ABSTRACT High-throughput phenotyping (HTP) using vegetation indices (VIs) is an important data source for managing plant breeding programs and can be a promising tool in indirect selection. This study hypothesized that VIs are correlated with agronomic traits in corn, and therefore, HTP can be an auxiliary tool for selecting superior genotypes. The objectives were: i) to analyze the association between agronomic traits and VIs, and ii) to identify superior corn hybrids for the evaluated traits using multivariate techniques. Ten corn hybrids (AGRI 330, AGRI 340, FS575PWU, KTZ006VP3, MG545AW, MG580PW, MG711PW, MZ1780, MZ1952, and TROPI 102) were evaluated for plant height (PH), stem diameter (SD), and grain yield (GY). The VIs studied were NDVI, NDRE, EVI, GNDVI, SAVI, and MSAVI. Pearson’s correlation network was constructed to analyze the relationship between the variables, and a canonical analysis was performed to verify the inter-relationship between the variables and hybrids. The VIs evaluated are strongly positively correlated with each other and with PH. The most productive hybrids are MG545AW, FS575PWU and KTZ006VP3. Hybrid MZ1952 has higher correlations with VIs and PH. The findings reveal that VIs can be excellent auxiliary variables for selecting agronomically superior genotypes, being a promising alternative to increase corn breeding efficiency.

ABSTRACT Peppers need to be characterized for the in situ conservation of species that might be important for breeding purposes. In this study, we characterized the diversity of 23 pepper genotypes through eight qualitative and 12 quantitative descriptors. The pepper samples were collected from the city of Manaus and in the municipalities of Rio Preto da Eva and Iranduba in the state of Amazonas. We performed descriptive analysis, analysis of variance, Pearson’s correlation analysis, and principal component analysis (PCA) and used the unweighted pair group method with arithmetic mean (UPGMA) to analyze the data. The differences between the genotypes for all descriptors evaluated were significant. Among the genotypes collected, four pepper species were identified, including Capsicum chinense (14 genotypes), Capsicum frutescens (two genotypes), Capsicum baccatum (one genotype), and Capsicum annuum (four genotypes), and two other genotypes were obtained with unidentified species. The PCA and UPGMA clustering methods could be used to efficiently differentiate between the genotypes by applying quantitative and qualitative descriptors of fruits and seeds. These methods helped to identify different genotypes (G1 and G15) and separate them into distinct groups associated with the distribution of species based on quantitative data only. Overall, our findings suggested that the genetic variability in Capsicum available in the State of Amazonas might be useful in breeding programs.

RESUMO As pimentas precisam ser caracterizadas para a conservação in situ da espécie que possam ser importantes para fins de reprodução. Neste estudo, caracterizamos a diversidade de 23 genótipos de pimenta por meio de oito descritores qualitativos e 12 quantitativos. As amostras de pimenta foram coletadas na cidade de Manaus e nos municípios de Rio Preto da Eva e Iranduba no estado do Amazonas. Realizamos análise descritiva, análise de variância, análise de correlação de Pearson e análise de componentes principais (ACP) e usamos o método de grupo de pares não ponderados com média aritmética (UPGMA) para analisar os dados. As diferenças entre os genótipos para todos os descritores avaliados foram significativas. Dentre os genótipos coletados, foram identificadas quatro espécies de pimenta, sendo elas Capsicum chinense (14 genótipos), Capsicum frutescens (dois genótipos), Capsicum baccatum (um genótipo) e Capsicum annuum (quatro genótipos), e outros dois genótipos foram obtidos com espécies não identificadas. Os métodos de agrupamento ACP e UPGMA podem ser usados para diferenciar eficientemente os genótipos por meio da aplicação de descritores quantitativos e qualitativos de frutos e sementes. Esses métodos ajudaram a identificar diferentes genótipos (G1 e G15) e separá-los em grupos distintos associados à distribuição das espécies com base apenas em dados quantitativos. No geral, nossos achados sugerem que a variabilidade genética em Capsicum disponível no Estado do Amazonas pode ser útil em programas de melhoramento.

RESUMO: Compreender a diversidade genética e contornar os problemas causados pela interação genótipos por ambientes é uma etapa importante em programas de melhoramento. Este estudo teve como objetivo estimar a diversidade genética e aplicar a metodologia de biplot genótipo + genótipo por ambiente (GGE biplot) em doze genótipos de algodão avaliados quanto ao rendimento da fibra, comprimento da fibra, resistência da fibra e micronaire. A estimativa dos componentes de variância e dos parâmetros genéticos foi feita através do método da máxima verossimilhança restrita e a predição dos valores genotípicos por meio da melhor predição linear não enviesada. Os métodos de Tocher modificado e análise de componentes principais (PCA) foram utilizados para quantificar a diversidade genética entre os genótipos. O método GGE biplot foi conduzido para encontrar os melhores genótipos em relação à adaptabilidade e estabilidade. As técnicas de Tocher e PCA permitiram a formação de clusters de genótipos semelhantes com base em uma estrutura multivariada. O GGE biplot indicou que os genótipos IMACV 690 e IMA08 WS foram altamente adaptáveis e estáveis para as principais características do algodão. O cruzamento dentre os genótipos IMACV 690 e IMA08 WS é o mais recomendado para aumentar o desempenho das principais características na cultura do algodão.

ABSTRACT: Understanding the genetic diversity and overcoming genotype-by-environment interaction issues is an essential step in breeding programs that aims to improve the performance of desirable traits. This study estimated genetic diversity and applied genotype + genotype-by-environment (GGE) biplot analyses in cotton genotypes. Twelve genotypes were evaluated for fiber yield, fiber length, fiber strength, and micronaire. Estimation of variance components and genetic parameters was made through restricted maximum likelihood and the prediction of genotypic values was made through best linear unbiased prediction. The modified Tocher and principal component analysis (PCA) methods, were used to quantify genetic diversity among genotypes. GGE biplot was performed to find the best genotypes regarding adaptability and stability. The Tocher technique and PCA allowed for the formation of clusters of similar genotypes based on a multivariate framework. The GGE biplot indicated that the genotypes IMACV 690 and IMA08 WS were highly adaptable and stable for the main traits in cotton. The cross between the genotype IMACV 690 and IMA08 WS is the most recommended to increase the performance of the main traits in cotton crops.

ABSTRACT. In cotton crops, the cotton seed yield significantly contributes with the success of any cultivar. However, other traits are considered when an ideotype is pointed out in the selection, such as the fiber quality traits. The aim of this study was to applied genotype by yield*trait (GYT) biplot to a multi-environment trial data of cotton genotypes and selected the best genotypes. For this end, thirteen genotypes from nineteen trials were assessed. Seven traits were evaluated [cotton seed yield (SY), fiber percentage (FP), fiber length (FL), fiber uniformity (FU), short fiber index (SFI), fiber strength (FS), and elongation (EL)] and residual error variances structures [identity variance (IDV) and diagonal (Diag)] were tested by bayesian information criterion. After, the REML/BLUP approach was applied to predict the genetic values of each trait and the selective accuracy were measured from the prediction. Then, the GYT-biplot were applied to the data. For SP and SFI traits, the model with Diag residual variance was indicated, whereas for SY FL, FU, FS, and EL traits the model with IDV residual variance demonstrated the best fit to the data. Values of accuracy were higher than 0.9 for all traits analyzed. In the GYT-biplot acute angles were find for all traits relations, which means high correlation between the yield*traits combination. Besides that, the correlation still can be seen in the GYT-biplot, as shown by the magnitudes of the angles between the pairs Yield*FU-Yield*FS and Yield*FS-Yield*EL. Also, the GYT-biplot indicates the genotype G4 with the best performance for Yield*FS, Yield*SFI, Yield*FU, Yield*FL, and Yield*FP combined. The genotypes G4, G1, G13, G8, and G9 represent those genotypes with yield advantage over the other cultivars. Then, the genotype G4 combines all desirable characteristics and demonstrate have large potential in the cotton breeding. The GYT approach were valuable and were highly recommended in cotton breeding programs for selection purpose in a multivariate scenario.

Resumo O objetivo deste trabalho foi selecionar, simultaneamente, híbridos pré-comerciais de sorgo granífero com alta adaptabilidade e estabilidade da produção de grãos, por meio de modelagem mista, em 20 ambientes, por seis anos. O material vegetal avaliado consistia de 57 híbridos comerciais de sorgo granífero. Em todos os experimentos, os híbridos foram arranjados em delineamento fatorial triplo; alguns experimentos utilizaram um fatorial 6x6, e outros, um fatorial 5x5. Os parâmetros de adaptabilidade e estabilidade foram obtidos com base na predição por média harmônica da performance relativa dos valores genéticos (MHPRVG). Os modelos mistos mostraram-se adequados para analisar a interação genótipo x ambiente (GxE) e os estudos de adaptabilidade e estabilidade genotípica do sorgo granífero. Os híbridos que se destacam, considerando-se todos os ambientes, são 1G282, A9904, 50A50, A9902 e XB6022. O híbrido A9904 destaca-se em ambientes favoráveis, com rendimento de grãos acima da média. Apenas 1G282 está entre os cinco melhores híbridos para cada grupo de ambientes e é o melhor híbrido de sorgo granífero quanto ao rendimento, à adaptabilidade e à estabilidade. Os valores genotípicos preditos com base em médias genotípicas podem ser usados em relação aos ambientes com o mesmo padrão de interação GxE por estarem livres da interação GxE.

Abstract The objective of this work was to simultaneously select pre-commercial grain sorghum hybrids with high adaptability and yield stability, through mixed modeling, in 20 environments, during six years. The evaluated plant material consisted of 57 commercial grain sorghum hybrids. In all experiments, hybrids were arranged in a triple lattice design; some experiments used a 6x6 lattice, and others, a 5x5 lattice. Adaptability and stability parameters were obtained based on the prediction by harmonic mean of the relative performance of genotypic values (HMRPGV). The mixed models proved to be adequate to analyze the genotype x environment (GxE) interaction and the genotypic adaptability and stability studies on grain sorghum. The hybrids that stand out, considering all environments are 1G282, A9904, 50A50, A9902, and XB6022. The A9904 hybrid stands out in favorable environments, with a grain yield above average. Only 1G282 is among the five best hybrids for each group of environments, and it is the best grain sorghum hybrid for yield performance, adaptability, and stability. The predicted genotypic values based on genotypic means can be used in the environments with the same GxE interaction pattern because they are free of the GxE interaction.

ABSTRACT Grain yield is a constant concern in soybean breeding programs for being highly influenced by the environment. This trait can be significantly affected by the sowing times. Thus, for the correct cultivar recommendation, methods of stability and adaptability analysis are used to verify the genotype x environment interaction at different sowing times. In this study, ten soybean cultivars were evaluated in 2016/2017 and 2017/2018 seasons, at different sowing times. Each experiment was carried out in a randomized block design with three replications. The methods of Eberhart & Russel and Lin & Binns were used for cultivars recommendation for general, favorable, and unfavorable environments. Cultivars CS2728, BONUS, and BRS1074 show high yield and predictability and therefore are the most suitable for the region of Chapadão do Sul.

Abstract Multiple-trait best linear unbiased prediction (BLUP) is generally the most appropriate method to predict genetic values because it considers the genetic and residual correlations among traits and leads to higher selective accuracy. Thus, the aim of the present study was to analyze the interrelationships among traits related to cotton fiber length through path analysis under multiple-trait BLUP. To that end, 36 elite lines were evaluated in three envi ronments and phenotyped for fiber quality and agronomic traits. Variance components were estimated via residual maximum likelihood (REML). The genetic correlation coefficients among traits were obtained through the mixed model output, and a correlation network was constructed to graphically express these results. Subsequently, path analysis was performed considering fiber length as the main dependent variable. Genetic parameters indicate that the phenotypic variance for most traits is mainly composed of residual effects, which reinforces the need for more accurate statistical methods, such as multiple-trait BLUP. The results for genetic correlations and path analysis reveal the difficulty of selection based on important fiber quality traits, especially fiber length, since most traits show a low cause-and-effect relationship, while other important traits have an undesirable cause-and-effect relationship. Path analysis under multiple-trait BLUP proved to be suitable and useful for studying interrelationships among traits related to cotton fiber length.

Resumen La mejor predicción lineal insesgada de rasgos múltiples (BLUP) es, en general, el método más apropiado para predecir valores genéticos porque considera las correlaciones genéticas y residuales entre rasgos y conducta con mayor precisión selectiva. Por lo tanto, el presente estudio tuvo como objetivo estudiar las interrelaciones entre los rasgos relacio nados con la longitud de la fibra de algodón a través del análisis de ruta en BLUP de rasgos múltiples. Con este fin, se evaluaron treinta y seis líneas élite en tres ambientes y se fenotipificaron para muchos rasgos relacionados con la calidad de la fibra y los rasgos agronómicos. Los componentes de la varianza se estimaron mediante la máxima verosimilitud residual (REML). Los coeficientes de correlación genética entre rasgos se obtuvieron a través de la salida del modelo mixto, y para expresar gráficamente estos resultados se construyó una red de correlación. Posteriormente, realizamos el análisis de ruta considerando la longitud de la fibra como variable dependiente principal. Los parámetros genéticos indican que la variación fenotípica para la mayoría de los rasgos se compone principalmente de efectos residuales, lo que refuerza la necesidad de utilizar métodos estadísticos más precisos, como BLUP de rasgos múltiples. Los resultados encontrados para las correlaciones genéticas y el análisis de ruta revelan la dificultad de la selección basada en rasgos importantes de la calidad de la fibra, especialmente la longitud de la fibra, ya que la mayoría de los rasgos muestran una relación de causa y efecto muy baja, y otros rasgos importantes presentan una relación de causa y efecto no deseada. El análisis de ruta en BLUP de rasgos múltiples resultó ser adecuado y útil para el estudio de las interrelaciones entre los rasgos relacio nados con la longitud de la fibra de algodón.

Abstract Grain sorghum production has expanded during the off-season when rainfall oscillates and becomes insufficient. Aiming to obtain better adapted cultivars, breeding programs have sought new combinations of hybrids with earliness, high grain yield, and ideal plant height for harvesting. This study aimed to estimate de combining ability of grain sorghum lines, proposing a breeding strategy, to identify hybrids gathering high yield, earliness, and desired plant height. Thirty-six hybrids from crosses of 12 lines were evaluated at two sites in the Brazilian region known as Cerrado biome. The evaluated traits were: days to flowering, plant height, and grain yield. For the diallel analysis, Method 4 of Griffing adapted to partial diallel was adopted. By combining ability analysis, we identified promising lines to be used as parents to obtain more yielding, early, and ideal height hybrids. The findings allowed us to propose a breeding strategy, in which complex crosses should be performed to gather favorable alleles in new restorer and male-sterile lines. The hybrids 7, 9, 19, and 22 are the most suitable for growing in the evaluated sites.

Resumen El cultivo de sorgo granífero se ha expandido durante la temporada baja, cuando la lluvia es oscilante e insuficiente. Para obtener cultivares mejor adaptados a estas condiciones, los programas de mejoramiento han buscado nuevas combinaciones de híbridos con preco cidad, alto rendimiento de grano y altura ideal de la planta para la cosecha. Este estudio tuvo como objetivo estimar la capacidad de combinación de líneas de sorgo granífero, con el objetivo de proponer una estrategia de mejoramiento e identificar híbridos con alto rendimiento, precocidad y altura de plantas adecuadas para la cosecha. Treinta y seis híbridos obtenidos del cruzamiento entre 12 líneas fueron evaluados en dos sitios en la región brasilera denominada Bioma Cerrado. Los caracteres evaluados fueron: días hasta la floración, altura de la planta y el rendimiento de grano. Para el análisis del dialelo, se adoptó el Método 4 de Griffing adaptado al dialelo parcial. Por medio del análisis de capacidad de combinación, identificamos líneas promisorias para ser usadas como parentales con el fin de obtener híbridos de mayor rendimiento, precocidad y altura ideal. Los resultados nos permitieron proponer una estrategia de mejoramiento, donde cruzamientos complejos deben ser realizados para reunir alelos favorables para los caracteres en nuevas líneas restauradoras y macho estériles. Los híbridos 7, 9, 19 y 22 son los más adecuados para el cultivo en los sitios evaluados.

RESUMO: A interação genótipo × ambiente (G × E) desempenha um papel essencial na expressão fenotípica e pode provocar dificuldades na seleção genética. Assim, o presente estudo teve como objetivo estimar parâmetros genéticos e comparar diferentes estratégias de seleção no contexto de modelos mistos para melhoramento da soja. Para isso, foram utilizados dados referentes à avaliação de 30 genótipos em dez ambientes, referentes à característica produtividade de grãos. Os componentes de variância foram estimados pela máxima verossimilhança restrita (REML) e os valores genotípicos foram preditos pela melhor previsão imparcial linear (BLUP). Efeitos significativos dos genótipos e interação G × E foram detectados pelo teste da razão de verossimilhança (LRT). Correlação genotípica baixa foi obtida entre os ambientes indicando interação G × E do tipo complexa. A acurácia seletiva foi muito alta, indicando alta confiabilidade. Os resultados mostraram que os genótipos de soja mais produtivos apresentam alta adaptabilidade e estabilidade.

ABSTRACT: The genotype × environment (G×E) interaction plays an essential role in phenotypic expression and can lead to difficulties in genetic selection. Thus, the present study aimed to estimate genetic parameters and to compare different selection strategies in the context of mixed models for soybean breeding. For this, data referring to the evaluation of 30 genotypes in 10 environments, regarding the grain yield trait, were used. The variance components were estimated through restricted maximum likelihood (REML) and genotypic values were predicted through best linear unbiased prediction (BLUP). Significant effects of genotypes and G×E interaction were detected by the likelihood ratio test (LRT). Low genotypic correlation was obtained across environments, indicating complex G×E interaction. The selective accuracy was very high, indicating high reliability. Our results showed that the most productive soybean genotypes have high adaptability and stability.

RESUMO: Em ensaios multi-ambientes, grandes redes experimentais são utilizadas para a avaliação de genótipos, tentando contornar o efeito que a interação genótipo por ambiente desempenha na seleção genotípica. Neste estudo, objetivamos testar diferentes estruturas de variância residual e medir o ganho de seleção de genótipos de algodão, baseados em produtividade, adaptabilidade e estabilidade, simultaneamente. Doze genótipos de algodão foram plantados em 10 ambientes, sendo determinados o comprimento da fibra (CF), a resistência da fibra (RF), a micronaire (MIC) e produtividade de fibras (PF). A seleção do modelo para diferentes estruturas de variância residual (homogênea e heterogênea) foi testada usando o Critério de Informação de Akaike (AIC) e o Critério de Informação Bayesiano (BIC). Os componentes de variância foram estimados através de máxima verossimilhança restrita e os valores genotípicos foram preditos através da melhor predição linear não viesada. A média harmônica do desempenho relativo dos valores genéticos (HMRPGV) foram aplicadas para seleção simultânea para adaptabilidade, estabilidade e produtividade. De acordo com o BIC, a estrutura residual heterogênea apresentou o melhor ajuste para a característica PF, enquanto a estrutura residual homogênea apresentou o melhor ajuste para as características CF, RF e MIC. A acurácia seletiva foi alta, indicando confiabilidade da predição. O método HMRPGV foi capaz de selecionar para estabilidade, adaptabilidade e produtividade, simultaneamente, com notável ganho de seleção para cada característica.

ABSTRACT: In multi-environment trials (MET), large networks are assessed for results improvement. However, genotype by environment interaction plays an important role in the selection of the most adaptable and stable genotypes in MET framework. In this study, we tested different residual variances and measure the selection gain of cotton genotypes accounting for adaptability and stability, simultaneously. Twelve genotypes of cotton were bred in 10 environments, and fiber length (FL), fiber strength (FS), micronaire (MIC), and fiber yield (FY) were determined. Model selection for different residual variance structures (homogeneous and heterogeneous) was tested using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The variance components were estimated through restricted maximum likelihood and genotypic values were predicted through best linear unbiased prediction. The harmonic mean of relative performance of genetic values (HMRPGV) were applied for simultaneous selection for adaptability, stability, and yield. According to BIC heterogeneous residual variance was the best model fit for FY, whereas homogeneous residual variance was the best model fit for FL, FS, and MIC traits. The selective accuracy was high, indicating reliability of the prediction. The HMRPGV was capable to select for stability, adaptability and yield simultaneously, with remarkable selection gain for each trait.

ABSTRACT An accurate, efficient and informative statistical method for analyses of genotype × environment (G × E) interactions is a key requirement for progress in any breeding program. Thus, the objective of this study was to quantify individual variation in reaction norms using random regression models fitted through Legendre polynomials in eucalyptus (Eucalyptus spp.) breeding. To this end, a data set with 215 eucalyptus clones of different species and hybrids evaluated in four environments for diameter at breast height (DBH) and Pilodyn penetration (PP) was used. Variance components were estimated by restricted maximum likelihood, and genetic values were predicted by best linear unbiased prediction. The best-fitted model for DBH and PP was indicated by the Akaike information criterion, and the significance of the genotype effects was tested using the likelihood ratio test. Genetic variability between eucalyptus clones and very high accuracies ( r g ^ g ≥ 0 . 90) were detected for both traits. Reaction norms and eigenfunctions generated genetic insights into G × E interactions. This is the first study that quantified individual variation in reaction norms using random regression models fitted through Legendre polynomials in eucalyptus breeding and demonstrated the great potential of this technique.

Soybean is the most economically important oilseed in the world, being one of the main sources of oil and protein in human and animal food. The objective of this work was to select F3 segregating populations for agronomic and physiological traits and vegetation indices (VIs). The experiment was carried out in the 2018/2019 crop seasons in a randomized block design with three replicates and 10 F3 populations (P1, P2, P3, P4, P5, P6, P7, P8, P9, and P10). The evaluated traits were as fallows: number of days to maturity (NDM); number of pods per plant (NPP); number of nodes per plant (NNP); number of lateral stems per plant (NSP); grain yield (GY); photosynthesis (A); stomatal conductance (gs); internal CO2 concentration (Ci); transpiration (E); NIR, Red-edge, Red and Green wavelengths; and NDVI and NDRE vegetation indices. Regarding the physiological traits evaluated in soybean F3 populations, the P8 and P6 populations presented the highest averages for photosynthesis, while the P3, P10, and P9 populations presented the highest Ci, E, and gs. For VIs evaluated in soybean F3 populations, the populations P7, P2, and P1 presented higher averages for NIR and Red-edge wavelengths. P5 and P4 showed a higher NDRE, and P8 showed a higher red wavelength. For agronomic traits, population P2 showed better results for the traits NNP and NSP. Populations P1 and P3 had a higher NDM. The P7 population presented the highest NPP, and the P6 population presented the highest GY.

La soja es la oleaginosa más importante económicamente en el mundo, siendo una de las principales fuentes de aceite y proteína en la alimentación humana y animal. El objetivo de este trabajo fue seleccionar las poblaciones segregantes de soja F3 en función de las características agronómicas y fisiológicas y los índices de vegetación. El experimento se llevó a cabo en la zafra de 2018/2019 con un diseño de bloques al azar con tres repeticiones y diez poblaciones F3 (P1, P2, P3, P4, P5, P6, P7, P8, P9 y P10). Los caracteres evaluados fueron: número de días hasta la madurez (NDM); número de vainas por planta (NPP); número de nudos por planta (NNP); número de tallos laterales por planta (NSP); rendimiento de granos (GY); fotosíntesis (A); conductividad estomática (gs); concentración interna de CO2 (Ci); transpiración (E); bandas NIR, Red-edge, Rojo y Verde; y los índices de vegetación NDVI y NDRE. Para los caracteres fisiológicos evaluados, las poblaciones P8 y P6 tuvieron las medias más altas de A, y las poblaciones P3, P10 y P9 tuvieron los valores más altos de Ci, E y gs. Para los índices de vegetación evaluados, las poblaciones P7, P2 y P1 presentaron medias más altas para el NIR y Red-edge, conjuntamente con las poblaciones P5 y P4 que presentaron NDRE más altos y la población P8 presentó longitudes de onda Rojas más altas. Para los caracteres agronómicos, la población P2 mostró mejores resultados para NNP y NSP. Las poblaciones P1 y P3 obtuvieron un mayor NDM. La población P7 mostró mayor NPP, y la población P6 mayor GY.

ABSTRACT Jatropha curcas L. is a perennial plant with great potential for biodiesel production. Thus, the aim of this study was to select J. curcas genotypes for bioenergy purpose considering multitrait, multiharvest and effective population size. To this end, a data set with 70 J. curcas families, obtained through controlled crosses, evaluated in a randomized block design, with six replications, three plants per plot, spaced at 4 × 2 m was used. The following traits were evaluated: plant height in the 2014 and 2015 crop seasons; canopy projection in the row (2015 crop season); canopy projection between rows (2015 crop season); and grain yield (2015, 2016, and 2017 harvests). The mixed model methodology was used to estimate the variance components and to predict the genetic values. Superior genotypes were selected through Multitrait index based on factor analysis and genotype-ideotype distance (FAI-BLUP index), considering the effective population size. According to this index, 98% of all additive genetic variability were summarized in three factors. By adopting the selection intensity that allows maintaining the effective population size equal to 30 (i.e., selection of 214 individuals), the predicted gains were 15.56, 21.75, 4.16, and 5.37% for the traits grain yield in the 2015 and 2016 harvests, canopy projection in the row, and canopy projection between rows in the 2015 crop season, respectively. Thus, the results suggest that the FAI-BLUP index can be successfully used in J. curcas breeding.

RESUMO O objetivo no trabalho foi avaliar o efeito de dessecantes químicos e épocas de colheita sobre a qualidade fisiológica e expressão enzimática em sementes de soja. O delineamento experimental foi blocos casualizados, com quatro repetições, em esquema fatorial (4 × 3 + 1): quatro dessecantes (paraquat - 2 L ha-1, glufosinato de amônio - 2 L ha-1, diquat - 1,5 L ha-1, saflufenacil 40 g ha-1) aplicado no estádio fenológico R7.1 (início do amarelecimento das folhas), com três épocas de colheita (0; 14 e 28 dias após o estádio fenológico R8) mais o controle (ausência de dessecante e colhida em R8). Foi avaliada a qualidade fisiológica das sementes de soja por meio de testes em laboratório e a expressão enzimática das enzimas malato desidrogenase, álcool desidrogenase, esterase, isocitrato liase e para a superóxido dismutase. A colheita de sementes no estádio R8+14 promove as maiores perdas na qualidade das sementes, todavia, não só tempo de atraso afeta, mas a ocorrência de precipitações é decisiva, sendo que chuva de 50 mm, já proporciona deterioração das sementes de soja. Os dessecantes diquat e paraquat foram os que promoveram os menores e maiores danos à qualidade fisiológica das sementes, respectivamente. A expressão das enzimas álcool desidrogenase, esterase e isocitrato liase são eficientes no monitoramento e com adequada correlação com a qualidade fisiológica. A malato desidrogenase e superóxido dismutase apresentam baixa correlação com os testes fisiológicos realizados nas sementes de soja.

ABSTRACT This study aimed to evaluate the effect of chemical desiccants and harvest times on the enzymatic expression and physiological quality of soybean seeds. The experiment was carried out in a randomized block design with four replications, in a factorial scheme (4 × 3 + 1) with four desiccants (paraquat - 2 L ha-1, ammonium glufosinate - 2 L ha-1, diquat - 1.5 L ha-1, and saflufenacil - 40 g ha-1). These were applied at the phenological stage R7.1 (beginning of leaf yellowing) and at three harvest times (0, 14, and 28 days after the phenological stage R8). There was also a control treatment (no desiccant, harvested at R8). The physiological quality of soybean seeds and the enzymatic expressions of malate dehydrogenase, alcohol dehydrogenase, esterase, isocitrate lyase, and superoxide dismutase were evaluated in laboratory tests. Seeds harvested at the R8 + 14 stage led to the highest losses in seed quality. However, the 50 mm rainfall also affected seed deterioration. The desiccants diquat and paraquat provided the lowest and the highest damage to the seed physiological quality, respectively. The expression of the enzymes alcohol dehydrogenase, esterase, and isocitrate lyase were efficient and had an adequate correlation with the physiological quality. Malate dehydrogenase and superoxide dismutase had no satisfactory relation with the physiological tests performed with soybean seeds.

Abstract This work aimed to study different seeding rates in soybean, at management zones determined by the mapping of the soil apparent electrical conductivity (ECa) and its relationships with plant phenology and grain yield (GY). The experiment consisted of a completely randomized design, with six replications. The plant population ranged between 311,000, 360,000, and 422,000 plants ha-1, and the fixed population (360,000 plants ha-1). The management zone with the least yield potential, received the highest seed population. The management zone with the highest plant vigor potential, received the lowest seed population. The variables plant height, Normalized Difference Vegetation Index (NDVI) at 50, 66, and 92 days after emergence (DAE), one hundred-grain weight, and GY were analyzed. ECa maps can be used to decide the seed population of the soybean. The decision strategy of increasing 20% of the seed soybean population on the smaller ECa map zones, and decreasing 20% seed population on higher ECa zones was effective and resulted in similar GY, even with the negative pressure of the high resistance of penetration (RP) values in some zones. GY map variability was influenced by ECa 0-0.2 m, by NDVI at 92 DAE and by RP 0.4-0.6 m soil layer.