grass carp, start aquaculture Improved fish, hybrid fish Buffalo, cattle, goat, pig, chicken Upland ploughing, improved pig, ducks Animal vaccination Farm outside village Intercropping Pineapple/tree fences on slopes Mulching Manual irrigation of paddy rice Chieng Khoi Dam Concrete irrigation channels Mud irrigation channels Note: 1955 denotes the baseline year recalled in the group interviews

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4.2 Diffusion of Selected Innovations in the Research Villages From the pre-survey of innovations, 18 improved crops, livestock, agricultural techniques, and soil conservation methods were selected. Table 8 presents the respective adoption rates among the sample households. Some innovations have found wide acceptance while others rarely spread.

Table 8: Adoption rates of observed innovations in Ban Me and Ban Tum Adoption rate (%) Adoption rate (%)

Hybrid maize 97.50 Improved pig 43.75Hybrid rice 97.50 Improved duck 27.50

Improved cotton 55.00 Direct seeding of rice 6.25Min. fertilizer on hybrid maize 97.50 Upland ploughing 91.25Min. fertilizer on impr. cotton 55.00 Tree fences on slopes 33.75

Min. fertilizer on vegetables 18.75 Mulching 86.25Lychee 28.75 Motorcycle 88.75Longan 57.50 Official credit 58.75Pomelo 37.50 Stable outside village 33.75

Figure 8 shows adoption curves for 18 selected innovations, as the cumulated adoption is plotted over time. The curves are tailed to the left indicating a slow start of diffusion and roughly show the S-shaped curve suggested from literature. Some innovations have been introduced up to 50 years ago while others are very recent. The adoption curves also differ widely in speed and extent of diffusion. The adoption of improved crop varieties (diagram a) has been rapid. After the beginning of the reforms, the Vietnamese government released and actively promoted new crop varieties aimed at increasing food production. Especially hybrid rice and hybrid maize were promoted in order to replace traditional varieties, which have lower yields and are used only for self-consumption. Hybrid maize additionally offers an income opportunity to farmers since it is used for industrial starch production and animal feed.

The farmers quickly accepted hybrid maize and hybrid rice. In 2006, the adoption rate for both crops reached 97.5% among the sample households. Together with hybrid rice and maize came the adoption of multiple mineral fertilizers and pesticides. Improved cotton diffused rapidly, reaching 55% of the farm households within 6 years of introduction. This is probably due to strong promoting activities by the Hanoi Cotton Company, which sells the seeds together with pesticides and fertilizers as a package and buys the cotton products. Furthermore, this company lowers constraints to initial purchase by giving the seeds as a credit to the farmers. The adoption of mineral fertilizers on hybrid maize and improved cotton happened almost simultaneously with adoption of the respective crop itself (diagram b).

Improved fruit varieties were introduced relatively long ago and diffused at relatively low speed with some acceleration in the last decade (diagram c). The

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farmers usually grow some fruit trees in their home yard but do not grow fruit trees commercially. They market their fruit to a small extent and mostly use them for own consumption. These fruit crops are not new in the region, so in the past, farmers subsequently replaced old trees, lacking land to plant more trees. Diffusion is only notable for improved longan, reaching 57.5% of the farm households. Improved livestock varieties (diagram d) in the two villages diffused slow and unsteady. The traditional varieties are low input livestock, kept around the house without requiring much labour.

Figure 8: Adoption curves for 18 innovations diffusing in Ban Me/ Ban Tum

a) Improved crop varieties b) Use of mineral fertilizers

c) Improved fruit varieties d) Improved livestock

e) Soil preparation and conservation f) Motorcycle, credit, stable outside village

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Most soil preparation and conservation techniques (diagram e) are known for several decades. However, they diffused slowly and only accelerated since the nineties or not at all. Upland ploughing and mulching are most widely accepted among farmers. Upland ploughing makes the hilly slopes arable, and is probably more widely used since the widespread cultivation of the more intensive cash crops hybrid maize and improved cassava in the upland. Mulching and contour planting of trees (treefences) on the hillsides are soil conservation techniques and spread simultaneously with increased use of the upland fields. Mulching means leaving chopped stems and leaves on the field after harvest, and does not bear additional costs for farmers, thus lowering constraints to adoption. Direct seeding in lowland rice never took off. This technique is labour saving, but yields are generally lower and weed infestation is higher. These directly perceived disadvantages and lack of experience in the community might be the reason that direct seeding never found a broad base of adopters.

The cumulated adoption of three further innovations is shown in Figure 8, diagram f. The surveyed households started in 1975 to build stables or small farmsteads outside the village. Traditionally, small and large ruminants are kept around the house and one member of the family would each day bring the animals to pastures outside the village for grazing. In informal talks, some villagers suggested that the high density of households within the villages and the labour required to bring animals to pastures everyday is an important factor for the farmers to build a stable or a small farmhouse near the pasture areas. Livestock is then kept there and some family member is regularly going to feed and look after the animals. The farmhouse outside the village often has a pond nearby and farmers feed their fish with manure. NEEF et al. (2006) reported that according to local authorities, the population of Yen Chau district has more than doubled in the past 20 years. Though there seems to be a pressure to move livestock outside of the village, until 2006 only around a third of the surveyed households had adopted this method. Concerning the adoption of this innovation, there are two obvious limitations, namely sufficient land and sufficient cash to build the stable, which could be a reason for the slow diffusion of this innovation.

Official credit and motorcycles are not innovations that came up in the group interviews, but have been included additionally. Motorcycles are a means of transport and can be used to carry products to the market or inputs to the farm. Motorcycles were first adopted in 1985 and since then reached an adoption rate of almost 90% among the surveyed households. Diffusion was relatively fast in the nineties. Official credits were first taken in 1990, and in 2006 almost 60% of the surveyed households had at least once taken an official credit from the local bank, or from one of the cooperatives or associations in the village. Especially the Women’s Union as a semi-formal actor in microfinance schemes plays an important role in bringing micro credits to the grassroots levels (PUTZEYS 2002). Adoption rates of official credit were steady and the resulting cumulated adoption curve is almost linear.

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4.3 Reasons for Non-adoption, Discontinuation, and Financing Sources Besides asking farmers in the interviews about time of adoption, the survey also identified reasons for non-adoption or the discontinued use of an innovation, and sources of finance.

Reasons for non-adoption are particular for different types of innovations (see Annex 4). For crops and fruit trees, land appears to be the major limiting factor among non-adopters (50 – 87.7% of sample households). In contrast, concerning livestock production, lack of cash, labour, and experience hinder adoption. While clearly lack of cash inhibits motorcycle adoption (100%), innovations like direct seeding of rice and using mineral fertilizer for vegetable production do not appear suitable to most farmers (41.3% and 52.3% respectively). Households usually have only a small garden around the house, and vegetable production is mostly for home consumption. This does not favour commercial production and hence, farmers do not see the need for mineral fertilizers.

Only a few households did not adopt the technique of ploughing the upland fields and the major constraint for them is lack of cash. The non-adopting households possess no buffalos, and hence lack draft power, which requires them to hire another farmer or rent a buffalo if they wanted to plough. Main hindrances for implementing soil conservation techniques are lack of experience or farmers do not regard the techniques as suitable in the sense that either they do not possess fields with steep slopes or they do not have problems with erosion. In contrast, farmers did not report lack of experience or suitability concerning keeping stables outside the village. Here, lack of land (77.4%) and labour (30.2%) followed by lack of capital (15.1%) are reasons for non-adoption. Since land is very scarce in the research area and much needed for crop cultivation, not many new adopters could be expected. One evident result of identifying reasons for non-adoption is that farmers did not feel that the surveyed innovations were too difficult for them.

For some innovations, many farmers in the sample stopped after adoption. This is especially evident for improved cotton, direct seeding of rice, and improved pig races (see Annex 5). The proportion of farmers who stopped after adoption is around 80% for the three innovations. Direct seeding of rice has been adopted by only a small number of households (n = 5), and the farmers stopped when they were not satisfied with lower yield. Concerning improved cotton, farmers reported that the variety was not suitable for their soil (54.1%) or did not have enough land (27%), followed by lack of labour (13.5%). Households that kept improved pig races discontinued due to low market prices (= not suitable 37%), lack of labour (33.3%), and lack of cash to purchase new piglets (22.2%).

Concerning official credit, 41% of the sample households are non-adopters. Around 2/3 stated they do not need credit, 1/3 are afraid of debt, and 3 sample households lacked official land use rights for agricultural or residential land which is

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needed as collateral. However, more than 90% of all sample households would generally like to take an official credit to buy an innovation.

In contrast, the main source of finance for innovations in the survey appeared to be own savings (see Annex 6). Only in four cases an innovation was purchased using official credit. Also informal credits from family members and friends, as well as from moneylenders play a minor role, while semi-formal credit from cooperatives, associations or unions was used for the total or partial financing of 39 innovations. As mentioned previously, the Hanoi Cotton Company promotes improved cotton and provides seeds, pesticides, and fertilizer as a credit to the farmer. Among the sample households, around 85% used this source of finance and repaid their debt after harvest. Fruit trees are either purchased with own savings or provided by friends or relatives as cuttings, while techniques like upland ploughing, mulching and planting tree fences in upland fields are mostly done by using own labour and therefore do not imply direct monetary costs to the households, which in turns lowers financial constraints to adoption.

4.4 Objectives of the Farm Households Farmers in the sample were asked to rank a set of six objectives according to their perception of importance for the own household. The most important goals for 80% of the sample households were to maximize their current income, creating a stable income and to maintain the land as a safety net for the family. 65% of the sample households see supporting the community and preserving the forest as the least important objective for the individual household, followed by 16.25% who do not wish to maximize their current income. Table 9 lists the relative frequency of the six objectives for the first and the last rank assigned by the sample households.

Table 9: Most and least important household objectives HH objectives in % of sample households Top rank Last rank

Maximize current income 25.0 16.2 Get a stable income 20.0 5.0

Maintain land as safety net for the family 35.0 5.0 Support the community 3.8 36.2

Preserve the forest 5.0 28.8 Preserve soil fertility 11.2 8.8

In order to see if the objectives of a household relate to its innovativeness, the top three goals of each household are examined. Using the innovativeness index, all households can be classified into five adopter groups: innovators (highest 2.5% of the innovativeness index), early adopters (2.5 – 16%), early majority (16 – 50%), late majority (50 – 84%), and laggards (lowest 16% of innovativeness index). Table 10 shows the relative frequencies of the top three household objectives classified by adopter category.

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Table 10: The top three household objectives by adopter categories

Relative frequency of top 3 HH objectives (%) Innovators

Early adopters

Early majority

Late majority Laggards

Maximize current income 100 73 56 70 69Get a stable income 0 73 59 78 54

Maintain land as safety net for the family

50 64 74 67 85

Support the community 50 9 11 26 23Preserve the forest 50 45 41 22 15

Preserve soil fertility 50 36 59 37 54Total # of households 2 11 27 27 13

Note: Percentages represent the proportion of households in the respective adopter group

All households classified as innovators stated that maximizing the current income is one of their top three objectives. This proportion is much lower in the remaining adopter groups, where 56 – 73% of the households indicated that maximizing the current income was among their top three household objectives. Innovators show a relatively high interest in increasing their income, and are less concerned about stabilizing their income. Early adopters are both eager to increase and stabilize their income, while the main concern of the early majority group is to maintain their land as a safety net for their family. Complementing this interest for security, the second frequent rating of the early majority group is to preserve the soil fertility and to have a stable income. Late majority adopters primarily want a stable income, followed by increasing it. Laggards see their main focus in maintaining their land as a safety net for their family, and are furthermore interested in increasing their income.

There is no clear tendency across the five adopter groups concerning the interest in increasing and stabilizing income. However, the wish to maintain land for the family as a safety net appears to be increasing from innovators to laggards (from 50 up to 85% of the households in the respective adopter group), and, except for innovators, this is also true for the objective to support the community. Later adopter groups seem to have greater interest in the two social objectives than earlier adopters do. While for preserving soil fertility again there is no tendency evident, the interest in preserving the forest is declining from earlier adopters to later adopter groups, where only 15% of laggard households express a high interest in preserving the forest as compared to 50% of innovator households (see Table 10).

Fisher’s exact tests have been conducted to check the observed differences between the adopter groups. However, the tests yielded no statistically significant results.

Figure 9 shows the relationship between the revenues from agriculture and innovativeness by adopter categories. The boxes contain 50% of the households in the

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respective adopter group, ranging from the first to the third quartile. As discussed above, the objective to increase the current income showed no clear tendency across adopter categories. In Figure 9, earlier adopters are clearly better off concerning their revenues from agricultural production in 2005. The correlation coefficient between innovativeness and agricultural revenues was r = 0.52. However, it is not clear if more innovative farmers benefited from early adoption of profitable innovations, or if farmers are more innovative because they have more resources and can therefore afford to implement the innovations.

Figure 9: Box-Whisker-Plots of revenues from agriculture by adopter category

4.5 Characteristics of Innovative Farmers Several mild outliers were observed in the linear regression model but not removed since no errors of data entry were detected. No multicollinearity was found by using variance inflation factors (VIF), and constant error variance was confirmed by the Breusch-Pagan/Cook-Weisberg test for heteroskedasticity. Residuals of the model approach normality (Shapiro-Wilk W test for normal data). By graphical comparison of augmented partial residual plots, no major nonlinear relationships among the explanatory variables and the dependent variable were found. The Ramsey RESET test used to identify omitted or irrelevant variables in the model was not significant, hence indicating no relevant variables were omitted.

The regression model was run using 11 explanatory variables and 80 observations. The sum of squared residuals is 9331 with a square root of the Mean Square Residual of 11.71. The R² indicates that the model explains 53% (adj. R² = 0.46) of the variation of the dependent variable as compared to a model without

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explanatory variables, and the group of explanatory variables show a significant relationship with the dependent variable (p < 0.001). Table 11 represents linear regression results where the index of innovativeness is the dependent variable.

The age and education of the household head, cultivated agricultural area and pond area as well as the farmer’s contacts with extension bodies appear to be significant predictors of innovativeness. The remaining variables are not significant, which means their estimated coefficients are not significantly different from 0. The parameter estimates tell about the relationship between the independent variables and the dependent variable. These estimates indicate the percentage increase in innovativeness resulting from a one unit increase in the predictor, while controlling for all other factors.

Education of the household head (p < 0.05) and extension contacts (p < 0.01) have the highest coefficients, and are dummy variables. The meaning is that if the household head received higher education, the innovativeness index of the household increases by 6.5, and if the farmer has above average contacts with extension bodies, the index increases by 7.8. The pond area and the agricultural area managed by the household appear significant at the 5% level and are positively related to innovativeness with a coefficient of 2.53 and 0.76 respectively. Age of the household head is positively correlated with innovativeness, but with a small coefficient of 0.24 and only significant at the 10% level.

As expected, the coefficient for the variable ‘dependency_ratio’ is negative, and above average membership in the village, as well as reading farming journals and the household head going to the markets have positive coefficients, although none on the variables is significant. Contradicting expectations, the number of buffalos and visits to the city show negative signs, though these were not significant.

Table 11: Results of linear regression model Innovativeness (%) Unit Coefficient Std. Err.

Age_hhead (years) 0.239 * (0.134) Education_hhead (dummy) 6.491 ** (3.134)

Dependency_ratio (contin.) -3.944 (3.222) Area_cult1000 (1000 sqm) 0.759 ** (0.328)

Area_pond1000 (1000 sqm) 2.528 ** (1.006) Buffalo (number) -0.689 (1.244)

Membership (dummy) 4.725 (3.062) Extension (dummy) 7.823 *** (2.878)

Farmjournal (dummy) 4.613 (3.171) City_bin (dummy) -1.640 (3.169)

Hhead2market (dummy) 0.034 (3.570) Constant 6.817 (7.020)

p-value 0.001 R² (adj. R²) 0.53 (0.47)

Note: * p < 0.1, ** p < 0.05, *** p < 0.01

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4.6 Binary Choice Model to Explain Adoption / Non-adoption The logit model results in Table 12 on page 43 displays the model coefficients as odds ratios. The original log odds from logit regression, expressed as ln(P/1-P), are transformed by raising e to the power of the logistic coefficient, and may then be more easily interpreted as the odds of adoption versus non-adoption (UCLA 2007). If the coefficient is

ββ xPP +=− 0)1/ln( , (11)

then raising e to the power of the logistic coefficient yields the odds ratio OR (probability of adoption divided by probability of non-adoption):

ββ xePP +=−= 0)1/(OR (12)

An odds ratio of 1 is similar to an OLS estimator which is 0. It has no effect on the odds of adoption. This can also be explained if the log odds is 0, then e0 = 1. If the odds ratio OR>1, then the odds of adoption is increased by the factor of OR and decreased if OR<1. Making interpretation of the results even easier, STATA offers to compute the predicted probabilities of positive outcome for the dependent variable.

For better comparison, the models in Table 12 all used the same explanatory variables. In a trial and error process, a backward stepwise logit regression (removal of p > 0.2) was conducted for all innovations but did not yield better results with respect to the computed log-likelihoods and McFadden’s R². In the motorcycle model, two variables predict a successful outcome perfectly, which leads to dropped observations. In this case, the two variables were removed in order to include all observations into the model.

The cotton model is significant at a 10% level and all other estimated models are significant at the 5% level or better. While McFadden’s R² is generally low for all models (0.17 – 0.43), the percentage of correctly classified predictions ranges from 70 to 91%. The Hosmer-Lemeshow or chi-square goodness-of-fit test was conducted to assess the model fits. If Hosmer-Lemeshow goodness-of-fit is not significant, then the model has adequate fit. However, this does not mean that the model necessarily explains much of the variance in the dependent variable, only that much or little it does explain is significant. The cotton and the pig model appear not to have adequate fit since the goodness-of-fit tests yield significant results.

Demographic Variables

In the linear regression of variables explaining overall innovativeness, age of the household head was significant at a 10% level. In the logit models in Table 12, only the model for longan and credit show a significant relationship with age of the household head. One additional year increases the odds of adoption by the factor 1.05 and 1.07 respectively. In the case of longan adoption, a 30 year-old household head has a predicted probability of adoption of only 0.42, while the predicted probability

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rises to 0.68 for a 50 year old farmer, all other variables held constant at their mean. For all seven models, increasing age does not generally increase the odds of adoption.

Having received secondary or higher education appears to be significant at the 10% level or better in three of the logit models. The results are somewhat conflicting since there is no clear direction. While higher education increases the odds of adoption by a factor of 16.9 and 3.4 for the motorcycle and credit model, it decreases the odds of keeping the animals in a stable outside the village by a factor of 0.16. Expressed in predicted probability of stable adoption, having higher education decreases the probability from 0.54 to 0.16, holding other factors constant at their mean. Also in the longan model, higher education decreases the odds of adoption, while in all other models, education has a positive or no impact on adoption.

If the dependency ratio increases, more ‘dependent’ persons aged below 15 or above 64 live in a household relative to persons aged between 15 and 64. Thus, a lower ratio should favour adoption of an innovation. This variable is significant only in the pig model, decreasing the odds of adoption by the factor 0.21 if the dependency ratio increases by one unit. A higher dependency ratio increasing the odds of adoption occurs only in the longan and stable model, but the odds are not significant.

Productive Assets

Although agricultural land is an important factor if one looks into adoption decisions, the variable ‘Area_cult1000’ does not appear to be significant in the logit models. It is positive for most models, and has low standard errors in all seven models.

The size of the pond does not seem to have clear effects on the odds of adoption. With some innovations, an increasing pond size decreases the odds, with other innovations it increases the odds of adoption. Pond size is highly significant in increasing the odds of stable adoption.

An increasing number of buffaloes has significant effects on decreasing the odds of motorcycle and credit adoption, while in the remaining 5 models the effects are not significant and not clearly positive or negative in direction.

Social Interaction and Information

An above average participation within the village community is represented by the variable ‘membership’ with the binary outcomes 0 and 1. Membership is significant in the stable and treefence model, and neither significant nor clearly positive or negative for the remaining models.

An above average contact with extension services is significant in three models at different significance levels (see Table 12), and the odds ratios for the variable ‘extension’ are larger than 1 in all models, implicating a generally positive effect on the odds of adoption.

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In contrast, reading a farming journal regularly does not seem to have a significant impact on the odds of adoption, but in the treefence model reading a farming journal significantly decreases the odds by a factor of 0.17.

A household’s visits to cities can be seen as opportunities to gather information. The model included above and below average frequency of visits to the city, and no significant effects were found. Since the household head usually is the decision maker in agriculture, information gathered could be more important if he/she goes to the market himself. The variable ‘Hhead2market’ is significant at a 10% level in increasing the odds of longan and pig adoption. The other models do not suggest a significantly positive or negative effect on the odds of adoption.

Among social interaction and information variables, ‘extension’ seems to have the most straightforward and strongest effects in increasing the odds of adoption.

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Table 12: Logit model fits and parameter estimates across selected innovations Cotton Longan Pig Motorcycle Stable Treefence Credit

Adoption rate 55% 57.5% 43.75% 88.75% 33.75% 33.75% 58.75% Odds Ratios

(Std. Err.)

Age_hhead (years) 0.96(0.03)

1.05(0.03)

* 0.99(0.03)

1.07(0.05)

1.03(0.04)

1.02(0.03)

1.07(0.03)

**

Edu_hhead (dummy) 2.08(1.28)

0.68(0.48)

2.19(1.41)

16.89(20.15)

** 0.16(0.16)

* 1.00(0.66)

3.35(2.16)

*

Dependency_ratio (contin.) 0.43(0.28)

1.32(1.01)

0.21(0.17)

* 0.90(1.08)

3.77(3.41)

0.74(0.49)

0.54(0.35)

Area_cult1000 (1000 sqm) 1.09(0.07)

1.02(0.07)

1.08(0.08)

0.97(0.10)

1.00(0.09)

1.11(0.08)

1.05(0.07)

Area_pond1000 (1000 sqm) 1.15(0.21)

1.51(0.45)

0.66(0.19)

2.05(1.49)

6.68(3.25)

*** 0.94(0.26)

0.85(0.16)

Buffalo (number) 0.71(0.17)

1.39(0.41)

0.86(0.22)

0.41(0.20)

* 0.79(0.26)

1.17(0.29)

0.45(0.13)

***

Membership (dummy) 1.58(0.91)

0.90(0.59)

1.50(0.93)

0.95(0.90)

4.84(4.47)

* 4.62(3.19)

** 0.69(0.41)

Extension (dummy) 3.19(1.76)

** 7.13(4.53)

*** 2.43(1.42)

2.16(2.20)

4.14(3.19)

* 1.66(0.94)

1.67(0.98)

Farmjournal (dummy) 1.08(0.66)

0.98(0.67)

2.17(1.48)

- 0.95(0.75)

0.17(0.12)

** 1.80(1.17)

City_bin (dummy) 0.46(0.30)

0.97(0.65)

1.71(1.15)

- 3.10(2.69)

1.33(0.88)

0.45(0.30)

Hhead2market (dummy) 0.74(0.51)

4.27(3.46)

* 3.97(3.29)

* 0.46(0.61)

1.15(1.09)

0.59(0.42)

2.43(1.71)

Log Likelihood -45.77 -38.92 -40.93 -18.32 -29.30 -41.32 -43.21 LR Chi Square 18.57 31.25 27.80 19.64 43.69 19.65 22.03

p-value 0.07 0.00 0.01 0.02 0.00 0.05 0.02 Observations 80 80 80 80 80 80 80

McFadden’s R² 0.169 0.286 0.253 0.349 0.427 0.192 0.203 % correctly classified 75.00 78.75 75.00 91.25 82.50 76.25 70.00

Hosmer-Lemeshow goodness-of-fit test; p-value 0.03 0.22 0.05 0.87 0.70 0.26 0.25

Note: * p < 0.1, ** p < 0.05, *** p < 0.01

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4.7 Ordinal Choice Model to Analyze Adoption over Time All models in Table 13 were checked for the parallel regression assumption. The coefficients are again displayed as odds ratios. Because the model assumes proportional odds, it estimates parallel equations over the levels of the dependent variable and can therefore use the same coefficients in each equation. If k is the level of the dependent variable, the farmers who are in groups greater than k are compared to those who are in groups less than or equal to k. So for the interpretation of the odds ratios follows that for a one unit change in the independent variable, the odds for being in a group that is greater than k versus less than or equal to k, the odds are increased or decreased by the odds ratio.

All models of the ordered logistic regression appear to be significant at a 5% level or better. However, McFadden’s R² are relatively low ranging from 0.09 to 0.16 and the percentage of overall correctly predicted cases ranges from 46 – 50% of the sample households (see Table 13). This is not surprising as compared to the binary logit model, because now we have five groups in the dependent variable that have to be predicted instead of only adopters and non-adopters.

Concerning cotton adoption, the cultivated area and the number of buffalos a household possesses have significant coefficients at the 10% level, while extension is associated at the 5% significance level. For a one unit increase of the cultivated area (1 unit ≈ 1000 sqm), the odds of the early adopter group versus the combined later adopter groups and non-adopters are 1.11 times greater, holding all other variables constant. Because of the proportional odds assumption, the same increase, 1.11 times, is found between the combined early adopters and early majority as compared to the combined later adopters and non-adopters, given that all other variables are held constant. For all combinations of groups where combined higher categories are compared versus the remaining combined lower categories, the odds are 1.11 times greater for the combined higher categories. For one more buffalo in the household, the odds of combined higher categories versus combined lower categories decrease by 0.66, given that all other variables are held constant. If extension increases from 0 to 1 (from below average to above average extension contacts), the odds of any combination of higher categories versus combined lower categories increase by the factor 2.66.

Concerning longan adoption, the age of the household head and extension are significant at 10% and at 5% level respectively. The odds of any combination of higher categories versus combined lower categories increase by 1.04 if the household head is one year older, holding all other variables constant. Increasing extension contacts from below average to above average (from 0 to 1), increases the odds of being in a combined higher category versus combined lower categories by 2.77.

Table 13 shows, that for motorcycles, the age and education of the household head, the number of buffalos, and whether someone in the household regularly reads a

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farming journal, are significant variables that determine adoption. While higher education, and reading a farming journal increase the odds of combined higher adopter categories versus combined lower categories, an increasing number of buffalos decreases the odds of higher categories. Higher age is positively associated with earlier adoption, the odds of higher versus lower combined categories increase by 1.05 if the age of the household head increases one year, given that all other variables are held constant. Concerning credit adoption, the same variables are significant like for motorcycle adoption. The direction of age, education, buffalos and farming journal are the same as for motorcycle adoption, and only differ in their magnitude (see Table 13).

Table 13: Ordered logistic models and parameter estimates Cotton Longan Motorcycle Credit

Adoption rate 55% 57.5% 88.75% 58.75% Odds Ratios

(Std. Err.)

Age_hhead (years) 0.98

(0.02) 1.04

(0.02)* 1.05

(0.02)** 1.05

(0.02) **

Edu_hhead (dummy) 1.72(0.88)

1.17(0.61)

4.49(2.35)

*** 3.47 (1.81)

**

Dependency_ratio (continuous) 0.53(0.30)

1.10(0.55)

1.12(0.58)

0.83 (0.44)

Area_cult1000 (1000 sqm) 1.11(0.06)

* 1.04(0.06)

1.06(0.06)

1.06 (0.06)

Area_pond1000 (1000 sqm) 1.26(0.25)

1.27(0.19)

1.27(0.23)

0.81 (0.14)

Buffalo (number) 0.66(0.14)

* 1.16(0.23)

0.70(0.14)

* 0.50 (0.11)

***

Membership (dummy) 1.32(0.66)

1.00(0.51)

1.15(0.55)

0.49 (0.26)

Extension (dummy) 2.66(1.23)

** 2.77(1.33)

** 0.96(0.44)

1.31 (0.62)

Farmjournal (dummy) 1.42(0.73)

0.75(0.39)

2.77(1.40)

** 2.52 (1.33)

*

City_bin (dummy) 0.48(0.25)

0.78(0.41)

2.15(1.06)

0.98 (0.51)

Hhead2market (dummy) 0.94(0.55)

2.01(1.24)

1.35(0.76)

1.23 (0.73)

Log Likelihood -103.64 -104.73 -103.81 -104.32 LR Chi Square 21.06 21.14 40.34 25.21

p-value 0.03 0.03 0.00 0.01 Observations 80 80 80 80

McFadden’s R² 0.092 0.092 0.163 0.108 % correctly classified 47.50 50.00 46.25 47.50

Note: * p < 0.1, ** p < 0.05, *** p < 0.01

Across all four models, though not being significant in all cases, the education of the household head and the area cultivated by the household increase the odds to be in a higher (≈ earlier) category. In the four models, either extension or reading a farming journal is significant and increases the odds of higher categories. The overlap of farmers who both read a farming journal and newspapers regularly is around 80%.

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Therefore, the variable ‘Farmjournal’ also represents general information, which might explain why the parameter estimate is significant for motorcycle and credit adoption. The age of the household head generally is significant and increases the odds of higher categories, except for cotton adoption, where it is slightly decreasing the odds and not significant. The number of buffalos in a household is significant in three models, and decreasing the odds of higher adopter categories. The odds increase only in one model, where the coefficient is not significantly related to the outcome variable. The dependency ratio in the household, pond area, membership within the village, city visits, and market visits by the household head are not significant in the four models and show no clear tendency.

While the total correctly classified cases amount to 46 – 50% across the four models, the correct predictions in each adopter category differ widely. Generally, for innovations which still have a large proportion of non-adopters, the correct prediction of non-adopters is around 90% and the correct prediction of the small groups of adopters is low, not exceeding 50% (see Table 14). Motorcycles have found widespread adoption, and almost 90% of the sample households have adopted this means of transport. Prediction of the early and late majority is best for this innovation, since here the early and late majority form the largest groups. Table 14 shows the proportion of correct predictions for each adopter category.

Table 14: Correct predictions of adopter categories % correctly

predicted by adopter category:

Early adopters

Early majority

Late majority Laggards

Non-adopters

Cotton 14.3 18.2 18.8 0.0 88.9Longan 0.0 28.6 29.4 0.0 91.2

Motorcycle 15.4 50.0 73.9 30.8 33.3Credit 12.5 50.0 0.0 0.0 87.9

The results of the ordinal logistic models are largely consistent with the results from the binary choice models. Except for longan adoption, the ordinal models resulted in more significant independent variables, or showed increased significance of the parameter estimates as compared to the binary logistic models of the respective innovation. The parameters’ directions were similar in the ordinal and the binary models. The ordinal models appear to be more sensitive with respect to the independent variables. This is probably because compared to the binary logit model there are fewer observations per outcome of the dependent variable.

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4.8 Information Sources for Three Innovations The theory of innovation diffusion identifies the spread of information as an essential aspect of the diffusion process, and earlier adopters are thought to use different information channels than later adopters. To test this hypothesis, respondents were asked to identify sources of information about three different innovations before they adopted. The respondents were allowed to select within nine cards symbolizing different information sources. The top three answers of each farmer were classified into three groups. First, external sources include extension agents, the Farmers’ Union, traders, researchers, private companies and other people from outside the village. Second, mass media was classified as a separate group and comprises TV, radio and newspapers. The third group consists of people from the own village, such as friends, neighbours as well as the village headman and the village loudspeaker. Respondents were furthermore classified into threshold groups by their year of adoption. Threshold groups were defined as innovators (mean time of adoption -2 Std. Dev.), early adopters (-2 Std. Dev. to -1 Std. Dev.), early majority (-1 Std. Dev. to mean time of adoption), late majority (mean time of adoption +1 Std. Dev.), and laggards (> mean time +1 Std. Dev.) (ROGERS 1995). If necessary, the thresholds were rounded (e.g. 1994.4 equals 1994 and 1994.8 equals 1995).

Figure 10 shows the share of different sources of information about hybrid maize, keeping a stable outside the village and motorcycle. These three innovations are basically different. Hybrid maize represents a ‘physical’ innovation, and keeping a stable outside the village is a new technique or idea. A motorcycle is a means of transport for agriculture and therefore a new asset, while it is also used for personal contacts and represents a status symbol. According to theory, we would expect that early adopters acquired more information from outside, while later adopters rely mostly on internal sources. However, Figure 10 shows no clear relationship between the earliness of adoption and sources of information.

Extension workers, traders and other people from outside dominated information about hybrid maize and there is a slightly decreasing tendency for later adopters (see Figure 10). Internal information sources represent around 30 – 40% in all different adopter categories, and play the main role for late majority adopters. However, mass media as a source of information increased for later adopters. A reason might be that early adopters did not have much access to mass media about agricultural innovations due to the political and economic situation in the late 1980ies.

For keeping a stable outside the village, innovators relied on external sources like extension agents, while mass media and contacts within the village were not as important. This graph is a bit confusing since there is only one early adopter, and this farmer tried this technique due to personal circumstances. According to the farmer, it was his own idea and he did not have outside information before adoption. In the subsequent adopter categories, internal sources of information alternate with external sources and mass media. However, only 27 farmers in the survey adopted this

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technique. Therefore, the diffusion process is probably not finished and what we call late majority or laggards today might be classified as early majority in several years.

Figure 10: Sources of information about three different innovations Hybrid maize

Adopter categories: n =

Innovators 2

Early adopters 18

Early majority 16

Late majority 30

Laggards 12

Total adopters 78

Stable outside village


Innovators 3

Early adopters 1

Early majority 9

Late majority 11

Laggards 3

Total adopters 27

Motorcycle


Innovators 3

Early adopters 10

Early majority 22

Late majority 29

Laggards 7

Total adopters 71

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Interpersonal contacts within the own village clearly dominated as sources of information about motorcycles. Mass media and other external sources of information are on a low level across all innovativeness categories and stagnate or decrease for later adopters. However, since the farmers use motorcycles to visit friends and they constitute a symbol of wealth, strong dominance of internal sources of information about motorcycles seems to be a logical consequence.

Summarizing the above findings, we did not observe the expected information pattern. There is no visible relationship between sources of information and time of adoption. The top answers of farmers were tested against the year of adoption using two-sample T-tests, and showed no significant results. In order to include the top three answers of farmers, the share of internal sources was calculated for each farmer. These resulting shares were compared against each other using a T-test and two-sample Wilcoxon rank-sum (Mann-Whitney) test, with the year of adoption being the continuous and the ordinal variable respectively. One would assume that higher shares of internal sources correlate with later adoption. However, this was the case for none of the investigated innovations as the conducted tests yielded insignificant results.

4.9 Network Analysis of Survey Households The previous section explored the idea that different types of information influence different adopter categories, or in other words, the type of information a potential adopter accesses has influence on the adoption time. The present section focuses on the pattern of relationships among the households of the two surveyed villages and infers to innovative behaviour among the sample households. Table 15 shows the correlation coefficients between the household’s exposure or threshold level and time of adoption for three different innovations. The results indicate that later time of adoption is positively associated with higher personal network threshold values.

Table 15: Relationship (correlation coefficient) between time of adoption and personal network Proportion of peers who adopted earlier Adoption time of… Hybrid maize Motorcycle Stable

Hybrid maize 0.33** Motorcycle 0.61**

Stable outside village 0.33** Note: ** p < 0.05

A combined threshold value was derived from the average of the three single threshold values. This combined value was tested for its relation with the innovativeness index based on the adoption times of all surveyed innovations (see section 3.6 for a detailed explanation of the index). The two values are negatively correlated (r = -0.52) at a 5% significance level. Figure 11 depicts the personal threshold level by the corresponding innovativeness level of a sample household. The

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line of fitted values shows the predicted relation between the two values based on linear regression. Higher threshold values are generally associated with lower innovativeness levels and vice versa. However, it is still possible that a household is innovative with respect to the personal network and not very innovative with respect to the overall sample.

Figure 11: Personal network thresholds and system innovativeness

Figure 12 below presents a so-called sociomatrix of the friendship relations in the two surveyed villages. It shows a rough overview of the two-village network and uses directed ties that show who nominated whom as a friend. The circles each represent one household in the villages, where full circles represent sample households and empty circles are households that were not included in the survey. The household which are located in the centre of the graph, have more friendship ties than households which are located in the outer sphere. Only around one third of the households in the two villages were surveyed so that many households remain unconnected (n = 52; see lower left corner of Figure 12).

For each innovation examined in the present survey, the measure of centrality and personal network density were used to test statistically significant differences between adopters and non-adopters. It can be seen in Table 16 that for some innovations, the centrality degree is significantly higher for adopters while personal network density appears to be significantly different only for mulching in upland. The centrality degree is significant mainly for physical innovations, that is, for fruit trees, livestock, and motorcycle adoption. Households who took official credit have higher centrality degrees than non-adopters. Centrality degree and personal network densities are generally not significantly different for field crops and agricultural methods.

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Figure 12: Sociogram of household-to-household contacts in the study villages

Note: Full circles are sample households; empty circles are non-sample households (n = 233 households, 345 ties, 52 households unconnected).

Table 16: Statistically significant relationships between personal network characteristics and the adoption/non-adoption of innovations (using T-tests)

Innovation adoption: Centrality

degreePersonal

network densityHybrid maize

Mineral fertilizer on maize Hybrid rice

Improved cotton Mineral fertilizer on cotton

Lychee *Longan

Grapefruit **Improved pigs ***

Improved ducks **Motorcycle *

Fertilizer on vegetables Upland ploughing

Stable outside village Direct seeding in rice Tree fence on slopes Mulching in upland **

Official credit *Note: * p < 0.1, ** p < 0.05, *** p < 0.01

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5 Discussion “Diffusion is the process by which an innovation is communicated through certain channels over time among the members of a social system” (ROGERS 1995).

The present study found many innovations that are currently diffusing in the research area. Furthermore, some innovations are interrelated and diffusion paths depend on one another. The introduction and diffusion of innovations has considerably sped up in the early nineties. Especially after 1994, many farmers adopted innovations. Technologies or methods that have been strongly promoted by government agencies or private companies have had greater adoption rates. The main constraints to adoption identified in the present study were lack of land, cash, and labour. While for some innovations farmers indicated lack of experience and information, they did not consider the innovations being too difficult or complex for them. If innovations required initial investment, the main sources of finance were own savings, followed by credits from shopkeepers and semi-formal credits from local organisations.

Concerning different innovativeness of the sample households, financial objectives were important for the majority of all households and did not show a tendency across categories. However, the agricultural revenues in 2005 were higher in more innovative households. Households with lower innovativeness had higher interest in social objectives as compared to innovative households. Linear, binary logit, and ordinal logistic regression models showed that higher age and education of the household head and contacts with extension services favoured earlier adoption decisions. However, no statistically significant differences between communication channels were found between earlier and later adopters of three innovations. Personal exposure levels within the friendship networks prior to adoption were correlated with overall innovativeness of the household. While the centrality degree of a household was significantly greater for adopters of some innovations, the comparison of personal network densities of adopters and non-adopters did not explain adoption.

The following sections discuss the main findings described above. While the first section discusses the introduction and diffusion paths of selected innovations, the second section focuses on communication channels and the household networks. Concerning socioeconomic variables and communication behaviour, the third section discusses the results of the linear, binary logit, and ordinal logistic regression analyses.

5.1 Innovation Diffusion over Time The results from the pre-survey group interviews (see Annex 2 and Annex 3 for timeline) and the household interviews were not consistent regarding the introduction years of the respective innovations. According to household interviews, the innovations were adopted earlier than the results from the timeline. This might be due

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to recall problems by the participants of the group interviews and by the respondents in the survey. Since the survey for the present study could collect data only at one point in time, the years of adoption are based on recall. The recall problem usually aggravates over time since people tend to forget more and more (ROGERS 1995). Important occasions in the life of people might help to remember adoption decisions relative to the occasion. The Vietnamese War and the Communist Period mark important cornerstones in the farmers’ lives. In the group interviews, the farmers were asked about the general introduction of innovations. It is likely that farmers mainly referred to their own adoption behaviour. Therefore, it is suggested that the recall data obtained from the individual household survey are largely consistent with real adoption curves.

The cumulated adoption curves obtained from the survey show that more farmers adopted innovations in the mid or late 1990ies. In a survey on income diversification in the Northern Uplands of Vietnam, MINOT et al. (2006) found that more than 40% of the farmers reported that the encouragement by extension services and local authorities was an important factor for them to adopt new crops. In Yen Chau district, an extension unit was established in 1994 (MINOT et al. 2006). Many of the examined innovations in the present survey were adopted by a large number of farmers after 1994, which might confirm a strong influence by government extension services.

5.2 Diffusion through Certain Communication Channels The results from the examination of information sources for three different innovations suggest that there is no significant difference between earlier and later adopters in the research villages (see section 4.8). However, this is in contrast with earlier findings from literature (LIN and BURT 1975; ROGERS 1995; SCHREINEMACHERS et al. 2006).

The examined innovations are hybrid maize, motorcycles, and keeping stables outside the villages. Hybrid maize is a well-known innovation that has deeply diffused among the sample households. In the early 1990ies, government agencies strongly supported the dissemination of hybrid maize varieties, which resulted in widespread adoption by farmers. Additionally, the growing livestock and poultry sector created a market for maize as feed (THANH HA et al. 2004). The massive campaigning could be a reason why information channels do not strongly differ between earlier and later adopters because the information reached all farmers regardless of the time of adoption. One approach to explain differences of adoption times is the time that a farm household exists. The number of years since the household head is making decisions in agriculture is denoted by farm management experience. This variable was used as a proxy for farm existence. There is a shortcoming because household heads could have changed in one farm household and this does not necessarily indicate the duration of farm existence. The time of farm management ranged from 1 to 56 years among the household heads in the sample (mean 18.4 years, Std. Dev. 10.9). Farm

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households that were established a few years ago might be later in adoption of hybrid maize than farms that were established several generations before. Wald’s T-test was used to compare the farm management experiences of the earlier 50% of adopters to the later 50% of adopters. The test was significant at a 1% level with the mean experience of 22.9 years for earlier adopters (Std. Dev. 10.2) and 14.9 years for later adopters (Std. Dev. 10.4). Consequently, the five adopter groups were tested for farm management experience in a one factorial analysis of variance, which reported a significant difference between the groups at the 10% level. Farm management experience is naturally correlated with age of the household head (r = 0.67). In contrast to the farm management experience, however, the ANOVA test on adopter groups of hybrid maize was not significant for the age of the household heads (p = 0.22). These results suggest that the duration of farm management by a household head might be used as a proxy for farm existence and correlates with significantly different adoption times despite its shortcomings.

From informal talks with village members, moving livestock into stables outside the village appeared to be necessary because of high population density in the villages. Factors obviously inhibiting the adoption are lack of land, labour, and cash (see Annex 4 for reasons of non-adoption). Regarding this innovation, not the stable itself is the innovation, but shifting it outside the village is the new technique. Therefore, farmers know the method to keep animals in a stable and do not need to gather information from outside. Because population pressure is high and to bring livestock to faraway grazing land is time consuming, keeping animals close to the pastures seems to be a logical conclusion, given the farmer has enough resources to implement it. Previous knowledge of stables, population pressure, and resource availability might lead the farmer to ‘invent’ the innovation him/herself, thus decreasing the importance of information channels.

Concerning motorcycle adoption, a possible reason for little differences among adopter categories might be that most information channels are irrelevant for this innovation. Motorcycles are no agricultural innovation by itself although farmers use them to transport products to the market. However, motorcycles are a part of daily life in Vietnam and are a symbol of wealth that replaces the former bicycles. Probably most farmers have heard about this innovation long before adoption, only lacking the cash for purchase. Therefore, it is not surprising that information sources do not differ significantly between earlier and later adopters.

SCHREINEMACHERS et al. (2006) found that network structures help to explain innovation diffusion, but are not sufficient. Also household and innovation characteristics have to be taken into account. The network analysis of the households for the present study was not complete because only sample households were questioned about their network of friends. For adoption decisions, comparing differences in densities of the personal networks was not very successful. This is because the friends of a household were not asked for their relation among each other unless they were part of the survey sample. To overcome this problem, sample

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households could be questioned for the relations among their network of friends. This would provide additional insight without the necessity to interview every household in the village. This approach to “ego-centric networks with alter connections” was also mentioned by HANNEMAN and RIDDLE (2005). Even with a relatively small sample size in a larger population, personal network densities are easier to obtain than with full network methods. The measure of centrality degrees used in the network analysis reflected the number of in- and outgoing ties of each household. Also the centrality degrees did not completely reflect reality, because ingoing ties from non-sample households could not be considered. However, there were significant differences between adopters and non-adopters of some innovations.

One result was that adopters of improved lychee and longan varieties had higher centrality degrees than non-adopters. Concerning fruit trees, a large proportion of adopters adopted the improved varieties without financing costs because they had received the cuttings from friends or relatives (see Annex 6). A smaller proportion of farmers also received young improved ducks from friends or relatives. Therefore, farmers with a higher centrality degree might be more likely to receive tree cuttings or young ducks from friends. Concerning ducks and especially improved pigs, a substantial proportion of farmers stated as reasons for non-adoption that they lacked information and experience. More central farmers might again receive more information from peers who already have experience. The same might apply for official credits but this was not investigated. Motorcycles are a means of transport, and a symbol of wealth. Farmers use them frequently to visit friends. More central households might be more exposed to other motorcycle owners and feel it is easier to keep in touch with friends who live further away in the village.

5.3 Diffusion among the Members of a Social System Results from the survey showed that across different adopter categories, the objective to increase current income was stated most frequently by innovators and earlier adopters, however, there was no tendency observable. Nevertheless, earlier adopter categories showed to have generally higher revenues from agricultural production than later adopters did. During the interviews, respondents often were not alone but joined by relatives, friends or neighbours. This circumstance might have led to biased responses concerning the farmer’s objectives. Additionally, a farmer who already gains relatively high revenues might not strive equally hard to increase revenues as compared to a poorer farmer. The relationship between innovativeness and higher income has been mentioned in literature (ROGERS 1995), although this does not involve a cause and effect statement.

The members of a social system have different objectives, and differ regarding socioeconomic features and communicative behaviour, which was expected to influence adoption decisions. The socioeconomic and communication variables derived from the survey were employed in three different regressions. In the ordinary least squares regression, overall innovativeness of farm households was assessed for

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the influence of independent variables, while in the binary and ordinal logistic regressions adoption decisions about selected innovations were examined.

The results from the OLS regression with overall innovativeness as the dependent variable suggested that variables concerning the age and education of the household head, cultivated agricultural and pond area as well as participation and contacts with extension services were significantly associated with the outcome variable. Other variables concerning productive assets like labour and draft power, participation in social village life, and market orientation of the farmers yielded no significant relation with innovativeness. However, the examined innovations from the survey were very diverse and did not only concern agriculture. While some had no or very low investment costs, other innovations had high monetary and/or labour requirements. Some innovations increased productivity while others attempted to preserve soil fertility. While most studies of innovation adoption analyse the diffusion of one specific technology, the examination of overall innovativeness across 18 innovations is a different approach taken in the present study. DIEDEREN et al. (2003) found in a study of Dutch farmers that this approach yielded more robust results because the innovativeness was not linked to a specific innovation. However, as mentioned above, a disadvantage is that different innovations are put together. The innovations have different characteristics and consequently farmers with different characteristics might find them more or less suitable for adoption. This is also suggested by the results from the binary and the ordered logistic models of this study. Models for different innovations yielded considerably different associations between independent and dependent variables. Classification of innovations into similar clusters might have overcome this problem, but has not been pursued because adoption rates for some innovations were very low.

Demographic Variables

The age of the household head appeared in literature as an important factor to influence adoption decisions. In the linear regression model on innovativeness, higher age was significantly related to greater innovativeness at the 10% level. In the binary and ordinal logistic models, this relation depended much on the specific innovation. However, when the variable age was significant, there was always a positive association with the dependent variable, let this be innovativeness, the adoption decision concerning a specific innovation, and the earliness of the adoption decision. Although a number of studies found age to be negatively related to adoption, some show a positive relation (MCNAMARA et al. 1991; ADESINA and BAIDU-FORSON 1995; COMER et al. 1999). Also HOGSET (2005) found a strong positive correlation between the age and the time of adoption of improved natural resource management techniques. In the linear regression model and the ordinal logistic models estimated in the present study, the variable age is more important than in the binary logistic models. This is not surprising because the former models account for the specific time of adoption while the binary logistic model only estimates parameters for adoption decisions (0/1).

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The fact that the household head received secondary or higher education was significantly associated with higher innovativeness, and with earlier adoption of innovations. However, the relation of higher education to adoption decisions (0/1) was sometimes negative. Higher education might correlate with earlier adoption or generally greater innovativeness in the study sample, but not with the decision to reject or adopt an innovation. Higher education represents a greater “ability to receive, decode, and understand information” about innovations (NELSON and PHELPS 1996). The present survey asked respondents about reasons for non-adoption and discontinuance of innovations. Farmers did not regard the examined innovations being too difficult for them (see Annex 4). Higher education might thus be related to the earliness of adoption, and did not appear as a hindering factor to adoption itself.

The dependency ratio was intended to reflect the share of the more productive and higher educated generation as well as to give a rough representation of the labour endowment of the household. However, it was neither significantly related to innovativeness, adoption decisions except for improved pig adoption, nor to timing of adoption decisions. Reasons might be that 10% of the household heads were 64 years or older, and thus the older generation was making decisions in these households even when young people were present. Also using the dependency ratio as a proxy for labour endowment is not very exact, because family members aged below 15 or above 64 years also work on the farm, sometimes providing a considerable amount of the household labour.

Productive Assets

Agricultural area and pond area were found to be significant predictors of overall innovativeness. However, these variables were not significantly associated to most adoption decisions of the logistic models. A possible reason is that the innovativeness index includes a larger number of innovations which require cropping area whereas the logistic models look at particular innovations such as motorcycle or credit adoption. Additionally, the amount of agricultural land per household does largely reflect the distribution policy in 1997 when every resident of the villages received the same amount of land. As explained earlier, land was redistributed regardless of the area a household had contributed before. Many farmers adopted innovations earlier than 1997, and no information was available to determine land endowment of the households before 1997.

Concerning the number of buffalos a household owns, the parameter estimates in the models were not expected except for credit adoption. Buffalos are a cash deposit for farmers and may be sold if money is needed. Therefore, a farmer with more buffalos might be less dependent on credit or even more unwilling to take a credit because of interest rates or the fear of debt. However, this does not explain why generally a higher number of buffalos was associated with lower innovativeness, decreasing odds of adoption and later adoption of innovations. An underlying factor of this variable might be that buffalos are relatively short-term assets and thus, their

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number in the household might vary considerably over time so that the present data are not consistent when used to explain adoption decisions several years ago.

Social Interaction and Information

The variable ‘membership’ reflected the social participation of a household within the village, whereas ‘extension’ and ‘farmjournal’ represent contacts with extension services, and ‘city_bin’ and ‘hhead2market’ were used to determine a household’s market orientation and openness. Surprisingly, the variables representing social participation within the village and market orientation or openness were found to be significant only in few models. Using dummy variables might not properly capture the potential associations with the dependent variables. Additionally, party or village meetings might not necessarily deal with agriculture. Concerning market orientation, the subsistence based agriculture in the two research villages might be a reason for the lack of statistically significant relations to adoption decisions.

A farmer’s contacts with extension services and active information seeking by reading farming journals appeared to have significant positive associations with innovativeness, adoption decisions (not in all models significant but nevertheless increased the odds of adoption), and earlier adoption of innovations. These results are consistent with theory on innovation diffusion. A change agent introduces an innovation, which some farmers adopt early and pass their knowledge on to peers who might be later adopters (ROGERS 1995).

Summary

In both the linear and the ordinal logistic regression models, the timing of adoption is an underlying dependent variable. Higher age and education of the household heads, and greater information seeking through contacts with extension bodies were identified as factors that influenced earlier adoption. Although the binary logit model of adoption showed better model fits across different innovations, the ordinal logistic model promises to account for earlier adoption as well. Consistent with this, only the contacts with extension services showed a clear relation across the binary models for different innovations. Other variables had specific relations with the considered innovation, and were not clear in direction. Also MINOT et al. (2006) reported from a survey that extension agents had considerable influence on adoption decisions of farmers in the Northern Uplands of Vietnam.

Concluding Remarks

59

6 Concluding Remarks Based on a survey of 80 randomly selected households in two Black Thai villages in Northern Vietnam, this study showed that farmers tend to be more innovative when they are older and have higher education. Furthermore, their households have higher land endowment and larger ponds used for aquaculture. Farmers with more contacts to extension bodies are more innovative.

No clear relation between innovativeness of farmers and their personal objectives for the farm could be established. Innovative farmers did not strive stronger for higher income than less innovative farmers did. However, innovative farmers were found to have generally higher revenues from agriculture. This could serve as a means to identify more innovative farmers in the future because the innovativeness index in the present study was based on a wide variety of innovations which started diffusing at different times. It can be expected that farmers who were more innovative in the past over a wide range of different innovations will continue to be so in the future.

The survey yielded a wide range of innovations and investigating all of them would have exceeded the limits of the analyses. Cumulated adoption curves generally had long starting phases with slow adoption rates. However, for highly promoted innovations like hybrid rice, hybrid maize, and improved cotton, diffusion has been rapid. Likewise, results from the regression models indicate that contacts with extension bodies such as extension agents, Farmers’ Unions, agricultural training, and farming journals have had a crucial impact on speeding up adoption of innovations. However, the less innovative farmers in the survey were also the ones who had the lowest agricultural revenues in 2005. Therefore, poorer farmers should receive more assistance from extension services.

The main constraints to adoption that farmers perceived were lack of land, cash, experience, and labour. Lack of land implies that farmers would prefer to adopt land intensive innovations as compared to innovations with a low return per unit of land. Lack of cash favours the adoption of low cost innovations, and adoption of innovations with higher costs could be facilitated by credits. While farmers in the sample did rarely use official credit to finance innovations, in addition to own savings, credits by shopkeepers or companies were used to finance more costly innovations such as improved cotton. Also semi-formal credits from local cooperatives or Unions might help to facilitate adoption for poorer households.

Lack of experience, another inhibiting factor to adoption, might be overcome by trial sites from the extension services and regular agricultural trainings. In only around 50% of the sample households, a family member received agricultural training from which almost one half had the last training in 2003 or earlier. However, due to staff limitations, the agricultural extension department in Yen Chau will most likely be unable to provide such information. Local Farmers’ Unions might be able to fill this gap and self-organize more training in cooperation with extension agents from the

Concluding Remarks

60

Yen Chau department. Additionally, simple leaflets on a picture basis with instructions, advantages, and disadvantages could be circulated for more complex innovations. This has been done previously in a project to promote sustainable coffee production in the Central Highlands of Vietnam (KUIT 2006, pers. comm.).

Analyzing household networks was not particularly helpful to distinguish adopters from non-adopters or to explain diffusion. One reason was the lack of sufficient network data because only one third of the households in the research villages were surveyed. Additionally, the survey asked for friendships and not for professional relations. Nevertheless, some additional insights could be gained concerning the possible relation of centrality degrees to adoption decisions for specific innovations. Also the personal thresholds to adoption within the personal network were significantly correlated to innovativeness.

Finally, the binary and ordinal logistic models identified significant relations between independent variables and adoption decisions. Moreover, the models showed that different factors have significant influence depending on the respective innovation. However, one should not forget that logistic models (and ordinal models even more so) require larger sample sizes than linear regression models to report robust results. Therefore, this study can only indicate rough trends which have to be confirmed with larger sample sizes. Additionally, the two research villages were located close to the district centre of Yen Chau, and have relatively favourable relief conditions including a large water dam that provides them with irrigation water. Therefore, research projects in more remote areas with less favourable conditions and low market access are needed to confirm the findings from this case study. Ordinal logistic models should be further explored with larger sample sizes to explain factors influencing earlier adoption since binary choice models can only distinguish between two different outcomes.

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Annex 1: Descriptive statistics of variables used in regression analyses Dependent variables Description of variables used in regression analyses Mean Std. Dev. Min. Max. Index Innovativeness [in %]; continuous 38.35 15.92 2.64 74.37 Ad_cotton_bin Adoption of improved cotton [0 = no; 1 = yes]; dummy 0.55 0.50 0.00 1.00 Ad_longan_bin Adoption of improved longan [0 = no; 1 = yes]; dummy 0.57 0.50 0.00 1.00 Ad_pig_bin Adoption of improved pig varieties [0 = no; 1 = yes]; dummy 0.44 0.50 0.00 1.00 Ad_motorcycle_bin Adoption of motorcycle [0 = no; 1 = yes]; dummy 0.89 0.32 0.00 1.00 Ad_stable_bin Adoption of stable outside village [0 = no; 1 = yes]; dummy 0.34 0.48 0.00 1.00 Ad_treefence_bin Adoption of tree fences of hillsides [0 = no; 1 = yes]; dummy 0.34 0.48 0.00 1.00 Credit_bin Adoption of official credit [0 = no; 1 = yes]; dummy 0.59 0.50 0.00 1.00 Rank_cotton Adoption of improved cotton [0 = laggards; 1 = late adopters; 2 = late majority;

3 = early majority; 4 = early adopters]; discrete 1.29 1.39 0.00 4.00 Rank_longan Adoption of improved longan [see rank_cotton]; discrete 1.40 1.41 0.00 4.00 Rank_motorcycle Adoption of motorcycle [see rank_cotton]; discrete 2.21 1.23 0.00 4.00 Rank_credit Adoption of official credit [see rank_cotton]; discrete 1.47 1.45 0.00 4.00 Socioeconomic variables Age_hhead Age of household head, years; continuous 44.91 12.24 23.00 84.00 Edu_hhead Education of household head

[0 = not educated or primary school; 1 = secondary or higher education]; dummy 0.59 0.49 0.00 1.00 Dependency_ratio The ratio of persons in a household aged below 15 or above 64,

who depend on the persons in working age; continuous 0.52 0.43 0.00 2.00 Area_cult1000 Area cultivated by the household per 1000 sqm; continuous 11.77 4.82 1.45 24.20 Area_pond1000 Pond area of the household per 1000 sqm; continuous 1.26 1.48 0.00 10.50 Buffalo Number of buffalos owned by the household; continuous 1.11 1.16 0.00 5.00 Communication behaviour Membership The household participates in village meetings, local cooperatives, and is a member of the

People’s Party [0 = participation below average; 1 = above average]; dummy 0.64 0.48 0.00 1.00 Extension The household is member of the Farmer’s Union, contacted the extension agent during the

previous year, and at least one family member has received agricultural training [0 = below average; 1 = above average]; dummy 0.50 0.50 0.00 1.00

Farmjournal One member of the household regularly reads a farming journal [0 = no; 1 = yes]; dummy 0.28 0.45 0.00 1.00 City_bin Frequency of visits to two important cities/markets

[0 = below average; 1 = above average]; dummy 0.33 0.47 0.00 1.00 Hhead2market The household head goes to the market him-/herself [0 = no; 1 = yes]; dummy 0.80 0.40 0.00 1.00

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Annex 2: List of innovations by year of introduction (baseline 1955) List of innovations: 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000

Crops Local paddy rice Improved rice Manure application Chemical pesticides Pump sprayer Mineral fertilizer Hybrid rice Local upland rice Local maize Sell to cooperative Improved maize Own marketing Hybrid maize Mineral fertilizer Chemical pesticides Private trader Local cassava Improved cassava Local cotton Improved cotton Chemical pesticides Mineral fertilizer Local vegetables Chemical pesticides Improved varieties Marketing Manure Mineral fertilizer Local mango Improved mango Pineapple

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Annex 3: List of innovations by year of introduction (baseline 1955), continued List of innovations: 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000

Banana Jackfruit Sugarcane Lychee Longan Pomelo Manure on fruit trees Mineral fertilizer on fruit trees Chemical pesticides on fruit trees Livestock Fishery Keeping wild fish in ponds Raise grass carp, start aquaculture Improved fish Hybrid fish Buffalo/ cattle Plough in upland Farm outside village Goat Local pig Improved pig Duck raising Animal vaccination Soil conservation Intercropping Pineapple/tree fences on slopes Mulching Irrigation of paddy rice Manual irrigation Mud irrigation channels Chieng Khoi Dam Concrete irrigation channels

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Annex 4: Reasons for Non-adoption of Innovations Lack of Percentage of responses

by reasons for non-adoption: (%)

not suitable

too difficult

no access cash land water labour

Infor-mation

exper-ience other

Sum of responses

n =Hybrid maize 0 0 50 0 50 0 0 0 0 0 2Hybrid rice 0 0 0 0 0 0 0 0 50 50 2Improved cotton 22.2 0 0 0 69.4 0 19.4 0 0 5.6 42Lychee 8.8 0 5.3 0 87.7 0 0 0 0 1.8 60Longan 0 0 11.8 0 85.3 0 0 0 0 2.9 34Pomelo 12 0 10 2 80 0 0 2 0 2 54Improved pig 2.2 0 0 53.3 4.4 0 24.4 4.4 24.4 6.7 54Improved duck 13.8 0 0 31 17.2 22.4 24.1 1.7 8.6 3.4 71Motorcycle 0 0 0 100 0 0 0 0 0 0 9Fertilizer on vegetables 52.3 0 0 1.5 20 0 1.5 0 1.5 18.5 62Upland ploughing 14.3 0 0 57.1 14.3 0 0 0 0 14.3 7Farm outside village 0 0 0 15.1 77.4 0 30.2 0 0 1.9 66Direct seeding of rice 41.3 0 2.7 0 0 6.7 4 17.3 64 1.3 103Tree fence on slope 34 0 15.1 0 22.6 0 0 9.4 28.3 15.1 66Mulching 9.1 0 0 0 0 0 36.4 0 36.4 9.1 10Sum of responses n = 114 0 23 65 224 18 56 22 85 35

Note: Multiple responses possible. Answers reflect % of sample households.

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Annex 5: Reasons for Discontinuation of Innovations Lack of Proportion of

discontinuation among adopters (%)

Percentage of responses by reasons for discontinuation: (%)

not suitable

Too difficult cash land water labour other

Sum of responses

n =2.6 Hybrid maize 100 0 0 0 0 0 0 21.3 Hybrid rice 100 0 0 0 0 0 0 1

84.1 Improved cotton 54.1 0 0 27 8.1 13.5 13.5 4313.0 Lychee 0 0 0 0 0 0 100 36.7 Pomelo 50 0 0 50 0 0 0 2

77.1 Improved pig 37 0 22.2 0 0 33.3 25.9 3227.3 Improved duck 16.7 0 0 16.7 16.7 33.3 33.3 79.9 Motorcycle 14.3 0 42.9 0 0 0 42.9 76.7 Fertilizer on vegetables 100 0 0 0 0 0 0 17.4 Farm outside village 0 0 0 0 0 100 0 2

80.0 Direct seeding rice 100 0 0 0 0 0 0 418.5 Tree fence on slope 60 0 0 40 0 0 0 5

Sum of responses n = 45 0 9 14 4 18 20


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Annex 6: Sources of Finance for Innovations Credit from Percentage of responses

by financing source:

(%) No cost Own

savings

Family member/

friend

Shop-keeper/

Company Bank

Cooperative/Association

/ UnionMoney-

lender other

Sum of responses

n =Hybrid maize 0 84.6 1.3 6.4 0 10.3 0 0 80Mineral fertilizer on maize 0 61.5 1.3 12.8 0 26.9 0 1.3 81Hybrid rice 1.3 87.2 0 2.6 1.3 7.7 0 0 78Improved cotton 0 20.5 0 84.1 0 0 0 0 46Fertilizer on impr. cotton 0 18.2 0 88.6 0 2.3 0 0 48Lychee 39.1 60.9 0 0 0 0 0 0 23Longan 32.6 67.4 0 0 0 0 0 0 46Pomelo 43.3 56.7 0 0 0 0 0 0 30Improved pig 0 88.6 2.9 0 2.9 5.7 0 0 35Improved duck 9.1 86.4 0 0 4.5 0 0 0 22Motorcycle 0 94.4 5.6 2.8 1.4 0 1.4 0 75Fertilizer on vegetables 0 100 0 0 0 0 0 0 15Upland ploughing 97.3 1.4 1.4 0 0 0 0 0 73Stable outside village 11.1 81.5 7.4 0 0 3.7 0 0 28Tree fences on slopes 92.6 3.7 0 0 0 0 0 3.7 27Sum of responses n = 139 417 10 95 4 39 1 2


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