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As wheat blast is one of the most damaging disease of wheat worldwide and extremely important in Brazil, in this work we are trying to summarize the scientific research direction and temporal trends about the disease. We summarized the evolution of scientific research from the Thomson Reuters Web of Science database, through bibliometrix analysis of the temporal trends of authors, organizations and journals. During 31 years, 521 publications approach wheat blast was published, with an annual growth rate of 3.86%. Different countries were identified, being the USA, Brazil and China the lead of publications. The main organizations are Brazilian federal institutions Empresa Brasileira de Pesquisa Agropecuária, followed by Kobe University, Kansas State University, Agriculture Research Service of United States Department of Agriculture. A total of 168 journals were identified in wheat blast publications in bibliometric analysis, being Phytopathology the journal with most publications about wheat blast, with 14.2% of publications (74 documents). Considering the 521 documents, the bibliometric analysis reported a total of 1941 authors, and thirteen authors are ranked as the main authors by h-index. Among the 20 most relevant terms, in keywords and keywords plus, in the documents related to wheat blast emphasize that three main ones are related to the fungus and the culture, being “Magnaporthe grisea”, “Wheat” and “Pyricularia oryzae”. These authors, organizations and journals have a great importance in plant disease research, and in addition they are recognized by worldwide researches. In this sense, the breeding programs should be encouraged in the search for blast.

In this study, the aim of this study was to identify the source of resistance using KASP markers developed for Rpp1 - Rpp5 and screening for resistance in field trials in F2 populations. Ten F2 soybean (Glycine max (L.) Merrill) populations derived from crosses between rust-susceptible (55I57RSF IPRO, 63I64RSF IPRO) and rust-resistant sources (PI 200492, PI 594538A, PI 587880A, PI 594723, PI 230970, PI 506764, PI 459025A and PI 200487) were evaluated. All F2 plants were individually evaluated in field conditions for ASR phenotypic reactions and classified according to sporulation level. KASP markers were developed according to assays associated with Rpp genes available at SoyBase. Based on a slight difference in map position and different phenotypic disease reactions of PI 200492, we suggest that PI 594723 carries a resistance gene Rpp1-b. The Rpp1-b gene from PI 594723 was mapped on Chr 18 in a 12.4 cM region. The PIs carrying Rpp1-b (PI 594723, PI 587880A, and 594538A) showed strong resistance to ASR compared to the lines carrying Rpp1 (PI 200492). A total of 26 KASP markers were significantly associated (P < 0.01) with ASR resistance. Among those, M1, M5, and M6 (Rpp1), M13 and M14 (Rpp2), M16, M17 and M20 (Rpp3), M25 and M26 (Rpp4), and M27 and M28 (Rpp5) have the potential to be used in marker-assisted selection strategies. [ABSTRACT FROM AUTHOR]

Soybean [Glycine max(L.) Merril] is one of the main crops produced worldwide, and on‐farm yields have increased considerably in the last decades in Brazil. We evaluated the genetic gain for agronomic, phenological, and end‐use quality traits in 29 cultivars in the South Region, and in 38 cultivars in the Midwest Region in Brazil, released from 1966 to 2011. Field trials were conducted in Macroregions 1, 2, and 4, in 2016–2017, 2017–2018, and 2018–2019 crop seasons. The best linear unbiased predictors (BLUP) of the cultivars were obtained for each trait using a linear model. The BLUPs were regressed with the year of release using linear and quadratic regression models. The rates of genetic gain for seed yield ranged from 11.98 to 15.31 kg ha–1yr–1(0.33 to 0.42% yr–1) in the South Region, and from 13.58 to 21.84 kg ha–1yr–1(0.47 to 0.77% yr–1) in the Midwest Region. New cultivars presented taller plants and more seed oil content, oil and protein yield, and lower seed weight, days to flowering, days to maturity, and seed protein content than old cultivars in the South Region, although with differences between the Macroregions. In the Midwest Region, new cultivars showed higher seed oil content, oil and protein yield, and lower bottom pod height and seed protein content than old cultivars. Our results showed that breeding programs have been efficient to improve soybean yield and other traits across the years, without yield plateaus in sight. We observed positive genetic gain for seed yield in both evaluated regions.The breeding efforts over the years have changed important soybean traits.The seed yield has not reached its plateau in the South and Midwest Regions of Brazil.

Pyramiding Rppgenes 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 Rppgenes were developed, and the F3lines were evaluated to phenotypic resistance and yield performance. Our results suggest that the Rpp1‐bof different resistant sources (PI 587880A, PI 594723, PI 4538A) have higher resistance and yield performance compared with other Rppgenes. Rpp‐pyramided populations carrying two and three genes showed an increase in resistance compared with populations carrying a single Rppgene. The best gene combinations were Rpp1‐b+ Rpp5, showing neither uredinia formation nor sporulation (immunity + RB1 phenotype reaction) in more than 70% of the F3population. Rpp‐pyramided lines developed in this study have the potential to be used in backcross programs for improving ASR resistance. Rpp‐pyramided populations carrying two and three Rppgenes 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.

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.

Wheat (Triticum aestivumL.) is one of the most important crops in the world. Brazil is one of the largest importers of this cereal, and wheat breeding programs are attempting to increase productivity and reduce external dependence. The objectives of this study were to quantify the spring wheat genetic gain in Brazil between 1984 and 2014 using data from multienvironment trials, and to verify if the genetic gain stagnated after the 2000s. This study used a highly unbalanced dataset containing grain yield (GY) data from 187 spring wheat trials that were performed at 25 locations between 2002 and 2015. In total, 126 cultivars, released between 1985 and 2014, were evaluated over 14 crop seasons. The cultivar evaluation indicated that there was a genetic gain in GY of 33.9 kg ha−1yr−1(r2= 0.53, P< 0.01), which represents an increase of 1.28% yr−1. The dataset showed a linear regression for the genetic gain and indicates that wheat genetic gain has not stagnated in Brazil after the 2000s.

The application of an adequate rate and splitting of nitrogen is essential for wheat grain yield and protein content. The aim of this work was to adjust nitrogen management approaches regarding agronomic performance and protein content of wheat cultivars in various environments. Field experiments were conducted under no-tillage system on soybean mulch during the 2011 and 2012 growing seasons in Londrina and Pato Branco regions. The experimental design was a randomized block in split plot with four replicates. Four wheat cultivars (IPR Catuara TM, BRS Gaivota, Quartzo, CD 120) were tested with six nitrogen (N) management forms. Were evaluated: number of ears per unit area (NEA); plant height (PH); thousand-kernel weight (TKW); test weight (TW); grain yield (GY); and protein content (PC). The combined ANOVA (p ≤ 0.01) and Tukey's test (p ≤ 0.01) were used. The interaction between cultivars and environments influence all yield components, GY and PC. The interaction management forms of N and environments affected the TKW, NEA, GY and PC. The results showed that in low-rainfall environments, nitrogen topdressing could be suppressed with no negative effects on GY or PC. Under ideal weather conditions, the GY of wheat cultivars was enhanced on application of 60 kg⋅ha-1 N of urea at the beginning of tillering as well 20 kg⋅ha-1 of N at booting. Matching the appropriate cultivars to the ideal growth environment is essential for achieving high GY values. The nitrogen forms on the topdressing do not influence the PC of cultivars in Pato Branco. [ABSTRACT FROM AUTHOR]

Several studies have shown a significant increase in wheat (Triticum aestivumL.) grain yield (GY) worldwide during the 20th century as a result of genetic and environmental improvement. The objective of this study was to measure the genetic gains achieved in a wheat breeding program maintained by the Central Cooperative of Agricultural Research (COODETEC) in Brazil through the annual evaluation of lines in multienvironment trials. The dataset was composed of 836 advanced trials and was evaluated in 40 representative locations to assess the value for cultivation and use (VCU) regions in the south, southeast, and central‐west areas of the country from 2004 to 2013. Each trial consisted of 25 treatments (lines + checks) in a randomized complete block design with three replicates. A linear mixed model was fitted to predict the genetic values of each genotype per year and a restricted maximum likelihood algorithm to estimate the variance components. These genetic values were used to compute the genetic gain over the years. The overall estimated genetic gain of the breeding program was 61.59 kg ha−1yr−1(1.68% yr−1) for GY, although it was partially counterbalanced by the estimated negative environmental effects. The genetic gain per VCU region ranged from 31.38 (VCU 4) to 115.33 kg ha−1yr−1(VCU 1). After accounting for the environmental changes over years, the yield gain was 39.40 kg ha−1yr−1(1.10% yr−1), ranging from −0.82 (VCU 3) to 3.35% yr−1(VCU 1). The assessment of Brazilian cultivars released between 1998 and 2014 showed genetic gains in GY of 34.8 kg ha−1yr−1(1% yr−1), demonstrating that the efforts of Brazilian research institutions to develop cultivars have produced additive results. The implications of genetic gain estimates on breeding programs are discussed.

Soybean [Glycine max(L.) Merr.] maturity group (MG) is an important concept used to determine the most suitable macroregion and edaphoclimatic region (ECR) in which soybean can best use the available resources. This classification is based on the number of days between sowing and maturation in the soybean life cycle. The MG is related to photoperiod; thus, the longer the photoperiod, the shorter the MG of cultivars must be to have an adequate life cycle. However, there is no consensus on which MGs are the most suitable for each region to improve grain yield. The objective of this study was to identify suitable soybean MGs for cultivation in the macroregions and ECRs in Brazil. During 4 yr of evaluation, grain yield data from 247 yield trials over 83 locations, encompassing four macroregions and 14 ECRs in Brazil, were used. Cultivars were grouped according to their MG for statistical analyses. Using these groups, the ideal genotypes and performance according to local analyses were determined. The best adapted and most productive cultivars were those with an intermediate MG in their predefined adaptation region (both macroregions and ECR). The maturities that performed the best in each macroregion were as follows: M1 (cultivars in MGs 5.3–5.9) M2 (cultivars in MGs 6.0–7.0), M3 (cultivars in MGs 7.1–7.9), and M4 (cultivars in MGs 7.7–8.4). A lower productivity was observed in cultivars in extreme MGs for each macroregion. Breeding program efforts should target the MGs identified as ideal for each ECR to develop cultivars with a greater chance of achieving high yields and with greater adaptability to the specific region.

Multi-environment trials often yield unbalanced datasets, thus necessitating the estimation of missing values. It is unknown whether this estimation affects the graphic characteristics of genotype plus genotype-by-environment interaction (GGE) biplots. Therefore, our objectives were to investigate the effects of different percentages of missing values on the number of significant principal components (PCs) and on mega environments, “winner” (highest-performing) genotypes, and the amount of variation explained by the PCs. Two complete sets of two-way data from wheat (Triticum aestivumL.) were used. The first set consisted of the original data (Data1, from which we created scenarios with 0, 30, and 60% missing data. For the second dataset (Data2), we removed 50% data from the original dataset, estimated missing values to make it a new complete dataset, and created scenarios like those for Data1. Missing values were estimated via expectation-maximization–GGE (EM–GGE) and EM–additive main effects and multiplicative interaction (EM–AMMI) methods. The percentage of variation explained by the PCs was affected by the percentage of missing data; a large percentage of missing values considerably increased the amount of variation explained by PC 1 and PC 2 and reduced the complexity of the genotype-by-environment interaction because two PCs accounted for more than 80% of the variation, instead of the three PCs that were required to explain the variation in the original dataset. The EM–GGE estimation method was able to maintain the original conformation of the ‘which-won-where’ biplot when =30% of estimated data were used. The EM–GGE was superior to the EM–AMMI method for estimating missing data. The estimation of more than 30% of the data should be avoided because it can lead to significant changes in mega environment conformation and the identification of “winner” genotypes.

The sensitivity of wheat crop to environmental variations frequently results in significant genotype (G) x environment (E) interaction (GEI). We compared statistical methods to analyze adaptability and stability of wheat genotypes in value for cultivation and use (VCU) trials. We used yield performance data of 22 wheat genotypes evaluated in three locations (Guarapuava, Cascavel, and Abelardo Luz) in 2012 and 2013. Each trial consisted of a complete randomized block design with three replications. The GEI was evaluated using methodologies based on mixed models, analysis of variance, linear regression, multivariate, and nonparametric analysis. The Spearman's rank correlation coefficient was used to verify similarities in the genotype selection process by different methodologies. The Annicchiarico, Lin and Binns modified methodologies, as well as the Harmonic Mean of the Genetic Values (HMGV) allowed to identify simultaneously highly stable and productive genotypes. The grain yield is not associated with Wricke, Eberhart and Russell stability parameters, scores of the first principal component of the AMMI1 method, and GGE biplot stability, indicating that stable genotypes are not always more productive. The data analyzed in this study showed that the AMMI1 and GGE biplot methods are equivalent to rank genotypes for stability and adaptability. [ABSTRACT FROM AUTHOR]

The nitrogen use efficiency (NUE) is defined as the capacity of a given genotype in take advantage of the applied nitrogen (N) and transform it in biomass and grains. The objective of this study was to evaluate 12 wheat cultivars as to the NUE and its components. The experiment was conducted in a controlled environment, in a randomized block design with three replications. Twelve wheat cultivars were submitted to four N supply levels (0, 80, 160 and 240 kg of N·ha-1). The data were submitted to analysis of variance, means multiple comparison, polynomial regression, and path analysis. The nitrogen remobilization efficiency (NRE) was the main NUE component of the evaluated cultivars, in both low and high conditions of nitrogen fertilization. In the cultivars average, the nitrogen utilization efficiency (NUtE) presented reduction tendency as the N supply was increased, tending to stabilization at the dose of 231 kg of N·ha-1. The wheat cultivars Mirante, TBIO Itaipu, BRS Parrudo, and TBIO Iguaçu were the most efficient on the N use, and the first two were also efficient in remobilizing the N from the phytomass to the grains. [ABSTRACT FROM AUTHOR]

The objectives of this study were to investigate the pattern of genotype environment interaction and identify megaenvironments (ME), essential test locations, and suitable genotypes for each ME. The genotype plus genotype by environment interaction (GGE) biplot and additive main effects and multiplicative interaction analysis (AMMI) were used to demonstrate the potential of using a graphical biplot to analyze the genotype by environment interaction (GEI) in data of multienvironment trials (MET). These trials of the Central Cooperative Agricultural Research (COODETEC) evaluated 36 advanced breeding lines and 27 check cultivars for 3 years (2008–2010) at 12 test locations. Yield data were analyzed using the genotype plus GGE and AMMI biplot methods. The test environments were classified into two ME (i.e. ME1: Castro, Guarapuava, Não‐Me‐Toque, Abelardo Luz, and Cachoeira do Sul; and ME2: Campo Mourão, Dourados, Palmital, Palotina, Ponta Porã, and Rolândia. In ME1, the locations Guarapuava, Cachoeira do Sul, and Abelardo Luz were classified as ideal, while in ME2, Ponta Porã, Dourados and Palotina were close to ideal. Guarapuava was the essential test location in ME1; however the identification of an essential test location in ME2 was unclear. Superior cultivars and advanced lines were identified in both ME that could be valuable for spring wheat (Triticum aestivumL.) improvement or cultivars released.

Copyright of Bragantia is the property of Instituto Agronomico de Campinas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bragantia is the property of Instituto Agronomico de Campinas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bragantia is the property of Instituto Agronomico de Campinas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bragantia is the property of Instituto Agronomico de Campinas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bragantia is the property of Instituto Agronomico de Campinas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bragantia is the property of Instituto Agronomico de Campinas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bragantia is the property of Instituto Agronomico de Campinas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bragantia is the property of Instituto Agronomico de Campinas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)