Soybean adaptability and stability analyzes to the organic system through AMMI, GGE Biplot and mixed models methodologies

Pradebon, Leonardo Cesar; Carvalho, Ivan Ricardo; Loro, Murilo Vieira; Port, Eduarda Donadel; Bonfada, Bruno Mihlbeier; Sfalcin, Inaê Carolina; Santos, Iago Jardim; Bruinsma, Gabriel Mathias Weimer; Silva, Jose Antonio Gonzalez da; Alban, Aljian Antônio; Challiol, Márcio Alberto

doi:10.1590/0103-8478cr20220262

Cited by 9 publications

(3 citation statements)

References 20 publications

(18 reference statements)

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“…For that, the REML/BLUP methodology has helped to select genotypes in crops such as soybeans (PRADEBON et al, 2023;KEHL et al, 2022;KNEBEL et al, 2021), wheat (LORO et al, 2023), white oats (SCHMIDT et al, 2023) and corn (CARVALHO et al, 2017). This methodology makes it possible to estimate variance components and genetic parameters that indicate the proportion of the phenotype that is determined by the genetic makeup.…”

Section: Port Et Almentioning

confidence: 99%

Early selection of resilient progenies to seed yield in soybean populations

Port,

Carvalho,

Pradebon

et al. 2024

Cienc. Rural

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This study carried out early selection of soybean progenies that are productive and resilient to environmental conditions. The experiment took place at the Genetic Breeding Program of UNIJUI (University of the Northwest of the State of Rio Grande do Sul), located in Ijuí - RS, Brazil. The experiment used augmented blocks design with interim checks. The regular treatments correspond to 24 soybean F2 populations and the common treatments were 18 commercial checks, arranged in four replications. At full physiological maturity, in each experimental unit, five plants were randomly collected to obtain seed weight per plant (SWP, g). The Jinks and Pooni methodology was used to calculate the probability of extracting superior lineages from the evaluated populations. The best control and promising cultivars to compose the parent bank are BMX FORÇA RR, FUNDACEP 66 RR and TMG 7062 IPRO. Jinks and Pooni’s methodology identified populations IRC001, IRC002, IRC017, IRC019, IRC028, IRC030, IRC032, IRC033, IRC035, IRC036, IRC039 and IRC040 as having high potential for extraction of superior lineages.

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Section: Port Et Almentioning

confidence: 99%

Early selection of resilient progenies to seed yield in soybean populations

Port,

Carvalho,

Pradebon

et al. 2024

Cienc. Rural

Self Cite

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“…This fluctuation results from an environmental component, that is, from the interaction of genotypes × environments ( G × E ). Some positioning studies of white oat genotypes (Loro et al., 2022; Savick et al., 2023; Silva et al., 2015), wheat (Loro et al., 2023), and soybean (Pradebon et al., 2023) seek to understand the G × E interaction through methodologies like biometrics models to promote greater assertiveness in positioning.…”

Section: Introductionmentioning

confidence: 99%

Decomposition of phenotypic variation of white oats by meteorological and geographic covariables

Schmidt¹,

Carvalho²,

Silva³

et al. 2023

Agronomy Journal

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White oat (Avena sativa L.) is a cold season grass, with a very important role, both in animal and human food, due to the chemical composition of its grains, which have, among other beneficial components, fiber soluble food products, beta‐glucans. Therefore, the demand for grains of the crop is maximized and it is important to increase grain production. This can be achieved through the adequate positioning of the genotypes in the growing environments to maximize the agronomic performance of the crop. Thus, the objective of this work was to decompose meteorological and geographic variables in the positioning of white oat genotypes. The study took place considering 39 genotypes of white oats in 21 environments (in 13 years), in six municipalities of Rio Grande do Sul, two locations in the state of Santa Catarina, ten in Paraná and three municipalities in the state of São Paulo. The average grain yield (kg ha−1) of the genotypes in each environment was used to determine the adaptability and stability of the genotypes through the application of the Genotype and Genotypes by Environments Interaction (GGE biplot) biometric method and the reaction norm. The reaction norm pointed out that the genotypes UPFPS Farroupilha, UPFA Gaudéria and URS Guará demonstrated general adaptability to all environments, while FAEM 006 and URS Charrua expressed stability, high grain yield and higher than average genetic value. The GGE biplot graphically demonstrated that the genotypes URS Monarca and IPR Artemis demonstrated the highest grain yield and high stability in the environments Eldorado do Sul ‐ RS (E7) and Pelotas ‐ RS. Identifying genotypes with superior agronomic performance in specific environments minimizes the effects of genotype x environment interaction. These white oat genotypes can be used as sources of alleles for the development of new genotypes.This article is protected by copyright. All rights reserved

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“…O Brasil é o maior produtor mundial de soja, seguido pelos Estados Unidos (PRADEBON et al, 2023). Dados da safra 2022/2023, demonstram uma produtividade estimada de 151.4 milhões de toneladas de grãos, em uma área aproximada de 43,52 milhões de hectares (CONAB, 2023), sendo que nos últimos 12 anos, a área de produção da oleaginosa no país aumentou 42.5 % e 56.2 % no volume total produzido.…”

Section: Introductionunclassified

Comportamento espectral da soja e predição de produtividade com uso do NDVI

Possebon,

Gonçalves,

Gomes

et al. 2023

Tecnologia E Inovação Em Ciências Agrárias E Biológicas Avanços Para a Sociedade Atual

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O uso de imagens de sensoriamento remoto possibilita a estimativa da produtividade da cultura da soja através de índices de vegetação, sendo um dos mais utilizados o NDVI. O objetivo foi determinar o comportamento do NDVI durante o ciclo de desenvolvimento da soja, sua correlação com a produtividade e estimar através de regressões lineares a produtividade de grãos. Os dados foram obtidos em Dom Pedrito-RSda safra agrícola 2021/2022, em área de18.44 ha. Foram selecionadas dezesseis imagens durante o ciclo de cultivo da soja de satélites da constelação Planet com cálculos de NDVI para cada imagem, além da extração de dados de produtividade de grãos de monitor Gen4 Command Center 4600 da John Deere. Uma malha amostral de 262 polígonos com área de 900 m2 cada foi utilizada para extração da produtividade e NDVI utilizando o software QGIS Białowieża, versão 3.22.14 e exportados em formato .XLS para análise em planilhas. Os dados de produtividade foram agrupados em 5 (cinco) classes de acordo com a porcentagem em relação a produtividade média: < 85% da média; 85 a 95% da média; 95 a 105% da média, 105 a 115% da média e > 115% da média. Metade dos dados amostrais (131 polígonos) foram utilizados para análise de correlação e regressões lineares simples quando as correlações fossem fortes e significativas e a outra metade para validação dos modelos gerados. A validação dos modelos foi realizada pela raiz quadrada do erro quadrado médio (RMSE) e a raiz quadrada do erro quadrado médio normalizado (nRMSE) e o coeficiente de determinação (R2). Os resultados demonstraram que o NDVI possui comportamento exponencial da semeadura a colheita da soja com maior valores encontrados próximos aos 92 DAS (dias após semeadura). NDVI dos 68 aos 80 DAS e dos 120 a 126 DAS produziram correlação na média de 0.96 com a produtividade da soja. Os modelos gerados aos 80, 120 e 126 DAS permitiram estimar a produtividade de soja via NDVI com erro médio de ± 167.51 kg ha-1 sendo que o NDVI pode prever com antecedência de 70 dias da colheita e precisão de ± 246,17 kg ha-1 a produtividade em soja para o talhão estudado. Conclui-se que o NDVI pode ser utilizado para estimar a produtividade da soja.

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Soybean adaptability and stability analyzes to the organic system through AMMI, GGE Biplot and mixed models methodologies

Cited by 9 publications

References 20 publications

Early selection of resilient progenies to seed yield in soybean populations

Early selection of resilient progenies to seed yield in soybean populations

Decomposition of phenotypic variation of white oats by meteorological and geographic covariables

Comportamento espectral da soja e predição de produtividade com uso do NDVI

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