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Article

Influence of Psychological Factors on Dairy Farmers’ Intentions to Adopt Environmental Sustainability Practices in Paraná State, Brazil

by
Jessica Ortega de Jesus Sangali
1,
Ferenc Istvan Bánkuti
1,2,*,
Julio Cesar Damasceno
2 and
Henrique Leal Perez
2
1
Programa de Pós-Graduação em Produção Sustentável e Saúde Animal—PPS/UEM, Departamento de Medicina Veterinária, Universidade Estadual de Maringá, Estrada da Paca, S/N, Umuarama 87507-190, PR, Brazil
2
Programa de Pós-Graduação em Zootecnia—PPZ/UEM, Departamento de Zootecnia, Universidade Estadual de Maringá, Av. Colombo, 5790, Bloco J45, Maringá 87020-900, PR, Brazil
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(11), 4500; https://doi.org/10.3390/su16114500
Submission received: 17 April 2024 / Revised: 17 May 2024 / Accepted: 23 May 2024 / Published: 25 May 2024
(This article belongs to the Special Issue Sustainability in the Global Dairy Sector: Challenges and Opportunities)
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Abstract

:
Efforts worldwide have been dedicated to developing strategies for reducing the environmental impacts arising from agricultural production. In developing countries, such as Brazil, where agricultural production stands as one of the most important economic sectors, meeting institutional and market requirements for sustainability is essential for ensuring the country’s competitiveness. This study investigated the intention of Brazilian dairy farmers to adopt environmental sustainability practices. The sample comprised 100 dairy farms in Paraná State, Brazil. The data were analyzed using structural equation models and discussed from the perspective of the Theory of Planned Behavior. The results showed that farmers’ intentions to adopt sustainability practices is not associated with socioeconomic or production characteristics. Structural equation modeling identified three constructs explaining farmers’ intentions to adopt sustainability practices, namely attitude (ATT), subjective norms (SN), and perceived behavioral control (PBC). ATT and SN had a positive and significant influence, explaining 90% (R2 = 0.90) of the farmers’ intentions toward sustainability adoption. The lack of influence of the PBC construct suggests that farmers perceive themselves as having limited ability to adopt sustainability practices, mainly attributed to a lack of knowledge and financial resources, low self-confidence, and a heavy reliance on others for the implementation of sustainability actions.

1. Introduction

Globally, agricultural production has struggled to find a balance between economic, social, and environmental sustainability. From an economic perspective, crop production generates jobs and foreign exchange for producing countries. Socially, it contributes to food security and domestic development. Environmentally, however, agriculture can exert negative impacts on a global scale. Several countries have implemented public policies aimed at achieving a balance between sustainability dimensions, demonstrating the need to reduce the environmental impacts of crop production [1,2]. In Europe, for instance, failure to comply with institutional and market requirements for sustainability has caused many farmers to leave the agricultural sector [3]. In developing countries such as Brazil, where crops are produced on a large scale and the economy is strongly dependent on agricultural production, achieving sustainability is a significant concern. Failure to meet sustainability requirements may have serious economic and social impacts for the country.
Among the three pillars of sustainability, namely economic, social, and environmental sustainability, environmental factors appear particularly relevant in the context of agricultural systems, given the direct relationship between crop production and environmental conditions. Agricultural production can have deleterious effects on natural resources, such as water and soil, as it is associated with increased greenhouse gas emissions, deforestation, biodiversity loss, and climate change [4]. Studies have shown that the adoption of sustainable practices is a crucial strategy to mitigate the negative impacts of crop production, warranting high priority from farm managers [1,4,5]. Here, sustainable practices are defined as actions that, when implemented in place of others, result in reduced environmental impacts [1,2,4].
Some investigations suggested that institutional efforts have been ineffective in steering animal production systems toward sustainability. As extensively documented, farmers only adopt environmental sustainability practices in food production systems if they perceive such practices as important, accept them, and show the intention of implementing behavioral changes [1,2,6]. The Theory of Planned Behavior (TPB) has been widely used in studies assessing farmers’ intentions to adopt less-common behaviors [6,7,8,9]. The TPB posits that individual behavior is influenced by intention, which, in turn, is governed by three psychological constructs, namely attitude, subjective norms, and perceived behavioral control [10].
This study examined the intention of dairy farmers in Paraná State, Brazil, to adopt environmental sustainability practices. The following three hypotheses were proposed based on the TPB [10]: H1, attitude has a positive and significant influence on farmers’ intentions to implement environmental sustainability actions on the farm; H2, subjective norms have a positive and significant influence on farmers’ intentions to implement environmental sustainability actions on the farm; and H3, perceived behavioral control has a positive and significant influence on farmers’ intentions to implement environmental sustainability actions on the farm.

2. Materials and Methods

2.1. Study Site

Agricultural production is one of the most important sectors of the Brazilian economy, accounting for almost a quarter of the country’s gross domestic product [11]. Brazil ranks among the world’s largest producers of several agricultural commodities, including beef, chicken meat, coffee, and sugarcane [12]. Despite this, most Brazilian farms (77%) are family farms with small-to-medium scales of production. Family farms occupy 23% of the total agricultural land available in the country and represent the main source of income for both farmers and their families [13].
Dairy production holds significant economic and social importance in Brazil. The country boasts more than 1 million dairy farms, which together produced 34.6 billion liters of milk in 2022, placing the country as the third largest milk producer in the world [12,14]. Among the Brazilian states, Paraná (24°36′ S 51°23′ O) ranks as the second largest in terms of milk production volume, accounting for 13% of the national production. As in other regions of the country, the Paraná dairy sector is mainly represented by small- and medium-scale family farms [14]. This study was conducted in the northwest region of Paraná State (Figure 1). The area comprises 7900 dairy farms and about 105,000 dairy cows, which account for about 7% of the state’s milk production [14].
Although small-scale family farms are predominant, important dairy corporations operate in the state, mainly in the southeast and southwest. The municipalities of Castro, Carambeí, Ponta Grossa, Toledo, and Marechal Candido Rondon have the highest milk production in the country. In these large-scale systems, milk production is highly specialized and characterized by high technology adoption and hired labor, unlike other regions of the state. These dairy basins account for 75% of the total milk volume and house 60% of the dairy farms in Paraná State [2].

2.2. Data Collection

Data were collected between February and December 2023. Dairy farmers were selected from contact lists provided by technical assistance and public rural extension companies operating in Paraná State. Additionally, farmers previously contacted by the research team were also included in the list. Farmers were randomly selected and invited to participate in the study. During the first contact, the dairy farmers received an explanation and were presented with some examples of environmental sustainability practices. These included (i) reducing the use or ensuring the rational use of natural resources, such as water and soil; (ii) reducing, eliminating, and properly disposing of elements that pose a risk of environmental, human, and animal contamination, such as pesticide and medication packaging and animal carcasses; and (iii) reducing greenhouse gas emissions or enhancing greenhouse gas capture through conservation practices, such as crop–livestock integration, rotational grazing, utilization of animal waste as biofertilizers, and preservation of forested areas.
Data collection was performed by a single researcher (the first author) by using two semi-structured questionnaires. The first questionnaire was administered to 15 dairy farmers. The aim of this step was to identify the main advantages, disadvantages, challenges, capabilities, and social groups influencing farmers’ intentions to adopt sustainability practices in their dairy system. This qualitative survey allowed us to better understand the barriers and facilitators for the study sample, as performed in previous studies [15]. The data collected using the first questionnaire were used to formulate quantitative questions for the second questionnaire. The quantitative questionnaire was administered to 100 dairy farmers in northwest Paraná State, Brazil. About 70% of the responses were collected on site (at the farm). The remaining responses were obtained remotely via video calls because of schedule incompatibilities with the farmers. The questionnaires were approved by the Standing Committee on Human Research Ethics (COPEP, CAAE protocol No. 50176121.3.0000.0104) at the local university.
The quantitative questionnaire was divided into two sections. The first section assessed structural and production variables of dairy systems and social variables related to farmers and their families. The second section of the questionnaire measured farmers’ intentions to adopt sustainability practices based on TPB assumptions [10]. According to the TPB, an agent’s intention (dependent construct) is influenced by three (independent) constructs, that is, attitude, subjective norms, and perceived behavioral control [10]. In the context of the current study, the TPB constructs are defined as follows: intention (INT), which refers to farmers’ intentions to adopt environmental sustainability practices on the farm; attitude (ATT), which describes farmers’ attitudes toward environmental sustainability practices, whether favorable or not; subjective norms (SN), which indicate farmers’ perceptions of social pressures from relevant persons (family members and technical advisors) to adopt environmental sustainability practices; and perceived behavioral control (PBC), which describes farmers’ perceptions of their own capacity to implement environmental sustainability practices.
The TPB constructs were measured from the responses to eighteen questionnaire items, including four for INT, four for ATT, five for SN, and five for PBC. These questions were rated on a Likert scale, as recommended [10]. Responses ranged from 1 to 5, with 1 being the most negative and 5 the most positive [3] (Table 1).

2.3. Data Analysis

For the general characterization of the sample, structural, production, and social data were analyzed using descriptive statistics (mean, standard deviation, maximum, minimum, and mode). Then, the relationship between the INT construct and structural, production, and socioeconomic variables was assessed using Spearman’s correlation (rho) analysis [9]. Confirmatory factor analysis was used to determine the variables composing INT, using factor loadings (>0.5) and Cronbach’s alpha (>0.7) as criteria [16].
Structural equation modeling was used to assess the TPB constructs. This method provides a preliminary assessment of the measurement model. The model was subjected to confirmatory factor analysis and assessed for reliability. The following reliability measures were evaluated: the average variance extracted (AVE), where values above 50% were deemed acceptable; construct reliability (CR), which must be greater than 0.7; and Cronbach’s alpha, which must be greater than 0.7 [16,17]. Model validation included the assessment of the following fit indices: the root mean square error of approximation (RMSEA), 95% confidence intervals (CI), the comparative fit index (CFI), the Tucker–Lewis index (TLI), and the standardized root mean squared residual (SRMR). The CFI and TLI should be greater than 0.95, whereas the RMSEA and SRMR should be less than 0.08. The chi-squared (χ2) value was also evaluated; χ2 values of less than 5.0 were deemed acceptable [16].
Following validation of the measurement model, the next step was to assess the structural model. This model should adequately represent relationships between constructs, allowing for the measurement of multiple regressions and the evaluation of their strengths [16]. The structural model was assessed by determining the coefficient of determination (R2) and beta coefficient (β). Hypotheses were accepted or rejected at a significance level of p < 0.05. The collected variables were tabulated and analyzed using the Jamovi software version 2.3.21.

3. Results

3.1. Socioeconomic and Production Characteristics

The mean age of the interviewed dairy farmers was 49.93 ± 12.32 years. The farmers had a mean of 18.63 ± 12.41 years of experience in dairy farming. The sample included farmers with different levels of education, with a mean of 7.98 ± 3.86 years. The majority of the farmers (62.0%) reported not receiving any form of technical assistance for dairy production, such as those offered by public agencies, cooperatives, and dairies.
The dairy farms were highly heterogeneous with regard to production characteristics. The mean daily production was 165.92 ± 246.47 L of milk. The mean farm size was 31.36 ± 72.33 ha. The area used specifically for milk production, including pasture and animal handling areas, was 19.74 ± 44.12 ha. The mean number of lactating cows was 16.06 ± 12.45 cows. As for herd characteristics, the farmers reported that 90.0% of their cows were crossbreds, mostly Dutch or Jersey cows crossed with zebu bulls (e.g., Nellore), and 10.0% were purebreds (Girolando, Dutch, or Jersey).
Regarding milking systems, 25.0% of the farmers reported using manual milking, 44.0% used mechanical bucket milking, 21.0% used pipeline milking systems, and only 10.0% used a fully closed milking system. Milk cooling was performed in immersion tanks by 4.0% of the farmers, community cooling tanks by 33.0% of the farmers, and individual cooling tanks by 63.0% of the farmers.
No significant correlations were found between INT and age (rho = 0.008, p = 0.936), education (rho = 0.017, p = 0.870), experience in dairy activity (rho = −0.056, p = 0.580), total farm area (rho = −0.008, p = 0.938), milk production area (rho = 0.048, p = 0.638), number of lactating cows (rho = −0.106, p = 0.295), and production volume (rho = 0.068, p = 0.501).

3.2. Measurement Model

For the validation of the measurement model, the items INT1, SN1, PBC1, and PBC3 were removed because they had factor loadings lower than |0.5|. The retained items had a reliability index of 95% and factor loadings equal to or greater than |0.58| (Table 2). The consistency measures of the measurement model indicated that all indicators were adequate. The AVE was greater than 0.5, and CR and Cronbach’s alpha were greater than 0.7. The measurement model also had adequate fit indices [4], namely χ2 = 106, df = 69, p < 0.003, RMSEA = 0.07 (95% CI = 0.04–0.09), CFI = 0.96, and TLI = 0.95.

3.3. Structural Model

For the structural model, there were positive interaction coefficients between INT and ATT, SN, and PBC. The strongest correlation was observed between INT and ATT (0.75). Among the three constructs, the interaction was only non-significant for PBC (p > 0.05) (Table 3). Therefore, hypotheses H1 (attitude has a positive and significant influence on farmers’ intentions to implement environmental sustainability actions on the farm) and H2 (subjective norms have a positive and significant influence on farmers’ intentions to implement environmental sustainability actions on the farm) were accepted, and hypothesis H3 (perceived behavioral control has a positive and significant influence on farmers’ intentions to implement environmental sustainability actions on the farm) was rejected (Table 3).
The fit index of the model was adequate (R2 = 0.90), indicating that ATT and SN together explained 90% of the farmers’ intentions to adopt sustainability practices in milk production. Complementarily, Figure 2 shows the strengths of the relationships between the items and their respective constructs. For ATT, the lowest β was 0.93 (ATT3). For SN and PBC, the lowest β values were 0.75 (SN3) and 0.60 (PBC2 and PBC5), respectively. For INT, the lowest β was 0.89 (INT2).

3.4. Data Analysis

Structural equation modeling was used to assess the TPB constructs. This method provides a preliminary assessment of the measurement model. The model was subjected to confirmatory factor analysis and assessed for reliability. The following reliability measures were evaluated: the average variance extracted (AVE), where values above 50% were deemed acceptable; construct reliability (CR), which must be greater than 0.7; and Cronbach’s alpha, which must be greater than 0.7 [16,17]. The model validation included the assessment of the following fit indices: the root mean square error of approximation (RMSEA), 95% confidence intervals (CI), the comparative fit index (CFI), the Tucker–Lewis index (TLI), and the standardized root mean squared residual (SRMR). The CFI and TLI should be greater than 0.95, whereas the RMSEA and SRMR should be less than 0.08. The chi-squared (χ2) value was also evaluated; χ2 values of less than 5.0 were deemed acceptable [16].
Following validation of the measurement model, the next step was to assess the structural model. This model should adequately represent relationships between constructs, allowing for the measurement of multiple regressions and the evaluation of their strengths [16]. The structural model was assessed by determining the coefficient of determination (R2) and beta coefficient (β). Hypotheses were accepted or rejected at a significance level of p < 0.05. The collected variables were tabulated and analyzed using the Jamovi software version 2.3.21.

4. Discussion

4.1. Production and Socioeconomic Characteristics

The age (49.93 ± 12.32 years) and education level (7.98 ± 3.86 years) of the analyzed farmers were in agreement with those of dairy farmers in Paraná State [18]. The dairy farms were found to be heterogenous with regard to structural and production characteristics, as reported in similar studies conducted in Paraná State [19]. The milk production (165.92 ± 246.47 L/day), number of lactating cows (16.06 ± 12.45), farm area (31.36 ± 72.33 ha), and milk production area (19.74 ± 44.12 ha) of the analyzed farms were higher than those of other dairy production systems in the state [18]. Therefore, the studied farmers had a higher degree of adaptation to market demands, particularly the concerning minimum production volume.
These results can be explained in part by the farmers’ needs to meet current institutional and market demands, which have evolved over the past 20 years [18]. The current demands aim at professionalization of dairy activity, as exemplified by the increase in production scale and product quality. Such factors have led many farmers to abandon the activity. Those who managed to adapt remained in the market. These farmers are generally younger, with a higher level of education and greater use of production technologies, which allow for achieving a greater production scale and milk quality [19].

4.2. Correlation between INT and Socioeconomic and Production Characteristics

No significant correlations were found between INT and the variables age, education level, experience in dairy activity, total farm area, milk production area, number of lactating cows, or production volume. This finding shows that the intention to adopt sustainability practices in dairy production is not associated with the socioeconomic characteristics of farmers, nor the production or structural characteristics of dairy farms.

4.3. Farmers’ Intentions to Adopt Sustainability Practices in Dairy Production

A positive and significant relationship was found between the variables that defined INT, ATT, and SN. Thus, the structural model indicated that two constructs (ATT and SN) had a positive and significant influence on the farmers’ intentions to adopt environmental sustainability practices. PBC did not exert a significant influence (Table 3). Non-validation of the three TPB constructs has been observed in several studies about farmers’ intentions [6,8], representing a common situation for TPB models. The two constructs explained 90% of the variance in the farmers’ intentions, which was considered an adequate result. Hair et al. (2009) argued that structural models explaining more than 75% of the variance in a dataset are considered highly satisfactory. Previous studies using the same method and theoretical input to analyze dairy farmers’ intentions in Brazil reported variances (R2) of from 49.3% to 76.0% [20].
ATT was the most important factor determining farmers’ intentions to adopt environmental sustainability practices in the coming years (Table 3). Other studies have also reported the great importance of attitudes in determining farmers’ intentions to use technologies and conservation practices in production systems. Given the importance of the ATT factor, it is recommended to adopt practices designed to encourage farmers to make better decisions [21,22]. Therefore, it can be inferred that farmers perceive the adoption of sustainability practices in a positive/favorable way, not representing barriers to their adoption.
The high β values of the ATT items are further evidence of the importance of this construct to farmers. ATT4 (How important is it for you to increase the use of environmental sustainability practices in dairy production in the coming years?) had the highest β value (0.97). By contrast, ATT3 (How necessary is it for you to increase the use of environmental sustainability practices in dairy production in the coming years?) had the lowest value (0.93). This result, although quite positive, may indicate that the need to adopt environmental sustainability practices is relatively less important than the other items comprising ATT (Figure 2). Thus, according to farmers’ perceptions, the need for changes in environmental sustainability practices stemming from standards or explicit requirements by buyers (e.g., the dairy industry) is less important than farmers’ beliefs. Such a relationship is supported by the lack of contracts between dairy farmers and the industry throughout Brazil [23].
The poor enforcement of production standards within the Brazilian dairy sector may contribute to farmers’ perceptions of the low importance of adopting sustainability practices in the face of institutional demands. The study [1] argued that issues related to regulatory compliance are less important than production factors in the decisions of farmers to use sustainable production practices. The study [2] considered that although the institutional environment has directed efforts toward mitigating the environmental impacts of agricultural production, positive results will only be seen if farmers adopt sustainable practices. Overall, it can be said that dairy farmers’ intentions to adopt sustainability practices is more related to their own beliefs about environmental issues than to the need to comply with laws and regulations.
There was a positive and significant relationship between the variables that defined INT, ATT, and SN. Therefore, SN had a positive and significant effect on the farmers’ intentions to adopt environmental sustainability practices, being the second most important construct (Table 3). This finding indicates that people who are important to farmers influence them in a positive way to adopt environmental sustainability practices. Previous studies analyzing subjective norms found that family, other farmers, and technical advisors can influence farmers’ intentions and decisions [24,25,26]. The studies [26,27] identified the importance of subjective norms in determining farmers’ intentions toward adopting environmental sustainability practices.
The high β values of the SN items (Figure 2) indicate that the dairy farmers believed that individuals who hold importance in their lives would support them in the adoption of environmental sustainability practices. This group of individuals includes family members, other farmers, and members of associations and cooperatives. SN4 (Would most rural technical advisors approve if you increased the use of environmental sustainability practices in dairy production in the coming years?) had the highest score among the SN items (Figure 2). Thus, it is shown that technical advisors are important to farmers and bear responsibility as catalysts of changes in the milk production system. The study [28] demonstrated that public or private technical assistance programs can be determinant of the behavior of farmers toward the adoption of sustainability practices. The study [25] stated that technical assistance aims to facilitate interactions, learning, and innovation in agricultural systems and is therefore useful in the development of agricultural production.
The lowest score among the SN items was that of SN3 (Would most farmers who are like you approve if you increased the use of environmental sustainability practices in dairy production in the coming years?) (Figure 2). Although the β value of SN3 (0.75) was the lowest among the SN items, it is still considered high, indicating that other farmers have a positive perception about the adoption of environmental sustainability practices. Some authors demonstrated the influence of peers, technical advisors, and family members on farmers’ intentions to adopt environmental sustainability practices. The study [26] found that the decision of farmers to adopt such practices was influenced only by subjective norms, not being influenced by attitude or perceived behavioral control. The cited study observed that close farmers were the most influential, followed by experts and technical assistance professionals. Similarly, [6] reported that farmers’ intentions to adopt sustainability practices is mainly influenced by the opinions of other farmers.
Among the three constructs analyzed in this research, PBC was the only one to not significantly influence the farmers’ intentions (Table 3). PBC allows for analyzing farmers’ perceptions about their own capacity to adopt environmental sustainability practices. The higher and more positive the perception, the greater the tendency to adopt such practices. This result indicates that farmers do not perceive themselves as having the capacity to adopt sustainability practices in their farms, as exemplified by PBC1, PBC2, PBC3, PBC4, and PBC5 (Table 1).
Some studies also reported a low importance of PBC in determining farmers’ intentions [8,9]. However, others observed a greater importance of PBC in the intention to incorporate sustainability practices [28]. Therefore, barriers to the adoption of such practices may vary depending on the analyzed group, region, production characteristics, and cultural aspects, among others. The study [4] stated that the economic benefits of sustainable practices, resulting, for example, from the reduced use of inputs and increased yields, are not yet clear to farmers, reducing their adoption. For the farmers surveyed, the low perception of financial returns or the need to increase costs may be an obstacle to the adoption of sustainable practices in dairy production. In this sense, public and private information and training actions could be defined. Besides that, the farmers’ perceived lack of capacity to adopt environmental sustainability practices indicates the need for public and private actions aimed at developing capacity, training, and support. An example of such an effort is the provision of specific credit lines for environmental sustainability projects. Furthermore, actions that promote knowledge and understanding of such practices can help farmers meet institutional and market demands.
The results of this research allow us to suggest public or private actions to increase the adoption of sustainability practices by dairy farms, namely the generation of financial incentives, such as credit subsidized by the government or partner companies (e.g., dairy industries) for the adaptation of farms; farmer training and qualification; and acquisition of technologies. Such actions can be facilitated by their promotion through organized collectives, such as cooperatives and associations. Several authors argued that information exchange and knowledge generation are greater when farmers participate in collective production arrangements. The study [18] stated that information exchange and training through collective arrangements can increase the competitiveness of dairy farms. Furthermore, training, awareness, and incentives for the adoption of environmental sustainability practices should also be directed to the group of people considered important by farmers, such as family members and rural technical advisors. Thus, positive reinforcement of these groups may contribute to the adoption of sustainability actions in the analyzed dairy farms.

4.4. Study Limitations

This study has some limitations. The results cannot be extrapolated to other production systems or regions, as different groups of farmers may respond differently to the adoption of sustainability measures. Another limitation is the possibility that the farmers misinterpreted the items of the questionnaire. We are aware that although explanations and examples about sustainability practices were provided, errors in interpretation might have occurred. Another limitation of this study is that the data collection was performed in a single event, thus not allowing for a temporal analysis. Time series studies in dairy farms could provide more realistic results about farmers’ intentions to adopt environmental sustainability practices. Limitations of this kind are inherent in any research that focuses on issues that are difficult to measure and are fundamentally less objective in nature. Other studies have already identified such limitations [29].

5. Conclusions

A positive and significant relationship was observed between the variables INT, ATT, and SN. Therefore, ATT and SN had a positive and significant effect on the intention (INT) of dairy farmers to adopt environmental sustainability actions in the coming years. PBC did not exert such an effect on INT. The greater importance of the INT construct indicates that farmers have a positive perception of adopting sustainability practices in dairy production. In addition, the importance of the SN construct represents reinforcement from people important to the farmers to adopt sustainability practices in dairy production. These results suggest that there is a good chance that dairy production systems will change in the coming years, making them more responsive to current institutional and market demands and thus more viable in the medium and long term. The results also showed that, for the evaluated sample, the farmers’ intentions to adopt such measures in dairy production was not associated with socioeconomic or production characteristics.

Author Contributions

Conceptualization, F.I.B., J.C.D., H.L.P. and J.O.d.J.S.; methodology, F.I.B. and J.C.D.; software F.I.B. and J.C.D.; validation, F.I.B., J.C.D., H.L.P. and J.O.d.J.S.; formal analysis F.I.B. and J.C.D.; investigation, F.I.B. and J.O.d.J.S.; resources, F.I.B., J.C.D. and J.O.d.J.S.; data curation, F.I.B. and J.C.D.; writing—original draft preparation, F.I.B., J.C.D., J.O.d.J.S. and H.L.P.; writing—review and editing, F.I.B., J.C.D., J.O.d.J.S. and H.L.P.; visualization, F.I.B., J.C.D. and J.O.d.J.S.; supervision, F.I.B.; project administration, F.I.B. and J.O.d.J.S.; funding acquisition, F.I.B. and J.O.d.J.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brasil (CAPES), finance code 001.

Institutional Review Board Statement

This study was conducted in accordance with the Committee on Human Research Ethics (COPEP, CAAE protocol No. 50176121.3.0000.0104) at the local university.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Dataset is in moderation—Mendeley Data 20 August 2021.

Acknowledgments

The authors would like to thank the National Council for Scientific and Technological Development (CNPq), procedure 303291/2021-4.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Brazil and Paraná State.
Figure 1. Brazil and Paraná State.
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Figure 2. Relationship of intention (INT) with attitude (ATT), subjective norm (SN), and perceived behavioral control (PBC). Rectangles represent the items used to assess dairy farmers’ intentions to adopt environmental sustainability practices in the coming years. Circles represent latent constructs. Arrows represent dependency relationships between constructs and measured items. The values in each arrow represent the β value and express the strength of the relationship between items and constructs and between constructs.
Figure 2. Relationship of intention (INT) with attitude (ATT), subjective norm (SN), and perceived behavioral control (PBC). Rectangles represent the items used to assess dairy farmers’ intentions to adopt environmental sustainability practices in the coming years. Circles represent latent constructs. Arrows represent dependency relationships between constructs and measured items. The values in each arrow represent the β value and express the strength of the relationship between items and constructs and between constructs.
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Table 1. Questionnaire used to measure intention (INT), attitude (ATT), subjective norm (SN), and perceived behavioral control (PBC) constructs of the Theory of Planned Behavior.
Table 1. Questionnaire used to measure intention (INT), attitude (ATT), subjective norm (SN), and perceived behavioral control (PBC) constructs of the Theory of Planned Behavior.
ItemQuestionResponses (1–5)
INT1Do you intend to increase the use of environmental sustainability practices in dairy production?Definitely no–definitely yes
INT2How strong is your intention to increase the use of environmental sustainability practices in dairy production in the coming years?Very weak–very strong
INT3How likely are you to increase the use of environmental sustainability practices in dairy production in the coming years?Not likely–very likely
INT4Do you plan to increase the use of environmental sustainability practices in dairy production in the coming years?Definitely no–definitely yes
ATT1How good would it be for you to increase the use of environmental sustainability practices in dairy production in the coming years?Very poor–very good
ATT2How beneficial would it be for you to increase the use of environmental sustainability practices in dairy production in the coming years?Not advantageous–very advantageous
ATT3How necessary is it for you to increase the use of environmental sustainability practices in dairy production in the coming years?Not necessary–very necessary
ATT4How important is it for you to increase the use of environmental sustainability practices in dairy production in the coming years?Not important–very important
SN1Do most people who are important to you think you should increase the use of environmental sustainability practices in dairy production in the coming years?Strongly disagree–strongly agree
SN2Would most people whose opinion you value approve if you increased the use of environmental sustainability practices in dairy production in the coming years?Not likely–very likely
SN3Would most farmers who are like you approve if you increased the use of environmental sustainability practices in dairy production in the coming years?Not likely–very likely
SN4Would most rural technical advisors approve if you increased the use of environmental sustainability practices in dairy production in the coming years?Not likely–very likely
SN5Would most dairies in your area approve if you increased the use of environmental sustainability practices in dairy production in the coming years?Not likely–very likely
PBC1If you wanted to increase the use of environmental sustainability practices in dairy production in the coming years, would you have enough knowledge?Definitely no–definitely yes
PBC2If you wanted to increase the use of environmental sustainability practices in dairy production in the coming years, would you have enough resources?Definitely no–definitely yes
PBC3How confident are you that you can overcome the barriers that prevent you from increasing the use of environmentally sustainable practices in dairy production in the coming years?Definitely not confident–definitely confident
PBC4Is the increased use of environmental sustainability practices in dairy production in the coming years solely dependent on you?Definitely no–definitely yes
PBC5Is increasing the use of environmental sustainability practices in dairy production in the coming years under your control?Definitely no–definitely yes
Table 2. Standardized factor loadings, Cronbach’s alpha, average variance extracted (AVE), and reliability of the constructs included in the measurement model.
Table 2. Standardized factor loadings, Cronbach’s alpha, average variance extracted (AVE), and reliability of the constructs included in the measurement model.
INT ATT SN PBC
Factor loadingINT20.80ATT10.92SN20.70PBC50.58
INT30.87ATT20.91SN30.61PBC40.87
INT40.87ATT30.89SN40.92PBC20.59
ATT40.94SN50.69
Cronbach’s alpha0.89 0.95 0.85 0.72
AVE0.85 0.91 0.71 0.59
Construct reliability0.91 0.95 0.86 0.77
INT, intention; ATT, attitude; SN, subjective norm; PBC, perceived behavioral control. INT 2, 3, and 4; ATT 1, 2, 3, and 4; SN 2, 3, and 4; and PCB 2, 4, and 5 are shown in Table 1.
Table 3. Results of the structural model.
Table 3. Results of the structural model.
HRelationshipEstimateSDLower
95% CI		Upper
95% CI		Standardized
β		zpOutcome
H1ATT→INT0.750.040.660.840.8115.8<0.001 *Accepted
H2SN→INT0.210.060.090.340.213.47<0.001 *Accepted
H3PBC→INT0.020.04−0.060.110.030.590.55Rejected
INT, intention; ATT, attitude; SN, subjective norm; PBC, perceived behavioral control; H, hypothesis; SD, standard deviation; CI, confidence interval. * p < 0.05.
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Sangali, J.O.d.J.; Bánkuti, F.I.; Damasceno, J.C.; Perez, H.L. Influence of Psychological Factors on Dairy Farmers’ Intentions to Adopt Environmental Sustainability Practices in Paraná State, Brazil. Sustainability 2024, 16, 4500. https://doi.org/10.3390/su16114500

AMA Style

Sangali JOdJ, Bánkuti FI, Damasceno JC, Perez HL. Influence of Psychological Factors on Dairy Farmers’ Intentions to Adopt Environmental Sustainability Practices in Paraná State, Brazil. Sustainability. 2024; 16(11):4500. https://doi.org/10.3390/su16114500

Chicago/Turabian Style

Sangali, Jessica Ortega de Jesus, Ferenc Istvan Bánkuti, Julio Cesar Damasceno, and Henrique Leal Perez. 2024. "Influence of Psychological Factors on Dairy Farmers’ Intentions to Adopt Environmental Sustainability Practices in Paraná State, Brazil" Sustainability 16, no. 11: 4500. https://doi.org/10.3390/su16114500

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