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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.

Wheat crop is very sensitive to osmotic stress conditions. As an abiotic stress, drought may exert a considerable effect on the levels of specialized metabolites in plants. These metabolites may exert beneficial biological activities in the prevention or treatment of disorders linked to oxidative stress in plants and humans. Furthermore, osmoprotector accumulation helps wheat to increase the maintenance of osmotic balance. Therefore, identifying wheat genotypes with better drought tolerance is extremely important. In this sense, this research aimed to understand agronomic, physiological and biochemical responses of spring wheat strains and cultivars to drought stress, under field conditions, and jointly select strains via multi-trait index. We evaluated agronomic, physiological and biochemical variables in 18 genotypes under field condition. The results demonstrated that all variables were affected by the drought. Most genotypes were significantly reduced in grain yield, except VI_14774, VI_14668, VI_9007 and TBIO_ATON. The variables related to photosynthesis were also affected. An increase above 800% was observed in proline contents in genotypes under drought. Sodium and potassium also increased, mainly for VI_131313 (Na), while VI_130758 and VI_14774 presented increased K. We evaluated the antioxidant potential of the different strains and the total content of phenolic compounds. The most drought-responsive genotypes were BRS_264, VI_14050 and VI_14426. Reduced grain yield and photosynthetic variables, and increased specialized metabolism compounds are due to plant defense mechanisms against drought conditions. Furthermore, variation in genotypes can be explained by the fact that each plant presents a different defense and tolerance mechanism, which may also occur between genotypes of the same species. Four strains were selected by the multivariate index: VI_14055, VI_14001, VI_14426 and VI_1466. Such results allow us to predict which genotype(s) performed best in semi-arid environments and under climatic fluctuations.

Background The development of superior summer maize hybrids with high-yield potential and essential agronomic traits, such as resistance to lodging, is crucial for ensuring the sustainability of maize cultivation. However, the task of identifying and breeding genotypes that exhibit exceptional performance and stability across multiple environment conditions, while considering a wide range of traits, is challenging. Given the backdrop of global climate change, understanding which climate variables and soil properties most significantly impact environmental similarity is essential for selecting hybrids with improved adaptability to regions with diverse climatic and soil conditions. This study aimed to integrate envirotyping techniques (ETs) with a multi-trait selection approach to carry out a comprehensive evaluation of maize genotypes for performance and stability. Results The grain yields of 13 maize hybrids, along with their four critical agronomic parameters, were assessed in the Huang-Huai-Hai Plain of China across 40 locations in eight provinces. By considering 20 years of climatic factors and soil covariates, these 40 locations were divided into six mega-environments (MEs) based on similar long-term weather patterns and soil characteristics. Additive main effects and multiplicative interaction (AMMI) analyses revealed that genotype (G), environment (E), and the GxE interaction had significant effects on all agronomic parameters ( P  < 0.001). The mean performance and stability of the genotypes in each mega-environment were assessed, allowing for the identification of superior hybrids using the multi-trait stability index (MTSI). In two of the MEs (ME2 and ME3), only two hybrids, HY321 and HY9112, were selected concurrently. Conclusion Overall, this study provides valuable insights into the effects of ETs on maize hybrids and enhances our understanding of GxE interactions in multi-environment trials. This understanding is essential for improving maize cultivation practices and breeding program in diverse environments.

Identifying agronomic traits correlated to grain yield can be very useful for soybean [Glycine max (L.) Merr.] breeding, especially if these traits can be measured through unmanned aerial vehicle high‐throughput phenotyping rather than through manual measurements. The objective of the present study was to assess the association between canopy coverage and soybean grain yield through different statistical methodologies. A panel with 97 soybean genotypes was evaluated in two field experiments conducted in Paraná State, Brazil. Canopy coverage was determined by using an RGB camera coupled to a drone. Images taken during flights at phenological stages V3‐V4, V5‐V6, V7‐V8, and V9‐R1 were used to calculate canopy coverage based on the green pixel ratio in each experimental unit. There were significant genotype × environment interactions in all evaluated traits. Selective accuracy values (0.73–0.96) revealed indirect yield selection efficiency based on canopy coverage. High genetic correlation estimates (0.76) were observed between grain yield and canopy coverage at flowering in one of the assessed environments. These results were confirmed through genetic correlation coefficient decomposition in direct and indirect effects and of gain estimates presenting indirect selection. Thus, canopy coverage data remotely collected using drones to soybean indirect selection for grain yield can be a promising strategy to accelerate genetic gains in soybean breeding programs. Core Ideas Use of high‐throughput phenotyping in soybean breeding programs for yield prediction. High efficiency in the response correlated by indirect selection for grain yield using drones. High estimate of genotypic correlation is observed between canopy coverage and grain yield in soybean breeding.

With widespread cultivation, Cucurbita moschata stands out for the carotenoid content of its fruits such as β and α -carotene, components with pronounced provitamin A function and antioxidant activity. C. moschata seed oil has a high monounsaturated fatty acid content and vitamin E, constituting a lipid source of high chemical–nutritional quality. The present study evaluates the agronomic and chemical–nutritional aspects of 91 accessions of C. moschata kept at the BGH-UFV and propose the establishment of a core collection based on multivariate approaches and on the implementation of Artificial Neural Networks (ANNs). ANNs was more efficient in identifying similarity patterns and in organizing the distance between the genotypes in the groups. The averages and variances of traits in the CC formed using a 15% sampling of accessions, were closer to those of the complete collection, particularly for accumulated degree days for flowering, the mass of seeds per fruit, and seed and oil productivity. Establishing the 15% CC, based on the broad characterization of this germplasm, will be crucial to optimize the evaluation and use of promising accessions from this collection in C. moschata breeding programs, especially for traits of high chemical–nutritional importance such as the carotenoid content and the fatty acid profile.

The selection of combinations that include the best parents and the understanding of the genetic effects controlling the traits can be made using diallel analysis. This study selected parents with a high frequency of favorable alleles and segregating populations with the greatest potential to produce superior progenies, as well as to understand the genetic effects controlling the studied traits. For this end, 15 parents were divided into two groups and crossed in a 7×8 partial diallel scheme, resulting in a total of 56 hybrid combinations. Some of the F1 seeds were advanced to the F2 generation. The combinations in both generations (F1 and F2) were evaluated in an experimental field in a randomized complete block design, with two replicates, between June and October 2022, in Viçosa, Minas Gerais, Brazil. Days to heading, days to maturation, plant height, spike length, hundred-grain weight, and plot yield were assessed. A diallel analysis was performed using the Geraldi and Miranda-Filho model for partial diallel, adapted from Griffing’s method. Our results suggested a predominance of additive effects. The parents CD 1303, CD 151, BRS 254, ORSFEROZ, ORS Guardião and ORSSENNA exhibited favorable alleles for different traits. In total, 41 combinations were selected, 20 from F1 and 21 from F2 generations. Among these, seven populations were identified as having high genetic potential for producing superior progenies. RESUMO: A seleção de combinações que apresentem os melhores genitores e o conhecimento sobre os efeitos gênicos que controlam as características, são possibilitadas pelo uso da análise dialélica. O objetivo deste estudo, foi selecionar os genitores com alta frequência de alelos favoráveis e populações segregantes com maior potencial para produzir progênies superiores, e entender os efeitos genéticos no controle das características estudadas. Assim, 15 genitores foram divididos em dois grupos e cruzados em esquema de dialelo parcial 7×8, obtendo o total de 56 combinações híbridas. Parte das sementes F1 foram avançadas para geração F2. As combinações em duas gerações (F1 e F2) foram avaliadas em campo experimental, em delineamento em blocos ao acaso, com duas repetições, entre os meses de junho e outubro de 2022, em Viçosa, Minas Gerais, Brasil. As variáveis avaliadas foram espigamento, dias até a maturação (ciclo), altura da planta, comprimento da espiga, massa de cem grãos e produção de parcela. A análise dialélica foi realizada pelo modelo de Geraldi e Miranda-Filho, para dialelo parcial, adaptado do método de Griffing. O efeito aditivo predominou para o controle dos caracteres analisados. Os genitores CD 1303, CD 151, BRS 254, ORSFEROZ, ORS Guardião e ORSSENNA, apresentaram frequência de alelos favoráveis para diferentes características. No total, 41 combinações foram selecionadas, sendo, 20 em F1 e 21 em F2. Destas, foram identificadas sete populações de alto potencial genético para originar progênies.

The peach is a traditional crop in the south of Rio Grande do Sul State, Brazil, where 35 million to 50 million cans of peaches in syrup are produced annually. All the raw materials for the peach canneries come from cultivars originated from the peach breeding program of the Brazilian Agricultural Research Corporation (Embrapa Temperate Agriculture), which started before the establishment of Embrapa at the Experimental Station of Pelotas, then managed by the Ministry of Agriculture. The objective of this study was to estimate the genetic progress in phenological and production traits of canning peach resulting from the peach breeding program of Embrapa Temperate Agriculture over 53 years. We divided the data records considered in the estimation of genetic progress into two periods, 1964–1984 and 1985–2017. The following traits were evaluated: days to maturity, fruit development period, number of fruits, fruit weight, fruit yield, and soluble solids content. Genetic gain was observed for early maturation, shorter fruit development period, and increased fruit yield.

The development process of a new wheat cultivar requires time between obtaining the base population and selecting the most promising line. Estimating genetic parameters more accurately in early generations with a view to anticipating selection means important advances for wheat breeding programs. Thus, the present study estimated the genetic parameters of F2 populations of tropical wheat and the genetic gain from selection via the Bayesian approach. To this end, the authors assessed the grain yield per plot of 34 F2 populations of tropical wheat. The Bayesian approach provided an adequate fit to the model, estimating genetic parameters within the parametric space. Heritability (h2) was 0.51. Among those selected, 11 F2 populations performed better than the control cultivars, with genetic gain of 7.80%. The following populations were the most promising: TbioSossego/CD 1303, CD 1303/TbioPonteiro, BRS 254/CD 1303, Tbio Duque/Tbio Aton, and Tbio Aton/CD 1303. Bayesian inference can be used to significantly improve tropical wheat breeding programs. RESUMO: O processo de desenvolvimento de uma nova cultivar de trigo requer tempo entre a obtenção da população base e a seleção da linhagem mais promissora. Estimar parâmetros genéticos com mais precisão nas primeiras gerações com vistas a antecipar a seleção significa avanços importantes para os programas de melhoramento de trigo. Assim, o presente estudo estima os parâmetros genéticos de populações F2 de trigo tropical e o ganho genético da seleção via abordagem Bayesiana. Para tanto, os autores avaliaram a produtividade de grãos por parcela de 34 populações F2 de trigo tropical. A abordagem Bayesiana proporcionou um ajuste adequado ao modelo, estimando parâmetros genéticos dentro do espaço paramétrico. A herdabilidade (h2) foi de 0,51. Dentre as selecionadas, 11 populações F2 obtiveram desempenho superior às cultivares controle, com ganho genético de seleção de 7,80%. As seguintes populações foram as mais promissoras: Tbio Sossego/CD 1303, CD 1303/Tbio Ponteiro, BRS 254/CD 1303, Tbio Duque/Tbio Aton e Tbio Aton/CD 1303. A inferência Bayesiana pode ser usada para melhorar significativamente programas de melhoramento de trigo tropical.

The study of genotypic relationships between drought tolerance indices and agronomic traits of interest in wheat breeding is useful for designing selection strategies. The objective of this research was to investigate the cause-and-effect relationships between agronomic traits and drought tolerance indices through the analysis of canonical correlations. Two trials (control and stress) were conducted in winter 2020 in Viçosa, MG, Brazil. The traits evaluated were: (days for heading, plant height, mass and number of grains per spike, mass of one hundred grains, and grain yield). Grain yield data from the control and stress conditions were used to construct five drought tolerance indices. The data were subjected to mixed model analysis for estimation of genetic parameters and prediction of genotypic values (REML/BLUP), and then the genotypic values were used to calculate the correlation coefficients between the traits. Two groups of traits were established for the study of canonical correlations, the first group consisting of agronomic traits and the second by drought tolerance indices. There was a significant genotype effect for all evaluated traits. The canonical pairs were significant, which indicated the existence of dependence between the groups. Days to heading trait can be used in the indirect selection of wheat genotypes for drought tolerance. RESUMO: O estudo das relações genotípicas entre índices de tolerância à seca e caracteres agronômicos de interesse no melhoramento de trigo é útil para traçar estratégias de seleção. Objetivou-se com este trabalho investigar as relações de causa e efeito entre características agronômicas e índices de tolerância à seca via análise de correlações canônicas. Dois ensaios (controle e estresse) foram conduzidos no inverno de 2020 em Viçosa, MG, Brasil. Foram avaliados os caracteres (dias para o espigamento, altura da planta, massa e número de grãos por espiga, massa de cem grãos e rendimento de grãos). Os dados de produtividade dos ensaios de controle e estresse foram utilizados para construir cinco índices de tolerância à seca. Os dados foram submetidos à análise de modelos mistos para estimação dos parâmetros genéticos e predição dos valores genotípicos (REML/BLUP), em seguida, os valores genotípicos foram utilizados para calcular os coeficientes de correlação entre os caracteres. Dois grupos de caracteres foram estabelecidos para o estudo das correlações canônicas, sendo o primeiro grupo constituído pelas variáveis agronômicas e o segundo pelos índices de tolerância à seca. Houve efeito significativo de genótipo para todas as características avaliadas. Os pares canônicos foram significativos, o que indicou a existência de dependência entre os grupos. O caráter dias para o espigamento pode ser utilizado na seleção indireta de genótipos de trigo para tolerância à seca.