Diffuse system simulating wheat productivity by nitrogen and temperature in the use of biopolymers

Mamann, Ângela Teresinha Woschinski De; Silva, José Antônio Gonzalez da; Scremin, Osmar Bruneslau; Trautmann, Ana P. B.; Argenta, Cláudia Vanessa; Matter, Ester Mafalda

doi:10.1590/1807-1929/agriambi.v24n5p289-297

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…Therefore, the favorable climate for wheat is described as that of milder temperatures and high solar radiation, without rain large volumes, with adequate distribution for maintaining stored soil moisture (Guarienti et al, 2004;Silva et al, 2016). Excess heat, in addition to inducing quantitative and qualitative losses in production, shortens the cycle length, reduces leaf area, plant height and percentage of flower fertilization, acceler filling period and decreasing the average weight of the grains (Ribeiro et al, 2012;Mamann et al, 2020). Therefore, the guarantee of food security with quantity and quality of grains is dependent on the weather conditions of wheat growing (She 2018).…”

Section: Resultsmentioning

confidence: 99%

“…Nitrogen fertilization is quite usual and important in wheat crop, however, determining dosages and the most efficient conditions of use is a challenge in production systems (Mantai et al, 2016;Barbosa et al, 2016). The possibility of analyzing the behavior and simulations of the nutrient in the productivity biomass and grains can qualify more efficient and sustainable managements (Costa et al, 2018;Mamann et al, 2020). The definition of the N-fertilizer dose is a function of the organic matter content of the soil, the previous crop and the grain yield expectations (Mantai et al, 2016;Marolli et al, 2017).…”

Section: Resultsmentioning

confidence: 99%

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Research Article The supply of nitrogen fertilizer in wheat to the highest environmental quality with satisfactory yield

Trautmann¹,

Silva²,

Rosa³

et al. 2021

Genet. Mol. Res.

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The management of nitrogen in wheat based on biological and environmental indicators can ensure productivity with a reduction in environmental impact. The objective of the study was to develop a more sustainable management of nitrogen use in wheat, considering the dose as a fraction of the total supply based on an estimate of the maximum technical and economic efficiency, and expected grain yield, in systems of succession of high and reduced release of N-residual, during acceptable and unfavorable crop years. The study was conducted from 2012 to 2018, in Augusto Pestana, RS, Brazil. Two experiments were conducted in each agricultural year and in each rotation system (soybean/wheat and corn/wheat), one to quantify the biomass yield and the other to determine grain yield. The experimental design was a randomized block with four repetitions in a 5 x 3 factorial, for doses of N-fertilizer (0, 30, 60, 90, 120 kg ha -1 ) and ways of supplying the nutrient [full condition (100%) at the phenological stage V 3 (third expanded leaf); fractionated (70%/30%) at phenological stage V 3 /V 6 (third and sixth expanded leaf) and fractionated (70%/30%) at phenological stage V 3 /R 1 (third expanded leaf and ear differentiation)], in the ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 20 (3): gmr18903 A.B.P. Trautmann et al. 2soybean/wheat and corn/wheat succession systems with high and reduced N-residual release, respectively. More efficient use of nitrogen was obtained based on wheat grain productivity when applied at the full dose at the third expanded leaf stage. The supply of the maximum dose of nitrogen with an expectation of 3 t ha -1 ensures satisfactory productivity with reduced nutrient losses to the environment, especially in unfavorable years for cultivation, regardless of the succession system.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Research Article The supply of nitrogen fertilizer in wheat to the highest environmental quality with satisfactory yield

Trautmann¹,

Silva²,

Rosa³

et al. 2021

Genet. Mol. Res.

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“…de ferramentas para estimar o desempenho do cereal [11]. Também com trigo, foi utilizado um modelo fuzzy para estimar os valores de produtividade de biomassa e grãos de trigo pelo nitrogênio e não linearidade da temperatura máxima do ar nas condições de uso do biopolímero hidrogel [4].…”

Section: Anounclassified

Lógica Fuzzy na Previsão da Produtividade de Grãos de Aveia pelo Nitrogênio e Condições Meteorológicas

Rosa¹,

Mantai²,

Peter³

et al. 2022

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

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UNIJUÍ, IJUÍ, RSResumo.A modelagem matemática via lógica fuzzy tem permitido simular sistemas complexos de comportamentos lineares e não lineares e auxiliar na busca de soluções nas mais diversas áreas da ciência, com na agricultura. O emprego de lógica fuzzy pode permitir simulações e proposições mais precisas da produtividade dos grãos de espécies, em função de variáveis controladas e não controladas. A aveia branca é um cereal com destaque de seus derivados na alimentação humana e sua produtividade está associada com a adubação através do nitrogênio e as condições meteorológicas. O objetivo deste trabalho é empregar a lógica fuzzy para simular a produtividade de grãos de aveia aliado aos processos envolvendo variáveis meteorológicas não controláveis e considerando o uso do nitrogênio. O delineamento experimental foi de blocos casualizados com 4 repetições, seguindo um esquema fatorial 4 x 2, com quatro doses de nitrogênio (0 kg ha −1 , 30 kg ha −1 , 60 kg ha −1 e 120 kg ha −1 ) e duas cultivares (Barbarasul e Brisasul), no sistema de cultivo soja/aveia nos anos agrícolas de 2011 a 2016. No desenvolvimento da lógica fuzzy foi considerada a função de pertinência triangular e o método de inferência Mamdani. A lógica fuzzy mostra-se eficaz na simulação da produtividade de grãos de aveia em função das variáveis não controladas aliadas a doses de nitrogênio e se ajusta aos processos de biossistemas, representando inovação com potencialidade de uso na perspectiva de simulação, essencialmente em situações onde é necessário lidar com a incerteza e subjetividade, comuns na agricultura.Palavras-chave. Avena sativa L., modelagem matemática, simulação, nitrogênio 1 Introdução A modelagem Matemática é um processo dinâmico muito utilizado em biossistemas para a obtenção e validação de modelos capazes de realizar previsões de produtividade, de diversas culturas [10,13]. Dentre as ferramentas da modelagem matemática tem-se o uso da lógica fuzzy, que é caracterizada como um dos ramos da inteligência artificial [5,9]. A lógica fuzzy é conhecida como sistemas de conjuntos nebulosos, que tem sido utilizada para tratar de problemas com características complexas, sendo capaz de capturar informações incertas, em geral, determinadas por expressões linguísticas, e convertê-las para um formato numérico de fácil manipulação, tornando-se solução para problemas até então não solucionáveis por técnicas clássicas [1,14]. Na agricultura, a

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“…Mathematical and computational models describing agricultural processes can assist in developing and validating technologies and managements that are more adequate from a technical, economic, and environmental point of view (De Mamann et al, 2020). In this sense, artificial intelligence techniques have emerged as an alternative for simulating and optimizing agricultural systems (Trautmann et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in the Simulation of Fungicide Management Scenarios for Satisfactory Yield and Food Safety in oat Crops

Rosa¹,

Dornelles²,

Silva³

et al. 2023

RGSA

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Purpose: The objective of the study is to analyze the management of the fungicide that seeks a longer interval between the last application and the harvest of oat grains, with an indication of the cultivars with the highest satisfactory productivity without application in the grain filling. Validate artificial neural network models in the expectation of satisfactory productivity with food security, through the interaction between management, pathogen, genotype, and environment. Method/design/approach: The study was conducted in 2015, 2016, 2017, in a randomized block design, in a 22 x 4 factorial scheme, for 22 white oat cultivars (recommended and no longer present in the current Brazilian recommendation) and 4 fungicide application conditions (no application; one application at 60 days after emergence (DAE); two applications, 60 and 75 DAE; and three applications, at 60, 75, 90 DAE), with three repetitions. Results and conclusion: The oat cultivars URS Altiva, URS, Guará, URS Charrua, FAEM 4 Carlasul, IPR Aphrodite, and UPFPS Farroupilha showed satisfactory yields in absence of fungicide applications. URS Altiva, FAEM 4 Carlasul, and IPR Aphrodite showed significant yield increases with fungicide application before grain filling. Artificial neural networks are efficient in predicting productivity and are a reliable alternative for simulating scenarios to validate more sustainable management practices. Originality/value: An unprecedented study seeking more sustainable managements to reduce the use of fungicides in the control of oat diseases and development of simulation models by artificial intelligence, providing opportunities for analysis of different scenarios of the complex interaction plant, pathogen, and environment.

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Diffuse system simulating wheat productivity by nitrogen and temperature in the use of biopolymers

Cited by 5 publications

References 21 publications

Research Article The supply of nitrogen fertilizer in wheat to the highest environmental quality with satisfactory yield

Research Article The supply of nitrogen fertilizer in wheat to the highest environmental quality with satisfactory yield

Lógica Fuzzy na Previsão da Produtividade de Grãos de Aveia pelo Nitrogênio e Condições Meteorológicas

Artificial Intelligence in the Simulation of Fungicide Management Scenarios for Satisfactory Yield and Food Safety in oat Crops

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