Rafael Galbieri

The objective of this work was to evaluate the reproduction of Meloidogyne enterolobii and Meloidogyne incognita and the caused symptoms in two soybean (Glycine max) cultivars under different inoculum concentrations. Two greenhouse experiments were conducted using the M7198IPRO (moderately resistant to M. incognita) and IMA 731 IPRO (susceptible to M. incognita) soybean cultivars. Each plant was inoculated with 0, 300, 1,000, 3,000, 9,000, and 27,000 nematodes from both species. Meloidogyne enterolobii causes a more severe root galling in soybean plants and also exhibits a higher reproduction rate on soybean roots. Therefore, this species overcomes the resistance found in soybean cultivars that are moderately resistant to M. incognita.

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.

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.

Crop yield estimation supported by satellite remote sensing data can provide expeditious and strategic information for farmers’ decision-making. Most recent forecasting methods have indicated a promising pathway based on machine learning algorithms. However, validation performances, demand for big data and their inherent inexplicability have not yet consolidated a substantial differential to replace methods based on simpler and more understandable models. This paper proposes an approach based on simple linear models fitted from vegetation indices (VIs) assessed at regular intervals of time series derived from MODIS satellite images, aiming to forecast cotton yields in representative areas of the Brazilian Cerrado. Data from 281 commercial production plots were taken to train (167 plots) and test (114 plots) linear regression models relating seed cotton yield and nine well-known VIs averaged in 15-days intervals. Among the evaluated VIs, EVI (Enhanced Vegetation Index) and TVI (Triangular Vegetation Index) showed the lowest root mean square errors (RMSE) and the highest determination coefficients. The best periods for in-season yield prediction were from the 90-105 to 135-150 days after sowing (DAS), i.e. phenological phases corresponding to boll development, open boll and fiber maturation, with lowest RMSE of about 750 kg ha-1 and R2=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 its respective VIs.

Plant growth-promoting rhizobacteria (PGPR) can ensure the sustainability of agricultural growth. The objectives of this study were to characterize and determine the effects of Bacillus subtilis, isolate IMA Bs/170005, applied as co-inoculant in the soybean planting furrow. In all treatments, the seeds had been pre-inoculated with Bradyrhizobium japonicum. The experiments were carried out in a greenhouse, and in the field. Different doses of formulated (8.10⁸ spores mL^-1) product with B. subtilis (0; 0.2; 0.4; 0.8; 1.2; 1.6 and 2.0 L ha^-1) were tested. The isolate proved efficient for in vitro auxin production. Under greenhouse conditions, the response to B. subtilis co-inoculation consisted of an increase of up to 26% in length of the root system. In the field, co-inoculation in the furrow proved beneficial for crop growth and yield and can be recommended. The best response rate was 0.4 L ha^-1. At this dose, averaged over 20 and 40 days after sowing and compared to the control with Bradyrhizobium inoculation alone, increases of 5.3% were observed for plant height, 14.8% for shoot fresh weight, 14.1% for shoot dry weight, 8.5% for root dry weight and 6.5% for soybean yield, demonstrating the efficiency of this B. subtilis isolate as a PGPR.

Main conclusion Host-derived suppression of nematode essential genes decreases reproduction of Meloidogyne incognita in cotton. Abstract Root-knot nematodes (RKN) represent one of the most damaging plant-parasitic nematode genera worldwide. RNAi-mediated suppression of essential nematode genes provides a novel biotechnological strategy for the development of sustainable pest-control methods. Here, we used a Host Induced Gene Silencing (HIGS) approach by stacking dsRNA sequences into a T-DNA construct to target three essential RKN genes: cysteine protease (Mi-cpl), isocitrate lyase (Mi-icl), and splicing factor (Mi-sf), called dsMinc1, driven by the pUceS8.3 constitutive soybean promoter. Transgenic dsMinc1-T₄ plants infected with Meloidogyne incognita showed a significant reduction in gall formation (57–64%) and egg masses production (58–67%), as well as in the estimated reproduction factor (60–78%), compared with the susceptible non-transgenic cultivar. Galls of the RNAi lines are smaller than the wild-type (WT) plants, whose root systems exhibited multiple well-developed root swellings. Transcript levels of the three RKN-targeted genes decreased 13- to 40-fold in nematodes from transgenic cotton galls, compared with those from control WT galls. Finally, the development of non-feeding males in transgenic plants was 2–6 times higher than in WT plants, indicating a stressful environment for nematode development after RKN gene silencing. Data strongly support that HIGS of essential RKN genes is an effective strategy to improve cotton plant tolerance. This study presents the first application of dsRNA sequences to target multiple genes to promote M. incognita tolerance in cotton without phenotypic penalty in transgenic plants.

The advance of cotton farming in the Brazilian savannah boosted and made possible a highly technified, efficient and profitable production, elevating the country from the condition of cotton fiber importer in the 70s to one of the main exporters so far. Despite the increasing contribution of technologies such as transgenic cultivars, machines, inputs and more efficient data management, in recent years there has been a stagnation of cotton productivity in the State of Mato Grosso (MT). Data Mining (MD) techniques offer an excellent opportunity to assess this problem. Through the rules-based classification applied to a real database (BD) of cotton production in MT, factors were identified that were affecting and consequently limiting the increase in productivity. In the pre-processing of the data, we perform the attributes, selection, transformation and identification of outliers. Numerical attributes were discretized using automatic techniques: Kononenko (KO), Better Encoding (BE) and combination: KO + BE. In modeling the rule algorithms used were PART and JRip, both implemented in the WEKA tool. Performance was assessed using statistical metrics: accuracy, recall, cost and their combination using the I_FC index (created by the authors). Results showed better performance for the PART classifier, with discretization by the KO + BE technique, followed by binary conversion. The analysis of the rules made it possible to identify the attributes that most impact productivity. This article is an excerpt from an ICMC/USP Professional Master's Dissertation in Science carried out in São Carlos-SP/BR.

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.

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...