A cotton (Gossypium hirsutum) field in the state of Minas Gerais in Brazil was identified in March 2019 with plants showing reduced size, chlorosis, and root galls in a cotton cultivar with resista...

Satellite remote sensing data expedite crop yield estimation, offering valuable insights for farmers’ decision making. Recent forecasting methods, particularly those utilizing machine learning algorithms like Random Forest and Artificial Neural Networks, show promise. However, challenges such as validation performances, large volume of data, and the inherent complexity and inexplicability of these models hinder their widespread adoption. This paper presents a simpler approach, employing linear regression models fitted from vegetation indices (VIs) extracted from MODIS sensor data on the Terra and Aqua satellites. The aim is to forecast cotton yields in key areas of the Brazilian Cerrado. Using data from 281 commercial production plots, models were trained (167 plots) and tested (114 plots), relating seed cotton yield to nine commonly used VIs averaged over 15-day intervals. Among the evaluated VIs, Enhanced Vegetation Index (EVI) and Triangular Vegetation Index (TVI) exhibited the lowest root mean square errors (RMSE) and the highest determination coefficients (R²). Optimal periods for in-season yield prediction fell between 90 and 105 to 135 and 150 days after sowing (DAS), corresponding to key phenological phases such as boll development, open boll, and fiber maturation, with the lowest RMSE of about 750 kg ha⁻¹ and R² of 0.70. The best forecasts for early crop stages were provided by models at the peaks (maximum value of the VI time series) for EVI and TVI, which occurred around 80–90 DAS. The proposed approach makes the yield predictability more inferable along the crop time series just by providing sowing dates, contour maps, and their respective VIs.

Soil bulk density (ρ_b) is vital for assessing soil organic carbon (SOC) and nutrient stocks, as well as for modeling soil processes. Although ρ_b can be measured using traditional methods, these are labor-intensive and time-consuming. Consequently, there is a growing interest in developing pedotransfer functions (PTFs) to predict ρ_b based on more easily measured soil properties. The vast Brazilian territory, with different climates and biomes, presents a challenge for development of national-scale PTFs, particularly for ρ_b. In this study, a comprehensive dataset was compiled from various sources to develop ρ_b PTFs across Brazil. The dataset includes particle size distribution (PSD), SOC, ρ_b, sampling depth, soil order, land use, and geographic coordinates, allowing for the incorporation of additional numerical and categorical variables. Rigorous data preprocessing ensured quality and reliability. PTFs were developed using multiple linear regression (MLR) and Random Forest (RF) models. Model accuracy was evaluated using mean absolute error, bias, root mean square error (RMSE), and coefficient of determination. Both MLR and RF models accurately predicted ρ_b, with log-transformed PSD and SOC emerging as key predictors. The RF model slightly outperformed the MLR model (RMSE = 0.12 vs. 0.13 g cm⁻³) on the test dataset, underscoring the importance of environmental and categorical variables in predicting ρ_b. The developed PTFs, along with other PTFs for Brazil, were applied to estimate SOC stocks across different biomes and land uses. Best estimations were obtained with the RF model, with an R² of 0.97, emphasizing the value of categorical variables in improving SOC stock estimations.

This research aimed to evaluate management of Meloidogyne incognita in cotton (Gossypium hirsutum L.) using a rotation with castor bean (Ricinus communis L.), a potential cash crop. Reproduction of the nematode on castor bean in the greenhouse and effect of its planting on nematode populations on cotton in the field were tested. The effect of castor bean cake on nematode populations and its direct effect on cotton yield were also evaluated. Castor bean was a non-host to M. incognita in the trials. In addition, a preliminary field study showed that castor bean produced 1.2 t seed/ha when used in M. incognita infested areas, reduced populations of the nematode, and improved cotton productivity in the following season. Castor bean cake also reduced M. incognita populations. However, castor bean cake cannot be applied at doses above 100 kg/ha in the cotton planting furrow due to a phytotoxic effect on the crop. The most appropriate rate was 60 kg/ha applied to the furrow, decreasing in the nematode population at 80 days after planting and gall index as well as increasing cotton yield.

Yield maps from combine harvesters are essential in precision agriculture for capturing within-field variability and guiding variable-rate input management. However, in large-scale systems such as those in the Brazilian Cerrado, these maps are often inconsistent due to calibration errors, use of multiple harvesters, and complex post-processing. Orbital remote sensing offers an alternative by providing consistent vegetation index (VI) data for crop monitoring and yield estimation. This study developed regression models relating Sentinel-2 VIs (EVI, TVI, NDVI, and NDRE) to cotton yield data obtained from combine harvesters across 30 commercial plots in Mato Grosso, Brazil, over six cropping seasons (2019–2024), totaling 76 plot-season datasets. Vegetation indices were grouped into 15-day intervals based on days after sowing, and a logistic growth function was applied in the regression modeling. Model performance evaluated using 15 independent plot-seasons showed good pixel-level accuracy, with RMSE of 0.695 t ha⁻¹ and R² of 0.78, with EVI performing slightly better. At the plot scale, mean yield predictions across all datasets achieved an RMSE of 0.41 t ha⁻¹, reflecting the higher reliability of module-based yield measurements. These results demonstrate the potential of Sentinel-2 VIs combined with logistic regression to predict cotton yields in the Cerrado, complementing or replacing harvester-based monitoring.