In the cultivation of maize, the leaf area index (LAI) serves as an important metric to determine the development of the plant. Unmanned aerial vehicles (UAVs) that capture RGB images, along with random forest regression (RFR), can be used to indirectly measure LAI through vegetative indices. Research using these techniques is at an early stage, especially in the context of maize for silage. Therefore, this study aimed to evaluate which vegetative indices have the strongest correlations with maize LAI and to compare two regression methods. RFR, ridge regression (RR), support vector machine (SVM), and multiple linear regression (MLR) regressions were performed in Python for comparison using images obtained in an area cultivated with maize for silage. The results showed that the RGB spectral indices showed saturation when the LAI reached 3 m² m⁻², with the VEG (vegetable index), COM (combination), ExGR (red–green excess), and TGI (triangular greenness index) indices selected for modeling. In terms of regression, RFR showed superior performance with an R² value of 0.981 and a root mean square error (RMSE) of 0.138 m² m⁻². Therefore, it can be concluded that RFR using RGB indices is a good way to indirectly obtain the LAI.

The use of artificial intelligence tools in agriculture is growing. In particular, the use of ensemble models. However, there are still few reviews on the use of these models in the study of the impacts of climate change on agriculture. Therefore, the aim of this article is to review the use of such models and perform three key tasks: (1) identify topics in which ensemble models are used, (2) determine the most widely applied model and the predominant crops and regions, and (3) explore future applications and challenges. As a result, it was noted that the first studies, dating back to 2011, applied ensemble models to model invasive species. Since then, research has focused on changes in temperature and precipitation, with at least one study published every year. The most cited studies have dealt with land use classification, emphasizing its relevance to climate change studies. Notably, studies on carbon storage in soil and its capacity to remove CO₂ from the atmosphere have become increasingly relevant. This analysis highlights the growing importance of ensemble models in climate-related agricultural research, outlining trends and key areas for future exploration.

In recent years, particularly in 2024, there has been an escalation in the frequency and intensity of megafires in the state of São Paulo, Brazil. This state, the most industrialized in the country, has seen extensive land-use changes in recent decades, with agriculture extending upon areas previously dedicated to other uses and forests. The practice of family farming, which is distinguished by its smaller operational areas and the majority involvement of the family that owns the land, has the potential to influence the occurrence of fires, but few studies have explored the link between agricultural practices (especially the difference between family and other farming types) and fire intensity. This study aims to assess whether the higher presence of family-farming establishments in different municipalities reduces fire incidents. The results indicate that the municipalities with the highest presence of family farming present lower percentages of burned areas. The increased diversity in crop types and the presence of forest cover within these municipalities have been identified as contributing factors to this reduced fire rate and burned areas. These findings underscore the need for public policies that support family farming as a strategy to reduce fires and protect vulnerable farmers in rural landscapes.

Maize plays an important role in global agriculture, with significant productive value in various regions. Effective water management is of paramount importance in this crop as water stress at specific vegetative stages (e.g., V6) can have a detrimental impact on its development and productivity. Accordingly, we aim to compare various regression models with the aim of indirectly estimating the value of canopy water content (CWC) and equivalent water thickness (EWT) in maize. To this end, vegetative indices were calculated and related to CWC and EWT using different wavelengths [visible, red, red-edge (RE), and near infrared] obtained from Sentinel satellite images. The indices calculated using the red edge band demonstrated the strongest correlation with water content. Four regression models were constructed using different indices as input variables. The regression models utilized in this study were multiple linear regression (MLR), ridge regression (RR), random forest regression (RFR), and AdaBoost regression (AR). Ensemble models, such as the AR, are seldom cited in the literature (there are no studies in the literature on measuring CWC and EWT for maize) and may serve as alternatives for measuring different crop development parameters. This hypothesis was validated in this study by demonstrating the superior performance of this model compared with the others used here. In conclusion, the AR model demonstrated superior performance compared with MLR, RR, and RFR, with an R2 of 0.991 for CWC and 0.972 for EWT. RFR exhibited a value of 0.982 and 0.948 for these two water content indices. Therefore, the methodology employed proved effective in obtaining both CWC and EWT over the analyzed period.