The sowing density on oat productivity indicators

Romitti, Marcos Vinicios; Dornelles, Eldair Fabricio; Silva, Jose Antonio Gonzalez da; Marolli, Anderson; Mantai, Rúbia Diana; Scremin, Osmar Bruneslau; Arenhardt, Emílio Ghisleni; Brezolin, Ana Paula; Reginatto, Douglas Cézar; Scremin, Ari Higino; Lima, Andressa Raquel Cyzeski De; Silva, Dionatas Rodrigues Da

doi:10.5897/ajar2016.12095

Cited by 8 publications

(4 citation statements)

References 31 publications

(37 reference statements)

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“…Data obtained in the field regarding the different seeding densities were subjected to second-order polynomial regression analysis, commonly used in the optimization and simulation of factors with quantitative training levels (Romitti et al, 2017). After meeting the assumptions of homogeneity and normality by Bartlett's test, analysis of variance of regression was conducted to detect main and interaction effects.…”

Section: Methodsmentioning

confidence: 99%

“…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. Since the expression of oat grain yield depends on seeding density (Silva et al, 2012;Romitti et al, 2017), using different densities in the alteration of grain yield may serve as basis for training the network and validate the use of AI for simulation and optimization.…”

Section: Introductionmentioning

confidence: 99%

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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: Methodsmentioning

confidence: 99%

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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“…Oats are considered a multi-purpose cereal, used for soil cover due to the high volume of straw it produces (Godoy et al, 2016;Queiroz et al, 2017), and for animal feed in the form of pasture, hay, silage, and as an ingredient of feed concentrates (Romitti et al, 2017;Dornelles et al, 2018). For human consumption, it is possible to produce numerous products, with nutritional and functional qualities superior to those of other cereals (Sancho and Pastore, 2016;Mantai et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Research Article A combination of regression and internal point methods as a hybrid model for estimating oat plant productivity

Marolli¹,

Silva²,

Rosa³

et al. 2021

Genet. Mol. Res.

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The internal points method (IPM-Carvalho), with regression analysis, can generate an efficient hybrid model for estimating oat grain productivity. We tested a combination of the internal points method and regression to estimate straw productivity. We also applied this methodology to forecast a harvest index in the elaboration of a hybrid model to estimate oat grain productivity, taking into account nitrogen management and growth regulator use, with biological and environmental indicators. Simulation of oat yield as a function of nitrogen and growth regulator applications, with biological and environmental inputs, can assist in the development of more efficient and sustainable management for this crop. Two experiments were conducted during 2013, 2014, and 2015; one was used to quantify biomass yield and the other to determine grain yield and plant lodging. The experimental design was randomized blocks with four replications in a 4 x 3 factorial scheme in the sources of variation, which were growth regulator (0, 200, 400 and 600 mL ha -1 ) and nitrogen (30, 90 and 150 kg ha applications. The environmental parameters that were included were rainfall and maximum air temperature. The nitrogen was applied as urea at the expanded fourth leaf stage. The growth regulator was trinexapac-ethyl applied at the ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 20 (2): gmr18756 A. Marolli et al. 2 stage between the 1st and 2nd visible stem node. Straw productivity was obtained by the IPM model with nitrogen dose and rainfall inputs. The harvest index was obtained by regression as a function of the growth regulator doses. The combination of the internal points method to estimate straw productivity with the use of regression in the forecast of the harvest index proved to be a useful model for estimating oat grain productivity based on biological and environmental parameters, together with nitrogen and growth regulator applications.

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“…4.2). Other investigators have also found a quadratic response or no response in grain yield Ciha, 1983;Peltonen-Sainio and Jarvinen, 1994;Marcos et al, 2017) and are different than those in which yields increased linearly Peltonen-Sainio and Jarvinen, 1994;Benaragama and Shirtliffe, 2013).…”

Section: Grain Yieldmentioning

confidence: 78%

Production, perceptions and limitations of organic small grains in Iowa

Weisberger

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The sowing density on oat productivity indicators

Cited by 8 publications

References 31 publications

Artificial intelligence in seeding density optimization and yield simulation for oat

Artificial intelligence in seeding density optimization and yield simulation for oat

Research Article A combination of regression and internal point methods as a hybrid model for estimating oat plant productivity

Production, perceptions and limitations of organic small grains in Iowa

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