Multi-trait selection in multi-environments for performance and stability in cassava genotypes.

Genotype-environment interaction (GEI) presents challenges when aiming to select optimal cassava genotypes, often due to biased genetic estimates. Various strategies have been proposed to address the need for simultaneous improvements in multiple traits, while accounting for performance and yield stability. Among these methods are mean performance and stability (MPS) and the multi-trait mean performance and stability index (MTMPS), both utilizing linear mixed models. This study's objective was to assess genetic variation and GEI effects on fresh root yield (FRY), along with three primary and three secondary traits. A comprehensive evaluation of 22 genotypes was conducted using a randomized complete block design with three replicates across 47 distinct environments (year x location) in Brazil. The broad-sense heritability (H2) averaged 0.37 for primary traits and 0.44 for secondary traits, with plot-based heritability (hmÉ¡2) consistently exceeding 0.90 for all traits. The high extent of GEI variance (σÉ¡xe2) demonstrates the GEI effect on the expression of these traits. The dominant analytic factor (FA3) accounted for over 85% of the total variance, and the communality (ɧ) surpassed 87% for all traits. These values collectively suggest a substantial capacity for genetic variance explanation. In Cluster 1, composed of remarkably productive and stable genotypes for primary traits, genotypes BRS Novo Horizonte and BR11-34-69 emerged as prime candidates for FRY enhancement, while BRS Novo Horizonte and BR12-107-002 were indicated for optimizing dry matter content. Moreover, MTMPS, employing a selection intensity of 30%, identified seven genotypes distinguished by heightened stability. This selection encompassed innovative genotypes chosen based on regression variance index (Sdi2, R2, and RMSE) considerations for multiple traits. In essence, incorporating methodologies that account for stability and productive performance can significantly bolster the credibility of recommendations for novel cassava cultivars.

RGB image-based method for phenotyping rust disease progress in pea leaves using R.

BACKGROUND: Rust is a damaging disease affecting vital crops, including pea, and identifying highly resistant genotypes remains a challenge. Accurate measurement of infection levels in large germplasm collections is crucial for finding new resistance sources. Current evaluation methods rely on visual estimation of disease severity and infection type under field or controlled conditions. While they identify some resistance sources, they are error-prone and time-consuming. An image analysis system proves useful, providing an easy-to-use and affordable way to quickly count and measure rust-induced pustules on pea samples. This study aimed to develop an automated image analysis pipeline for accurately calculating rust disease progression parameters under controlled conditions, ensuring reliable data collection. RESULTS: A highly efficient and automatic image-based method for assessing rust disease in pea leaves was developed using R. The method's optimization and validation involved testing different segmentation indices and image resolutions on 600 pea leaflets with rust symptoms. The approach allows automatic estimation of parameters like pustule number, pustule size, leaf area, and percentage of pustule coverage. It reconstructs time series data for each leaf and integrates daily estimates into disease progression parameters, including latency period and area under the disease progression curve. Significant variation in disease responses was observed between genotypes using both visual ratings and image-based analysis. Among assessed segmentation indices, the Normalized Green Red Difference Index (NGRDI) proved fastest, analysing 600 leaflets at 60% resolution in 62 s with parallel processing. Lin's concordance correlation coefficient between image-based and visual pustule counting showed over 0.98 accuracy at full resolution. While lower resolution slightly reduced accuracy, differences were statistically insignificant for most disease progression parameters, significantly reducing processing time and storage space. NGRDI was optimal at all time points, providing highly accurate estimations with minimal accumulated error. CONCLUSIONS: A new image-based method for monitoring pea rust disease in detached leaves, using RGB spectral indices segmentation and pixel value thresholding, improves resolution and precision. It rapidly analyses hundreds of images with accuracy comparable to visual methods and higher than other image-based approaches. This method evaluates rust progression in pea, eliminating rater-induced errors from traditional methods. Implementing this approach to evaluate large germplasm collections will improve our understanding of plant-pathogen interactions and aid future breeding for novel pea cultivars with increased rust resistance.

Genetic gains in tropical maize hybrids across moisture regimes with multi-trait-based index selection.

Unpredictable weather vagaries in the Asian tropics often increase the risk of a series of abiotic stresses in maize-growing areas, hindering the efforts to reach the projected demands. Breeding climate-resilient maize hybrids with a cross-tolerance to drought and waterlogging is necessary yet challenging because of the presence of genotype-by-environment interaction (GEI) and the lack of an efficient multi-trait-based selection technique. The present study aimed at estimating the variance components, genetic parameters, inter-trait relations, and expected selection gains (SGs) across the soil moisture regimes through genotype selection obtained based on the novel multi-trait genotype-ideotype distance index (MGIDI) for a set of 75 tropical pre-released maize hybrids. Twelve traits including grain yield and other secondary characteristics for experimental maize hybrids were studied at two locations. Positive and negative SGs were estimated across moisture regimes, including drought, waterlogging, and optimal moisture conditions. Hybrid, moisture condition, and hybrid-by-moisture condition interaction effects were significant (p ≤ 0.001) for most of the traits studied. Eleven genotypes were selected in each moisture condition through MGIDI by assuming 15% selection intensity where two hybrids, viz., ZH161289 and ZH161303, were found to be common across all the moisture regimes, indicating their moisture stress resilience, a unique potential for broader adaptation in rainfed stress-vulnerable ecologies. The selected hybrids showed desired genetic gains such as positive gains for grain yield (almost 11% in optimal and drought; 22% in waterlogging) and negative gains in flowering traits. The view on strengths and weaknesses as depicted by the MGIDI assists the breeders to develop maize hybrids with desired traits, such as grain yield and other yield contributors under specific stress conditions. The MGIDI would be a robust and easy-to-handle multi-trait selection process under various test environments with minimal multicollinearity issues. It was found to be a powerful tool in developing better selection strategies and optimizing the breeding scheme, thus contributing to the development of climate-resilient maize hybrids.

Multi-trait selection for mean performance and stability of maize hybrids in mega-environments delineated using envirotyping techniques.

Under global climate changes, understanding climate variables that are most associated with environmental kinships can contribute to improving the success of hybrid selection, mainly in environments with high climate variations. The main goal of this study is to integrate envirotyping techniques and multi-trait selection for mean performance and the stability of maize genotypes growing in the Huanghuaihai plain in China. A panel of 26 maize hybrids growing in 10 locations in two crop seasons was evaluated for 9 traits. Considering 20 years of climate information and 19 environmental covariables, we identified four mega-environments (ME) in the Huanghuaihai plain which grouped locations that share similar long-term weather patterns. All the studied traits were significantly affected by the genotype × mega-environment × year interaction, suggesting that evaluating maize stability using single-year, multi-environment trials may provide misleading recommendations. Counterintuitively, the highest yields were not observed in the locations with higher accumulated rainfall, leading to the hypothesis that lower vapor pressure deficit, minimum temperatures, and high relative humidity are climate variables that -under no water restriction- reduce plant transpiration and consequently the yield. Utilizing the multi-trait mean performance and stability index (MTMPS) prominent hybrids with satisfactory mean performance and stability across cultivation years were identified. G23 and G25 were selected within three out of the four mega-environments, being considered the most stable and widely adapted hybrids from the panel. The G5 showed satisfactory yield and stability across contrasting years in the drier, warmer, and with higher vapor pressure deficit mega-environment, which included locations in the Hubei province. Overall, this study opens the door to a more systematic and dynamic characterization of the environment to better understand the genotype-by-environment interaction in multi-environment trials.

MGIDI: a powerful tool to analyze plant multivariate data.

BACKGROUND: Commonly, several traits are assessed in agronomic experiments to better understand the factors under study. However, it is also common to see that even when several traits are available, researchers opt to follow the easiest way by applying univariate analyses and post-hoc tests for mean comparison for each trait, which arouses the hypothesis that the benefits of a multi-trait framework analysis may have not been fully exploited in this area. RESULTS: In this paper, we extended the theoretical foundations of the multi-trait genotype-ideotype distance index (MGIDI) to analyze multivariate data either in simple experiments (e.g., one-way layout with few treatments and traits) or complex experiments (e.g., with a factorial treatment structure). We proposed an optional weighting process that makes the ranking of treatments that stands out in traits with higher weights more likely. Its application is illustrated using (1) simulated data and (2) real data from a strawberry experiment that aims to select better factor combinations (namely, cultivar, transplant origin, and substrate mixture) based on the desired performance of 22 phenological, productive, physiological, and qualitative traits. Our results show 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 ranked the Albion cultivar originated from Imported transplants and the Camarosa cultivar originated from National transplants as the better factor combinations. The substrates with burned rice husk as the main component (70%) showed satisfactory physical proprieties, providing higher water use efficiency. The strengths and weakness view provided by the MGIDI revealed that looking for an ideal treatment should direct the efforts on increasing fruit production of Albion transplants from Imported origin. On the other hand, this treatment has strengths related to productive precocity, total soluble solids, and flesh firmness. CONCLUSIONS: Overall, this study opens the door to the use of MGIDI beyond the plant breeding context, providing a unique, practical, robust, and easy-to-handle multi-trait-based framework to analyze multivariate data. There is an exciting possibility for this to open up new avenues of research, mainly because using the MGIDI in future studies will dramatically reduce the number of tables/figures needed, serving as a powerful tool to guide researchers toward better treatment recommendations.

Stability Indices to Deciphering the Genotype-by-Environment Interaction (GEI) Effect: An Applicable Review for Use in Plant Breeding Programs.

Experiments measuring the interaction between genotypes and environments measure the spatial (e.g., locations) and temporal (e.g., years) separation and/or combination of these factors. The genotype-by-environment interaction (GEI) is very important in plant breeding programs. Over the past six decades, the propensity to model the GEI led to the development of several models and mathematical methods for deciphering GEI in multi-environmental trials (METs) called "stability analyses". However, its size is hidden by the contribution of improved management in the yield increase, and for this reason comparisons of new with old varieties in a single experiment could reveal its real size. Due to the existence of inherent differences among proposed methods and analytical models, it is necessary for researchers that calculate stability indices, and ultimately select the superior genotypes, to dissect their usefulness. Thus, we have collected statistics, as well as models and their equations, to explore these methods further. This review introduces a complete set of parametric and non-parametric methods and models with a selection pattern based on each of them. Furthermore, we have aligned each method or statistic with a matched software, macro codes, and/or scripts.

MGIDI: toward an effective multivariate selection in biological experiments.

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 through a Monte Carlo simulation study where the percentage of success in selecting traits with desired gains is compared with classical and modern indexes under different scenarios. Two real plant datasets are used to illustrate the application of the index from breeders and agronomists' points of view. Our experimental results indicate that MGIDI can effectively select superior treatments/genotypes based on multi-trait data, outperforming state-of-the-art methods, and helping practitioners to make better strategic decisions toward an effective multivariate selection in biological experiments. AVAILABILITY AND IMPLEMENTATION: The source code is available in the R package metan (https://github.com/TiagoOlivoto/metan) under the function mgidi(). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multivariate diallel analysis by factor analysis for establish mega-traits.

In plant breeding, the dialelic models univariate have aided the selection of parents for hybridization. Multivariate analyses allow combining and associating the multiple pieces of information of the genetic relationships between traits. Therefore, multivariate analyses might refine the discrimination and selection of the parents with greater potential to meet the goals of a plant breeding program. Here, we propose a method of multivariate analysis used for stablishing mega-traits (MTs) in diallel trials. The proposed model is applied in the evaluation of a multi-environment complete diallel trial with 90 F1's of simple maize hybrids. From a set of 14 traits, we demonstrated how establishing and interpreting MTs with agronomic implication. The diallel analyzes based on mega-traits present an important evolution in statistical procedures since the selection is based on several traits. We believe that the proposed method fills an important gap of plant breeding. In our example, three MTs were established. The first, formed by plant stature-related traits, the second by tassel size-related traits, and the third by grain yield-related traits. Individual and joint diallel analysis using the established MTs allowed identifying the best hybrid combinations for achieving F1's with lower plant stature, tassel size, and higher grain yield.

Production of biquinho pepper in different growing seasons characterized by the logistic model and its critical points / Produção de pimenta biquinho em diferentes épocas de cultivo caracterizadas pelo modelo logístico e seus pontos críticos

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 case. The production of the cultivars in E1 and E2 were well characterized by the estimated logistic models, and the most productive cultivar was Airetama biquinho in all evaluated seasons. This cultivar also presented higher concentration of production. The two cultivars did not differ significantly with regards to productive precocity. For E3, it was not possible to interpret the parameters in the same way as for E1 and E2, since the use of the linear model did not allow the same interpretations performed for the nonlinear model, reaffirming its applicability horticultural crops of multiple harvests.

RESUMO: O objetivo deste estudo foi caracterizar a produção de pimenta biquinho através da interpretação dos parâmetros do modelo Logístico e seus pontos críticos obtidos pelas derivadas parciais da função, bem como indicar qual a melhor cultivar e a melhor época de cultivo para locais de clima subtropical. Conduziu-se um experimento em esquema fatorial 2x3 sendo duas cultivares de pimenta biquinho (BRS Moema e Airetama biquinho), em três épocas de cultivo (E1: outubro de 2015, E2: 01 de novembro 2015 e E3: janeiro de 2016). Ajustou-se o modelo logístico para massa de frutos em função da soma térmica acumulada, e calculou-se os pontos críticos através das derivadas parciais do modelo com a finalidade de caracterizar o desempenho produtivo da cultura através da interpretação biológica destes parâmetros. Temperaturas próximas a 0 ºC durante o experimento foram letais às plantas, e por isso, para a época 3, ajustou-se um modelo de regressão linear. A interpretação dos parâmetros do modelo Logístico e seus pontos críticos permitiram que a produção das cultivares nas épocas 1 e 2 fossem caracterizadas, sendo que a cultivar mais produtiva é Airetama biquinho em todas as épocas de transplante. Essa cultivar também apresenta maior concentração de produção no período. Quanto a precocidade produtiva as duas cultivares não diferiram significativamente. Sobre a época 3, não foi possível interpretar da mesma forma, pois o ajuste do modelo linear não permite as mesmas interpretações realizadas para o modelo não linear, reafirmando a sua aplicabilidade em cultura olerícolas de múltiplas colheitas.

Phenotypic variance of black oat growing in crop seasons reveals genetic effects predominance.

This work aimed to determine variance components and genetic parameters, as well as phenotypic, genetic and environmental correlations among black oat (Avena strigosa) families grown in different crop season. Seventy-six black oat families and three controls (BRS Madrugada, BRS Centauro, BRS 139 Neblina) were evaluated in two crop seasons (2016 and 2017), using families with intercalary controls experimental design. The results reveled high potential of black oat families to compose a breeding program, due to families and controls variance were similar, variance components expressed greater genetic variance origin for crop season. Panicle weight and panicle grain weight presented high heritability and, these are correlated with panicle length. Thus, these traits can be used to select superior genotypes. Divergent meteorological conditions between crop seasons expressed few variations among phenotypic, genetic and environmental correlations, and it did not alter magnitude and sense of phenotypic and genetic correlations.

Productive variability, border use and plot size in trials with cherry tomato / Variabilidade produtiva, uso de bordadura e tamanho de parcela em ensaios com tomate cereja

ABSTRACT: Knowing 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 cherry tomatoes carried out in greenhouses and the use of border does not bring benefits in terms of reduction of coefficient of variation or reduction of cases of variance heterogeneity among rows or columns. Plots with a size equal to or greater than two plants make possible to use the completely randomized design in the cherry tomato trials in greenhouses.

RESUMO: Conhecer a variabilidade dentro de ambientes protegidos é crucial para a escolha do delineamento experimental a ser utilizado nestas condições. O objetivo do estudo foi avaliar a variabilidade de produção de frutos em ambiente protegido cultivado com tomate cereja e, verificar o efeito do uso de bordaduras e tamanho de parcela na redução dessa variabilidade. Para isso, dados de um teste de uniformidade realizado em estufa com tomate cereja cv. 'Lili' foram utilizados. A massa fresca total de frutos por planta foi considerada, sendo estas plantas dispostas em fileiras de cultivo paralelas às aberturas laterais da estufa e, também, foram dispostas em colunas perpendiculares a estas aberturas. Para gerar as bordaduras, diferentes cenários foram projetados excluindo linhas e colunas, usando diferentes tamanhos de parcela. Em cada cenário, a homogeneidade das variâncias entre as fileiras e colunas restantes foi testada. Não há variabilidade na produção de frutos entre fileiras ou colunas em ensaios com tomate cereja, realizado em estufas, sendo que o uso de bordaduras não traz benefícios em termos de redução do coeficiente de variação ou dos casos de heterogeneidade de variância entre fileiras ou colunas. As parcelas com um tamanho igual ou superior a duas plantas tornam possível utilizar o delineamento inteiramente casualizado nos ensaios com tomate cereja em estufas.