Rice (Oryza sativa L.) is one of the most important crops globally. However, its production faces significant challenges due to climate change, reduced arable land, and increased demand. In this context, the present study conducted a systematic literature review (SLR) on technological advances in rice production in Latin America. Recognized scientific databases were consulted, and rigorous inclusion and exclusion criteria were applied to synthesize current knowledge on the subject. The results show that the main innovations include genetically improving varieties with greater resistance to biotic and abiotic stresses; implementing advanced water management techniques, such as intermittent irrigation; and applying biofertilizers and organic amendments to improve soil fertility. Additionally, precision agriculture tools, such as remote sensing and artificial intelligence-based modeling, have optimized crop monitoring and input efficiency. Brazil, Mexico, and Colombia are the main generators of rice production technologies in the region. Despite the progress made, challenges remain regarding the adoption of these innovations by producers, highlighting the need for comprehensive policies to facilitate technology transfer. This review establishes a foundation for researchers and policymakers interested in the sustainable development of rice production in Latin America.

Introduction. Defoliation studies on sugarcane in Mexico are scarce, and it is unknown at what age of the plant and in which season of the year it should be carried out. Objective. To evaluate the effect of artificial defoliation in sugarcane at different stages of growth and its response on quality and yield parameters. Materials and methods. The experiment was conducted during the 2020-2021 harvest between the months of April and May, in a plot located in a ratoon of the cultivar COLPOSCTMEX 06-039, within the supply area of the Ingenio Presidente Benito Juárez in the Chontalpa region, Tabasco. A completely randomized block design was used, where the defoliation treatments consisted of removing the entire leaves of the stem until leaving the +5 leaf (H5). Defoliation treatments were applied at 8, 9, and 10 months of crop age, plus a control without defoliation, with five replicates per treatment. Growth parameters evaluated were plant height and diameter, number of stalks, and leaf area index (IAF) and normalized difference vegetation index (NDVI). At harvest, °Brix and sugarcane yields of sugarcane were measured. Results. It was found that growth and quality parameters were reduced by total defoliation treatments. Treatment 2 showed the highest values of plant height and diameter, lower NDVI, and stalk yield at harvest, and defoliation affected the IAF. °Brix were not affected by different defoliation treatments. Conclusion. Defoliation at nine months of age increased agricultural yield and did not affect juice quality.

Yield estimation in sugarcane systems remains a major challenge in tropical regions due to the reliance on destructive, labor-intensive, and spatially limited field measurements. Although remote sensing has been widely used for crop monitoring, its predictive performance is often constrained when spectral information is used in isolation. This study proposes a data fusion framework integrating multitemporal Sentinel-2 spectral bands with meteorological variables to improve sugarcane biomass prediction under tropical conditions. A commercial field was monitored throughout the 2022–2023 growing season, and machine learning models, including random forest (RF), support vector machine (SVM), and multiple linear regression (MLR), were developed to estimate stem, foliage, and total biomass. To reduce potential spatial data leakage caused by spatial autocorrelation within the field, model performance was evaluated using Spatial Block Cross-Validation. Results showed that integrating spectral and meteorological data consistently improved predictive performance compared to spectral-only and weather-only scenarios. Spectral bands exhibited stronger relationships with biomass than derived vegetation indices, while maximum temperature and solar radiation were identified as key drivers of biomass variability. RF combined with spectral–weather fusion achieved the highest predictive performance, reaching R² values up to 0.95, RMSE values as low as 5296.35, and rRMSE values close to 18% for stem biomass, consistently outperforming SVM and MLR. In contrast, spectral-only scenarios produced lower predictive accuracy and higher prediction errors across all biomass variables. This study provides one of the first field-scale implementations under humid tropical conditions in southeastern Mexico, where georeferenced yield data remain scarce.

Objective: To optimize the composting process of sugarcane filter-pressed mud, straw, and ash as an alternative to reduce the environmental contamination of sugarcane cultivation in the Santa Rosalía sugar mill (Ingenio Santa Rosalía) of Chontalpa, Tabasco, Mexico.Design/Methodology/Approach: A completely randomized experimental design in a factorial arrangement was used, with compost mixtures and the aeration times as study factors. Ten treatments were generated with six repetitions each. The study variables were pH, organic matter (%), and nitrate (NO 3 - ) and ammonium (NH 4 + ) content.Results: The compost mixture factor had significant effects on compost quality parameters. Aeration times did not affect the quality of the different compost mixtures. Chemical characteristics of pH and organic matter did not present differences between the treatments. Study Limitations/Implications: The use of industrial sugarcane residues generates quality organic fertilizers through the composting process; therefore, it is necessary to test more residues, such as molasses and vinasses.Findings/Conclusions: The 100% sugarcane filter-pressed mud and the 100% sugarcane filter-pressed mud + 0.5% N treatments presented the highest amount of NO 3 - and NH 4 + and were therefore considered the best treatments. All treatments were classified as mature composts, rich in organic matter, with alkaline pH, and made ofsteady materials.