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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 F 2 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., (© 2023. Springer Nature Limited.)

Genome-wide selection (GWS) has been becoming an essential tool in the genetic breeding of long-life species, as it increases the gain per time unit. This study had a hypothesis that GWS is a tool that can decrease the breeding cycle in Jatropha. Our objective was to compare GWS with phenotypic selection in terms of accuracy and efficiency over three harvests. Models were developed throughout the harvests to evaluate their applicability in predicting genetic values in later harvests. For this purpose, 386 individuals of the breeding population obtained from crossings between 42 parents were evaluated. The population was evaluated in random block design, with six replicates over three harvests. The genetic effects of markers were predicted in the population using 811 SNP's markers with call rate = 95% and minor allele frequency (MAF) > 4%. GWS enables gains of 108 to 346% over the phenotypic selection, with a 50% reduction in the selection cycle. This technique has potential for the Jatropha breeding since it allows the accurate obtaining of GEBV and higher efficiency compared to the phenotypic selection by reducing the time necessary to complete the selection cycle. In order to apply GWS in the first harvests, a large number of individuals in the breeding population are needed. In the case of few individuals in the population, it is recommended to perform a larger number of harvests.

Jatropha (Jatropha curcas) has become one of the most important species for producing biofuels. Currently, Genotype x Environment (GxE) interaction is the biggest challenge that breeders should solve to increase the section accuracy in the plant breeding. Therefore, the objectives in this study were to estimate the parameters in the 180 half-sib families in Jatropha evaluated for five production years, to verify the significance of the GxE interaction variance, to evaluate the adaptability and stability for each family based on three prediction methods, to select superior half-sib families based on the adaptability and stability analyses, and to predict the accuracy for the sixth production year. Jatropha half-sib families were classified and selected using the follow adaptability and stability methods: linear regression, bi-segmented linear regression and mixed models concepts called harmonic mean of the relative performance of genetic values (HMRPGV). The prediction accuracy was estimated by the Pearson correlation between the predicted genetic values by adaptability and stability methods and the phenotypic value in the sixth production year. In result, most half-sib families were classified as general adaptability and general stability for the evaluated traits. The selection gain obtained via HMRPGV was higher than other methods. The prediction accuracy for the sixth production year was 0.45. Therefore, HMRPGV is efficient to maximize the genetic gain, and it can be a useful strategy to select genotype with high adaptability and stability in Jatropha breeding as well as other species that should be evaluated for many years to take a suitable selection accuracy., Competing Interests: The authors have declared that no competing interests exist.

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]

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]

In Brazil, grain sorghum has been mainly grown in Cerrado biome during the off‐season, as a succession crop, after the soybean harvest. At this period, rainfall is erratic and insufficient, causing drought stress in pre‐ or post‐flowering of grain sorghum crops. In this scenario, the identification of both drought‐tolerant and irrigation responsive hybrids has been one of the main targets of sorghum breeding programmes. The hypothesis of this research was that grain sorghum genotypes respond differently to different moments of water deficit. This study aimed to evaluate the agronomic performance of sorghum hybrids under full irrigation and drought stress at pre‐ and post‐flowering, in order to identify hybrids tolerant to drought stress and responsive to the water supply. Field trials were carried out in Nova Porteirinha during the 2017/2018 and 2018/2019 crop seasons. Twelve grain sorghum single‐hybrids were evaluated for days to flowering and grain yield in three water availability conditions: drought stress at pre‐flowering, drought stress at post‐flowering and full‐irrigation (control). Tolerant hybrids were identified under both pre‐ and post‐flowering drought stress, as well as hybrids responsive to irrigation. The hybrids 1G282 and 116,748 stood out in the 2017/18 season for high yield and earliness under both drought‐stress conditions, while the hybrid 1G282, AG1090, AS4625 and DKB590 stood out for the 2018/19 season in both stress condition. The hybrid A9904 and 1G282 stood out for its responsiveness to water supply, being recommended to high‐tech farming systems that employ irrigation or for in‐season cultivation. [ABSTRACT FROM AUTHOR]

One of the main objectives of soybean breeding programs is the search for genotypes that are both high yielding and tolerant to abiotic stresses. Brazilian Cerrado, the main grain‐producing region in the country, is characterised by naturally acidic and low fertility soils that consequently have low base saturation. Therefore, identifying genotypes with good performance for favourable and low base saturation stress conditions is of fundamental importance for crop improvement. The objective of this study was to select F3 soybean populations for multiple traits under contrasting base saturation conditions. Field experiments were carried out in two crop seasons in randomised block design with three replicates and 10 F3 populations (P1, P2, P3, P4, P5, P6, P7, P8, P9 and P10). In the first experiment, F3 populations were evaluated without base saturation correction (V = 30%, low saturation), while in the second experiment, limestone was applied 3 months before the start to raise the base saturation to 60% (recommended saturation). We evaluated physiological, nutritional, agronomic and spectral variables. Multivariate analysis was performed to know the populations' performance and the interrelationship between variables and populations in the saturation conditions evaluated. The F3 populations showed differential performance for all traits across the conditions tested, enabling the identification of the best populations for each specific condition and both base saturation environments. Population P2 stood out under both low and recommended base saturation conditions and can be used in F4 plant selection to obtain superior lines for physiological and agronomic traits and vegetation indices. [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]

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]

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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]

Highlights • Genetic correlations of low to moderate magnitude were observed between harvests. • Multiple-trait BLUP / Additive Index procedure allowed for the genetic selection. • This procedure can be efficiently applied on Jatropha curcas breeding. Abstract Despite being a species with great potential for biodiesel production, little research has been done on the breeding of Jatropha curcas , mainly with respect to its yield across harvests. Thus, the present study was carried out to analyze longitudinal data via multiple-trait Best Linear Unbiased Prediction (BLUP) for the genetic improvement of Jatropha curcas. The experiment was set up as a randomized block design with two blocks and five plants per plot. The seed yield of 730 individuals of 73 half-sib families was evaluated over six years. Variance components and genetic parameters were estimated via Restricted Maximum Likelihood (REML). The Additive Index was used for ranking and selection purposes. Genetic correlations of low to moderate magnitude were observed between pairs of harvests. The Multiple-trait BLUP / Additive Index procedure allowed for the selection of superior families based on the predicted genetic values, considering all the harvests. Therefore, it can be efficiently applied in the breeding of Jatropha curcas. [ABSTRACT FROM AUTHOR]

Diallel crosses in an unbalanced scheme were carried out in Jatropha to (i) evaluate the additive and non-additive genetic components; (ii) select parents through the general combining ability; (iii) estimate the specific combining ability used in the crosses; and (iv) verify the existence of the maternal effect and inbreeding depression. The experiment was carried out in a complete diallel scheme with four progenitors, unbalanced for the number of crosses. The experimental design consisted of a randomized block, with 5 replications and 3 plants per plot. The following characteristics were evaluated: stem diameter (SD), number of branches (NB), plant height (PH), canopy projection on the row (CPR), canopy projection between rows (CPB), mass of hundred grains (MHG) and grain yield (GY). Estimates of variances were obtained using the method of restricted maximum likelihood, while breeding values were estimated by the best linear unbiased prediction. It was concluded that the additive effect was predominant in the genetic control for SD, CPR, and CPB; the dominance effect was predominant for PH, NB, and GY; there was a cytoplasmic effect and nuclear genes of the female parent for all evaluated traits; parents 107 and 190 are promising for reducing the size and increasing the grain yield; there was inbreeding depression for SD and GY; and the favorable crosses for increasing GY were 190x107 and 190x190. [ABSTRACT FROM AUTHOR]

Asian rust affects the physiology of soybean plants and causes losses in yield. Repeatability coefficients may help breeders to know how many measurements are needed to obtain a suitable reliability for a target trait. Therefore, the objectives of this study were to determine the repeatability coefficients of 14 traits in soybean plants inoculated with Phakopsora pachyrhizi and to establish the minimum number of measurements needed to predict the breeding value with high accuracy. Experiments were performed in a 3x2 factorial arrangement with three treatments and two inoculations in a random block design. Repeatability coefficients, coefficients of determination and number of measurements needed to obtain a certain reliability were estimated using ANOVA, principal component analysis based on the covariance matrix and the correlation matrix, structural analysis and mixed model. It was observed that the principal component analysis based on the covariance matrix out-performed other methods for almost all traits. Significant differences were observed for all traits except internal CO2 concentration for the treatment effects. For the measurement effects, all traits were significantly different. In addition, significant differences were found for all Treatment x Measurement interaction traits except coumestrol, chitinase and chlorophyll content. Six measurements were suitable to obtain a coefficient of determination higher than 0.7 for all traits based on principal component analysis. The information obtained from this research will help breeders and physiologists determine exactly how many measurements are needed to evaluate each trait in soybean plants infected by P. pachyrhizi with a desirable reliability. [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 cotton breeding programs, it is necessary to identify genotypes with predictable behavior on the length of fibers, and which are responsive to environmental variations, in specific or broad conditions. The aim of this study was to employ the methodology of mixed models for simultaneous selection of cotton genotypes with greater length of fibers, adaptability and stability. It was evaluated 36 lines in three trials located in Apodi (2013 and 2014) and Santa Helena (2013,) in a randomized block design with two replications. Genetic parameters were estimated by the restricted maximum likelihood/best linear unbiased predictor method (REML/BLUP) and the selection was based on the method of harmonic mean of the relative performance of genetic values. The genotypes CNPA 2012-55, CNPA 2012-58, CNPA 2012-62 and CNPA 2012-64 can be grown in all tested environments, since they gather greater length of fibers, stability and adaptability. [ABSTRACT FROM AUTHOR]