Banana Farmers’ Adoption of Sustainable Agriculture Practices in the Vietnam Uplands: The Case of Quang Tri Province

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Agriculture and Agricultural Science Procedia 5 ( 2015 ) 67 – 74

Peer-review under responsibility of the Faculty of Agriculture, Chiang Mai University doi: 10.1016/j.aaspro.2015.08.010

ScienceDirect

1st International Conference on Asian Highland Natural Resources Management, AsiaHiLand

2015

Banana Farmers’

Adoption of Sustainable Agriculture Practices in

the Vietnam Uplands: the Case of Quang Tri Province

Nguyen Van Thanh

a,b

,

Chinawat Yapwattanaphun

a,

*

a_{Department of Horticulture, Faculty of Agriculture, Kasetsart University, Bangkok 10900, Thailand} b_{Centre for the Rural Development in Central Vietnam, Hue University of Agriculture and Forestry, Hue city 47, Vietnam}

Abstract

This study evaluated factors influencing banana farmer’s adoption of sustainable agricultural practices (SAPs) in the uplands of Quang Tri province, Vietnam. Stratified sample technique was used to randomly select 300 respondents from two upland districts of the province with an aid of the structured questionnaire. The study found 65% of the SAPs were not adopted or adopted in a low rate; whereas, 35% of others had a high adoption rate. Moreover, the study ascertained that sustainable agricultural perception, economic status, extension courses, education and feasibility of practices were effective factors on banana farmers’ SAPs.

Peer-review under responsibility of the Faculty of Agriculture, Chiang Mai University.

Keywords: adoption, banana farmer, upland, sustainable agriculture

1.Introduction

Concern about the negative impacts of modern agriculture to human health, natural environment and resources has prompted development and diffusion of an alternative called sustainable agriculture. Sustainable agriculture is the approach that not only makes to better uses of natural goods and services for human needs without damaging the environment, but minimizes the use of external inputs. Besides, it enables farmers to use their knowledge and skills more effectively (Shiri et al. 2012). However, the decision of farmers to adopt a new agricultural technology depended on complicated factors. One of these factors that influence farmers' perception is the characteristics of the

* Corresponding author. Tel.: + 66-2579-0308; fax: + 66-2579-0308 E xt. 112. E-mail address: agrcwy@ku.ac.th

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new technology. Other factors influencing farmers' adoption are resource endowments, socio-economic status, demographic characteristics, and access to institutional services (Negatu and Parikh, 1999).

The Vietnam uplands occupy three-quarters of natural inland territory and it constitutes of 30% of the country’s

population with a majority belonging to the ethnic minorities. The uplands of Vietnam are associated with lowest income and highest rate of poverty (BTI, 2014). The livelihoods of upland population mainly depend on agricultural production activities (An, 2006). Cassavas, bananas, coffees, beans and upland rice are the major crops in the uplands. However, upland people still use many unsustainable cultivation techniques and bad habits such as monocultures, burning of crop residue, poor fertility management, tillage, etc. which results in low yields, increasing insect pests and diseases on crops, natural resources degradation and environmental pollution in the uplands of Vietnam (Schmitter et al. 2010). In addition, the poor linkage between production and consumption results in low and fluctuation product prices. Hence, improving production and consumption practices towards sustainable agriculture has been considered as a strategy to address above mentioned problems in the uplands of Vietnam.

Although Vietnam government has been having efforts in promoting farm-level sustainable agriculture practices (SAPs), related research on upland farmers’ SAPs and the factors that influence their adoption of these practices has been lacking. Hence, it is vital to conduct studies related to this field in order to help the agricultural institutions of Vietnam to design better extension programs for the uplands. Present study was conducted as a case study on banana farmers due to bananas are the major crop the Vietnam uplands. The objectives of this study were as follows:

(1) To measure banana farmers’ SAPs;

(2) To determine the factors influencing banana farmers’ adoption of SAPs in the Vietnam uplands.

2.Literature review

There has been a large range of factors that influence adoption of SAPs. Hence, it is essential to establish a theoretical base to model the relationships between explanatory variables and adoption of SAPs.

Some factors commonly found in previous studies related to adoption of SAPs can be grouped into 2 categories: (1) socioeconomic characteristics of farmers including age, education, farm experience, perception, income, land ownership, farm size, information-seeking behaviour, extension, etc; (2) Behavioural control factors such as perceived characteristics of new technologies and access to resources.

2.1 Socioeconomic characteristics of farmers

Age of farmers is often hypothesized to have an impact upon adoption of SAPs. Some studies found farmers’ age

had a significant influence on the adoption of SAPs in (Kassie et al. 2013; Zdenka and Michal, 2013). Formal education of farmers is expected to associate with the adoption of SAPs. Numerous studies found that education of farmers tends to positively influence their decision to adopt SAPs (Ngombe et al. 2014; Teklewold et al. 2013) while study of Clay et al. (1998) did not find influence of education levels on the adoption of these practices.

Farm experience was revealed to have significant and positive influence on the adoption of SAPs by studies conducted by Adeola (2010) and Tosakana et al. (2010) but it was not found significant influence on the adoption in the studies of Rezvanfar et al. (2009) and Arellanes and Lee (2003). Besides, Ngombe et al. (2014) and De Souza Filho et al. (1999) revealed farm labor was associated with the adoption of SAPs while the study conducted by Okuthe (2014) did not find its influence on the adoption of these practices.

Income is assumed to associate to the adoption in some studies. Caviglia Harris (2003) found that farm income of farmers had positive influence on SAPs adoption while (Ngombe et al. 2014) stated that off-farm income had negative influence on the adoption. Arellanes and Lee (2003) revealed that household income was not significantly influenced the adoption of SAPs.

Regarding land ownership, several studies did not found its influence on the SAPs adoption (Adeola, 2010; Arellanes and Lee, 2003; Ngombe et al. 2014) while it was found to have significant and positive influence on the use of SAPs in the study of Okuthe (2014). Besides, property size is often, but not usually associated with the SAPs adoption (Ghadim et al. 2005). For example, De Souza Filho et al. (1999) and Adeola (2010) ascertained that farm size had a significant influence on adoption of soil conservation practices but Wollni and Andersson (2014) did not

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find its influence on the adoption of these practices.

In terms of perception, some previous studies considered perception as a certain prerequisite to SAPs adoption (Mahboubi et al. 2005; Stonehouse, 1991). Otherwise, Rogers (2003) states that perception of a technology leads to its adoption. De Souza Filho et al. (1999) showed that the adoption of SAPs will increase as farmers have positive perception of negative effects of chemicals on health and environment while Hall et al. (2009) revealed perceived importance of sustainable practices is significant influential factor on the adoption of SAPs. Tosakana et al. (2010)

found that farmers’ perceived yield risk and uncertainty was an important factor influencing the adoption of SAPs.

Information sources play an important role in decision-making process to reduce risks and uncertainties to enable farm households to make right decision on the adoption of SAPs (Petros, 2010). Several studies showed that farmers’ access to mass media (TV and Radio) was identified to relate with the adoption of SAPs (Alonge and Martin, 1995; Okuthe, 2014). However, in study’s Ngombe et al. (2014), radio was the factor that no had influence

on the adoption of SAPs.

Extension polices are associated with the sustainable technological adoption. Several studies revealed contact with extension, participation in training courses are effective factors on adoption of SAPs (Kassie et al. 2013; Okuthe, 2014; Timprasert et al. 2014). Gecho and Punjabi (2011) found that adoption of improved maize technology was associated to farmers’ participation in demonstration while Zegeye and Haileye (2001) ascertained that

attendance of training contributed positively to farmers’ decision to adopt SAPs. 2.2 Behavioural control factors

The theory of planned behaviour is a social psychological theory applied to predict and interpret specific behaviours (Ajzen, 1991). This theory states perceived difficulty in conducting actions is an important factor influencing on the adoption of technologies (Hattam, 2006). Perceived feasibility of practices was found as an effective factor on the adoption of SAPs (Hallet al. 2009; Hattam, 2006; Karami and Mansoorabadi, 2008). Besides, regarding access to resources, Okuthe (2014) found positive influence of inputs such as improved seed and fertilizers on the adoption while rent labour was not found significant influence on the SAPs adoption in the study of

D’Souza et al. (1993). Some studies ascertained credit use had positive influence on adoption of SAPs (Kassie et al.

2013; Ngombeet al. 2014; Okuthe, 2014) but the study of Wollni and Andersson (2014) no found its influence on the adoption.

3.Materials and Methods

3.1 Research site and data

Quang Tri province located in the Northern Central Vietnam has five upland districts that make up 67% of the natural land area (317,965 ha) in the province. Bananas, coffees and cassavas, rubbers are major crops in the uplands of Quang Tri. Most banana production areas (90.5%) in Quang Tri are distributed in the uplands with 2,814 ha. Dakrong and Huong Hoa upland districts located in the West of Quang Tri province have four ethnic groups namely Kinh, Pa Co, Van Kieu and Pa Hy, and had population of 37,190 and 78,854 people, respectively (QTSO, 2012).

This study was conducted as a survey research by adopting a structured questionnaire to gather primary data during period of January - August, 2014. The target population of the study consisted of all banana households in two upland districts of Quang Tri province, namely Dakrong and Huong Hoa. The stratified random sampling techniques were used to select 300 respondents in the research area.

3.2 Variables and measurements

Socio-economic characteristics: These include variables age, education, economic status, farm experience, farm size, access to agricultural informational sources, participation in extension courses and sustainable agricultural perception. Sustainable agriculture perception variable was used to measure farmers’ perception in 27 statements

related to sustainable agricultural concepts and practices.

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crop rotation, non-chemical weed control, biological pest control, etc.) was assessed by this variable.

Access to input resources: measured regarding accessing inputs of banana production including machines (grass-cutters, water pumps), fertilizers, pesticides, rent labour and bank credit.

SAPs: This variable was measured regarding performing SAPs in farmers’ banana production. Adoption of a

sustainable agricultural practice received a score of 1 and non-adoption received a score of 0. Table 1 Description of variables used in the regression model

Variable name Variable description and unit of measurement

1. Dependent variable

Adoption of SAPs SAPs adoption index (min. = 3, max. = 14)

2. Independent variables

Age Age of household heads (years) Labour Labour of households (labour)

Education Number of years in school of household heads (years) Economic status Economic status (above poverty line = 1; 0 otherwise) Farm experience Number of years in farm experience (years)

Farm size Banana area of households (ha)

Land tenure Land tenure (Land owner = 1, 0 otherwise) Extension courses Participation in extension courses (times/year)

TV’s agricultural programs Watching TV’s agricultural programs (times/month) Radio’s agricultural programs Listening to agricultural programs on radio (times/month) Agricultural newspaper and books Reading agricultural newspaper and books (times/year) Sustainable agricultural perception Farmers perception of sustainable agriculture (scores) Feasibility of practices Feasibility of SAPs (scores)

Labour assess Access to rent labour (access to labour = 1; 0 otherwise) Machine access Access to machines (access to machines = 1; 0 otherwise) Fertilizer access Access to fertilizers (access to fertilizer = 1; 0 otherwise) Pesticide access Access to pesticides (access to pesticide = 1; 0 otherwise) Credit access Access to bank credit (access to credit = 1; 0 otherwise)

3.3 Data analysis

Data analysed use the Statistical software SPSS 16. Descriptive statistics (frequencies, percentage, mean and standard deviations) and multiple regression were used to analyse the data.

Farmers were categorized as "low", "fairly low", "fairly high" and "high" SAPs adopters based on the collective

‘adoption score’ defined by mean and standard deviation as follows: A = Low: A ൑ Mean - SD; B = Fairly low: Mean - SD < B ൑ Mean; C = Fairly high: Mean < C ൑ Mean + SD; D = High: Mean + SD < D ൑ Max.

4.Results and Discussion

4.1 Banana farmers’ socio-economic characteristics

The study showed that 70% of respondents were males; their age ranged from 18 to 72 and the mean age was 41 years. Number of years in school of 59% of the respondents was 4.3 years; their experience years ranged from 1 to

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30, and the mean was 8.6 years. The mean household size was 5.4 members per household; 43% of the households have economic status of above poverty line; 20% was Kinh ethnic group; 16% used credit for inputs; and the farm size was 1.3 ha. No respondents participated in farm groups. The mean time of respondents watched agricultural TV programs and listened to radio in agricultural programs were 3.1 times and 0.25 times per month respectively; while the mean time of the respondents read agricultural newspaper or books and participated in extension courses were 0.8 times and 0.7 times per year, respectively.

4.2 The measurement of banana farmers’ SAPs

Table 2 indicates classification of banana farmers in adoption of SPAs. As shown that only 4.3% of the respondents ranked in high adoption group of SAPs; whereas, most respondents placed in low group (33%), fairly low group (32.0%) and fairly high group (30.7%) in terms of the adoption of SAPs.

Table 2: Classification of banana farmers based on SAPs

Groups Frequency Percent Cumulative percent

Low 99 33.0 33.0

Fairly low 36 32.0 65.0

Fairly high 92 30.7 95.7

High 13 4.3 100 Total 300 100

Table 3 presents banana farmers’ SAPs. The result showed the practices having high rate of adoption were related to harvest and post-harvest, use of native knowledge, cattle management in fields, crop rotation and weed control; whereas, the practices applied in low rate or not applied was related to application of inputs, quality management of products, consumption, production cooperation and product label establishment.

Firstly, regarding the uses of inputs, about 98% of banana households did not applied any fertilizers. Fertilizers are essential for providing nutritional needs for growth and development of crops when the soil nutrient supply is insufficient. Fertilizers especially organic fertilizers are used to improve soil fertility and prevent land degradation (Roy, 2012). Besides, most respondents still used native variety and did not control diseases for seedlings by chemicals before growing. In addition, no households applied the irrigation system for banana production in the research area. In the uplands, most bananas are grown in sloping soils; thus, it is crucial to set up an irrigation system to provide water for development needs of bananas (Table 3).

Although, biological control is an effective way to control various pest and diseases and to reduce environmental pollution (DeBach and Rosen, 1991), no farmers in the research area used this technique. However, only 35.7% farmers adopted herbicides to control weeds. Besides, crop intercrop and rotation are considered an effective solution to improve soil fertility, reduce soil erosion, control development of weeds, pest and diseases and reduce yield loss (Rusinamhodzi et al. 2012); however, the rate of the respondents adopted these techniques was low, only with 39.7% and 52.3%, respectively (Table 3).

Farmer groups play a vital role in improving members’ knowledge and experience to access to production resources as well as increasing negotiating strength and establishing product certification and label (Barham and Chitemi, 2009). Unfortunately, no respondents participated in farmer groups. Moreover, cooperation between farmers and enterprises in product consumption by contracts helps farmers to gain profits more through value adding and reduce price fluctuation in the market which is the common trend in agricultural production. However, the study showed there were not contracts implemented in selling products between farmers and enterprises. Furthermore, to improve consumption market and product prices, it is vital to register product labels and manage product quality that

play an importance role in improving consumers’ perception and belief about products and improving product prices. Nevertheless, no respondents applied these techniques (Table 3).

Indigenous knowledge in the uplands, Vietnam is rich and diversified. It have been developed and adopted over centuries to conserve natural resources, human health, and farming systems (Trung et al. 2007). Fortunately,

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application of indigenous knowledge was adopted with the high rate (86%) (Table 3). Table 3: The measure of banana farmers’ SAPs

No. SAPs Practices applied (%)

1 Conducting soil test before applying fertilizers - 2 Seedlings controlled diseases by chemicals before growing -

3 Improved variety 1.3

4 Limiting tilling and hoeing 21.0

5 No pasturing cattle in production areas 84.3

6 Proper use of pesticides 14.0

7 Application of irrigation -

8 Application of inorganic fertilizers 2.0 9 Application of organic fertilizers (green and animal manure) 1.7 10 Crop intercrop (legumes or annual crops) 39.7

11 Mulches 15.0

12 Rotation of crops 52.3

13 No-chemical weed control 64.3

14 Biological pest and diseases control 0.3 15 Application of indigenous knowledge 86.0 16 Using fresh equipment to harvest fruits 54.7

17 Fruits harvested at maturity 90.3

18 No fruits touch the land after harvesting 56.7 19 Fruits preserved fresh materials 61.3

20 Participating in farmer groups -

21 Products sold for enterprises by contracts -

22 Products registered the label -

23 Products checked quality very year -

Finally, results showed that most respondents (90.3%) harvested mature fruits. According to Rahman (2007), immature fruits have poor quality upon ripening due to high water loss and mechanical damage. The maturity of fruits is ideal condition for consumption. Using fresh equipment and materials to harvest and manage fruits prevents contamination and spoilage of the produce (Rahman, 2007). However, the rate of households adopted these techniques was low (Table 3).

4.3 Factors influencing banana farmers’ SAPs adoption

To determine the factors influencing farmer’s SAPs adoption, a multiple regression analysis with stepwise method was carried out. The regression model incorporated all of the independent variables which had significantly correlations with the adoption. The dependent variable was the respondent’s adoption towards selected SAPs index, which was defined as their scores obtained from the statements associated with 23 selected practices.

There were 18 independent variables entered in the model, out of which only 5 variables had a significant

influence at the 5% level on farmers’ SAPs adoption. As shown in Table 4, the partial regression coefficients of sustainable agriculture perceptions, economic status, extension courses, education and feasibility of practices were

found to have a positive influence on respondents’ adoption of the selected SAPs. The ଶ_{adj value of 0.385 with F}

value of 38.389 indicated the power of model for prediction its significance at 0.01 level of probability and revealed that 38.5% of variance in SAPs adoption could be explained by these five independent variables.

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Table 4: Results of multiple regression analysis

Predictor variables B Beta t p

Constant 1.539 - 1.940 0.050

Sustainable agricultural perception 0.048 0.314 5.789 0.000

Economic status 0.745 0.195 3.792 0.000

Extension courses 0.289 0.160 3.481 0.001

Education 0.077 0.164 3.068 0.002

Feasibility of practices 0.100 0.133 2.567 0.011 F = 38.398 Sig.= 0.01 R = .629 ଶ_{adj = .385}

The variable that had the greatest influence on farmers’ SAPs was “sustainable agricultural perception” with the

ߚ = 0.314 (p<0.01). This means one standard deviation increase in the sustainable agricultural perception increase the adoption of SAPs by 0.314 standard deviation. The value ߚ= 0.195 (p<0.01) associated with “economic status”

implies that for every standard deviation change in economic status, the adoption of SAPs will increase by 0.195 standard deviations. This finding is supported by D’Souza et al. (1993). Besides, “extension courses” (ߚ = 0.160,

p<0.01), “education”ሺߚ = 0.160, p<0.01) and “feasibility of practices” ሺߚ = 0.133, p<0.05) were also factors

influencing significantly farmers’ SAPs adoption (Table 4).

5.Conclusion and Recommendations

The study pointed out that 65% of the banana households were of low adoption group of SAPs while 35% of ones belonged to high adoption group of SAPs. The result showed practices that had high rate of adoption were related to harvest and post-harvest, indigenous knowledge use, cattle management in fields, crop rotation and weed control; whereas, the practices adopted in low rate or not adopted were related to input uses (soil, water, chemicals and seedling), quality management of products, linkage in consumption, production cooperation and product label establishment. In addition, the study revealed that there were 5 factors that had a significant influence on farmers’

SAPs adoption, namely sustainable agricultural perception, economic status, extension courses, education and feasibility of practices including sustainable agricultural perception had the most influence on the adoption of SAPs.

The study recommends that efforts to enhance banana farmers’ adoption of SAPs should be focused on

improving sustainable agricultural perception especially the perception in application of inputs as well as enhancing knowledge and skills of market access and production cooperation. Moreover, extension courses should be

implemented more to improve farmers’ knowledge and skills in terms of SAPs.

Acknowledgment

We are indebted profoundly to the IDRC-SEARCA scholarship project, the Southeast Asian Regional Center for Graduate Study and Research in Agriculture (SEARCA) for their financial support in carrying out of this study. References

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