Effect of Initial Inoculum on the Temporal and Spatial Dynamics of Wheat Blast Under Field Conditions in Bolivia.

Epidemiological studies to better understand wheat blast (WB) spatial and temporal patterns were conducted in three field environments in Bolivia between 2019 and 2020. The temporal dynamics of wheat leaf blast (WLB) and spike blast (WSB) were best described by the logistic model compared with the Gompertz and exponential models. The nonlinear logistic infection rates were higher under defined inoculation in experiments two and three than under undefined inoculation in experiment one, and they were also higher for WSB than for WLB. The onset of WLB began with a spatial clustering pattern according to autocorrelation analysis and Moran's index values, with higher severity and earlier onset for defined than for undefined inoculation until the last sampling time. The WSB onset did not start with a spatial clustering pattern; instead, it was detected later until the last sampling date across experiments, with higher severity and earlier onset for defined than for undefined inoculation. Maximum severity (Kmax) was 1.0 for WSB and less than 1.0 for WLB. Aggregation of WLB and WSB was higher for defined than for undefined inoculation. The directionality of hotspot development was similar for both WLB and WSB, mainly occurring concentrically for defined inoculation. Our results show no evidence of synchronized development but suggest a temporal and spatial progression of disease symptoms on wheat leaves and spikes. Thus, we recommend that monitoring and management of WB should be considered during early growth stages of wheat planted in areas of high risk.

Tar Spot: An Understudied Disease Threatening Corn Production in the Americas.

Tar spot of corn has been a major foliar disease in several Latin American countries since 1904. In 2015, tar spot was first documented in the United States and has led to significant yield losses of approximately 4.5 million t. Tar spot is caused by an obligate pathogen, Phyllachora maydis, and thus requires a living host to grow and reproduce. Due to its obligate nature, biological and epidemiological studies are limited and impact of disease in corn production has been understudied. Here we present the current literature and gaps in knowledge of tar spot of corn in the Americas, its etiology, distribution, impact and known management strategies as a resource for understanding the pathosystem. This will in tern guide current and future research and aid in the development of effective management strategies for this disease.

Temporal Dynamics of Wheat Blast Epidemics and Disease Measurements Using Multispectral Imagery.

Wheat blast is a devastating disease caused by the Triticum pathotype of Magnaporthe oryzae. M. oryzae Triticum is capable of infecting leaves and spikes of wheat. Although symptoms of wheat spike blast (WSB) are quite distinct in the field, symptoms on leaves (WLB) are rarely reported because they are usually inconspicuos. Two field experiments were conducted in Bolivia to characterize the change in WLB and WSB intensity over time and determine whether multispectral imagery can be used to accurately assess WSB. Disease progress curves (DPCs) were plotted from WLB and WSB data, and regression models were fitted to describe the nature of WSB epidemics. WLB incidence and severity changed over time; however, the mean WLB severity was inconspicuous before wheat began spike emergence. Overall, both Gompertz and logistic models helped to describe WSB intensity DPCs fitting classic sigmoidal shape curves. Lin's concordance correlation coefficients were estimated to measure agreement between visual estimates and digital measurements of WSB intensity and to estimate accuracy and precision. Our findings suggest that the change of wheat blast intensity in a susceptible host population over time does not follow a pattern of a monocyclic epidemic. We have also demonstrated that WSB severity can be quantified using a digital approach based on nongreen pixels. Quantification was precise (0.96 < r> 0.83) and accurate (0.92 < ρ > 0.69) at moderately low to high visual WSB severity levels. Additional sensor-based methods must be explored to determine their potential for detection of WLB and WSB at earlier stages.

Regenerative potential, metabolic profile, and genetic stability of Brachypodium distachyon embryogenic calli as affected by successive subcultures.

Brachypodium distachyon, a model species for forage grasses and cereal crops, has been used in studies seeking improved biomass production and increased crop yield for biofuel production purposes. Somatic embryogenesis (SE) is the morphogenetic pathway that supports in vitro regeneration of such species. However, there are gaps in terms of studies on the metabolic profile and genetic stability along successive subcultures. The physiological variables and the metabolic profile of embryogenic callus (EC) and embryogenic structures (ES) from successive subcultures (30, 60, 90, 120, 150, 180, 210, 240, and 360-day-old subcultures) were analyzed. Canonical discriminant analysis separated EC into three groups: 60, 90, and 120 to 240 days. EC with 60 and 90 days showed the highest regenerative potential. EC grown for 90 days and submitted to SE induction in 2 mg L-1 of kinetin-supplemented medium was the highest ES producer. The metabolite profiles of non-embryogenic callus (NEC), EC, and ES submitted to principal component analysis (PCA) separated into two groups: 30 to 240- and 360-day-old calli. The most abundant metabolites for these groups were malonic acid, tryptophan, asparagine, and erythrose. PCA of ES also separated ages into groups and ranked 60- and 90-day-old calli as the best for use due to their high levels of various metabolites. The key metabolites that distinguished the ES groups were galactinol, oxaloacetate, tryptophan, and valine. In addition, significant secondary metabolites (e.g., caffeoylquinic, cinnamic, and ferulic acids) were important in the EC phase. Ferulic, cinnamic, and phenylacetic acids marked the decreases in the regenerative capacity of ES in B. distachyon. Decreased accumulations of the amino acids aspartic acid, asparagine, tryptophan, and glycine characterized NEC, suggesting that these metabolites are indispensable for the embryogenic competence in B. distachyon. The genetic stability of the regenerated plants was evaluated by flow cytometry, showing that ploidy instability in regenerated plants from B. distachyon calli is not correlated with callus age. Taken together, our data indicated that the loss of regenerative capacity in B. distachyon EC occurs after 120 days of subcultures, demonstrating that the use of EC can be extended to 90 days.

Artificial neural networks as auxiliary tools for the improvement of bean plant architecture.

Classification using a scale of visual notes is a strategy used to select erect bean plants in order to improve bean plant architectures. Use of morphological traits associated with the phenotypic expression of bean architecture in classification procedures may enhance selection. The objective of this study was to evaluate the potential of artificial neural networks (ANNs) as auxiliary tools in the improvement of bean plant architecture. Data from 19 lines were evaluated for 22 traits, in 2007 and 2009 winter crops. Hypocotyl diameter and plant height were selected for analysis through ANNs. For classification purposes, these lines were separated into two groups, determined by the plant architecture notes. The predictive ability of ANNs was evaluated according to two scenarios to predict the plant architecture - training with 2007 data and validating in 2009 data (scenario 1), and vice versa (scenario 2). For this, ANNs were trained and validated using data from replicates of the evaluated lines for hypocotyl diameter individually, or together with the mean height of plants in the plot. In each scenario, the use of data from replicates or line means was evaluated for prediction through previously trained and validated ANNs. In both scenarios, ANNs based on hypocotyl diameter and mean height of plants were superior, since the error rates obtained were lower than those obtained using hypocotyl diameter only. Lower apparent error rates were verified in both scenarios for prediction when data on the means of the evaluated traits were submitted to better trained and validated ANNs.

Genetic diversity among cotton cultivars in two environments in the State of Mato Grosso.

Using commercial cultivars to compose crossing blocks in cotton is a promising strategy, because these materials have desirable agronomic and technological characteristics. The objective of this study was to evaluate the genetic diversity among 16 cotton cultivars cultivated in two environments in the State of Mato Grosso, the largest national producer, using agronomical and technological traits. There was significant effect to cultivars for all traits, while genotype x environment interaction was significant only for average boll weight, short fiber index, and maturity of fibers. Therefore, because of the presence of genotype x environment interaction for three traits, we chose to study genetic diversity among cotton cultivars separately in each environment and investigate the interaction impact on the diversity among genotype pairs. Based on agronomical and technological performance and genetic diversity among cultivars in both environments, the most promising cross involves FM 910 and LD CV 02. We also observed that lint percentage and average boll weight presented a higher discrimination capacity in both environments.

Regularized quantile regression applied to genome-enabled prediction of quantitative traits.

Genomic selection (GS) is a variant of marker-assisted selection, in which genetic markers covering the whole genome predict individual genetic merits for breeding. GS increases the accuracy of breeding values (BV) prediction. Although a variety of statistical models have been proposed to estimate BV in GS, few methodologies have examined statistical challenges based on non-normal phenotypic distributions, e.g., skewed distributions. Traditional GS models estimate changes in the phenotype distribution mean, i.e., the function is defined for the expected value of trait-conditional on markers, E(Y|X). We proposed an approach based on regularized quantile regression (RQR) for GS to improve the estimation of marker effects and the consequent genomic estimated BV (GEBV). The RQR model is based on conditional quantiles, Qτ(Y|X), enabling models that fit all portions of a trait probability distribution. This allows RQR to choose one quantile function that "best" represents the relationship between the dependent and independent variables. Data were simulated for 1000 individuals. The genome included 1500 markers; most had a small effect and only a few markers with a sizable effect were simulated. We evaluated three scenarios according to symmetrical, positively, and negatively skewed distributions. Analyses were performed using Bayesian LASSO (BLASSO) and RQR considering three quantiles (0.25, 0.50, and 0.75). The use of RQR to estimate GEBV was efficient; the RQR method achieved better results than BLASSO, at least for one quantile model fit for all evaluated scenarios. The gains in relation to BLASSO were 86.28 and 55.70% for positively and negatively skewed distributions, respectively.

Comparison between different selection indices in energy cane breeding.

Sugarcane breeding programs have been adapting to a new market demand: aside from high sucrose yield per hectare, the sector needs new cultivars with higher fiber percentages. The selection of sugarcane clones based on phenotype alone is a complex task. The selected clones should display high performance in a series of yield- and quality-related traits. Selection indices can provide information about which clones can best combine the traits of agronomic interest. In this study, different selection indices were evaluated in a population of 220 clones. The following traits were evaluated: weight of 10 stalks with straw, weight of 10 stalks with no straw, tons of cane per hectare with straw, tons of cane per hectare with no straw, sucrose content, fiber percentage, and tons of fiber per hectare. The selection indices of Smith (1936) and Hazel (1943) and Mulamba and Mock (1978), the base index (Williams, 1962), and the index of Pesek and Baker (1969) were used. The selection index of Mulamba and Mock (1978) without economic weight estimates, the index of Mulamba and Mock with economic weights based on heritability, and the index of Pesek and Baker (1969) with the desired gains based on genetic standard deviations were efficient for the selection of energy cane clones with good fiber yield, sucrose content, and tons of cane per hectare.

Genetic gains from selection for fiber traits in Gossypium hirsutum L.

Brazil is among the five largest producers of cotton in the world, cultivating the species Gossypium hirsutum L. r. latifolium Hutch. The cultivars should have good fiber quality as well as yield. Genetic improvement of fiber traits requires the study of the genetic structure of the populations under improvement, leading to the identification of promising parent plants. To this end, it is important to acquire some information, such as estimates of genetic variance components and heritability coefficients, which will support the appropriate choice of the breeding strategy to be employed as well as enable the estimation of gains from selection. This study aimed to evaluate some agronomic characteristics, such as fiber quality and yield, estimating genetic parameters for the purpose of predicting earnings. Twelve cultivars of cotton, including four male progenitors (CNPA 01-42, BRS Verde, Glandless, and Okra leaf) and eight female progenitors (Delta opal, CNPA 7H, Aroeira, Antares, Sucupira, Facual, Precoce 3, and CNPA 8H), were used in performing crosses according to design I, proposed by Comstock and Robinson (1948). The experimental design was a randomized block with four replications. We observed genetic variability among all traits as well as higher efficiency of selection for the gains related to traits. Our results showed that the combined selection presented the highest genetic gains for all traits. For fiber length, the female/male selection and the combined selection resulted in the highest genetic gain.

Determination of the optimal number of markers and individuals in a training population necessary for maximum prediction accuracy in F2 populations by using genomic selection models.

Genomic selection is a useful technique to assist breeders in selecting the best genotypes accurately. Phenotypic selection in the F2 generation presents with low accuracy as each genotype is represented by one individual; thus, genomic selection can increase selection accuracy at this stage of the breeding program. This study aimed to establish the optimal number of individuals required to compose the training population and to establish the amount of markers necessary to obtain the maximum accuracy by genomic selection methods in F2 populations. F2 populations with 1000 individuals were simulated, and six traits were simulated with different heritability values (5, 20, 40, 60, 80 and 99%). Ridge regression best linear unbiased prediction was used in all analyses. Genomic selection models were set by varying the number of individuals in the training population (2 to 1000 individuals) and markers (2 to 3060 markers). Phenotypic accuracy, genotypic accuracy, genetic variance, residual variance, and heritability were evaluated. Greater the number of individuals in the training population, higher was the accuracy; the values of genotypic and residual variances and heritability were close to the optimum value. Higher the heritability of the trait, higher is the number of markers necessary to obtain maximum accuracy, ranging from 200 for the trait with 5% heritability to 900 for the trait with 99% heritability. Therefore, genomic selection models for prediction in F2 populations must consist of 200 to 900 markers of major effect on the trait and more than 600 individuals in the training population.

Diallel analysis to choose parents for black bean (Phaseolus vulgaris L.) breeding.

In this study, conducted in two different seasons, we aimed to choose parents to obtain promising segregating populations for the extraction of black bean (Phaseolus vulgaris L.) lines that are superior in terms of disease resistance, plant architecture, and grain yield. Twelve parents were arranged in two groups to compose a partial diallel in a 5 x 7 scheme. Group 1 was composed of parents with black grains and erect plant architecture, while group 2 was composed of parents that had carioca grains and were resistant to the main fungal diseases that occur in the common bean. The following traits were evaluated: severity of angular leaf spot (ALS), plant architecture (PAG), and grain yield (YIELD). The data were analyzed according to a partial diallel model using parents and F1 hybrids. In the genetic control of ALS and PAG, additive effects were predominant, while for YIELD, additive effects were predominant in one season and dominance effects were in another season, because it is a more complex trait than ALS and PAG. For YIELD, we observed an interaction between general combining ability and specific combining ability between seasons. The genes that control ALS, PAG, and YIELD were in eight of the 12 parents evaluated in the diallel. The cultivar 'BRS Estilo' is suitable to use as a parent in common bean breeding in terms of ALS, PAG and YIELD. Recurrent selection is the most recommended option for simultaneously breeding for PAG, YIELD, and resistance to angular leaf spot in bean culture.

Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs.

The aim of the present study was to propose and evaluate the use of factor analysis (FA) in obtaining latent variables (factors) that represent a set of pig traits simultaneously, for use in genome-wide selection (GWS) studies. We used crosses between outbred F2 populations of Brazilian Piau X commercial pigs. Data were obtained on 345 F2 pigs, genotyped for 237 SNPs, with 41 traits. FA allowed us to obtain four biologically interpretable factors: "weight", "fat", "loin", and "performance". These factors were used as dependent variables in multiple regression models of genomic selection (Bayes A, Bayes B, RR-BLUP, and Bayesian LASSO). The use of FA is presented as an interesting alternative to select individuals for multiple variables simultaneously in GWS studies; accuracy measurements of the factors were similar to those obtained when the original traits were considered individually. The similarities between the top 10% of individuals selected by the factor, and those selected by the individual traits, were also satisfactory. Moreover, the estimated markers effects for the traits were similar to those found for the relevant factor.

Selection in sugarcane based on inbreeding depression.

This study aimed to evaluate the gene action associated with yield-related traits, including mean stalk weight (MSW), tons of sugarcane per hectare (TCH), and fiber content (FIB) in sugarcane. Moreover, the viability of individual reciprocal recurrent selection (RRSI-S1) was verified, and the effect of inbreeding depression on progenies was checked. The results were also used to select promising genotypes in S1 progenies. Eight clones (RB925345, RB867515, RB739359, SP80-1816, RB928064, RB865230, RB855536, and RB943365) and their respective progenies, derived from selfing (S1), were evaluated. Several traits, including the number of stalks, MSW, soluble solids content determined in the field, stalk height, stalk diameter, TCH, soluble solids content determined in the laboratory, sucrose content, and FIB were evaluated in a randomized block design with hierarchical classification. The results showed that the traits with predominant gene action associated with the dominance variance of MSW and TCH were most affected by inbreeding depression. The FIB, with predominant additive control, was not affected by selfing of the clones, and the RB867515⊗, RB928064⊗, RB739359⊗ and RB925345⊗ progenies performed best. Therefore, the use of S1 progenies for RRSI-S1 in sugarcane breeding programs is promising, and it should be explored for the future breeding of clones with high FIB levels.

Evaluation of the efficiency of artificial neural networks for genetic value prediction.

Artificial neural networks have shown great potential when applied to breeding programs. In this study, we propose the use of artificial neural networks as a viable alternative to conventional prediction methods. We conduct a thorough evaluation of the efficiency of these networks with respect to the prediction of breeding values. Therefore, we considered eight simulated scenarios, and for the purpose of genetic value prediction, seven statistical parameters in addition to the phenotypic mean in a network designed as a multilayer perceptron. After an evaluation of different network configurations, the results demonstrated the superiority of neural networks compared to estimation procedures based on linear models, and indicated high predictive accuracy and network efficiency.

Potential of hypocotyl diameter in family selection aiming at plant architecture improvement of common bean.

Cultivars of common bean with more erect plant architecture and greater tolerance to degree of lodging are required by producers. Thus, to evaluate the potential of hypocotyl diameter (HD) in family selection for plant architecture improvement of common bean, the HDs of 32 F2 plants were measured in 3 distinct populations, and the characteristics related to plant architecture were analyzed in their progenies. Ninety-six F2:3 families and 4 controls were evaluated in a randomized block design, with 3 replications, analyzing plant architecture grade, HD, and grain yield during the winter 2010 and drought 2011 seasons. We found that the correlation between the HD of F2 plants and traits related to plant architecture of F2:3 progenies were of low magnitude compared to the estimates for correlations considering the parents, indicating a high environmental influence on HD in bean plants. There was a predominance of additive genetic effects on the determination of hypocotyl diameter, which showed higher precision and accuracy compared to plant architecture grade. Thus, this characteristic can be used to select progenies in plant architecture improvement of common beans; however, selection must be based on the means of at least 39 plants in the plot, according to the results of repeatability analysis.

Comparison of methods used to identify superior individuals in genomic selection in plant breeding.

The aim of this study was to evaluate different methods used in genomic selection, and to verify those that select a higher proportion of individuals with superior genotypes. Thus, F2 populations of different sizes were simulated (100, 200, 500, and 1000 individuals) with 10 replications each. These consisted of 10 linkage groups (LG) of 100 cM each, containing 100 equally spaced markers per linkage group, of which 200 controlled the characteristics, defined as the 20 initials of each LG. Genetic and phenotypic values were simulated assuming binomial distribution of effects for each LG, and the absence of dominance. For phenotypic values, heritabilities of 20, 50, and 80% were considered. To compare methodologies, the analysis processing time, coefficient of coincidence (selection of 5, 10, and 20% of superior individuals), and Spearman correlation between true genetic values, and the genomic values predicted by each methodology were determined. Considering the processing time, the three methodologies were statistically different, rrBLUP was the fastest, and Bayesian LASSO was the slowest. Spearman correlation revealed that the rrBLUP and GBLUP methodologies were equivalent, and Bayesian LASSO provided the lowest correlation values. Similar results were obtained in coincidence variables among the individuals selected, in which Bayesian LASSO differed statistically and presented a lower value than the other methodologies. Therefore, for the scenarios evaluated, rrBLUP is the best methodology for the selection of genetically superior individuals.

Superiority of artificial neural networks for a genetic classification procedure.

The correct classification of individuals is extremely important for the preservation of genetic variability and for maximization of yield in breeding programs using phenotypic traits and genetic markers. The Fisher and Anderson discriminant functions are commonly used multivariate statistical techniques for these situations, which allow for the allocation of an initially unknown individual to predefined groups. However, for higher levels of similarity, such as those found in backcrossed populations, these methods have proven to be inefficient. Recently, much research has been devoted to developing a new paradigm of computing known as artificial neural networks (ANNs), which can be used to solve many statistical problems, including classification problems. The aim of this study was to evaluate the feasibility of ANNs as an evaluation technique of genetic diversity by comparing their performance with that of traditional methods. The discriminant functions were equally ineffective in discriminating the populations, with error rates of 23-82%, thereby preventing the correct discrimination of individuals between populations. The ANN was effective in classifying populations with low and high differentiation, such as those derived from a genetic design established from backcrosses, even in cases of low differentiation of the data sets. The ANN appears to be a promising technique to solve classification problems, since the number of individuals classified incorrectly by the ANN was always lower than that of the discriminant functions. We envisage the potential relevant application of this improved procedure in the genomic classification of markers to distinguish between breeds and accessions.

Artificial neural networks reveal efficiency in genetic value prediction.

The objective of this study was to evaluate the efficiency of artificial neural networks (ANNs) for predicting genetic value in experiments carried out in randomized blocks. Sixteen scenarios were simulated with different values of heritability (10, 20, 30, and 40%), coefficient of variation (5 and 10%), and the number of genotypes per block (150 and 200 for validation, and 5000 for neural network training). One hundred validation populations were used in each scenario. Accuracy of ANNs was evaluated by comparing the correlation of network value with genetic value, and of phenotypic value with genetic value. Neural networks were efficient in predicting genetic value with a 0.64 to 10.3% gain compared to the phenotypic value, regardless the simulated population size, heritability, or coefficient of variation. Thus, the artificial neural network is a promising technique for predicting genetic value in balanced experiments.

Repeatability of agronomic traits in Panicum maximum (Jacq.) hybrids.

When evaluating plants, in particular perennial species, it is common to obtain repeated measures of a given trait from the same individual to evaluate the traits' repeatability in successive harvests. The degree of correlation among these measures defines the coefficient of repeatability, which has been widely utilized in the study of forage traits of interest for breeding. The objective of the present study was to evaluate the repeatability of agronomic traits in Panicum maximum hybrids. Hybrids from three progenies totaling 320 hybrids were evaluated in an incomplete-block design, with consideration of production and morpho-agronomic traits. Of the production traits, total dry matter and leaf dry matter showed the highest repeatability and varied from 0.540 to 0.769, whereas stem dry matter had lower coefficients (0.265-0.632). Among the morpho-agronomic traits, plant height and incidence of Bipolaris maydis had higher coefficients (0.118-0.460). The repeatability values of the agronomic traits were low-to-moderate, and six evaluations were sufficient to provide accuracy in the selection of hybrids regarding total dry matter, leaf dry matter, plant height, and incidence of B. maydis, whereas the other traits require more repeated measures to increase reliability in the prediction of their response.

Increasing genetic variability in black oats using gamma irradiation.

The black oat (Avena strigosa Schreb) is commonly used for forage, soil cover, and green manure. Despite its importance, little improvement has been made to this species, leading to high levels of genotypic disuniformity within commercial cultivars. The objective of this study was to evaluate the efficiency of different doses of gamma rays [(60)Co] applied to black oat seeds on the increase of genetic variability of agronomic traits. We applied doses of 0, 10, 50, 100, and 200 Gy to the genotype ALPHA 94087 through exposure to [(60)Co]. Two experiments were conducted in the winter of 2008. The first aimed to test forage trait measurements such as plant height, dry matter yield, number of surviving tillers, and seedling stand. The second test assessed seed traits, such as yield and dormancy levels. Gamma irradiation seems not to increase seed yield in black oats, but it was effective in generating variability for the other traits. Tiller number and plant height are important selection traits to increase dry matter yield. Selection in advanced generations of mutant populations can increase the probability of identifying superior genotypes.