Tiago Olivoto

Silva, C. M., Mezzomo, H. C., Casagrande, C. R., Lima, G. W., Olivoto, T. & Nardino, M. (2021). Selection of tropical wheat lines based on classical and modern parameters of adaptability and stability. Bulg. J. Agric. Sci., 27 (5), 933–941 The differential response of lines to the variation of environments makes it necessary to use robust biometric tools for efficient evaluation and selection of wheat lines in tropical regions of Brazil. The objective of this work was to select tropical wheat lines with high grain yield, adaptability, and stability for the Central region of Brazil using classic and modern methodologies. For this purpose, three experiments were conducted in the agricultural years of 2018 and 2019 in the state of Minas Gerais, Brazil. The treatments were 26 tropical wheat lines developed by the UFV’s Wheat Breeding Program, in addition to two commercial cultivars (checks). Four methodologies were used of adaptability and stability: AMMI, GGE Biplot, Lin and Binns and ...

Image analysis based on color thresholding is the reference method for measuring severity as percent area affected. It is deemed to produce accurate results, usually considered the “true” severity value. More than a dozen applications have been used for the task in phytopathometry studies, but none was coded in R language. Here we introduced and evaluated pliman, a suite for the analysis of plant images. In particular, we show functions for computing percent severity based on RGB information contained in image palettes prepared by the user. Six image collections, totaling 249 images, from different diseases (wheat tan spot, soybean rust, olive leaf spot, rice brown spot, bean angular spot, and Xyllela fastidiosa on tobacco) exhibiting a range of symptomatic patterns and severity were used to evaluate the agreement of pliman predictions with measures by three other software: APS Assess, LeafDoctor, and ImageJ. Three users independently prepared three image palettes (each representing leaf background, symptomatic, or healthy leaf tissue) by manually inspecting and subsetting these target areas of the images. Pliman predictions by a joint palette (by joining images by the three users into one) were highly concordant ( ρ c  > 0.98) with measures by the other software for all but Xylella fastidiosa on tobacco ( ρ c  = 0.49). The error for the latter may be due to the low contrast between symptomatic and healthy tobacco tissues. Users showed to be a source of variation in the overall concordance depending on the disease. Reduction in the image resolution (< 1 megapixel) did not impact the results. Combined with parallel processing, the use of low image resolution (1078 × 680) decreased processing time, resulting in pliman being ~ 150 to ~ 700 times faster than existing tools for disease quantification. Pliman showed great potential to produce accurate measures and accelerate studies involving plant disease severity measurements, especially for the batch processing of large sets of image collections.

Maize ( coefficient estimates and path analysis. This critical review discusses some systematic errors that have been observed in impacts on accuracy of path analysis. In a first moment, an approach about the maize crop, origin, characteristics and biometric models commonly used in genetic breed crop is presented. Some obstacles found in estimates of path coefficients and the methods used to adjust them are discussed. We also present evidences and a theoretical explanation that some data arrangement methods currently used, may be overe coefficients in scientific studies. Data from a literature search revealing the accuracy of path analysis of some research are presented and discussed. In a last moment, we present a future perspective about how the correct estima improve the accuracy of path analysis, underscoring the need for research directed to this objective.

ABSTRACT. The objective of this study was to estimate the coefficient of repeatability and the number of measurements required for production and quality variables in a strawberry crop. An experiment was conducted with two strawberry cultivars from two origins grown in four substrate mixtures, totaling 16 treatments, evaluated in a randomized block design with four replications. Mass (MF) and number (NF) of fruits per plant were evaluated as measures of production, and total soluble solids (SST), titratable acidity (AT) and firmness (FIR) of fruits during the crop cycle were evaluated as measures of quality. Subsequently, the repeatability coefficient was estimated by the following methods: analysis of variance (ANOVA), principal component analysis using a correlation matrix (PCcor), principal component analysis using a variance-covariance matrix (PCcov) and structural analysis (SA). The number of measurements was adjusted for each studied variable based on determination coefficient...

Mixed models and multivariate analysis are powerful tools for selecting superior genotypes in plant breeding programs. The BLUP (best linear unbiased prediction) method has been used to predict genetic values without environmental effects. Furthermore, the FAI-BLUP (ideotype-design index) procedure is especially valuable for plant breeding because of multiple-trait selection. This study aimed to determine the genetic potential of advanced wheat generations using REML/BLUP in combination with multivariate techniques for the selection of superior genotypes. The experiment consisted of eleven wheat (Triticum aestivum L.) genotypes. The experimental design was randomized blocks, with three replications. Plant height, spike insertion height, number of tillers, number of spikelets, kernel width, hectoliter weight and kernel weight per plant were determined. The genetic parameters were estimated using the REML/BLUP methodology, and the FAI-BLUP index was calculated using predicted genetic ...

This is a simulated data set containing 1000 genotypes and 32 traits, to be used in the Monte Carlo simulation of the draft paper "MGIDI: a novel multi-trait index for genotype selection in breeding programs" by Tiago Olivoto and Maicon Nardino

Maize is one of the most important cereals in the world. The productive potential of this crop is closely associated with nitrogen (N) fertilization, thus, studies focused on this subject are important in the development of cropping strategies. The aim of this work was to evaluate the effects of N split and different type of urea on important agronomic traits of the maize crop. A randomized complete block design in a 2×2+1 factorial treatment design with four replications was used. The factorial levels were composed of two types of urea (common and coated) and two nitrogen splits (V3 and V3+V8), plus the control treatment (without urea application). Important agronomic traits such as grain yield, biological productivity, and yield components were assessed. It was verified that there is no difference between the common or coated urea on grain yield and its components. On the other hand, the split of nitrogen into V3 and V8 stages is an efficient strategy to improve grain yield as wel...

This study aimed to identify important traits for indirect selection and to evaluate the variability among black oat populations through cause and effect relationships and canonical variables. Fourteen (14) black oat populations were collected in the 2013 cropping season which were evaluated in the laboratory, and then in the field in the 2014 cropping season. The seed width has a high and positive association with physiological quality of black oat seeds. The number of grains and thousand-grain weight has greater direct effects on the grain yield of black oat; thus, these traits may be considered for indirect selection in earlier phases of future black oat breeding programs. Targeted crosses between black oat genotypes coming from Salvador das Missoes with genotypes coming from the other studied locations should be promising to obtain recombinant offspring in the future.

Soy is the main summer culture established in Brazil. Currently the soybean crop is increasing in the second harvest, in this sense it is necessary to know the contribution of the characteristics to grain yield in the different environments in condition of second harvest. The experimental design used was randomized blocks, in a 2 x 8 factorial design, being two cultivation environments (Salvador das Missoes and Tenente Portela) and eight soybean genotypes with indeterminate growth habit (BMX Elite, NS 5959, NS 5909, BMX Alvo, BMX Potencia, BMX Tornado, BMX Turbo, BMX Garra). With the exception of yield components that differed among genotypes, the number of legumes with three grains, thousand-kernel weight of legumes with three grains presented stronger and positive linear trends with the yield of grains. The genotypes NS5959, BMX Potencia and BMX Turbo obtained the largest total grain mass, with the greatest contribution of thousand-kernel weight of legumes with two and three grain...

EnglishKnowing the productive variability within protected environments is crucial for choosing the experimental design to be used in that conditions. Thus, the aim of the present study was to assess the variability of fruit production in protected environment cultivated with cherry tomatoes and to verify the border effect and plot size in reducing this variability. To this, data from an uniformity test carried out in a greenhouse with cherry tomato cv. ‘Lili’ were used. Total fresh mass of fruits per plant was considered being these plants arranged in cropping rows parallel to the lateral openings of the greenhouse and also the same plants arranged in columns perpendicular to these openings. To generate the borders, different scenarios were designed by excluding rows and columns and using different plot sizes. In each scenario, homogeneity of variances among the remaining rows and columns was tested. There is no variability of fruit production among rows or columns in trials with c...

GEN = GENOTYPE FACTOR BLOCK = BLOCK FACTOR FLO = FLOWERING DIS = DISEASE GY = GRAIN YIELD HW = HECTOLITER WEIGHT FLH = FLAG LEAF HEIGHT EH = EAR HEIGHT PH = PLANT HEIGHT NSE = NUMBER OF SPIKLETS PER EAR NGS = NUMBER OF GRAINS PER SPIKLETS EL = EAR LENGTH EW = EAR WEIGHT NGE = NUMBER OF GRAINS PER EAR GME = GRAIN MASS PER EAR HIE = HARVEST INDEX OF THE EAR

ABSTRACTThe multi-trait genotype-ideotype distance index (MGIDI) was used to select superior treatments in experiments with strawberries. Twenty-three productive, qualitative, physiological, and phenological traits with negative and positive desired gains were accessed in 16 treatments, a combination of two cultivars (Albion-neutral days, and Camarosa-short days), two transplants origins (National and Imported), and four organic substrates mixes (Crushed sugarcane bagasse, burnt rice husk, organic substrate, and Carolina commercial substrate). Our results suggest that most of the strawberry traits are influenced by the cultivar, transplant origin, cultivation substrates, as well as by the interaction between cultivar and transplant origin. The MGIDI index indicated that the Albion cultivar originated from imported transplants grown in substrates where the main component (70%) is burnt rice husk provides desired values for 20 of a total of 22 traits, which represents a success rate o...

Guar, the most popular vegetable, is tolerant of drought and is a valuable industrial crop enormously grown across India, Pakistan, USA, and South Africa for pharmaceutically and cosmetically usable galactomannan (gum) content present in seed endosperm. Guar genotypes with productive traits which could perform better in differential environmental conditions are of utmost priority for genotype selection. This could be achieved by employing multivariate trait analysis. In this context, Multi-Trait Stability Index (MTSI) and Multi-Trait Genotype-Ideotype Distance Index (MGIDI) were employed for identifying high-performing genotypes exhibiting multiple traits. In the current investigation, 85 guar accessions growing in different seasons were assessed for 15 morphological traits. The results obtained by MTSI and MGIDI indexes revealed that, out of 85, only 13 genotypes performed better across and within the seasons, and, based on the coincidence index, only three genotypes (IC-415106, IC...

Strawberry is an economically and socially important crop in several regions worldwide. Thus, studies that provide information on topics in strawberry growth are important and must be constantly updated. The aims of this study were to fit a logistic growth model to describe strawberry fruit production and to estimate the partial derivatives of the fitted model in order to estimate and interpret the critical points, in addition to using multivariate analyses. To do this, data on 16 treatments [combinations of two cultivars (Albion and Camarosa), two origins (national and imported), and four mixed organic substrates (70% crushed sugar cane residue + 30% organic compost, 70% crushed sugar cane residue + 30% commercial substrate, 70% burnt rice husk + 30% organic compost, and 70% burnt rice husk + 30% commercial substrate)] conducted in a randomized complete block design (RCBD) with four replicates were used. A logistic model was fitted to the accumulated fruit production stratified by ...

Motivation Multivariate data are common in biological experiments and using the information on multiple traits is crucial to make better decisions for treatment recommendations or genotype selection. However, identifying genotypes/treatments that combine high performance across many traits has been a challenger task. Classical linear multi-trait selection indexes are available, but the presence of multicollinearity and the arbitrary choosing of weighting coefficients may erode the genetic gains. Results We propose a novel approach for genotype selection and treatment recommendation based on multiple traits that overcome the fragility of classical linear indexes. Here, we use the distance between the genotypes/treatment with an ideotype defined a priori as a multi-trait genotype–ideotype distance index (MGIDI) to provide a selection process that is unique, easy-to-interpret, free from weighting coefficients and multicollinearity issues. The performance of the MGIDI index is assessed ...

The objective of this study was to estimate the coefficient of repeatability and the number of measurements required for production and quality variables in a strawberry crop. An experiment was conducted with two strawberry cultivars from two origins grown in four substrate mixtures, totaling 16 treatments, evaluated in a randomized block design with four replications. Mass (MF) and number (NF) of fruits per plant were evaluated as measures of production, and total soluble solids (SST), titratable acidity (AT) and firmness (FIR) of fruits during the crop cycle were evaluated as measures of quality. Subsequently, the repeatability coefficient was estimated by the following methods: analysis of variance (ANOVA), principal component analysis using a correlation matrix (PCcor), principal component analysis using a variance-covariance matrix (PCcov) and structural analysis (SA). The number of measurements was adjusted for each studied variable based on determination coefficients of 0.80,...

ABSTRACT: The objective of this study was to characterize the production of biquinho pepper through the interpretation of parameter estimates from the logistic model and its critical points obtained by the partial derivatives of the function, and to indicate the best cultivar and growing season for subtropical climate sites. For this, a 2x3 factorial experiment was conducted with two cultivars of biquinho pepper (BRS Moema and Airetama biquinho) in three growing seasons (E1: October 2015, E2: November 2015, E3: January 2016). The logistic non-linear model for fruit mass was specified as a function of the accumulated thermal sum, and the critical points were calculated through the partial derivatives of the model, in order to characterize the productive performance of the crop by the biological interpretation of the estimates of the three set parameters. In E3, temperatures close to 0 ºC during the experiment were lethal to the plants, and a linear regression model was used in this c...

Canonical correlations analyzes are being used in the agrarian sciences and constitute an important tool in the interpretation of results. This analysis is performed by complicated mathematical equations and it is only possible to use it thanks to the development of computational software, which allow different interpretations of results, and it is up to the researcher to choose according to his knowledge. Canonical correlations can be interpreted using canonical weights, canonical loadings, or canonical cross-loadings. In Brazil, most of the works that use these analyzes interpret the canonical weights. Therefore, this study aims to show, through an analysis of canonical correlations, the best way to interpret the results, so that they are presented in the most reliable way possible. Data from an experiment with two cultivars of biquinho pepper seeded in 5 light spectrums were performed. The variables were root length and volume, plant height, number of leaves, fresh shoot and root...

The implementation of a network of maize trials is an onerous task, so breeding programs seek to eliminate redundant environments, remaining only contrasting ones. The objective was to perform the homogeneous environment grouping by studying the G×E interaction through different environmental stratification methods, as well as to compare the efficiency of these methods. Four methods were used: environmental dissimilarity (Djj); Decomposition of the genotype × environment interaction (G×E) into simple and complex parts by Cruz and Castoldi; Pearson correlation coefficient; and factor analysis. Twenty-five single-cross corn hybrids and three commercial cultivars were tested in eight cultivation environments in a randomized complete block design with three replicates in the evaluation of grain yield. Quedas do Iguaçu and Cascavel can be reduced into only one test environment by methods of factor analysis and Pearson correlation. The environments of Pato Branco and Ampére are grouped by...

Multi-environment trials (MET) are crucial steps in plant breeding programs that aim increasing crop productivity to ensure global food security. The analysis of MET data requires the combination of several approaches including data manipulation, visualization, and modeling. As new methods are proposed, analyzing MET data correctly and completely remains a challenge, often intractable with existing tools.Here we describe the metan R package, a collection of functions that implement a workflow-based approach to (a) check, manipulate and summarise typical MET data; (b) analyze individual environments using both fixed and mixed-effect models; (c) compute parametric and non-parametric stability statistics; (c) implement biometrical models widely used in MET analysis; and (d) plot typical MET data quickly.In this paper, we present a summary of the functions implemented in metan and how they integrate into a workflow to explore and analyze MET data. We guide the user along a gentle learni...

3 Supplemental R codes 14 3.1 Predictive accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.1.1 Printing the means of RMSPD estimates . . . . . . . . . . . . . . . . 15 3.1.2 Plotting the RMSPD estimates . . . . . . . . . . . . . . . . . . . . . 16 3.2 Estimating the response variable using the AMMI model . . . . . . . . . . . 17 3.3 Estimating the WAAS index . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.3.1 Number of axes based on F-test . . . . . . . . . . . . . . . . . . . . . 19 3.3.2 Number of axes declared manually . . . . . . . . . . . . . . . . . . . 22 3.4 Other AMMI-based stability indexes . . . . . . . . . . . . . . . . . . . . . . 23 3.5 Estimating the WAASB index . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.5.1 Diagnostic plot for residuals . . . . . . . . . . . . . . . . . . . . . . . 24 3.5.2 Printing the model outputs . . . . . . . . . . . . . . . . . . . . . . . 25 3.5.2.1 Likelihood Ratio Tests . . . . . . . . . . . . . . . . . . . . . 25 3.5.2.2 Variance components and genetic parameters . . . . . . . . 25 3.5.2.3 Some useful information . . . . . . . . . . . . . . . . . . . . 26 3.5.2.4 The WAASB object . . . . . . . . . . . . . . . . . . . . . . 27 3.5.2.5 BLUP for genotypes . . . . . . . . . . . . . . . . . . . . . . 28 3.5.2.6 Plotting the BLUP for genotypes . . . . . . . . . . . . . . . 29 3.5.2.7 BLUP for genotype-environment interaction . . . . . . . . . 29 3.5.3 Eigenvalues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.5.4 Phenotypic means . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.5.5 Biplots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.5.5.1 biplot type 1: PC1 x PC2 . . . . . . . . . . . . . . . . . . . 32 3.5.5.2 biplot type 2: GY x PC1 . . . . . . . . . . . . . . . . . . . 33 3.5.5.3 biplot type 3: GY x WAASB . . . . . . . . . . . . . . . . . 33 3.5.5.4 biplot type 4 : nominal yield and environment PCA1 . . . . 34 3.6 The WAASBY index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.6.1 Different scenarios of WAASBY estimation . . . . . . . . . . . . . . . 36 3.6.2 Ranks of genotypes depending on the number of IPCA . . . . . . . . 37 3.6.2.1 Ranks of genotypes depending on the WAASB/GY ratio . . 37