A Proposal of Oat Productivity Simulation by Meteorological Elements, Growth Regulator and Nitrogen

Marolli, Anderson; Silva, José Antônio Gonzalez da; Scremin, Osmar Bruneslau; Mantai, Rúbia Diana; Trautmann, Ana P. B.; Mamann, Ângela Teresinha Woschinski De; Carbonera, Roberto; Kraisig, Adriana Roselia; Krüger, Cleusa Adriane Menegassi Bianchi; Arenhardt, Emílio Ghisleni

doi:10.4236/ajps.2017.89141

Cited by 5 publications

(3 citation statements)

References 36 publications

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“…This need becomes more necessary for cultivated agroecosystems, as they represent real situations of non-linear behavior and act directly on yield. Therefore, models that simulate the yield of agricultural crops are increasingly sought after, contributing to the realization of studies and validation of more efficient and sustainable managements (Costa et al, 2016;Marolli et al, 2017c;Piekarski et al, 2017). In this perspective, Carvalho et al (2009) developed a model of internal parameters (IPM) for the calculation of dry oat matter, such model considers ( , ) to be the response function (t ha -1 ) in relation to the water layer w (mm) and the nitrogen dose (n) applied in topdressing (kg ha -1 ).…”

Section: Resultsmentioning

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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Section: Resultsmentioning

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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“…When using agrometerological models to estimate crop productivity, data collected from meteorological stations are usually used, which have limitations regarding the spatial representativeness of the results (Junges & Fontana, 2011). Simulating oat productivity in Rio Grande do Sul, Marolli et al (2017) developed a mathematical model that showed efficiency in simulating the oat grain yield through meteorological parameter like thermal sum, radiantion and precipitation, nitrogen fertilization and growth regulator.…”

Section: Introductionmentioning

confidence: 99%

Chemical and physical attributes of five Oxisols as predictors of shoot dry mass of white oats

2021

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In no-tillage systems, oats are one of the species most used as a cover crop in the subtropical region of Brazil. This study aims is to determine a set of chemical and physical properties of five Oxisols after surface liming, and to determine whether these properties are related to the shoot dry mass production of white oat (Avena sativa L.) variety IPR Aphrodite, in order to establish a model to predict its yield. For this purpose, a field experiment was conducted during 2015 and 2016 in Londrina County in the State of Paraná with samples of B horizon collected from five Oxisols with clay content ranging from 15-80 dag kg -1 arranged in microplots since 2004. Soil pH in CaCl 2 , calcium, magnesium, potassium, potential cation exchange capacity (CEC), base saturation, microporosity, total porosity, field capacity, permanent wilting point, clay and sand of the Oxisols were the parameters that most influenced the dry mass production of white oats. Oxisol extrinsic factors such as values pH and base saturation, as well as the calcium and magnesium contents, positively altered with surface liming, boosting the production of oat dry mass production.

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“…Predicting with efficiency has been a complex activity, due to the different managements, types of soils and climates (Crusiol et al, 2016;Marolli et al, 2017). Mathematical models that seek efficient simulations, describing complex interactions with the nonlinear effects of agroecosystems are nincreasingly being sought (Rosa et al, 2015;Mantai et al, 2017). In this perspective, fuzzy models are techniques that allow the description of complex systems, produced from rules elaborated by specialists, providing their experience to the elaboration of an inference system of the type "If <condition> Then <result>" (Barros & Bassanezi, 2010;Silva et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Simulation of wheat yield by nitrogen and nonlinearity of environmental conditions

Trautmann

Silva

Binelo

et al. 2020

Rev. bras. eng. agríc. ambient.

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Fuzzy logic can simulate wheat productivity by assisting crop predictability. The objective of the study is the use of fuzzy logic to simulate wheat yield in the conditions of nitrogen use, together with the effects of air temperature and rainfall, in the main cereal succession systems in Southern Brazil. The study was conducted in the years 2014, 2015 and 2016, in Augusto Pestana, RS, Brazil. The experimental design was a randomized block design with four repetitions in a 4 x 3 factorial scheme for N-fertilizer doses (0, 30, 60, 120 kg ha-1) and nutrient supply forms [100% in phenological stage V3 (third expanded leaf); (70%/30%) in the phenological stage V3/V6 (third and sixth expanded leaf) and; fractionated (70%/30%) at the phenological stage V3/E (third expanded leaf and beginning of grain filling)], respectively, in the soybean/wheat and corn/wheat systems. The pertinence functions and the linguistic values established for the input and output variables are adequate for the use of fuzzy logic. Fuzzy logic simulates wheat grain yield efficiently in the conditions of nitrogen use with air temperature and rainfall in crop systems.

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A Proposal of Oat Productivity Simulation by Meteorological Elements, Growth Regulator and Nitrogen

Cited by 5 publications

References 36 publications

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

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

Chemical and physical attributes of five Oxisols as predictors of shoot dry mass of white oats

Simulation of wheat yield by nitrogen and nonlinearity of environmental conditions

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