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Flavonoids are compounds that result from the secondary metabolism of plants and play a crucial role in plant development and mitigating biotic and abiotic stresses. The highest levels of flavonoids are found in legumes such as soybean. Breeding programs aim to increase desirable traits, such as higher flavonoid contents and vigorous seeds. Soybeans are one of the richest sources of protein in the plant kingdom and the main source of flavonoid derivatives for human health. In view of this, the hypothesis of this study is based on the possibility that the concentration of isoflavones in soybean seeds contributes to the physiological quality of the seeds. The aim of this study was to analyze the content of flavonoids in soybean genotypes and their influence on the physiological quality of the seeds. Seeds from thirty-two soybean genotypes were obtained by carrying out a field experiment during the 2021/22 crop season. The experimental design was randomized blocks with four replications and thirty-two F3 soybean populations. The seeds obtained were subjected to germination, first germination counting, electrical conductivity and tetrazolium vigor and viability tests. After drying and milling the material from each genotype, liquid chromatography analysis was carried out to obtain flavonoids, performed at UPLC level. Data were submitted to analysis of variance and, when significant, the means were compared using the Scott-Knott test at 5% probability. The results found here show the occurrence of genotypes with higher amounts of flavonoids when compared to their peers. The flavonoid FLVD_G2 had the highest concentration and differed from the others. Thus, we can assume that the type and concentration of flavonoids does not influence the physiological quality of seeds from different soybean genotypes, but it does indirectly contribute to viability and vigor, since the genotypes with the highest FLVD_G2 levels had better FGC values. The findings indicate that there is a difference between the content of flavonoids in soybean genotypes, with a higher content of genistein. The content of flavonoids does not influence the physiological quality of seeds, but contributes to increasing viability and vigor. [ABSTRACT FROM AUTHOR]

Making plant breeding programs less expensive, fast, practical, and accurate, especially for soybeans, promotes the selection of new soybean genotypes and contributes to the emergence of new varieties that are more efficient in absorbing and metabolizing nutrients. Using spectral information from soybean genotypes combined with nutritional information on secondary macronutrients can help genetic improvement programs select populations that are efficient in absorbing and metabolizing these nutrients. In addition, using machine learning algorithms to process this information makes the acquisition of superior genotypes more accurate. Therefore, the objective of the work was to verify the classification performance of soybean genotypes regarding secondary macronutrients by ML algorithms and different inputs. The experiment was conducted in the experimental area of the Federal University of Mato Grosso do Sul, municipality of Chapadão do Sul, Brazil. Soybean was sown in the 2019/20 crop season, with the planting of 103 F2 soybean populations. The experimental design used was randomized blocks, with two replications. At 60 days after crop emergence (DAE), spectral images were collected with a Sensifly eBee RTK fixed-wing remotely piloted aircraft (RPA), with autonomous takeoff control, flight plan, and landing. At the reproductive stage (R1), three leaves were collected per plant to determine the macronutrients calcium (Ca), magnesium (Mg), and sulfur (S) levels. The data obtained from the spectral information and the nutritional values of the genotypes in relation to Ca, Mg, and S were subjected to a Pearson correlation analysis; a PC analysis was carried out with a k-means algorithm to divide the genotypes into clusters. The clusters were taken as output variables, while the spectral data were used as input variables for the classification models in the machine learning analyses. The configurations tested in the models were spectral bands (SBs), vegetation indices (VIs), and a combination of both. The combination of machine learning algorithms with spectral data can provide important biological information about soybean plants. The classification of soybean genotypes according to calcium, magnesium, and sulfur content can maximize time, effort, and labor in field evaluations in genetic improvement programs. Therefore, the use of spectral bands as input data in random forest algorithms makes the process of classifying soybean genotypes in terms of secondary macronutrients efficient and important for researchers in the field. [ABSTRACT FROM AUTHOR]

Currently, Brazil is the largest producer and exporter of soybeans in the world. Most of this cultivation is concentrated in the Cerrado region, which has soils with low boron levels. Boron performs functions that are directly linked to plant performance. The objective of this study was to evaluate the agronomic performance of soybean cultivars with and without boron fertilization. Two field experiments were carried out in the agricultural years 2018/2019 and 2019/2020. Each experiment was carried out in a randomized block design with four replications and 10 soybean cultivars (Desafio, Foco, Bonus, Maracaí, 7067, 7110, 7739, 8372, 7100, and Population). Boron fertilization was carried out at the V3 stage of the crop using ulexite (10% of boron) at a rate of 3194 kg ha−1 and 0.0 kg ha−1 of B. The application of boron to the soil increased plant height, pod insertion height, number of branches, main stem diameter, and number of pods per plant, in addition to increasing the cycle of these cultivars. The cultivars 7110, 7739 and Desafio did not statistically differ in terms of grain yield in response to boron fertilization. The cultivars Foco, Bonus, Maracaí, 7067, 8372, 7100, and Population responded favorably to this fertilization. Furthermore, genetic breeding programs incorporate advanced strategies, such as the use of boron fertilization, in order to improve the performance of the selected genotypes. Implementing boron fertilization as an integral part of breeding programs helps not only to achieve high-yielding cultivars but also to optimize key agronomic traits. This integrated approach not only boosts breeding research but also provides a solid basis for sustainable and efficient agricultural practices. [ABSTRACT FROM AUTHOR]

Soil salinity affects plant growth, compromising sugarcane cultivation in regions with great production potential. Saccharum complex genotypes that respond positively to growth under saline environment can be used in the diversification of sugarcane cultivars to obtain greater economic returns. The objective of this study was to evaluate growth‐related traits of Saccharum genotypes grown under the presence and absence of salinity. The experiment was carried out in a 32 × 2 factorial scheme in a randomized block design with three replicates. The first factor consisted of 32 genotypes of the Saccharum complex and the second factor consisted of the presence and absence of salinity. The salinity provided higher mean values than the environment without salinity for plant height in the genotypes G9, G11, G13, G22 and G28, leaf number for G9 and G24, leaf area index for G9 and stem diameter for G1, G11 and G24. Among the genotypes tested, G1, G9, G11, G13, G22, G24 and G28 were the most promising genotypes and could be used for breeding new sugarcane cultivars of enhanced salinity tolerance. [ABSTRACT FROM AUTHOR]

Using spectral data to quantify nitrogen (N), phosphorus (P), and potassium (K) contents in soybean plants can help breeding programs develop fertilizer-efficient genotypes. Employing machine learning (ML) techniques to classify these genotypes according to their nutritional content makes the analyses performed in the programs even faster and more reliable. Thus, the objective of this study was to find the best ML algorithm(s) and input configurations in the classification of soybean genotypes for higher N, P, and K leaf contents. A total of 103 F2 soybean populations were evaluated in a randomized block design with two repetitions. At 60 days after emergence (DAE), spectral images were collected using a Sensefly eBee RTK fixed-wing remotely piloted aircraft (RPA) with autonomous take-off, flight plan, and landing control. The eBee was equipped with the Parrot Sequoia multispectral sensor. Reflectance values were obtained in the following spectral bands (SBs): red (660 nm), green (550 nm), NIR (735 nm), and red-edge (790 nm), which were used to calculate the vegetation index (VIs): normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), green normalized difference vegetation index (GNDVI), soil-adjusted vegetation index (SAVI), modified soil-adjusted vegetation index (MSAVI), modified chlorophyll absorption in reflectance index (MCARI), enhanced vegetation index (EVI), and simplified canopy chlorophyll content index (SCCCI). At the same time of the flight, leaves were collected in each experimental unit to obtain the leaf contents of N, P, and K. The data were submitted to a Pearson correlation analysis. Subsequently, a principal component analysis was performed together with the k-means algorithm to define two clusters: one whose genotypes have high leaf contents and another whose genotypes have low leaf contents. Boxplots were generated for each cluster according to the content of each nutrient within the groups formed, seeking to identify which set of genotypes has higher nutrient contents. Afterward, the data were submitted to machine learning analysis using the following algorithms: decision tree algorithms J48 and REPTree, random forest (RF), artificial neural network (ANN), support vector machine (SVM), and logistic regression (LR, used as control). The clusters were used as output variables of the classification models used. The spectral data were used as input variables for the models, and three different configurations were tested: using SB only, using VIs only, and using SBs+VIs. The J48 and SVM algorithms had the best performance in classifying soybean genotypes. The best input configuration for the algorithms was using the spectral bands as input. [ABSTRACT FROM AUTHOR]

The search for cotton (Gossypium hirsutum L.) genotypes gathering high yield and fiber quality is the main objective of breeding programs. The objectives of this study were: (a) to verify which effects contribute the largest fraction of the total phenotypic variance of cotton fiber quality traits and (b) to recommend the best genotypes for these traits. Eight competition trials of cotton cultivars were carried out between the 2017 and 2019 seasons. Field trials were carried out in Alagoinha (crops seasons 2017, 2018, and 2019) and Barbalha (2017 and 2018) in a randomized block design with three repetitions. Fifteen genotypes developed by the cotton plant breeding program of Embrapa Algodão were evaluated. The following fiber quality traits were evaluated: fiber percentage (FP), average boll weight (BW), upper‐half mean length, fiber uniformity (UNIF), short fiber index (SFI), fiber strength (STR), elongation (ELG), micronaire (MIC), and fiber maturity. Genotypic values (GVs) for the evaluated traits were estimated. The total phenotypic variance of the fiber quality traits evaluated is mostly composed of genotypic effects. Only the traits FP, BW, and ELG showed significant genotypes × sites interaction. The genotype G1 has high GVs for the traits FP, BW, fiber length, and fiber uniformity. The G15 stood out by gathering high values for UNIF, SFI, and STR. Core Ideas: Cotton genotypes gathering high yield and fiber quality is the main objective of breeding programs.The total phenotypic variance of the fiber quality traits evaluated is mostly composed of genotypic effects.Only the traits fiber percentage, boll weight, and elongation showed significant genotypes—sites interaction. [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]

The search for high‐yielding genotypes and that are tolerant to abiotic stresses has been a major goal in plant breeding. Thus, the use of technologies such as precision agriculture associated with remote sensing tools for plant phenotyping has increased. The hypothesis of this research was that soya bean genotypes respond differently to low and adequate base saturation levels in the soil and that vegetation indexes can be efficient auxiliary tools in plant phenotyping for this purpose. The objective of this study was to evaluate the nutritional status and agronomic performance of soya bean genotypes grown in low and recommended base saturation conditions using high‐throughput phenotyping. The research was carried out in 2017/2018 and 2018/2019 crop seasons, in which two field experiments in each season were installed. In experiment I, genotypes (P1, P2, P3, P4, P5, P6, P7, P8, P9 and P10) were evaluated without soil correction (low saturation condition). In experiment II, liming was performed three months before sowing of the genotypes to raise the base saturation to 60% (recommended saturation). Canopy spectral behaviour at the following wavelengths was evaluated: green (550 nm), red (660 nm), red edge (735 nm) and near‐infrared (790 nm), and the vegetation indices (Vis) NDVI, SAVI, EVI and MSAVI were calculated. The variables evaluated were leaf calcium and magnesium contents and grain yield. The use of VI's was efficient in assessing the performance of genotypes soya bean at different base saturation levels. The EVI showed moderate correlation with the nutritional and agronomic variables measured in each base saturation level. The approach used enabled both to identify genotypes tolerant to low base saturation soils and the ones with better response to liming. [ABSTRACT FROM AUTHOR]

High fiber quality is one of the most desired traits in the upland cotton breeding. To this end, breeding programs have developed hybrids with high heterotic effect, which provide genetic combinations and segregating populations from which superior genotypes can be selected. The objectives of this study were: (1) to identify superior genitors based on the general combining ability (GCA), (2) to indicate the best crossbreeds through the specific combining ability (SCA), and (3) to estimate phenotypic and genotypic correlations for the following fiber traits: length, uniformity, short fiber index, strength, elongation, micronaire, maturity, reflectance, degree of yellowing, and spinning index. The experiments were carried out in two environments in the semi-arid region of Brazil and two harvests (2015 and 2019) to evaluate six parents and fifteen F1's hybrids obtained by diallel crossings. The design used was random blocks with three replicates. The following traits were evaluated: length, uniformity, short fiber index, strength, elongation, micronaire, maturity, reflectance, degree of yellowing, and spinning index. Analysis of variance, diallel analysis and genetic correlations were performed. The parents with higher GCA were: TAMB 139-17, PSC 355, IAC 26, and FM 993. The crossings with highest SCA were: TAMB 139-17 × TAMCOT CAMD-E, CNPA 04 2080 × PSC 355, TAMB 139-17 × IAC 26, FM 993 × CNPA 04 2080. Positive genetic correlations were found between length, uniformity, strength, and spinning index. Micronaire had positive correlations with maturity. The short fiber index showed negative correlations with length, uniformity, strength, elongation, degree of yellowing, and spinning index. [ABSTRACT FROM AUTHOR]

Drought stress, salinity and aluminium toxicity are considered the most critical abiotic factors limiting the growth of soya bean plants, accounting for the low yield of the crop in the Cerrado region. Our hypothesis is that soya bean cultivars have differential morphological responses to stressful environmental conditions. Thus, an experiment was carried out with the purpose of identifying and recommending soya bean genotypes that simultaneously combine adaptability and stability for morphological traits when subjected to stressful environmental conditions. Drought, saline and aluminium stresses were induced in seeds of 25 soya bean cultivars. Seed germination, and length and dry mass of the seedlings were measured. For each trait, adaptability and stability were evaluated by the Lin & Binns method and simultaneously considering all traits for the set of environments. There was significant GxE interaction for all traits evaluated. The genotypes 97R50 IPRO, BMX Bônus IPRO and 98R31 IPRO simultaneously combine adaptability and stability for all traits measured. These genotypes can be cultivated both under optimal and abiotic stress conditions, such as those investigated in this study. Additionally, they can be used as parents in crossing blocks aiming at obtaining genotypes resistant to drought, saline and aluminium toxicity stresses. [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]

Drought and salinity are the main limiting environmental factors that restrict the establishment of soya bean plants. In order to recommend genotypes for cultivation under adverse drought and saline stress conditions, multi‐environment trials (MET) are needed. However, MET analysis is usually performed considering a single trait, which provides lower reliability in recommending genotypes when compared to multi‐trait analysis. Thus, this study was carried out to investigate the stability of multi‐trait stability index (MTSI) in 46 soya bean cultivars under the effects of drought and saline stress on seed germination and initial seedling growth. Drought and saline stresses were imposed by seed exposure to −0.20 MPa iso‐osmotic solutions with polyethylene glycol—PEG 6000 (119.6 g/L) or NaCl (2.36 g/L) for 12 days at 25°C. The germination rate, seedling length and seedling dry matter were measured. We showed here how genotypic stability can be quantified by MTSI when comparing drought and salinity conditions in relation to non‐stressful environment (control) and how this index can be employed under different conditions. When considering the index for multiple environments, we can select as the most stable genotypes TMG 716 RR, FPS Antares RR, AS 3610 IPRO, NS 7300 IPRO and FPS Solar IPRO among the 46 tested genotypes. Owing to high stability and gains with selection verified for these genotypes under salinity and drought conditions, they can be used as genitors in breeding programs aimed at obtaining offspring with higher resistance to antibiotic stresses. [ABSTRACT FROM AUTHOR]

Reaction norms fitted through random regression models (RRM) have been widely used in animal and plant breeding for analyses of genotype × environment (G × E) interaction. However, in annual crops, they remain unexplored. Thus, this study aimed to evaluate the applicability and efficiency of RRM fitted through Legendre polynomials as a tool to recommend cotton (Gossypium hirsutum L.) genotypes. To this end, a data set with 12 genotypes of cotton evaluated in 10 environments for fiber length (FL) and fiber fineness was used. The restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) procedure was used to estimate the variance components and to predict the genetic values. Results showed that there was genetic variability among cotton genotypes and that the reaction norms over the environmental gradient illustrated the G × E interaction. Very high selective accuracies (rĝg>0.90) were found for both traits in all environments, which indicates high reliability in the genotype's recommendation. The areas under the reaction norms were calculated for the recommendation of genotypes for unfavorable, favorable, and overall environments. Regarding genotypes recommendation, areas under reaction norms allow recommending genotypes for unfavorable and favorable environments, as well as for overall recommendation, for both traits. This study is the first considering reaction norms fitted through RRM for the recommendation of cotton genotypes and demonstrated the potential of this technique in cotton breeding, besides its great potential to deal with G × E interactions. [ABSTRACT FROM AUTHOR]

The genotype by environment interaction is essential in any plant breeding program. Methodologies allowing the evaluation of nonlinear genotype responses to environmental variation allied to prior beliefs on unknown parameters bring new insights for breeders. In this context, we aimed to propose a Bayesian segmented regression model to infer on phenotypic adaptability and stability of cotton (Gossypium L.) cultivars. The efficiency of using informative and minimally informative prior distributions in the selection of cultivars was also investigated. Randomized complete-block design experiments to study fiber yield (kg/ha) of 16 cotton genotypes were carried out in eight different environments in the State of Mato Grosso, Brazil. The proposed methodology was implemented using the free software R through the rbugs package. Bayesian segmented regression model was able to recommend cotton genotypes for cultivation, allowing to exploit the nonlinear pattern of genotype responses to environmental variation to find out the "ideal" genotype. The use of suitable prior information reduces the ranges of the credibility intervals, implying in higher precision of parameter estimates and, consequently, in reliable genotypes selection. [ABSTRACT FROM AUTHOR]

Photosynthetic efficiency has become the target of several breeding programs since the positive correlation between photosynthetic rate and yield in soybean suggests that the improvement of photosynthetic efficiency may be a promising target for new yield gains. However, studies on combining ability of soybean genotypes for physiological traits are still scarce in the literature. The objective of this study was to estimate the combining ability of soybean genotypes based on F2 generation aiming to identify superior parents and segregating populations for physiological traits. Twenty-eight F2 populations resulting from partial diallel crossings between eleven lines were evaluated in two crop seasons for the physiological traits: photosynthesis, stomatal conductance, internal CO2 concentration, and transpiration. General combining ability (GCA) of the parents and specific combining ability (SCA) of the F2 populations were estimated. Our findings reveal the predominance of additive effects in controlling the traits. The genotype TMG 7062 IPRO is the most promising parent for programs aiming at photosynthetic efficiency. We have also identified other promising parents and proposed cross-breeding with higher potential for obtaining superior lines for photosynthetic efficiency. [ABSTRACT FROM AUTHOR]

A gain in association was obtained by using a new multivariate index with alleles of TaALMT1 and TaMATE1B genes compared with a previously published index. Wheat is among the major grain crops. Its growth can be affected by aluminum toxicity, an important stress factor that limits plant productivity. Thus, an efficient means to select wheat genotypes that are resistant to this stress is required. Here, 172 wheat cultivars and 23 synthetic hexaploid wheat plants showing different alleles for TaALMT1 and TaMATE1B and different levels of Al resistance in field conditions (phenotyped in two crop seasons) were studied. The modified Lin and Binns method was applied to obtain the phenotype data in a multivariate manner aiming at a general identification of genotypes based on the multiple traits evaluated. The genotype x year interaction was significant for all traits that were evaluated in three growth stages (tillering, silking, and maturation). The top 20 Al-resistant genotypes obtained by the multivariate method were highlighted as having the best combination of the main desirable traits. Correlation analysis showed a gain in the association of the multivariate index with alleles of TaALMT1 and TaMATE1B genes compared with a previously published index. The superior TaMATE1B allele (having an insertion in the promoter region) contributed a greater Al resistance independent of the TaALMT1 allele. The higher association with Al resistance qualifies the selection index described in this study to be used for wheat breeding programs with the aim of improving the performance of wheat when grown under Al stress. [ABSTRACT FROM AUTHOR]

Cotton (Gossypium spp.) provides ~90% of the world's textile fibre. The aim of this study was to use the principal additive effects and multiplicative interaction (AMMI) model under the Bayesian approach to recommend cotton genotypes for the Central-West region of Brazil. Eight trials with upland cotton genotypes were conducted during the 2008–09 harvest in the State of Mato Grosso, Brazil. The experiment included a randomised block design with 16 genotypes. The genotypes were evaluated for fibre yield, length and strength. Chains were simulated via the Markov chain Monte Carlo method with 300 000 iterations for the parameters of the Bayesian AMMI model. From the chains generated, the first 20 000 burn-in observations were discarded and samples were taken by jumping every 20 observations (thin). Bayesian analysis provided additional results to those obtained by the frequentist approach, highlighting the credibility regions in the biplot for the genotypic and environmental scores. Bayesian AMMI model allowed identification of a genotype that can be widely recommended; this genotype has genotypic values above the overall mean for the three evaluated traits and did not contribute to the genotype × environment interactions observed in these traits. In addition, adaptability of genotypes to specific environments was observed, which makes it possible to capitalise the positive effect of the genotype × environment interaction. Cotton is a crop of great industrial importance. However, cotton cultivars have different productivities and fibre qualities when submitted to different environments. In this work, we use modern statistical techniques to identify cotton cultivars that have stable behaviour for these characteristics throughout Brazilian environments [ABSTRACT FROM AUTHOR]

The performance of textile processes and the quality of the products depends on the several cotton (Gossypium hirsutum L.) fiber quality traits, such as micronaire index, fiber length, and fiber strength. This research aims to recommend cotton genotypes for the Brazilian Cerrado based on fiber yield and fiber quality traits. Nineteen cotton cultivar variety trials were performed in the 2013–2014 and 2014–2015 crop seasons. Each trial was conducted as a randomized complete block design with 12 treatments and four replications. The evaluated traits were fiber yield, fiber length, fiber strength, and micronaire. For each trait, a modified Lin and Binns method was used to recommend the best genotypes for all environments (favorable and unfavorable). This method was also used in a multivariate manner, aiming at a general recommendation of genotypes based on the multiple traits evaluated. The genotype × environment interaction was significant for all evaluated traits. Genotypes with high adaptability and stability were identified for each trait. The multivariate method identified IMA 08 WS and BRS 335 as the genotypes that had the best combination of main desirable traits. The analysis further revealed that new crosses could be performed between genotypes with high yield adaptability and stability and genotypes with adequate fiber quality traits. [ABSTRACT FROM AUTHOR]

The aim of this work was to answer the following question: can influential points modify the recommendation of genotypes, based on regression methods, in the presence of genotype × environment (G × E)? Therefore, we compared the parameters of the adaptability and stability of three methodologies based on regression in the presence of influential points. Specifically, were evaluated methods based on simple, non-parametric and quantile (τ = 0.50) regressions. The dataset used in this work corresponds to 18 variety trials of cotton cultivars that were conducted in the 2013–2014 and 2014–2015 crop seasons. The evaluated variable was the cotton fiber yield (kg/ha). Once we noticed that the effect of G × E interaction is significant, the statistical procedures adopted for the adaptability and stability analysis of the genotypes. To verify the presence of a possible influential point, we used the leverage values, studentized residuals (SR), DFBETAS and Cook's distance. As a result, the influential points can modify the recommendation of genotypes, based on regression methods, in the presence of G × E interaction. The non-parametric and quantile (τ = 0.50) regressions, which are based on median estimators, are less sensitive to the presence of influential points avoiding misleading recommendations of genotypes in terms of adaptability. [ABSTRACT FROM AUTHOR]

To increase yield, breeding programs must search for innovative and efficient methodologies for the development and selection of superior genotypes. Therefore, this study aimed to compare the single- and multiple-trait best linear unbiased prediction (BLUP) in genetic selection of parents and hybrids of grain sorghum. For this, an experiment conducted in alpha-lattice design with two replications was used. Flowering time (FT), plant height (PH), and grain yield (GY) were evaluated in 502 grain sorghum hybrids obtained by the cross of ten restorer lines and 54 male-sterile lines. Variance components were estimated via restricted maximum likelihood (REML). Significant effects of restorer lines, of male-sterile lines, and of specific combining ability were detected by the likelihood ratio test (LRT). The estimates of variance components, genetic parameters, and correlations were similar when obtained via single- and multiple trait-BLUP. Considering hybrids, the multiple-trait BLUP resulted in slightly higher predicted selection gain for the three evaluated traits and, therefore, can be efficiently applied in the genetic selection of grain sorghum. [ABSTRACT FROM AUTHOR]

In multi-environment trials (MET), large networks are assessed for results improvement. However, genotype by environment interaction plays an important role in the selection of the most adaptable and stable genotypes in MET framework. In this study, we tested different residual variances and measure the selection gain of cotton genotypes accounting for adaptability and stability, simultaneously. Twelve genotypes of cotton were bred in 10 environments, and fiber length (FL), fiber strength (FS), micronaire (MIC), and fiber yield (FY) were determined. Model selection for different residual variance structures (homogeneous and heterogeneous) was tested using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The variance components were estimated through restricted maximum likelihood and genotypic values were predicted through best linear unbiased prediction. The harmonic mean of relative performance of genetic values (HMRPGV) were applied for simultaneous selection for adaptability, stability, and yield. According to BIC heterogeneous residual variance was the best model fit for FY, whereas homogeneous residual variance was the best model fit for FL, FS, and MIC traits. The selective accuracy was high, indicating reliability of the prediction. The HMRPGV was capable to select for stability, adaptability and yield simultaneously, with remarkable selection gain for each trait. [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]

This study aimed to evaluate the adaptability and phenotypic stability of cowpea genotypes using a nonlinear regression analysis and multivariate analysis. Experiments were performed at four sites in Brazil using a randomized blocks design with 20 treatments and four replications. The adaptability and stability of genotypes were evaluated by Toler nonlinear regression and genotype plus genotype 3 environment (GGE) biplot methodologies. Most of the genotypes revealed linear response patterns, with no differences regarding the favorable and unfavorable environments. Regarding the genotype classification for stability and adaptability, the Toler and GGE biplot methodologies are congruent. Genotypes MNC99-537F-4, MNC00-561G-6, MNC99542F-5, and Patativa have high overall adaptability and adequate yield. Therefore, they should be recommended for cultivation in the tested environments. Genotypes closer to the ideotype by the GGE biplot method are considered doubly desirable by the nonlinear method. [ABSTRACT FROM AUTHOR]

• Paricá is a native species of Amazon used in the laminated wood industry. • Brazilian half-sib progenies of paricá has high genetic variability. • Combined selection strategy provides the greatest genetic gains for paricá. Paricá (Schizolobium parahyba var. amazonicum) is a tropical species native to the Amazon region and is continually growing for wood exploitation potential, especially in the laminated wood industry. In Brazil, there are still no breeding programs for this species. Therefore, it is necessary to assess its genetic diversity and use appropriate selection strategies to develop genotypes that serve the consumer market. This research hypothesized that half-sib families of paricá have genetic variability for characteristics favorable to wood production. Thus, the aim was to evaluate different selection strategies in paricá genotypes for growth traits. Half-sib progenies from 58 paricá matrices were planted in the experimental area of Embrapa Agrossilvipastoril in the municipality of Sinop-MT. The seeds used for obtaining the seedlings were collected in the states of Mato Grosso, Rondônia, Acre, Tocantins, and Pará. The traits evaluated were: diameter at breast height and plant height. Variance components and heritability were estimated, and subsequently, the genetic gains with the selection were predicted by four selection strategies: combined selection, among and within-families selection, mass selection, and stratified mass selection. The results obtained by analysis of variance showed the existence of genetic variability among families for the traits evaluated, which revealed the possibility of obtaining genetic gains and thereby success with the selection. All selection strategies showed to be suitable for obtaining genetic gains with a selection of the best paricá progenies aimed at wood production. However, the combined selection provided the highest genetic gains. The results reported in this research made it possible to direct the strategies of the first genetic breeding program for paricá in Brazil. [ABSTRACT FROM AUTHOR]