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1. Introduction
This study proposes an approach to the digitalization of the agricultural phase in different production chains in Uruguay. Its relevance lies in the sector’s significant contribution to the national economy, the profound transformations that agriculture has recently undergone, and the expansion of information and communication technologies.
Uruguay is characterized by its insertion in the global market through agricultural and agro-industrial exports. While this has impacted the growth and development of the national economy, the fluctuations reflected in GDP per capita have not enabled closing the social welfare gap1. This characteristic is fundamental to understanding the profound transformations occurring at the national level, driven by a strong flow of investments into the global commodity market. The transformations experienced by Uruguayan agriculture in the 21st century are such that Piñeiro and Moraes2 compare them with those of the late 19th century, with the introduction of fencing and livestock crossbreeding. Among the changes, the increase and formalization of wage relations, indicated as a process of proletarianization3, and the displacement of the local landowning bourgeoisie2 stand out.
This scenario is part of the reconfiguration of a global agri-food regime, defined by profound transformations in trade and an intensification of the global division of agricultural labor, supported by two fundamental pillars: biotechnology and information technologies4. This shift replaces the agro-industrial model with a new logic of accumulation known as agribusiness, characterized by cross-sectoral integration, the prioritization of the global consumer needs, the standardization of technologies, and land concentration5. These dynamics manifest in diverse ways across territories; in Uruguay, technological change is central to understanding contemporary rurality and the possibilities for future transformation. The need for technological change has been highlighted as one of the main constraints for national development, particularly given the low rate of investment in science, technology, and innovation6. In this context, digitalization is seen as fundamental for transformation, enhancing the efficiency, control, and management across production chains7.
Based on this scenario, some questions that motivated the development of this study arise: What contributions does digitalization make to different productive sectors? What digital tools are available in the country? Who develops them, and who are their intended users? What role do they play within production systems?
Given the limited national research on digital technologies in the agricultural sector, this study aims to approach the presence of digital technologies in the agricultural phase by identifying tools available in agriculture, livestock, and dairy. Through a proposed classification, it analyzes and discusses the current state of affairs while raising questions for future inquiry from the perspective of agrarian social sciences.
1.1 Digitalization in Agricultural Production
Digital technologies are visualized by Patiño and Rovira8 as essential tools to design, produce, and commercialize goods and services across sectors of the economy. Digital agriculture profoundly transforms agri-food systems. Also known as agriculture 4.0 or smart farming, it integrates advanced technologies such as robotics, artificial intelligence, big data, and automation to optimize resources, lower costs, and enhance sustainability. It relies on information and communication technologies (ICT) for the management of properties, improving both quantity and quality of products, while optimizing human work9.
Digitalization applies ICTs across all stages of the food system, improving the flow of information, logistical efficiency, and food traceability 10)(11) . These systems cover the entire food chain and are considered crucial for food security and economic development 12)(13) . Digitalization also replaces human strength and cognition with sophisticated robots and algorithmic systems14.
As for the concept of digitization, a distinction is made in English between digitization (converting analog information to digital) and digitalization (adoption and use of digital technologies), although in Spanish this difference is not always recognized. Some authors use them interchangeably, while others highlight the influence of digitalization on product platforms and organizational logics 15)(16) . In this article, the term digitalization will be used broadly, focusing on the availability of digital tools and technologies.
In Latin America, the adoption of technologies such as GPS systems, mapping tools, mobile apps and remote sensing is expanding, with Brazil and Argentina leading the way, followed by Uruguay and Chile17. This process requires responsibility and reflection on the inclusive and exclusive effects of digital technologies and their role in shaping transitions to future agriculture and food systems11. In addition, strategic management using digital technologies can contribute to sustainable development in rural areas, increasing productivity and technological efficiency18.
The emergence of digital technologies in agriculture, as well as their aforementioned promotional arguments, is part of agricultural innovations. This concept is applicable in various areas, including the agricultural sector, where it unfolds in a complex and relational way 19)(20) . According to Johnson and Lundvall21, social innovation focuses on satisfying unmet human needs, conceived as a cumulative, interactive, and institutionalized process that takes place in specific learning environments. This approach highlights the importance of learning processes within innovation systems. Arocena and Sutz22 agree that innovation systems improve with greater connectivity and cooperation between actors, facilitating learning spaces that guide innovation towards the resolution of substantive social problems.
For some authors, big data technologies have the potential to significantly alter the relationships between actors in the agri-food system, facilitating greater efficiency and precision in agricultural practices23. Also, this type of tool can modify the power dynamics among the “players” of the food system and raise concerns over the privacy of the data collected23. Another aspect identified as relevant in the literature is the interoperability of technological systems, considered essential to maximize the effectiveness of these technologies, but which in turn increases vulnerability to cyber attacks24.
Digitalization in agri-food systems is a disruptive process that, while promising benefits, can also intensify inequalities and foster exclusion and conflict. Alcántara and others25 highlight that differential access to these technologies can widen existing gaps, resulting in unequal adoption and conflicts among different actors in the agri-food sector. In this regard, Carolan11 introduces the concept of “cruel optimism” to describe situations where technologies, perceived as solutions, end up exacerbating pre-existing problems, creating unforeseen dependencies, and reinforcing existing inequalities.
This article is structured in five sections. The first introduces the research problem and explains its conceptual framework. The second one describes the methodology used in the study. In the third section, the results and their analysis are presented, followed by a discussion in the fourth section, and ending with some conclusions.
2. Materials and Methods
This study is part of an exploratory study1, based on the proposal of grounded theory to construct conceptualizations and categories emerging from the data26.
To identify available technologies in Uruguay’s agricultural, livestock, and dairy sectors, information was compiled from secondary sources, including public reports (from public, public-private and private institutions and organizations), academic articles (national and linked to the subject), business data (extracted mainly from websites or social networks), journalistic articles (related to the subject and somewhat recent), among others. Most of the information came from business data and public reports, collected primarily from websites2 between February and March 2024. Keywords such as digital tool, digital technology, agricultural software, and digitalization were used individually and in association with each of the productive sectors surveyed. The selection of the items was based on their economic contribution to the national GDP, land use, and the number of producers involved3.
Based on the conceptual framework adopted for the research, digital tools or technologies were considered in this study as computational instruments (platforms, programs, applications, devices) that support production system design and management, productivity growth, process optimization, and value addition.
2.1 General Characteristics of the Productive Sectors Studied
Uruguayan livestock farming covers approximately 12.8 million hectares27, with its main forage resource being the natural grasslands. It is concentrated in the north, northeast, and center of the country, highlighting its heterogeneity in scale, specialization, applied technologies, and integration with other activities28. According to the latest agricultural census, there are 25,523 livestock farms, mainly oriented to breeding29, with predominance of mixed systems (sheep and cattle graze together). Currently, the stock is 11.6 million cattle and 6.2 million sheep27. Livestock contributes around 5% of GDP, representing 24% of national exports (4.6% of the world beef market). These exports correspond to 94% of industrially processed products, and the rest correspond to live cattle sales 27)(31) .
The dairy sector comprises 2963 producers4, including dairy farms and artisanal cheese makers32. Of this total, 72% supply milk to plants, with Conaprole processing about 70% of that volume33. The area covers approximately 649,000 hectares, with almost half of the dairy farms being between 50 and 199 ha. The productive nucleus is located to the southwest and along the coast of the Uruguay River27. This sector ranks ninth as the world's exporter of dairy products, commercializing 70% of national production to over 60 markets27, being one of the sectors that generates the highest added value.
On the other hand, agriculture5 is mainly located on the southern and northern coastlines, and central and southern regions of the country, covering an area of approximately 1.3 million hectares (harvest 22/23)27. Historically, the area was dominated by winter crops over summer crops, but since 2010, this relationship has been reversed34, mainly due to the “soybean boom” that began in 2003, driven by the increase in its international price35. This scenario brought new farmers who intensified and diversified production36. Agriculture represents 46.1% of the total valued agricultural production27, with the main summer crops being soybeans, corn, and sorghum, and winter crops including wheat, barley, and rapeseed. Its commercialization is export-oriented, representing 26.5% of agricultural exports27.
In recent years, all three sectors have shown strong productivity growth, largely due to intensification processes driven by technological adoption and diverse technical advances31.
3. Results
3.1 Proposed Classification of the Digital Technologies Surveyed
The survey identified 80 digital tools and technologies (DT) used by these sectors: 41 main DTs were surveyed for livestock, 25 for agriculture, and 14 for dairy.
For their classification, the study by Lachman and López37 was taken as a relevant reference, which categorizes agtech services by their main uses. Based on this, the present study expands the categories according to the objectives these technologies address:
1. PROCESS CONTROL EFFICIENCY (40 DT) (Table 1): It brings together those technological tools that improve processes and productive outcomes, while also aiming at cost minimization and more effective cost control. It includes three dimensions:
Process control and automation (21 DT): Related to the monitoring and follow-up of processes, they perform tasks in an automated way, report on status through records and data, among others, with varying degrees of human interaction.
Production simulation (4 DT): They improve production processes through data and information processing, generating outputs from specific inputs, which, through simulation models, provide results for decision-making.
Activity and resource management (15 DT): Enable management, administration, or planning of systems, involving a global scale.
2. CONNECTIONS AND EXCHANGES (27 DT) (Table 2): Technological tools that allow for communication, articulation, transactions, and networking between the different actors in agri-food systems.
Commercialization (3 DT): Designed to optimize and diversify the methods of buying and selling products and services, facilitating the direct connection between producers, distributors and consumers.
Dissemination of information (5 DT): Aimed at expanding and diversifying the approach of information between the different actors.
Information or alert systems (19 DT): Provide rapid and useful data and information at scales larger than individual production systems, and contribute to decision-making.
3. REGULATIONS AND CERTIFICATIONS (13 DT) (Table 3): Encompasses those necessary -whether mandatory or not- for carrying out processes in the different sectors, enabling compliance with relevant accreditations and requirements.
Table 1: Technological tools found in the Production Efficiency category
| PRODUCTION EFFICIENCY | ||
| Process Control and Automation | Production Simulation | Activity and Resource Management |
| Soil Moisture Sensor | INIA-Grass | Farm Management Software |
| Pivot Irrigation | MEGanE | Water Level Sensor Reservoirs |
| Automated Ground Leveling Tools | UPIC PRO | Load Calculator |
| Variable seeding density | EfiCarne | Property Indicators Calculator |
| Variable Rate Fertilization | in Grazing | |
| Selective application of herbicides | Pasture growth visualizer | |
| Control Sections Application | Comprehensive Property Management | |
| Harvest and Productivity Mapping Monitor | Uruguay Family Farming Improvement Project (UFFIP) | |
| Technology Application Teejet Spray Select | OvinApp | |
| CuthillHydroReader and DropLeaf | Livestock Management System (SGG) | |
| Drone | Grazing net | |
| Conditioning and harvesting of wool | Mu App | |
| CLU-sheep count | Competitive Production Project | |
| Electronic Scales | GRASS-FED Certification Program | |
| Caravan reader | Lecheck | |
| Livestock Environmental Assessment | ||
| SRGen Program | ||
| SULAR Program | ||
| Diagnosis of ovarian activity and pregnancy | ||
| G. PGanad | ||
| Voluntary milking system |
Table 2: Technological tools found in the category Connections and Exchanges
| CONNECTIONS AND EXCHANGES | ||
| Commercialization | Dissemination of Information | Information Systems or Alerts |
| Grain Price Coverage | Meteorological Radars Network Extension | INIA-SARAS |
| Virtual Productive Animal Auctions | Water Information Viewer | INIA-DONCast |
| Muuu | Culti Data Viewer | Phytosanitary Application Management and Monitoring System |
| Technology Guides | Comprehensive Monitor of Risks and Impacts (MIRA) | |
| TV and Radio Plan | Predator Bigging and Attack Information System (SIAAP) | |
| Web and App Geographic Information System (SIGRAS) | ||
| -Chillindex | ||
| -Prediction of conditions predisposing to heat stress (meat) | ||
| -Agroclimatic data bank -Monitoring forage productivity of natural grasslands | ||
| -Monitoring (APAR) vegetation growth indicator | ||
| -Normalized vegetation index | ||
| CultiDatos_uy | ||
| National Livestock Information System for Producers (SNIG) | ||
| Bovine genetic evaluations | ||
| Sheep genetic evaluations | ||
| Farmer's website (web) | ||
| INAC info (App) |
Table 3: Technological tools corresponding to the Regulations and Certifications category
| REGULATIONS AND CERTIFICATIONS |
| Supply Registration and Management System |
| Agricultural Traceability |
| Land Use and Management Plan Registration |
| Property and transit guides |
| Environmental Sustainability Certifiers |
| Livestock traceability |
| Livestock traceability |
| Property and transit guides |
| Supply Registration and Management System (SRGA) |
| Digital transformation in Uruguay's artisanal cheese factory |
| Dairy Effluent Management System Certification |
| Export Digitalization |
| Sanitary certification for the export of milk and dairy products |
4. Discussion
A prevalence of DT was observed across all three productive sectors, particularly in process control and automation, linked to process control efficiency. This suggests an orientation towards efficient and sustainable agricultural production systems, characterized by lower costs and reduced reliance on human labor, which involve the various sectors and agents that participate in the production chains. Collectively, these tools optimize production processes through the regulation of variables for an increase in efficiency and productivity. They operate through the implementation of monitoring systems and automatic sensors that can adjust conditions in real time based on specific data, optimizing the use of resources and improving yields. Examples include livestock programs to control wool conditioning and harvesting records (SIRO), platforms for bovine and sheep genetic improvement records (SRGen and SULAR); in precision agriculture, tools such as environmental management (variable seeding density and yield mapping with harvest monitors), and in dairy, robotic milking systems.
Technological tools also strengthen decision-making, since they enable the collection, analysis, and processing of large volumes of data in real time. The improvement of production simulation models allows predicting the impact of different management strategies on yields, supporting the selection of optimal options. An example of the three addressed areas is the availability of eco-physiological prediction models (INIA-Gras).
Automation and process control align with environmental sustainability by minimizing the impact of agricultural practices. Examples include the Livestock Environmental Assessment tool, technologies for precision in the application of agricultural inputs (selective application of herbicides and fertilization with variable doses), and the certification of the Dairy Effluent Management System. These tools not only lower production costs but also minimize input losses due to excess, contributing to more sustainable production.
Another aspect to point out is resilience to changes in the production environment. The ability to adapt quickly to changing conditions can be considered as another significant effect of automation and process control. In the face of climate variability, these technologies can mitigate risks by ensuring the continuity of production. Nationally, this is evident in the Pasture Growth Visualizer for beef and dairy systems, and in the Soil Moisture Sensors in irrigated agriculture.
The organization of work changes with the widespread adoption of DTs, moving from intensive and manual tasks, variable in their precision, to managing standardized agricultural systems. In the case of livestock, an example is the sheep counting; in dairy, the organization of herd management tasks (MuApp), and in agriculture, the implementation of the drone.
As for the category of Connections and Exchanges, the survey for the three productive sectors reflects a differential distribution but is less heterogeneous than the category analyzed above. It is possible to account for the predominance of information systems or alerts indicating a trend towards the valorization of the collection, analysis, and efficient distribution of data for decision-making. The ability of these systems to provide real-time data and alerts on crop, climatic, or market conditions is key to the resilience and adaptability of production systems. An example of these three sectors is SIGRAS, which provides real-time national soil water balance data.
Likewise, dissemination of information also emerges as key to the transmission of knowledge on sustainable practices, new technologies, market trends, and development opportunities. The preference for these technologies evidences the value given to information. A shared example across the three analyzed sectors is the extension of the network of meteorological radars.
Regarding the commercialization dimension, the technologies surveyed suggest room for improvement in the platforms and tools that facilitate the commercialization of products. Efficient commercialization is essential for market access and profit maximization. Some examples include televised and online livestock auctions such as Pantalla Uruguay, Plaza Rural, Lote 21, and the Muuu application.
Finally, within the category related to regulations and certifications, Uruguay offers emblematic tools such as the national bovine traceability system (SNIG), which records all cattle (meat and milk), from birth to final destination. Agriculture also includes traceability schemes and sustainability certifications; an example of this is the Land Use and Management Plans.
The presence of the categories analyzed in the different sectors shows particular digitization strategies. This research suggests that the relationship between the agricultural and industrial phases shapes the way in which technologies are adopted. While the dairy sector reflects strong industry leadership, in livestock and agriculture, DT adoption remains fragmented and often uncoordinated among chain actors.
This raises the need to further explore different elements that underlie digital tools, such as the distribution of power both in the control of data and in the appropriation of the aforementioned tools. In this regard, Rotz and others24 suggest that large corporations can centralize control over data, exacerbating inequalities among farmers, which can lead to the exclusion of smallholders. This phenomenon echoes other global processes such as supermarket consolidation, financialization and data hoarding, and land concentration 12)(38) .
5. Conclusions
The approach to the DTs available in Uruguayan agriculture has not yet been explored in the agrarian social sciences. This review of agriculture, dairy, and livestock in Uruguay has evidenced their availability in the three sectors, although their presence is uneven, and classified them into three categories according to the objective of each digital technology: efficiency of process control, connections and exchanges of the production chain, regulations, and process certifications.
The analysis of these DTs reflects a strategic orientation of Uruguay's agricultural sector towards the adoption of responsible and sustainable practices, driven by a demanding global market for safe, high-quality products with low environmental impact. Consumer preferences would explain the presence of digital technologies that ensure process transparency and traceability from production to final destination.
Likewise, the predominance of digital technologies that seek to quantify, control, and manage processes and resources highlights the relevance of data generation and information management to make informed decisions, leading to the efficient use of productive, monetary, natural, human, and financial resources, and more. This requires capacity-building and training for actors, as well as stronger integration across agri-food system stages. Nonetheless, the study has some limitations: i) restrictions on access to information, given that a large part of digital tools and technologies are for private use and belong to companies; ii) rapid evolution of technologies and availability of new ones, which could make the proposed typology obsolete; iii) complexity and diversity of productive sectors, which require selection criteria, and iv) the exploratory nature of the study, which did not allow for an in-depth study of the influence that digitalization has on the different types of producers and actors in agricultural production, among others. These limitations and the results obtained raise new questions for future research, such as: can all actors in the agricultural sector incorporate digital tools and technologies in the same way? What impact does digitalization have on agricultural work? How do digital technologies influence producer-extension interaction? What role do enabling infrastructure, digital literacy, information ownership, etc., play in the digitalization process?
