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The use of digital technology in low- and middle-income countries (LMICs) is shaped by multiple factors that can enable or hinder adoption. Understanding these factors is crucial, as LMICs face many constraints that limit the benefits of digital innovation. Therefore, this paper thoroughly evaluates the literature to identify the key determinants influencing the adoption of digital agriculture technologies in LMICs. A total of 30 relevant publications, dated between 2019 and May 2025, were retrieved from the academic databases Google Scholar, Scopus, and Web of Science through a systematic search and analysis approach with clearly defined inclusion criteria to ensure relevance and comparability. This study highlights that socioeconomics, agro-ecological, technological, institutional, situational, social, and behavioral factors are the most frequently discussed influences on digital agriculture technology adoption. Nonetheless, only a few studies have examined all of the components of the complex adoption process, and the majority were only concerned with assessing the impact of a single factor. The findings suggest that these factors positively influence the adoption of digital agriculture technologies, but their effects vary across contexts. The study recommends that policymakers, practitioners, and development agencies adopt integrated strategies that address multiple barriers simultaneously to enhance the uptake and sustained use of digital agriculture technologies.

Artificial intelligence is rapidly entering dairy barns and poultry houses. Computer vision systems monitor lameness and body condition, acoustic models infer thermal and respiratory stress, sensor networks support herd- and flock-level digital twins, and cloud platforms translate these data into management recommendations. These tools are increasingly framed as climate-smart technologies that support feed efficiency, earlier health interventions, and tighter environmental control across livestock value chains. Yet the energy use, carbon intensity, and hardware lifecycles of the AI systems themselves remain largely invisible in livestock sustainability frameworks. Current policy instruments in the European Union and United States promote AI as a sustainability enabler, but rarely require disclosure of AI's computational energy use, emissions, and material impacts as a condition for “climate-smart” claims. This creates a governance gap as AI becomes embedded in climate programs and supply-chain narratives. This Perspective argues that climate-smart livestock policy must move from AI-for-agriculture to Green-AI-for-agriculture, where computational sustainability is integral to mitigation and adaptation. Drawing on recent work in AI regulation, digital agriculture, sustainable computing, and agri-food data spaces, we propose five guiding principles for governing Green AI in livestock systems: energy transparency, boundary harmonization, architectural proportionality, climate-first design, and FAIR compute metadata. Building on these principles, we outline a three-tier framework differentiating expectations for smallholders, commercial operations, and export-oriented value chains. A 2026–2030 roadmap identifies how funders, regulators, technology providers, and supply-chain actors can embed computational carbon into climate-smart livestock strategies without excluding producers with limited digital capacity.

BackgroundAccess to timely and reliable agricultural information remains a major challenge for smallholder farmers in South Africa's rural municipalities. Information and Communication Technologies (ICTs) have emerged as transformative tools to enhance communication, knowledge sharing, and service delivery in agricultural extension systems. This study investigates how ICTs improve smallholder farmers' access to extension services in the Ingquza Hill Local Municipality, Eastern Cape Province.MethodsA descriptive survey design was employed. Data were collected from 104 smallholder farmers using a structured questionnaire administered through face-to-face interviews. Respondents were selected through a combination of convenience and random sampling to ensure diversity and representativeness. Quantitative data were analyzed using descriptive statistics, including frequencies and percentages, to describe patterns of ICT access, utilization, and constraints.ResultsThe majority of respondents (64.7%) were women, with 20.2% aged between 26 and 30 years. ICT adoption for agricultural purposes was widespread (75%), and 65.5% of farmers relied primarily on mobile phones to access extension information. WhatsApp (44.1%) and Facebook (44.1%) were identified as the most commonly used digital platforms for obtaining agricultural advice. However, persistent barriers such as poor internet connectivity (39.4%), high data costs (38.5%), and limited digital literacy (18.3%) constrained optimal utilization of ICT-based extension services.Discussion and conclusionThe findings demonstrate that ICTs particularly mobile and social media platforms play a pivotal role in bridging information gaps and improving smallholder farmers' access to agricultural extension services. The study concludes that digital tools have become essential for promoting inclusivity and efficiency in agricultural communication systems. To enhance the effectiveness of ICT-based extension services, the study recommends targeted investments in rural ICT infrastructure, digital literacy training, and multi-stakeholder partnerships to promote sustainable, digitally enabled agricultural transformation in Ingquza Hill Local Municipality.

Digital agriculture technologies provide potential for increased yield and quality of wheat grain with an optimized input use related to site-specific conditions. This review aims to present the global distribution of digitalization in wheat production, to identify the core digital technologies applied in wheat management, and to address challenges and future directions for ensuring the security of producing this staple food. For this purpose, a systematic literature review based on the PRISMA 2020 guidelines was conducted, and 113 peer-reviewed papers within the period of 2015–2025 were selected and examined. The highest number of research papers refers to Asia (37.4%), followed by Europe (17.4%) and North America (15.7%). The majority of the papers related to the field of remote sensing, more specifically, in 40.2% of the papers, satellites are listed as a platform, followed by UAVs (in 33.0% of studies). The review reveals uneven global distribution of digitalization, with a significant need for improvement in less developed countries to address food safety in a more balanced way. This comprehensive analysis proposes integration of the current state of digitalizing wheat production with future opportunities for large, but moreover, for small and medium farmers, along with strong support for the policies.

Among the forage species, especially in semiarid ecosystems, cactus pear is exceptional because of its high tolerance to adverse conditions and high productivity. Due to this alone, several studies have been conducted to identify the main technologies for this crop. Despite being consolidated and integrated, the cactus pear production system has limited accessibility, technical assistance, and availability of information for those dedicated to its production. This study aimed to present a digital platform, website, and applications to provide technical information on the cactus pear and demonstrate the efficiency of these applications through experimental data. On this digital platform, applications were made available for predicting the productivity of cactus pear using artificial neural networks (ANN) on a computer with routines in the R software and by simple linear regression (SLR) on smartphones on the Android system of the MIT App Inventor 2 platform. In addition, using the smartphone app, it is possible to obtain the cladode area through multiple linear regression (MLR). It is also possible to obtain the estimates of the experimental plot sizes by the maximum modified curvature, linear and quadratic methods with plateau response, relative information, comparison of variances, and convenient plot size. The platform provides technical information associated with the cactus pear crop from different sources (dissertations, theses, articles) and formats (video classes and teaching resources), offline for applications, and online with download for publications, dissertations, theses and articles, video classes, and several didactic resources. The biomathematical models integrated with the applications were highly precise in predicting the phenomena, in which the variation explained by the models in the prediction of responses for future observations had R² values of 0.95, 0.72, and 0.92, respectively, for productivity with computer-ANN and smartphone-SLR, and for the cladode area with a smartphone - MLR.

Crop protection information, such as how to control emergent and outbreak crop diseases and pests, as well as the latest research, regulations, and quality control measures for pesticides and fertilizers, is important to farmers. Rural smallholder farmers in Tanzania have traditionally relied on government agricultural officers who visit them in their villages to provide this crop protection information. However, these officers are few and cannot reach all the farmers on time. This means that farmers fail to make critical farming decisions on time, which can lead to low crop productivity. In this study, we aim to provide farmers with reliable and instant crop protection information by developing a system based on the Short Message Service (SMS) and the Web. This system automatically replies to farmers’ requests for the latest crop protection information in the Swahili language through SMS on a mobile phone or a Web system. The findings reveal that our proposed system can provide farmers with crop protection information at lower cost (500 times cheaper) than the existing Tigo Kilimo system. Furthermore, our proposed system’s deep learning model is effective in understanding and processing Swahili natural language SMS queries for crop protection information with an accuracy of 96.43%. This crop protection information will help farmers make better critical farming decisions on time and improve crop productivity.

Climate change is a variation in the normal behavior of the climate. These variations and their effects will be seen in the coming years, the most imminent being anomalous fluctuations in atmospheric temperature and precipitation. This scenario is counterproductive for agricultural production. This study evaluated the effect of climate change on oil palm production for conditions in the Central Pacific of Costa Rica, in three simulation scenarios: the baseline between the years 2000 and 2019, a first climate change scenario from 2040 to 2059 (CCS1), and a second one from 2080 to 2099 (CCS2), using the modeling framework APSIM, and the necessary water requirements were established as an adaptive measure for the crop with the irrigation module. A decrease in annual precipitation of 5.55% and 7.86% and an increase in the average temperature of 1.73 °C and 3.31 °C were identified, generating a decrease in production yields of 7.86% and 37.86%, concerning the Baseline, in CCS1 and CCS2, respectively. Irrigation made it possible to adapt the available water conditions in the soil to maintain the baseline yields of the oil palm crop for the proposed climate change scenarios.

The article discusses modern legal, managerial, digital approaches to ensuring the sustainable development of agriculture. The norms and standards of agricultural risk insurance, budget lending, organic agribusiness modeling and food safety developed by international institutions are evaluated. Methods of comparative, legal, informational analysis and scientific generalization of theoretical knowledge revealed organizational problems of budgeting and quality control of agricultural products, preventing food losses, and combating falsification of food products. It was noted that legal measures and management standards optimize agribusiness segments, reduce the number of intermediaries, and improve the trust of suppliers and consumers through direct links. The digitalization of agriculture contributes to the transparency of data exchange while reducing information imbalances and transaction costs for participants in agricultural markets. The connection of standards and norms of legal regulation of sustainable development of agriculture with food security in the EU and the EAEU is shown. The conclusions are indicated by the positive prospects for digital management of agribusiness subsidies and digital budgeting as the level increases in the EU and the EAEU. Unlike the EU, the EAEU is building a common space for organic and digital agriculture on the principles of Eurasian integration and cooperation.

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.

Smart farming is a development that has emphasized information and communication technology used in machinery, equipment, and sensors in network-based hi-tech farm supervision cycles. Innovative technologies, the Internet of Things (IoT), and cloud computing are anticipated to inspire growth and initiate the use of robots and artificial intelligence in farming. Such ground-breaking deviations are unsettling current agriculture approaches, while also presenting a range of challenges. This paper investigates the tools and equipment used in applications of wireless sensors in IoT agriculture, and the anticipated challenges faced when merging technology with conventional farming activities. Furthermore, this technical knowledge is helpful to growers during crop periods from sowing to harvest; and applications in both packing and transport are also investigated.