Contract Owner’s Best Commanding for Sweet Potato Farming Based on the Theory of Planned Behavior

Abstract

The purpose of this study is to examine and compare different psychological and sociodemographic factors for contracting sweet potato production for farmers with different statuses based upon the theory of planned behavior (TPB). Sustainable production provides contract owners with a sufficient amount of both food crops and a source of bioethanol clean energy. The impact of such factors on potential farmers based on the TPB for a particular contract type is estimated with the data collected in three major sweet potato production cities/counties in Taiwan through the probit model and multinomial logit model. The average size of the surveyed farms is 1.64 ha. The results consistently show that the factors of attitude toward the advantages of contract farming, subjective norms regarding contract farming, perceived contract farming control, and behavior intention have very significant impacts on the selection of contract farming types for professional farmers and brokers. These results indicate that the contract owners will gain the greatest advantage through commanding any factor in TBP for these two groups of farmers, as they have an incentive to manage the sources of sweet potatoes at the best conditions before they have the agreement with the contract owners, either as the supply of bioethanol energy raw materials, supply of food crops, or supply of food processing materials.

1. Introduction

Agricultural production is apparently affected by all kinds of climate factors. Under globalization, it has become increasingly difficult for small-scale agricultural management to compete with similar agricultural products imported from countries with large-scale agricultural operations. Contract farming is one way for those countries with small scales operations to engage in agricultural production that is part of a planned scheme. This can help maintain a balance between production and marketing and further reduce farmers’ possible losses due to market risk [1].

Moreover, food security, agricultural development, provision of bioethanol energy, and utilization of a guaranteed number of products on hand can also be maintained via contract farming [2]. Contract farming means that the contract owner and farmer enter into an agreement whereby one side offers the other side land and labor and provides it with a variety of promises regarding the quality, required size, quantity, and price of the contract items [3]. From the contract owner’s viewpoint, it is likely to achieve an efficient utilization and allocation of resources with a sufficient amount of production. This normally results in a long-term cooperation between the contract owner and the farmer and in both sides being better off [4,5].

Ipomoea batatas lam, commonly referred to as sweet potato, was originally produced in South America. It is currently ranked as the sixth grain crop in the world and as the fifth grain crop in developing countries. The production area is mainly in Asia, which accounts for 90% of the overall amount grown in the world, with China, Indonesia, and India being the three major producers [6]. According to statistics compiled by the International Potato Center, this short-term crop is an important source of food supply. A sweet potato weighing 125 g can provide 125 g of vitamin A for a pre-school child. This can help counter the adverse effects of slow learning or the threat of death for 0.14 billion children [7].

According to statistics compiled by the Council of Agriculture, Executive Yuan (COA) for 1950–1990 in Taiwan, the total sweet potato cultivation acreage in Taiwan is 230,000 ha and the total production is approximately 2500–3400 thousand kilotons [8]. Over the past 35 years or so (1986–2019), the total acreage of sweet potatoes has decreased from 22,062 ha in 1986 to 8407 ha in 2019. The total value has increased by approximately 63 million USD, or even double the amount with the exchange rate between the USD and TWD at the end of the most recent full year, i.e., 2020, when it was 1:28.09. In spite of the small-scale acreage cultivation, the value of total production has been maintained, or has become even higher due to the diversification of demand [8].

In the years 1996–2019, the three regions in Taiwan with the highest sweet potato production were Yunlin, Changhua, and Taichung. Each accounting for 40%, 18–20%, and 8–10% of the total production in Taiwan, respectively [9]. Among these, one of the townships in Yunlin County, Shuilin township, has been named as the hometown of the sweet potato due to its geographic advantages, environment, climate, and soil conditions. A total of 1180 ha has been cultivated and accounts for 32% of the total acreage under production in Yunlin. Farmers in Shuilin township have long cooperated with agribusiness contract owners through contract farming. The contracted acres have consistently increased over the years. The total area under cultivation is 542 ha in Changhua County and 633 ha in Taichung City. Many farmers in both the county and city have joined production and sales teams (PSTs) or cooperative farms [9].

The average farm size is less than 1 hectare in Taiwan. This has resulted in high production costs and low agricultural income. Participating in contract farming is one way to enlarge the farming size and stabilize and increase agricultural income [9,10]. The incentives and motivations for farmers to participate in contract farming are concerned not only with the content of the contract, but also with the other services provided by the contract, such as technological support, the supply of seeds and seedlings, the variable contracted quantity, and/or price to maintain high revenue overall [11]. From the contract owner’s viewpoint, a stable supply of sweet potatoes with a guaranteed quality via contract farming results in sufficient production for all needed uses.

Notwithstanding the advantage of contract farming, there exists a certain risk for contract farming arrangements. Banana plantation production has been destructed by Panama disease in the Philippines; this has brought about high risk for both sides of contractors [12]. Moreover, disputes might arise from unequal bargaining power or diverse interpretations of inequality of bargaining power among development planners, contract owners, and farmers [13]. These circumstances stated above might be abated if both sides of the contract are well-matched. That is, if contract owners encounter brokers or professional farmers who either have a high incentive to manage and gather small farmers or have larger farm sizes, then both sides have compatible tolerance to bear the possible loss from the disease or other damage to the contracted farms. Similarly, when contract owners face brokers or professional farmers, the unequal bargaining power is much less likely to occur. Additionally, the agreement between contract owners and brokers or professional farmers can reduce all the related transaction costs for the contracts.

To determine farmers’ attitudes, norms, or perceptions regarding the contract, the theory of planned behavior (TPB), modified from the theory of reasoned action (TRA) proposed by Fishbein and Ajzen in 1975 [14] and subsequently elaborated by Ajzen [14,15,16]. is used for this purpose. The theory is capable of integrating the multidimensions of attitude, subjective norms, and perceived behavioral control to explain the possibility of having certain behavioral intentions and further encouraging particular behaviors. The idea of the TPB has been applied to different behaviors in relation to various types of consumption, medical health, and transportation [15]. Studies conducted by [17,18,19] are examples applied to consumption or health.

The TPB has also been applied to contract farming for different kinds of agricultural products. Examples can be found in studies of dairy production in the Netherlands [20], apples in China [21], and peanuts in Senegal [22]. Each study finds that attitudes, subjective norms, and perceived behavioral control have played a relatively important role in participating in a specific type of contract farming. In addition to those essential psychological factors, Adnan et al. [23] have observed the role of sociodemographic factors among farmers of corn production in Bangladesh, and Nguyen et al. [24] for coffee production in Vietnam for specific kinds of behavior adopted.

With the supply chains for all kinds of materials and products, food being no exception, having been interrupted globally since early 2019 due to the Coronavirus pandemic (COVID-19), access to necessary nutrition has become a challenge for many countries. The supply of sweet potatoes is a relatively easy way to make up for undernutrition or malnutrition under such circumstances. In addition to its essential role in food crop supply, the sweet potato has been deemed to be one of the promising feedstocks for ethanol production and has been found to be more efficient than other raw materials for biofuel generation [25,26,27,28]. Efforts to engage in biofuel production from sweet potatoes have also been launched in Taiwan and are expected to make further progress when emissions of greenhouse gases are further restricted [29,30]. Thus, the provision of a stable and sufficient amount of sweet potatoes has become imperative. Although Afzal et al. [31] have comprehensively reviewed the benefits of sweet potato cultivation to achieve multiple sustainable development goals of no poverty, no hunger, and affordable and clean energy, no specific evidence has been provided to determine which factors have crucial, influential, and persistent impacts on the existence of contracts.

Furthermore, of all the studies conducted on contract farming, it is obvious that the bulk of the research has been concentrated on knowing the influential factors in the TPB, either of a psychological or sociodemographic type, for a specific type of behavior or action. As the content and conditions of the contract significantly affect the selection of the contract and will certainly influence farmers’ income, mastering different types of contract designs offered to potential farmers is not only essential for farmers, who should know which type of contract offers them the best deal, but also for contract owners, who can “collect” sufficiently large amounts of produce from small-scale farming to efficiently provide food crops and clean energy generation. It is expected that psychological and sociodemographic factors will have different impacts on contracts between potential farmers with different statuses and contract owners. It is then necessary to have an in-depth understanding of the importance of each factor to potential farmers in selecting a specific type of contract.

To fill this gap in the literature, the first purpose of this study is to use the TPB to examine whether there is an impact of past experience in contract farming on selecting specific types of contract farming in the future. The second purpose is to compare different psychological and sociodemographic factors in the TPB for different potential farmer statuses, i.e., homesteaders, farmers from cooperative farms, farmers from PSTs, professional farmers, or brokers, when selecting different types of contracts. Subsequently, the third purpose is to demonstrate the impact of the factors in the TPB for a particular contract type on potential farmers. The data were collected from potential farmers in the three major sweet potato production areas in Taiwan. The effect of each essential factor in the TPB is ascertained by comparing the change in the corresponding probability of selecting a specific type of contract farming.

2. Theory for Determining the Selection of a Sustainable Contract Farming Type

The component of attitude in the TPB denotes the evaluation of the preference for specific objects and targets. The stronger the attitude toward specific objects and targets is, the higher the probability of exhibiting a specific behavior will be. An objective norm means pressure from others, such as friends, relatives, and/or colleagues, imposed on an individual’s intention to determine whether to express a particular type of behavior. As with perceived behavioral control, it is the opportunity and the degree of difficulty for all kinds of resource control perceived by an individual that will give rise to the intention to engage in a particular form of behavior. When an individual perceives that the barriers are fewer, the stronger the perceived behavioral control will be [17]. Based upon the behavioral intention defined by [14], the extent of the behavioral intention will be determined by how much effort or willingness the individual commits to the action. It is expected that homesteaders, members of PSTs, those from cooperative farms, professional farmers and brokers have different attitudes, norms, perceptions, and intentions toward behavior in regard to different contract types. Thus, it is reasonable to examine the selection of the contract separately.

Regarding the application of the TPB in the selection of contract farming, the study conducted by [32] indicates that the farmer’s risk attitude toward market fluctuations will influence the farmer’s contractual behavior. Other services, such as the provision of transportation or harvest assistance by the contract owner, are also incentives for farmers to participate. The farmer’s attitude toward the reputation of the contract owner, i.e., the agribusiness, such as the unexpected termination or breaking of a contract, is an essential factor. The farmer’s subjective norm toward an increase in revenue from agriculture is also a key factor for farmers to participate in contract farming [33,34].

Past experience might also have an impact on the selection of the contract farming type in the future. The compensation offered by a contract owner to guarantee the contracted price is higher than the market price [5]. In some cases, this indicates that the decision will be affected by friends, other farmers, or propaganda from the government [20]. The perception of information accessibility difficulties, the ability to acquire cultivation technology, fiduciary loans, and field management are also perceived behavioral control factors that determine participation in contract farming [35]. Farmers perceive contract farming as saving farming pressure, time and effort, and completing the cultivation process in a friendly manner. The more farmers participate in contract farming, the more the farmers will be attracted. That is, herding behavior will sometimes take place.

In addition to the above factors, the stronger the behavioral intention toward contract farming, the higher the probability of engaging in a specific type of behavior will be. The behavioral intention is usually revealed by the sharing of the advantages of a certain kind of behavior with other farmers and relatives who are not yet involved in contract farming. A study performed by [21] found that sharing the advantages of contract farming with others usually originates from cost-saving. Three types of contracts are designed for the case at hand here, namely, purchasing all sweet potatoes without sweet potato size classification, purchasing them with size classification, and making adjustments for a bullish market. The framework of the TPB used in this study is shown in Figure 1. It is modified from [15]. Behavioral intention is placed in parallel with other psychological and sociodemographic factors as a potential factor, as previous studies show that behavioral intention and the performance of behavior are consistently highly correlated [16,17,18].

3. Research Methodology

3.1. Influential Factors and Their Treatment

From the above framework, factors can be categorized into two parts to determine the type of contract farming. One category is related to the factors in the TPB, and the other category concerns the sociodemographic characteristics [36]. The factors related to the TPB are the attitudes toward the advantages of participating in contract farming (Att), the subjective norms regarding the decisions of others and the information offered by the contract (Sub), the perceived behavioral control regarding the contract (Per), and the behavioral intention regarding the suggestions to others and possible changes in contract farming (Int). The specific questions for each factor are listed in

Table 1. Specific questions for each factor in the TPB.

Attitude Towards the Advantages of Participating in Contract Farming

My agricultural revenue will increase and remain stable if I participate in contract farming.

The advantage of participating in contract farming will enable me to obtain healthy seeds and seedlings and learn about the safe use of pesticides.

The advantage of participating in contract farming will assist me in harvesting and transportation.

Signing a contract with an agribusiness with a good reputation will result in multiple guarantees.

Subjective norms regarding the decisions of others and information offered by the contract

My decision to participate in contract farming is affected by the government, media, and all kinds of agricultural workshops.

My decision to participate in contract farming is influenced by friends, relatives, and other farmers.

I have confidence due to the past good experiences of contract farming.

I will sign the contract depending upon whether or not the contract owner will provide additional compensation when the market price is higher than the contracted price.

Perceived behavioral control in contract farming

The clearer the contract activity information is, the more that farmers will participate in contract farming.

Contract farming will not increase the pressure of farming.

I am willing to participate in contract farming because it is friendly to the environment and farm community.

I will participate in contract farming if the contract owner maintains a high quality.

Behavioral intention of my suggestions to others or to change to contract farming

I will suggest that friends, relatives, neighbors, and other farmers participate in contract farming.

I currently have a sweet potato contract, but I am willing to change to a contract owner with a good reputation.

. This category of factors in the TPB is measured using a 5-point Likert scale where 1 indicates “very much disagree” and 5 indicates “very much agree” [37].

The sociodemographic factors that have been chosen include gender (Gender), age (Age), educational level (Edu), the number of persons in farming in the family (Agricrop), farming experience (Agriexp), marriage status (Marriage), and income from agricultural practices (Income), in order to examine their impacts on the selection of contract type. More attention is paid to certain characteristics of contract farming than others [11]. Thus, “if a farmer is concerned about whether the dispute will be resolved by the contract” (Conagu) and “if a farmer is concerned about other services provided by the contract” (Conoth) are two independent factors to be observed. The definitions, mean values, and standard deviations for all factors used in the estimation are listed in

Table 2. Variables used in the estimation and their definitions and mean values.

Variable	Definitions	Mean Value	Standard Deviation
Dependent variables
$P$	Latent variable for a farmer who has participated in contract farming in the past three years; 1 if yes, 0 if no	0.318	0.466
$y$	Selection of contract type from six designed contracts	3.604	1.921
Independent variables
$Att$	Total scores of four attitude questions regarding contract farming	15.672	2.695
$Sub$	Total scores of four subjective norm questions	13.709	1.637
$Per$	Total scores of four perceived control behavior questions	15.341	2.343
$Int$	Total scores of two behavioral intention questions	7.647	1.496
$Per–2$	Score of perceived control behavior—Contract farming will not increase pressure of farming	3.709	0.758
$Per–3$	Score of perceived control behavior—I am willing to participate in contract farming because it is friendly to the environment and farming community	3.853	0.820
$Per–4$	Score of perceived control behavior—I will have good contract conditions if the contract owner is of high quality	3.915	0.762
$Homest$	Dummy variable: 1 if farmer is a homesteader; 0 otherwise	0.796	0.403
$Cofarm$	Dummy variable: 1 if farmer is from a cooperative farm; 0 otherwise	0.010	0.099
$PST$	Dummy variable: 1 if farmer is from a PST; 0 otherwise	0.149	0.356
$Profarmer$	Dummy variable: 1 if farmer is a professional farmer; 0 otherwise	0.027	0.163
$Broker$	Dummy variable: 1 if farmer is a broker; 0 otherwise	0.011	0.131
$Conagu$	Dummy variable: 1 if farmer is concerned about disputes being resolved by the contract; 0 otherwise	0.107	0.309
$Conoth$	Dummy variable: 1 if farmer is concerned about other services provided by the contract; 0 otherwise	0.274	0.446
$Taichang$	Dummy variable for farmland location: 1 if it is located in Taichung; 0 otherwise	0.189	0.392
$Changhua$	Dummy variable for farmland location: 1 if it is located in Changhua; 0 otherwise	0.224	0.417
$Yunlin$	Dummy variable for farmland location: 1 if it is located in Yunlin; 0 otherwise	0.587	0.492
$Gender$	Dummy variable for gender: 1 if male; 0 otherwise	0.639	0.481
$Age$	Age (years)	53.823	13.925
$Marriage$	Dummy variable for marriage status: 1 if married; 0 otherwise	0.081	0.400
$Edu$	Years of education (years)	10.206	3.348
$Agricrop$	Number of persons in farming (persons)	2.507	0.677
$Agriexp$	Years of farming experience (years)	15.100	8.977
$Income$	Annual income of the potential farmer (USD)	14,831.000	16.361

. Experiences in farming (Agriexp) are classified into three kinds to demonstrate the farmer’s proficiency in agricultural practices [38]. One consists of those with an average farming experience of 5 years, another of those with an average farming experience of 15 years, and the other, those with a farming experience of 25 years. The average farming experience is 15.1 years.

Farmland location and status are also included. The farmland location is designated by the three county/city regions in which the farmland is located. It is believed that different factors in the TPB will have different impacts on the different statuses of the potential farmers in charge, i.e., “homesteader”, “farmer from a cooperative farm”, “farmer from a PST”, “professional farmer”, and “broker”. Thus, the different statuses of potential farmers are distinguished to have different combinations with those TPB factors stated above. In this way, it will be possible to observe the impact of each TPB factor on each type of farmer. Furthermore, a question is designed to determine whether the experience of contract farming in the past three years will have an impact on the participation in contract farming in the future and the selection of the contract type [39].

Six types of contracts for selection are categorized as three major forms of contracts, namely, purchase all, ordinary purchase, and adjustment for a bullish market, as shown in Figure 2. Uncertainty regarding product prices is the biggest concern of farmers [40,41]. Thus, the design of the contract type focuses on the price assurance to ensure a large number of sweet potatoes or compensation offered for the most preferred size of sweet potato for food crops. Among these, there are two specific types under the purchase all contract and three different types under the ordinary purchase contract. One type of purchase all contract is that the contract owner purchases all the sweet potatoes and pays 15% more than the market price for superior ones, designated as “purchase all-1”. The other type of purchase all contract is if the number of medium-sized sweet potatoes is greater than the contracted amount by at least 10%, in which case a bonus amount of 249–356 USD will be offered per hectare, designated as “purchase all-2”.

The three ordinary purchase contracts include one type where the contract owner purchases a specific amount of the large (more than 525 g), the medium (between 375 g and 525 g), and the small (between 75 g and 337.5 g) sweet potatoes according to the local market price, designated as “ordinary purchase-1”. The second type is similar to the “ordinary purchase-1”, but the contract owner will pay 20% more than the contracted price for the medium-sized amount with at least 20% or more according to the prior agreement, designated as “ordinary purchase-2”. A bonus of 249–356 USD per hectare will be offered to the farmer if the price of the medium-sized sweet potato is 15% more than the price in the previous contract, designated as “ordinary purchase-3”. The third type is purchased according to the large, the medium-sized, and the small sweet potatoes based on the market price level of the city/county. However, when the market price increases, the contract price will be adjusted close to the bullish market, designated as “adjustment for bullish market”.

3.2. Sampling

Under the 95% confidence level and where the sampling error is less than 0.05, the normalized value for such a condition is 1.96. Assuming that the probability of answering each questionnaire for each type of sweet potato farmer is 50%, then the size of the effective sample is computed as Equation (1):

$$N=P(1-P){\left[Z(\frac{\alpha }{2})/e\right]}^2$$

(1)

where N is the number of observations, P is the probability, $\alpha$ is the value of correctness, $Z(\alpha /2)$ is the normalized constant term, and e is the acceptable sample error. Accordingly, the sample size is 384.16 and is rounded up to 385.

The pretest was conducted in early May 2021. The list of potential farmers was obtained from the agricultural bureaus in these cities/counties. The final questionnaires were assigned in proportion to the numbers of homesteaders, farmers in cooperative farms, farmers from PSTs, professional farmers, and brokers in Taichung City, Changhua County, and Yunlin County. The final number of completed questionnaires was 402, which was more than the number of observations computed in Equation (1), including 76 from two major sweet potato growing townships in Taichung City, 90 from two main sweet potato growing townships in Changhua County, and 236 from three major sweet potato growing townships in Yunlin County. The sweet potato crop of these 402 surveyed farmers was the second cropping. Thus, the cultivated lands for sweet potatoes have the same size as the major crop, i.e., the rice crop. The total size of 402 sweet potato farmers is 659 ha, which accounts for 22.05% of the total farm hectares for the seven surveyed townships in the above three counties/cities. The average surveyed sweet potato farm size is 1.64 ha. This size is larger than the average farm size for homesteaders. The larger size results from contract owners’ targets also being on farmers other than homesteaders. Farmers of other statuses are professional farmers, brokers, farmers in cooperative farmers, and farmers from PSTs. These types of farmers normally cultivate larger farm sizes than homesteaders do.

The formal survey was conducted from the middle of May to June of 2021. A common problem confronted by potential farmers in these areas is that the contract farming is entered into orally. Disputes arise when the market fluctuates. There is no guaranteed contracted price. This inevitably serves to deepen the mistrust between the contract owners and farmers. Thus, there is room for contract owners or agribusinesses to offer systematic and guaranteed contracts to existing or potential farmers.

3.3. Specification for Participation in Contract Farming in the Past Three Years

In order to capture the factors and marginal effects that influenced the decision to participate in sweet potato contract farming in the past three years and the selection of different contract schemes for potential farmers with different statuses, this study calculates the cross-product of attitude, subjective norms, perceived behavioral control, and behavioral intention for five different statuses of potential farmers. The cross-product terms of farmers with different statuses and each of the associated factors are in order to reinforce their performance [42].

The probit model was used to analyze the probability of the factors that affected the farmers who participated in contract farming over the past three years. It is expected that past experience might have an influence on the willingness of farmers to participate. However, the influence depends upon the positive or negative experiences from the past. $p_i$ is a latent variable with a value of 1 if the farmer participated in contract farming, and with a value of 0 otherwise. The probit model is expressed as Equation (2):

$$P_i=\{\begin{array}{l}1,P_i^{*}\ge 0\\ 0,\mathrm{otherwise}\end{array}$$

(2)

$$\begin{array}{ll}\hfill \mathrm{Prob}(P_i=1)& =\mathrm{Prob}(P_i^{*}\ge 0)\hfill \\ & =\mathrm{Prob}(P_i^{*}(●)-{\epsilon }_i\ge 0)\hfill \\ & ={\mathrm{F}}_{\epsilon }(P_i^{*}(●))\hfill \end{array}$$

(3)

where ${\mathrm{F}}_{\epsilon }(P_i^{*}(●))$ is the cumulative distribution function of ${\epsilon }_i$ and ${\epsilon }_i$ is assumed to be a normal distribution. The distribution is written as Equation (4):

$${\mathrm{F}}_{\epsilon }(\Delta P_i^{*}(●))=\frac{1}{{(2\pi )}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}}{f}_{-\infty }^{\Delta P_i^{*}(•)e^{\frac{c^2}{2}}dc}$$

(4)

The coefficients in $\Delta P_i^{*}(●)$ are obtained by the estimation of the maximum likelihood function stated as Equation (5):

$$\ln L={\displaystyle \sum _{i=1}^n\left\{P_i\ln F_{\epsilon }(\Delta P_i^{*}(●))+(1-P_i)\ln (1-F_{\epsilon }(\Delta P_i^{*}(●)))\right\}}$$

(5)

The estimation of $\Delta P_i^{*}(●)$ is specified as Equation (6) below:

$$\begin{array}{ll}\hfill P_i^{*}& ={\alpha }_0+{\alpha }_{1}At{t}_i●Homes{t}_i+{\alpha }_{2}At{t}_i●Cofar{m}_i+{\alpha }_{3}At{t}_i●PS{T}_i+{\alpha }_{4}At{t}_i●Profarme{r}_i\hfill \\ & +{\alpha }_{5}At{t}_i●Broke{r}_i+{\alpha }_{6}Su{b}_i●Homes{t}_i+{\alpha }_{7}Su{b}_i●Cofar{m}_i+{\alpha }_{8}Su{b}_i●PS{T}_i+{\alpha }_{9}Su{b}_i●Profarme{r}_i\hfill \\ & +{\alpha }_{10}Su{b}_i●Broke{r}_i+{\alpha }_{11}Pe{r}_i●Homes{t}_i+{\alpha }_{12}Pe{r}_i●Cofar{m}_i+{\alpha }_{13}Pe{r}_i●PS{T}_i\hfill \\ & +{\alpha }_{14}Pe{r}_i●Profarme{r}_i+{\alpha }_{15}Pe{r}_i●Broke{r}_i+{\alpha }_{16}In{t}_i●Broke{r}_i+{\alpha }_{17}In{t}_i●Cofar{m}_i\hfill \\ & +{\alpha }_{18}In{t}_i●PS{T}_i+{\alpha }_{19}In{t}_i●Profarme{r}_i+{\alpha }_{20}In{t}_i●Broke{r}_i+{\alpha }_{21}Per-2_i+{\alpha }_{22}Per-3_i\hfill \\ & +{\alpha }_{23}Per-4_i+{\alpha }_{24}Conag{u}_i+{\alpha }_{25}Conot{h}_i+{\alpha }_{26}Taichan{g}_i+{\alpha }_{27}Changhu{a}_i+{\alpha }_{28}Gende{r}_i\hfill \\ & +{\alpha }_{29}Ag{e}_i+{\alpha }_{30}Marriag{e}_i+{\alpha }_{31}Ed{u}_i+{\alpha }_{32}Agricro{p}_i+{\alpha }_{33}Agriex{p}_i+{\alpha }_{34}Incom{e}_i+{\epsilon }_i,\hfill \\ & \hfill i=1,\dots ,402\end{array}$$

(6)

All the $\alpha$s in Equation (6) are coefficients to be estimated and ${\epsilon }_i$ is a random error term.

3.4. Specification for the Type of Contract Farming Selected

The selection scheme in Figure 2 needs to be estimated using a multinomial logit model. Since there are J nonsequential types of selection, j = 1, 2,…, J, the multinomial logit influenced by K explanatory variables $x$ is shown as Equation (7):

$$\ln \left[\frac{p(y=j|x)}{p(y=J|x)}\right]={\beta }_{0j}+{\displaystyle \sum _{k=1}^K{\beta }_{1j}{x}_k}$$

(7)

In the general formation of J types of selection, when selection J is used as the reference, the logit of J − 1 is written as Equation (8):

$$\begin{array}{ll}\ln \left[\frac{p(y=j|x)}{p(y=J|x)}\right]={\beta }_1+{\displaystyle \sum _{k=1}^K{\beta }_{1k}{x}_k}& \\ \ln \left[\frac{p(y=j|x)}{p(y=J|x)}\right]={\beta }_2+{\displaystyle \sum _{k=1}^K{\beta }_{2k}{x}_k}& \\ \hfill ·\hfill & & \\ \hfill ·\hfill & & \\ \hfill ·\hfill & & \\ \ln \left[\frac{p(y=(J-1)|x)}{p(y=J|x)}\right]={\beta }_{(J-1)}+{\displaystyle \sum _{k=1}^K{\beta }_{(J-1)k}{x}_k}& \end{array}$$

(8)

Among all the selections, when the last selection, i.e., J, is used as the reference type, then the probability of selecting the J type is computed as Equation (9):

$$p(y=j|x)=\frac{{\ell }^{{\beta }_j+{\displaystyle {\sum }_{k=1}^K{\beta }_{jk}}{x}_k}}{1+{\displaystyle {\sum }_{j=1}^{J-1}{\ell }^{{\beta }_j+{\displaystyle {\sum }_{k=1}^K{\beta }_{jk}}{x}_k}}}$$

(9)

The marginal effect for factor $x$ in selecting the J type can be computed by Equation (10):

$$\raisebox{1ex}{$\partial p(y=j|x)$}\!\left/ \!\raisebox{-1ex}{$\partial x$}\right.=\raisebox{1ex}{$\partial \left[\frac{{\ell }^{{\beta }_j+{\displaystyle {\sum }_{k=1}^K{\beta }_{jk}}{x}_k}}{1+{\displaystyle {\sum }_{j=1}^{J-1}{\ell }^{{\beta }_j+{\displaystyle {\sum }_{k=1}^K{\beta }_{jk}}{x}_k}}}\right]$}\!\left/ \!\raisebox{-1ex}{$\partial x$}\right.$$

(10)

There are six contract selections for the case at hand, and the latent variable y has six options, 1, 2, …,6.

The contract type selected for reference under the multinomial logit model is the “adjustment for bullish market”. The results for all the other five specific contract types are then related to the reference one. It is reasonable to assume that different factors in the TPB will have different impacts on potential farmers with different statuses. Since there are five types of farmers and four essential factors in the TPB, 20 cross-product terms are generated. Under such a framework, the selection of each contract is specified as in Equation (11):

$$\begin{array}{ll}\hfill y_i& ={\beta }_0+{\beta }_{1}At{t}_i●Homes{t}_i+{\beta }_{2}At{t}_i●Cofar{m}_i+{\beta }_{3}At{t}_i●PS{T}_i+{\beta }_{4}At{t}_i●Profarme{r}_i\hfill \\ & +{\beta }_{5}At{t}_i●Broke{r}_i+{\beta }_{6}Su{b}_i●Homes{t}_i+{\beta }_{7}Su{b}_i●Cofar{m}_i+{\beta }_{8}Su{b}_i●PS{T}_i+{\beta }_{9}Su{b}_i●Profarme{r}_i\hfill \\ & +{\beta }_{10}Su{b}_i●Broke{r}_i+{\beta }_{11}Pe{r}_i●Homes{t}_i+{\beta }_{12}Pe{r}_i●Cofar{m}_i+{\beta }_{13}Pe{r}_i●PS{T}_i\hfill \\ & +{\beta }_{14}Pe{r}_i●Profarme{r}_i+{\beta }_{15}Pe{r}_i●Broke{r}_i+{\beta }_{16}In{t}_i●Homes{t}_i+{\beta }_{17}In{t}_i●Cofar{m}_i\hfill \\ & +{\beta }_{18}In{t}_i●PS{T}_i+{\beta }_{19}In{t}_i●Profarme{r}_i+{\beta }_{20}In{t}_i●Broke{r}_i+{\beta }_{21}{\mathrm{xb}2}_i+{\beta }_{22}Per-2_i+{\beta }_{23}Per-3_i\hfill \\ & +{\beta }_{24}Per-4_i+{\beta }_{25}Conag{u}_i+{\beta }_{26}Conot{h}_i+{\beta }_{27}Taichan{g}_i+{\beta }_{28}Changhu{a}_i+{\beta }_{29}Gende{r}_i\hfill \\ & +{\beta }_{30}Ag{e}_i+{\beta }_{31}Marriag{e}_i+{\beta }_{32}Ed{u}_i+{\beta }_{33}Agricro{p}_i+{\beta }_{34}Agriex{p}_i+{\beta }_{35}Incom{e}_i+{\mu }_i,\hfill \\ & \hfill i=1,\dots ,402\end{array}$$

(11)

In Equation (11), all $\beta$s are coefficients to be estimated and ${\mu }_i$ is the random error term.

4. Results and Discussion

4.1. Influence of Participation in Contract Farming in the Past Three Years

Before the analyses, all explanatory variables were tested for multicollinearity, with variance inflation factors (VIFs) being employed for this purpose. The results indicate, that in addition to the cross-product terms, such as $At{t}_i●Homes{t}_i$, the VIF for all other individual variables is less than 10. The VIFs for those cross-product terms that are greater than 10 are not detrimental to the estimation results [43,44]. For all the other 14 explanatory variables, in addition to those with cross-product terms, none of the variables has a VIF larger than 10, and the average VIF for these 14 explanatory variables is 2.11. The problem of multicollinearity is, thus, avoided in the related estimations.

The estimation results shown in

Table 3. Estimation results for farmers’ contract participating in the past three years.

Variable	Estimated Coefficient		Standard Deviation	Z Value
$Att●Homest$	0.0178		0.040	0.4400
$Att●Cofarm$	−81.5307	***	3.905	−20.8800
$Att●PST$	0.0010		0.078	0.0100
$Att●Profarmer$	−9.1438	***	0.439	−20.8200
$Att●Broker$	0.4357	***	0.115	3.7700
$Sub●Homest$	−0.1040	**	0.058	−1.7900
$Sub●Cofarm$	−40.5207	***	2.153	−18.8200
$Sub●PST$	0.1119		0.102	1.1000
$Sub●Profarmer$	−0.0472		0.143	−0.3300
$Sub●Broker$	−1.2524	***	0.196	−6.3900
$Per●Homest$	0.1770		0.113	1.5600
$Per●Cofarm$	86.9117	***	4.234	20.5300
$Per●PST$	0.0629		0.144	0.4400
$Per●Profarmer$	0.3500	*	0.186	1.8800
$Per●Broker$	−3.0254	***	0.211	−14.3400
$Int●Homest$	0.0487		0.076	0.6400
$Int●Cofarm$	64.2399	***	3.152	20.3800
$Int●PST$	0.0379		0.143	0.2600
$Int●Profarmer$	18.6112	***	0.770	24.1700
$Int●Broker$	6.9086	***	0.176	39.1800
$Per-2$	0.0193		0.193	0.1000
$Per-3$	−0.2296		0.165	−1.3900
$Per-4$	−0.4427	**	0.194	−2.2800
$Conagu$	−2.0694	***	0.566	−3.6600
$Conoth$	−0.3467	**	0.171	−2.0300
$Taichang$	−1.0090	***	0.239	−4.2200
$Changhua$	−0.6729	***	0.228	−2.9500
$Gender$	0.3305	*	0.170	1.9500
$Age$	−0.0080		0.009	−0.9200
$Marriage$	0.1551		0.199	0.7800
$Edu$	−0.0502	*	0.030	−1.6800
$Agricrop$	−0.0660		0.128	−0.5100
$Agriexp$	0.0263	**	0.013	1.9800
$Income$	0.0067	***	0.002	3.8000
$Cons$	0.4213		1.034	0.4100
Wald χ² (34) = 9957.19
Pseudo R² = 0.2562
Number of samples: 402

Note: Numbers with *, **, and *** indicate that the estimated coefficients are significantly different from zero at the 10%, 5%, and 1% significance levels, respectively.

indicate that the attitude, subjective norms, perceived behavioral control, and behavioral intention in the TPB had different impacts on the participation in contract farming for the past three years. As with the sociodemographic factors, the overall result indicates that farmers with more experience in farming and with higher income had a high probability of participating in contract farming in the past three years. The factors further indicate that the younger the farmer is, the lower the probability of having participated in contract farming in the past is. The geographical areas also show that sweet potato farmers in Yunlin County have a high probability of signing contracts. The estimated Equation (6) is used to forecast a new variable for estimating the participation in selecting contract farming in the future.

4.2. The Marginal Effect of TPB Factors in Selecting Contract Farming

The results of using the multinomial logit model in contract farming selection are listed in

Table 4. Estimation results of selecting different types of contract farming.

Variable	Purchase All−1		Purchase All−2		Ordinary Purchase−1		Ordinary Purchase−2		Ordinary Purchase−3
$Att●Homest$	−0.0391		0.0401		0.2276	**	0.0182		0.1105
	(0.4400)		(0.3600)		(2.4200)		(0.1600)		(0.9900)
$Att●Cofarm$	−62.1880	***	−103.3594	***	−5.5636		221.3747	***	11.0664
	−(2.9900)		−(4.8200)		−(0.3200)		(11.0400)		(0.5300)
$Att●PST$	−0.4250	**	0.0328		−0.0293		−0.8375		−0.3692	*
	−(2.1400)		(0.1300)		−(0.1600)		−(1.3900)		−(1.6900)
$Att●Profarmer$	−28.8693	***	−11.3434	***	−12.2736	***	−14.8333	***	−24.0887	***
	−(20.8200)		−(10.0800)		−(10.2000)		−(11.8900)		−(18.0600)
$Att●Broker$	−37.2029	***	27.8646	***	−27.2737	***	−37.1603	***	−5.0462
	−(18.7800)		(4.1400)		−(11.2800)		−(13.8200)		−(1.2200)
$Sub●Homest$	−0.0705		−0.0489		−0.0199		0.0164		−0.3232	**
	−(0.4600)		−(0.2900)		−(0.1500)		(0.1100)		−(2.2600)
$Sub●Cofarm$	−20.1870	**	−64.4128	***	−2.8986		113.4620	***	6.2898
	−(1.9600)		−(6.0100)		−(0.3400)		(11.2800)		(0.5900)
$Sub●PST$	−0.7821	**	−0.1133		0.0129		0.7072	**	0.5353
	(2.5000)		−(0.4100)		(0.0400)		(2.0900)		(1.3400)
$Sub●Profarmer$	−38.5030	***	−26.5836	***	−28.6723	***	−29.4701	***	−45.4254	***
	−(22.7200)		−(13.7300)		−(14.9200)		−(14.0400)		−(22.2500)
$Sub●Broker$	−1.9020	***	27.1355	***	10.7783	***	1.0997	*	−4.6687	***
	(3.0300)		(21.8000)		(14.1200)		(1.7200)		−(7.2000)
$Per●Homest$	−0.4726	**	−0.2061		−0.7121	**	−0.1038		−0.5456	*
	−(1.7100)		−(0.6200)		−(2.2200)		−(0.3900)		−(1.7200)
$Per●Cofarm$	−61.8344	***	115.6468	***	5.9432		−236.5677	***	−12.7490
	(2.7800)		(5.0300)		(0.3200)		−(11.0600)		−(0.5600)
$Per●PST$	−0.2508		−0.1366		−0.1390		0.0682		−0.5164
	−(0.8200)		−(0.3200)		−(0.4400)		(0.1000)		−(1.4300)
$Per●Profarmer$	−52.4286	***	46.6615	***	55.1834	***	54.8063	***	83.5076	***
	(23.2600)		(13.4800)		(15.8100)		(13.7400)		(21.8900)
$Per●Broker$	−46.5618	***	−51.2414	***	21.5727	***	43.2508	***	−4.2143
	(17.9600)		−(6.2900)		(6.7700)		(12.3700)		−(0.6500)
$Int●Homest$	−0.5609	***	−0.0460		0.1106		0.2419		0.0972
	(3.1900)		−(0.2600)		(0.6700)		(1.0800)		(0.5400)
$Int●Cofarm$	−41.0789	**	90.7804	***	3.7048		−175.8752	***	−9.3614
	(2.5200)		(5.3800)		(0.2700)		−(11.0500)		−(0.5600)
$Int●PST$	−0.2436		−0.0130		−0.5538		0.4349		−0.5038
	−(0.7500)		−(0.0300)		−(1.4100)		(0.8600)		−(1.3600)
$Int●Profarmer$	−17.5290	***	−22.7144	***	−35.2066	***	−27.5358	***	−39.4574	***
	(12.1700)		−(8.6200)		−(12.2700)		−(9.9800)		−(14.9200)
$Int●Broker$	−22.1926	***	−2.5781		−5.7815	***	−13.7115	***	26.2136	***
	−(19.8700)		−(0.9800)		−(4.8600)		−(10.2500)		(5.6300)
$xb2$	−2.6362		1.1994		−0.2209		1.6604		1.8908
	−(0.8200)		(0.3500)		−(0.0800)		(0.5500)		(0.6100)
$Per-2$	−0.5582		0.2369		0.8128	*	−0.2636		0.3058
	(1.3400)		(0.4900)		(1.8800)		−(0.5800)		(0.6100)
$Per-3$	−0.0851		0.4453		0.7966		−0.0124		0.5770
	(0.2100)		(0.9500)		(1.5500)		−(0.0300)		(1.2000)
$Per-4$	−0.5796		0.0846		0.5864		−0.3523		0.3167
	(1.1600)		(0.1400)		(1.1900)		−(0.7800)		(0.5600)
$Conagu$	−0.2363		−0.2934		1.0016		1.4465		0.1986
	−(0.1700)		−(0.2200)		(0.9400)		(1.2800)		(0.1600)
$Conoth$	−0.1471		0.8715		0.2770		0.7603		0.4301
	(0.3000)		(1.5600)		(0.4800)		(1.2500)		(0.8700)
$Taichang$	−0.4081		0.1362		−1.5564	*	1.1776		0.0429
	−(0.3800)		(0.1200)		−(1.7000)		(1.2100)		(0.0400)
$Changhua$	−0.4146		0.0995		−1.1593	*	0.2244		0.9359
	(0.5100)		(0.1200)		−(1.6900)		(0.2500)		(1.1800)
$Gender$	−0.6555		0.9687		0.1341		−0.4398		−0.5294
	(1.5000)		(1.7700)		(0.3200)		−(0.8400)		−(1.0300)
$Age$	−0.0093		−0.0160		−0.0237		−0.0003		0.0197
	(0.4500)		−(0.6900)		−(1.1000)		−(0.0100)		(0.9000)
$Marriage$	−0.0421		−0.4586		0.2995		0.0383		−0.2851
	−(0.0900)		−(0.9100)		(0.5900)		(0.0700)		−(0.5200)
$Edu$	−0.1976	**	−0.0154		−0.1125		0.0426		−0.0333
	−(2.4000)		−(0.1700)		−(1.3800)		(0.4800)		−(0.3900)
$Agricrop$	−0.2435		−0.5144		0.5056		0.5312		0.6067	*
	(0.8000)		−(1.3900)		(1.6300)		(1.4200)		(1.8600)
$Agriexp$	−0.0124		−0.0646		0.0386		0.0056		−0.0145
	−(0.3100)		−(1.5600)		(1.0300)		(0.1300)		−(0.3400)
$Income$	−0.0048		−0.0020		−0.0050		0.0009		0.0025
	(0.5700)		−(0.2200)		−(0.5700)		(0.1200)		(0.3500)
$Cons$	−0.6417		1.8766		−1.4417		−2.1117		2.1048
	−(0.2200)		(0.5800)		−(0.5000)		−(0.7200)		(0.7200)

Note: Numbers with *, **, and *** indicate that the estimated coefficients are significantly different from zero at the 10%, 5%, and 1% significance levels, respectively.

. The results show that whether or not the farmers had experience in contract farming in the past three years, and their sociodemographic factors will not affect the selection of any type of contract farming in the future. The results show that the actual available and existing contract conditions have different impacts on farmers with different statuses. One contract is necessarily selected as the reference in the multinomial logit estimation. The contract with the “adjustment for bullish market” is selected as the reference type.

Thus, each estimated coefficient in Table 4 is the change in a specific factor in relation to the change in the probability of selecting a particular contract type listed in the table compared to the contract with the “adjustment for bullish market” type for the corresponding factor. For instance, the estimated coefficient for the factor under the “purchase all-1” contract type is −0.0391, which means that the probability of selecting “purchase all-1” is lower than that of selecting the “adjustment for bullish market” contract type for the attitude of homesteaders. Similar explanations are applicable for all the other coefficients. It is not easy to interpret and understand the absolute impact of a particular factor on selecting a specific contract type in terms of probability.

The marginal effect for each factor under each type of contract is, thus, computed in

Table 5. Marginal Effects of Different Factors in the TPB on Selecting the Contract Type for Different Types of Farmers.

■ Factor and Type of Contract Farmers	Type of Contract
	Purchase All-1 A		Purchase All-2 B		Ordinary Purchase-1 C		Ordinary Purchase-2 D		Ordinary Purchase-3 E		Adjustment for Bullish Market F
■ Attitude
■ Professional farmer	−3.1212		0.4492		0.7197		0.1596	*	−1.4414		3.2387
■ Broker	−5.2256		3.9308		−1.8854		−2.1348		2.1098		3.2236
■ Cooperative farm	−15.3113		−9.1097		−0.7596	*	22.7687		2.2555	*	0.1931	*
■ Production and sales team	−0.0091	*	0.0264	*	0.0406		−0.0588	*	−0.0198	*	0.0520	*
■ Homesteader	−0.0404	*	−0.0031	*	0.0264		−0.0059	*	0.0066	*	−0.0149	*
■ Subjective norm
■ Production and sales team	0.1055		−0.0422		−0.0604	*	0.0355	*	0.0327	*	−0.0712	*
■ Broker	−0.5251		2.0586		1.1744		−0.2969		−1.6233		−0.7902
■ Professional farmer	−2.5023		0.1705	*	−0.0260	*	−0.0994	*	−3.1697		5.6309
■ Cooperative farm	−5.4263		−5.8661		−0.7516	*	11.4634		0.8943	*	−0.2992	*
■ Homesteader	0.0035	*	0.0032	*	0.0111	*	0.0103	*	−0.0447		0.0167	*
■ Perceived behavioral control
■ Cooperative farm	15.423		10.2742		0.9635	*	−24.2426		−2.4207	*	−0.0351	*
■ Broker	8.0267		−5.8471		1.2109		2.9468		−3.4995		−2.8596
■ Professional farmer	1.0831		−0.1279	*	1.2238		0.6869		6.631		−9.5033
■ Production and sales team	−0.0155	*	0.0046	*	0.0086	*	0.0263	*	−0.0614	*	0.0373	*
■ Homesteader	−0.0249	*	0.0148	*	−0.0586	*	0.0276	*	−0.0323	*	0.0737
■ Behavioral intention
■ Cooperative farm	10.4673		8.1456		0.7505	*	−17.9311		−1.6096	*	0.1514	*
■ Professional farmer	7.8763		−0.7791		−3.6951		−1.3904		−4.7752		2.7578
■ Homesteader	0.0907		−0.0217		−0.0154	*	0.0045	*	−0.0190	*	−0.0392
■ Broker	−4.7309		0.0624	*	−0.4225	*	−1.0682		5.5231		0.6487
■ Production and sales team	−0.0089	*	0.0174	*	−0.0598	*	0.0658	*	−0.0554	*	0.0411	*

Note: Numbers in bold font indicate that there is a positive marginal effect for the change in the corresponding variable for specific farmers under different types of contract farming. The letters A, B, …, F are used to designate the name of the contract for presentation in figures later. Numbers with an “*” indicate that the estimated coefficients are insignificantly different from zero. For a complete presentation, the marginal effects of those insignificant variables are displaced for comparison purposes if necessary, and also meaningful.

. The marginal effect refers to the impact of a change in a particular factor in the TPB on the change in the probability of selecting a particular contract farming type. The results obviously show that farmers with different statuses have different preferences in terms of perception, attitude, subjective norms, or behavioral intention for each type of contract, and all these factors dominate their past contract farming experiences. The marginal effect in Table 5 can also be observed according to different viewpoints. One viewpoint involves inspecting a particular status of a farmer and comparing the probability of the change in a specific factor in the TPB when selecting a certain contract.

The other viewpoint involves observing and comparing the probability of a change in a typical factor in the TPB for farmers with different statuses for participating in a particular type of contract farming. The significance in Table 5 is out carried from the estimation results in Table 4. It can be observed that most of the significant factors in terms of the contract types designated in this study were affected by the psychological factors in the TPB. It is very easy to compare the probability influencing each factor either from the viewpoint of a particular farmer or from the standpoint of a designated type of contract farming, as shown in Figure 3 and Figure 4.

5. Conclusions

This study has adopted the theory of planned behavior to explore the factors that have determined the participation of farmers in contract farming in the past. The results were carried further to examine the impact of all kinds of psychological factors in the TPB, the sociodemographic factors of potential farmers, and farmland conditions in terms of the selection of the six kinds of contract farming designated in this study. One type comprises purchasing all sweet potatoes without regard to size classification or a fixed amount or a specific percentage of bonuses that are paid for the most preferred size over that agreed in the contract. Another type includes an ordinary purchase with a size classification, where a higher price is paid for the most preferred size with a certain percentage, or where a fixed amount of bonus is paid for the most preferred size over that in the agreement. The other type involves the price being adjusted when the market price increases.

A total of 402 effective potential farmers were surveyed in the three major sweet potato production counties and cities in Taiwan. The results from the dataset demonstrated that farmers who participated in sweet potato contract farming in the past did not have an impact on the selection of either type of contract farming. The psychological factors in the TPB, i.e., attitude, subjective norm, perception, and behavioral intention, however, did have an impact and had different impacts on potential farmers with different statuses when selecting the type of contract farming.

The results show that although the psychological factors in the TPB are significant in one contract or in the others for homesteaders or farmers from PSTs, the impact that these factors have on them is relatively small compared to those for the other three types of farmers, i.e., professional farmers, brokers, and farmers from cooperative farms. Homesteaders or farmers from PSTs basically take actions independently and individually, although farmers from PSTs are very likely to share information with each other.

Farmers from cooperative farms have a more positive attitude and a more subjective norm toward contract farming. Thus, strengthening these factors will increase the probability of potential farmers from cooperative farms selecting contracts to obtain the amount based on medium-sized sweet potatoes with a markup on the prior agreement. As for professional farmers and brokers, when their perceived behavioral control factor and behavioral intention factor are enhanced, they will have a relatively high probability of selecting a contract that provides assurance to ensure a large number of sweet potatoes or that adjusts due to rising market prices. Since these two types of potential farmers can serve as agents between homesteaders and farmers from PSTs and contract owners, they will naturally have more positive perceptions than others of contract farming behavior, whether of agribusinesses or food processing companies. Thus, contracts that are mutually beneficial both to the farmers’ income and to the contract owners’ acquisition of quantities of sweet potatoes will be selected.

To conduct and complete the survey, it was necessary to overcome the major challenges during the outbreak of the COVID-19 pandemic. To a certain degree, this resulted in the study’s survey being focused on counties and cities located in the central parts of Taiwan, although these are the three major sweet potato production areas on the island. However, certain counties and cities in the southern part of Taiwan are also important production areas. If a survey for the southern part can be conducted, there will be a more complete picture of the sweet potato farmers’ contract farming behavior in Taiwan.