Abstract Aerial spraying is a fundamental strategy for maintaining operational efficiency and productivity in Brazilian agriculture. In recent years, the rapid expansion of remotely piloted aircraft (drones) has intensified the debate over their technical, operational, and economic performance compared with traditional manned agricultural aircraft. In this context, this study developed a mathematical model to evaluate and compare the operational capacity of manned agricultural aircraft (EMB-202 and EMB-203) and unmanned aircraft (AGRAS T10, T20P, T30, and T40) under three application rates: 5, 10, and 20 L ha⁻1. Operational data were obtained through direct inquiries with 41 aircraft maintenance facilities certified by the National Civil Aviation Agency (ANAC) and 41 agricultural drone dealerships accredited by manufacturers across Brazil's major agricultural regions. The results indicated that five AGRAS T40 drones operating simultaneously can achieve a daily operational capacity comparable to that of the EMB-203 at an application rate of 5 L ha⁻1, reaching approximately 900 ha day⁻1 while requiring an acquisition cost approximately 3.6 times lower than that of a new manned aircraft. Kernel density estimation revealed a greater concentration of manned aircraft in the state of Mato Grosso, whereas unmanned aircraft were more concentrated in the state of São Paulo. These findings indicate that agricultural drones are a competitive alternative to manned aircraft in terms of operational capacity and acquisition costs for medium-sized farms, with strong potential for technological and operational expansion into other agricultural regions of Brazil, particularly the Northeast.
Abstract Air-assisted sprayers are widely used in ornamental nurseries, where canopy architecture variations challenge the ability of sprayers to provide adequate spray deposition while minimizing drift. In this context, precision variable-rate sprayers have shown the potential to improve targeting efficiency and reduce application rates. This research evaluated the field performance of a laser-guided variable-rate sprayer by comparing deposition and coverage uniformity in ash tree canopies and drift losses with those of a conventional constant-rate sprayer. Deposition was quantified using a fluorescent tracer on artificial targets, and coverage was assessed with water-sensitive papers placed within the canopy and on the ground. Drift was measured at different downwind distances. Treatments consisted of two sprayer types (conventional air-assisted and air-assisted variable-rate sprayers) and three travel speeds (3.2, 5.6, and 8.0 km h⁻1). The variable-rate sprayer reduced the application rate by 64%. Both sprayers provided similar deposition and coverage in the canopy without a significant effect of travel speed in the evaluated range. Ground deposition near the treated area was comparable between sprayers and was unaffected by the travel speed. The variable-rate sprayer reduced airborne drift by 92 and 94% and ground drift by 58 and 98% at 5 and 15 m downwind, respectively.
Abstract The final quality of cotton produced is critically influenced by the characteristics of the fibers. The objective of this study was to map and evaluate the spatial correlation between cotton lint productivity and lint quality parameters measured by the High Volume Instruments (HVI) using geostatistical analysis. The x and y axes of a Cartesian coordinate system were defined, and a geostatistical experimental grid was established. The sampling grid consisted of 100 equally spaced points arranged in a square grid with 9.9 m spacing. The analyses performed included cotton lint productivity (PR), spinning consistency index (SCI), micronaire (Mic), upper half average length (UHML), short fiber index (SF), strength (Str), and leaf grade (TrId). Results were presented through semivariograms and kriging maps of the studied attributes, showing zones with the observed values. Results also indicate that cotton (cultivar FM 978 6LTP) grown in the Chapadões region, located in the Midwest of Brazil, has fiber test results very similar to those found in cotton varieties in different parts of the world such as Africa, Europe, and Asia. PR, SCI, Mic, UHML, and SF showed spatial variability. The attributes SCI and UHML showed a negative correlation with cotton lint productivity (R2 = 0.96 and R2 = 0.96 respectively), while SF showed a positive correlation (R2 = 0.60) with cotton lint productivity.
Abstract This study used machine learning techniques to predict leaf nitrogen content from canopy hyperspectral data of sugarcane (Saccharum officinarum L.), thereby providing a rapid and non-invasive alternative to traditional laboratory analysis. The experiment was conducted in Piracicaba, São Paulo, Brazil, using ground-based spectroradiometry to obtain the canopy spectral signatures at different vegetative stages and under varying nitrogen doses. Spectral data were acquired at 67, 99, 144, 164, 200, 228, 255, and 313 days after cutting followed by leaf sampling and laboratory quantification of the leaf nitrogen content (LNC). Partial least squares regression, random forest, and extreme gradient boosting models were applied to predict the LNC. The relationship between the spectral response and leaf nitrogen was primarily associated with the visible and RedEdge regions, which are sensitive to chlorophyll absorption, pigment concentration, and crop nutritional status. Among the evaluated models, PLSR achieved the best performance, indicating that canopy hyperspectral sensing may be used as a complementary approach for nitrogen monitoring under field conditions; however, its predictive accuracy remained moderate. Overall, the results suggest that VIS-near-infrared region (NIR) hyperspectral data, combined with machine learning techniques, can support nitrogen monitoring in sugarcane. However, further validation under expanded field conditions is required before its implementation in operational nutrient management.
Abstract Supported by data published by the Brazilian Agricultural Research Corporation (Embrapa), based on information from the Rural Environmental Registry System (SiCAR), this study analyzes the Das Posses Stream catchment (Extrema, Minas Gerais, Brazil), which is part of the Cantareira Water Supply System that supplies the city of São Paulo. The objectives were to evaluate the current forest cover and estimate the total irrigable area of the catchment. Data analysis and hydrological calculations showed that: (i) 18.47% of the Das Posses catchment still needs to be restored or designated as preservation areas; (ii) from the perspective of increasing agricultural productivity, the irrigable area supported by direct water withdrawal is limited; (iii) the use of reservoirs would allow irrigation of 85.2% of the agricultural area within the catchment; and (iv) adopting appropriate irrigation techniques can increase the productivity of the cultivable area of the Das Posses Stream catchment.
