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.

Rice (Oryza sativa L.) systems rotated with perennial pastures have intensified in South America to increase annual grain productivity, but the effects on rice yield and soil quality remain poorly understood. We evaluated rice grain yield, crop and pasture biomass production, and soil organic carbon (SOC) and total nitrogen stocks (0–15‐cm depth) in three rice‐based rotations over 8 yr in Uruguay. Treatments were: (a) rice–pasture [a 5 yr rotation of rice–ryegrass (Lolium multiflorum Lam.)–rice, then 3.5 yr of a perennial mixture of tall fescue (Festuca arundinacea Schreb.), white clover (Trifolium repens L.), and birdsfoot trefoil (Lotus corniculatus L.)], (b) rice–soybean [a 2‐yr rotation of rice–ryegrass–soybean (Glycine max [L.] Merr.)–Egyptian clover (Trifolium alexandrinum L.)], and (c) rice–cover crop (an annual rotation of rice–Egyptian clover). Rice after soybean or pasture achieved the highest yield (9.8 Mg ha^–1), 9% higher than rice after rice in the rice–pasture and rice–cover crop systems. Estimated belowground biomass under rice–pasture (2.7 Mg ha^–1) was 12 and 42% greater than under rice–cover crop and rice–soybean rotations, respectively. Rice–pasture showed an increase of 0.6 Mg ha^–1 yr^–1 of SOC; no changes were observed in the intensified rotations replacing pasture with additional rice or soybean. All systems sustained soil total N. These results provide insights for implementing sustainable rice‐based rotations, with rice–pasture being the only system that increased SOC while achieving high rice yields and belowground biomass productivity.

The rice sector is facing the challenge of increasing rice yields while maintaining or improving input use efficiency. The purpose of this study was to determine the most effective vegetation indices for monitoring nitrogen uptake (N uptake) under different irrigation techniques. The study was conducted in Uruguay over two rice-growing seasons. A split plot experimental design featured two irrigation treatments (main plots): continuous flooding (C) and alternate wetting and drying (AWD). The nitrogen-rate (N-rate) treatments (split plots) included no nitrogen, the recommended N-rate based on soil analyses, and two additional doses (±50% of the recommendation). The plant N uptake relationships with selected drone-based vegetation indices (VIs) were assessed at panicle initiation. The presence or absence of standing water during image collection affected the VIs and their relationships with N uptake. The relationships estimated for traditional irrigation may not be applicable for AWD. The SCCCI was the top index with a significantly stronger relationship with N uptake under the C (R² = 0.84) and AWD (R² = 0.71) irrigation techniques in relation to all evaluated vegetation indices. The Clre, NDRE², NDRE, and CLg also had a significant relationship with N uptake under both irrigation treatments in both seasons, though their average R² values of 0.75, 0.74, 0.73, and 0.71, respectively, were lower than the SCCCI (average R² = 0.78). The findings would assist rice growers for selecting effective VIs for remote crop monitoring.

The high water demand of rice cultivation is mainly due to flood irrigation, which requires large volumes not only to meet evapotranspiration needs, but also due to losses from percolation, lateral seepage, and surface runoff. In addition to lowering water use efficiency, surface runoff may transport nutrients. This study aimed to calibrate and validate a daily water balance model to estimate surface runoff losses across three rice-growing seasons. During the first two seasons, different model components were calibrated by comparing simulated and observed water depths. In the final season, the calibrated model was validated using direct runoff measurements obtained from weirs and flowmeters. Results showed strong agreement between model estimates and direct measurements of water depth and surface runoff. Linear regression models showed good fit, with coefficients of determination (R²) above 0.80 for water depth and 0.79 for runoff. A validated daily water balance model, combined with periodic monitoring of water depth, proved to be a reliable tool for estimating surface runoff during the rice-growing season. Total runoff—from irrigation, rainfall, and final drainage—represented between 7.5% and 18% of the total water input. This approach offers a practical tool for improving irrigation water management and understanding runoff-driven nutrient transport.