Digital agriculture (DA) can contribute solutions to meet an increase in healthy, nutritious, and affordable food demands in an efficient and sustainable way. South America (SA) is one of the main grain and protein producers in the world but the status of DA in the region is unknown. A systematic review and case studies from Brazil, Argentina, Uruguay, and Chile were conducted to address the following objectives: (1) quantify adoption of existing DA technologies, (2) identify limitations for DA adoption; and (3) summarise existing metrics to benchmark DA benefits. Level of DA adoption was led by Brazil and Argentina followed by Uruguay and at a slower rate, Chile. GPS guidance systems, mapping tools, mobile apps and remote sensing were the most adopted DA technologies in SA. The most reported limitations to adoption were technology cost, lack of training, limited number of companies providing services, and unclear benefits from DA. Across the case studies, there was no clear definition of DA. To mitigate some of these limitations, our findings suggest the need for a DA educational curriculum that can fulfill the demand for job skills such as data processing, analysis and interpretation. Regional efforts are needed to standardise these metrics. This will allow stakeholders to design targeted initiatives to promote DA towards sustainability of food production in the region.

Nitrogen Sufficiency Index (NSI) is an important nitrogen (N) stress indicator for precision N management. It is usually calculated using variables such as leaf chlorophyll meter readings (SPAD) and vegetation indices (VIs). However, no consensus has been reached on the most preferred variable. Additionally, conventional NSI (NSI_uni) calculation assumes N being the sole yield-limiting factor, neglecting other factors such as soil water variability. To tackle these issues, this study compared various variables for NSI calculation and evaluated two new N stress indicators in minimizing the impact of confounding water treatment. The following ground- and aerial-derived variables were compared for NSI_uni calculation: SPAD, sampled leaf and canopy N content (LNC, CNC), LNC and CNC estimated using hyperspectral images acquired by an Unmanned Aerial Vehicle, and three VIs (Normalized Difference Vegetation Index (NDVI), Normalized Red Edge Index (NDRE), and Chlorophyll Index) from the hyperspectral images. Results demonstrated that ground-measured variables outperformed aerial-based variables in deriving N-responsive NSI. Especially, LNC derived NSI_uni responded to N treatment significantly in ten out of thirteen site-date datasets. For the second objective, a modified NSI (NSI_w) and the NDRE/NDVI ratio were compared to NSI_uni. NSI_w reduced water treatment effects in over 80% of the datasets where NSI_uni showed evident impacts. NDRE/NDVI performed similarly to NSI_w, with the notable advantage of not requiring prior knowledge of soil water spatial distribution. This research pioneers the optimization of N stress indicators by identifying the best variables for NSI and mitigating the effects of soil water variability. These advancements significantly contribute to precision N management in complex field conditions.

Optimizing nitrogen (N) fertilization is essential for enhancing nitrogen use efficiency (NUE), maximizing crop yields, and minimizing environmental impacts. Sensor-based technologies, integrated with variable rate applications, present a promising approach to site-specific N management. However, their effectiveness can differ across crops, soils, and topographic properties. This study compared sensor-based N management with conventional grower practices in corn and wheat over 17 on-farm site-years. Additionally, we evaluated key site-specific factors influencing sensor performance on a 57 on-farm trial dataset. Our results showed that sensor-based N management significantly improved NUE in corn compared to Grower conventional practices, reducing on average 40 kg N ha^-1 without compromising yield. However, in wheat, the differences were not statistically significant across all trials, suggesting that crop-specific responses affect sensor effectiveness. Our findings highlight that corn field yield productivity, its variability, and soil texture were the most influential factors affecting sensor-based NUE. Sensor-based approach in corn outperformed grower practices in moderate to high-variability fields. These results suggest that while sensor-based N management enhances NUE in corn, its effectiveness in wheat may vary more. This study provides valuable insights into the practical limitations and site-specific factors influencing the success of sensor-based technologies, aiding in developing improved decision-support tools for precision nitrogen management.

Digital agriculture is emerging as the next green revolution, helping farmers to make data‐informed decisions that increase productivity and optimize resource use. Understanding farmers' perceptions of current barriers and future opportunities for technology adoption is necessary for sustainable agriculture. This study aimed to identify trends and patterns in farmers' perceptions of digital agriculture technology adoption. A survey was distributed across most of the Midwest and adjacent region of the United States, collecting 247 responses. Results showed 93% used digital agricultural technology, with long‐term (>10 years) adoption of auto‐guidance (59%), yield mapping (56%), and variable rate technologies (36%). Perceived financial profitability was a key driver of technology adoption (36%), followed by input optimization (18%) and productivity (16%). The main barrier was high cost relative to perceived benefit (31%), followed by small farm size (16%) and equipment incompatibility (14%). Environmental benefits emerged as a tertiary motivator, and their lower prioritization suggests that farmers focus more on the economic dimension of sustainability when adopting new technologies. Fertilizer efficiency (27%), pest management (18%), and water management (13%) were top challenges to address. Findings suggest that digital agriculture adoption is primarily driven by economic considerations, with cost‐benefit analysis and entry costs as key determinants in US Midwest and adjacent region farming systems.