Mixed Use of Chemical Pesticides and Biopesticides among Rice–Crayfish Integrated System Farmers in China: A Multivariate Probit Approach

Abstract

Biopesticides are perceived as a feasible alternative to chemical pesticides, providing an effective approach to pest management while mitigating the undesirable effects of chemical pesticide overuse. Yet, due to the distinctive attributes of the two types of pesticides, farmers often adopt a combination of both. This study aimed to probe the interdependent relationship underlying the mixed use of chemical pesticides and biopesticides by farmers in the rice–crayfish integrated system (RCIS) in Hubei province, China. We also sought to identify influencing factors and associated consequences of this practice. Given that the adoption of chemical pesticides and biopesticides by farmers is not mutually exclusive, a multivariable probit model was utilized to estimate simultaneous pesticide applications. Survey data from 736 RCIS farmers revealed that 10.50% of the sample farmers employ a combination of chemical pesticides and biopesticides. A substitution relationship was observed between the adoption of chemical pesticides and biopesticides by farmers, whereas no significant complementary relationship was found in the application of the two types of pesticides. The primary motivation for the mixed use of pesticides by farmers is to achieve superior pest control efficacy. Notably, when integrating the use of pesticides, farmers pay less attention to factors such as resource endowments and multiple production objectives. Further analysis indicated that farmers display significant concern for pesticide attributes including insecticidal efficacy, toxicity, insecticidal spectrum, and validity period. The mixed use of pesticides has resulted in a decreased frequency of pesticide application but has escalated the cost. While the mixed use of pesticides elevated the selling price of rice, no significant improvement was observed in yield and output value. The insights garnered from this study offer strategic implications for policymakers on how to enhance the judicious adoption of pesticides by farmers.

1. Introduction

Chemical pesticides are prevalently utilized in agricultural production as a means to enhance yield quality and quantity [1], safeguarding crops against pests, weeds, and diseases. Nevertheless, the indiscriminate application of chemical pesticides has notably polluted the ecological environment [2], compromised biodiversity, escalated pest resistance risk [3], impaired human health [4], jeopardized food security, and augmented farmers’ production costs [5]. The deleterious impacts linked with chemical pesticide usage have necessitated a shift towards biopesticide application as a non-toxic, ecological means of pest control [6]. As an effective alternative to chemical pesticides, biopesticides are characterized by high efficacy, target specificity, reduced chemical residues, and minimized environmental risks [1,7,8,9,10]. In China, biopesticides constituted nearly 10% of the total pesticide production and output value in 2021, a figure significantly lower than the 25–60% range observed in developed countries (Data from Agrochemical Information Net: http://jsppa.com.cn/news/jingji/6174.html, accessed on 20 January 2022). As of 2023, there are 45,531 registered pesticide products in China, of which only 2048 are biopesticide products, representing a mere 4.5% of the total (Data from China Farmer Network: https://www.farmer.com.cn/2023/07/13/99932483.html, accessed on 13 July 2023). This prompts the question of why the adoption of biopesticides in China has been sluggish despite the presence of various economic and political incentives.

However, biopesticides are typically host-specific, expensive to produce, sensitive to environmental conditions, less persistent in soil environments, and less effective within short time frames. They also have a shorter shelf life compared to their chemical counterparts [9,10,11,12]. These drawbacks have impeded the widespread acceptance and commercial usage of biopesticides [13]. Furthermore, the complete transition from chemical pesticides to biopesticides is time-consuming [14,15,16]. Additionally, farmers face challenges in the appropriate application of biopesticides due to limitations in their resource endowments, such as financial, technical, and physical capital [14]. The primary focus remains not on ensuring food safety [17]. Therefore, rather than completely substituting chemical pesticides with biopesticides, farmers are likely to gradually transition, indicating a combined use of both types of pesticides.

Prior studies have predominantly focused on either farmers’ use of chemical pesticides [5,18,19,20,21,22] or biopesticides [23,24,25]. However, some research has highlighted the combined adoption of these pesticides. Wuepper, Roleff [26] found that Swiss fruit farmers advised by private extension services were more inclined to use a combination of insecticides and preventive measures. Gould, Brown [3] noted that the combination of pesticides is often recommended at the field level to slow resistance evolution. Abtew, Niassy [7] showed that farmers in Kenya use mixtures of chemicals to manage grain legume pests and ensure pesticide effectiveness. Shishir, Bhowmik [27] observed that cabbage and cauliflower farmers substitute chemical pesticides with Bt biopesticide preparations to ensure food safety without compromising yield. Constantine, Kansiime [28] reported that Kenyan farmers used a biopesticide product in conjunction with chemical pesticides. Uribe-Gutierrez, Moreno-Velandia [29] found that while a new biopesticide demonstrated high efficacy against grey mould, chemical fungicides were still needed to control downy mildew and anthracnose diseases of blackberry. While the mixed use of pesticides by farmers has been discussed in some studies, most have focused on influencing factors. Few have further explored the mechanism of their interdependent relationship and related consequences.

Chemical pesticides and biopesticides can both fulfill farmers’ goal of pest control [30]. However, in the face of certain pest resistance issues, farmers may substitute some chemical pesticides with biopesticides to achieve enhanced pest control efficacy. Conversely, due to the variability in the efficacy of biopesticides influenced by the application environment, farmers may substitute a portion of biopesticides with chemical pesticides. As a result, farmers adopt a mixed-use strategy with these two types of pesticides due to their interchangeable relationship. On another note, beyond pest control, farmers’ pesticide applications can also be constrained by their resources and influenced by their multifaceted production objectives. Although biopesticides are more host-specific, meaning their effectiveness is confined to limited target pests [13], farmers may need to increase the frequency of biopesticide use to ensure effective pest control. Additionally, proper application of biopesticides demands considerable time to identify and scout pests, as well as to set up specific infrastructure [25]. Critically, the efficacy of biopesticides is susceptible to climatic conditions [13], necessitating more labor input from farmers. In contrast, chemical pesticides are typically more labor-saving and cost-effective [31], making farmers’ application of these pesticides less constrained by resource endowments. Moreover, farmers usually pursue multiple production goals, e.g., self-consumption and market participation [17,32]. Consequently, farmers must consider both food safety and maximum benefits in their adoption of pesticides. Unfortunately, it is challenging for a single pesticide to fulfill farmers’ diverse demands simultaneously [33]. As such, farmers are motivated to combine the use of chemical pesticides and biopesticides, effectively reducing the inconvenience of separate pesticide applications [3,16,17,27,29]. The mixed use of chemical pesticides and biopesticides, owing to their complementary properties, aligns well with farmers’ resource endowments and diverse production purposes in practice. This approach suggests a different set of agricultural extension policies compared to those based on their substitutional relationship.

Drawing upon the discussions above, the aim of this study is to scrutinize the relationship concerning farmers’ utilization of chemical pesticides and biopesticides, their influencing factors, and resultant outcomes. To this end, utilizing survey data from 736 farmers involved in the integrated rice–crayfish system in Hubei province, China, we employed a multivariate probit model to collectively estimate the application of chemical pesticides and biopesticides by farmers. This study offers several contributions. Primarily, considering the factors of production endowments and multifaceted production objectives (including food safety, cost minimization, and pest control), farmers are inclined to simultaneously use chemical pesticides and biopesticides as complements due to their distinct attributes. Hence, beyond the theoretical substitution perspective discussed in previous studies, their complementary relationship also warrants inclusion within the analytical framework. Secondarily, past research has given limited focus to farmers’ combined usage of chemical pesticides and biopesticides and the underlying mechanism. This study aids in elucidating the logic behind farmers’ pesticide applications, thereby facilitating the gradual implementation of biopesticides. The conclusions drawn from this study bear significant implications for pest management policies aimed at enabling judicious pesticide usage by farmers. We propose the initiation of a pest management project integrating the use of chemical pesticides and biopesticides, with the aim of improving biopesticide implementation and reducing chemical pesticide application.

The remaining sections of the paper are structured as follows: Section 2 outlines the materials and methods. Section 3 details the descriptive statistical results, empirical results, and discussions. Section 4 presents the conclusions.

2. Materials and Methods

2.1. Rice–Crayfish Integrated Systems in China

The Rice–Crayfish Integrated Systems (RCIS), characterized by the concurrent cultivation of crayfish and rice in the same paddy field [34], has experienced accelerated growth in China since 2016. In 2021, the cultivation area for RCIS extended to approximately 1.4 million hectares (or 0.21 million mu in Chinese units), which represented 52.95% of the overall rice–aquaculture integrated systems (Data from China Fisheries Association Network: http://www.china-cfa.org/xwzx/xydt/2022/0531/732.html, accessed on 31 May 2022). Owing to its positive impacts on nutrient use efficiency [34], soil quality [35], biological diversity [36], water quality [37], and greenhouse gas emissions [38], RCIS has been acknowledged as a sustainable agricultural system. Simultaneously, RCIS permits farmers to utilize fully the natural biological resources inherent to paddy fields, resulting in heightened productivity, favorable market pricing, adaptability, and compatibility with traditional rice cultivation [39,40]. Consequently, the proliferation of these systems has been demonstrated to be a persuasive strategy for augmenting farmers’ revenue, ensuring the supply of rice and aquatic products, as well as catalyzing rural revitalization [39,41]. Contrary to the high degree of mechanization in singular rice cultivation, this system leans toward being labor and capital-intensive, especially throughout the crayfish production process. Additionally, the crayfish residing in paddy fields exhibit sensitivity towards chemical inputs, which curtails the over-application of pesticides and fertilizers, whilst promoting increased use of biochemical substances to safeguard aquatic organisms [42]. Therefore, farmers engaging in RCIS are more inclined to utilize biopesticides.

2.2. Differences between Chemical Pesticides and Biopesticides

Chemical pesticides and biopesticides represent two primary strategies for pest management, crucial for maintaining crop yield and quality. These two classes of pesticides significantly differ in their insecticidal mechanisms, application conditions, industrial production, and levels of toxic residues. Biopesticides, which often surpass their chemical counterparts in performance, are projected to gain a significant market share in the forthcoming period [43]. Typically, biopesticides are derived from natural sources, designed to manage pests using non-toxic mechanisms, and they are more target-specific, leave fewer toxic residues, have a lower incidence of pest resistance, and are less hazardous to human health, the environment, and biodiversity [13,30].

However, in contrast to chemical pesticides, biopesticides are more costly to produce, have specific requirements for shelf-life [44], are less efficacious, and are slower at managing pests and diseases [45] They are also more sensitive to environmental conditions. Consequently, the unique characteristics of biopesticides may lead to increased cost implications and labor for farmers [9]. In summary, the varying attributes of the two types of pesticides have resulted in their relative advantages and disadvantages.

In the context of China, biopesticides are primarily classified into microbial, biochemical pesticides, botanicals, natural enemies, and agricultural antibiotics [46]. Each of these biopesticides holds certain advantages over chemical pesticides in various aspects. A detailed comparative analysis of their strengths is presented in

Table 1. Attributes of chemical pesticides and biopesticides.

Attributes	Chemical Pesticides	Biopesticides
Broader spectrum	☑	☐
Faster efficacy	☑	☐
More convenient to implement	☑	☐
High chance to cause pest resistance	☑	☐
More environmentally friendly	☐	☑
Lower costs for producing	☑	☐
Less toxic to the human body	☐	☑

Notes: The comparison of attributes is all relative. Source: Ayilara, Adeleke [9], Kumar, Ramlal [43] and Mishra, Dutta [15].

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2.3. Survey Data

The selection of Hubei province as the research area was predicated on several considerations. Initially, the region committed to the rice–crayfish integrated system (RCIS) production accounted for an estimated 42.38% of mainland China’s domestic production area in 2021 (Data from Jingzhou Agriculture Bureau network: http://nyj.jingzhou.gov.cn/ywbk/scy/202206/t20220620_740817.shtml, accessed on 20 June 2022, Data from The People’ Government of Hubei Province Network: https://www.hubei.gov.cn/hbfb/bmdt/202202/t20220221_4006309.shtml, accessed on 21 February 2022). Subsequently, Hubei’s agricultural departments initiated an extension program known as the ‘zero growth’ policy in 2015, aimed at enhancing the application of biopesticides and diminishing the use of chemical pesticides.

The study area and sample farmers were chosen using a stratified random sampling method. Nine cities, namely Xiantao, Honghu, Jianli, Shishou, Gongan, Jianling, Shashi, Qiangjiang, and Tianmen, which are the primary RCIS production regions in Hubei (Figure 1), were selected, accounting for the distribution, developmental stage of RCIS, and the economic development level within these cities. Subsequently, three towns in each city were chosen. Within each town, two or three villages were selected, and from these villages, 10–15 farmers were randomly targeted as respondents. The village-level sample selection was refined through a farmers’ name list provided by the local village committee, ensuring an enhanced level of randomness.

A team of trained researchers, specializing in agricultural economics and management, conducted face-to-face interviews with the respondents who are responsible for farming decisions. Standardized questionnaires were utilized to gather information based on the interviewees’ responses. The questionnaires were divided into three sections: household demographic information, household agricultural input and output data, and village characteristics. After excluding observations that lacked key information and the data of rice monoculture farmers, the final dataset for analysis comprised 736 RCIS farmers.

2.4. Model and Variables

We are examining the interplay between farmers’ use of chemical pesticides and biopesticides. To delineate the variable of mixed-use pesticides, we posed two questions: ‘Did you utilize chemical pesticides in the rice field in 2021?’ and ‘Did you utilize biopesticides in the rice field in 2021?’ Positive responses to both inquiries defined a farmer’s mixed use of chemical pesticides and biopesticides. In our study, mixed use of pesticides signifies farmers employing chemical pesticides and biopesticides either concurrently or alternatively in one or more applications throughout the rice growing season in 2021. Farmers have two alternatives to manage pest infestations. If they opt for chemical pesticides, the usage of biopesticides is likely to decrease, and vice versa. As such, farmers’ adoption of chemical pesticides and biopesticides is not mutually exclusive but is rather highly correlated. Using univariate techniques in our analysis may overlook the interdependent relationship between these two adoption decisions [47]. The multivariate probit (MVP) model is beneficial for estimating several correlated binary outcomes simultaneously [48].

Consequently, in line with the methodologies of Ehiakpor, Danso-Abbeam [49] and Belderbos, Carree [50], we applied a multivariate probit model to estimate a farmer’s dual-choice of using biopesticides and chemical pesticides through simulated maximum likelihood. Empirically, the dual decision of pesticide adoption by farmers can be outlined in Equation (1). Equations (2) and (3) denote the specification of the binary dependent variable.

$$\left\{\begin{array}{c}{y}_b^{*}={\mathsf{{\rm X}}}_1{\beta }_1+{\epsilon }_1\\ y_c^{*}={\mathsf{{\rm X}}}_2{\beta }_2+{\epsilon }_2\end{array}\right.$$

(1)

$$y_b=\left\{\begin{array}{c}0, if y_b^{*}\le 0\\ 1, if y_b^{*}>0\end{array}\right.$$

(2)

$$y_c=\left\{\begin{array}{c}0, if y_c^{*}\le 0\\ 1, if y_c^{*}>0\end{array}\right.$$

(3)

Let $y_b^{*}$ and $y_c^{*}$ denote unobservable latent variables representing the potential outcomes of farmers’ adoption of chemical pesticides and biopesticides, respectively. The observed household and farm-specific characteristics affecting farmers’ decisions to use biopesticides and chemical pesticides are captured by ${\mathsf{{\rm X}}}_1$ and ${\mathsf{{\rm X}}}_2$, respectively. The corresponding parameters to be estimated are denoted by ${\beta }_1$ and ${\beta }_2$. The unobservable error terms ${\epsilon }_1$ and ${\epsilon }_2$ follow a two-dimensional normal distribution with zero conditional means and can be specified as ${\epsilon }_1,{\epsilon }_2~\mathrm{BVN}\left(0,0,1,1,\rho \right)$. Here, $\rho$ signifies the pairwise correlation coefficient between error terms ${\epsilon }_1$ and ${\epsilon }_2$ in relation to the farmers’ two pesticide adoption equations within the model.

$$\left(\begin{array}{c}{\epsilon }_1\\ {\epsilon }_2\end{array}\right)\tilde{N}\left\{\left(\begin{array}{c}0\\ 0\end{array}\right)\right.,\left.\left[\begin{array}{cc}1& \rho \\ \rho & 1\end{array}\right]\right\}$$

(4)

Theoretically, $\rho =0$ denotes that $y_b$ and $y_c$ are not correlated, suggesting that independent univariate probit models could be applied to estimate farmers’ decisions regarding $y_b$ and $y_c$. Conversely, $\rho \ne 0$ indicates a correlation between $y_b$ and $y_c$ [49,51]. Specifically, $\rho >0$ signifies that the two pesticide adoptions are complementary, and the application of one may depend on the other. In contrast, the adoptions are considered substitutional when $\rho <0$.

In addition, we further delineate the influencing factors of farmers’ adoption of two types of pesticides. Drawing upon previous studies, we categorize these factors into three sets of characteristics. The individual set comprises the household head’s age, education [7], risk preference [18,52], experience with rice–crayfish integrated system cultivation, extension attendance [53,54,55], and perception concerning food safety and the environment [56,57,58]. The household set includes farm size [59], agricultural income, financial status, and agricultural labor within the household. The farmland set encompasses pest outbreaks [60] and specific farmland characteristics [59]. Detailed definitions of the variables and the corresponding descriptive results can be found in

Table 2. Definition and statistics of variables.

Variable	Description	Mean	Std Dev.
Biopesticides use	Whether use biopesticides: 0 = no, 1 = yes	0.248	0.432
Chemical pesticides use	Whether use chemical pesticides: 0 = no, 1 = yes	0.826	0.379
Age	Age of household head (year)	54.83	8.896
Education	Education level of household head (year)	7.999	2.700
Risk preference	Attitude toward risk, range [0, 1]	0.369	0.382
Experience	Years of engagement in rice–crayfish integrated system	4.420	2.360
Agricultural extension	Participation in agricultural extension trainings (times)	1.956	2.191
Food safety perception	Concern for food safety: Likert 1–5 points, 1 = very unimportant, 5 = very important	3.135	0.894
Environmental perception	Aware about environmental protection: Likert 1–5 points, 1 = very unimportant, 5 = very important	3.735	0.835
Farm size	Household’s total farmland (hectare)	2.356	2.695
Agricultural income	Proportion of agricultural income to the total family income (%)	0.538	0.307
Financial status	Proportion of owned finance to the total investment in 2020 (%)	93.15	18.91
Agricultural labor	Household agricultural labor (person)	1.937	0.592
Cooperative membership	Whether join any agricultural cooperation: 0 = no, 1 = yes	0.226	0.419
Pest outbreak	Whether experienced pest outbreak in 2020: 0 = no, 1 = yes	0.283	0.451
Land contiguous condition	Farmland operation in continues area: 0 = no, 1 = yes	0.413	0.493
Jingzhou city	The sample RCIS farmer in Jingzhou city: 0 = no, 1 = yes	0.655	0.476

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3. Results and Discussion

3.1. Farmers’ Mixed Use of Chemical Pesticides and Biopesticides

Figure 2 displays the descriptive statistics of farmers’ pesticide application. Among the RCIS farmers in the sample, 10.5% utilize both biopesticides and chemical pesticides concurrently. Those solely relying on chemical pesticides account for 72.13%, while farmers exclusively using biopesticides constitute approximately 14.27%. Additionally, 3.1% of the sampled RCIS farmers have abstained from using any pesticides. However, a discrepancy is observed in the descriptive statistics shown in Table 2, where 24.8% of the sample farmers are recorded as adopting biopesticides, and the remaining 82.6% are found using chemical pesticides. Consequently, a one-dimensional focus on either enhancing biopesticides’ use or curtailing chemical pesticides’ use may overlook the combined utilization of chemical pesticides and biopesticides by farmers.

3.2. Relationship between Biopesticides and Chemical Pesticides Adoption and Their Influencing Factors

The results of the multivariate probit (MVP) model estimation are presented in

Table 3. Multivariate probit simulation results for farmers pesticide use.

	Model (1)		Model (2)		Model (3)		Model (4)
	Bio	Chemical	Bio	Chemical	Bio	Chemical	Both Type
	Coeff (S.E)	Coeff (S.E)	Coeff (S.E)	Coeff (S.E)	Coeff (S.E)	Coeff (S.E)	Coeff (S.E)
Farm size	−0.091 *** (0.032)	0.088 *** (0.033)					−0.069 ** (0.033)
Age	−0.006 (0.007)	0.006 (0.007)	−0.009 (0.009)	0.007 (0.008)	0.002 (0.012)	0.019 (0.013)	−0.007 (0.007)
Education	0.039 * (0.022)	0.009 (0.023)	0.058 ** (0.026)	0.021 (0.027)	0.006 (0.041)	−0.013 (0.039)	0.004 (0.023)
Risk preference	−0.031 (0.158)	0.109 (0.162)	−0.112 (0.197)	0.101 (0.184)	−0.071 (0.272)	0.353 (0.298)	−0.028 (0.172)
Experience	0.029 (0.023)	−0.042 * (0.023)	0.000 (0.034)	−0.028 (0.031)	0.021 (0.033)	−0.039 (0.035)	0.035 (0.022)
Agricultural extension	0.059 ** (0.026)	−0.011 (0.027)	0.096 *** (0.034)	−0.018 (0.034)	0.009 (0.040)	−0.016 (0.042)	0.032 (0.026)
Environmental perception	−0.001 (0.076)	−0.030 (0.082)	−0.124 (0.105)	0.095 (0.096)	0.154 (0.119)	−0.266 ** (0.127)	0.042 (0.082)
Food safety perception	0.187 *** (0.069)	−0.004 (0.071)	0.205 ** (0.089)	−0.016 (0.078)	0.130 (0.127)	0.040 (0.153)	0.078 (0.073)
Agricultural income	0.047 (0.195)	0.254 (0.218)	−0.121 (0.235)	0.238 (0.236)	0.355 (0.396)	0.899 ** (0.443)	−0.273 (0.220)
Financial status	−0.003 (0.003)	0.002 (0.003)	−0.006 (0.004)	0.001 (0.004)	0.001 (0.005)	0.002 (0.005)	−0.005 (0.003)
Agricultural labor	−0.080 (0.090)	0.115 (0.097)	−0.067 (0.108)	0.092 (0.103)	−0.102 (0.166)	0.526 *** (0.201)	−0.047 (0.096)
Cooperative membership	−0.370 ** (0.153)	0.135 (0.149)	−0.202 (0.199)	0.080 (0.190)	−0.467 ** (0.234)	0.306 (0.256)	−0.078 (0.153)
Pest outbreak	0.236 * (0.131)	−0.153 (0.132)	0.309 * (0.162)	−0.122 (0.149)	0.072 (0.234)	−0.201 (0.277)	0.195 (0.137)
Land condition	0.021 (0.119)	0.250 ** (0.124)	−0.060 (0.143)	0.314 ** (0.147)	0.058 (0.214)	0.094 (0.218)	−0.103 (0.129)
Jingzhou city	−1.488 *** (0.124)	0.760 *** (0.124)	−1.744 *** (0.155)	0.760 *** (0.151)	−1.015 *** (0.203)	0.711 *** (0.229)	−0.772 *** (0.123)
Constant	−0.098 (0.658)	−0.400 (0.750)	0.821 (0.829)	−0.829 (0.837)	−1.638 (1.116)	−1.080 (1.336)	−0.102 (0.688)
Log pseudolikelihood	−532.828		−365.458		−146.229		−272.844
Wald test	201.459		174.449		54.080		50.417
$\rho$	−0.822 (0.041)		−0.801 (0.052)		−0.905 (0.034)
N	736		499		237		736

Robust standard errors in parentheses, *** p < 0.01, ** p < 0.05, and * p < 0.1. Joint probability of success: 0.123. Joint probability of failure: 0.041.

. Model (1) encompasses the estimation derived from the full sample set. To further test the robustness of the MVP, two methods were employed to validate the findings: group regression and the binary probit model. Model (2) and Model (3) represent estimated results, focusing on sub-samples of smallholder farmers and large-scale farmers, respectively. In accordance with the World Bank’s definition of smallholders, farms with a size of fewer than 2 hm² (30 mu in Chinese units) are classified as small-scale farmers [61]; otherwise, they are regarded as large-scale farmers. In Model (4), we redefine $y_{bc}=1$ to signify farmers using both chemical pesticides and biopesticides simultaneously, with $y_{bc}=0$ otherwise. Model (4) specifically utilizes the binary probit model.

In Model (1), the likelihood ratio test statistics, with Chi2(1) = 180.389 and p-value = 0.0000, refute the null hypothesis positing no correlation between the unobserved error terms across the two pesticide use equations. This finding confirms the interdependent relationship between farmers’ adoption of chemical pesticides and biopesticides. Consequently, the application of the MVP model is deemed appropriate. Analogous results were also observed in the group regression analyses of Model (2) and Model (3).

Furthermore, the $\rho$ value in Model (1) is −0.844, statistically significant at the 1% level, signifying a significant substitutional relationship between farmers’ adoption of chemical pesticides and biopesticides. For the subsamples analyzed in Model (2) and Model (3), representing small farmers and large farmers, respectively, the $\rho$ values are −0.846 and −0.925, both negative and statistically significant at the 1% level. These outcomes confirm the hypothesis of a substitution relationship between the utilization of chemical pesticides and biopesticides for both smallholder and large-scale farmers. Such a pattern might be attributed to the fact that farmers primarily deploy pesticides for pest control, often overlooking other production purposes such as improving food safety and mitigating harmful environmental consequences. This tendency is particularly pronounced in China, where farmers generally lack knowledge about biopesticides application techniques [62] and sufficient technical guidance for proper implementation [63]. Other studies have revealed that 68.3% of sampled farmers would prefer to use biopesticides if they offer a similar controlling effect to chemical pesticides [64]. Nevertheless, our studies have not identified a complementary relationship between the use of different pesticides. In China, chemical pesticides still dominate agricultural production [65], with biopesticides accounting for merely 10% of the market share, far below the 25% to 60% range prevalent in developed countries (Data from Agrochemical Information Net: http://jsppa.com.cn/news/jingji/6174.html, accessed on 20 January 2022). Few farmers use biopesticides as complements to chemical pesticides, especially with the escalating labor costs. Since biopesticides are sensitive to abiotic factors like ultraviolet radiation and high temperatures, their efficacy is often unstable, necessitating multiple applications throughout a single growing season [66], thus increasing farmers’ production costs and labor inputs. Hence, no significant complementary relationship was observed in our study regarding farmers’ mixed use of chemical pesticides and biopesticides.

Based on the results from Model (1) through Model (4), an in-depth exploration into the influencing factors of farmers’ pesticide adoption can be found in Table 3. With regard to household characteristics, the education level of the household head was found to positively influence the adoption of biopesticides. This suggests that farmers with a higher education level are more likely to use biopesticides than those who are less educated. Such an observation might be attributed to the fact that better-educated farmers have mastered more knowledge concerning variables such as light and temperature, allowing them to appropriately implement biopesticides. This result aligns with the study by Abtew, Niassy [7], which reveals that education significantly contributes to farmers’ understanding of grain legume pests. On the contrary, cultivation experience with RCIS has a negative and significant effect on farmers’ adoption of chemical pesticides. This is plausible, as farmers engaged with RCIS for years have acquired more knowledge about pesticide usage, thus mitigating the likelihood of chemical pesticides’ overuse. The study’s findings also uncover that farmers attending more agricultural extensions are more inclined to adopt biopesticides. This inclination may be traced back to the dissemination of knowledge and technical guidance offered by agricultural extension services, enhancing farmers’ comprehension and practical capabilities regarding biopesticides. This finding resonates with the study of Huang, Li [53]. Additionally, the analysis denotes that food safety perception exerts a positive and significant influence on farmers’ biopesticide adoption, a sentiment echoed in the study by Bagheri, Emami [56].

Farm size exhibits a positive and significant association with farmers’ adoption of chemical pesticides, while exerting a negative and significant effect on biopesticide adoption. This trend can be attributed to the fact that biopesticides are generally more expensive than chemical pesticides [14]. Consequently, farmers managing larger scales would face higher costs and potentially unstable pest control efficacy when utilizing biopesticides. This uncertainty subjects large-scale farmers to heightened economic risks, thus negatively impacting their propensity to adopt biopesticides. In contrast, chemical pesticides, owing to their mass production, are often associated with higher efficacy and lower cost, aiding large-scale farmers in stabilizing production output and controlling production expenses. Furthermore, the reduction in labor input stands as an undeniable advantage of chemical pesticides in pest control [31]. Such evidence is corroborated in other studies as well. For example, Huang, Luo [17] demonstrated that farmers tended to apply biopesticides to self-consumption fields and chemical pesticides to fields yielding products for sale. In addition, farmers with smaller farmland are usually less constrained by labor input, enabling them to utilize biopesticides as alternatives. Similar findings are documented in the study by Hu and Rahman [67]. Interestingly, cooperative membership is negatively correlated and has a significant effect on farmers’ biopesticide adoption. This result contradicts the findings of Zheng, Guo [14], who concluded that organizations voluntarily formed by farmers are more inclined to use biopesticides through collective implementation to overcome high costs and complex application techniques. Furthermore, the results also indicate that households experiencing pest outbreaks are more likely to adopt biopesticides. This may be a result of farmers’ concerns regarding pest resistance. Considering that pest resistance to pesticides is a concurrent major issue [3], the application of biopesticides can diminish the incidence of such resistance [15].

3.3. Farmers’ Concerns for Pesticides Attribute and Application

To better comprehend farmers’ behaviors in adopting pesticides, we analyzed their concerns regarding pesticide attributes and applications. In accordance with previous studies, aspects such as insecticidal efficacy, toxicity, insecticidal spectrum, and valid period have been extensively explored [17,68]. Detailed results are presented in Figure 3 and Figure 4.

Figure 3 reveals that the majority of farmers regard insecticidal efficacy as the primary consideration, reflecting their foremost concern with yield loss due to pest infestation. Several studies have demonstrated the reduction of crop yield attributed to inadequate pest management strategies [69,70,71]. Thus, insecticidal efficacy emerges as farmers’ paramount concern. Specifically, 97.47% of mixed-use farmers exhibit concern about insecticidal efficacy, while only 62.97% of farmers adopting chemical pesticides consider it, a figure below the average for the sample farmers. Evidently, farmers utilizing both chemical and biological pesticides allocate a higher proportion to insecticidal efficacy as the primary consideration. Additionally, a segment of farmers includes toxicity in their choice of pesticides, with mixed-use pesticide adopters showing the least concern about toxicity. The insecticidal spectrum and valid period also rank among farmers’ priorities, accounting for 16.20% and 12.71% of the total sample farmers, respectively. This is attributed to the high correlation between insecticidal spectrum, valid period, and labor input. Given the constraints of the labor force and the high supervision and coordination costs of hired labor [72], a narrow insecticidal spectrum and a short valid period may expose farmers to the risk of production uncertainty, potentially exacerbating the burden on household labor. Notably, among them, farmers adopting biopesticides exhibit more concern about the insecticidal spectrum and valid period for pesticide use than the other two types of farmers. It should be highlighted that about a quarter of the sample farmers adopting chemical pesticides demonstrate no specific concern for pesticide use, indicating that they may apply pesticides indiscriminately.

Identifying critical periods for pesticide application, selecting appropriate types of pesticides, and adhering to the recommended application rates are not only vital for combating pests more effectively but also for mitigating health-related risks to the environment [73,74]. Therefore, we analyzed farmers’ preferences for pesticide application, focusing on variety, dosage, and timing. Figure 4 illustrates that in choosing pesticide varieties, most farmers rely on their experience, while salesmen’s advice also accounts for a significant proportion across all three types of farmers. Schreinemachers, Grovermann [57] found that farmers’ pesticide overuse was positively correlated with seeking advice from pesticide sellers. Regarding pesticide usage, 66% of chemical pesticide-using farmers determined dosages based on their cultivation experience, and about half of the mixed-use pesticide adopters referred to their neighbors’ usage. The study by Zhang, Li [58] also confirmed the influential role of neighbors in farmers’ pesticide utilization. Meanwhile, 18.9% of biopesticide-using farmers have followed agricultural technicians’ guidance, and only a small fraction adhered to the instruction labels across all three types. Evidence from Bagheri, Emami [68] further indicates that farmers rarely consult labels before pesticide application. Additionally, many farmers have not paid significant attention to the timing of pesticide use, especially those adopting chemical pesticides. They applied pesticides indiscriminately, often neglecting considerations for specific rice growth periods (e.g., seedling growth stage, tillering stage, and jointing stage). Approximately a quarter of mixed-use pesticide-adopting farmers would apply pesticides in the presence of severe diseases, insect pests, or weeds in the paddy field.

Regrettably, few pesticides in China possess the combined attributes of high insecticidal efficacy, broad-spectrum applicability for various crop types, low toxicity, and convenient operation. This scarcity may partially explain farmers’ mixed use of chemical pesticides and biopesticides, as only a combination of different pesticides can fulfill their diverse requirements. This observation has been corroborated by other studies as well. Srinivasan, Sevgan [69] demonstrated that vegetable farmers are more inclined to adopt biopesticides rather than chemical pesticides. Abtew, Niassy [7] documented that farmers in Kenya rotate the use of chemical pesticides and biopesticides to maintain insecticidal efficacy. Furthermore, the majority of the sampled farmers have not sought professional guidance from agricultural technicians, relying more on personal and neighbors’ cultivation experiences. This reliance may be one of the significant factors leading to farmers’ mixed use of pesticides, as the combined application aligns with their endowments and satisfies multiple requirements, particularly in the absence of sufficient professional technical guidance.

3.4. Consequences of Farmers’ Mixed Use of Pesticides

To investigate the consequences of farmers’ mixed use of pesticides, five result indicators were examined, specifically times of application, costs, rice yield, rice price, and rice output value, as depicted in Figure 5. The average values and error bar graph for these five outcomes across all three types of pesticide use can be observed in Figure 5.

Regarding pesticide input, statistical analysis reveals that farmers’ mixed use of pesticides significantly decreased the frequency of pesticide application. The average number of applications for farmers using chemical pesticides is 2.7 times, which reduces to 2.1 times for those employing a combination of chemical pesticides and biopesticides or solely utilizing biopesticides. This reduction may be attributed in part to biopesticides’ non-toxic mechanism, which controls pests while allowing beneficial insects to thrive in treated crops [75]. As a result, it reduces the need for frequent pesticide application. In terms of pesticide costs, mixed users incur a cost of CNY 2400 ha⁻¹, higher than the cost for separate adoption of biopesticides (CNY 1750 ha⁻¹) or chemical pesticides (CNY 2100 ha⁻¹). This observation has been supported by other studies reporting higher costs for biopesticides application [28,76,77].

In the context of rice output, though farmers using mixed pesticides command the highest rice selling price, their rice yield and output value are lower than those of farmers solely using chemical pesticides. Additionally, the performance of biopesticides-using farmers in yield, selling price, and output value is inferior to the other two categories of farmers. This may be due to the fact that single biopesticide adoption still involves technical application risks and may not achieve the desired pest control efficacy. Conversely, the application of chemical pesticides remains pivotal in sustaining grain yield [7]. Moreover, the larger error bar of the selling price for farmers adopting biopesticides illustrates that green production rice with fewer chemical residues has considerable market potential, appealing to consumers willing to pay higher prices [78].

4. Conclusions

The excessive use of chemical pesticides and its associated detrimental effects have been widely criticized. Biopesticides are considered a feasible alternative to chemical pesticides. However, due to differing attributes of these two types of pesticides, some farmers opt for a combination, depending on their production factor endowments and production objectives. Although the overuse of chemical pesticides and the use of biopesticides by farmers have been well-studied, little is known about their combined use, the interrelationships involved, and the ensuing consequences. Utilizing data from 723 farmers engaged in a rice–crayfish integrated system in Hubei province, China, we investigated the interdependent relationship between the mixed use of chemical and biopesticides, the influencing factors, and the associated outcomes. Our main findings are as follows.

Initially, our results demonstrated that mixed use of chemicals and biopesticides is not common among Chinese farmers, with approximately 10.50% of the sampled farmers using both types simultaneously. Secondly, a substitution relationship was verified between farmers’ adoption of chemical pesticides and biopesticides for pest control, but no significant complementary relationship was observed in our study. Thirdly, farmers placed significant emphasis on pesticide attributes such as insecticidal efficacy, toxicity, insecticidal spectrum, and the validity period. The decision on pesticide variety was primarily based on their experience, with 66% of the farmers using chemical pesticides determining the dosage based on their cultivation experience. Approximately half of the farmers employing mixed-use pesticides referred to their neighbors’ usage. However, many farmers did not adequately consider the timing of pesticide use, especially those applying chemical pesticides. Finally, farmers utilizing a combination of pesticides reduced the frequency of pesticide application but incurred increased costs. While mixed use of pesticides increased the selling price of rice, they did not improve yield or output value.

Given our study’s findings, we propose several policy implications. Firstly, chemical pesticides, due to their low cost and high efficacy in pest control, continue to predominate in agricultural production. Complete substitution of chemical pesticides with biopesticides is likely to take time, and the mixed use of these two may persist and even expand. Additional research should focus on the attributes of biopesticides, including their stable efficacy and lesser environmental sensitivity, which could reduce labor and costs for farmers. Consequently, the acceleration of biopesticide adoption and reduction of chemical pesticide use are encouraged. Ultimately, considering the challenges still hampering biopesticide application, it may not be feasible to solely rely on either biopesticides or chemical pesticides in the short term. More agricultural extension services should be provided, especially technical guidance for the proper application of biopesticides, advocating for a balanced use of biopesticides with existing chemical pesticides, thereby advancing pest management and promoting sustainable agriculture.