Smart Agriculture in Ecuador: Adoption of IoT Technologies by Farmers in Guayas to Improve Agricultural Yields

Abstract

The adoption of digital technologies, such as the Internet of Things (IoT), has emerged as a key strategy to improve efficiency, sustainability, and productivity in the agricultural sector, especially in contexts of modernization and digital transformation in developing regions. This study analyzes the key factors influencing the adoption of IoT technologies by farmers in the province of Guayas, Ecuador, and their impact on agricultural yields. The research is grounded in innovation diffusion theory and technology acceptance models, which emphasize the role of perception, usability, training, and economic viability in digital adoption. A total of 250 surveys were administered, with 232 valid responses (92.8% response rate), reflecting strong interest from the agricultural sector in digital transformation and precision agriculture. Using structural equation modeling (SEM), the results confirm that general perception of IoT (β = 0.514), practical functionality (β = 0.488), and technical training (β = 0.523) positively influence adoption, while high implementation costs negatively affect it (β = −0.651), all of which are statistically significant (p < 0.001). Furthermore, adoption has a strong positive effect on agricultural yield (β = 0.795). The model explained a high percentage of variance in both adoption (R² = 0.771) and performance (R² = 0.706), supporting its predictive capacity. These findings underscore the need for public and private institutions to implement targeted training and financing strategies to overcome economic barriers and foster the sustainable integration of IoT technologies in Ecuadorian agriculture.

1. Introduction

According to FAO [1], agriculture has historically been a key activity for economic and social development. Although it represents only 4% of world GDP, in developing countries can represent more than 25% [2,3]. In Ecuador, and especially in the province of Guayas, agriculture is an essential economic pillar, facing challenges such as climate change, population pressure and the need for sustainable production [4,5].

Since the Green Revolution, technology has been instrumental in increasing agricultural productivity [6]. However, this model based on the expansion of the production of a few staple crops has generated environmental and sustainability challenges that require new innovative solutions [7].

In this sense, digital technologies have become an essential part in business development and in improving the quality of life [8]. Their implementation in agriculture has made it possible to optimize production processes, make more efficient use of resources, and reduce the environmental impact [9]. Globally, this sector is a key part of value chains, since it provides fundamental services for economic stability, certifying food security and social sustainability [10,11,12]. However, major challenges remain, such as population growth, climate change and the urgency of achieving more sustainable food production. According to United Nations estimates, by 2050, the world population will reach 9.7 billion people [13,14].

The incorporation of Information and Communication Technologies (ICTs) includes tools such as online platforms, mobile applications, monitoring systems and digital communication, which support the implementation of digital and precision agriculture practices. These technologies enable farmers to access up-to-date data, automate processes and manage resources efficiently. In this context, tools such as the Internet of Things (IoT), Big Data, Artificial Intelligence (AI) and Blockchain have established themselves as fundamental pillars of this transformation. IoT refers to the interconnection of devices, sensors, machinery and systems through the Internet, allowing the collection, transmission and analysis of data in real time. Precision agriculture is based on the use of sensors and geospatial systems to apply inputs in a differentiated and efficient manner, optimizing production processes. Within this digital ecosystem, IoT is particularly relevant because of its capacity to generate key data on the state of crops, environmental conditions and other determinants of agricultural yields [15,16,17].

Smart agriculture based on the integration of digital technologies to accurately and efficiently manage production processes offers a promising way to respond to global challenges such as population growth, climate change and the need to ensure food security [18]. However, in agricultural regions such as Guayas, its adoption still faces significant barriers, including limited access to technological infrastructure, limited technical training and certain negative perceptions about its usefulness.

Nevertheless, it is noteworthy that, in contrast to other agricultural regions in Latin America, producers in Guayas show an open and receptive attitude towards the use of digital technologies. This disposition could be related to greater contact with previous technological experiences, academic initiatives or local agricultural modernization policies that have allowed farmers to become familiar with the advantages of these tools. This favorable attitude constitutes a solid basis for promoting more effective digital transformation processes in the region.

In this context, the survey applied in this study addressed aspects such as familiarity with and the current use of connected devices (e.g., sensors and digital platforms), perceptions of their benefits and barriers to their adoption, as well as the motivations that influence the integration of these tools into daily agricultural practices.

Therefore, this article aims to analyze the adoption of IoT technologies by farmers in the province of Guayas, in order to improve agricultural yields. In particular, it aims to examine the relationship between variables such as technical knowledge, perception of benefits, operational challenges, implementation cost and institutional support, with the intention to use and effectively adopt these technologies. The research seeks to provide contextualized evidence that will contribute to the design of policies and strategies aimed at promoting digital transformation in Ecuadorian agriculture.

2. Literature Review

In recent years, agriculture has experienced significant technological advances that have transformed crop monitoring and management practices. Smart agriculture has emerged as a key strategy to address challenges such as climate change, resource scarcity and growing food demand.

One of the most relevant technologies is the IoT, which allows the interconnection of sensors, machinery and software to collect and analyze data in real time. Several authors, such as Shafique et al. [19] have documented that this technology facilitates accurate decision making on irrigation, fertilization, and pest control, improving efficiency and reducing environmental impact [20], as illustrated in Figure 1. The adoption of digital tools allows not only to optimize production processes, but also to anticipate critical events and maximize efficiency in the use of natural resources [18].

2.1. A Smart Farming Approach to Yield Prediction

Crop yield is an essential component of agricultural production, the accurate prediction of which represents one of the greatest challenges for farmers and agronomists. This task is influenced by multiple factors, including soil properties, climatic conditions, seasonal fluctuations, seed quality, pest and disease management, nutrient availability, and the efficient use of water and other inputs [22,23].

In this scenario, smart agriculture, supported by emerging technologies such as IoT and AI, is emerging as a comprehensive solution to address these complexities [17,24]. The evolution of precision agriculture has given way to more complete systems that integrate in-field sensors, satellites, drones, GPS, cloud platforms and machine learning algorithms, which optimize agronomic practices and substantially improve decision making [16,25].

Several studies support this perspective. Balducci et al. [22] developed experiments aimed at predicting future harvests, reconstructing missing data and detecting sensor errors through geospatial analysis. Garg et al. [26] proposed a system that combines IoT sensors with machine learning and deep learning algorithms to identify crop diseases, reducing the need for manual labor. Likewise, Adel et al. [27] designed an IoT monitoring architecture for potato and tomato crops, focused on the early detection of diseases such as late blight, thus facilitating preventive intervention. Li et al. [28], on the other hand, developed an intelligent system for greenhouse management that applies an improved k-means algorithm to analyze environmental data and set optimal parameters for successive agricultural cycles.

Furthermore, from a technical perspective, the effectiveness of IoT systems in agriculture depends on a robust communication infrastructure. Factors such as bandwidth, latency, network reach, data encryption, and system scalability are critical to ensure connectivity, multiple sensor handling, and the secure transmission of large volumes of information [20].

The use of distributed sensors and intelligent monitoring systems has facilitated the collection of real-time data on critical variables, such as soil moisture, temperature, nutrient levels and the presence of pests or diseases. When processed through predictive models based on machine learning, these data allow for more accurate yield estimates, even weeks in advance of harvest [29,30].

2.2. IoT Technologies in Agriculture

The incorporation of the Internet of Things (IoT) technologies has gained relevance within the agricultural field. Agricultural producers have increasingly begun to recognize the benefits that these tools offer, both in monitoring crop development and in optimizing production processes and yield prediction.

One of the main advantages of the IoT system is its ability to optimize the use of essential resources, such as water for irrigation, fertilizers, insecticides, and pesticides. Also, its applications are very diverse, including soil condition monitoring, plant growth monitoring, and overall crop yield increase [31]. A study conducted in the United States showed that 70% of farmers were already familiar with IoT technologies and were positive about their adoption [32].

Similarly, recent research proposes that, given the considerable pace of technological development currently underway, IoT is likely to continue to establish itself as a key component in multiple agricultural activities [18]. Among its most prominent functions are data collection through smart sensors, the use of connected mobile devices, data storage in the cloud, the automation of routine tasks, and decision making based on real-time information [31].

However, the effective implementation of these technologies presents significant challenges. These include the need to discover useful patterns from historical records, the proper processing of large volumes of unstructured data, the proper handling of sensor-generated images and videos, as well as the seamless integration of all this information across digital platforms [13]. Additionally, adoption may be limited by the high costs of accessing these technologies, the lack of technical training among farmers, and the need to strengthen information security systems in the agricultural environment [33,34].

The role of government also emerges as a crucial factor in this process. According to Goo and Heo [35], the involvement of the state contributes to reducing uncertainties surrounding technological adoption, thus facilitating its expansion. Also, Chong and Ooi [36] in a study on the implementation of RosettaNet standards in Malaysia, highlighted that public policies such as subsidies and tax exemptions played an important role in encouraging the use of emerging technologies. Similarly, Marakarkandy et al. [37] showed that government support is positively correlated with the adoption of new technological solutions.

2.3. Studies on Technology Acceptance, Usability and Adoption in Agriculture

Several studies have explored the acceptance and usability of digital technologies applied to the agricultural sector, particularly those based on IoT. In Malaysia, for example, research revealed that factors such as yield expectation, enabling conditions, perceived enjoyment, trust and technological autonomy positively influence the perception and acceptance of IoT [38]. Complementarily, the usability of an IoT-based agricultural irrigation system was evaluated, finding that the ease of use of the interface is critical to support decision making in both experienced users and those with less technological familiarity [39].

In Iran, a study of attitudes and intentions toward adopting precision farming technologies identified individual innovativeness, trust, perceived usefulness, and ease of use as key factors [40]. Similarly, research in North Dakota showed that larger farming operations and those oriented toward corn cultivation are more likely to adopt technologies such as variable rate application, while adoption is less common among wheat producers [41].

Social influence has also been the subject of analysis. A study in Taiwan on agricultural and food traceability systems found that social image, system visibility and system quality significantly influence perceived usefulness, ease of use and, ultimately, the intention to reuse the system [42]. On the other hand, the applicability of the extended Technology Acceptance Model in Malawi and Zambia revealed significant differences between men and women in perceptions of ease of use, expected usefulness and climate risk, suggesting the need to design gender-differentiated strategies [43].

In Australia, the acceptance of automated technologies in cotton plantations was studied, identifying that social and personal factors have a considerable weight in the decision to adopt such technologies. In this context, perceived usefulness was highlighted as a determining variable for different producer profiles [44]. In addition, the development and testing of IoT-based agricultural systems have been well received by users, who value their ease of use, attractiveness, innovation and functionality compared to traditional monitoring methods [45].

Finally, a systematic review of the literature yielded 16 studies which identified perceived effectiveness as the most recurrently evaluated factor in investigations into the acceptance of technologies in agriculture. However, other important aspects such as the opportunity costs, ethical implications and associated risks have been underestimated. This review highlights the need for formal instruments and standards that enable the adequate evaluation of the acceptance of technologies in agricultural contexts [46]. Costs of application (COST)

In the case of Latin America, although progress has been made in the adoption of IoT technologies, there are still significant challenges related to the lack of resources, limited knowledge and weak digital infrastructure. Despite these limitations, relevant initiatives have been developed in areas such as environmental monitoring, water management and smart irrigation systems, mainly using low-cost and open source solutions. In addition, there is a growing trend towards the adoption of Industry 4.0 technologies. However, in order to achieve long-term sustainable implementation, institutional capacity building and adequate public policies are required [47].

2.4. Barriers to ICT Adoption in Agriculture

Adoption of ICT in Agriculture: From Digital Literacy to Knowledge Management

According to the United Nations [48], ICTs have acquired a fundamental role in boosting agricultural competitiveness. Their application covers practically all levels of production and management within the sector’s enterprises and along agrifood chains. Although this is not an exhaustive list, there are several areas within an agricultural company, as well as in the rural environment, where these technologies can be implemented with very positive results.

The use of tools such as precision agriculture, sensors, global positioning systems (GPS) and digital management systems contributes not only to production yields, but also to a lower use of agrochemicals and better disease management [49]. In the context of climate change, ICTs make it possible to implement solutions for more efficient water use, early warning systems and climate information networks, thanks to technologies such as remote sensing and real-time monitoring [48]. The adoption of ICTs in agriculture is progressive and depends on various factors such as the size and level of development of the company. From basic uses to the implementation of complex systems such as ERP or cloud computing, the investment required grows along with the benefits. Therefore, promotion strategies should range from digital literacy to the adoption of advanced solutions, also considering the strengthening of external digital support systems to achieve knowledge-intensive agriculture [50]. Figure 2 shows the habits where ICTs can be developed in an agricultural enterprise and the stages of ICT adoption.

In Latin America, many farmers face a significant barrier to adopting digital technologies due to their low educational level. In countries such as Mexico and Paraguay, for example, more than half of the farmers barely reach basic education [48,51]. This reality represents a structural challenge that cannot be solved in the short term, since changes in the education system have a medium- and long-term impact. Therefore, it is necessary to design strategies that mitigate these limitations through training programs, access to adequate information and activities that foster interest in technologies [48].

Age has also been identified as a possible obstacle. In general, farmers in the region are over 50 years old, which could hinder their approach to ICTs. However, research in countries such as the Dominican Republic, Colombia and Chile shows that young people are the ones who use these tools the most [52]. Even so, there is no direct relationship between age and technology use; rather, this connection seems to be mediated by educational level. Countries such as Uruguay, with a more homogeneous education between generations, show a more balanced adoption of ICTs among age groups. On the contrary, in Chile, where older generations had less access to education, young people show greater digital mastery [53].

Despite these conditions, there is evidence that older farmers can incorporate technologies if they receive adequate training and are properly motivated. This suggests that, beyond age, factors such as education, market demand, and access to adapted training methodologies are determinants for effective digital inclusion [48].

In the case of Ecuador, according to the 2022 census [54], agriculture represents the second main economic activity in the country (14.1%) after commerce. In addition, 18.7% of men and 7.3% of women are engaged in this sector. This reaffirms the importance of promoting the use of technologies in agriculture as a tool to improve competitiveness and reduce structural gaps, especially among populations with less access to formal education or located in rural areas.

2.5. Research Gap

Despite the growing global interest in digital transformation in the agricultural sector, most existing studies on the adoption of IoT technologies in Ecuadorian agriculture have focused mainly on the technical aspects of implementation, such as sensor design, monitoring platforms and the automation of agricultural processes [55,56]. However, there is limited understanding of how farmers perceive these technologies, especially in strategic regions such as the Guayas province, one of the most representative agricultural areas of the country.

Few studies have systematically addressed the factors that affect farmers’ willingness to adopt IoT technologies, including their level of digital familiarity, the perception of benefits versus barriers, and the role of institutional or governmental support policies. This gap represents a constraint to the development of effective strategies to promote technology adoption in specific rural contexts.

Through research focused on the perception and adoption of IoT technologies by farmers in Guayas, this study seeks to address this gap in the literature. It is hoped that the findings will provide a better understanding of the contextual factors that influence technological adoption intentions in this region. These factors will include educational level, perceived usefulness and ease of use of technologies, operational challenges, and degree of institutional support. This knowledge can serve as a basis for the formulation of public policies, training programs and support strategies that favor the successful implementation of smart agriculture in Ecuador.

Objective and hypotheses

Objective 1. 

To analyze the influence of the use of IoT technologies in crop yield prediction in the province of Guayas.

Hypothesis 1. 

The use of IoT technologies significantly improves farmers’ ability to predict crop yields by enabling the real-time monitoring of critical agronomic variables.

Objective 2. 

Determine the factors that influence the adoption of IoT technologies by farmers in Guayas, considering technical, economic, and attitudinal aspects.

Hypothesis 2. 

The adoption of IoT technologies by farmers in Guayas is positively influenced by the perception of usefulness and ease of use, as well as by the technical training received; and negatively influenced by the initial implementation costs.

Objective 3. 

To examine the role of the educational level and institutional support in the willingness of farmers to incorporate IoT technologies in their productive practices.

Hypothesis 3. 

There is a positive relationship between the educational level of farmers and their willingness to adopt IoT technologies, and this willingness increases when there are training programs and institutional support.

3. Methodology

3.1. Research Design

This research aimed to analyze the adoption of Internet of Things (IoT) technologies by farmers in the province of Guayas, Ecuador, in order to improve agricultural yields and promote smarter and more sustainable practices. A quantitative approach was adopted, which allowed numerical data to be systematically collected and analyzed. This methodology facilitated the verification of hypotheses through statistical analysis, providing empirical evidence that supported the understanding of the factors that influenced technological adoption in the Ecuadorian agricultural context.

3.2. Description of the Study Area

This study focused on the province of Guayas (Figure 3), located in the coastal region of Ecuador. This area is bordered to the north by Manabí, to the south by the province of El Oro and the Pacific Ocean, and covers an area of 17,139 km². Its topography, with altitudes between 0 and 100 m above sea level, and its tropical climate, characterized by an average annual temperature of 25 °C, create ideal conditions for different types of crops, making it a strategic area for analyzing the integration of IoT technologies in agriculture.

3.3. Sampling Technique

To guarantee the representativeness of the results, stratified and random sampling was applied to the agricultural population of the province of Guayas. The total population of farmers, whose list was obtained from databases provided by agricultural and local extension institutions, was stratified taking into account criteria such as farm size, geographic location and type of main crop.

Subsequently, a simple or systematic random selection was made in each stratum using SPSS statistical software (version 26.0.0.0.0), ensuring that the sample reflected the actual distribution of producers in the region.

The total population of farmers in the province of Guayas, according to agricultural statistics for 202,357, is approximately 237,829 people employed in the agricultural sector. To determine the sample size, the finite population formula [57] was used, with a confidence level of 95% (z = 1.96), a margin of error of 5%, and considering that the expected proportion of adoption of IoT technologies was unknown, a conservative value of 50% was assumed (p = 0.5). Applying the corresponding formula and the population correction, a minimum sample size of 383 farmers was estimated. In the present study, 250 questionnaires were distributed, of which 232 were answered completely and validly, which represents a response rate of 92.8% and is considered adequate given the field conditions.

3.4. Data Collection

Data collection was carried out in person, through structured interviews, applied directly in the field. This modality was key to achieving a high response rate and an adequate understanding of the instrument by the respondents. The field work was organized with the support of local technicians and was carried out by a team of trained interviewers, made up of eighth semester students in agricultural and social sciences, who received prior training on the content of the questionnaire, interview techniques and ethical considerations.

To ensure data quality, consistency controls were implemented during data entry and validation. In addition, random reviews of the forms were performed to verify the plausibility of the responses. Only complete and consistent questionnaires were considered for analysis, resulting in a total of 232 valid responses.

The instrument used consisted of a structured questionnaire with closed-ended questions distributed in nine analytical dimensions: general perception of IoT, practical functionality, intention to use and recommendation, costs, technical training, educational level, institutional support, IoT adoption and agricultural performance. Most of the items were formulated under a five-point Likert scale, ranging from “strongly disagree” (1) to “strongly agree” (5); however, other scales of the same type, such as “not at all likely” (1) to “very likely” (5), and “not at all” (1) to “very likely” (5), were also incorporated, depending on the nature of each item.

To guarantee the validity of the instrument, the questionnaire was designed, taking as a reference two previously validated surveys: the one proposed by Isma et al. [58] and the one adapted by Martillo et al. [55], which were contextualized to the agricultural reality of the province of Guayas.

Likewise, in order to provide greater transparency and facilitate an understanding of the structure of the instrument, the questionnaire by dimension is presented in Appendix A (

Table A1. Distribution of items of the questionnaire by dimension and scale used.

Dimension	Question	Scale
General perception of IoT in agriculture (GP)	How much do you agree that IoT is important for the future of agriculture?	Strongly disagree Disagree Neither agree, nor disagree Agree Strongly agree
	How much do you agree that IoT could improve productivity and optimize resource use (water, fertilizer, energy) on your farm?
	How much do you agree that IoT adoption will generate economic growth in the agricultural sector?
Practical and operational functionality of the IoT (POF)	How much do you agree that IoT could improve the quality and quantity of your agricultural production?	Strongly disagree Disagree Neither agree, nor disagree Agree Strongly agree
	How much do you agree that you could monitor and control your crops remotely using IoT?
	How likely do you agree that IoT would allow you to forecast and control production in real time?
Intention to use (IUTR)	How likely are you to use IoT technologies (sensors, drones, automation) in your farming in the future?	Not at all likely Unlikely Neutral Likely Very likely
	How likely are you to recommend the use of IoT to other agribusinesses to improve agricultural productivity?
	How likely are you to increase the use of IoT technologies in your crops in the coming years?
Costs of application (COST)	How much do you agree that using IoT could reduce waste and better control costs in your agricultural production?	Strongly disagree Disagree Neither agree, nor disagree Agree Strongly agree
	The advantages of using IoT in agriculture outweigh the disadvantages of not using it.
	How much do you agree that IoT would make it easier to collect data and manage your farm?
Technical Training (TRAI)	How much do you think you need to improve your knowledge of IoT in agriculture?	Not at all Not at all Neutral Fairly Very much
	How interested would you be in receiving training on IoT to improve agricultural performance?
	How easy do you think it is to use IoT technologies (such as sensors or drones) in your agricultural work?
Educational Level (EDU)	How much do you know about the application of IoT in agricultural crop monitoring?	Not at all Not at all Neutral Fairly Very much
	How important do you think technology is in improving agricultural productivity?
	How much do you incorporate technology in your current agricultural production process?
Institutional Support (IS)	Would you use electrical devices at any stage of the agricultural process?	Strongly disagree Disagree Neither agree, nor disagree Agree Strongly agree
	Do you consider that there are factors that prevent the implementation of technological systems in agricultural production?
	I consider that IoT is a useful tool to improve food security in the province of Guayas.
Adoption of IoT (ADOP)	Adopting IoT for crops is a good idea.	Strongly disagree Disagree Neither agree, nor disagree Agree Strongly agree
	I would be willing to voluntarily use IoT soon.
	I would be willing to routinely use IoT to improve crop production.
Agricultural performance (PERF)	You could increase productivity using IoT.	Strongly disagree Disagree Neither agree, nor disagree Agree Strongly agree
	I could improve resource utilization efficiency using IoT.
	I believe that using IoT can improve productivity.

) at the end of the article as complementary material.

3.5. Data Analysis

The information collected was processed using descriptive and inferential statistical techniques. In a first phase, frequency measures, percentage distribution and centralization parameters were used to describe the profile of the sample and provide an overall view of the data. Subsequently, inferential methods were implemented, including the calculation of Cronbach’s α coefficient to assess internal consistency, Pearson correlation analysis and multiple linear regression, with the aim of examining possible associations between variables.

The procedures were executed in SPSS (Statistical Package for the Social Sciences) software, recognized for its efficiency in handling complex databases. Likewise, given the sample size (n ≥ 9 per item), it was considered pertinent to apply a factor analysis to explore the underlying structure of the data [59]. Subsequently, IBM SPSS AMOS version 23 software was used to perform confirmatory factor analysis (CFA), by means of which the convergent and discriminant validity of the latent variables was examined, as well as the statistical significance of the structural regression coefficients. This phase made it possible to validate the relationship model proposed in the hypotheses. In addition, R Studio version 2024.12.1 was used to create the geographic location map of the study, providing a visual geospatial component to the context analysis.

4. Results

This section presents the findings obtained from the analysis of data collected through structured questionnaires applied to farmers in the province of Guayas, Ecuador. The purpose was to examine the factors that influence the adoption of Internet of Things (IoT) technologies to improve crop yields through the respective monitoring, as well as to evaluate the level of knowledge, perception, current use, and intention of future use of these technological tools in the local agricultural context.

The results are presented according to the statistical analyses carried out, providing empirical evidence that allows responding to the objectives set and evaluating the hypotheses formulated. This section seeks to contribute to the understanding of the elements that favor or hinder the incorporation of IoT technologies by agricultural producers in Guayas, with a view to improving the yield and sustainability of their crops.

4.1. Characteristics of the Sample

The target population of the study was composed of farmers from different rural areas of the province of Guayas. For data collection, a stratified and random sample was designed, based on territorial and productive criteria, in order to guarantee representativeness. A total of 250 questionnaires were distributed, of which 232 were duly completed and considered valid for the analysis, representing a response rate of 92.8% as detailed in

Table 1. Sociodemographic and productive profile of farmers in Guayas.

	Frequency	Percentage %
Gender
Men	181	78.017
Woman	51	21.983
Age (years)
Less than 25 years old	19	8.190
25–34	41	17.672
35–44	62	26.724
45–54	87	37.500
>55	23	9.914
Educational level
Incomplete primary school	27	11.638
Primary complete	82	35.345
Secondary	91	39.224
Third level	23	9.914
Fourth level or higher	9	3.879
Years of experience in agriculture
Less than 5 years	13	5.603
5–10.	21	9.052
11–15.	35	15.086
16–20	67	28.879
More than 20 years	96	41.379
Approximate size of your agricultural land
Less than 1 ha	23	9.914
1–5 ha	34	14.655
6–10 ha	73	31.466
More than 10 ha	102	43.966
Internet access on your farm or community
Yes	129	55.603
No	103	44.397

. This rate, obtained through face-to-face surveys, is considered excellent according to Fincham [60], which strengthens the validity and reliability of the data collected.

In terms of socio-demographic composition, 78.0% of respondents were men and 22.0% were women, which is similar to official INEC statistics (2023), which report a male predominance in the Ecuadorian agricultural sector (71.4% men and 28.6% women). This distribution evidences a persistent gender bias in agricultural activities.

In terms of age, producers between 45 and 54 years of age were the largest group, followed by those between 35 and 44 years of age, indicating that the agricultural labor force is mostly composed of older people. Regarding education level, 39.2% indicated that they had completed secondary education, while only 13.8% had higher education (third level, fourth level, or more), reflecting that most farmers have basic training.

Regarding farming experience, the results reveal that 41.4% of the farmers have worked more than 20 years in the sector, and 28.9% have between 16 and 20 years of farming experience. In relation to the size of the land, 43.4% cultivate more than 10 hectares, and 31.5% manage between 6 and 10 hectares, which reinforces the economic importance of their productive activity in the region. Finally, 55.6% of respondents have Internet access on their plots, while the remaining do not have this resource, which may influence the effective adoption of digital technologies.

4.2. Internal Validity and Consistency of the Instrument

In order to guarantee the methodological quality of the study, the internal reliability of the questionnaire was evaluated using Cronbach’s alpha coefficient. This analysis made it possible to verify the consistency of the responses between the items that make up the measurement instrument. The questionnaire consisted of 27 items organized around three key dimensions: general perception of the IoT, practical and operational functionality, and the intention to use, train, and recommend.

The results presented in

Table 2. Descriptive statistics and internal reliability of the measurement instrument.

Mean	Variance	Sta. Deviation	Cronbach’s Alpha α	N of Items
2.981	0.790	0.888	0.701	27

show that the reliability coefficient was α = 0.848, indicating a high level of internal consistency among the questions in the questionnaire. This confirms that the instrument is reliable and suitable for measuring farmers’ perceptions and attitudes towards the adoption of IoT technologies to improve agricultural yields, through crop monitoring. Furthermore, the overall descriptive values (mean, standard deviation) suggest a favorable trend towards the use of these technologies, with responses generally clustering around a positive view of IoT in agriculture.

4.3. Exploratory Factor Analysis

To assess construct validity, an exploratory factor analysis (EFA) was performed using the unweighted least squares method, an appropriate technique when seeking to identify underlying patterns in the data [61,62]. Before applying this analysis, sample adequacy was checked using the Kaiser–Meyer–Olkin index (KMO) and Bartlett’s test of sphericity. The KMO index value was 0.757, indicating acceptable sample adequacy, and Bartlett’s test was statistically significant (p < 0.05), validating the feasibility of the factor analysis.

Similarly, all items obtained factor loadings greater than 0.40, which corroborates the relevance of the item within its factor. The scale reliability corresponding to each factor was defined through Cronbach’s alpha: General perception of IoT in agriculture (a = 0.956), practical and operational functionality of IoT (a = 0.951), intention to use, training and recommendation (a = 0.960), costs of application (0.959), technical training (0. 955), level of education (0.952), institutional support (0.957), adoption of IoT (0.949), and agricultural performance (0.960), which demonstrates excellent internal consistency, meeting the minimum standards accepted in quantitative research (α ≥ 0.70), in

Table 3. Exploratory factor analysis (rotated factor matrix).

Item	Factor
	1	2	3	4	5	6	7	8	9
GP1					0.956
GP2					0.950
GP3					0.955
POF1								0.947
POF2								0.959
POF3								0.949
IUTR1		0.954
IUTR2		0.962
IUTR3		0.954
COST1			0.961
COST2			0.959
COST3			0.947
TRAI1						0.947
TRAI2						0.961
TRAI3						0.947
EDU1							0.952
EDU2							0.945
EDU3							0.952
IS1				0.956
IS2				0.959
IS3				0.955
ADOP1									0.947
ADOP2									0.940
ADOP3									0.948
PERF1	0.955
PERF2	0.959
PERF3	0.957
α de Cronbach	0.956	0.951	0.960	0.959	0.955	0.952	0.957	0.949	0.960

Note. GP: General perception of IoT in agriculture; POF: Practical and operational functionality of the IoT; IUTR: Intention to use, training and recommendation; COST: Costs of application; TRAI: Technical training; EDU: Educational level; IS: Institutional support; ADOP: Adoption of IoT; PERF: Agricultural performance.

, the rotated component matrix can be observed.

Furthermore, as can be seen in

Table 4. Model fit indices (Hypothesis 1).

Fit Measures	Index	Value	Recommended Value
Absolute Adjustment Measures	CMIN/DF	1.213	(<2)
	RMSEA	0.032	(≤0.05)
Incremental Adjustment Measures	CFI	0.985	[0.9–1]
	TLI	0.955	[0.9–1]
	NFI	0.977	(0.9–1)
Parsimony-Adjusted Measures	Pratio	0.773	(0.5–1)
	PCFI	0.770	(0.5–1)
	PNFI	0.760	(0.5–1)
	AIC	139.857	-

, the communalities ranged from 0.898 to 0.911, exceeding the recommended threshold of greater than 0.5, which shows that the common variance is the most representative of the total variance.

4.4. Confirmatory Factor Analysis

The exploratory factor analysis made it possible to structure the data and lay the foundations for the next step in the validation of the model. In this sense, a confirmatory factor analysis (CFA) was applied using IBM SPSS AMOS software, version 23, with the purpose of evaluating the quality of the fit of the proposed theoretical model and empirically testing the relationships proposed in the hypotheses. This tool was used to examine the convergent and discriminant validity of the latent variables, as well as the statistical significance of the structural regression coefficients.

In order to analyze the first research objective and test hypothesis H1, a structural equation model (SEM) was developed that incorporates both direct and mediated relationships between the constructs. Figure 4 illustrates the estimated theoretical model, which considers that the adoption of IoT technologies (ADOP) has a direct effect on agricultural yield (PERF). However, such adoption is understood as a mediating variable, influenced in turn by several antecedent factors, including the general perception of IoT (GP) and its practical and operational functionality (POF), which were included in the model based on previous literature by Isma et al. [58] and Martillo et al. [55] and their empirical relevance in technological adoption processes in agricultural environments.

In this framework, the model is not limited to a purely predictive function, but seeks to explain the causal relationships between the constructs, based on theories of technological behavior. The constructs were defined and empirically validated by confirmatory factor analysis, and detailed information on each dimension, item and scale item is presented in Appendix A.

The results obtained (Table 4) support an adequate fit of the theoretical model to the empirical data. Overall, the indicators show a good fit: CMIN/DF = 1.213 and RMSEA = 0.032 (both within the recommended values), as well as high values in the incremental indices (CFI = 0.985; TLI = 0.955; NFI = 0.977), and parsimony measures within the acceptable range (Pratio = 0.773; PCFI = 0.770; PNFI = 0.760). The AIC obtained was 139.857, which supports the efficiency of the proposed model.

Furthermore, the structural results confirmed Hypothesis H1 (

Table 5. Regression estimates and SEM model determination coefficients (Hypothesis 1).

Unstandardized Regression Weights
Relation	Estimate	S.E.	C.R.	p	Significance
ADOP ← GP	0.575	0.04	14.281	***	Significant
ADOP ← POF	0.806	0.053	15.256	***	Significant
PERF ← ADOP	0.795	0.046	17.435	***	Significant
Standardized Regression Weights
Relation			Standardized Estimation
ADOP ← GP			0.582
ADOP ← POF			0.658
PERF ← ADOP			0.840
R2 (Coefficients of Determination-Squared Multiple Correlations)
Endogenous Variable			R2 (Variance Explained)
ADOP			0.771
PERF			0.706

Note. The arrows represent directional effects from the predictor variable to the outcome variable. Asterisks indicate levels of statistical significance: *** p < 0.001.

) by showing significant effects of key variables on IoT adoption and agricultural yield (

Table 6. SEM model fit indicators proposed to validate (Hypothesis 2).

Fit Measures	Index	Value	Recommended Value
Absolute Adjustment Measures	CMIN/DF	3.528	(<2)
	RMSEA	0.111	(≤0.05)
Incremental Adjustment Measures	CFI	0.926	[0.9–1]
	TLI	0.910	[0.9–1]
	NFI	0.901	(0.9–1)
Parsimony-Adjusted Measures	Pratio	0.819	(0.5–1)
	PCFI	0.759	(0.5–1)
	PNFI	0.738	(0.5–1)
	AIC	401.411	-

): General perception of IoT in agriculture (β = 0.575; p < 0.001) and the practical and operational functionality of IoT (β = 0.806; p < 0.001) positively influenced technology adoption (ADOP), which in turn had a significant effect on agricultural yield (PERF) (β = 0.795; p < 0.001). The variance explained by the model was high (R²_ADOP = 0.771; R²_PERF = 0.706), which supports the predictive and structural validity of the model proposed.

To analyze Objective 2 and Hypothesis 2, the SEM model was performed, which showed a good overall fit (Figure 5 and Table 6).

The evaluated model (Table 6) presents a moderate fit that, although not optimal according to some absolute criteria is considered acceptable for investigations with complex structures such as this one. The incremental fit indices, such as the CFI (0.926), TLI (0.910) and NFI (0.901), are within the established range. As for the absolute fit indicators, the RMSEA obtained was 0.111 (90% CI: 0.097–0.124), which exceeds the recommended threshold of 0.08. However, studies such as those of MacCallum et al. [62] point out that RMSEA values between 0.08 and 0.10 can be interpreted as a mediocre fit, and that even values above 0.10 can be acceptable in models with multiple parameters, especially in exploratory studies. For its part, the CMIN/DF index reached a value of 3.528, which is higher than the ideal limit of 3.0, but still considered tolerable according to authors such as Whittaker and Schumacker [63], who argue that values between 2 and 5 are acceptable in complex structural models. Despite these limitations, the standardized structural coefficients in

Table 7. Regression estimates of the structural model applied to Hypothesis 2.

Unstandardized regression Weights
Relation	Estimate	S.E.	C.R.	p	Significance
ADOP ← GP	0.396	0.032	12.235	***	Significant
ADOP ← POF	0.466	0.041	11.416	***	Significant
ADOP ← COST	−0.978	0.274	−3.570	***	Significant
ADOP ← TRAI	0.593	0.163	3.444	***	Significant
Standardized Regression Weights
Relation			Standardized Estimation
ADOP ← GP			0.514
ADOP ← POF			0.488
ADOP ← COST			−0.651
ADOP ← TRAI			0.523

Note. The arrows represent directional effects from the predictor variable to the outcome variable. Asterisks indicate levels of statistical significance: *** p < 0.001.

reveal strong and statistically significant relationships between the latent variables, which supports the validity of the proposed model to explain the adoption of IoT technologies by farmers in Guayas.

The results of the structural equation model show that the adoption of IoT technologies by farmers in Guayas is significantly influenced by several technical, economic and attitudinal factors. Specifically, the general perception of IoT (β = 0.514, p < 0.001), practical functionality (β = 0.488, p < 0.001) and technical training (β = 0.523, p < 0.001) have a positive and significant effect on adoption. In contrast, initial implementation costs exert a negative and significant effect (β = −0.651, p < 0.001). Thus, Hypothesis 2 is confirmed, since it highlights that strengthening the technical training of farmers and demonstrating the usefulness of IoT in the field can increase the adoption rate, while costs represent a barrier that should be addressed with subsidies, financing or government incentives.

Similarly, the results obtained from the structural analysis empirically validate Hypothesis 3 (Figure 6), which stated that the educational level, technical training, and institutional support positively affect farmers’ willingness to adopt IoT technologies in their production practices. The model (

Table 8. Unstandardized estimates of the SEM model for Hypothesis 3.

Unstandardized Regression Weights
Relation	Estimate	S.E.	C.R.	p	Significance
ADOP ← IS	0.430	0.031	13.929	***	Significant
ADOP ← EDU	0.442	0.037	11.941	***	Significant
ADOP ← TRAI	0.387	0.113	3.418	***	Significant
ADOP ← IUTR	0.329	0.1029	11.508	***	Significant

Note. The arrows represent directional effects from the predictor variable to the outcome variable. Asterisks indicate levels of statistical significance: *** p < 0.001.

) reveals significant standardized coefficients for all the relationships examined: (EDU) → (ADOP) (β = 0.442; p < 0.001), (IS) → (ADOP) (β = 0.430; p < 0.001), (TRAI) → (ADOP) (β = 0.387; p < 0.001), (IUTR) → (ADPO) (β = 0.329; p < 0.001). These values reinforce the importance of institutional support in the process of incorporating technological innovations in the agricultural sector (

Table 9. SEM model fit indicators proposed to validate (Hypothesis 3).

Fit Measures	Index	Value	Recommended Value
Absolute Adjustment Measures	CMIN/DF	3.694	(<2)
	RMSEA	0.114	(≤0.05)
Incremental Adjustment Measures	CFI	0.938	[0.9–1]
	TLI	0.925	[0.9–1]
	NFI	0.918	(0.9–1)
Parsimony-Adjusted Measures	Pratio	0.819	(0.5–1)
	PCFI	0.819	(0.5–1)
	PNFI	0.752	(0.5–1)
	AIC	385.658	-

Note. Values in parentheses indicate thresholds for good model fit, while values in brackets represent acceptable fit ranges.

).

5. Discussion

The findings of this study provide a comprehensive understanding of the factors that determine the adoption of the Internet of Things (IoT) in agriculture in the province of Guayas, Ecuador. It is evident that this process does not depend exclusively on technological availability, but is mediated by variables such as perceived usefulness, institutional support, technical training and the economic conditions of the rural environment.

The favorable attitude towards the use of IoT observed in Guayaquil producers can be explained by the influence of previous agricultural modernization programs and by the growing exposure to technological tools, which coincides with studies conducted in other regions of Latin America. In this sense, Strong et al. [64] identified that the adoption of IoT in Brazilian farmers was driven by the functionality, simplicity and social acceptance of the technology. Likewise, Cornejo Olivares et al. [47] reported that, during the COVID-19 pandemic, the use of automated systems such as smart irrigation increased, improving production efficiency in the region.

A critical aspect is the role of the institutional environment. The presence of technical support networks and specialized training positively influences confidence and the willingness to experiment with new digital tools, as also evidenced by Iliopoulos et al. [65], who argue that continuous training reduces technological uncertainty. This confirms that knowledge is not enough; accompaniment and field assistance are essential to achieve effective adoption.

On the other hand, the perception of economic risk remains an important barrier. Many farmers consider the initial cost of IoT implementation to be high, which is consistent with the reports of Iliopoulos et al. and Stone [65,66], who indicate that digital technologies are not yet perceived as affordable for small- and medium-sized farmers. Such structural barriers limit the scaling up of IoT, despite its obvious potential benefits.

In this context, it is essential that public policies are oriented towards creating enabling conditions for technological adoption, especially through financial incentives, such as subsidies, tailored credit lines, and collaborative investment schemes, as suggested by Chong and Ooi [36] and Marakarkandy et al. [37]. From a methodological point of view, the measurement instrument used showed high internal consistency, with alpha coefficients above 0.94 in all dimensions. The empirically validated structural equation model (SEM) presented a good overall fit, with outstanding explanatory power (R² = 0.771 for adoption and R² = 0.706 for performance). These results reinforce the validity of the proposed theoretical framework and support the usefulness of the dimensions considered in predicting technological adoption in similar agricultural contexts.

Recommendations for Future Research

This study has certain limitations that should be considered when interpreting its results; among them, the sample size and the demographic composition of the participants. Although the methodological design contemplated a stratified and random sampling, the majority of respondents were men over 45 years of age, which reflects the dominant structure of the agricultural sector in the province of Guayas. However, a low participation of farmers over 55 years of age was identified, which is not attributed to a methodological bias, but to contextual factors such as physical limitations, generational replacement processes, and their lower availability during the face-to-face data collection. For future research, we suggest implementing strategies aimed at achieving the greater inclusion of this age group, in order to more accurately capture their perspectives, needs and level of technological adoption.

It is also advisable to expand the geographic coverage and increase the diversity of the profile of the participants, including women farmers and young producers, who were underrepresented in this research, in a balanced way. This expansion would generate a more holistic understanding of the process of adoption of digital technologies in diverse rural environments.

Additionally, it is recognized that, although this study focused on perceptual and operational variables associated with IoT use, one of the most determinant structural factors in rural areas is the availability of connectivity infrastructure. Limited Internet or mobile network coverage can be a critical barrier to the adoption of these technologies, regardless of the level of farmer acceptance or awareness. Therefore, it is recommended that future research incorporate this component in its analysis, to provide a more comprehensive view of the technical constraints that affect the implementation of digital solutions.

It is also suggested that longitudinal studies are conducted to observe how technological adoption evolves over time, identifying both the dynamics of change and the factors that favor its sustainability. Similarly, it would be pertinent to delve deeper into the economic, social and cultural barriers faced by different segments of farmers, as well as the impact of public policies, training programs and specialized financing.

Finally, it is suggested that quantitative approaches are complemented with qualitative studies to gain a deeper understanding of individual perceptions, motivations and resistance to technological adoption. Likewise, integrating variables related to environmental sustainability and economic efficiency would provide a more comprehensive view of the real impact of the use of IoT on agricultural productivity and the conservation of natural resources.