Factors Determining the Farmers’ Decision for Adoption and Non-Adoption of Oil Palm Cultivation in Northeast Thailand

Abstract

Many tropical regions are promoting the cultivation of oil palm. In this regard, different national and international organizations promote oil palm cultivation in Thailand. However, socio-economic and institutional factors are very important to be considered in the adoption of its cultivation. This study aims first to determine the various socio-economic and institutional factors in adopting oil palm cultivation, and second, to explore the role of these stated factors in the extent of the adoption of oil palm. The data were collected through a semi-structured questionnaire in Seka of Bueng Kan province of Thailand. The double-hurdle model was used for the estimation of the farmers’ decision to adopt the new technology and the extent of adoption. Results show that the adoption and extent of adoption of oil palm cultivation were positively influenced by gender (male), oil palm training, and access to extension services, while the size of landholding per family member and age negatively influenced its adoption/extent. To increase the adoption rate of oil palm cultivation, young, educated farmers should be encouraged by providing training and credit and extension services should be made accessible.

1. Introduction

At present, the world’s energy situation is highly dependent on non-renewable energy sources such as fossil fuel and coal, which is the main source of greenhouse gasses [1]. Moreover, if not managed properly, these energy sources have a high chance of depletion over time, thereby creating energy security problems for future generations [2]. The best option to tackle these problems is renewable sources of energy. Popp et al. [3] revealed that bioenergy is an important component of the renewable energy mix in the European Union (EU) and has set the ambitious target to increase the share of renewable energy consumption to 20 percent by 2020 [4]. Regarding this, the EU has increased renewable energy consumption from 13.2% in 2010 to 18% in 2018. Around the globe, after Indonesia and Malaysia, Thailand is the third-largest producer and exporter of palm oil [5], and has the potential for palm oil production. According to the recent report of the International Renewable Energy Agency, a projected 141,600 jobs in the European Union will be related to biofuels in 2020. With an estimated 863,000 jobs, Brazil continues to be the top biofuel employer in the world, then Indonesia with 555,900 biofuel employees, followed by Colombia with 187,500, Thailand with 133,900, Malaysia with 61,400, and the Philippines with 34,300 [6]. Statistical Review of World Energy 2011 stated that “Asia is emerging as an important biofuel producer, with an annual average growth rate of 33%, based largely on first-generation biofuels [7]. Chanthawong and Dhakal [8] found that for bioethanol, Thailand and the Philippines are higher producers and have relatively larger sugarcane plantations and the biggest cassava planting areas for feedstock production compared to other countries in the region.

Due to its opportunities and effects on the social and environmental spheres, palm oil is contentious [9]. The main direct environmental implications of oil palm growth are the loss of natural habitats, reduced woody biomass, and peatland draining during site preparation [10]. Such conversion often results in lower biodiversity, poorer water quality, higher greenhouse gas emissions, and smoke and haze when the fire is used [11]. Large international and domestic corporations’ industrial oil palm expansion is frequently linked to social issues such as land grabbing and disputes [12], labour exploitation [9], social injustice, and reductions in village wellbeing [13]. In addition to the effects on human health (for example, respiratory ailments and conjunctivitis), such fires can influence wildlife and atmospheric systems [14]. For instance, particles from fires can distort evaporation, reflect solar energy, and encourage drought [15]. However, in producing nations, oil palm is a highly prized crop that promotes economic growth in areas with few other options for agricultural development [16]. In addition to this, it is argued that the palm oil trade plays a role in reducing poverty, providing nutritional benefits, sustaining livelihoods, and supporting a number of SDGs, which bolsters the argument that efforts should be made to increase the sustainability of palm oil production and trade [17].

Thailand has inadequate access to national oil reserves, leading to less production; a significant portion of oil consumption is covered by import [18]. Thailand is rich in natural gas as it holds huge reserves for it due to large reserves; the country’s production has increased substantially over time. However, the country is dependent on the imports of natural gas to overcome the rising internal demand for fuel [18]. According to REEP [19] report, Thailand’s consumption of primary energy is highly reliant on fossil fuels. The primary energy utilization is estimated to be over 80 percent of the county’s total energy consumption, and oil was 39% of the total energy consumption in 2010, down from nearly half in 2000. As the economy expanded and Thailand became more industrialized, it consumed double the amount of oil in the transport sector and the industrial sector compared to 2000 [20]. Natural gas is the second-largest consumed fuel that, to some extent, replaced the oil demand [19]. Thailand’s new Alternative Energy Development Plan has an ambitious target that emphasizes the consumption of new renewable energy target of 30% of the total final energy consumption by 2036, and to reduce the dependence on fossil fuel [21]. According to a survey conducted in 2013 to evaluate household energy consumption, it was shown that the highest proportion of energy was contributed by petroleum products such as gasoline and various gases (70.9%), followed by electricity (27.1%), and the lowest contribution was from charcoal and firewood (2.0%) [22].

Thailand falls in the position of major producer countries for biofuel in Asia and is one of the foremost ASEAN countries to form policies to promote biofuel production to decrease dependency on primary energy import [7]. High resource availability enables Thailand to produce both ethanol and biodiesel. The major resources contributing to production are cassava, sugarcane, and palm oil [23,24]. The Thai government’s decision to expand oil palm plantations is based not only on producing biofuel for alternative energy but also on reducing the disparity between rural and urban areas in terms of economic development [25]. Being the second-largest consumer of energy in ASEAN and heavily relying on imported energy, Thailand promoted and expanded oil palm cultivation in Northeast Thailand [26]. The Thai government implemented a policy to substitute fossil fuel diesel with B2 biodiesel (a combination of regular diesel with 2% biodiesel) in 2008. Resultantly, the compulsory use of blended biodiesel increased the production of B100 in 2009 and 2010. The positive outcome of the policy was a significant increase in biodiesel production from 68 million liters (ML) to 448 ML from 2007 to 2008 and 610 ML in 2009 [7,26]. The latest data show that Thailand is predicted to generate 1.33 billion liters (351.35 million gallons) of biodiesel in 2022, a decrease from 1.658 billion liters in 2021, 1.843 billion liters in 2020, and 1.845 billion liters in 2019 [27]. In addition to this, exports of biodiesel are predicted to reach 65 ML in 2021, up from 7 ML in 2021, and 3 ML in 2020 [27].

Thailand’s government encourages the development and usage of biodiesel [28]. The Prime Minister attended the COP 21 summit in Paris in December 2015 to discuss a new global accord on climate change. Thailand then filed its First Biennial Update Report (BUR) with the UN Framework Convention on Climate Change (UNFCCC). Concurrently, the Climate Change Master Plan (2015–2050) was created in order to reduce GHG emissions [29]. One of the strategies mentioned to help achieve this goal is the production of biodiesel. The commercial development of palm-oil-based biodiesel has been encouraged by the Thai government [30]. Fresh fruit bunches (FFBs) are used to create crude palm oil (CPO), which is then converted into pure biodiesel called B100. When mixed with fossil fuel at 2%, 3%, 5%, and 7%, respectively, the biodiesel blending rates are referred to as B2, B3, B5, and B7 [29,30]. The revenue of oil palm farmers, the price of cooking oil, the cost of input for the food processing industries, and cost savings from currency exchange can all be affected socio-economically by the production of biodiesel from palm oil feedstock and the blending strategy [31]. Thailand has a high possibility for increased production of both ethanol and biodiesel due to the huge availability of raw materials [32]. Thailand is capable of producing biofuel from both first- and second-generation feedstock; however, government policy is prioritizing the use of first-generation crops (produced from crops directly from the fields, such as cereals, maize, sugar beet, and cane). At present, much research is being conducted on the use of second- and third-generation resources for biofuel production (biofuels are produced from residual and waste products). Rice, sugarcane, cassava, and oil palm are four major agricultural crops of the country. Every year, these crops generate a large amount of residue and by-products and these potential sources of bioenergy have been fully utilized [33]. The beginning of biodiesel production was small-scale but recently, due to the government policies to meet ambitious national biofuel targets and mandatory biodiesel blending targets, it has emphasized the expansion of the production area [7]. However, scaling up the production process has a potential effect on food security, land use, deforestation, and the environment [8].

The adoption of new crops or technology is highly associated with its generated profit [34]. However, literature shows that even if the adoption of technologies is cost-effective and the technologies are highly profitable, they may not be adopted by the farmers [35,36]. Adoption of new technology involves high cost; however, the production cost is only one of the factors influencing farmers’ decisions towards adopting technology [37]. Economies of scale, risk avoidance, and access to credit are some of the associated factors that impact the decision to allocate farm size for technology adoption [38,39]. Technology adoption chances rise with farm size increase [40] on reduced tillage, [41] with rate technology, and with participating in the Conservation Reserve Program [42]. The major reason for not adopting oil palm cultivation is the unavailability of the cultivation area or limited land that is already occupied by rice and rubber [43]. Another major reason was the lack of capital, as oil palm requires high initial investment [44]. The other reason for non-adoption is farmers do not want to clear rubber and plant oil palm because they have already invested a high amount in rubber [43].

At the government level, the acceptance of diversification is based on the incentives provided to the targeted farmers [45]. However, at the farmers’ level, the decision to adopt or participate in diversification is highly based on economic terms, such as new technology that needs to be more beneficial than the traditional method. In addition, individualized farm visits by the extension officers can solve farmers’ issues [46]. However, due to the extension-to-farmer low ratio, extension officers are rarely able to address all farmers’ demands through farm visits [47]. Other influencing factors for diversification are highly affected by access to information [48] and the availability of land, labour, capital, and market opportunity [49]. The latest information shows that Crude Palm Oil consumption projects to expand from an accelerated export growth of roughly 30–40%, spurred by the food security worries from the COVID-19 scenario and the Russia-Ukraine war [50]. In addition to this, the farm-gate pricing for oil palm (fresh fruit bunches) surged dramatically in October 2020, up 85 percent over the same period last year with the implementation of mandatory B10 [51]. Oil palm prices kept rising in 2021, pushed by declining supplies in Malaysia and Indonesia, which together accounted for almost 90% of world supplies. Furthermore, the Russian invasion of Ukraine, which is disrupting trade in sunflower oil, increased the price of palm oil as India reportedly purchased more palm oil from Malaysia and Indonesia to secure domestic supplies of cooking oil. At the same time, the trade in Ukrainian sunflower oil was in jeopardy [51].

According to the Thai Office of Agricultural Economics, there was a million rai (1600 square meters) of oil palm in 2021, with Palm Oil reaching a record 16.8 million tons (+7.3%) compared to 15.7 million tons in 2020, with Oil Palm production per hectare reaching 2761 kg. The total yearly extraction of Crude Palm Oil (CPO) was 2.96 million tons (+11.8%), up from 2.65 million tons in 2020 [50]. Moreover, the country has 131 crude palm oil extraction facilities, and their combined machinery generates around 5.6 million tons of crude palm oil annually. Major manufacturers make investments in breeding, developing, and expanding palm species. The supply of CPO is expected to increase by 6–7% this year, primarily due to plantations increasing by an average of 1–2 hundred thousand rai every year as a result of government policy encouraging farmers to expand their land. The projected annual CPO volume is 3.1–3.2 million tons [50]. It is important to explore the factors influencing the adoption of oil palm [52] in Thailand. Previous studies have analysed the socio-economic factors influencing the adoption of new technologies [47,53]; however, it is our first attempt to simultaneously use the adoption and extent of adoption in the model. Therefore, this study aims first to determine the various socio-economic and institutional factors in adopting oil palm cultivation, and second, to explore the role of these stated factors in the extent of adoption of oil palm cultivation.

2. Theoretical Framework

The study is based on rationale choice theory (RCT) in the agriculture industry. It explains the farmers’ decision to participate in some activities based on their rationale choice [54,55,56]. This indicates that farmers had a reputation for weighing potential advantages against probable disadvantages before making decisions [55,57]. RCT implies that individual action is instrumental; an individual’s action is explained by the actor’s desire or intention to achieve specified goals [58]. The fundamental tenet of RCT is that individuals in a society make decisions in an effort to maximize their advantages and minimize their costs that reflect the patterns of behaviour in that community [54,59]. However, the RCT has some limitations in its generalisability regarding socio-cultural factors. However, the theory has been extended by Ahmad Rizal, et al. [54]. A study in Kenya, for example, demonstrates how, via collective effort, farmers can receive more important information, which is one of the major challenges in southern nations. As a result, they gained better market access and increased their income [60]. Therefore, this study is designed to investigate the role of farmers’ and institutional characteristics [54,60,61] in adopting oil palm cultivation (Figure 1).

3. Methods

3.1. Selection of Study Area

The Thai government, under the Ministry of Agriculture and Cooperative, launched a pilot project in 2005 to promote the expansion of oil palm plantation in the Northeast [43]. Ministry of Agriculture observed Nong Khai, Ubon Ratchathani, Sakon Nakhon Phanom, and Mukdahan as high-potential areas for oil palm expansion [62]. Under the observation of the Department of Agriculture, the Ministry of Agriculture promoted the pilot project in these areas. Similarly, Nong Khai province was observed to be a potential area for expansion, and at that time, the Seka district was under Nong Khai [43]. Later in 2011, it was separated from Nong Khai and came under Bueng Kan province. Bueng Kan province was selected for this study because it occupied the second-largest harvested area along with the second-highest production and yield after Ubon Ratchathani in Northeast Thailand [63]. Presently, 24.85 thousands of rai of land is under cultivation for palm oil- producing 512 kg of yield per rai with 16.05% oil extraction in Bueng Kan province [25]. Seka sub-district lies in the southern part of the province, near the border with Sakon Nakhon province. It consists of 9 sub-districts and 135 villages. Seka district covers a total land area of 611,518 rai (97,842.88 Ha), equivalent to 22.73 percent of the total land area of Bueng Kan province. Farmers with landholding up to 15 hectares (93.75 rai) (1 Rai = 0.16 Hectare) bought oil palm seedlings from Nong Khai Research and Development Centre. Data of farmers were obtained from Nong Khai Agriculture Research and Development Centres. The study villages are shown in Figure 2.

3.2. Sample Design and Selection of the Sample Households

The sampling design was divided into three steps. First, Bueng Kan province was selected as this area occupied the third-largest oil-palm-planted area and the second-largest harvested area in Northeast Thailand [43]. Second, stratified sampling was employed by oil palm farmers and non-oil palm farmers in equal numbers. Third, for the selection of the oil-palm-cultivating household, the simple random sampling method was employed. The size of the sampled household was determined by considering the total population cultivating oil palm in the Seka sub-district. Employing Yamane [64] formula with a 95% confidence level and error precision = ±13%, the sample size was calculated (Equation (1)). The total number of farmers in Seka district who bought oil palm seedlings from Nong Khai Agricultural Research and Development Centre from 2006 to 2015 was 582; out of that number, 234 farmers were only from the Seka sub-district.

$$n=\frac{N}{(1+N{e}^2)}$$

(1)

where N = Total oil palm farmers of Seka sub-district who bought seedlings from Nong Khai Agricultural Research and Development Centre, n = sample size, and e = Precision error. The sample size for oil palm farmers was 47.23 ≈ 48. The total number of sample sizes taken for oil pam farmers was 50 households, and an equal proportion of non-oil palm farmers were taken as samples, i.e., 50 households; the total sample size was 100 households. Six villages—Huai Phak Kha, Na Ngua, Sai Panya, Udom Sap, Non Sung Nuea, and Non Mueat Ae—with farmers who bought oil palm seedlings from Nong Khai Agricultural Research Centres were selected for the study. Other non-oil palm farmers were also selected in the same proportion in the same village to make a comparative study.

3.3. Data Collection

Secondary data were collected from the Office of Agricultural Economics [65] about oil palm crop statistics, land use, and production. The primary data were obtained directly from farmers through a questionnaire survey from June to July 2018. Farmers who cultivated oil palm and who were not involved in oil palm cultivation were sampled, and interviews were conducted. To obtain the primary data, a specific survey was undertaken by interviewing local farmers.

3.4. Theoretical Model of Double-Hurdle Model

A double-hurdle model was used when two components contribute to a process. The first process was the decision to do something, i.e., the adoption or non-adoption of oil palm cultivation in this study. The second process was the decision on the extent of adoption, such as the area allocated for adopting oil palm. This model involves two independent decisions (conditional/observables) and unique decisions. The main objective of using this particular study is to comprehend both the probability of the component of adoption and the extent or intensity of adoption. The use of the double-hurdle model by Cragg [66] was to run two hurdles in parallel to make decisions, where the assumption is that samples should pass through two hurdles [67].

Explanatory variables used in this analysis are the same for both hurdles. The double-hurdle model in this study was used to make a decision because many of the independent variables observed were dummy variables. It simplifies the Tobit model by taking into account a different first hurdle (Equation (2)) that embraces farmers’ decision to participate in cultivation and the second hurdle (Equation (3)) exemplifies the choice of oil palm farmers for the extent of adoption [67]. Both choices can be displayed as reliant on or autonomous of each other. Moreover, the decisions achieved can be demonstrated consecutively, but most of the studies prefer to represent them differently and make a separate decision [68]. The empirical model is presented in Equation (4).

The first hurdle—decision to adopt oil palm (A)—is expressed as

$$A_i^{*}=a{x}_i+u_i;u_i~N\left(0,1\right)$$

(2)

with $A_i=1$ if $A_i^{*}>0$ and $A_i=0$ if $A_i^{*}\le 0$

The second hurdle—intensity of adoption equation (I)—is expressed as

$$I_i^{*}=\beta z_i+v_i;v_i~N\left(0,{\sigma }^2\right)$$

(3)

with $I_i=I_i^{*}$ if $I_i^{*}>0$ and $A_i=1$ if $I_i=0$, otherwise.

The empirical model of double-hurdle is

Y_i = β₀ + β₁X_1i + β₂X_2i + β₃X_3i + β₄X_4i + β₅X_5i + β₆X_6i + β₇X_7i + β₈X_8i + β₉X_9i + β₁₀X_10i + β₁₁X_11i + β₁₂X_12i + Ɛ_i

(4)

where:

i = 1………100

β_i = coefficients

X_i = independent variables

Y_i = percentage of land under oil palm cultivation

Ɛ_i = error term

3.5. Marginal Effects

Unlike linear models, Probit and Tobit models results could not be interpreted, as a one-unit change in independent variables would bring a change in the dependent variable by the value of coefficients β_i; however, this could be interpreted as a unit increase in the independent variable would increase the Z score by the coefficient β. The βs in linear models are marginal values (Equation (5)). However, interpretation will be different in these models (Equation (6)).

Y = β₀ + β₁X₁ + β₂X₂ + β₃X₃……+β₁₂X₁₂

(5)

$$\frac{\partial Y}{\partial X_i }=\beta$$

Marginal effects Probit and Tobit estimation:

Y_i = Φ (β₀ + β₁X₁ + β₂X₂ + β₃X₃……+β₁₂X₁₂)

(6)

$$\frac{\partial Y}{\partial X_i }={\beta }_i\phi ({\beta }_0+{\beta }_1{X}_1+{\beta }_2{X}_2+{\beta }_3{X}_3\dots \dots \dots +{\beta }_{12}{X}_{12})$$

Equation (6) shows that marginal effects in the Probit and Tobit model depend not only on the value of β but also on all variables in the equation. That is the reason to set all variables at their means or medians. In our study, the variables were set at their means. To avoid complexities in interpretations of coefficients from the Probit model, marginal effects were calculated through statistical software, STATA-15; this program can directly estimate Equation (6). Therefore, a unit change in the independent variables may increase or decrease the probability of occurrence in the dependent variable. The first and second models in the double-hurdle model include two categories of variables: socio-economic characteristics and institutional characteristics. In this section, the main focus of the study was to determine the major contributing factors for the choice of adopting oil palm cultivation or the willingness of farmers to change their cultivation practice and, simultaneously, the choice of oil palm farmers to contribute a percentage of their available land for oil palm cultivation.

3.6. Description of the Variables

Gender plays an important role in terms of decision-making for adoption or participation in new technology [69], and it also determines the decision in the rate of adoption, i.e., the extent or percentage of land contributed to oil palm cultivation. It is measured as a binary variable, 0 for females and 1 for males. Age is a continuous variable that is anticipated to have a positive influence on the adoption of technology [70]. Exposure to education (years of schooling) enhances farmers’ ability to analyse the situation and make decisions for adoption or non-adoption based on the evaluation [69]. Household size is measured as a continuous variable; the larger the household number, the higher the probability of adoption is expected to be positive as the chances of labour availability increase. However, the estimate is based on the family member who is dependent on the area (ha). This has the inverse effect on adoption because the higher the dependency of household members on the land, the higher the demand for food, and ultimately, fewer chances for adoption. Landholding or farm size determines the economic status in society and it is also a determining factor for adoption [69]. Larger farmers are more likely to adopt the technology than smallholder farmers. Households’ income per capita is another important factor in determining the need for the adoption or non-adoption of new technology [71]. If a household has a high income per capita, it may not want to take the risk for new technology. Likewise, less income of household members also assures less access to technology adoption due to lack of investment and less willingness to take the risk due to the minimum per capita income. Availability or access to credit through various sources can have a positive influence on farmer’s decision to adopt new technology [72], whereas no access to credit leads to termination of the plan for adoption. This can be measured as a dichotomous variable where access to credit is 1 and non-access is 0. In this study, access to credit is determined based on the sources of formal credit sources, that is, from the Bank for Agriculture and Agricultural Cooperatives (BAAC), and informal sources (village fund) of credit. Oil palm training received by the farmers, access to extension services, and members of the village development group are represented by the dummy variables (0 = no access, 1 = access); furthermore, these variables add up to the decision for the adoption or non-adoption of the technology.

4. Results

4.1. Oil Palm Statistics in Thailand

Secondary data were obtained from the Office of Agricultural Economics [65] about the land use and production of oil palm (Figure 3). The data analysis shows that there is an increasing trend in both production and land use for palm oil cultivation.

4.2. Oil Palm Production in Different Regions of Thailand

Data in

Table 1. Oil palm cultivation statistics in Thailand (year 2021).

Region	Cultivated Area (rai)	Harvested Area (rai)	Productivity (ton)	Yield/Harvested Area (kg)
Northland	92,609	83,529	102,263	1224.28
Northeast	248,634	232,813	339,723	1459.21
Central Region	556,116	530,621	1262,197	2378.72
South	5,398,792	5,186,773	15,199,545	2930.44

show that most of the oil palm is produced in the south of Thailand in terms of area under cultivation and productivity in the year 2021. The second largest is the central region, while the northeast is also notable in crop statistics.

4.3. Oil Palm Production in Bueng Kan Province

Oil palm cultivation is considered a popular crop in the Bueng Kan province of Thailand. Data mentioned in Figure 4 illustrate that with the passage of time, oil palm production has increased in Bueng Kan province.

4.4. Socio-Economic Characteristics of the Respondents

Of the total respondents, the majority (40) were male oil palm farmers. Farmers were mostly older in age, i.e., above 50 years, and this was the average age in both groups of farmers. The education of oil palm farmers was higher (8.08 years) than non-oil palm farmers. Likewise, the landholding per family size was higher (1.85 ha) for oil palm farmers than for non-oil palm farmers (0.89 ha). However, the labour per hectare was lower for oil palm farmers (

Table 2. Descriptive statistics of the study variables.

Variables	Oil Palm Farmers	SD	Non-Oil Palm Farmers	SD
X1. Gender	Male: 40 Female: 10		Male: 33 Female: 17
X2. Age of household head (Years)	53.70	10.43	55.08	11.66
X3. Education of household head (Years of schooling)	8.08	3.88	7.50	3.89
X4. Landholding per family member (Ha)	1.85	1.35	0.89	0.71
X5. Household labour per hectare	0.58	0.51	1.02	0.62
X6. Current income per capita (USD)	1427.91	952.93	1242.78	999.56
X7. Food expenditure per capita (USD)	573.78	267.02	521.36	259.79
X8. Formal Credit (BAAC)	Accessed = 20 Not Accessed = 30		Accessed = 23 Not Accessed = 27
X9. Informal Credit (Village fund)	Accessed = 31 Not Accessed = 19		Accessed = 29 Not Accessed = 21
X10. Oil palm training	Accessed = 36 Not Accessed = 14		Accessed = 10 Not Accessed = 40
X11. Access to extension services	Accessed = 41 Not Accessed = 9		Accessed = 45 Not Accessed = 5
X12. Member of Village Development Group	Yes = 23 No = 27		Yes = 21 No = 29

). Current income/expenditures per capita were higher among oil palm farmers than non-oil palm farmers. Non-oil palm farmers accessed credit more than oil palm farmers in numbers. Oil palm farmers received more training on oil palm cultivation than non-oil palm farmers. However, non-oil palm farmers have accessed more extension services than oil palm farmers.

4.5. Adoption and Extent of Adoption of Oil Palm Cultivation

Before running the double-hurdle regression model, multiple collinearities between the independent variables were checked through the variance inflation factor (VIF). None of the independent variables in the regression analysis had VIF greater than 10, proving no multi-collinearity. The outcome of the double-hurdle model can be seen in

Table 3. Determination of Decision for Adoption of Oil Palm and Percentage of Land Contributed to Oil Palm Plantation.

Independent Variables	Double Hurdle
	Adoption of Oil Palm		Extent of Adoption
	Coefficient	T Value	Coefficient	T Value	VIF
Gender	5.105 ** (2.60)	1.96	18.686 ** (7.956)	2.35	2.15
Age of household head	−0.115 * (0.077)	−1.48	−0.772 *** (0.291)	−2.65	1.13
Education of household head	0.458 (0.318)	1.44	1.606 * (0.914)	1.76	1.35
Land holding per family member	−5.559 * (3.284)	−1.69	−27.743 *** (8.162)	−3.40	1.26
Household labour per hectare	2.166 (3.706)	0.58	33.873 *** (11.261)	3.01	1.61
Current income per capita	−0.0014 (0.0017)	−0.81	−0.0035 (0.00294)	−1.19	1.24
Food expenditure capita	0.000402 (0.0029)	0.14	0.00423 (0.00976)	0.43	1.40
Formal Credit (BAAC)	0.846 (2.787)	0.30	9.189 (6.363)	1.44	1.18
Informal Credit (Village fund)	1.883 (1.704)	1.10	18.495 ** (7.923)	2.33	1.33
Oil palm training	6.71 ** (3.395)	1.98	31.106 *** (6.907)	4.50	2.5
Access to extension services	5.089 * (2.956)	1.72	19.968 *** (5.787)	3.45	1.18
Member of Village Development Group	−3.374 (2.402)	−1.41	−20.158 * (10.486)	−1.92	1.25
Log likelihood	−9.0473672		−231.46424
Pseudo R2	0.8695		0.1884
Prob > Chi2	0.0000		0.0000
LR Chai2 (12)	120.53		107.49
Number of observations	100		100

Notes: ***, **, * shows the significance at 1%, 5%, and 10% levels, respectively; figures in parenthesis are standard errors.

below. The first part of the table presents the results of the decision to adopt oil palm plantation. Gender as an independent variable has a positive coefficient (5.105) in the first hurdle, which is significant at a 95% confidence level with a t-value of 1.96. It implies that when the value of gender changes from 0 (female) to 1 (male), respondents are more likely to adopt oil palm cultivation. Furthermore, the decision-makers’ age is another critically important factor for major household decisions. Age has a negative coefficient (−0.115), while significant at a 90% confidence level with a t-value of −1.48 in the first hurdle, representing that there was an inverse relationship of age with the adoption of oil palm cultivation. Likewise, the negative coefficient (−5.559) of family member dependent per hectare and its statistical significance at 90% confidence implied that with the increase in the number of family members, the decision to adopt oil palm cultivation decreases. Furthermore, the positive coefficient (6.71) at a 95% significance level with a t-value of 1.98 in the first hurdle indicates that farmers’ access to oil palm training has an influential effect on the adoption of oil palm cultivation. Local extension service providers were the easy and accessible sources of information for the farmers. The results, i.e., positive coefficient 5.089 and t-value 1.72 at 90% level of significance, verify that farmers receiving extension services were more likely to adopt oil palm plantation.

In the second part of the double-hurdle model, we have estimated the extent of the adoption of oil palm cultivation (Table 2). The estimated coefficient for gender represents that it had a highly significant influence on the adoption of oil palm, with a positive coefficient (18.686) and t-value of 2.35, which is significant at a 95% confidence interval. Moreover, the negative coefficient (−0.772) with high significance at a 99% confidence interval indicates an increase in age, decreasing the adoption extension. Education of the households’ head significantly influenced the extent of the adoption of oil palm cultivation. It has a positive coefficient of 1.606 and a t-value of 1.76, significant at a 90% confidence interval. The results interpreted that if household heads were educated, they were likely to go for the adoption of oil palm in a larger area. Likewise, for the results for the adoption of oil palm cultivation and family member per hectare, the negative coefficient (−27.743) and high significance with 99% confidence interval indicate that when family members increase, the adoption of oil palm plantation area decreases. The decision for adoption was not dependent on household labour availability, whereas the results of the decision for the extent of adoption were highly influenced by the availability of household labour. The positive coefficient (33.873) is significant at a 99% confidence interval. This result represents that if the household has a higher amount of labour, their decision to increase the extent of oil palm adoption based on the area available increases. Moreover, a positive coefficient (18.495) significant at a 95% confidence interval shows that informal sources of credit were influencing factors for the larger area under adoption. The availability of oil palm training and access to extension services were positively (31.106) and significantly (99% level of confidence) influencing the extent of oil palm cultivation.

The best fit of the model was observed from the Chi2 value (p = 0.000), which showed the high significance of the model. The maximum likelihood estimates of the parameters in the double-hurdle model characterized the behaviour of farmers towards adopting oil palm plantation. The log-likelihood ratio test revealed the best fit of this model. The ultimate outcome of the model indicated that three variables with a positive coefficient and two variables with a negative coefficient influenced the choice of farmers to adopt oil palm cultivation. Similarly, out of twelve, six variables with a positive and three with a negative coefficient influenced the decision of households’ heads to go for extended cultivation of oil palm.

4.6. Marginal Effects

The probability of oil palm adoption was influenced positively by the gender of households’ heads (

Table 4. Marginal Effect at Mean.

Variables	Adoption of Oil Palm	Extent of Adoption
Gender	0.483 * (0.271)	7.521 ** (3.180)
Age	−0.022 (0.019)	−0.311 *** (0.116)
Education	0.087 (0.084)	0.646 * (0.377)
Landholding per family member (Ha)	−1.059 (0.843)	−11.166 *** (82.788)
Household labour per hectare	0.412 (0.727)	13.633 *** (4.322)
Current income per capita	−0.0002694 (0.00027)	−0.0014 (0.0012)
Food expenditure per capita	0.00000766 (0.00055)	0.0017 (0.0040)
Formal Credit (BAAC)	0.209 (0.789)	3.698 (2.667)
Informal Credit (Village fund)	0.604 (0.564)	7.444 ** (3.397)
Oil palm training	0.988 *** (0.038)	12.529 *** (2.777)
Access to extension services	0.97 *** (0.122)	8.036 *** (2.533)
Member of Village Development Group	−0.907 *** (0.226)	−8.113 * (4.139)

Notes: ***, **, * shows the significance at 1%, 5%, and 10% levels, respectively; figures in parenthesis are standard errors.

). The result indicated that if the households’ head is male, the probability of adoption of oil palm increases by 0.483, holding all variables at their means, which is significant at a 90% confidence level. Likewise, access to oil palm training and extension service had a positive effect on adoption. The probability of a decision to adopt oil palm plantation increases by 0.988 in the availability of training and by 0.97 in access to extension services. Contrary to expectations, members of the village development group (VDG) had a negative effect on oil palm adoption decisions. When there is a change from non-member to a member of VDG, the probability in the decision to adopt oil palm cultivation decreases by 0.907.

Adoption and non-adoption of oil palm cultivation and the extent of adoption are two independent decisions made by farmers. The extent or percentage of area to be used for oil palm plantation was the decision after the first decision to adopt, and there were various socio-economic and institutional factors contributing to that decision. Gender positively affected the extent of adoption. When a household head was male, the probability to contribute a larger portion of the area for oil palm cultivation was increased by 7.521. The probability of the percentage of adoption was negatively influenced by the age of households’ heads. A unit increase in the age of households’ heads decreases the probability of expansion of land by 0.311. A unit increase in the years of schooling of a household head increases the probability of expansion by 0.646. The dependence of household members on land for food is also an important factor in determining the option for the adoption of technology. The outcome of the marginal effect showed that a unit increase in family members had a negative effect on the decision of the area that contributed to oil palm production. The probability of the extent of adoption with a unit increase in family members decreases by 11.166. The availability of household labour per hectare had a positive effect on the extent of adoption. A unit increase in household labour increased the probability of the extent of adoption by 13.633. Similarly, informal credit, training on oil palm cultivation, and extension services increased the probabilities for the extent of adoption. However, a farmer who is a member of VDG decreases the probability of the extent of adoption by 8.113.

4.7. Reasons for Adoption (Farmers’ Perceptions)

The results of multiple responses provide the other reasons for the switching of crops to oil palm for the adopting farmers and reasons for not switching their previous crops to oil palm. The highest percentage (27.3%) of farmers mentioned the reason for switching to oil palm was due to the unsuitability of their land (

Table 5. Multiple Responses for Decision for Adoption of Oil Palm.

Reasons for Adoption	N (n = 50)	Percentage (%)
1. Suggestion from extension service providers	15	19.5
2. Suggested by neighbours and commercial farmers	17	22.1
3. Low average cost of production	7	9.1
4. Continues income and less labour intensive	12	15.6
5. Governmental support	5	6.5
6. Unsuitable land for rice	21	27.3
Total	77	100

). The other reasons for switching crops were suggestions from neighbours and commercial farmers and extension service providers.

4.8. Reasons for Non-Adoption (Farmers’ Perceptions)

Results mentioned in

Table 6. Multiple Responses for Non-adoption of Oil Palm.

Reasons for Non-Adoption	N (n = 50)	Percentage
1. Limited land	42	70
2. Lack of labour	5	8.3
3. Higher profit from rubber	6	10
4. Lack of irrigation	4	6.7
5. Lack of access to credit	3	5
Total	60	100

show that in the case of non-oil palm farmers, the major reason was the availability of limited land (70%). Although these farmers are not adopting oil palm cultivation, most are already cultivating rubber and do not want to switch their crops. In the present context, rubber prices are very low; however, non-oil palm farmers do not want to change their crops with the hope that rubber prices will increase in the near future. The other few responses were high profit from rubber and lack of household labour.

5. Discussion

The findings of the study revealed that Palm Oil cultivation is an increasing trend in Thailand in different parts. This might be due to the reason that oil palm provides consistent and regular income throughout the year, and agricultural labor requirements are quite minimal [73]. In most circumstances, Thai farmers have the freedom to pick the crops they want to raise. Furthermore, the finding of this research showed various influencing factors for the adoption of oil palm cultivation. For instance, gender plays a key role in deciding to adopt oil palm cultivation. The conclusion from this outcome could be made that household decisions are dominated in male-headed households, and there is a gender bias in embracing this technology. However, mixed results are obtained from developing countries about the role of male-headed households in technology adoption. For instance, Islam, et al. [67] reported that gender played a role in new technology adoption in Bangladesh. However, Gebre, et al. [69] revealed that in Ethiopia, the male-headed household adopted less improved crop varieties than females. Therefore, it implies that gender role is different from region to region depending on the socio-cultural environment. The findings of our study show that when farmers’ age increases, they are more willing to be stable and try to avoid the risk of adopting the new farming system. The result of the analysis showed that when the household head’s age increased, he/she was less likely to adopt oil palm cultivation. The reasons may be that, with age, their interest and ability to understand new technology decreases. A study from Trinidad revealed that age has a negative association with the use of text messaging for extension services [46]. This shows that old farmers are risk-averse, whereas young farmers are more prone to risk-taking and are more likely to go for adoption. This result was in accordance with Gedikoglu [37]. They stated that in Missouri and Iowa, the young farmers might be more innovative than old farmers, and they value future net benefits more than the old farmers. Therefore, they adopted the adoption of energy crops more than old-age farmers. When there is an increase in the number of family members, the land availability per capita decreases. If the landholding is small and the family members are more, the head of the family is less likely to go for new technology adoption. This was to minimize the risk of failure of the crop and future consequences. Our findings are in agreement with those of Doss and Morris [74], who revealed that the amount of land owned was positively influencing the probability of new technology adoption in Ghana.

Furthermore, the findings show that when the farmers were trained in oil palm cultivation, they were more likely to adopt oil palm cultivation. This outcome of the study represents the need for intensive care and management for the commercial cultivation of oil palm. Farmers deprived of technical knowledge are subsequently less likely to adopt the technology. Likewise, institution and extension service providers are also an integral part of a change in the decision at the household level [75]. However, Gedikoglu [37] revealed that farmers’ willingness to grow is not impacted by their interaction with extension services. Support from the government to oil palm farmers, suggestions from extension service providers, and success stories of commercial farmers are the influencing factors for the adoption of oil palm cultivation [76]. The farmers will adopt smart agriculture and innovations if they are simplistic, easily communicated, socially accepted, and have larger degrees of functionality [49]. Extension officers should respond to farmers’ requests and information needs [46].

All the positive coefficients that are significant in the first hurdle indicate the likelihood of adoption. The observation can be made based on its corresponding variable. Similarly, the positive coefficient in the second hurdle signifies that corresponding variables are increasing the extent of adoption. The extent of oil palm adoption (i.e., the percentage of land allocated for oil palm cultivation) by a household was based upon various socio-economic and institutional factors. The decision after adopting oil palm is crucial for allocating the land for oil palm cultivation that is influenced by the many independent variables that are the same as for adoption. The outcome showed that after deciding to adopt oil palm cultivation, the next level of decision becomes more critical, i.e., the decision to expand oil palm cultivation.

The findings of the study showed that if household heads are male, the extent of adoption increases. They were allocating more land for the cultivation of oil palm in percentage compared to their female counterparts. Our data show that most of the oil palm farmers were male. Likewise, age was in a positive association with the extent of oil palm cultivation. The estimates of the study indicated that younger farmers were more enthusiastic about increasing their area under oil palm than older farmers. This implies that as age increases, the extent of adoption of oil palm is more likely to increase. Our findings are in disagreement with those of Euler, et al. [77], who reported that in Sumatra, Indonesia, the higher the age, the more the extent of oil palm cultivation. Furthermore, if farmers were educated, they were likely to go for the adoption of oil palm in larger areas. The level of education of households’ heads (decision-makers) played a crucial role in the adoption of biofuel technology. It was also a significant factor in making decisions that were economical for households and to what extent to adopt in the case of the area so that it was profitable [67]. This study is consistent with Saqib, et al. [78], who revealed that farmers’ schooling on reduced tillage enhances the efficiency of the adoption decision.

This study further revealed that if the family member increased in proportion more than the landholding, the extent of adoption decreased. For instance, if the family size is larger, farmers may decide to use a large portion of the area for food crop cultivation. They may need more food for the family. Koundouri, et al. [53], showed that with an increase in human capital, the adoption of new technology increases. However, we have used landholding per family member; this should not be considered landholding in absolute terms, which is very important to measure it in relative terms. It might be possible that a small family with less landholding is in a better position than a large family with a large landholding. The availability of household labour per hectare was also one of the important factors for the extent of the adoption of oil palm cultivation. This result was according to our expectations. More labour resources are needed for palm oil production, but less than for rubber plantation [77]. Our results are in agreement with Doss and Morris [74]. They revealed that in new technology adoption, more labour is needed.

To adopt any new technology, there is an involvement of high cost and access to credit, and its use is an important factor contributing to making a decision to adopt or discard the technology [52,69]. Access to informal sources of credit was more popular than formal sources among oil palm farmers. There was a positive relationship between the use of credit from village funds and areas under oil palm cultivation. Farmers mentioned that pilot projects were initiated for the expansion of the oil palm cultivation area in 2005. At present, those are no more effective, and there is not much support from the government institution for the oil palm growers. Therefore, they did not have access to credit for oil palm cultivation from formal sources such as BAAC. This may be the reason for the use of credits from village funds.

Access to extension services and oil palm training were the associated factors in the extent of adoption. This implies that if the oil palm farmers were getting training for managing their crops along with access to extension services from service providers, they were likely to increase their area of adoption. Our results were similar to those of Hardjono, et al. [39]. They revealed that in Indonesia, among the smallholder, access to extension services was important for the extent of palm oil production. Farmers’ association with the farmers’ groups was influencing their extent of adoption. This implies that the farmers were influenced by their peers. These results were contradicted by the findings of Martey, et al. [79], who revealed that fertilizer use intensity was positively influenced by the membership of the association. Oil palm cultivation is associated with several environmental and sociocultural costs [9]. However, the findings from the multiple responses of farmers revealed that their land is unsuitable for rice cultivation. Therefore, land use for oil palm cultivation has less opportunity cost in Thailand than in other countries.

Limitations and Strengths of the Study

The study was conducted in one of the districts in Thailand. We did not cover its environmental and sociocultural aspects, which can enhance the potential costs associated with oil palm cultivation. This study was conducted in 2018; the results of the fresh data might have different results. However, this study can be considered a baseline study for future research. For instance, what are the impacts of oil palm cultivation on food security and the environment? In addition to this, many lessons and policy implications can still guide the stakeholders.

6. Conclusions

The outcome of the study provides an understanding of factors (socioeconomic and institutional) contributing to the decision to adopt oil palm and the extent of area for adoption. The results revealed that gender, age, number of family members dependent per hectare, and availability of more household labour ensure the larger extent of oil palm adoption. Furthermore, the availability of credit, access to training regarding new technology, and extension services were associated with the adoption and extent of adoption of oil palm cultivation. The overall implication of the result was resource constraint; farmers had less accessibility to the technology, and proper access and adequacy of finance and resources were the restricting factors prohibiting smallholders from going for oil palm adoption. Moreover, there are also negative externalities associated with oil palm cultivation. Therefore, sourcing global vegetable oils present broader trade-offs that must be taken into account in order to achieve sustainable development, which includes delivering SDG 2 on agriculture and SDG 15 on biodiversity simultaneously (along with contributions to SDG 7 on energy and SDG 13 on climate) [9]. To grow more oil palm, it is very important to convince farmers through extension services officers in simplistic, easily communicated, and socially accepted ways [47]. The Thai government should ensure easy access to credit without complexities. Thailand’s BAAC and Department of Agricultural Extension should ensure that they are providing formal credit, training, and extension services for oil palm cultivation practices.