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Pyramiding Rpp genes is a promising strategy to achieve a more durable resistance against Asian soybean rust (ASR). The objective of this study was to develop Rpp‐pyramided populations and evaluate their resistance and yield in field conditions. Fifteen pyramided populations carrying different combinations of Rpp genes were developed, and the F3 lines were evaluated to phenotypic resistance and yield performance. Our results suggest that the Rpp1‐b of different resistant sources (PI 587880A, PI 594723, PI 4538A) have higher resistance and yield performance compared with other Rpp genes. Rpp‐pyramided populations carrying two and three genes showed an increase in resistance compared with populations carrying a single Rpp gene. The best gene combinations were Rpp1‐b + Rpp5, showing neither uredinia formation nor sporulation (immunity + RB1 phenotype reaction) in more than 70% of the F3 population. Rpp‐pyramided lines developed in this study have the potential to be used in backcross programs for improving ASR resistance. Core Ideas: Rpp‐pyramided populations carrying two and three Rpp genes show higher resistance to ASR.Rpp1‐b + Rpp5 could be an interesting option to achieve resistance.Rpp‐pyramided lines have potential to be used in backcross programs for improving ASR. [ABSTRACT FROM AUTHOR]

The objective of this work was to assess the breeding influences in different agronomic and physiological traits in Brazilian soybean cultivars, released between 1965 and 2011, to identify traits associated with modern cultivars. A total of 29 cultivars were evaluated in two locations in the 2016/17 crop season. Genotype selection based on agronomic and physiological traits was determined using GYT (Grain Yield*Trait) methodology, which uses the Superiority Index to rank genotypes by mean of all traits. Grain Yield is combined with other target traits and shows the strengths and weaknesses of each genotype. Soybean breeding improved desirable traits during the 46 years of evaluation. Superiority index can be a powerful tool for breeders to obtain high genetic gains in the future. The cultivars DMario 58i, TMG 7161RR and TMG 7262 RR stand out as the best cultivars but present different sets of desirable traits. The traits grain yield, harvest index, number of pods per plant, reproductive-vegetative ratio, photosynthetic rate and transpiration rate are core traits which can be evaluated in soybean breeding programs. [ABSTRACT FROM AUTHOR]

An optimal number of test locations and replications in yield trials allows improvements in the efficiency of a breeding program and selection reliability. The objectives of this study were to verify the optimal number of test locations and replications per test location in edaphoclimatic regions (ECR) and macroregions (MRs) of soybean adaptation in Brazil. Grain yield data from 184 soybean trials in 57 locations was evaluated in crop seasons 2012–2013 to 2015–2016. The number of replications currently used (3) is sufficient to achieve selection reliability and reduce costs in all MRs. However, the optimal number of replications needs to be determined for each location. Locations that require a high number of replicates to achieve H = 0.75 must be removed and/or replaced to improve selective accuracy. The optimal mean number of test locations in macroregions MR1 and MR2 is seven locations per ECR, and in MR3 and MR4, it is five locations per ECR. Thus, analyses of the optimal number of locations and replications may be applied to other regions to improve the yield trial efficiency of soybean cultivars. [ABSTRACT FROM AUTHOR]

Recently developed selection indexes provide solutions for plant breeding, using linear‐bilinear models that consider factors as fixed or random. This work aimed to compare the multitrait selection indexes based on factor analysis and ideotype‐design (FAI‐BLUP), GGE biplot, and grain yield × trait index (GYT), and proposes the use of predicted genetic values together with the GYT index (best linear unbiased prediction used in grain yield*trait index, GYT‐BLUP). In addition, this work indicates the best index to select superior soybean [Glycine max (L.) Merr.] genotypes, closer to the ideotype. Data from 35 homozygous soybean lines and four checks, were obtained from trials conducted in six locations in the southern region of Brazil in the 2014/2015 crop season. The grain yield, yield components, morphological and grain composition were evaluated. Phenotypic data were used for GGE biplot and GYT analysis, using the software GGE biplot. Genetic values were predicted with mixed models considering genotype and location as random and fixed effects, respectively. Thus, genetic values were used in GYT‐BLUP and FAI‐BLUP indexes. These methods were compared by Spearman's rank correlation. Genetic gains obtained by indexes and traits were estimated. Soybean lines L1 and L22, and cultivars C3 and C4 were selected based on their performance for multiple traits, for indexes used. Thus, we suggest to combined FAI‐BLUP and GYT‐BLUP indexes. The GYT‐BLUP has a high importance for grain yield, which was related to all other traits. FAI‐BLUP gave similar weights for all traits. So, combining different approaches can provide better answers to breeders. [ABSTRACT FROM AUTHOR]

Mega‐environment (ME) identification is the first step for evaluating, selecting and recommending genotypes within a target region (TR). The present study aimed to (a) identify MEs, using GGE biplot methods, in Brazilian edaphoclimatic region (ECR) 402 of soybean cultivation, located in the Mato Grosso State (the TR) and (b) compare the performance of genotypes within the TR and in each ME using fixed and mixed models. Data from three years of soybean yield trials, 19 genotypes and 22 environments were used. The biplots GGE, GGL + GGE and GGS + GGE were implemented to identify the MEs. Two MEs were identified in the TR. ME1 presents a higher altitude, farms which use a higher level of fertilizer inputs and a higher occurrence of the soybean cyst nematode (SCN) than ME2. When selection and recommendation are made based on MEs, genotypes with both broadly and specific adaptation can be selected. This action can improve grain yield in the entire target region. [ABSTRACT FROM AUTHOR]

Brazil is one of the largest soybean ( Glycine max L. Merrill) producers in the world, as well as main exporter. Little information is available about both the composition of Brazilian genotypes in respect to soluble sugars contents such as sucrose, glucose, raffinose and stachyose, and the effect of genotype × environment interaction (GEI). This study evaluated the soluble sugar contents, the GEI and the genetic variability of highly cultivated cultivars and new soybean lines. A sample of 46 commercial Brazilian cultivars and 35 pre-commercial lines were evaluated at six locations during the 2014/15 crop season, in a randomized complete block design with three replications. Soluble sugars were evaluated using an infrared spectroscopy (NIR) method. The average contents of sucrose, glucose, raffinose and stachyose were 3.3, 1.62, 0.39 and 3.07% which were lower than the levels found in other countries. All field traits were highly influenced by the environment. The cultivar NS 6121 RR presented a reduced (and stable across the environments) raffinose and stachyose content and can be used as a source of novel genes in breeding programs. Brazilian soybean presents genotypic differences which can be exploited to reduce or increase soluble sugar contents in breeding programs. There is a strong GEI for sucrose, glucose, raffinose and stachyose contents for Brazilian environments. The evaluated genotypes presented reduced contents of raffinose and stachyose, which are considered antinutritional factors. The new soybean lines presented similar soluble sugar contents to the tested cultivars. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the experimental precision of different methods of statistical analysis for trials with large numbers of soybean genotypes, and their relationship with the number of replicates. Soybean yield data (nine trials; 324 genotypes; 46 cultivars; 278 lines; agricultural harvest of 2014/15) were used. Two of these trials were performed at the same location, side by side, forming a trial with six replicates. Each trial was analyzed by the randomized complete block, triple lattice design, and use of the Papadakis method. The selective accuracy, least significant difference, and Fasoulas differentiation index were estimated, and model assumptions were tested. The resampling method was used to study the influence of the number of replicates, by varying the number of blocks and estimating the precision measurements. The experimental precision indicators of the Papadakis method are more favorable as compared to the randomized complete block design and triple lattice. To obtain selective accuracy above the high experimental precision range in trials with 324 soybean genotypes, two repetitions can be used, and data can be analyzed using the randomized complete block design or Papadakis method [ABSTRACT FROM AUTHOR]

In this study, we summarized the evolution of scientific research about fungus Phakopsora pachyrhizi Sydow & Sydow affecting soybean [Glycine max (L.) Merrill]. We retrieved research articles from the Thomson Reuters Web of Science database that reported studies of the Asian soybean rust (ASR) from 1945 to 2018. We applied the scientometric analysis of the temporal trends of authors, journals, research partnerships, and resistance genes to ASR. Several studies aiming to understand fungal etiology, testing, or improve strategies to control the pathogen were developed. The temporal trends in publications showed an increase in the number of publications between 2005 and 2013. This is directly related to the dissemination of ASR in South and later North America, which facilitates field research and an increase in grant opportunities. The USA had the highest number of published articles (42%), followed by Brazil, Germany, Japan, and Argentina. The leading organizations associated with these publications are from Brazil and the USA. 19% of articles pertaining to ASR were published in the journal Plant Disease. Our results also showed that among the seven dominant genes identified for resistance to ASR, Rpp1, and Rpp2 are the most studied. Several countries, institutions, and plant scientists are deeply involved in ASR research and have been making efforts to control this disease and ensure food security worldwide. [ABSTRACT FROM AUTHOR]

Yield increase in soybean [Glycine max (L.) Merr.] has occurred due to plant breeding, as well as the improvement in management practices. Genetic gain for important traits in soybean due to plant breeding in South Brazil has not been evaluated. The objective of this study was to measure the genetic progress of several soybean agronomic, phenological and physiological traits, in a historical set of cultivars released for cultivation in South Brazil from 1965 to 2011. Twenty-nine cultivars were evaluated in a three replicate randomized complete block design at two locations in the 2016/17 crop season. Mean genetic gain for yield was 40.06 kg ha−1 year−1 averaged across locations. The modern cultivars had higher biological yield (BY), harvest index (HI), number of pods per plant (NPP), and lower number of branches per plant, plant height, and lodging score compared to older releases. There was a reduction in the vegetative period, and increase in ratio between reproductive and vegetative periods (R/V). Photosynthetic rate (PN) and transpiration rate (Tr) also increased with year of release. The traits BY, NPP, HI, R/V, number of seeds per pod, PN, Tr, and chlorophyll content (Chl a and b) was highly associated with seed yield. Breeding strategies that maximize the R/V ratio, and prioritize indirect selection through HI, BY, NPP, PN, Tr, and Chl a and b, will likely contribute to improving soybean yield in the future. [ABSTRACT FROM AUTHOR]

Genomic selection (GS) can potentially accelerate genetic improvement of soybean [Glycine max L. (Merrill)] by reducing the time to complete breeding cycles. The objectives of this study were to (1) explore the accuracy of GS in soybean, (2) evaluate the contribution of intrapopulational structure to the accuracy of GS, and (3) compare the efficiencies of phenotypic selection and GS in soybean. For this, phenotypic and genotypic data were collected from 324 soybean genotypes (243 recombinant inbred lines and 81 cultivars) and GS was performed for five yield related traits. BayesB methodology with a 10-fold cross-validation was used to compute accuracies. The GS accuracies were evaluated for grain yield, plant height, insertion of first pod, days to maturity, and 1000-grain weight at eight locations. We found that GS can reduce the time required to complete a selection cycle in soybean, which can lead to increased production of this commercially important crop. Furthermore, genotypic accuracy was similar regardless of population structure correction. [ABSTRACT FROM AUTHOR]

Empirical decision-making on the number of plants to be sampled per experimental unit when assessing soybean grain yield is a commonly observed situation. The cause for this is unknowing how that decision affects experimental precision statistics, which has not yet been reported. Therefore, the aims of this study were: to analyze the response of experimental precision statistics as a function of the variations in the number of plants collected per experimental unit in highland and lowland systems; to define the optimal sample size per experimental unit for each precision statistic; and, to propose predictive models for estimating the precision of experiments with soybean. Field experiments were carried out in two locations of Rio Grande do Sul, Brazil, using 30 cultivars in the highland trials and 20 cultivars in the lowland trials. A validation trial was also performed using 8 cultivars. A randomized block design with three repetitions was used for all trials. The grain yield of 20 plants per experimental unit was assessed, thus totaling 9000 plants. In the validation experiment, grain yield was measured in 100 plants per experimental unit, totaling 2400 plants more. From these experiments, trials with sample scenarios of 1, 2, ..., 1000 plants per experimental unit were simulated with replacement. The confidence interval width of the precision statistics reduced as the number of plants measured per experimental unit increased, allowing a higher precision of its estimates. In general, up from values of ≥ 70%, precision gain, in the estimates of experimental precision statistic determined in soybean highland and lowland systems, tends to stabilize. The optimal sample number per experimental unit varied between systems and experimental precision statistics. The sampling of 18 plants per experimental unit is sufficient to estimate most precision indicators reliably. Interestingly, the mechanistic response of the confidence interval width is easily modeled and can be used for predicting experimental precision in highland and lowland cropping systems. From this analysis, a methodology for predicting experimental precision was outlined based on sample size per experimental unit, thus bringing new insight into future experimental planning for soybean experiments in highland and lowland systems in subtropical climate. [Display omitted] • The accuracy of the precision statistics was improved in greater sampling scenarios. • Eighteen plants per experimental unit were sufficient in highlands and lowlands. • The proposed methodology is adequate for subtropical climate highlands and lowlands. • Precision predictability is improved in samplings ≥ 18 plants per experimental unit. [ABSTRACT FROM AUTHOR]

• Multiple-trait FAI-BLUP was efficient for the selection of genotypes. • Genetic gain was achieved in traits related with soybean biodiesel quality. • Selection reduced linolenic fatty acids by up to 14%. Soybean (Glycine max (L.) Merrill) is an important source of oil for biodiesel production in Brazil. However, the traits related to oil content and fatty acid profile are not targeted by soybean breeding programs. Developing methods to increase oleic and reduce linolenic fatty acid content within soybean genotypes will increase the use of soybeans for food and feed, as well as biodiesel production. Biodiesel produced with high polyunsaturated fatty acids (PUFAs) content has a high tendency to undergo oxidation, making storage difficult and overall quality reduced. On the other hand, oils with high levels of monounsaturated fatty acids (MUFAs) are preferred for biodiesel production because oxidation is not increased, improve cold flow properties significantly and nitrogen oxide emissions are decreased. In addition, soybean oil with high concentration of PUFAs causes low oxidative stability, resulting in rancidity, a rapid decline of optimal flavor and reducing storage time of manufactured food products. 242 recombinant inbred lines (RILs) were evaluated in six locations in Brazil for grain yield, oil content, oil production, and fatty acid composition. A multi-trait index based on factor analysis and ideotype-design (FAI-BLUP index) was applied to identify genotypes best related with the ideotype for biodiesel production. This method is based on mixed models, with genotype and environment effects being considered as random and fixed, respectively. In addition, the best linear unbiased prediction (BLUP) method was used to predict the genetic values. Six RILs outperformed both parents and can be selected as genotypes more suitable for biodiesel production. Genetic selection reduced linolenic fatty acid content by 14%. Results indicated the presence of genetic variability for fatty acid composition. The significant increases in oleic and reductions in linolenic contents may be easily achieved with the introduction of mutant FAD2-1A and FAD2-1B genes through breeding programs or biotechnological strategies. [ABSTRACT FROM AUTHOR]