1 INTRODUCTION

The search for more sustainable agriculture and resilient to environmental challenges has motivated the governments of developed and developing countries, as well as companies belonging to the food production chain, to invest in new technologies and management techniques that protect the environment. (Hardoim, Martins & Martins, 2023). In this context, regenerative agriculture is a production model that aims to improve soil quality, promote biodiversity, capture carbon, preserve the water cycle, promote animal and human welfare, and promote profitable agricultural production (Mpanga et al., 2021; Day & Cramer, 2022).

Among the technological tools for regenerative agricultural production is bioinputs, defined by the Brazilian government through the National Bioinputs Program (Decree No. 10.375 of 2020 and amended by Decree No 11.940 of 2024) as “process or technology of plant, animal or microbial origin, intended for use in the production, storage and processing of agricultural products, aquatic production systems or planted forests, which positively affects growth, development and the response mechanism of animals, plants, microorganisms and derived substances and that interact with the products and the physical-chemical and biological processes” (Brazil, 2020, 2024).

In practice, the term bioinputs is used as a synonym for biofertilizers, biopesticides and inoculants, among other biologically based products, which were initially used in organic or agroecological agriculture, but currently play an increasing role in conventional agriculture. An alternative or a complement to fertilizers and phytosanitary products can reduce production costs (Vidal et al., 2020).

However, the perception of the adoption and use of biological products in the production system is still permeated by barriers and limitations by many farmers. Identifying the factors that determine such limitations, as well as the motivations for the use of this technology is essential for expanding practices in the agricultural environment. Therefore, the present study is guided by three important questions:

a) “What is the profile of grain farmers in the Brazilian Cerrado that contributes to the adoption or non-adoption of bioinputs on the farms?”

b) “Which source(s) of origin and update are determinant for the grain farmers to know the bioinputs technology that impacts the decision-making of the use of technology in the field?

c) “What are the barriers or limitations that prevent the adoption or increase in the use of bioinputs by grain farmers in the Brazilian Cerrado?”

The Brazilian Cerrado was chosen as the study site for this work because there has been movement on the part of grain farmers in this region to adopt the use of bioinputs on their farms, aimed at the production of large grain crops, especially soybeans.

2 THEORETICAL FRAMEWORK

2.1 Motivations for adopting the use of technology in regenerative agriculture

The motivations that lead farmers to adopt the use of technologies on their farms are not yet well established. However, researchers such as Ryan & Gross (1943), Kivlin & Fliegel (1967), Adesina & Zinnah (1993), Negatu & Parikh (1999), Rogers (2003), Prager & Posthumus (2010) and Ruzzante, Labarta & Bilton (2021) presented studies that show that the theory of agricultural technology adoption is multidisciplinary and combines elements of three main fields, called paradigms, which highlight the role of different factors in the rates and patterns of adoption of new technologies. Among the factors, three different paradigms can be highlighted: a) the diffusion of innovation, in which information is considered a critical parameter that controls the diffusion of an innovation through society; b) the economic restrictions, in which farmers aim to maximize utility, using unequally the resources destined for the adoption of technology; and c) the perception of the adopter, which allows for a level of subjectivity by stating that it is the perceived need to innovate and the perceived attributes of innovations that determine adoption behavior on the basis of cultural, contextual and individual factors.

In this context, Ruzzante, Labarta and Bilton (2021), using empirical studies on adoption theories, reported that the education of farmers, the size of the household and the property, access to credit, access to rural extension services and membership in organizations and cooperatives are positively correlated with the adoption of agricultural technologies. In addition, O’Donoghue, Minasny & McBratney (2022) emphasize that regenerative technologies have substantial support from the consumer market, the business sectors of the food chain and the farmers themselves. These stakeholders hope that regenerative agriculture can be confirmed as a system of agricultural production that increases the product quality and ensures the availability of resources to agriculture , such as soil, water, biota, renewable energy and human effort. Another relevant factor that encourages the adoption and expansion of regenerative agriculture technologies is social learning, through the exchange of knowledge and the sharing of the effects of technologies (Soto et al., 2021).

Finally, moving away from conventional agricultural models, which are dependent on chemical inputs, the adoption of sustainable food production systems, based on ecological practices and landscape management, is a change of no return (Lemke et al., 2024). Thus, scientific innovations aimed at this new model are fundamental in this new scenario. Among these innovations, the adoption of bioinputs has emerged as a sustainable alternative for the ecosystem (Getahun, 2020, Ejjem et al., 2023). The bioinputs market in Brazil is on the rise, driven both by technological advances and by the regulation of sustainable agricultural practices (Valente, 2024). According to CropLife Brazil (CLB), in the 2021–22 crop year, soybean was the crop with the highest participation of bioinputs, representing 42% of the total used in Brazil, followed by cotton, sugarcane and corn. In addition, the value of the Brazilian bioinputs market increased by approximately 154% in 2021–22 compared with that in the previous crop year (Valente, 2024). Therefore, the consumer market for bioinputs is growing, and thus, it is important to understand the motivations that lead farmers to adopt or expand the use of bioinputs.

2.2 Limitations/barriers to the adoption of technologies in regenerative agriculture

The adoption or expansion of bioinputs on farms, although promising, faces challenges inherent to this type of decision-making by farmers. Social, financial, situational, technological and operational constraints seem to be the main barriers and limitations for the bioinputs market. According to Lima (2023), the low technical and knowledge level of farmers, in addition to the shortage of specialized technical assistance and the technique of the use of biological products, are the main obstacles encountered by farmers. Another important concern is possible biological insecurity, considering that, given the possibility of production by the farmers himself, within the property (on farm production) of biological products. This production on farm can result in risks to plantations, the environment, and human health, through the selection of pathogens, requiring a regulation. This possible risk is minimized during industrial production, which involves more regulations and inspections.

In addition, there is a lack of notable examples in the business field, and farmer’s adopt the technology to act inductively, using examples from other countries and limiting the expansion of the use of bioinputs (Marques, 2022). According to Lemke et al. (2024), regulatory problems, lack of technical knowledge, cost and effectiveness of bioinputs are limitations for the biological products market. For these authors, interest in regenerative practices is related mainly to the expected profitability potential rather than the environmental benefits (Lemke et al., 2024).

Importantly, barriers and limitations are not mutually exclusive. According to Hurley et al. (2023), the main barriers to the adoption of techniques in regenerative agricultural systems are financial viability, lack of knowledge about the benefits of this practice, lack of models, lack of supporting policies and legislation, lack of experience in environmental programs, infrastructure requirements, and lack of technical assistance, among others. These barriers usually occur concomitantly, discouraging farmers from knowing, adopting and expanding the use of regenerative tools, such as bioinputs.

Thus, it is possible to observe the diversity of barriers and limitations to the adoption or expansion of the bioinputs market, and a multifactorial approach to better understand the desires and needs of these consumer-producers is necessary.

2.3 Means of disseminating information and knowledge

According to Silva et al. (2020), rural communication favors the circulation of information, both within the rural environment with farmers and agents of the agricultural environment, and in other sectors of the food production chain, , such as agribusinesses . In this context, Brazil began the process of disseminating information on new technologies through the publication of newsletters, lectures and radio programs. Additionally, when observing the relationship between a farmer and the technical-scientific information received, it is necessary to get closer to the farmer to encourage him to take part in the learning and dissemination of new technologies (Silva et al., 2020).

Therefore, as the farmer is the main decision maker within the property and, in most cases, is the one who interacts with the community, with the suppliers of inputs, among other professionals related to the agricultural sector, it is necessary for all those involved to communicate with each other effective communication to transmit information in a clear and reliable way, establishing interactions between organizations and producers (Januário, 2023).

Thus, given that agricultural production involves several risks and uncertainties, it is necessary for farmers to have adequate access to information for effective decision-making, including the adoption of new technologies and/or the expansion of the use of these technologies (Mtega, 2021). In this way, agricultural information is generated by various stakeholders, such as, for example, the farmers themselves, through experience acquired in the medium and long term, or through agricultural research, which must be presented through easy-to-use communication channels. access, in addition to the use of appropriate and simplified language (Munyua & Stilwell, 2013, Isaya et al., 2018, Mtenga, 2021).

Therefore, understanding farmers regarding the choices of appropriate communication channels is important for improving access to the dissemination and update knowledge of innovations and new technologies, such as bioinputs.

3 METHODOLOGY

The first stage of the research was based on semi structured and qualitative interviews, with the objective of allowing respondents to express their thoughts on the proposed theme, adoption or non-adoption of the use of bioinputs, as presented by Cerveira et al. (2024). In this stage, six grain farmers in the Rio Verde region, state of Goiás, reported their experiences, motivations and limitations regarding the adoption, or non-adoption, of bioinputs on their properties. The six farmers interviewed were selected due to the knowledge of the concepts about regenerative agriculture. In addition, the selected farmers were at different levels of adoption of regenerative technology, including non-adoption. Thus, the interviews were recorded with the permission of the interviewees and then transcribed for subsequent qualitative analysis of the texts. The recordings totaled 5 hours and 19 minutes of audio and 76 transcribed pages (Cerveira et al., 2024).

Based on the analysis of the qualitative data obtained in these interviews, a structured questionnaire was prepared for the present study, consisting of a set of closed questions on a 5-level Likert scale with the following options: unimportant; not unimportant; neither important nor unimportant; important; and very important. The details of the questionnaire, as well as its results, are presented in the next section.

The applied questionnaires contained questions related to the socioeconomic profile of farmers, such as age, sex, location, property area, production area, main production, family farming practices, in addition to knowledge about bioinputs, motivations and barrier limitations for the adoption of the technology.

First, the questionnaire was shared online among the main groups of grain grain farmers in the Brazilian Cerrado region. Owing to the low level of engagement of producers in answering the questionnaire, printed questionnaires were subsequently distributed to rural properties, thus increasing the degree of participation of farmers. In total, 122 questionnaires were answered and validated. The online distribution of questionnaires was carried out among groups of rural grain farmers, associated with Cooperatives and Rural Associations. As for the in-person distribution of questionnaires, this was carried out voluntarily by farm technicians, during routine technical visits to the properties, thus allowing 6 states to participate in the data collection.

In the first step, the analysis of the data obtained consisted of descriptive statistics, several graphs and a binomial logistic regression (Logit). Binomial logistic regression is a statistical analysis used to classify records based on the values of the input fields and categorical target field. For this purpose, the logit model calculates the values of the coefficients after providing an experimental dataset with known values of the dependent and independent variables. This statistical method is used to determine which producer characteristic(s) affect the adoption or non-adoption of bioinputs (Hair et al., 2009). With this same logic, the method also answers which factor(s) significantly affect(s) the motivation or limitation of the use of this technology.

In the second stage, the data obtained from these questionnaires were compared between the groups, i.e., between those who adopted the technology and those who did not, via the Mann‒Whitney U test and the chi-square test. The Mann‒Whitney test is used for the comparison of two unpaired groups, checking whether they belong to the same population, whereas the chi‒square test is a statistical test used to determine whether there is a statistically significant difference between the expected frequency and the observed frequency of categorical data, which leads to the assumption of an association between two qualitative variables. These tools were used to determine whether the source of origin and update the knowledge of bioinputs technology contributed to its adoption (Hair et al., 2009). For both stages, Jamovi® software was used (The Jamovi, 2022) and Stata® (Statacorp, 2023) as instruments for statistical measurements.

4 ANALYSIS AND DISCUSSION OF RESULTS

4.1 Descriptive profile of the grain farmers who participated in the study

Based on the responses obtained through the questionnaires administered to grain farmers, it was possible to establish a profile of the study participants, as shown in Table 1.

Table 1 shows that the age group of the grain farmers who participated in the study ranged from 18-82 years, with a mean of 47 years. The size of the farms ranged from 11—30,000 hectares. In view of this wide area, the median, which is approximately 130 hectares, is considered the best indicator for representing the most common farm area. In the productive area, it follows the same logic, with a size of 115 hectares being more representative (median). According to the categorical indicators, the main crop of agricultural production is soybean, with 91% of farmers admitting this chain as the main crop. The most representative gender of the respondents was male, with 91% of the respondents and 61.5% being self-declared family farmers. This self-declaration of the producer was validated in Brazilian legislation, which correlates one of the legal aspects, that is, the size of the property, to adjust this categorization. the relationship with the size of the property according to the law to be considered a family farmer. Finally, of the 122 respondents, 73% (or 89 grain farmers), declared that they knew the bioinputs technology and 33 did not know. Among the knowledgeable grain farmers, 60% (or 53 farmers) declared that they make use of this technology on their properties. Figure 1 represents such a distribution.

According to Figure 2, the states participating in the study were Minas Gerais (54 producers), Goiás (44), Mato Grosso (17), Mato Grosso do Sul (5), Pará and São Paulo, with 1 participant each.

The farmers declared that they were knowledgeable about bioinputs technology and were presented with a list of statements to verify how much such statements were adherent to their perceptions of the technology. They are as follows: i) I am interested in bioinputs technology (Interest_bio); ii) I am willing to grow crops via bioinputs (Willingness_bio); iii) I am motivated to use bioinputs (Motivation_bio); iv) I am able to grow crops via bioinputs (Able_bio); v) I am asked to cultivate them via bioinputs (Requested_bio); vi) I feel pressured to grow crops via bioinputs (Pressed_bio); vii) I believe that I can cultivate prioritizing the use of bioinputs (Prioritize_bio); and viii) I believe that I can grow crops by reducing the use of agrochemicals (Decrease_agroche). Figure 3 represents the results obtained from the average of the responses for each of the statements, considering two subsegments within the segment of knowledgeable producers: a) the subsegment of those who adopt the technology (blue) and b) the sub-segment of farmers who do not adopt the technology (orange).

Based on these results, shown in Figure 3, we hypothesize that agreement with the statements regarding the perception factors of bioinputs may have different effects among the segments that use and do not use the technology in question. This may of course be influenced by some of the characteristics of the socioeconomic profile, which will be analyzed in the next part of this article.

4.2 Effects of the perceptions and profiles of grain farmers that affect the adoption or non-adoption of bioinputs

Tables 3 and 4 present three binomial logistic regression analysis models in which the effect of the perception variables (based on the statements about the perception of bioinputs technology answered by the grain farmers) and the profile of the respondents in relation to the variable of technology adoption are presented. In the first model, the isolated effect of perceptions is considered, in the second model, the age variable is added, whereas in the third model, whether the farmers declares himself to be a family farmer is added. Neither the gender profile nor the type of production could be considered because both violate the mini-mum assumption for the number of respondents for each category (Hair et al., 2009). The production area and farm area cannot be used in the analysis because they have multicollinearity, i.e., they have a direct correlation with the family farmers, since such characteristics tie the size of the farm to be categorized as such (between other aspects) according to the law (Law No. 11.326, of July 24, 2006). This fact greatly reduces the measures of fit of the model.

Table 3 presents the fit measures of Model 1, with only the technology perception factor variables; Model 2, with the inclusion of age; and Model 3, with the inclusion of the self-declaration of family farmers. In all three models, the significance of the global model is observed; however, it is only in Model 3 that there is a significant factor that influences the adoption of bioinputs: not being a family farmer. Table 4 shows the three models and highlights the family farmer factor, which has a significance level of 1%, with an odds ratio of 59.387.

Thus, the fact the grain farmers know the bioinputs technology could be considered a first stimulus for its use. However, this fact, demonstrated with perception factors, makes it clear that there are no differences between the groups that use and do not use such technology. Age, by sequence, also had no influence on the adoption of technology by producers. Finally, whether being a family farmer or not has a significant effect on the adoption of bioinputs in the field: the analyses show that not being a family farmer increases the odds ratio of these farmers using bioinputs by 59 times. That is, nonfamily farmers who are knowledgeable about bio-input technology are expected to have a 98% chance of adopting bioinputs and a 2% chance of not adopting bioinputs. Owing to the high accuracy, specificity and sensitivity of the model, it is possible to predict this profile of grain farmers with high accuracy in the adoption of bioinputs.

After the binomial logistic regression analysis of the data presented, it was possible to observe in the model represented in Table 4 that the only factor that significantly influences the adoption or non-adoption of bioinputs by grain farmers in the Brazilian Cerrado is self-declared family farmers. Thus, it is possible to observe that age and perceptions regarding the technology of bioinputs do not have a significant influence at the 1% level on the decision to adopt (or not use) bioinputs.

To visualize these results , Figure 4 shows an estimate of the marginal influence of the characteristic "Family Farmer" (Fam_Agr), in which the average of the other factors estimated in Model 2 is considered and only the fact that being a family farmer or not varies and how this affects the distribution of probabilities of adoption of this new technology (the use of bioinputs).

Figure 4 shows that the distribution of nonfamily farmers is much greater and greater (because the minimum value observed was above 75%) than the distribution of family farmers and that the average probability (the point on this straight line) of the nonfamily farmer is also much greater and much closer to the maximum value obtained (100% probability). Once we have seen the main characteristics for this use of technology, now, in the next part of the article, we will see the main barriers and/or limitations for this adoption and/or for the increase of this use.

4.3 Perceptions of grain farmers regarding the barriers/limitations that prevent the adoption of or increase the use of bioinputs

To analyze the limitations of the adoption and increase in the use of bioinputs on farms, farmers were asked to rate the importance of the following statements: i) lack of knowledge on the subject limits the use of bioinputs (Lack_of_knowledge); ii) the lack of access to extension projects on the use of bioinputs makes their application difficult (Lack_TARE_use_bio); iii) the lack of access to extension projects on the management of bioinputs hinders their use (Lack_TARE_management_bio); iv) the lack of knowledge about the production of bioinputs on their farm limits their use (Lack_known_bio_product); v) the lack of specific tax incentives for the application of bioinputs limits their use (Ab-sence_tax_incentive_bio); vi) the lack of certification of bioinputs hinders their adoption (Lack_bio_certification); vii) the specificity of the bioinputs limits their use (High_specificity_bio); viii) the specificity of management limits their use (High_specificity_management_bio); ix) the absence of cooperatives to help produce bioinputs limits their use (No_coop _prod_bio); x) insufficient technical assistance hinders the adoption of bioinputs (Lack_TARE_bio); xi) poor quality technical assistance hinders adoption of the use of bioinputs (TARE_without_quality_bio); xii) lack of dissemination of bioinputs limits their use (Lack_disclosure_bio); and xiii) lack of awareness of bioinputs limits their use (Lack_conscience_bio).

The classification was carried out using a 5-level Likert scale, with the following options: 1) not important; 2) not very important; 3) neither important nor unimportant; 4) important; 5) very important, as shown in Figure 5.

Note: The blue curve represents the limitations indicated by grain farmers who adopt the use of bioinputs, and the orange curve represents the limitations indicated by grain farmers who do not adopt the use of bioinputs.

Figure 5 shows evidence that the lack of technical assistance and rural extension (TARE) is a significant aspect limiting the adoption or limiting the increase in the use of bioinputs by grain farmers in the Cerrado, which was confirmed in Table 5.

According to Table 5, grain farmers who do not have access to a satisfactory TARE have a 3.8-fold greater odds ratio of not adopting or, if they do, not increasing the use of bioinputs on their property. That is, a producer with a lack of TARE is 3.8 times more likely to not adopt bioinputs than to adopt them. It is clearly a limiting element in the use and expansion of technology.

4.4 Means of communication/dissemination that enable grain farmers to learn about and keep up to date with bioinputs technology

To analyze the means of communication/dissemination that allowed the grain farmers to update their knowledge about the bioinputs technology, the study participants were asked the following options regarding the source of knowledge about the technology and the source of updating and monitoring of changes in the technique: i) events/training/Lectures (Events); ii) input/consultancy companies (Companies); iii) research and education institution (University); iv) technical assistance and rural extension (TARE); v) digital communication (websites, webinars, podcasts, videos) (digital_media); vi) printed information (books, magazines, leaflets) (Print_material); vii) relatives (relatives); viii) other farmers or Professionals in the field (Professionals); ix) training/Demonstration (Demonstration); x) government agency (Gov); xi) nongovernmental organizations (NGOs); and xii) agricultural regenerative organizations (NGO_regenerative), as shown in Figure 6:

Analyzing Figure 6, it is not possible to observe explicit differences regarding sources of information (either to learn or to update) between the means of communication and dissemination used by grain farmers. The only observation is that there was no mention of sources of information originating from the government (governmental) or from relatives. For this reason, it is necessary to perform a statistical analysis to verify whether there is a difference between the means of knowledge cited by producers and whether they adopt the technology. Table 6 presents the statistical analysis of the data, showing the significance (or not) of the differences observed, except for the government source and relatives (not indicated by the grain farmers).

According to Table 6, it is possible to observe a significant difference at the 1% level between the sources presented:

• Events/Training/Lectures (Events);

• Input/Consultancy Companies (Companies);

• Research and Education Institution (University);

• Printed information (books, magazines, pamphlets) (Print_material);

• Training/Demonstration (Demonstration);

• Regenerative Agricultural Organizations (NGO_regenerative).

This difference demonstrates that such information sources, both for initial knowledge of the technology and for the maintenance of this knowledge, have a significant influence on the adoption of bioinputs technology. However, it is valid to consider that such an influence can be positive or negative. For this reason, another statistical tool, the chi-square test, was used. It evaluated the association between each of these knowledge sources with respect to whether to adopt the technology. Thus, identifying the sources of information for grain farmers regarding bioinputs technology is only part of this study. After that, we seek to understand how rural producers are affected by them in choosing the most appropriate channels of communication of information, and this information is presented below.

Table 7 presents the chi-square test analysis of the data obtained regarding the main sources of knowledge communication used by grain farmers who adopt or do not adopt bioinputs, either for knowledge of the technology or for improvement and technology updates.

Table 7 clearly shows that some sources of knowledge, whether as a new practice or to maintain knowledge, have positive effects, while others have negative effects. All test results were significant at the 5% level, thus indicating a strong association between technology adoption and the source of knowledge. When the expected number of respondents is greater and the actual result is smaller, it is expected that the item has a negative association, i.e., a greater number is expected than was observed. The reverse is also true. According to the results, the following conclusions can be shown:

• Events/Training/Lectures (Events) have a positive influence on grain farmers. The farmers who discover the technology and/or update themselves through events have a positive influence on its adoption.

• Input/Consultancy Companies (Companies) have a negative influence, either as an initial source of knowledge or update. The source of input companies negatively reverberates in the adoption of the practice.

• Research and education institutions (University) have positive effects on technology adoption.

• Printed information (books, magazines, leaflets) (Print_material) has a positive effect on technology adoption.

• Training/Demonstration (Demonstration) has a positive effect on technology adoption.

• Agricultural Regenerative Organizations (NGO_regenerative) have a positive effect on technology adoption.

5 CONCLUSIONS

According to the data presented, the main conclusions of this study are as follows: a) there is evidence that nonfamily farmers may be the most appropriate audience for the adoption and expansion of bioinputs, and b) there is a need for investment in qualified technical assistance to assist farmers in decision making and in the continued use of biological products.

It is noteworthy that grain farmers are increasingly informed about the importance of using biologically sourced products; however, there remains a lack of understanding regarding how the adoption and continued use of these products should be carried out. This finding challenges the traditional modernization theory, which often assumes that technology dissemination alone naturally leads to adoption. The adoption of bioinputs depends not only on knowledge availability but also on technical guidance and practical support. Therefore, it is essential that technical assistance and consultancy services are properly trained to adequately meet farmers’ demands. Raising farmers’ awareness of their role in establishing more balanced agriculture is essential for successful learning about the use of biological products, which require specific viability conditions as well as the use of appropriate equipment for their conservation and field application.

The results also indicate that there is specificity in the choice of information sources about bioinputs that influence the behavior of farmers regarding adoption or non-adoption. It is observed that farmers who chose not to adopt the use of bioinputs on their properties predominantly rely on input supply companies, along with technical consultancy from input resellers, as their main sources of knowledge and updates on biological products.

However, it is important to highlight that these technical companies, which often originate from conventional input resellers, may play a role in distancing farmers from adopting bioinputs. This occurs because the consultancies from these resellers have historically positioned themselves around the use of chemical substances, such as pesticides, which creates a perception of antagonism towards biological products. Consequently, rural producers may develop a sense of distrust regarding these consultancies when the focus is on adopting bioinputs. Specific studies are still needed to understand the reasons behind this distancing, but the hypothesis raised is related to the historical promotion of chemical products by these companies.

On the other hand, farmers who chose to adopt bioinputs on their properties remain updated through events, training courses, lectures, educational and research institutions, and regenerative agricultural organizations, acting as key agents in the learning process and, simultaneously, as disseminators of experiences acquired through agricultural practices resulting from the adoption of new technologies and agricultural innovations.

Therefore, offering various sources of technical knowledge can influence the interest of grain farmers in adopting this technology in the field. This information is relevant to the bioinputs industry, which can leverage these insights to improve the main forms of technology diffusion that reach farmers.

Public policies related to biological inputs, such as the National Bioinputs Program, created in 2020, may be important for South American countries such as Brazil. This is because the federal government’s promotion of biological products does not aim to break away from conventional agriculture or chemical inputs but rather to promote the coexistence of chemical and biological inputs towards the so-called bioeconomy. Biodiversity is a resource that can be explored and used to improve the ecological sustainability of agriculture.

Thus, sharing knowledge, quality technical assistance, and appropriate environmental policies, along with clear social, environmental, and economic indicators, are essential elements to motivate the adoption and expansion of bioinputs.

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