Adoption determinants, perceived benefits, and barriers to digital management systems in agriculture

Determinantes da adoção, benefícios percebidos e barreiras aos sistemas de gestão digital na agricultura

ABSTRACT:

This study examined the determinants, perceived benefits, and barriers to the adoption of Farm Management Information Systems (FMIS) in Brazilian agriculture, with a focus on the state of Mato Grosso do Sul. Digital transformation has become a strategic driver of efficiency, decision-making, and sustainability in rural management, yet the diffusion of FMIS remains limited. To investigate the factors shaping adoption, we conducted a survey with 103 farmers in August 2023 and analyzed the data using descriptive statistics and Probit modeling. The results indicated that financial constraints, distrust in digital technologies, and limited availability of qualified labor significantly reduce the likelihood of adoption. Conversely, FMIS are positively valued for their role in fiscal and accounting management, suggesting that their administrative benefits are more widely recognized than their operational potential. These findings underscore the importance of targeted strategies to promote digital inclusion in agriculture. Public policies and private initiatives aimed to improve training, facilitating access to credit, and strengthening digital infrastructure are essential to overcome current barriers. By addressing structural and behavioral challenges, FMIS adoption can be expanded, supporting the modernization and competitiveness of Brazilian agriculture.

Key words:
FMIS; technology adoption; farm management; innovation barriers; digital agriculture

RESUMO:

A transformação digital tornou-se um fator estratégico para a eficiência, a tomada de decisões e a sustentabilidade na gestão rural, mas a difusão dos SIGA ainda é limitada. Nesse sentido, este estudo procurou examinar os determinantes, os benefícios percebidos e as barreiras à adoção de Sistemas de Informação para Gestão Agrícola (SIGA) na agricultura brasileira, com foco no estado de Mato Grosso do Sul. Para investigar os fatores que influenciam a adoção, realizamos uma pesquisa com 103 agricultores, em agosto de 2023, e analisamos os dados utilizando estatística descritiva e modelagem Probit. Os resultados indicam que as restrições financeiras, a desconfiança em relação às tecnologias digitais e a limitada disponibilidade de mão de obra qualificada reduzem significativamente a probabilidade de adoção. Por outro lado, os SIGA são valorizados positivamente por seu papel na gestão fiscal e contábil, sugerindo que seus benefícios administrativos são mais amplamente reconhecidos do que seu potencial operacional. Essas descobertas ressaltam a importância de estratégias direcionadas para promover a inclusão digital na agricultura. Políticas públicas e iniciativas privadas voltadas para a melhoria da capacitação, a facilitação do acesso ao crédito e o fortalecimento da infraestrutura digital são essenciais para superar as barreiras atuais. Ao abordar os desafios estruturais e comportamentais, a adoção dos SIGA pode ser ampliada, apoiando a modernização e a competitividade da agricultura brasileira.

Palavras-chave:
SIGA; adoção de tecnologia; gestão agrícola; barreiras à inovação; agricultura digital

INTRODUCTION

The increasing complexity of agricultural production systems and the mounting pressure from globalized markets have pushed the agricultural sector toward a new era of digital transformation (BAI et al., 2023; SLOB et al., 2023). In this scenario, digital management technologies are no longer optional tools but strategic assets essential to ensuring efficiency, sustainability, and competitiveness in modern agribusiness (SENTURK et al., 2023). The transition to Agriculture 4.0 has been marked by the incorporation of data-driven solutions that enable producers to manage their resources more precisely, respond quickly to external shocks, and optimize production processes at every level of decision-making (SLOB et al., 2023). Digitalization has the potential to transform rural management by enhancing operational integration, strengthening monitoring capacity, and allowing for more intelligent, evidence-based planning (KARUNATHILAKE et al., 2023). Brazil is the world’s largest net exporter of food products, the leading producer of soybeans, and the second-largest beef producer, sustaining a thriving agricultural sector that makes significant contributions to GDP and employment (FAO, 2024). The state of Mato Grosso do Sul, which is the focus of this research, accounts for 6.7% of Brazil’s grain production and 8.0% of the total cultivated grain area in the 2023/24 season (CONAB, 2025a). Over the past decade, the state has experienced a 71.2% increase in grain production area (CONAB, 2025b). This means that it ranks as the fifth-largest producer and cultivated area in the country. While some authors have examined the adoption of precision agriculture technologies in sugarcane production in São Paulo (CARRER et al., 2022; MOZAMBANI et al., 2023) or the adoption of messaging and information exchange applications in cattle farming (MENDES et al., 2023), our literature review did not identify any studies that specifically investigated the diffusion of Farm Management Information Systems (FMIS) in the Mato Grosso do Sul using survey-based methodologies.

In Brazil, this digital wave is accompanied by the emergence of a vibrant ecosystem of agricultural technology startups (Agtechs) (DIAS, 2023), research institutions, and governmental initiatives aiming to promote innovation in farm management (BALATSOURAS et al., 2023). Despite these advancements, the diffusion of digital tools remains uneven. Challenges such as poor infrastructure, limited digital literacy, financial constraints, and cultural resistance continue to hinder adoption, especially among small and medium-sized farmers (DINELLI et al., 2022; LAJOIE-O’MALLEY et al., 2020). Within this context, Farm Management Information Systems (FMIS) emerge as a particularly promising innovation (CARRER et al., 2017). These systems offer integrated platforms that collect, organize, and analyze data from multiple farm operations, providing a unified environment for managerial, financial, and operational control (SORENSEN et al., 2010a; TERENCE & PURUSHOTHAMAN, 2020). When effectively implemented, FMIS contributes not only to increased productivity and cost-efficiency but also to environmental sustainability and risk mitigation (CARRER et al., 2017; MUANGPRATHUB et al., 2019).

However, FMIS adoption is far from widespread. Empirical evidence suggested that a variety of factors, including structural characteristics of the farm, individual attributes of the farmer, and perceived risks, strongly influence whether and how these systems are incorporated into daily agricultural practices. While some producers embrace digital technologies with enthusiasm, others hesitate due to uncertainty about return on investment, lack of trust in automated systems, or insufficient technical support. Understanding this divergence is critical to designing effective interventions, public policies, and business strategies that aim to democratize access to agricultural innovation and bridge the digital divide in rural regions (FOUNTAS et al., 2015; SORENSEN et al., 2010a; 2010b).

In this context, this study proposes to investigate how farm structure, producer profiles, and behavioral variables, particularly risk aversion, affect the adoption of FMIS. It also examines the main perceived barriers that prevent widespread implementation and explores the operational and administrative benefits that motivate adoption. By combining descriptive analysis with econometric modeling, this research contributes to a better understanding of the technological readiness of Brazilian agriculture and provides valuable insights for stakeholders interested in advancing rural digitalization.

The article is structured into five sections. Following this introduction, Section 2 presents a comprehensive literature review on digital agricultural management and the factors influencing technology adoption. Section 3 describes the research methodology, including sample design, data collection procedures, and analytical methods. Section 4 discusses the main findings, organized according to the tested hypotheses. Finally, Section 5 outlines the study’s conclusions and reflects on their implications for future research, agricultural policy, and technology diffusion strategies.

Literature review

The discussion about the factors that influence farmers’ decisions to adopt new technologies is a recurring and widely revisited issue in agricultural economic studies literature (CARRER et al., 2013; 2015; 2017; PIVOTO et al., 2019). Early adoption of technological innovations by farmers can generate significant competitive advantages, such as increased total productivity, when compared to those who either resist innovation or delay its implementation. This highlights the heterogeneous nature of the adoption process and underscores the importance of identifying and analyzing the key factors that influence it (CARRER et al., 2015; 2017; SORENSEN et al., 2010b).

The digital transformation of agriculture has significantly advanced the use of Information Technologies (IT) as strategic instruments for enhancing farm management. This evolution began with Decision Support Systems (DSSs), which allowed decision-making to incorporate not only agronomic variables but also financial and administrative considerations (FOUNTAS et al., 2015). Building on these foundations, modern intelligent farm management systems have emerged, integrating diverse technologies to boost production efficiency and competitiveness in the agricultural sector (SLOB et al., 2023). The concept of Smart Farming (SF) reflects the convergence of precision agriculture, information technology, and FMIS. Unlike traditional approaches focused solely on field variability, SF promotes real-time monitoring and management of diverse data sources, which supports faster and more accurate decision-making (PIVOTO et al., 2019; WOLFERT et al., 2017). The emergence of digital infrastructures and knowledge-sharing platforms has reinforced this shift, improving communication among stakeholders and enabling the dissemination of best practices (DE ALENCAR et al., 2017).

Field Management Software (FMS), the precursors to modern Farm Management Information Systems (FMIS), were instrumental in this evolution by providing integrated oversight of agricultural processes and laying the foundation for data-driven decision-making (SAIZ-RUBIO & ROVIRA-MÁS, 2020). These tools support critical operations such as input allocation, phytosanitary monitoring, and pesticide management, ultimately improving operational coherence. FMIS become foundational to strategic planning in agriculture, contributing directly to productivity gains and sustainability goals (AMMANN et al., 2022). Similarly, Enterprise Resource Planning (ERP) systems adapted for agriculture have enabled the automation and integration of financial, logistical, and production activities, thereby enhancing cost control and strengthening the competitiveness of agribusinesses (VARBANOVA et al., 2025; SANTOS et al., 2022).

Despite the promise of these digital tools, adoption remains uneven. Key barriers included high implementation costs, inadequate technical training, and increasing demand for specialized professionals. These challenges are especially critical in rural and resource-constrained regions, where disparities in access to infrastructure and human capital exacerbate digital inequality (CALLADO et al., 2007; KLERKX et al., 2019; KLERKX & ROSE, 2020; PIVOTO et al., 2019).

Understanding the determinants of FMIS adoption requires attention to the multifactorial nature of innovation in agriculture. Adoption is not a linear process but rather a dynamic one, influenced by changing perceptions, accumulated experience, and contextual factors such as infrastructure and policy support (CARRER et al., 2017). According to the literature, adoption is shaped by socioeconomic conditions, individual farmer characteristics, and cognitive-structural variables (JAKKU et al., 2019; MARRA et al., 2003; MICHELS et al., 2019; PIVOTO et al., 2019). Education, financial resources, and access to reliable information increase the likelihood of adoption, especially when farmers engage with networks of extension agents and support institutions (CARRER et al., 2013; 2015; 2017).

Individual characteristics, such as risk tolerance, confidence in digital tools, and expectations of economic return, also influence behavior (MARRA et al., 2003). Cognitive dimensions, including prior agricultural experience, perceptions of ease of use, and familiarity with technology, significantly affect the likelihood of adoption (ADRIAN et al., 2005; MICHELS et al., 2019; MOHR & KÜHL, 2021). Technologies perceived as intuitive and beneficial tend to be adopted more rapidly, while complex systems requiring advanced training often face resistance.

In summary, previous studies have advanced our understanding of technological adoption in agriculture, highlighting the role of socioeconomic, behavioral, and structural factors (CARRER et al., 2013; 2015; 2017; MICHELS et al., 2019). However, most empirical evidence comes from developed countries, and relatively little is known about the determinants of FMIS adoption in Latin America (KLERKX et al., 2019; KLERKX & ROSE, 2020). Even within the Brazilian context, survey-based analyses remain scarce and fragmented, with limited attention to specific states. This gap is particularly relevant for Mato Grosso do Sul, a leading agricultural producer (CONAB, 2025a; DIAS, 2023) that still faces structural constraints and heterogeneous farmer profiles. By analyzing both structural and behavioral variables through econometric modeling, this study advances the literature by providing original evidence from an underexplored but highly relevant agricultural region, while also offering insights with potential applicability to other contexts facing similar challenges.

A comprehensive understanding of these dynamics is crucial to designing effective strategies for promoting digital inclusion in agriculture. Public policies and private initiatives must target both structural and behavioral barriers, ensuring that farmers are equipped not only with tools, but also with the knowledge and support needed to integrate them successfully into daily farm management.

Research method

The combination of descriptive statistics and econometric models is a widely used methodological approach (CASINILLO & SERIÑO, 2022; DE SOUZA FILHO et al., 2023; MENDES et al., 2024; XIE et al., 2025).

Sample and data collection

Due to the absence of a centralized registry of rural properties or agricultural producers in Brazil, the exact number and complete records of grain producers in Mato Grosso do Sul remain unknown. For this reason, a non-probabilistic sampling method was adopted in this study, whereby eligible participants were grain producers (soybean and maize) located in specific macro-regions of the state (Maracaju and Dourados). This approach is particularly useful in agricultural research when access to producers is limited and when local knowledge is essential for data collection. It is also suitable when rapid data collection is required during critical periods of the agricultural calendar, such as planting seasons (KYVERYGA, 2019; MAMATOV et al., 2025). However, this sampling strategy may introduce selection bias and limit the generalizability of the findings.

The empirical investigation, quantitative in nature and cross-sectional in design, was conducted with a non-random sample of 103 farmers. Data collection took place between August and November 2023, through a structured, in-person questionnaire. To recruit respondents, the researcher relied on support from cooperatives, associations, and agribusiness firms in the region, which already maintained contact with farmers who matched the desired profile. The procedure involved approaching farmers upon arrival, presenting the study, and obtaining informed consent to participate. Data collection was carried out exclusively through face-to-face interviews conducted directly by the researcher. This strategy aimed to maximize respondent participation and to ensure systematic and controlled data acquisition (DEVELLIS & THORPE, 2021).

A critical consideration regarding the representativeness of our sample is the reported average farm size of 1,627 hectares, which is comparable to the state average of 3,459 hectares (CONAB, 2023; IBGE, 2023a). This contextual information is further corroborated by the state’s economic profile, where soybean accounts for 66.0% of the total financial value of agricultural production, followed by maize (16.6%) and sugarcane (12.4%) (IBGE, 2023b). Additionally, the technological environment is favorable, as rural internet access reached 84.8% of the population in 2024, approaching near-universal coverage due to public policy initiatives (GOVERNO DO BRASIL, 2025).

Therefore, the dataset employed in this study consists of primary and original microdata collected in the field, yielding a rich foundation for subsequent analysis. The instrument captured information on farm structural characteristics, producer profiles, and perceived barriers to the adoption of Farm Management Information Systems (FMIS). The survey employed closed-ended questions and five-point Likert scales to facilitate statistical analysis and hypothesis testing (DEVELLIS & THORPE, 2021; NEWMAN, 2003). To ensure instrument validity, a pilot test was conducted with 10 farmers, resulting in refinements to item wording and the addition of three new items (YIN, 2005).

Data analysis followed established guidelines, with emphasis on farmers’ perceptions regarding the drivers and constraints of FMIS adoption (FOSTER & ROSENZWEIG, 2010; BEIER & ACKERMAN, 2005; GROHER et al., 2020).

Conceptual model and hypotheses

The conceptual model developed in this study explores how individual, behavioral, and structural factors affect FMIS adoption among grain producers. The hypotheses were formulated to test the influence of personal characteristics, perceived benefits, risk aversion, and structural barriers.

Personal characteristics of the farmer

The decision to adopt new technologies is influenced by individual factors such as age and education level. Younger and more educated farmers tend to be more receptive to adopting innovations, while older farmers or those with limited access to education may be more resistant to FMIS implementation (NIKKILÄ et al., 2010; SORENSEN et al., 2010a).

Hypothesis H1: The personal characteristics of farm owners (age and education level) positively influence the adoption of FMIS.

Perceived benefits

FMIS are widely recognized as essential tools for the modernization of farm management. These systems enable process automation, integration of operational and administrative data, and real-time monitoring, thereby improving productivity and reducing costs. Moreover, they support strategic decision-making by transforming raw data into actionable knowledge (BIO, 2008; SOUZA & LOPES, 2024; PADOVEZE, 2009).

Hypothesis H2: The farmer’s perception of the benefits offered by FMIS positively influences adoption, particularly regarding operational and administrative management.

Risk aversion and resistance to change

Farming decision-making inherently involves risk, and the introduction of new technologies can create uncertainty regarding return on investment and system reliability. Farmers with higher levels of risk aversion may delay or avoid adopting FMIS, preferring to maintain traditional management practices (FOUNTAS et al., 2015).

Hypothesis H3: It is expected that higher risk aversion is associated with a lower likelihood of adopting FMIS.

Perceived barriers

Despite the potential advantages offered by FMIS, various factors may hinder their implementation, including high costs, lack of digital infrastructure, absence of specialized technical support, and insufficient workforce training. These obstacles limit farmers’ ability to fully leverage the benefits of these technologies, especially in small and medium-sized farms (TEKINERDOGAN et al., 2019).

Hypothesis H4: Farmers perceived that factors such as limited investment capacity, distrust in technology, lack of workforce qualifications, insufficient technical support, and inadequate infrastructure negatively influenced the adoption of innovative technologies and practices.

Probit model

The probit model has been widely employed for modeling qualitative response variables (AMEMIYA, 1981). This model is based on the cumulative normal distribution and is particularly suitable for situations in which the dependent variable is binary, such as decisions involving the adoption or non-adoption of a given technology.

The dependent variable reflects the farmer’s decision regarding FMIS adoption and is conditioned by a set of explanatory variables (X). The relationship between the explanatory variables and the adoption decision can be represented by the following latent equation:

y_i^*= 𝛽𝑋_i + e₁ i = 1, 2, …, N (1)

Where X represents the set of explanatory variables influencing FMIS adoption, β are the model coefficients, and u denotes the random error term. Since the latent variable (y _i ^*) is not directly observable, the adoption decision is represented in binary form:

y_i = 1 if y_i ^* > 0

y_i = 0 otherwise

This implies that if the farmer perceives benefits and adopts the FMIS, the variable y_i assumes the value of 1; otherwise, it takes the value of 0.

The probit model is estimated to use the Maximum Likelihood method, as it is nonlinear in its parameters. The change in the probability of adoption is obtained through the partial derivatives of the explanatory variables (GREENE, 2003). The interpretation of the estimated coefficients does not directly reflect marginal effects but rather the predicted probabilities, which vary according to the values of the explanatory variables.

For data analysis and model implementation, the statistical software RStudio was used (version 4.4.1), enabling the estimation process and the identification of key determinants of FMIS adoption among grain producers in the studied region.

Explanatory variables of the econometric model

The model included four groups of explanatory variables: (i) Demographic characteristics (age and education); (ii) Perceived barriers (capital investment, trust in technology, and skilled labor); (iii) Economic motivations (productivity, quality, and fiscal control); (iv) Risk aversion. Table 1 presents detailed descriptions of each variable.

Table 1
Description of variables that drive or hinder FMIS - Farm Management Information System adoption.

Variable	----------------------------Description----------------------------	---------------------Theoretical Foundation---------------------
Adoption of integrated systems (y)	A binary (dummy) variable, assuming a value of 1 if the farmer has adopted a Farm Management Information Systems (FMIS), and 0 otherwise.
Log_AGE (x1)	Age of the farmer, expressed in years.	The literature indicated that personal characteristics - such as age - play a decisive role in shaping technology adoption behavior. The inclusion of this variable is supported by empirical evidence from NIKKILÄ et al. (2010) and SORENSEN et al. (2010).
Education (x2)	Binary (dummy) variable taking a value of 1 if the farmer holds high school education, and 0 otherwise.	Higher levels of education enhance individuals’ cognitive capacity to assimilate technological innovations and are consistently found to be positively correlated with the adoption of agricultural technologies (LI et al., 2019).
Risk aversion (x3)	A proxy variable for the farmer’s risk propensity. It is measured using a Likert scale based on the farmer’s level of agreement with the following statement: “When it comes to business, I prefer the safer option, even knowing that I may earn less”. The scale ranges from 1 (strongly disagree) to 5 (strongly agree). Higher values indicate a greater inclination toward risk aversion.	Risk aversion represents a critical behavioral dimension in farmers’ decision-making processes. Its inclusion as an explanatory variable is justified by the findings of FOUNTAS et al. (2015), who emphasize the role of psychological and attitudinal factors in technology uptake.
Production (x4)	Ordinal variable measured on a Likert scale ranging from 1 (not important) to 5 (very important), based on the following statement: “Improving yields and productivity is an important reason to adopt a FMIS”	Motivational aspects - particularly those linked to operational efficiency and productivity improvement - emerge as key drivers of adoption. The conceptual grounding for these variable draws on the research of SOUZA & LOPES (2024), BIO (2008), and PADOVEZE (2009), which highlighted the economic rationale underlying adoption behavior.
Quality (x5)	Ordinal variable measured on a Likert scale from 1 (not important) to 5 (very important), in response to the statement: “Improving product quality is an important reason to adopt a FMIS”	Perceived value and expected benefits exert a direct influence on adoption intentions, whereas perceived risks tend to diminish such propensity (ZHANG et al., 2023).
Control (x6)	Ordinal variable measured on a Likert scale from 1 (not important) to 5 (very important), referring to the following statement: “Improving fiscal and accounting control is an important reason to adopt a FMIS”	Fiscal and accounting control constitutes a central motivation for the adoption of Farm Management Information Systems (FMIS). Studies by SOUZA & LOPES (2024), BIO (2008), and PADOVEZE (2009) emphasized that digital management tools contribute to greater transparency, traceability, and regulatory compliance within agricultural enterprises.
Capex (x7)	Ordinal variable measured on a Likert scale from 1 (strongly disagree) to 5 (strongly agree), based on agreement with the statement: “A potential barrier to adopting a FMIS is the farm investment capacity”	Implementation cost remains one of the most prominent barriers to technological adoption. The relevance of this constraint is substantiated by TEKINERDOGAN et al. (2019) and SMITH et al. (2019), who demonstrated that financial considerations often outweigh perceived long-term benefits.
Trust (x8)	Ordinal variable measured on a Likert scale from 1 (strongly disagree) to 5 (strongly agree), derived from the statement: “A potential barrier to adopting a FMIS is the lack of trust in the recommendations, or the incompleteness of solutions offered to meet the farmer’s needs”	Trust in technological systems and perceived risk exert a decisive influence on adoption outcomes. Greater trust fosters adoption, whereas elevated perceptions of risk act as deterrents (VERDOUW et al., 2021).
Labor (x9)	Ordinal variable measured on a Likert scale from 1 (strongly disagree) to 5 (strongly agree), in relation to the statement: “A potential barrier to adopting a FMIS is the lack of skilled labor to operate the software/technologies”	The shortage of workers equipped with the technical competencies necessary to operate digital technologies is widely acknowledged as a major impediment to adoption (MOYA et al., 2025). This structural limitation underscores the broader challenge of human capital development in digital agriculture.

• Source: elaborated by the authors (2024).

Table 2 presents the means and standard deviations of the main variables, comparing adopters and non-adopters to highlight statistically significant differences (ASHRAF et al., 2009).

Table 2
Descriptive statistics for model variables.

Adopters (43 adopters)			Non-adopters (60 non-adopters)
Variable	Mean	SD	Variable	Mean	SD
Log_AGE (x1)	56.517	12.581	Age (x1)	59.044	14.599
Education (x2)	0.552	0.502	Education (x2)	0.356	0.484
Risk aversion (x3)	3.690	0.842	Risk aversion (x3)	4.622	0.576
Production (x4)	3.759	0.865	Production (x4)	4.133	0.726
Quality (x5)	2.569	1.156	Quality (x5)	3.267	0.915
Control (x6)	4.793	0.409	Control (x6)	4.822	0.490
Capex (x7)	3.552	0.705	Capex (x7)	3.156	0.737
Trust (x8)	3.310	0.821	Trust (x8)	3.444	0.693
Labor (x9)	2.828	0.861	Labor (x9)	2.222	1.020

• Source: Compiled by the authors (2024).

The statistical analysis, conducted through a Probit model, revealed key determinants for the adoption of FMIS (Farm Management Information Systems) among rural producers. To evaluate the model’s goodness-of-fit, several statistical tests were performed, including the log-likelihood, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and the model’s classification accuracy.

The log-likelihood for the fitted model was -41.54 with 10 degrees of freedom. This metric assesses how well the model fits the observed data, with less negative values indicating a better fit (GREENE, 2003). For comparison, the null model - containing no explanatory variable exhibited a log-likelihood of -70.57. This substantial difference indicates that the inclusion of explanatory variables significantly enhanced the model’s predictive power regarding FMIS adoption.

To supplement this evaluation, the AIC and BIC values were analyzed, as both are widely employed in model selection. The AIC for the Probit model was 103.09, compared to 143.14 for the null model. Given that AIC penalizes overfitting by favoring models with a better balance between fit and simplicity, lower values are preferable (AKAIKE, 1974). Similarly, the BIC for the fitted model was 129.43, versus 145.78 for the null model. The BIC imposes a stricter penalty for model complexity, thus favoring simpler, more parsimonious models (SCHWARZ, 1978). The lower AIC and BIC scores of the fitted model relative to the null model reinforce its superior predictive capacity.

Beyond fit criteria, the distribution of predicted probabilities was assessed. The predicted values ranged from 0.0027 to 0.9998, with a median of 0.605. This range suggested that the model effectively generates well-dispersed probabilities across the dependent variable’s spectrum, reflecting the influence of the independent variables. To evaluate the model’s predictive accuracy, the classification rate was calculated, reaching 78.64%, which demonstrates satisfactory performance within the applied context.

Additionally, a Likelihood Ratio Test (LRT) was conducted to compare the full model (including all explanatory variables) to the null model. The LRT yielded a value of 58.06 with 9 degrees of freedom, indicating a statistically significant difference between the models. The extremely low P-value (≈ 3.17e-09) underscores the relevance of the explanatory variables in predicting FMIS adoption, thus justifying their inclusion in the Probit model.

The results indicated that the Probit model offers a satisfactory fit to the data, as evidenced by the log-likelihood, AIC, BIC, and LRT statistics. The classification rate of 78.64% further supports the model’s predictive capability. Therefore, the Probit regression emerges as a statistically robust tool for analyzing binary outcomes. The choice of a Probit model is justified by its assumption that the relationship between the dependent and independent variables follows a cumulative normal distribution, making it an effective alternative to logistic regression in specific analytical contexts. Table 3 displays the detailed results of the Probit model.

Table 3
Probit model results: determinants of FMIS - Farm Management Information System adoption.

Variable	---------Estimate---------	--------Std. Error--------	---------Z-value---------	---------P-value---------
(Intercept)	-1.7663	3.7325	-0.4732	0.6361
Log_AGE (x1)	0.8729	0.7149	1.2211	0.2220
Capex (x7)	0.4780	0.2410	1.9832	0.0473*
Trust (x8)	-0.6026	0.2450	-2.4595	0.0139*
Labor (x9)	0.5151	0.2100	2.4524	0.0142*
Education (x2)	0.7594	0.3450	2.2008	0.0278*
Quality (x5)	-0.4406	0.1982	-2.2234	0.0262*
Production (x4)	-0.4524	0.2111	-2.1430	0.0321*
Control (x6)	0.6691	0.4438	1.5079	0.1316
Risk aversion (x3)	-0.7114	0.2573	-2.7647	0.0057**
Log-Likelihood	-41.5426
AIC	103.0851
BIC	129.4324
Null Deviance	141.1432
Residual Deviance	83.0851
Symbol	Significance Level (P-value)		----------------------------------------Interpretation----------------------------------------
***	P ≤ 0.001		Highly significant (strong evidence against the null hypothesis, H0)
**	0.001 <P ≤ 0.01		Very significant (substantial evidence against H0)
*	0.01 < P ≤ 0.05		Statistically significant at the conventional 5% level
.	0.05 < P ≤ 0.1		Marginally significant (suggestive but not conclusive evidence)
	P > 0.1		Not statistically significant (insufficient evidence to reject H0)

• Source: Compiled by the authors (2024).

RESULTS AND DISCUSSION

The results obtained from the Probit model estimation are presented and discussed in this section according to the hypotheses formulated in the conceptual framework. The analysis aimed to identify the main determinants that influence the adoption of FMIS among farmers, considering personal characteristics, perceived benefits, risk aversion, and structural barriers. Each hypothesis is examined individually, with attention to both descriptive statistics and econometric estimates, allowing for a more comprehensive understanding of how behavioral, economic, and institutional factors shape technology adoption decisions in the agricultural sector.

Hypothesis H1 - The personal characteristics of farm owners (age and education level) influence the adoption of FMIS

Hypothesis H1 proposed that personal characteristics, specifically, the farmer’s age and education level, would influence the likelihood of adopting FMIS. The descriptive statistics revealed that FMIS adopters had a slightly lower average log_AGE (56.52 years; SD = 12.58) compared to non-adopters (59.04 years; SD = 14.60). Additionally, the share of producers who completed high school was substantially higher among adopters (55.2%) than among non-adopters (35.6%), suggesting a possible link between education and technology adoption. These initial findings were further explored through the Probit regression results. The variable representing age, transformed into a logarithmic scale (log_AGE), was used to reduce the asymmetry in the sample distribution, which improves the validity of the Probit model’s inferences. Although, a formal comparison with a quadratic model was not performed, the choice of the logarithm is justified by its strong theoretical basis in the agricultural economics literature, as it adequately models the idea that returns (or the impact of age) are decreasing in technology adoption studies (GREENE, 2018). The variable exhibited a positive but statistically non-significant coefficient (β = 0.8729; P-value = 0.2220), indicating that while log_AGE might play a role in shaping familiarity with farm operations, it does not significantly affect the likelihood of adopting FMIS in the sample analyzed. In contrast, the variable Education (x₂) presented a positive and statistically significant coefficient (β = 0.7594; P-value = 0.0278), supporting the hypothesis that education positively influences FMIS adoption. This result aligns with previous studies indicating that higher levels of formal education are associated with better access to information, improved capacity for technological assimilation, and greater engagement with innovation-oriented networks (GENIUS et al., 2006; ASHRAF et al., 2009). Therefore, while age alone does not appear to be a determining factor, education plays a decisive role in equipping farmers with the cognitive and informational resources necessary to understand, evaluate, and implement digital management technologies.

Hypothesis H2 - perceived benefits

Hypothesis H2 suggested that the farmer’s perception of the benefits offered by FMIS, particularly in terms of operational efficiency, quality improvement, and cost control, would positively influence adoption. However, the results revealed a more nuanced scenario. The motivation for cost control (CONTROL) was the only benefit-related variable to exhibit a positive coefficient (β = 0.6691), though it was not statistically significant (P-value = 0.1316). This suggested that although FMIS adopters acknowledge the importance of fiscal and administrative management tools, this motivation alone does not decisively drive adoption behavior. The analysis showed that both productivity-related P-value = 0.0321) and quality-related (P-value = 0.0262) motivations exhibit negative and statistically significant coefficients, suggesting that focusing on these aspects reduces the likelihood of adopting FMIS (Farm Management Information Systems). Regarding productivity, the mean score among non-adopters was 4.133 (SD = 0.726), higher than that of adopters, 3.759 (SD = 0.865), indicating that the latter assign less importance to this factor, possibly because they rely on alternative forms of technological innovation, such as mechanization and biotechnology (FARRELL et al., 2022; ROSE et al., 2021). In terms of quality, the mean score was 3.267 (SD = 0.915) for non-adopters and 2.569 (SD = 1.156) for adopters, reinforcing that FMIS users also tend to consider this aspect less relevant. This perception may be associated with the view that quality improvements are more closely linked to good agricultural practices and certification schemes than to the use of digital technologies (EASTWOOD et al., 2022; KLERKX & JANSEN, 2021).

Hypothesis H3 - risk aversion and resistance to change

Hypothesis H3 posited that higher levels of risk aversion among farmers would be associated with a lower likelihood of adopting FMIS. The empirical results strongly support this hypothesis. The variable RISK AVERSION presented a negative and highly statistically significant coefficient (β = -0.7114; P-value = 0.0057) in the Probit model, indicating that producers who are more risk-averse are significantly less likely to implement FMIS. Descriptive statistics reinforce this conclusion: non-adopters reported a higher average score for risk aversion (4.622) compared to adopters (3.690), suggesting a clear behavioral distinction between the two groups. These findings align with the broader literature on technology adoption in agriculture, particularly studies emphasizing the role of uncertainty and perceived vulnerability in the decision-making process (FOUNTAS et al., 2015; MARRA et al., 2003). Farmers often associate digital innovations with high initial costs, a steep learning curve, and uncertain returns, factors that weigh heavily on those with conservative profiles or limited tolerance for uncertainty. The influence of risk aversion on FMIS adoption also resonates with the Technology Acceptance Model (TAM), which highlights perceived risk as a key inhibitor of user acceptance (DAVIS, 1989). In this context, promoting FMIS adoption requires not only improving technological performance and usability but also reducing perceived uncertainty through targeted actions, such as demonstration projects, pilot programs, technical support, and financial guarantees. By directly addressing the psychological and informational barriers faced by risk-averse producers, public and private initiatives can play a crucial role in mitigating resistance to change and fostering greater digital inclusion in rural areas.

Hypothesis H4 - perceived barriers

Hypothesis H4 asserted that structural and perceptual barriers - such as limited investment capacity, distrust in technology, shortage of qualified labor - would negatively impact the adoption of FMIS. The results confirmed this expectation, revealing the substantial weight of these barriers in the decision-making process. The Probit model identified three barrier-related variables as statistically significant: CAPEX (capital constraint), TRUST (confidence in technology), and LABOR (availability of skilled labor). The CAPEX variable (investment capacity) exhibited a positive and significant coefficient (0.4780; P-value = 0.0473). Since the question assessed factors that could lead to non-adoption of FMIS (Farm Management Information Systems), the result indicates that a greater perception of financial limitation increases the relevance of this factor as a barrier. Non-adopters reported a mean of 3.156 (SD = 0.737), whereas adopters reported a mean of 3.552 (SD = 0.705), highlighting that FMIS users perceive fewer financial constraints. This finding is consistent with the literature, which identifies acquisition, maintenance, training costs, and limited access to credit as significant barriers to the adoption of digital innovations, particularly among small and medium-sized producers (KLERKX & ROSE, 2020; FEDER et al., 1985). However, the TRUST variable (trust in technology) showed a negative and significant coefficient (-0.6026; P-value = 0.0139). Since the question addressed factors that could lead to non-adoption of FMIS, the result indicates that higher perceived trust (i.e., lower distrust) reduces the relevance of this factor as a barrier. Non-adopters reported a mean of 3.444 (SD = 0.693) and adopters 3.310 (SD = 0.821), highlighting greater perceived uncertainty among non-adopters. These findings align with the literature, which links hesitation to adopt technologies to lack of clear information and the need for adequate technical support and continuous training (ROSE et al., 2021; HALL & KHAN, 2003). In the case of the variable LABOR, related to the availability of skilled labor, presented a positive and significant coefficient (β = 0.5151; P-value = 0.0142), since the question addressed factors that could lead to the non-adoption of FMIS (Farm Management Information Systems), the result suggested that the perception of labor scarcity is understood as a barrier, but it may also reflect a compensatory effect: producers facing greater labor constraints tend to consider FMIS as an alternative to mitigate such deficiencies. This finding highlighted the heterogeneity of barriers, which do not operate in a unidimensional manner but may simultaneously restrict and stimulate adoption, depending on the producer’s profile and conditions. These results are consistent with the literature emphasizing the importance of technical training and institutional support as fundamental elements to enable digital transformation in agriculture (DESCONSI & DE SÁ, 2024; COSTA et al., 2023; EASTWOOD et al., 2019). Collectively, the results highlighted that FMIS adoption is not merely a matter of technological readiness, overcoming these barriers requires coordinated public and private strategies focused on financing, training, and the development of digital infrastructure capable of supporting sustainable technology diffusion across diverse rural contexts.

Final considerations of the results and discussion section

Taken together, the results of the Probit model offer a comprehensive view of the multifaceted factors influencing FMIS adoption among farmers. The hypothesis-driven analysis revealed that individual characteristics, particularly education level, play a meaningful role in shaping adoption behavior, while log_AGE, despite a suggestive trend, was not statistically significant. Contrary to expectations, motivations related to productivity and quality showed a negative association with adoption, suggesting that farmers may not perceive FMIS as the most direct path to operational gains. Instead, motivation tied to cost control and fiscal management, although not statistically significant, emerged as more aligned with the perceived function of these systems. Risk aversion was confirmed as a major inhibitory factor, reinforcing the idea that psychological and informational barriers often outweigh technical considerations. Structural and perceptual constraints, especially capital limitations, distrust in technology, and lack of qualified labor, also proved decisive, highlighting the need for systemic interventions that go beyond mere availability of digital tools. These findings underscore the complexity of the adoption process, which is shaped not only by rational cost-benefit calculations but also by social, behavioral, and institutional dynamics. As such, policies aimed at expanding FMIS adoption must address both economic and educational asymmetries, while fostering trust and technical capacity among rural producers. The next section builds upon these insights to discuss the broader implications of the study and suggested avenues for future research and action.

CONCLUSION

This study examined the determinants of Farm Management Information Systems (FMIS) adoption by farmers in Mato Grosso do Sul, one of Brazil’s leading agricultural regions. The application of Probit modeling, complemented by hypothesis testing, proved to be a methodologically sound approach for analyzing the probability of adoption. This methodology allowed for the identification of key explanatory variables and provided statistical validation for the conceptual framework, ensuring precision in identifying significant relationships between farmer characteristics and FMIS adoption.

The empirical results highlighted that FMIS adoption is shaped by a set of decisive factors that can be either drivers or inhibitors. Among the driving factors, education emerged as a fundamental and statistically significant pillar. The analysis suggested that producers with higher levels of education are more equipped to understand, assimilate, and integrate the technological and management complexities that FMIS demand.

Conversely, perceived barriers and unexpected motivators are crucial for understanding why many farmers do not adopt the technology. Structural constraints, such as limited investment capacity and the lack of skilled labor, were found to be statistically significant barriers, reinforcing the idea that digital adoption is not just a technological choice but also a matter of economic viability and human capital.

Risk aversion and distrust in technology also stood out as critical behavioral barriers, indicating that psychological and perceptual factors often outweigh the mere availability of the digital tool.

In a finding contrary to expectations, motivations linked to an increase in productivity and quality showed a negative and statistically significant association with adoption. This suggested that farmers may not view FMIS as the primary solution for optimizing these areas, possibly preferring other innovations or agricultural practices. In contrast, the motivation for cost control and fiscal management, while not statistically significant in the model, emerged as the main perceived benefit, indicating that the administrative and accounting function of FMIS is more readily recognized by producers.

The study reinforced the need for targeted public policies and private-sector strategies to facilitate FMIS adoption in Mato Grosso do Sul. Initiatives such as financing mechanisms, technical assistance programs, and risk-reducing incentives can help overcome adoption hurdles and promote a more inclusive digital transition in rural areas. As an original contribution, this research integrates economic, behavioral, and structural dimensions in the analysis of FMIS adoption, going beyond traditionally fragmented approaches. It also sheds light on a state that remains underexplored in digital agriculture research, providing insights into its specific agricultural profile and the barriers faced by local producers. While the empirical focus is regional, the results can serve as a reference for other agricultural areas with similar characteristics, particularly where structural and behavioral constraints shape the pace of technological adoption.

ACKNOWLEDGMENTS

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Brazil - Finance Code 001. The authors also thank the Graduate Program in Production Engineering (PPGEP) at the Universidade Federal de São Carlos (UFSCar) for the institutional support and the bibliographic resources provided through the CAPES Journals Portal, which were essential for the development of this research.

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• CR-2025-0361.R1
• DECLARATION OF CONFLICT OF INTEREST
  The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
• DATA AVAILABILITY STATEMENT
  The data supporting the findings of this study are derived from a Master’s dissertation titled “Sistemas digitais de gestão na agricultura - benefícios e desafios na perspectiva dos produtores”, developed within the Graduate Program in Production Engineering at the Universidade Federal de São Carlos (UFSCar). The complete dataset and detailed research findings are available in the UFSCar Institutional Repository at <https://hdl.handle.net/20.500.14289/23671>. Additional raw data may also be requested directly from the corresponding author.
• DECLARATION OF USE OF ARTIFICIAL INTELLIGENCE
  No AI was used in the conception and writing the present manuscript.

Edited by

• ASSOCIATE EDITOR:
  Alessandro Dal’Col Lúcio (0000-0003-0761-4200)

• SCIENTIFIC EDITOR:
  Daniel Arruda Coronel(0000-0003-0264-6502)

Data availability

The data supporting the findings of this study are derived from a Master’s dissertation titled “Sistemas digitais de gestão na agricultura - benefícios e desafios na perspectiva dos produtores”, developed within the Graduate Program in Production Engineering at the Universidade Federal de São Carlos (UFSCar). The complete dataset and detailed research findings are available in the UFSCar Institutional Repository at <https://hdl.handle.net/20.500.14289/23671>. Additional raw data may also be requested directly from the corresponding author.

Publication Dates

• Publication in this collection
  27 July 2026
• Date of issue
  2026

History

• Received
  09 July 2025
• Accepted
  12 Jan 2026
• Reviewed
  16 May 2026