Ana Carina da Silva Cândido

Flavonoids are essential molecules that affect plant growth and development and act as defense mechanisms against biotic and abiotic factors. This study aimed to assess whether the content of flavonoids in different species of Eucalyptus is related to tree growth. The objective of the work is (i) to identify whether there is a relationship between flavonoids, height and diameter among Eucalyptus species and (ii) to identify differences in flavonoid content between species. The experimental design was randomized blocks with three replicates, with 20 plants in each experimental plot. The treatments consisted of six Eucalyptus species: Eucalyptus camaldulensis , E. saligna , E. grandis , E. urophylla , C. citriodora and a GG100 clone (hybrid clone of E. urophylla ×  E. grandis ). This study measured plant diameter at breast height (DBH) and height (HC). Leaf samples were taken from the species in their respective repetitions for flavonoid evaluation, and subsequent separation and quantification of the isoflavones, daidzein (D1), daidzin (D2), genistein (G1), genistin (G2), using liquid chromatography. In general, the E. grandis species showed the best results for DAP and AP. D1 was higher than what? for C. citriodora and E. saligna . D2 was higher than what? for E. camaldulensis . The principal component analysis demonstrated a negative relationship among the variables AP and DAP with D1, D2, G1, and G2. With the results of this study, it can be inferred that there is no relationship between height, diameter, and flavonoids among the Eucalyptus species evaluated since the species with the lowest concentrations of isoflavones had the highest growth and diameter.

Silicon (Si) plays a critical role in regulating plant physiological processes, particularly through its influence on non-enzymatic antioxidant systems and amino acid metabolism. This study aims to assess soybean performance in response to both soil and foliar Si applications under well-watered and drought conditions, with the goal of enhancing Si accumulation in plant tissues and potentially strengthening the crop’s physiological responses to water deficit stress. This is especially pertinent given that the mechanisms underlying Si fertilization and its contribution to drought tolerance in soybean remain poorly understood. Greenhouse experiments were conducted using a 3 × 2 factorial design. The factors were: (i) three foliar Si treatments: control (no Si), potassium silicate (SiK; 128 g L⁻¹ Si, 126.5 g L⁻¹ K₂O, pH 12.0), and sorbitol-stabilized potassium silicate (SiKe; 107 g L⁻¹ Si, 28.4 g L⁻¹ K₂O, 100 mL L⁻¹ sorbitol, pH 11.8); and (ii) two soil water levels: well-watered (80% field capacity) and water-restricted (40% field capacity), the latter simulating tropical dry spells. Silicon was applied to the soil via irrigation and to the leaves via foliar spraying prior to the onset water restriction. All Si solutions were adjusted to pH 7.0 with 1 M HCl immediately before application. Potassium (K) levels were standardized across treatments through supplementary applications of KCl to both soil and foliage. Biometric and physiological parameters were subsequently measured. Sorbitol-stabilized Si enhanced Si accumulation in soybean tissues and improved plant resilience under both well-watered and drought conditions by promoting key physiological traits, including increased levels of daidzein and ascorbic acid levels, along with reduced amino acid concentrations. It also improved biometric parameters such as leaf area, root development, and number of pods per plant. These findings further support the role of Si as a beneficial element in enhancing stress tolerance and contributing to sustainable agricultural practices.

High-quality soybean seeds possess genetic, physical, and physiological characteristics that directly influence crop yield. The use of hyperspectral sensors combined with machine learning (ML) can streamline and accelerate seed germination testing. Therefore, the objectives of this study were: (i) to evaluate whether leaf and seed reflectance can effectively predict the physiological quality of soybean seeds using ML algorithms, and (ii) to identify which algorithm provides the highest prediction accuracy. Thirty-two soybean genotypes were evaluated in a controlled experiment. Leaves and seeds were analyzed using a hyperspectral sensor capable of measuring reflectance across the 350 to 2500 nm range. The resulting data were subjected to ML analysis with two types of input: spectral variables from leaves and seeds. The output variables predicted included germination test (GERM), electrical conductivity (EC), first germination count (FGC), vigorous tetrazolium test (VIG-TZ), and viable tetrazolium test (VIAB). Predictions were performed using stratified 10-fold cross-validation with ten repetitions (100 runs per model). All model parameters were set to the default configuration in Weka version 3.8.5. The ML models used for prediction included artificial neural networks (ANN), REPTree and M5P decision trees, random forest (RF), support vector machine (SVM), and ZeroR, with the latter serving as a control algorithm. The models showed consistent performance in predicting physiological variations in seeds, with better results when seed reflectance was used as input. For germination (GERM), the M5P, RF, and SVM algorithms obtained the highest correlations (r = 0.565–0.575). In predicting electrical conductivity (EC), M5P showed greater accuracy with leaf data (r = 0.506), while SVM performed best with seed data (r = 0.658). For first germination count (CPG), M5P was the most accurate with leaf data (r = 0.720), while M5P, RF, and SVM showed r between approximately 0.735 and 0.777 with seed data. In tetrazolium vigor (TZVG), RF showed the best performance (MAE 0.25), again highlighting seed reflection, which resulted in the lowest errors and highest correlations. Overall, the M5P, RF, and SVM algorithms achieved the most robust results, especially when used with seed spectral data. The highest germination prediction accuracy was achieved by the M5P, SVM, and RF algorithms for both input types. Seed reflectance yielded the best accuracy and the lowest MAE and RMSE values. Leaf reflectance also enabled accurate predictions, indicating that this input can serve as an early, in-field strategy for predicting soybean seed physiological quality.

Silicon (Si) plays a pivotal role in modulating plant physiological responses, particu-larly by influencing non-enzymatic antioxidant systems and amino acid metabolism. The objective of this study is to evaluate soybean performance in response to soil and foliar Si applications under both well-watered and drought conditions, aiming to en-hance Si accumulation in plant tissues and potentially amplify the crop’s physiological responses under water deficit stress. This is particularly relevant given that the mech-anisms underlying Si fertilization and its role in soybean stress tolerance remain insuf-ficiently explored. A greenhouse experiment was conducted using a 3×2 factorial de-sign. The factors were: i) three foliar Si treatments: a control (no Si), potassium silicate (SiK; 128 g L⁻¹ Si, 126.5 g L⁻¹ K₂O, pH 12.0), and sorbitol-stabilized potassium silicate (SiKe; 107 g L⁻¹ Si, 28.4 g L⁻¹ K₂O, 100 mL L⁻¹ sorbitol, pH 11.8); and ii) two soil water levels: water-restricted and well-watered, 40% and 80% of field capacity, respectively. The drought condition simulated tropical dry spells. Si was applied to the soil via irri-gation and to the leaves via foliar sprays prior to water restriction. All Si solutions were adjusted to pH 7.0 with 1M HCl immediately before use. Potassium (K) levels were balanced across all treatments using soil and foliar applications of KCl. Biometric and physiological attributes were measured. Sorbitol-stabilized silicon increased Si accumulation in soybean tissues and enhanced plant resilience under both well-watered and drought conditions by improving key physiological traits, such as elevated daidzein and ascorbic acid levels, along with reduced amino acid concentra-tions, as well as biometric parameters, including leaf area, root development, and number of pods per plant. These findings reinforce the role of Si as a beneficial element in promoting stress tolerance and advancing sustainable agricultural practices.

The study of the relationship between water availability, photosynthetic behavior, flavonoid accumulation, and antioxidant response offers new perspectives for enhancing nursery practices, resulting in more vigorous eucalyptus seedlings that are tolerant and have greater potential for field establishment. Under the hypothesis that different eucalyptus genetic materials show contrasting responses to water availability in the soil–plant–atmosphere system, this study aims to evaluate the physiological behavior of clones subjected to different irrigation intervals, with an emphasis on the role of flavonoids as antioxidants in mitigating the effects of water stress. The experimental design was structured in strips containing five eucalyptus clones and irrigation with different watering intervals: 1, 2, 4, and 8 days. Evaluations of net photosynthesis, transpiration, and instantaneous water use efficiency were performed. In addition to the physiological assessments, the flavonoids daidzein, genistein, and genistin were determined. Clones C1, C2, and C3 excelled in photosynthesis and transpiration at 2- and 4-day intervals, while C1 and C2 maintained superior performance even at an 8-day interval. WUE was highest in C5 and increased with water stress, showing a quadratic fit in all clones. Regarding flavonoid production, C1 and C3 showed greater daidzein accumulation, with a quadratic response to the withdrawal interval. Genistein showed a linear reduction only in C2, while genistein increased in C1, peaking around 11 days. Eucalyptus clones exhibit distinct physiological and biochemical responses to variations in irrigation intervals. More frequent irrigation favors photosynthetic activity and transpiration, particularly in clones C1, C2, and C3, whereas longer irrigation intervals reduce these processes but enhance water use efficiency, especially in C5.

The seed lots were evaluated based on their viability and vigor, which vary according to their origin and the locations where the seeds were produced. However, differences in vigor can be observed within a single seed lot, resulting from the deposition of photoassimilates. In this context, the hypothesis of this study is that distinct locations on the plant may produce seeds with different physiological quality. Therefore, the objective of this work was to evaluate how pod position influences the vigor of seeds from different soybean genotypes. Field experiments were conducted during the 2021/22 and 2022/23 crop seasons in Brazil. The experimental design was a randomized complete block with four replications and 32 soybean populations from the UFMS/CPCS Breeding Program. During the R4, R5, R6, and R7 reproductive stages of soybean, at the time of pod formation, the plants in each block were tagged with string to delimit the uppermost point at which pods had formed. Tagging was carried out as each stage change was verified, at approximately eight-day intervals. When analyzing how the pod position of the plant influences seed physiological variables, we found that position P1 was responsible for the best results for the variables evaluated, with the exception of genotypes G18 and G28. This result indicates that pods from the first position produce seeds with greater germination capacity and a higher ability to generate normal seedlings. However, the genotypes are still under development and, therefore, do not yet exhibit stability. Nevertheless, the results obtained highlight the relationship between the pod position of the plant and seed physiological variables. The position of the pods on the soybean plant influences the physiological quality of the seeds. In general, the P1 position, when the plants are in the R4 reproductive stage, with fully developed pods measuring 2 cm on one of the four upper nodes of the stem, is responsible for the best results in seed physiological quality tests for most of the soybean genotypes evaluated. These results indicate that pod position should be considered in breeding and seed production programs, since genotypes with greater physiological stability in the upper positions may be preferential in selection strategies. In the future, studies in different environments and evaluation of biochemical traits may confirm these patterns and contribute to the development of cultivars with higher seed quality and physiological uniformity.

Wheat genetic materials subjected to different irrigation levels may exhibit varying concentrations of elements such as zinc (Zn), iron (Fe), and calcium (Ca), depending on the cultivar and its response to irrigation. Additionally, these same conditions can lead to higher concentrations of bioactive compounds, such as flavonoids. Therefore, the objective of this study was to evaluate the concentrations of Zn, Fe, and Ca using X-ray fluorescence (XRF) and to determine the flavonoid content in different wheat and triticale genetic materials under distinct irrigation regimes. The experiment was conducted during the third cropping season (winter season) of 2023 at the experimental area of the Federal University of Mato Grosso do Sul. The experimental design adopted was a strip-plot arrangement, with the main plots consisting of three irrigation levels, corresponding to 30% (L3), 60% (L2), and 90% (L1) of crop evapotranspiration. Each irrigation level was subdivided into subplots comprising nine commercial cultivars of wheat and triticale: BRS Coleiro (G1, wheat), BRS Jaçanã (G2, wheat), BRS Atobá (G3, wheat), IPR Catuara (G4, wheat), IPR Caiapó (G5, triticale), IPR Aimoré (G6, triticale), BRS Gralha Azul (G7, wheat), IPR Potyporã (G8, wheat), and BRS Sabiá (G9, wheat). The concentrations of Fe, Zn, and Ca in the leaves were determined using energy-dispersive X-ray fluorescence (EDXRF). Flavonoid extraction and quantification in wheat and triticale leaves were performed on dried, ground samples and analyzed by ultra-performance liquid chromatography (UPLC); the flavonoids determined were daidzin, genistein, and genistin. The results demonstrate that the interaction between wheat and triticale genotypes and different irrigation levels significantly influences the foliar composition of flavonoids and micronutrients, highlighting promising genotypes for Fe, Zn, and bioactive compound accumulation, such as G4 and G5. The observed correlations, especially between Fe and Zn, reinforce the importance of these interactions in plant metabolism, with relevant implications for biofortification strategies and genotypic selection, particularly when higher irrigation levels are combined with superior materials for bioactive accumulation and nutrient uptake essential for wheat biofortification.

Biological nitrogen (N) fixation is a well-established practice in various legumes, such as soybeans. However, it has not been widely studied in cowpeas (Vigna unguiculata L. Walp). In this context, it is important to understand how the application of nitrogen-fixing bacteria, either alone or in association, can benefit the crop’s nitrogen demand. This study aimed to determine whether co-inoculation of Bradyrhizobium and Azospirillum favors nodulation and isoflavone production, and increases the nitrogen content, in cowpea crops. The experiment was set up using a randomized block design on two cowpea varieties, with seven treatments consisting of a control and the isolated application of Bradyrhizobium japonicum and Azospirillum brasilense, as well as different co-inoculation doses (75, 150, 225 and 300 mL per 50 kg of seed for each inoculant). There were four replications. Thirty days after emergence, the number of nodules and the dry masses of the nodules, roots and shoots of the plants were assessed. N content and isoflavone content in the fully developed third trifoliate leaf from the apex of the plants were also assessed. Statistical differences were observed between treatments for all analyzed variables, with higher values generally observed for co-inoculation treatments. Co-inoculation of B. japonicum and A. brasilense in cowpea seeds can be a viable and efficient practice. A dose of 75 mL of each inoculant favored nodule formation, root development and N content, as well as contributing to isoflavone production in the cowpea crop.

Spectral reflectance of plants can be readily associated with physiological and biochemical parameters. Thus, relating spectral data to amino acid contents in different genetic materials provides an innovative and efficient approach for understanding and managing genetic diversity. Therefore, this study had two objectives: (I) to differentiate genetic materials according to amino acid contents and spectral reflectance; (II) to establish the relationship between amino acids and spectral bands derived from hyperspectral data. The research was conducted with 32 soybean genetic materials grown in the field during the 2023–2024 crop year. The experimental design involved randomized blocks with four replicates. Leaf spectral data were collected 60 days after plant emergence, when the plants were in full bloom. Three leaf samples were collected from the third fully developed trifoliate leaf, counted from top to bottom, from each plot. The samples were taken to the laboratory, where reflectance readings were obtained using a spectroradiometer, which can measure the 350–2500 nm spectrum. Wavelengths were grouped as means of representative intervals and then organized into 28 bands. Subsequently, the leaf samples from each plot were subjected to quantification analyses for 17 amino acids. Then, the soybean genotypes were subjected to a PCA–K-means analysis to separate the genotypes according to their amino acid content and spectral behavior. A correlation network was constructed to investigate the relationships between the spectral variables and between the amino acids within each group. The groups formed by the different genetic materials exhibited distinct profiles in both amino acid composition and spectral behavior. Leaf reflectance data proved to be efficient in identifying differences between soybean genotypes regarding the amino acid content in the leaves. Leaf reflectance was effective in distinguishing soybean genotypes according to leaf amino acid content. Specific and high-magnitude associations were found between spectral bands and amino acids. Our findings reveal that spectral reflectance can serve as a reliable, non-destructive indicator of amino acid composition in soybean leaves, supporting advanced phenotyping and selection in breeding programs.