ABSTRACT The objective of the present work was to evaluate the influence of environmental variables on the interaction between genotypes and environments and to identify adapted and stable genotypes for grain seed yield. Twenty-one cultivars were evaluated in randomized blocks with four replications in the years 2016, 2017, and 2018 in the northeastern semi-arid region of Brazil, for seed yield and oil content. Factor regression methodologies and principal component analysis were used with predictions of the sum of the genotypic effects and the interaction to quantify the role of five environmental covariates in the genotype x environment interaction; the Harmonic Mean Method of Relative Performance of Genotypic Values was used for identifying adapted and stable genotypes. The covariance biplot model is useful for relating important environmental factors and indicating their relative effect on seed yield and oil content. Rainfall, relative humidity and maximum temperature contribute positively to increasing oil content while minimum temperature and solar radiation reduce it. Within the limits of the work, the maximum temperature positively influences grain production while the minimum reduces it. The most stable genotypes and those adapted for grain seed yield and oil content are BMX OPUS IPRO, P 98Y70 RR, BRS 333 RR, BRS 9280 RR, M 8644 IPRO, M 8372 IPRO, and ST 920 RR.

RESUMO O objetivo do presente trabalho foi avaliar a influência de variáveis ambientais na interação entre genótipos e ambientes e identificar genótipos adaptados e estáveis para a produção de sementes. Vinte e uma cultivares foram avaliadas em blocos ao acaso com quatro repetições nos anos de 2016, 2017 e 2018 no semiárido nordestino do Brasil, para rendimento de sementes e teor de óleo. Metodologias de regressão fatorial e análise de componentes principais foram utilizadas com previsões da soma dos efeitos genotípicos e a interação para quantificar o papel de cinco covariáveis ambientais na interação genótipo x ambiente; o Método da Média Harmônica de Desempenho Relativo de Valores Genotípicos foi usado para identificar genótipos adaptados e estáveis. O modelo biplot de covariância é útil para relacionar fatores ambientais importantes e indicar seu efeito relativo no rendimento de sementes e no teor de óleo. A precipitação, a umidade relativa e a temperatura máxima contribuem positivamente para aumentar o teor de óleo, enquanto a temperatura mínima e a radiação solar o reduzem. Dentro dos limites do trabalho, a temperatura máxima influencia positivamente a produção de grãos enquanto a mínima a reduz. Os genótipos mais estáveis e adaptados para rendimento de sementes de grãos e teor de óleo são BMX OPUS IPRO, P 98Y70 RR, BRS 333 RR, BRS 9280 RR, M 8644 IPRO, M 8372 IPRO e ST 920 RR.

ABSTRACT. This study aimed to evaluate the nutrition of commercial soybean crops in an agricultural frontier region using the diagnosis and recommendation integrated system (DRIS) and compositional nutrient diagnosis (CND) methods, as well as identify sufficiency ranges. The study was performed by collecting leaf samples (third trifoliate leaf without petiole) at flowering from commercial soybean crops in the states of Piauí and Maranhão, Brazil, and evaluating the crop yield by analyzing macro- and micronutrients in the plant tissue of 98 samples. The DRIS and CND methods were applied based on the cataloged data, followed by the generation of norms, analysis of relationships between yield and nutrients (selecting high-yield crops by the cumulative function of the data), generation of sufficiency ranges, and comparison of methods. The relationships obtained by the DRIS and CND indices with the yield and nutrients were significant, indicating that both methods can be employed for the evaluation of leaf nutrients in soybean. The sufficiency ranges from the DRIS and CND methods presented superior nutrient ranges in relation to the values proposed in the literature for macronutrients, except for nitrogen, and greater range amplitudes for micronutrients.

RESUMO Pesquisas recentes apontam resultados promissores da integração de dados orbitais utilizando o índice de vegetação NDVI, para monitorar e estimar a produtividade da soja. O objetivo do trabalho foi avaliar a proposição de modelos de regressão linear múltipla para estimativa da produtividade de grãos com uso do índice NDVI. A pesquisa foi realizada na Fazenda Celeiro, município de Monte Alegre do Piauí, PI, sendo a área de estudo de 200 ha. Foram utilizadas cinco imagens durante o ciclo de cultivo da soja, sendo uma do Landsat 8 e quatro do Sentinel 2. Efetuou-se análises de regressão entre dados de produtividade de grãos (variável predita), extraídos dos mapas de colheita, e dados espectrais de (variáveis preditoras) oriundos dos índices de vegetação (NDVI) de diferentes estádios de desenvolvimento da soja. A seleção dos modelos promissores foi efetuada pelo critério de informação de Akaike (AIC). Para validação dos modelos utilizou-se a raiz quadrada do erro quadrado médio (RMSE) e a raiz quadrada do erro quadrado médio normalizado (nRMSE) pela média da produtividade da soja no talhão. O modelo de regressão linear gerado com o índice de vegetação NDVI nos estádios de desenvolvimento V5-V6 e R2, mostrou-se promissor na predição da produtividade de grãos de soja, com erro médio de estimativa da ordem de 153,9 kg ha-1, o que representa 4,2% em relação aos dados medidos em campo.

ABSTRACT Recent researches have shown promising results for the use of orbital data using the Normalized Difference Vegetation Index (NDVI) to monitor and predict soybean grain yield. The objective of this work was to evaluate propositions of multiple linear regression models to predict soybean grain yield using NDVI. The research was carried out at the Celeiro Farm, in Monte Alegre do Piauí, PI, Brazil, in an area of 200 ha. Five images were collected during the soybean crop cycle: one from the Landsat 8 and four from the Sentinel 2. Regression analyses were carried out between grain yield data (predicted variable) extracted from harvest maps and spectral data (predictor variables) from NDVI of soybean crops at different developmental stages. The promising models were selected by the Akaike Information Criterion (AIC). The models were validated using Root Mean Square Error (RMSE) and Normalized Root Mean Square Error (nRMSE), considering the mean of soybean yield of the plot. The linear regression models developed with NDVI for the V5-V6 and R2 developmental stages showed promising results for the prediction of soybean grain yield, with mean error of predictions of 153.9 kg ha-1, representing 4.2% when compared to the data from field measures.

ABSTRACT Establishing sufficiency ranges and critical levels of nutrients are important for a correct evaluation of plant nutrition through leaf diagnosis. This study aimed to propose critical levels and sufficiency ranges of macro and micronutrients based on leaf diagnosis of soybean plants. The database used was generated from 86 samples of the third trifoliate leaf without petiole, collected during the flowering stage from soybean plants of the main cultivars used in the states of Piauí and Maranhão, Northeast region of Brazil. The results of macro and micronutrients and grain yield were used to calculate the critical level by the reduced normal distribution and boundary line methods, the latter was also used to generate the sufficiency ranges. Nutrient levels for 90 % maximum grain yield were considered for the critical level by the reduced normal distribution, and nutrient levels at the upper line of a dispersion diagram were considered for the boundary line method, using the relation between grain yield and nutrient concentration to generate sufficiency ranges for 95 and 99 % maximum grain yields. The critical levels generated by the boundary line method presented a larger number of deficient samples than the reduced normal distribution method, except for boron. The sufficiency ranges generated by the boundary line with 95 % of the maximum grain yield could not diagnose nutrient deficiency, except for copper. The critical levels by the reduced normal distribution and boundary line methods for leaf diagnosis of soybean were 40.2 and 42.1 g kg-1, 3.2 and 3.4 g kg-1, 17.6 and 19.5 g kg-1, 8.7 and 10.3 g kg-1, 4.7 and 4.9 g kg-1, 2.1 and 2.4 g kg-1, 44 and 44 mg kg-1, 5 and 12 mg kg-1, 125 and 145 mg kg-1, 33 and 34 mg kg-1, and 48 and 63 mg kg-1 for N, P, K, Ca, Mg, S, B, Cu, Fe, Mn, and Zn, respectively. The critical levels by boundary line showed better distribution for leaf diagnosis for excess, deficiency, and adequate nutrient levels. The sufficiency ranges by the boundary line method for N, P, K, Ca, Mg, S, B, Cu, Fe, Mn, and Zn were 38.6-45.7 g kg-1, 3.1-3.7 g kg-1, 18.3-20.7 g kg-1, 9.4-11.3 g kg-1, 4.4-5.3 g kg-1, 2.1-2.7 g kg-1, 35-53 mg kg-1, 10-14 mg kg-1, 131-159 mg kg-1, 23-46 mg kg-1, and 58-68 mg kg-1, respectively. The reduced normal distribution and boundary line methods allowed the generation of critical levels and sufficiency ranges for leaf diagnosis of soybean. The sufficiency range generated by the boundary line with 95 % maximum grain yield showed no prevalence of diagnosis of nutrient deficiency, except for copper.

Abstract: The aim of this study was to ascertain the association between the REML/BLUP and GGE Biplot methodologies for selection of superior genotypes in regard to adaptability and yield stability for various regions of the Middle North region of Brazil. Sixteen soybean genotypes were evaluated in eight environments during the 2015/2016 and 2016/2017 crop seasons, analyzing the following traits: number of days to maturity, plant height, one hundred seed weight, and grain yield. In this study, the REML/BLUP and the GGE Biplot methods are highly correlated in terms of genotype ranking for selection and recommendation purposes. The genotypes BRASBT13-0528, M8372 IPRO, and BRASBT13-0621 most approximate a hypothetical ideal genotype.

Resumo: O objetivo deste trabalho foi obter progênies de feijão-caupi (Vigna unguiculata) com inflorescência composta, pedúnculo curto, ciclo precoce, boa arquitetura de planta e alto potencial produtivo. O material genético inicial constituiu-se de 453 progênies F2:3, selecionadas a partir de dez cruzamentos e avaliadas em delineamento de blocos aumentados de Federer, com duas testemunhas. Dessas, selecionaram-se 60 progênies das gerações F4:5 e F4:6, que foram analisadas em dois experimentos sucessivos, ambos em delineamento látice 8x8, com quatro testemunhas e quatro repetições. A seleção precoce foi eficiente para fixação dos caracteres avaliados. Detectou-se ampla variabilidade genética entre e dentro das progênies. As herdabilidades e os ganhos genéticos estimados e realizados nas progênies F4:5 e F4:6 indicam a possibilidade de seleção de progênies precoces, com comprimento de pedúnculo curto, inflorescência composta e produtividade igual ou superior à das cultivares comerciais utilizadas como testemunhas.

Abstract: The objective of this work was to obtain cowpea (Vigna unguiculata) progenies with compound inflorescences, short peduncles, early cycle, good plant architecture, and high-yielding potential. The initial genetic material consisted of 453 F2:3 progenies, selected from ten crosses and evaluated using Federer’s augmented block design, with two controls. Of these, 60 progenies from the F4:5 and F4:6 generations were selected and assessed in two consecutive experiments, both in a 8x8 lattice design with four controls and four replicates. Early selection was efficient at fixing the evaluated traits. A wide genetic variability was detected among and within progenies. The estimated and obtained heritability and genetic gains in the F4:5 and F4:6 progenies indicate the possibility of selection of early progenies with short peduncles, compound inflorescences, and productivity similar to or greater than that of the commercial cultivars used as controls.