Aplicações E Limitações Da Modelagem Em Agricultura – Revisão

Corrêa, Simone Toni Ruiz; Lorençoni, Rogério; Neto, Durval Dourado; Scarpare, Fábio Vale; Vivian, Rafael; Ruiz, Eduardo Toni

doi:10.37856/bja.v86i1.75

Cited by 11 publications

(15 citation statements)

References 3 publications

(4 reference statements)

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“…Models that represent reality through one or more equations and algorithms, enable efficient and low-cost simulations (Corrêa et al, 2011;Mamann et al, 2017). This need becomes more necessary for cultivated agroecosystems, as they represent real situations of non-linear behavior and act directly on yield.…”

Section: Resultsmentioning

confidence: 99%

“…Although there are models for simulating the yield of grains in cereals, they do not simultaneously include management, biological and environmental indicators that are decisive for yield (Souza et al, 2013;Rosa et al, 2015). Models that represent reality through one or more equations and algorithms enable efficient and lowcost simulations (Corrêa et al, 2011;Mamann et al, 2017). Along this line, Carvalho et al (2009) developed an internal parameters model (IPM) for the calculation of dry oat matter.…”

Section: Introductionmentioning

confidence: 99%

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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%

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 main advantage of models computer simulation is related to the quickness productivity results obtained, when compared to field experiments. The use of crop simulation models results in reductions in the planting crops costs, obtaining a greater amount of information regarding plants responses to the environmental conditions evaluated and the creation of unknown scenarios (Corrêa et al, 2011).…”

Section: Organizationmentioning

confidence: 99%

Simulation and performance yield of soybean and maize crops by AquaCrop, in Campos Gerais, Paraná State

et al. 2020

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The computational models that simulate yield of agricultural crops are important to planning activities. The objective of this study was to verify the performance of AquaCrop model to simulate soybean and maize yield in Campos Gerais region, in different soil types. The AquaCrop was used to estimate yield, requiring climate, soil, crop and soil management input data. In the analysis were used data from 21 and 32 experiments with maize and soybeans, respectively, carried out in the ABC Foundation, from years harvest between 2006 and 2014. For soybean crop, the highest absolute and relative errors of productivity simulations occurred in less productive crops, due to the lack of rain during sowing, water deficit in the harvest or high temperatures in the first weeks after the plants emergence. The highest absolute and relative errors verified in the simulations with maize crop experiments did not allow defined pattern identification. The AquaCrop achieved “very good” and “excellent” performances in the simulations of soybean and maize yield it the analyzed locations. The soil type affected the results from the analyzes of the two crops, and the Latossolos provided better performance and higher correlation compared to other soil types.

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“…The development and application of modeling in agriculture is an important tool that can serve to guide research, technological management, and decisionmaking (Corrêa et al 2011). In this context, the use of mathematical models such as multiple linear regression and artificial neural networks allows correlation of agronomic traits with genotype performance in the field.…”

Section: Introductionmentioning

confidence: 99%

Prediction of grain yield, adaptability, and stability in landrace varieties of lima bean (Phaseolus lunatus L.)

Sousa

Silva

Lopes

et al. 2020

Crop Breed. Appl. Biotechnol.

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The aim of this study was to compare the Multiple Linear Regression and Artificial Neural Network models in prediction of grain yield of ten landrace varieties of lima bean and evaluate adaptability and stability through the Lin and Binns method for identification of the best performing variety. Trials were conducted in the municipalities of Teresina, PI, and São Domingos do Maranhão, MA, through measurement of 12 traits, except for grain yield in São Domingos do Maranhão. The parameters of Pearson and Spearman correlation, root mean square error, mean absolute error, and coefficient of determination were used to compare the models. The Artificial Neural Network proved to be more adequate for prediction of grain yield. Adaptability and stability analyses indicated that the environments are discriminant for selection of promising genotypes, and that the landrace variety Mulatinha can be recommended for planting in the municipalities.

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Aplicações E Limitações Da Modelagem Em Agricultura – Revisão

Cited by 11 publications

References 3 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

Simulation and performance yield of soybean and maize crops by AquaCrop, in Campos Gerais, Paraná State

Prediction of grain yield, adaptability, and stability in landrace varieties of lima bean (Phaseolus lunatus L.)

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