Redução do acamamento em aveia-branca com uso do regulador de crescimento etil-trinexapac

Hawerroth, Maraisa Crestani; Silva, José Antônio Gonzalez da; Souza, Clóvis Arruda; Oliveira, Antônio Costa de; Luche, Henrique de Souza; Zimmer, Cristiano Mathias; Hawerroth, F. J.; Schiavo, Jordana; Sponchiado, Julhana Cristina

doi:10.1590/s0100-204x2015000200003

Cited by 41 publications

(57 citation statements)

References 22 publications

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“…Oat (Avena sativa) is one of the most cultivated species in southern Brazil, for soil cover, crop rotation and food production (Castro et al, 2012;Hawerroth et al, 2015). Studies aiming at simulation and optimization with the oat crop through AI via artificial neural networks and genetic algorithms are inexistent in the Brazilian research, although they can contribute to important processes related to the management of the species.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in seeding density optimization and yield simulation for oat

Dornelles

Kraisig

Silva

et al. 2018

Rev. bras. eng. agríc. ambient.

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A B S T R A C TArtificial intelligence may represent an efficient strategy for simulation and optimization of important processes in agriculture. The main goal of the study is to propose the use of artificial intelligence, namely artificial neural networks and genetic algorithms, respectively, in the simulation of oat grain yield and optimization of seeding density, considering the main succession systems of southern Brazil. The study was conducted in a randomized complete block design with four replicates, following a 4 x 2 factorial scheme, for seeding densities (100, 300, 600 and 900 seeds m -2 ) and oat cultivars (Brisasul and URS Taura), in succession systems of corn/oats and soybean/oats. A multi-layered artificial neural network and a genetic algorithm were implemented in Java programming language, and the results obtained from this implementation were compared with traditional polynomial regression. The use of artificial intelligence through neural networks and genetic algorithms allows the efficient simulation of oat grain yield and better optimization of seeding density in comparison to polynomial regression, considering the main succession systems in southern Brazil.Inteligência artificial na otimização da densidade de semeadura e simulação da produtividade da aveia R E S U M O O uso de inteligência artificial pode representar uma estratégia eficiente de simulação e otimização de processos importantes na agricultura. O objetivo deste estudo é propor o uso de inteligência artificial via redes neurais artificiais e algoritmos genéticos, respectivamente, na simulação da produtividade de grãos de aveia (Avena sativa) e na otimização da densidade de semeadura, nos principais sistemas de sucessão do sul do Brasil. O estudo foi conduzido em blocos ao acaso com quatro repetições em esquema fatorial 4 x 2, para as densidades de semeadura (100, 300, 600 e 900 sementes m -2 ) e cultivares de aveia (Brisasul e URS Taura), nos sistemas de sucessão milho/aveia e soja/aveia. Implementou-se uma rede neural artificial de múltiplas camadas e um algoritmo genético, em linguagem de programação Java, e comparou-se os resultados obtidos desta implementação com análises tradicionais de regressão polinomial. O uso de inteligência artificial via redes neurais artificiais e algoritmos genéticos permite simular com eficiência a produtividade de grãos de aveia e melhor otimização da densidade de semeadura na comparação com regressão polinomial, considerando os principais sistemas de sucessão no sul do Brasil.

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Section: Introductionmentioning

confidence: 99%

Artificial intelligence in seeding density optimization and yield simulation for oat

Dornelles

Kraisig

Silva

et al. 2018

Rev. bras. eng. agríc. ambient.

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“…The use of cultivars that are more productive and responsive to nitrogen (N) fertilization is also decisive in the increase of yield (Benin et al, 2012;Silva et al, 2015). Fertilization with N-fertilizer is necessary due to the inefficient amount released by the soil and because it is the nutrient most absorbed by cereals (Costa et al, 2013;Hawerroth et al, 2015). However, the increment in N use combined with favorable climatic conditions stimulates vegetative growth, favoring plant lodging (Flores et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Lodging is the phenomenon in which the plant loses its vertical position, tipping and falling on the soil, which directly affects the yield and quality of oat grains, compromising harvest (Silva et al, 2012;Hawerroth et al, 2015). It is a characteristic that involves the interaction of innumerous factors, such as wind, rainfall, soil, plant density, genetic resistance and management techniques (Silveira et al, 2011;Silva et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…It is a characteristic that involves the interaction of innumerous factors, such as wind, rainfall, soil, plant density, genetic resistance and management techniques (Silveira et al, 2011;Silva et al, 2015). One alternative adopted in graminee crops like rice (Arf et al, 2012) and wheat (Schwerz et al, 2015) has been the use of growth regulators that modify the internode distance and plant architecture, making the stem more resistant to breaking and lodging (Hawerroth et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Biomass and grain yield of oats by growth regulator

Marolli

Silva

Romitti

et al. 2017

Rev. bras. eng. agríc. ambient.

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The use of growth regulator in oats can reduce plant lodging with reflections in biomass and grain yield. The objective of the study was to determine the feasibility and efficiency of using Trinexapac-Ethyl regulator in the growth of white oat under different conditions of N-fertilizer and years favorable and unfavorable for cultivation. In this study, two experiments were conducted in the years 2011, 2012 and 2013, one for quantifying biomass production rate and the other for the determination of grain yield and lodging. The experimental design was randomized blocks with four replicates, in a 4 x 3 factorial scheme, for growth regulator doses (0, 200, 400 and 600 mL ha-1) and nitrogen doses (30, 90 and 150 kg ha-1), respectively. There is a linear reduction of biomass rate with the increase in the growth regulator dose in oat, regardless of the condition of year and use of N-fertilizer. The growth regulator dose of 495 mL ha-1 efficiently reduces lodging with reduced, high and very high use of N-fertilizer, without reducing the yield of oat grains, in favorable, intermediate or unfavorable year for cultivation.

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“…White oat (Avenasativa L.) is acknowledgedas a grain producer withnutritional quality for human and animal feeding (Garcia et al, 2012;Hawerroth et al, 2015). In Southern Brazil, it is an alternative to wheat for the winterfarming, evidencing in recent years accentuated growth in the cultivated area, mainly, due to use of the *Corresponding author.…”

Section: Introductionmentioning

confidence: 99%

The sowing density on oat productivity indicators

Romitti¹,

Dornelles²,

Silva³

et al. 2017

Afr. J. Agric. Res.

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The sowing density adjustment in oatcan maximize the productivity expression.The aim of this study is to define the behavior of productivity expression of biomass, grains, straw andharvest indexthrough increasing sowing density in the main biotype cultivated in Southern Brazil. It proposesthe possibility of indicating higher sowing density to the productivity maximization of biomass and grains.With the densityadjusted to the grain productivity to simulate the reflexes on the biological and straw productivity and harvest index compared to recommendeddensity, considering high and reduced tillering cultivars in different succession systems.The study was carried out in 2013, 2014 and 2015 in randomized blockdesign with four replications in a 4 × 2 factorial scheme, for sowing density (100, 200, 300, 600 and 900 m -2 ) and oat cultivars (Brisasul and URS Taura), respectively, in the corn/oat and soybean/oat succession system.With the increase in sowing density, the biological and straw productivity evidence alinear behavior and the grain productivity and harvest index ofquadratic behavior, regardless of the cultivar,agricultural year and succession system. It is possible to indicate a higher sowing density to the biomass and grain productivity maximization with sowing density close to 500 seeds m -2 in the main succession systems.In high and reduced tillering cultivars, the adjusted density compared tothe recommended increasedthe biological and straw productivity, regardless of agricultural year and succession systems.

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Redução do acamamento em aveia-branca com uso do regulador de crescimento etil-trinexapac

Cited by 41 publications

References 22 publications

Artificial intelligence in seeding density optimization and yield simulation for oat

Artificial intelligence in seeding density optimization and yield simulation for oat

Biomass and grain yield of oats by growth regulator

The sowing density on oat productivity indicators

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