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 The objective of this study was to identify cowpea genotypes that fulfill the criteria of high grain yield, adaptability, and stability in the Mato Grosso do Sul (MS) region. Yield data from Value for Cultivation and Use (VCU) trials conducted in the municipalities of Dourados and Aquidauana/MS from March to August 2017 and 2018 were used, totaling four environments. A randomized block design with 14 treatments and four repetitions was adopted. Yield was evaluated by weighing the grains from the usable plot and converting the values to kilogram per hectare. After the analysis of variance, the interaction between genotypes and environments was evaluated by the methodologies of adaptability-stability analysis of Eberhart and Russell, Lin and Binns modified by Carneiro and GGE-Biplot. The genotypes Pingo-de-ouro 1-5-7, Pingo-de-ouro 1-5-5 and Bico-de-ouro 1-5-24 are stable, adaptable and productive for the state according to the complementary use of the methods.

ABSTRACT Artificial Intelligence has been widely applied in data prediction for better decision making and process optimization. In the post-harvest, the control of biotic and abiotic factors is fundamental for the conservation of seed quality. Meanwhile, the tetrazolium test has been used to evaluate seed quality, however, with several limitations that can lead to evaluation errors. Thus, machine learning models can be an alternative to predict the quality of soybean seeds, with gains in the speed of obtaining results in relation to laboratory analysis methods, making the processes more robust and with low operational cost. With this, the aim of this study was to identify the best machine learning model for predicting mechanical damage, vigor and viability of soybean seeds during storage, depending on different conditions (10, 15 and 25 ºC), packaging (with coating and uncoated) and storage times (0, 3, 6, 9 and 12 months). M5P decision tree (M5P) and Random Forest (RF) models showed the best performance for predicting seed vigor (r = 0.75 and MAE = 10.0), and viability (r = 0.85 and MAE = 5.1), and mechanical damage to seeds (r = 0.64 and MAE = 11.2). It was concluded that the Random Forest (RF) model was the one that best predicted the results of soybean seed quality, with a more simplified and agile analysis for the development of vigor and viability of soybean seeds in storage.

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 The wear rate of spray nozzles affects several factors. This study aimed to investigate the influence of spray nozzle wear rate on droplet characteristics, such as droplet size, droplet homogeneity, droplets prone to drift, and flow rate variation. Eight hydraulic nozzles models (AIXR-110-02, JSF-110-02, TT-110-02, DG-110-02, XR-110-02, ADI-110-02, XR-110-02, and AI-110-02) were subject to an accelerated wear test, and their effects were measured at six experimental wear times (0, 10, 20, 30, 40, and 50 hours). The following variables were evaluated: volume median diameter, percentage of spray volume containing droplets smaller than 100 μm (prone to spray drift), flow rate (L min-1), and relative span, which indicates droplet size homogeneity. The droplet population was measured using a laser particle size analyzer. The use of the spray nozzle tip influences the quality of the spray application due to the wear rate. This study revealed a linear increase in the flow rate and volume median diameter as functions of the wear rate increment. The droplet size increased over the wear time, which consequently modified the volume percentage of droplets smaller than 100 μm and significantly increased the relative span.

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 Objective: Abnormalities involving the TGFB1 gene and its receptors are common in several types of cancer and often related to tumor progression. We investigated the role of single nucleotide polymorphisms (SNP) in the susceptibility to cancer, their impact on its features, as well as the role of mRNA expression of these genes in thyroid malignancy. Materials and methods: We genotyped TGFB1, TGFBR1, and TGFBR2 SNPs in 157 papillary thyroid cancer (PTC) patients and 200 healthy controls. Further, we investigated RNA samples of 47 PTC and 80 benign nodules, searching for differential mRNA expression. Results: SNPs rs1800472 and rs1800469 were associated with characteristics of PTC aggressiveness. Effect predictor software analysis of nonsynonymous SNP rs1800472 indicated increasing protein stability and post-translational changes. TGFB1 mRNA expression was upregulated in PTC and downregulated in benign samples, differentiating malignant from benign nodules (p<0.0001); PTC from goiter (p<0.0001); and PTC from FA (p<0.0001). TGFBR1 mRNA expression was upregulated in goiter and PTC, but downregulated in FA, distinguishing PTC from goiter (p=0.0049); PTC from FA (p<0.0001); and goiter from FA (p=0.0267). On the other hand, TGFBR2 was downregulated in all histological types analyzed and was not able to differentiate thyroid nodules. Conclusion: TGFB1 polymorphism rs1800472 may confer greater activity to TGF-β1 in the tumor microenvironment, favoring PTC aggressiveness. Evaluation of TGFB1 and TGFBR1 mRNA levels may be useful to identify malignancy in thyroid nodules.

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.

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.

Extraction of inbred lines is a very important step in maize breeding since these lines will be used to obtain hybrids intended for the market. However, this expensive process, hinders genotype evaluation in contrasting environments, especially regarding nitrogen (N) content. This study aimed to evaluate the potential of maize populations for line extraction and select partially inbred lines under different soil nitrogen (N) availability. Five populations were evaluated regarding their potential for line extraction. Fifty-five S1 partially inbred lines were extracted from these populations. The top-cross method was used to evaluate lines by crossing them with two testers. Hybrids evaluation used 110 top-cross hybrids, five base populations, and six checks. Two trials were carried out in Dourados and Caarapó. One trial had adequate fertilization (high N) while the other was under stress condition (low N). Hybrid DKB 789 showed potential for extraction of partially inbred lines, aiming at selecting N use efficiency. Base population BP (07) was the most suitable for the extraction of partially inbred lines aiming at N use efficiency. Partially inbred lines BP (07) 13, BP (07) 14, and BP (07) 17 are the most suitable for the extraction of top-cross hybrids with high grain yield, efficiency, and responsiveness to N.

La extracción de líneas endogámicas es un paso muy importante para el mejoramiento del maíz, ya que estas líneas se utilizan para obtener híbridos destinados al mercado. Sin embargo, este es un proceso costoso que dificulta la evaluación de genotipos en ambientes contrastantes, especialmente respecto del contenido de Nitrógeno edáfico. El objetivo de este estudio fue evaluar el potencial de las poblaciones de maíz para la extracción de líneas y seleccionar líneas parcialmente endogámicas con diferente respuesta a la disponibilidad de nitrógeno (N). Se evaluaron cinco poblaciones en cuanto a su potencial de extracción de líneas. A partir de estas poblaciones, se extrajeron cincuenta y cinco líneas S1 (parcialmente endogámicas). El método de top-cross se usó para evaluar líneas cruzándolas con dos probadores. La evaluación de los híbridos utilizó 110 híbridos superiores, cinco poblaciones base y seis controles. Se realizaron dos ensayos en Dourados and Caarapó, uno con una fertilización adecuada (alto N) y otro en condiciones de estrés (bajo N). El híbrido DKB 789 mostró potencial para extraer líneas parcialmente endogámicas con el objetivo de mejorar la eficiencia de uso de N. La población base BP (07) fue la más adecuada para la extracción de líneas parcialmente endogámicas destinadas a eficiencia de uso de N. Las líneas BP (07) 13, BP (07) 14 y BP (07) 17 son las más adecuadas para la extracción de híbridos top-cross con alto rendimiento de grano, eficiencia y capacidad de respuesta a N.