Simulation of oat grain (Avena sativa) using its panicle components and nitrogen fertilizer

Mantai, Rúbia Diana; Silva, Jose Antonio Gonzalez Da; Arenhardt, Emílio Ghisleni; Scremin, Osmar Bruneslau; Mamann, Ângela Teresinha Woschinski De; Frantz, Rafael Z.; Valdiero, Antonio Carlos; Pretto, Rafael; Krysczun, Dionatan Ketzer

doi:10.5897/ajar2016.10943

Cited by 10 publications

(9 citation statements)

References 18 publications

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“…The StepWise technique allows to select potential components for simulation through multiple linear regression (Balbinot Júnior et al, 2005;Mantai et al, 2016). Dalchiavon et al (2012) selected through StepWise technique the number of panicles, panicle weight, number of spikelets per panicle and thousand-grain weight of rice to compose the multiple linear regression model in the simulation of grain yield.…”

Section: Resultsmentioning

confidence: 99%

“…Dalchiavon et al (2012) selected through StepWise technique the number of panicles, panicle weight, number of spikelets per panicle and thousand-grain weight of rice to compose the multiple linear regression model in the simulation of grain yield. Mantai et al (2016) simulated oat yield using the multiple model with the variables panicle harvest index, number of grains and spikelets per panicle and N. Table 4 shows the multiple linear regression equations for the simulation of wheat biomass yield under the N supply conditions in the cultivation systems. This simulation used the values presented in Table 2 and the potential variables validated by the StepWise technique (Table 3).…”

Section: Resultsmentioning

confidence: 99%

“…Mantai et al (2016) estimated oat yield through multiple linear regression based on panicle harvest index, number of grains and spikelets per panicle and N use. Leilah & Al-Khateeb (2005) simulated through multiple regression wheat yield under drought condition, using the variables grain weight per panicle, panicle harvest index and panicle length.…”

Section: Resultsmentioning

confidence: 99%

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Simulation of wheat biomass yield by thermal time, rainfall and nitrogen

Trautmann

Silva

Binelo

et al. 2017

Rev. bras. eng. agríc. ambient.

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A B S T R A C TWheat biomass yield focused on the production of quality silage is dependent on rainfall, temperature and nitrogen (N). The objective of the study was to validate the use of rainfall, thermal time and N as potential variables for the composition of the multiple linear regression model and simulation of wheat biomass yield for silage production under N supply conditions during the cycle, in the systems of succession. The study was conducted in 2012, 2013 and 2014, in randomized blocks with four replicates in 4 x 3 factorial, for N-fertilizer doses (0, 30, 60, 120 kg ha -1 ) and forms of N supply [single application (100%) in the stage V 3 (third expanded leaf); split application (70%/30%) in the stages V 3 /V 6 (third and sixth expanded leaves); split application (70%/30%) in the stages V 3 /E (third expanded leaf and beginning of grain filling)], respectively, in the systems soybean/wheat and maize/wheat. Rainfall and N are potential variables in the composition of the multiple linear regression model. Multiple linear regression models are efficient in the simulation of wheat biomass yield for silage under the N supply conditions during the cycle in the succession systems.Simulação da produtividade de biomassa do trigo pela soma térmica, precipitação e nitrogênio R E S U M O A produção de biomassa de trigo voltada para a elaboração de silagem de qualidade é dependente da precipitação, da temperatura e do nitrogênio. O objetivo no estudo foi validar o uso da precipitação, soma térmica e nitrogênio como variáveis potenciais para composição do modelo de regressão linear múltipla e a simulação da produtividade de biomassa do trigo na elaboração de silagem nas condições de fornecimento de nitrogênio durante o ciclo, nos sistemas de sucessão. O estudo foi conduzido em 2012, 2013 e 2014 em blocos ao acaso com quatro repetições em fatorial 4 x 3, para doses de N-fertilizante (0, 30, 60, 120 kg ha -1 ) e formas de fornecimento [único (100%) no estádio V 3 (terceira folha expandida); fracionado (70%/30%) no estádio V 3 /V 6 (terceira e sexta folha expandida) e fracionado (70%/30%) no estádio V 3 /E (terceira folha expandida e início do enchimento de grãos)] respectivamente, no sistema soja/trigo e milho/trigo. A precipitação e o nitrogênio são variáveis potenciais na composição do modelo de regressão linear múltipla. Os modelos de regressão linear múltipla são eficientes para simulação da produtividade de biomassa do trigo para silagem nas condições de fornecimento de nitrogênio durante o ciclo nos sistemas de sucessão.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Simulation of wheat biomass yield by thermal time, rainfall and nitrogen

Trautmann

Silva

Binelo

et al. 2017

Rev. bras. eng. agríc. ambient.

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“…The definition of the nitrogen dose in oats depends on the organic matter content of the soil, the previous crop and the grain yield expectations (Mantai et al, 2016;Silva et al, 2016). On the other hand, the most propitious moment for top dressing fertilization falls only on the phenology of the plant linked to the period of greatest nutrient deficiency in the formation of production components (Bredemeier et al, 2013;Mantai et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Time of Nitrogen Supply in Yield and Industrial Quality of Oat Grains by Agricultural Condition

Kraisig

Silva

Carvalho

et al. 2020

JAS

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The timing of nitrogen application in oats improves the efficiency of use of the nutrient, expressing the yield and industrial quality of grains. The objective of the study is to indicate the appropriate timing of nitrogen supply on the yield and industrial quality of oat grains, considering the use of low and high dose of nitrogen at sowing and favorable and unfavorable years for cereal growing in soybean/oat system. The experiment was conducted in the years 2016, 2017 and 2018, in Augusto Pestana, RS, Brazil. The experimental design was a randomized block with four replications in a 2 x 4 factorial, for two doses of nitrogen at sowing (5 and 25 kg ha-1), changing the top dressing dose by the total supplied of 70 kg ha-1, in the expectation grain yield of 4000 kg ha-1, with time of supply at 0, 10, 30 and 60 days after emergence. In an unfavorable and intermediate year for the growing of oats, it is necessary to increase the dose of the nutrient at sowing in order to favor greater amplitude of the time of application in top dressing. In a favorable year, whether low or high, this range of application is guaranteed. Regardless of the reduced and high dose of nitrogen at sowing, the greatest expression of grain and industry yield occurs with the nutrient applied at 30 and 35 days after emergence in an unfavorable year, 35 days in an intermediate year and 40 to 45 days in a favorable year, respectively.

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“…Fertilization plays a very important role in achieving a larger grain number per panicle. The application of nitrogenous fertilizers and crop rotation contribute to an increase in the spike and grain numbers per panicle (Rubia Diana Mantai et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Stability of grain number per panicle in a collection of autumn oat (Avena sativa L.) genotypes

Madoșă¹,

Ciulcă²,

Velicevici³

et al. 2019

J Proc Ener Agri

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The purpose of this study is to examine the stability of grain number per panicle in a collection of autumn oat genotypes under different climatic conditions. The biological material analysed consisted of 73 oat genotypes studied over a period of three years. The experimental data were obtained by biometric measurements and subsequently processed using the following linear regression models: Finlay-Wilkinson, Hardwick-Wood, Muir and Wrike. The grain number per panicle obtained varied according to the annual climatic conditions both as means and the intrapopulational variability. The Jefferson, Carrie and Florina cultivars, as well as the 4458, PA 725-4743 and PA 822-818 lines, exhibited a high dynamic stability associated with the above-average values of the grain number per panicle. Relative to this character, a total 53.10 % of the genotype x environment interaction is due to the heterogeneity of variances.

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Simulation of oat grain (Avena sativa) using its panicle components and nitrogen fertilizer

Cited by 10 publications

References 18 publications

Simulation of wheat biomass yield by thermal time, rainfall and nitrogen

Simulation of wheat biomass yield by thermal time, rainfall and nitrogen

Time of Nitrogen Supply in Yield and Industrial Quality of Oat Grains by Agricultural Condition

Stability of grain number per panicle in a collection of autumn oat (Avena sativa L.) genotypes

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