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Obtaining soybean genotypes that combine better nutrient uptake, higher oil and protein levels in the grains, and high grain yield is one of the major challenges for current breeding programs. To avoid the development of unpromising populations, selecting parents for crossbreeding is a crucial step in the breeding pipeline. Therefore, our objective was to estimate the combining ability of soybean cultivars based on the F2 generation, aiming to identify superior segregating parents and populations for agronomic, nutritional and industrial traits. Field experiments were carried out in two locations in the 2020/2021 crop season. Leaf contents of the following nutrients were evaluated: phosphorus, potassium, calcium, magnesium, sulfur, copper, iron, manganese, and zinc. Agronomic traits assessed were days to maturity (DM) and grain yield (GY), while the industrial traits protein, oil, fiber and ash contents were also measured in the populations studied. There was a significant genotype × environment (G × A) interaction for all nutritional traits, except for P content, DM and all industrial traits. The parent G3 and the segregating populations P20 and P27 can be used aiming to obtain higher nutritional efficiency in new soybean cultivars. The segregating populations P11 and P26 show higher potential for selecting soybean genotypes that combine earliness and higher grain yield. The parent G5 and segregant population P6 are promising for selection seeking improvement of industrial traits in soybean. [ABSTRACT FROM AUTHOR]

Tempering-drying time may affect the quality of polished rice. Hence, this study aims to evaluate the effect of drying-tempering time and air temperature relationships on the physical, physicochemical, and morphological qualities of polished rice. Herein, paddy with 20–24% (w.b.) moisture were subjected to drying and tempering with air temperatures of 75–115 °C, 115–130 °C, and 75–130 °C over 14 h to reduce their moisture content up to 12% (w.b.). With the increase in the paddy mass temperature to 49 °C, the whole grain yield and the percentages of protein, crude fiber, fat, and ash reduced; additionally, the morphology of starch and the composition of minerals were altered. The cumulative effects and prolonged time of the drying-tempering influenced the physical, physicochemical, and morphological qualities of the rice. In conclusion, paddy drying technologies with tempering periods should not be designed for temperatures not exceeding 75 °C and drying times not longer than 8 h for good system performance and rice quality. [ABSTRACT FROM AUTHOR]

Defining the ideal soybean stand is essential for obtaining maximum grain yield. As a hypothesis of the work, it was questioned whether it is possible to vary seeding population of soybean based on the physical–chemical attributes of soil related to grain yield. The objectives of this work were (i) to identify soil attributes that help obtain the maximum grain yield grown in the Cerrado region of Brazil; and (ii) to generate a model to define the seeding rate according to these attributes. The experiment was carried out in the 2017/2018 and 2018/2019 crop seasons. In the first year, plots with different rate-seeding were sown on site one and site two. In the second crop season, site three was sown at a variable rate based on the results found in 2017/2018. The pH, calcium, magnesium, phosphorus, potassium, organic matter and clay contents, cation exchange capacity, base saturation and apparent electrical conductivity of the soil were evaluated. The results were subjected to Pearson's correlation network, followed by path analysis and decision tree. The variable-rate seeding suggested by the decision tree was applied on site 3 and the similarity between the soil attributes contained in the decision tree and the grain yield was verified using the relative deviation coefficient technique. The apparent electrical conductivity of the soil and the clay contents are the soil attributes that should be used to determine the soybean seeding rate. Areas with the highest values of electrical conductivity and clay contents should receive the higher soybean rate seeding in the Cerrado region. [ABSTRACT FROM AUTHOR]

Background: Precision agriculture techniques are widely used to optimize fertilizer and soil applications. Furthermore, these techniques could also be combined with new statistical tools to assist in phenotyping in breeding programs. In this study, the research hypothesis was that soybean cultivars show phenotypic differences concerning wavelength and vegetation index measurements. Results: In this research, we associate variables obtained via high-throughput phenotyping with the grain yield and cycle of soybean genotypes. The experiment was carried out during the 2018/2019 and 2019/2020 crop seasons, under a randomized block design with four replications. The evaluated soybean genotypes included 7067, 7110, 7739, 8372, Bonus, Desafio, Maracai, Foco, Pop, and Soyouro. The phenotypic traits evaluated were: first pod height (FPH), plant height (PH), number of branches (NB), stem diameter (SD), days to maturity (DM), and grain yield (YIE). The spectral variables evaluated were wavelengths and vegetation indices (NDVI, SAVI, GNDVI, NDRE, SCCCI, EVI, and MSAVI). The genotypes Maracai and Foco showed the highest grain yields throughout the crop seasons, in addition to belonging to the groups with the highest means for all VIs. YIE was positively correlated with the NDVI and certain wavelengths (735 and 790 nm), indicating that genotypes with higher values for these spectral variables are more productive. By path analyses, GNDVI and NDRE had the highest direct effects on the dependent variable DM, while NDVI had a higher direct effect on YIE. Conclusions: Our findings revealed that early and productive genotypes can be selected based on vegetation indices and wavelengths. Soybean genotypes with a high grain yield have higher means for NDVI and certain wavelengths (735 and 790 nm). Early genotypes have higher means for NDRE and GNDVI. These results reinforce the importance of high-throughput phenotyping as an essential tool in soybean breeding programs. [ABSTRACT FROM AUTHOR]

Gas exchange analysis is an important technique, as the reduction in yield may be related to the decreased photosynthetic activity, due to adverse climatic factors in the growing site. The hypothesis of this study was that contrasting soil water conditions result in different photosynthetic performance in soybean genotypes. Thus, our objective was to analyse the physiological capacity in soybean genotypes under field conditions with optimal soil moisture and under water deficit. The experimental design was random blocks with 10 genotypes (P1, P2, P3, P4, P5, P6, P7, P8, P9 and P10) and three replicates. Individual analysis of variance was performed in both environments (irrigated and rainfed), and a correlation network between the traits was generated. We measured the traits net photosynthesis, stomatal conductance, internal CO2 concentration, instant water‐use efficiency, instant carboxylation efficiency and grain yield. Stressed plants reduce stomatal conductance and transpiration, but increase the instant water‐use efficiency as a defence mechanism in sub‐optimal soil moisture conditions. The P6 genotype obtained better physiological capacity under irrigated conditions, while the P10 genotype showed the better performance under rainfed conditions, which makes it tolerant to water stress. Our findings can contribute to the genotype formation and parental choice steps in breeding programs aimed at obtaining both irrigation‐responsive and drought‐tolerant genotypes. [ABSTRACT FROM AUTHOR]

Most of the Brazilian soils, especially in Cerrado biome, have high acidity and toxic aluminium, making the application of limestone an indispensable technique to obtain high yield in soybean crop. The hypothesis of this study was that contrasting base saturation provides changes in water‐use efficiency (WUE) and agronomic traits of soybean populations. Thus, we evaluated the WUE and agronomic performance of F3 soybean populations under different base saturation levels. Each experiment was conducted in a randomized complete blocks design with three replicates and 10 F3 populations (P1, P2, P3, P4, P5, P6, P7, P8, P9 and P10) for two crop seasons. Segregating populations were obtained by bulk method. In the first experiment, F3 populations were evaluated without correction of base saturation (V = 30%), while in the second experiment, limestone was applied three months before sowing to increase base saturation to 60%. The traits evaluated were as follows: calcium, magnesium and potassium contents, water‐use efficiency (WUE), plant height, first pod height, main stem diameter and grain yield. Our findings indicate that WUE and agronomic performance of F3 soybean populations depends on the saturation condition. We identified the P4 and P9 populations as the most promising for use in breeding programmes aiming at higher tolerance to abiotic stress caused by low base saturation in soil. [ABSTRACT FROM AUTHOR]

In soybean, there is a lack of research aiming to compare the performance of machine learning (ML) and deep learning (DL) methods to predict more than one agronomic variable, such as days to maturity (DM), plant height (PH), and grain yield (GY). As these variables are important to developing an overall precision farming model, we propose a machine learning approach to predict DM, PH, and GY for soybean cultivars based on multispectral bands. The field experiment considered 524 genotypes of soybeans in the 2017/2018 and 2018/2019 growing seasons and a multitemporal–multispectral dataset collected by embedded sensor in an unmanned aerial vehicle (UAV). We proposed a multilayer deep learning regression network, trained during 2000 epochs using an adaptive subgradient method, a random Gaussian initialization, and a 50% dropout in the first hidden layer for regularization. Three different scenarios, including only spectral bands, only vegetation indices, and spectral bands plus vegetation indices, were adopted to infer each variable (PH, DM, and GY). The DL model performance was compared against shallow learning methods such as random forest (RF), support vector machine (SVM), and linear regression (LR). The results indicate that our approach has the potential to predict soybean-related variables using multispectral bands only. Both DL and RF models presented a strong (r surpassing 0.77) prediction capacity for the PH variable, regardless of the adopted input variables group. Our results demonstrated that the DL model (r = 0.66) was superior to predict DM when the input variable was the spectral bands. For GY, all machine learning models evaluated presented similar performance (r ranging from 0.42 to 0.44) for each tested scenario. In conclusion, this study demonstrated an efficient approach to a computational solution capable of predicting multiple important soybean crop variables based on remote sensing data. Future research could benefit from the information presented here and be implemented in subsequent processes related to soybean cultivars or other types of agronomic crops. [ABSTRACT FROM AUTHOR]

Genetic study in maize (Zea mays) germplasm development is an important step to understand the genetic variability and complementarity between heterotic groups, as well as additive and dominance genetic effects. Thus, diallel analyses have been widely adopted with the aim of identifying the best parental lines and the best crosses. In addition, different environmental conditions make genetic selection difficult. Thus, the objectives of this study were to compare individual and joint analyses of a diallel design through a mixed model methodology for maize breeding, and to evaluate and interpret the genetic effects and their interaction with the environmental effect. Thirteen F2 hybrids were crossed in an incomplete diallel scheme. Seventy-eight inter-population hybrids, and thirteen self-pollinated parents were evaluated. Eleven traits were evaluated, and emphasis was given to the Grain Yield (GY) trait. The significance of the GY trait, specifically, varied across environments. Joint analysis, in particular, presented significance for dominance and additive by environment interaction effects. Joint analysis had the highest selective accuracies for six traits. The correlation coefficients showed similar results, with values from 0.15 to 0.50 between pairs of environments. Gains with selection, considering each environment, ranged from 9.17 to 20.66%, when five hybrids were selected. When direct gains were compared with indirect gains, combined analysis confirmed the high efficiency of selection. For selection of parents, combined analysis achieves the same results as direct selection when two parents were selected. [ABSTRACT FROM AUTHOR]

[Display omitted] • Soybean genotypes were grouped according to their grain yield and industrial traits. • Support Vector Machine and J48 algorithms performed better at classifying them. • Using the spectral bands selected in the study improved the classification accuracy of the algorithms. Employing visible and near infrared sensors in high-throughput phenotyping provides insight into the relationship between the spectral characteristics of the leaf and the content of grain properties, helping soybean breeders to direct their program towards improving grain traits according to researchers' interests. Our research hypothesis is that the leaf reflectance of soybean genotypes can be directly related to industrial grain traits such as protein and fiber contents. Thus, the objectives of the study were: (i) to classify soybean genotypes according to the grain yield and industrial traits; (ii) to identify the algorithm(s) with the highest accuracy for classifying genotypes using leaf reflectance as model input; (iii) to identify the best input data for the algorithms to improve their performance. A field experiment was carried out in randomized block design with three replications and 32 soybean genotypes. At 60 days after emergence, spectral analysis was carried out on three leaf samples from each plot. A hyperspectral sensor was used to capture reflectance between the wavelengths from 450 to 824 nm. Representative spectral bands were selected and grouped into means. After harvest, grain yield was assessed and laboratory analyses of industrial traits were carried out. Spectral, industrial traits and yield data were subjected to statistical analysis. Data were analyzed by the following machine learning algorithms: J48 (J48) and REPTree (DT) decision trees, Random Forest (RF), Artificial Neural Networks (ANN), Support Vector Machine (SVM), and conventional Logistic Regression (LR) analysis. The clusters formed were used as the output of the models, while two groups of input data were used for the input of the models: the spectral variables (WL) noise-free obtained by the sensor (450–828 nm) and the spectral means of the selected bands (SB) (450.0–720.6 nm). Soybean genotypes were grouped according to their grain yield and industrial traits, in which the SVM and J48 algorithms performed better at classifying them. Using the spectral bands selected in the study improved the classification accuracy of the algorithms. [ABSTRACT FROM AUTHOR]