Introduction
The global population is surging, with projections estimating 11.2 billion people on Earth by the end of the twenty-first century (UN World Population Prospects, 2022; Bhattacharya et al., Reference Bhattacharya, Tripathy, Swain and Mitra2024). This rise places immense demands on our food production systems, making the Sustainable Development Goal of ‘Zero Hunger’ an elusive target and highlighting the urgency of rethinking our approaches to food security (FAO, 2022).
Population growth exacerbates environmental challenges, as urbanization, industrialization, deforestation, and the widespread use of synthetic pesticides and chemical fertilizers severely impact ecosystems and trigger significant climatic consequences through increased greenhouse gas emissions and reduced carbon sequestration, ultimately leading to global warming (Brundtland et al., Reference Brundtland, Ehrlich, Goldemberg, Hansen, Lovins, Likens, Lovelock, Manabe, May, Mooney and Robert2012). These activities deplete arable land, extract essential nutrients from soils, and contaminate soil and groundwater with harmful substances, resulting in soil fertility degradation, reduced agricultural productivity, declining groundwater levels, and deteriorating water quality (Sidabutar et al., Reference Sidabutar, Namara, Hartono and Soesilo2017). The looming threat to environmental sustainability and food scarcity necessitates innovative and alternative solutions to ensure the resilience of agricultural systems, leading to the emergence of organic food production and consumption as potential remedies (Wijerathna-Yapa and Pathirana, Reference Wijerathna-Yapa and Pathirana2022).
Pesticides and fertilizers have traditionally been employed to boost agricultural output, particularly since the advent of the Green Revolution; however, their over-dependence has resulted in substantial consequences beyond simply improvements in production (Kaur, Kumar and Kaushik, Reference Kaur, Kumar and Kaushik2024). According to the World Health Organization (WHO), an estimated 150,000 people die annually from pesticide poisoning, particularly affecting regions with low to moderate incomes (WHO, 2020; Zaller, Reference Zaller2020). Moreover, the indiscriminate application of pesticides and herbicides disrupts natural ecological processes, contaminating water sources and food supplies, posing significant risks to human health, and aggravating environmental degradation (FAO, 2015; Govindharaj et al., 2021).
In India, the Green Revolution introduced advanced agricultural technologies, including chemical pesticides, to boost production (Pimentel, Reference Pimentel1996). However, persistent use of these chemicals has posed significant threats to human health, the environment, and soil microbiota, leading to decreased productivity, especially in states such as Uttar Pradesh, Punjab, and Haryana (Mishra et al., Reference Mishra, Arya, Tyagi, Grover, Mishra, Vimal, Sarita and Sharma2021). Additionally, xenobiotic pesticides (synthetic organic compounds with pesticidal activity) exhibit prolonged degradation rates, leading to their accumulation in living organisms over time and increasing concentrations as they move up the food chain, causing biodiversity loss and groundwater contamination (Jeffries et al., Reference Jeffries, Rayu, Nielsen, Lai, Ijaz, Nazaries and Singh2018). Given India’s agriculture-dependent economy, sustainable approaches such as organic farming practices are required to address these challenges.
Organic farming practices, rooted in traditional agricultural systems, have gained significant attention for their adherence to stringent regulations prohibiting synthetic inputs. These practices, including intercropping, mulching, and crop-livestock integration, aim to maintain crop productivity while safeguarding the ecosystem (Deshmukh, Khobragade and Dixit, Reference Deshmukh, Khobragade and Dixit2007). Biopesticides, derived from natural sources, are increasingly recognized as viable alternatives for producing food that is both safe and free from chemicals, posing fewer risks to humans, animals, and ecosystems compared with chemical pesticides (Heredia-R et al., Reference Heredia-R, Torres, Vasseur, Puhl, Barreto and Diaz-Ambrona2022).
Despite their potential benefits, farmers’ use of biopesticides remains limited, with many studies indicating low adoption rates (Lalani et al., Reference Lalani, Dorward, Holloway and Wauters2016) with biopesticides accounting for just 10% of the global pesticide market (Marrone, Reference Marrone2024) and only 4.2% of the pesticide market in India (Chakraborty et al., Reference Chakraborty, Mitra, Pal, Ganguly, Acharya, Minkina, Sarkar and Keswani2023). This disparity highlights the significant gap in their widespread usage. Understanding the intentions of farmers’ decisions in adopting biopesticides is crucial for promoting their uptake. Factors such as awareness, availability, affordability, and perceived effectiveness play pivotal roles in influencing farmers’ adoption decisions (Gennari and Navarro, Reference Gennari and Navarro2019).
The theory of planned behavior (TPB) and health belief model (HBM) frameworks have demonstrated their applicability across various domains, including agriculture and environmental management (Ataei et al., Reference Ataei, Gholamrezai, Movahedi and Aliabadi2021; Kaur, Kumar and Kaushik, Reference Kaur, Kumar and Kaushik2024). However, there is a gap in research concerning farmers’ intentions to adopt biopesticides in India within the framework of these theories. Understanding the underlying factors influencing farmers’ decisions to adopt biopesticides can inform targeted interventions and policy measures to promote sustainable pest management practices in Indian agriculture.
Farmers often face economic uncertainties in adopting conservation practices or biopesticides, affecting their productivity and crop yield (Meneguzzo and Zabini, Reference Meneguzzo and Zabini2021). Integrating psychological and economic perspectives is advisable to fully comprehend the adoption process. Various evaluation frameworks, including TPB and HBM, have been proposed to bridge the gap between psychological and economic disciplines. The role of government initiatives on the behavioral intention of farmers toward the adoption of biopesticides is less explored to date. Furthermore, this integrated model gives deeper insights into how farmers’ beliefs and attitudes influence their intention to adopt biopesticides.
The Indian government promotes biopesticide adoption through initiatives such as the National Mission on Sustainable Agriculture and the Sub-Mission on Agroforestry, aiming to enhance agricultural sustainability and reduce reliance on chemical pesticides (Gupta and Modi, Reference Gupta and Modi2022). However, there are several gaps in government initiatives for biopesticide adoption in India, which include the need for stronger implementation strategies, targeted education campaigns, and financial incentives to encourage farmers to switch from conventional pesticides, alongside streamlined regulatory processes for biopesticide registration and support for research and development in this field (Balkrishna et al., Reference Balkrishna, Rana, Sharma, Sharma and Arya2022).
By integrating key elements from HBM, TPB, and government initiatives as independent variables, this research aims to provide a comprehensive understanding of the factors driving farmers’ intentions to adopt biopesticides. This combined approach acknowledges the multidimensional nature of farmers’ behavior and considers a range of cognitive and motivational factors that may influence decision-making processes toward the promotion of sustainable agriculture. Through empirical investigation and analysis, the study seeks to contribute valuable insights into the determinants of biopesticide adoption among farmers, thereby informing strategies and policymakers for promoting sustainable pest management practices in agriculture.
Theoretical framework and hypotheses
Theory of planned behavior
In TPB, an individual’s behavior is determined based on their behavioral intention (Ajzen, Reference Ajzen2002). TPB analyzes behavioral intention by considering three belief-based structures: behavioral beliefs, normative beliefs, and control beliefs (Manstead and Parker, Reference Manstead and Parker1995). TPB proposes that intentions are influenced by the perceptions of advantageous or disadvantageous circumstances, which can be derived from previous encounters or observations of others participating in the activity (Heuckmann, Hammann and Asshoff, Reference Heuckmann, Hammann and Asshoff2019).
Attitude (AT), Subjective Norms (SN), and Perceived Behavioral Control (BC) are the three variables that define behavioral intention (Ajzen, Reference Ajzen2002). AT reflects an individual’s overall evaluation of the behavior or product/service (Conner, Reference Conner2020). SN refers to the perception of whether others believe the individual should engage in the behavior, representing the perceived pressure from significant individuals (Ajzen, Reference Ajzen2020). PBC indicates a person’s assessment of the ease or difficulty of performing the behavior (Abraham and Sheeran, Reference Abraham and Sheeran2003).
TPB explains that individuals decide to engage in a behavior when they perceive it as beneficial, believe they have control over it (PBC), and anticipate acceptance from significant others (Zolait, Reference Zolait2014). Previous researchers have shown that AT, SN, and PBC positively influence farmers’ intentions and support the application of TPB to analyze farmers’ intention to use green pesticides (Bagheri et al., Reference Bagheri, Bondori, Allahyari and Damalas2019; Gowda et al., Reference Gowda, Sendhil, Adak, Raghu, Patil, Mahendiran, Rath, Kumar and Damalas2021). Sarma (Reference Sarma2022) found that subjective norms had a significant effect on farmers’ behavioral intentions regarding pesticide use in Bangladesh. Similarly, Despotović, Rodić and Caracciolo (Reference Despotović, Rodić and Caracciolo2019) reported that attitudes, subjective norms, perceived behavioral control, and farm size together explained 49% of the variance in farmers’ intentions to adopt integrated pest management (IPM) practices. In another study, Wang et al. (Reference Wang, Chu, Deng, Lam and Tang2018) observed that perceived behavioral control, behavioral attitude, and subjective norms all positively influenced farmers’ intentions to comply with pesticide application standards, with perceived behavioral control emerging as the most influential factor and subjective norms as the least. However, the complexity of decision-making and certain behaviors cannot be explained with TPB alone (Garmendia-Lemus et al., Reference Garmendia-Lemus, Moshkin, Hung, Tack and Buysse2024). Therefore, there is a need to incorporate it with other behavioral theories to understand it better.
Health belief model
HBM is a psychological framework that helps in understanding and predicting health-related behaviors (Rosenstock, Reference Rosenstock1974). The model has found application in various domains, including organic farming, consumption of organic food, and different food choices (Wang et al., Reference Wang, Guo, Huang, Tang, Li and Yue2024). The various components of HBM contribute to the maintenance or cessation of behavior and have the ability to alter an individual’s attitude (Anuar et al., Reference Anuar, Omar, Ahmed, Saputra and Yaakop2020). Furthermore, demographic and socioeconomic factors largely influence the consumers’ attitudes toward biopesticides and organic food (Kabir et al., Reference Kabir, Biswas, Rahman, Islam and Tan2024). Research conducted by Nyang’au et al. (Reference Nyang’au, Mohamed, Mango, Makate and Wangeci2021) in Uganda found that those who had greater levels of education and money were knowledgeable of the adverse impacts of chemical pesticide usage and showed an affinity for taking risks, demonstrating an increased willingness to pay for biopesticides. On the contrary, old age, larger family size, and reliance on other community members reduced willingness to pay for biopesticides.
HBM comprises four constructs: severity, benefits, challenges, and health motivation. Perceived severity refers to an individual’s belief about the seriousness or severity of a disease or health issue, which can influence their health habits, for instance, the adverse effect of conventional pesticides and fertilizers on human health and the environment (Boudh and Singh, Reference Boudh and Singh2018). Health motivation refers to an individual’s inclination to engage in certain health behaviors, which is affected by individual perspectives, modifying influences, and the possibility of taking action (Carter and Kulbok, Reference Carter and Kulbok2002; Chrysochou and Grunert, Reference Chrysochou and Grunert2014). The perceived benefits and barriers are essential in determining the adoption of certain behaviors (Jones and Nies, Reference Jones and Nies1996). People are more inclined to support a behavior if they see advantages or benefits from it; for instance, organic farming can improve soil quality, reduce water contamination, maintain ecological balance, and produce healthy crops (Das, Chatterjee and Pal, Reference Das, Chatterjee and Pal2020). Perceived barriers refer to the perceived challenges that limit an individual from certain behaviors that have otherwise many health benefits (Nickerson et al., Reference Nickerson, Byrow, Pajak, McMahon, Bryant, Christensen and Liddell2020). Overcoming perceived barriers is essential while promoting healthier alternatives like biopesticides (Gunasekera, Wickramaratne and Madhushika, Reference Gunasekera, Wickramaratne and Madhushika2024). Ataei et al. (Reference Ataei, Gholamrezai, Movahedi and Aliabadi2021) reported that the HBM framework was more effective than the TPB framework, with TPB accounting for 52.2% of the variance in the intention to use green pesticides, while HBM explained 61.2% of the variance. Due to the relationships between each component of HBM, it is necessary to do more investigation on the intention to adopt biopesticides.
Government initiatives in India
Organic farming has gained significant traction in recent years as a sustainable alternative to conventional agricultural practices, driven by increasing consumer demand for organic agricultural products (Tal, Reference Tal2018). In response to this trend, the Indian government has implemented several initiatives aimed at promoting organic farming across the country. One of the primary schemes is the Paramparagat Krishi Vikas Yojana (PKVY) and the Mission Organic Value Chain Development for North Eastern Region, which have been operational since the fiscal year 2015–16 (Reddy, Reference Reddy2018).
These initiatives provide comprehensive support to organic farmers, encompassing various aspects such as organic production, certification, and marketing, along with post-harvest management, including processing and packaging (Charyulu and Biswas, Reference Charyulu and Biswas2011). The introduction of the Bhartiya Prakritik Krishi Padhati as a subscheme of PKVY from the fiscal year 2020–21 underscores the government’s commitment to promoting traditional indigenous practices such as Natural Farming (Balkrishna et al., Reference Balkrishna, Arya, Bhat, Chaudhary, Mishra, Kumar, Sharma, Sharma, Sharma and Gautam2024). The emphasis on eliminating synthetic chemical inputs and encouraging on-farm biomass recycling reflects a holistic approach toward sustainable agriculture (Oberč and Arroyo Schnell, Reference Oberč and Arroyo Schnell2020).
The emphasis on promoting organic farming along the course of the Ganges River, as highlighted in the Union Budget of 2022, underscores the government’s recognition of the environmental significance of organic agriculture (Varaprasad and Rao, Reference Varaprasad and Rao2024). The Bihar government’s organic corridor scheme along the Ganges River exemplifies state-level initiatives aimed at leveraging natural resources for sustainable agricultural development (Kumar et al., Reference Kumar, Bhattacharya and Banerjee2025). Through the Organic Farming Corridor Scheme, the Bihar government incentivizes farmers to adopt organic farming practices by providing financial assistance and technical support. However, the lack of specified criteria for selecting organic corridors along the Ganges River suggests a need for greater clarity and transparency in the implementation of such schemes (Shinde et al., Reference Shinde, Mishra, Bhonde and Vaidya2023). The government’s concerted efforts to promote organic farming in India through various schemes and initiatives include providing financial incentives and technical assistance and promoting indigenous farming practices. These initiatives aim to facilitate the transition toward sustainable agriculture and address the growing demand for organic agricultural products (Bisht, Rana and Pal Ahlawat, Reference Bisht, Rana and Pal Ahlawat2020).
Hypotheses
This research aims to analyze the impact of the TPB and HBM constructs, along with government initiatives as independent constructs, on the intentions of farmers to adopt biopesticides. To map the intention, the following hypotheses were framed:
H1: The perceived severity of pesticides significantly and positively influences farmers’ attitudes toward the use of biopesticides.
H2: Health motivation has a significant positive influence on farmers’ attitudes toward the use of biopesticides.
H3: The perceived benefits have a significant positive influence on farmers’ attitudes toward the use of biopesticides.
H4: Perceived challenges have a significant negative influence on farmers’ attitudes toward the use of biopesticides.
H5: Attitude (AT) has a significant positive influence on farmers’ intention to use biopesticides.
H6: Perceived behavioral control (BC) has a significant positive influence on farmers’ intention to use biopesticides.
H7: Subjective norms (SN) have a significant positive influence on farmers’ intention to use biopesticides.
H8: Government initiatives have a significant positive influence on farmers’ intention to use biopesticides.
H1–H4 were developed to understand the effect of these constructs on the attitude of farmers. Further, to gain a more detailed understanding of the psychological intention of farmers to adopt biopesticide, H5–H7 were developed. The independent construct, government initiatives, was tested in H8 to understand its direct effect on the intention of farmers to adopt biopesticides (Fig. 1).
Hypothesized model integrating health belief model, theory of planned behavior, and government initiatives.
Materials and methods
Sample size and survey location
The survey was carried out over a 5-month period from May to September 2024. The questionnaires were completed in face-to-face interviews. Based on the sample size estimation by Bartlett, Boucheron and Lugosi (Reference Bartlett, Boucheron and Lugosi2002), a comprehensive dataset of 540 farmers’ responses were collected from individuals across 4 districts (Fig. 2)—Patna, Bhagalpur, Purnea, and Saharsa—and 10 blocks: Punpun, Phulwari, Bakhtiarpur, Danapur, Pirpainti, Kahalgaon, Sultanganj, Sabour, Bhawanipur, and Mahishi in the state of Bihar, India. According to the principle of random stratified sampling, 5–7 villages were selected from each block, and 10–15 farmers were selected from each village. However, the data underwent further refinement by excluding multivariate outliers (data points that deviate significantly from the overall pattern of the data across multiple variables, identified by employing the Mahalanobis distance criterion following the guidance of Mullen, Milne and Doney, Reference Mullen, Milne and Doney1995; Kock and Hadaya, Reference Kock and Hadaya2018) and missed responses, resulting in a final sample size of 468 respondents (Table 1). These outliers were excluded because they can disproportionately influence statistical results, skewing the analysis and potentially leading to inaccurate conclusions.
Map of the survey area (Bihar, India).
Samples survey area
Research instrument
This study employed a cross-sectional survey design to understand the farmers’ behavioral intentions toward the adoption of biopesticides. This design allowed us to assess the sociodemographic characteristics, attitudes, and behavioral intentions of farmers in Bihar during the study period to provide an understanding of adoption patterns and influencing factors. To examine the hypotheses of this study, data were collected using a structured questionnaire designed to assess farmers’ behavioral intentions toward biopesticide adoption. The questionnaire items were sourced from prior research studies, and government initiatives as an independent variable were self-developed. The questions within the questionnaire were rated on a 5-point scale (Likert, Reference Likert1932). Additionally, we gathered sociodemographic information from the respondents, covering parameters such as age, gender, education level, family size, annual family income, land holding size, types of crop-grown farming systems, and whether they know about biopesticides and the way of application of pesticides and primary crop protection issues were also collected.
To ensure the validity of the questionnaire, a panel of eight experts from diverse backgrounds, including environmental studies, rural development, agriculture, plant protection, organic farming, market research, and management, reviewed the content. To further ensure reliability, a pilot study was conducted with 40 farmers through face-to-face interviews. This pilot study aimed to test the clarity and comprehensibility of the items. Based on the feedback, minor revisions were made to improve clarity and focus on the behavioral intention aspects of biopesticide adoption.
The Cronbach alpha values for the constructs in the pilot study were above 0.70 in most cases, indicating good internal consistency and confirming the reliability of the instrument. This provided a reliable and valid basis for assessing farmers’ behavioral intentions toward adopting biopesticides in the study region. The constructs adopted from different literature are enlisted in (Table 2). This comprehensive table provides an overview of the constructs and their respective details on behavior intention toward the adoption of biopesticide (Table A1 in the Appendix). It is a 5-point Likert Scale where the response can vary in the following manner: 5 = Strongly Agree, 4 = Agree, 3 = Neutral, 2 = Disagree, 1 = Strongly Disagree.
Constructs in the survey
Data analysis
A descriptive study of the sociodemographic profile of the farmers was conducted using EXCEL STAT software. Further, we employed the partial least squares structural equation modeling (PLS-SEM) method for data analysis using the SmartPLS software package version 4. The PLS-SEM technique, based on the PLS regression method, was used to examine the structural model. Out of the total 540 respondents collected, 468 samples met all the requirements for conducting structural equation modeling (SEM) and were considered suitable for further analysis (Nunnally and Bernstein, Reference Nunnally and Bernstein1978; Hair et al., Reference Hair, Black, Babin, Anderson, Black and Anderson2018).
Results
Sociodemographic profile
The survey participants’ demographic characteristics have been evaluated and presented in Table 3. According to the data, the majority of the respondents, specifically 78.2%, were male, while the remaining 21.79% reported as female. The age group of 31–60 years accounted for a substantial majority, nearly 65%. In terms of education, the majority of respondents have educational qualifications below intermediate (50.68%). Out of the total number of farmers, 61.32% had an annual income below 1 lakh, while around 53.2% of families consisted of four to six family members. In addition, the data indicated that 32.48% of the participants had been engaged in farming for over 30 years. A substantially higher proportion of farmers (47.86%) possess little landholding, consisting of less than 3 acres of agricultural land. When questioned about their yearly spending on pesticide application, 35.04% of the respondents reported spending between Rs.10,001 and Rs.30,000 on their farms. These data offer a comprehensive summary of the characteristics of the survey participants.
Sociodemographic profile and descriptive statistics of respondents (N = 468)
The data reveal an impressive majority of respondents (59.40%) practice organic farming, followed by 33.11% practicing integrated farming and 7.47% practicing conventional farming. The average value of 2.256 reflects that, on average, respondents are going toward organic and integrated farming systems, with a deviation of 0.584 indicating some variability in responses. Regarding pesticide usage frequency, 56% reported using pesticides frequently throughout the year due to crop losses, with around 95% experiencing losses due to pest attacks in the past 3 years (Table 4). The frequency of pesticide use on farms varies. A total of 56.41% of farmers use pesticides frequently, followed by 26.92% of farmers using pesticides occasionally. Additionally, 59.4% used biopesticides and 33.11% practiced IPM for crop protection, indicating a significant shift away from chemical pesticides. Furthermore, 92.5% of respondents were aware of biopesticides, and 82.47% held organic certification, reflecting a strong interest and adoption of organic farming practices for sustainable crop cultivation. The results indicate that 98.29% of farmers are familiar with biopesticides. In terms of actual usage, 92.52% of farmers have used biopesticides in their farming practices. This widespread adoption of biopesticides reflects a growing trend toward more sustainable and eco-friendly agricultural practices.
General information and descriptive statistics of farming systems
Graphical path analysis
SEM examines intricate associations between variables in research models. An analysis was conducted on the constructs, examining their interrelationships, significance, reliability, and effects on observable variables. The implications of each construct were also examined. The study performed reliability tests, such as Cronbach’s alpha (Cronbach, Reference Cronbach1951) and composite reliability, to assess the initial hypotheses that the items exhibit significant internal consistency within each construct (Fig. 3). The results, as shown in Table 5, confirm the assumptions, with most items demonstrating substantial internal consistency (α > 0.7) and (CR > 0.7) across the majority of constructs (Hair et al. Reference Hair, Black, Babin, Anderson and Tatham2006; Hair et al. Reference Hair, Hult, Ringle, Sarstedt, Danks and Ray2021). This validation highlighted the strong internal coherence of the majority of items in the survey. The Cronbach alpha value for perceived benefits (α = 0.624) in the HBM model, behavioral control (α = 0.646), and attitude (α = 0.683) in the TPB model was below the recommended threshold. Nevertheless, research with reliability coefficients above 0.6 (α > 0.6) also demonstrated a satisfactory degree of internal reliability (Kimberlin and Winterstein, Reference Kimberlin and Winterstein2008; Taber, Reference Taber2018). Hence the reliability of the different constructs in the integrated HBM, TPB, and government initiatives model was moderately accurate.
Structural equation modeling of the integrated health belief model, theory of planned behavior, and government initiatives.
Measurement model assessment
The evaluation of the measurement model, which included tests for item reliability, internal consistency reliability, and validity test, is mentioned in Table 5. Factor loadings were used to measure the reliability of an item, whereas composite reliability was employed to examine internal consistency reliability. Convergent validity was assessed using the average variance extracted (AVE).
Most of the factor loadings in this study except BC3 are above the threshold value of 0.5, determined by Hair, Ringle and Sarstedt (Reference Hair, Ringle and Sarstedt2013). The lower value for the factor loading of BC3 can possibly be due to the fact that, with increasing sample size, the threshold for factor loading gets reduced as mentioned by Hair et al. (Reference Hair, Risher, Sarstedt and Ringle2019). According to the guideline, the factor loading acceptable for a 60 sample size is 0.7, whereas it is 0.35 for a 250 sample size, and may be reduced further with an increased sample size (Afthanorhan, Awang and Aimran, Reference Afthanorhan, Awang and Aimran2020).
The composite reliability values for most constructs—except PB = 0.635, BC = 0.505, and AT = 0.697—were above the minimum allowed criterion of 0.7, and the AVE values were above the suggested threshold of 0.5, except for AT = 0.440 (Anderson and Gerbing, Reference Anderson and Gerbing1988). In spite of the lower AVE value observed in AT, the composite reliability value was higher than 0.6; therefore, the construct was not removed in this study (Fornell and Larcker, Reference Fornell and Larcker1981; Hair et al., 2010).
The R 2 value for attitude is 0.540, and for behavioral intention, it is 0.590, showing a reasonable predictive ability of the model for the dependent variable. Fifty-four percent of the changes in the AT of the HBM model were influenced by PB, HM, PC, and PS, whereas 59% of BI in the TPB model was influenced by SN, AT, BC, and the direct effect of GI. This value was found to be substantial to moderate according to Hair, Ringle and Sarstedt (Reference Hair, Ringle and Sarstedt2013).
Discriminant validity
Heterotrait–Monotrait ratio of correlations
Discriminant validity is a crucial aspect of research technique as it ensures that constructs that should not be associated are clearly separate from each other. The heterotrait–monotrait (HTMT) test is often used to evaluate discriminant validity. Kline (Reference Kline2023) suggested that the HTMT value should not exceed 0.85, while Gold, Malhotra and Segars (Reference Gold, Malhotra and Segars2001) offer a slightly higher threshold of 0.90 to establish discriminant validity. It is crucial for all HTMT values to be below the suggested threshold. Values above 0.9 suggest that the correlations between items within the constructs are lower compared with the correlations with items from other constructs (Table 6). The majority of the HTMT values in the study were within the norm, with the exception of BI → HM = 0.922, above the criterion of >0.90. By adhering to the specified threshold, the discriminant validity evaluation was made more robust, which in turn increased the credibility and validity of the study findings.
Heterotrait–monotrait ratio of correlations
Fornell–Larcker criterion
Evaluating the discriminant validity of the several conceptions being studied is of crucial significance (Aiken, Reference Aiken2002). Discriminant validity is important since it verifies that the constructs under investigation are separate from each other and do not merely measure the same underlying notion (Park, Lee and Chae, Reference Park, Lee and Chae2017). Moreover, the use of the Fornell–Larcker criterion has become a well-recognized method for assessing discriminant validity (Henseler, Ringle and Sarstedt, Reference Henseler, Ringle and Sarstedt2015; Rigdon, Reference Rigdon2012). The Fornell–Larcker criterion offers a quantitative approach to assessing the uniqueness of each component in a study. This criterion involves comparing the diagonal values in each construct with the values found in their respective rows and columns. If the diagonal values greatly exceed the values in the rows and columns, it suggests that each construct has a distinct variance that is not shared with other constructs in the study (Lin, Hung and Chen, Reference Lin, Hung and Chen2009). The Fornell–Larcker criterion assists researchers in assessing the distinctiveness of the constructs they are investigating (Arthur et al., Reference Arthur, Arkorful, Salifu and Abam Nortey2023). The Fornell–Larcker criterion is highly valuable for evaluating the accuracy of measurement models and research instruments (Aiken, Reference Aiken2002). By using this criterion, researchers may be certain of the efficacy of their measurement model in precisely representing the desired theoretical components (Table 7). This criterion also strengthens the credibility of the research instrument used in the study.
Fornell–Larcker criterion
Model assessment
All eight hypotheses formulated in the study exhibited statistically significant effects on behavioral intention, as evidenced by p-values below 0.05 (Table 8). The hypotheses that demonstrated an overall positive effect on attitude and behavioral intention toward the adoption of biopesticides are outlined as follows: Hypothesis 1 (H1) posited that perceived severity (PS) significantly and positively influences farmers’ attitudes toward the use of biopesticides, with the corresponding values of t = 10.908, VIF = 1.395, and p < 0.05. Similarly, Hypothesis 2 (H2) suggested that health motivation has a significant and positive impact on farmers’ attitudes toward biopesticide usage, with the values of t = 1.740, VIF = 1.002, and p < 0.05.
Model assessment
Furthermore, Hypothesis 3 (H3) proposed that perceived benefits have a significant positive influence on farmers’ attitudes toward biopesticide usage, with the values of t = 7.220, VIF = 1.399, and p < 0.05. Similarly, Hypothesis 4 (H4) indicated that perceived challenges play a significant and negative role in shaping farmers’ attitudes toward biopesticide usage, with the values of t = 5.101, VIF = 1.277, and p < 0.05. Moreover, the study found that attitude (AT) significantly and positively impacts farmers’ intention to use biopesticides, with the values of t = 5.101, VIF = 1.399, and p < 0.05. Additionally, perceived behavioral control (BC) significantly and positively affects farmers’ intention to use biopesticides, with the values of t = 3.029, VIF = 1.537, and p < 0.05, as per Hypothesis 6 (H6).
Furthermore, subjective norms (SN) exert a significant and positive influence on farmers’ intention to use biopesticides, with the values of t = 36.696, VIF = 1.021, and p < 0.05, aligning with Hypothesis 7 (H7). Lastly, Hypothesis 8 (H8) suggested that government initiatives significantly and positively influence farmers’ intention to use biopesticides, with the values of t = 2.512, VIF = 1.493, and p < 0.05; all relevant constructs surpassed the threshold values in terms of t-values, indicating a positive significant relationship with behavioral intention to adopt biopesticide products. Constructs with p-values below 0.05 exert a statistically significant effect on attitude and behavioral intention, leading to the rejection of the null hypotheses from H1 to H8.
Discussion
The growing global concerns surrounding the environmental and health impacts linked with conventional agricultural practices have prompted farmers toward organic farming. Concurrently, the adoption of biopesticides in agricultural practices has gained momentum owing to their potential benefits for the environment and human health (Essiedu, Adepoju and Ivantsova, Reference Essiedu, Adepoju and Ivantsova2020). However, despite this worldwide trend, the adoption of organic farming in India has been relatively slow. Therefore, this study aimed to unravel the factors influencing farmers’ attitudes and intentions to embrace organic farming and the adoption of biopesticides within their farming systems.
In India, small and marginal farmers constitute a significant majority, accounting for over 86.2% (Behera and France, Reference Behera and France2016). Additionally, Bihar, a state in India, exhibits a concerning poverty index, with over 50% of its population affected, as per the NITI Aayog National Multidimensional Index Report 2023. The ongoing research revealed that more than 32% of farmers have been practicing agriculture for over 30 years. This enduring engagement can be attributed to the traditional passing down of agricultural practices through generations, with farming being the primary occupation (Brodt, Reference Brodt2001). Moreover, there is a notable prevalence of low education rates among the respondents, with roughly one-fifth of them reported as illiterate possibly because they started farming at a very young age. The concerning aspect arises from the fact that around 60% of respondents reported a family income of less than 1 lakh, which is alarming. Despite such low incomes, many respondents reported having large family sizes (Kumar et al., Reference Kumar, Meena, Sharma, Poddar, Dhalliwal, Modi-Satish Chander Modi and Singh2011). This situation calls for government intervention and emphasizes the urgent need for increased awareness among them regarding population control, education, and alternative methods in farming to increase income.
A significant number of respondents reported that their primary farming system is organic and integrated farming. This trend may be attributed to the implementation of the ‘Organic Corridor Scheme’ by the Government of Bihar across 13 districts. Notably, a majority of respondents in our survey hailed from two of these districts: Bhagalpur and Patna. This underscores the impactful role of government initiatives in enhancing farmers’ awareness of organic farming practices. To further explore the influence of government initiatives on farmers’ intentions, the TPB and HBM models were integrated, with a control variable of Government Initiatives (GI). Given the necessity of validating models integrating various components, it becomes imperative to address the research gap identified in prior studies, which have not comprehensively examined the impact of all factors on the intention to use biopesticides (Moser et al., Reference Moser, Pertot, Elad and Raffaelli2008). Insights derived from this research serve as invaluable guidance for devising effective strategies and interventions aimed at the use of biopesticides among farmers.
Previous research has not thoroughly investigated the influence of all factors on an intention to use biopesticides, making this research innovative in its approach. By combining both TPB and HBM models, along with the effect of government initiatives, the predictive power of these models has been enhanced, particularly in the context of biopesticide adoption. Overall, the findings add to the existing literature that connects HBM factors to the usage of biopesticides. It also indicates that combining different theoretical frameworks can improve our understanding of farmers’ intentions to adopt sustainable agricultural methods.
The study’s findings revealed that many factors have a major impact on farmers’ inclination to employ biopesticides. The variables encompassed are perceived severity, benefits, motivation, attitude, subjective norms, and government initiative. Studies by Ali et al. (Reference Ali, Man, Farrah and Omar2020), Mwalupaso et al. (Reference Mwalupaso, Wang, Xu and Tian2019), and Gwara, Wale and Odindo (Reference Gwara, Wale and Odindo2022) have concluded that these constructs play a crucial role in influencing behavioral intention across different contexts.
As shown in Table 8, the HBM model reveals that the perceived severity exerts the highest positive and significant effect on farmers’ attitudes toward biopesticides. This suggests that farmers possess a keen awareness of the adverse impacts of excessive chemical fertilizer and pesticide use on the environment and agricultural productivity, thereby shaping their attitudes. This finding contradicts a study by Ataei et al. (Reference Ataei, Gholamrezai, Movahedi and Aliabadi2021), which reported no significant effect of perceived severity on the attitude of farmers in Iran. The disparity may develop from the robust promotion of organic and natural farming by the Government of India through various schemes and programs, fostering awareness among farmers nationwide regarding the harmful effects of chemical fertilizers and pesticides (Sarangthem et al., Reference Sarangthem, Haldhar, Mishra and Thakuria2023). Similarly, research conducted in Northern India highlights the positive and significant role of perceived severity in influencing the attitudes of vegetable and fruit farmers (Kaur, Kumar and Kaushik, Reference Kaur, Kumar and Kaushik2024).
The subjective norm exhibited the most substantial positive impact on farmers’ behavioral intention in the TPB model, suggesting the role of friends, family, other farmers, local agro-dealers, and TV to adopt biopesticides, which is consistent with prior research findings (Govindharaj et al., 2021; Kaur, Kumar and Kaushik, Reference Kaur, Kumar and Kaushik2024). In this context, it can be proposed that individuals, influenced by social dynamics, endeavor to fulfill the expectations of others. Hence, farmers’ perceptions of what constitutes appropriate behavior, as endorsed by their social circle, may significantly shape their decision-making processes (Martínez-García et al., Reference Martínez-García, Ugoretz, Arriaga-Jordán and Wattiaux2015). Our finding is supported by Lopes, Viriyavipart and Tasneem (Reference Lopes, Viriyavipart and Tasneem2020), which underscores the significance of social influence and peer pressure in shaping behavioral norms related to crop residue management in India. However, a study conducted in Syria reported a nonsignificant role of subjective norms on the behavioral intention of vegetable farmers to adopt organic farming (Issa and Hamm, Reference Issa and Hamm2017). Additionally, similar to our findings, a study conducted by Bai, Wang and Gong (Reference Bai, Wang and Gong2019) on a predominantly illiterate rural population highlighted the significant influence of subjective norms on their behavioral intentions. Therefore, we can suggest the direct role of literacy in subjective norms. In context to our findings, the study was conducted in Bihar, where the literacy rate is below the national average (Mehrotra and Kumar, Reference Mehrotra and Kumar2024). Lower literacy levels likely amplified the role of peer pressure and social influence in farmers’ decisions to adopt biopesticides. In addition, in places where formal education and access to independent information are limited, farmers often rely more heavily on their peers and community networks for guidance, especially when adopting relatively new or unfamiliar technologies like biopesticides (Fusar Poli et al., Reference Fusar Poli, Campos, Martinez Ferrer, Rahmouni, Rouis, Yurtkuran and Fontefrancesco2025).
The direct effect of government initiative on behavioral intention to adopt biopesticide was also found to be positive and significant. To address challenges related to farmers’ awareness, training, and educational programs on biopesticides, governmental support in the form of incentives and subsidies is crucial (Saha et al., Reference Saha, Thosar, Kabade, Pawar and Banerjee2023). Raising awareness among farmers about biopesticides and developing more schemes to promote organic farming are essential to overcome skepticism and encourage adoption. Government initiatives such as promoting biopesticides and declaring organic corridors can also increase adoption rates.
Farmers adopt biopesticide as a means to reduce disease risks and safeguard the environment, influenced by the successful experiences of other farmers who have reaped benefits from their usage. The influence of behavioral control (BC) on farmers’ intention to use biopesticides appears to be insignificant in a study by Kaur, Kumar and Kaushik (Reference Kaur, Kumar and Kaushik2024), contrary to the findings of our study. This discrepancy underscores the need for detailed further investigation into the specific construct influencing farmers’ decision-making regarding biopesticide usage.
There are numerous obstacles that prevent the widespread use of biopesticides in India. These include inadequate performance of biopesticide in the field, pricing issues, lack of awareness, financial limitations, and a lack of understanding (Dar et al., Reference Dar, Khan, Khan and Ahmad2019). Addressing these challenges requires collaboration between government and nongovernmental organizations (NGOs) to engage with farmers at the grassroots level and increase awareness of biopesticides. Government policies and initiatives are vital in enhancing research and development structures, refining policies, and promoting biopesticide adoption among farmers to ensure long-term food security and environmental sustainability (Chandler, Grant and Greaves, Reference Chandler, Grant and Greaves2010; Kumar et al., Reference Kumar, Bhattacharya and Banerjee2025).
The development of technological breakthroughs that can prolong the effectiveness of biopesticides’ active components and reduce their frequent use is also crucial. This will help biopesticides become more competitive with chemical pesticides (Mishra, Dutta and Arora, Reference Mishra, Dutta and Arora2020). Additionally, strengthening scientific and technological inputs in biopesticide development, improving farmer awareness, and increasing market availability are essential to bridge the gap between biopesticides and conventional chemical pesticides. Subjective norms and government initiative attitudes emerge as significant influencers of farmers’ behavioral intentions toward the adoption of biopesticides, presenting opportunities for governments to leverage social media and promotional activities in TV, by motivating local agro-dealers to encourage farmers toward the adoption of biopesticides.
Governments and NGOs can enhance the adoption of biopesticides by implementing several targeted initiatives, such as offering minimum support prices (MSPs) for crops cultivated with biopesticides (Reddy, Praveen and Mohan, Reference Reddy, Praveen and Mohan2024). This financial safety net could encourage farmers to adopt more sustainable farming practices (Aditya et al., Reference Aditya, Subash, Praveen, Nithyashree, Bhuvana and Sharma2017). For MSP programs to be effective, it is essential to enhance transparent certification procedures and monitoring systems that verify the use of biopesticides, thereby ensuring trust and transparency (Barwant et al., Reference Barwant, Ogidi, Yogita and Munje2025). Additionally, demonstrating the proper application and efficacy of biopesticides can enable extension services that can help farmers build confidence in these products (Dimitri, Oberholtzer and Pressman, Reference Dimitri, Oberholtzer and Pressman2025). These initiatives are particularly important in regions where adoption is hindered by misinformation or insufficient technical expertise. NGOs can enhance the adoption of biopesticides by facilitating farmer-to-farmer learning platforms and linking biopesticide users with microcredit opportunities (Pal and Singh, Reference Pal and Singh2021). Moreover, biopesticide interventions need to be incorporated into national IPM and soil health programs to ensure sustainable long-term outcomes. Support should also be derived from public–private partnerships, premium markets for organic produce, and ongoing monitoring and assessment. Contract organic farming agreements can also mitigate market risk for farmers by mandating that buyers, such as retailers or exporters, are committed to purchasing organically grown produce. Adoption is directly associated with guaranteed market access through the integration of biopesticides in contractual agreements. This model has proven effective in various countries; however, it requires robust institutional support to manage contracts fairly and resolve conflicts (Gramzow et al., Reference Gramzow, Batt, Afari-Sefa, Petrick and Roothaert2018; Ton et al., Reference Ton, Vellema, Desiere, Weituschat and D’Haese2018; Vicol et al., Reference Vicol, Fold, Hambloch, Narayanan and Pérez Niño2022). These programs should ultimately be integrated into a broader policy framework that prioritizes sustainable agricultural practices. Ensuring the initiation and sustainability of these initiatives requires collaboration among governmental organizations, NGOs, private sector stakeholders, and farmers’ cooperatives.
Conclusion
Despite global concerns about the environmental and health impacts of conventional agricultural practices, the adoption of organic farming and biopesticides remains relatively slow in India. Our research sheds light on the socioeconomic dynamics that shape farmers’ decisions in this regard. In India, where small and marginal farmers form a significant majority, persistent poverty and low education rates pose challenges to agricultural sustainability. Government initiatives, such as the ‘Organic Corridor Scheme’ in Bihar, play a crucial role in promoting organic farming awareness. Integrating theoretical frameworks such as TPB and HBM with a focus on government initiatives enhances our understanding of farmers’ intentions.
The present study aimed to model farmers’ behavioral intentions toward the adoption of biopesticides in India by integrating HBM, TPB, and government initiatives. The expanded model recognized all components as essential for investigating the factors involved in the adoption of biopesticides by farmers in India. Subjective Norms and Government Initiatives emerged as the most influential factors influencing farmers’ intention toward the adoption of biopesticides. Government initiatives were found to have a direct and positive impact on farmers’ intentions, emphasizing the importance of policy support in promoting sustainable agricultural practices. Addressing challenges such as a lack of awareness, pricing issues, and financial limitations requires collaborative efforts between government agencies and NGOs. It is vital to focus on reducing costs and enhancing the market availability of high-quality biopesticides to narrow the gap between biopesticides and conventional pesticides. Research and development frameworks and raising awareness among farmers are crucial measures for attaining sustainable long-term food security and environmental preservation.
Limitations and future research directions
The research investigates farmers’ intentions to use biopesticides within the framework of TPB, where behavioral intention acts as a principal predictor of actual behavior. This provides a significant understanding of farmers’ readiness to use biopesticides; however, future studies might strengthen these findings by monitoring the actual implementation of these intentions through longitudinal studies. A significant limitation observed in this study relates to the measurement model, where several constructs—namely Behavioral Control, Perceived Benefits, and Attitude—recorded Composite Reliability (CR), AVE, and Cronbach’s alpha values that fell slightly below the widely accepted thresholds. While these constructs were retained in the research due to their strong theoretical relevance and established use in prior literature, the results indicate potential limitations in internal consistency and convergent validity. Additionally, the relatively small sample size may have contributed to these discrepancies, as smaller samples can lead to statistical instability and affect measurement robustness. Future studies should seek to refine these constructs, potentially by rewording items or expanding item pools, and aim for larger, more diverse samples to strengthen reliability and validity. Another measurement-related concern was the discriminant validity between Behavioral Intention and Health Motivation, as the HTMT ratio between these constructs slightly exceeded the recommended 0.9 threshold. Although the constructs are theoretically distinct, their empirical overlap suggests a need for further refinement. Future research may consider rewording items to better differentiate between the two or exploring the possibility of merging them into a higher-order construct if conceptual overlap persists.
Furthermore, the study focused solely on a particular geographical area; therefore, more participants from various states of India might give a better insight into the intentional behavior of farmers all over the country. Moreover, the small sample size compared to the entire farming community in India is a significant challenge. The limited existing field research on farmers’ biopesticide use restricts the broader understanding of the issue. Future research could consider incorporating social and demographic factors to augment findings. Integrating Risk Perception Theory, the Innovation Diffusion Model, and the Social Cognitive Model into the analysis could provide valuable insights into farmers’ perceptions of pesticide usage. Studying behavioral changes over time through longitudinal studies could assess the effectiveness of marketing efforts in promoting biopesticide adoption.
Data availability statement
The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.
Acknowledgements
The authors express their gratitude to the respondents for their invaluable feedback and insightful comments, which greatly enriched the quality of this study.
Funding statement
This research was funded by Indian Institute of Technology Kharagpur.
Competing interests
The authors declare no competing interests.
Ethical standards
The study was approved by the Institute Ethical Committee, Sponsored Research and Industrial Consultancy, Indian Institute of Technology Kharagpur (Approval No. IIT/SRIC/DEAN/2024). All the respondents provided informed consent before participating in the survey.
Disclosure of use of AI tools
I hereby declare that the QuillBolt software is used to enhance the readability and language of this paper.
Appendix
Indicator loadings and significance for the measurement model
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