Plants respond to biotic and abiotic pressures by changing their biophysical and biochemical aspects, such as reducing their biomass and developing chlorosis, which can be readily identified using remote-sensing techniques applied to the VIS/NIR/SWIR spectrum range. In the current scenario of agriculture, production efficiency is fundamental for farmers, but diseases such as target spot continue to harm soybean yield. Remote sensing, especially hyperspectral sensing, can detect these diseases, but has disadvantages such as cost and complexity, thus favoring the use of UAVs in these activities, as they are more economical. The objectives of this study were: (i) to identify the most appropriate input variable (bands, vegetation indices and all reflectance ranges) for the metrics assessed in machine learning models; (ii) to verify whether there is a statistical difference in the response of NDVI (normalized difference vegetation index), grain weight and yield when subjected to different levels of severity; and (iii) to identify whether there is a relationship between the spectral bands and vegetation indices with the levels of target spot severity, grain weight and yield. The field experiment was carried out in the 2022/23 crop season and involved different fungicide treatments to obtain different levels of disease severity. A spectroradiometer and UAV (unmanned aerial vehicle) imagery were used to collect spectral data from the leaves. Data were subjected to machine learning analysis using different algorithms. LR (logistic regression) and SVM (support vector machine) algorithms performed better in classifying target spot severity levels when spectral data were used. Multivariate canonical analysis showed that healthy leaves stood out at specific wavelengths, while diseased leaves showed different spectral patterns. Disease detection using hyperspectral sensors enabled detailed information acquisition. Our findings reveal that remote sensing, especially using hyperspectral sensors and machine learning techniques, can be effective in the early detection and monitoring of target spot in the soybean crop, enabling fast decision-making for the control and prevention of yield losses.

Fungicides used to control the Asian soybean rust are composed almost entirely of mixtures involving the systemic (triazoles) and meso-systemic (strobilurins) modes of action. However, to control the soybean downy mildew, only protective fungicides are available. The present study aimed to evaluate the effectiveness of fungicides with different modes of action in the control of downy mildew and Asian soybean rust. A field experiment was carried out in a randomized block design, with seven treatments [negative control; difenoconazole at V7 + (azoxystrobin + cyproconazole) at R2 and R5.1; (tebuconazole + carbendazim) at V7 + (azoxystrobin + tebuconazole) at R2 and R5.1; carbendazim and mancozeb at V7 + (azoxystrobin + tebuconazole) at R2 and R5.1; (tebuconazole + carbendazim) at V7 + mancozeb in V7 + (azoxystrobin + tebuconazole) at R2 and R5.1; (tebuconazole + carbendazim) at V7 + (azoxystrobin + tebuconazole) at R2 and R5.1 + mancozeb at R2 and R5.1; (tebuconazole + carbendazim) at V7 + azoxystrobin + tebuconazole at R2 and R5.1 + mancozeb at R2 and R5.1 + carbendazim at R5.1] and four replicates. The following variables were evaluated: disease severity based on diagrammatic scales, phytotoxicity, product effectiveness, defoliation, number of pods per plant, number of seeds per pod, 1,000-seed weight and yield. The treatments with mancozeb addition showed to be more efficient in reducing the severity of mildew and Asian soybean rust and defoliation, providing increased seed yields for the crop. For the soybean downy mildew, the treatments with addition of mancozeb to benzimidazole, strobilurins and triazoles were more efficient in reducing the severity and for the control.

Most of the tropical soils that are intended for pastures are degraded or are at a certain stage of degradation. In this context, the use of nitrogen fertilization increases the quantity as well as the quality of the fodder produced and also accelerates growth, tillering, leaf production, and consequently, expansion of the aerial region and the root system. The present review of the literature aims to determine how the control of the source, location, time, and the application of a right dose of nitrogen fertilizer influences and benefits the entire ecosystem in tropical pastures with the correct use of 4R management, along with increasing the forage yields in these areas. The results showed that in tropical pastures, the recommended N dose varies with the cultivar used and the expected forage production and ranges from 50 to 500 kg N ha–1 year–1, irrespective of division in grazing cycles, with distribution in the entire pasture area

Maize and forage sorghum crops have a relevant economic importance in Midwest Brazil and there is a need for more information for their intercropping as a strategy for the farmers to reduce inputs. The objective of this work is evaluate if forage sorghum and maize intercropping effects agronomic traits, the bromatological characteristics of silage from the consortium at different times of silo openings, and the economic viability of all crop systems. This experiment was carried on in the field with seven treatments, consisting of monoculture and intercropping of corn and sorghum hybrids. Monoculture of sorghum Agri 001 and maize Agri 320, as well as in maize Agri 320, Agri 340 and Agri 104 intercropped with sorghum Agri 001 had the highest biomass yield. Intercropped maize did not have lodging, reduction in height, nor decrease in the first ear height in relation to the intercropped plants. Maize hybrids intercropped with sorghum led to a better fermentation pattern of silages, reducing ammonia concentrations. However, due the higher contents of NDF and the lower TDN contents of sorghum, nutritional values of intercropped silages were generally smaller than when they were composed only with maize. It was observed that the monoculture of sorghum Agri 001 for silage showed a higher profit compared to the other treatments.

Foi avaliada a presença de Curtobacterium flaccumfaciens pv. flaccumfaciens em 37 amostras de sementes de feijoeiro, dos grupos preto e carioca, produzidas em municípios do Estado de Santa Catarina, nas safras 2004/2005 e 2005/2006. Para a detecção, as sementes foram maceradas e alíquotas de sua suspensão foram transferidas para o meio de cultura semi-seletivo MSCFF. A identidade dos isolados obtidos foi comprovada por meio da observação da morfologia celular, coloração diferencial de Gram, tolerância a NaCl a 7% e patogenicidade em cultivares de feijoeiro suscetíveis. Detectou-se a presença de Curtobacterium flaccumfaciens pv. flaccumfaciens em 23 amostras (62,2%), indicando a importância das sementes como fonte de inoculo inicial.The presence of Curtobacterium flaccumfaciens pv. flaccumfaciens was evaluated in 37 common bean seed samples, of the black and carioca groups, produced in counties of Santa Catarina State, Brazil. The seeds were soaked and aliquots of the suspension formed were placed on the surface of the semi-selective medium MSCFF. Species identification was done by evaluation of the cell morphology, feature Gram test, feature tolerance to 7% of NaCl and pathogenicity tests. Curtobacterium flaccumfaciens pv. flaccumfaciens was detected in 23 samples (62.2 %), showing the importance of the seeds as source of inoculum

Using multispectral sensors attached to unmanned aerial vehicles (UAVs) can assist in the collection of morphological and physiological information from several crops. This approach, also known as high-throughput phenotyping, combined with data processing by machine learning (ML) algorithms, can provide fast, accurate, and large-scale discrimination of genotypes in the field, which is crucial for improving the efficiency of breeding programs. Despite their importance, studies aimed at accurately classifying sorghum hybrids using spectral variables as input sets in ML models are still scarce in the literature. Against this backdrop, this study aimed: (I) to discriminate sorghum hybrids based on canopy reflectance in different spectral bands (SB) and vegetation indices (VIs); (II) to evaluate the performance of ML algorithms in classifying sorghum hybrids; (III) to evaluate the best dataset input for the algorithms. A field experiment was carried out in the 2022 crop season in a randomized block design with three replications and six sorghum hybrids. At 60 days after crop emergence, a flight was carried out over the experimental area using the Sensefly eBee real time kinematic. The spectral bands (SB) acquired by the sensor were: blue (475 nm, B_475), green (550 nm, G_550), red (660 nm, R_660), Rededge (735 nm, RE_735) e NIR (790 nm, NIR_790). From the SB acquired, vegetation indices (VIs) were calculated. Data were submitted to ML classification analysis, in which three input settings (using only SB, using only VIs, and using SB + VIs) and six algorithms were tested: artificial neural networks (ANN), support vector machine (SVM), J48 decision trees (J48), random forest (RF), REPTree (DT) and logistic regression (LR, conventional technique used as a control). There were differences in the spectral signature of each sorghum hybrid, which made it possible to differentiate them using SBs and VIs. The ANN algorithm performed best for the three accuracy metrics tested, regardless of the input used. In this case, the use of SB is feasible due to the speed and practicality of analyzing the data, as it does not require calculations to perform the VIs. RF showed better accuracy when VIs were used as an input. The use of VIs provided the best performance for all the algorithms, as did the use of SB + VIs which provided good performance for all the algorithms except RF. Using ML algorithms provides accurate identification of the hybrids, in which ANNs using only SB and RF using VIs as inputs stand out (above 55 for CC, above 0.4 for kappa and around 0.6 for F-score). There were differences in the spectral signature of each sorghum hybrid, which makes it possible to differentiate them using wavelengths and vegetation indices. Processing the multispectral data using machine learning techniques made it possible to accurately differentiate the hybrids, with emphasis on artificial neural networks using spectral bands as inputs and random forest using vegetation indices as inputs.