Modelos agro-meteorológicos na estimativa da produtividade da cultura da soja na região de Ponta Grossa, Estado do Paraná

Araújo, Marla Alessandra de; Souza, Jorge Luiz Moretti de; Tsukahara, Rodrigo Yoiti

doi:10.4025/actasciagron.v33i1.5062

Cited by 3 publications

(6 citation statements)

References 12 publications

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“…Therefore, both methods can be used equally to estimate soybean yield (Table 2). These results are slightly lower than those obtained by Araújo et al (2011), who found an estimated yield of 3567 kg ha −1 , but for only one region and growing season in the state of Paraná. The average estimated yield was 12% higher than the average yield officially reported in Brazil and 6% higher than the average observed in the state (CONAB, 2018).…”

Section: Yield Estimationscontrasting

confidence: 77%

“…An HI value of 0.26 was used as a calibration index in this study. Moreover, the indices obtained in the literature do not allow obtaining statistically satisfactory yield estimates with the agrometeorological models (Araújo et al, 2011). Steduto et al (2012) recommended that HI values should be calibrated for each region.…”

Section: Yield Estimationsmentioning

confidence: 99%

“…However, the results are the same as those shown in Section "Water balance", regardless of the used water balance method. Additionally, the indices found in the literature do not allow obtaining statistically satisfactory yield estimates using agrometeorological models (Araújo et al, 2011). Steduto et al (2012) recommended that HI should be calibrated for each region.…”

Section: Model Calibrationmentioning

confidence: 99%

See 2 more Smart Citations

Crop Modeling With Less Data: The Fao Model for Soybean Yield Estimation

2021

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Section: Yield Estimationscontrasting

confidence: 77%

Section: Yield Estimationsmentioning

confidence: 99%

Section: Model Calibrationmentioning

confidence: 99%

See 1 more Smart Citation

Crop Modeling With Less Data: The Fao Model for Soybean Yield Estimation

2021

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“…A partir da identificação dos modelos agrometeorológicos que melhor descrevem o comportamento da cultura da soja em uma determinada região, é possível utilizar algoritmos para a simulação da produtividade (Silva-Fuzzo et al, 2015;Klering et al, 2016), ou mesmo inseri-los em sistemas de apoio à decisão (Araujo et al, 2011).…”

Section: Introductionunclassified

Produtividade de soja em consequência do atraso da colheita e de condições ambientais

Tsukahara

Fonseca

Silva

et al. 2016

Pesq. agropec. bras.

Self Cite

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Resumo -O objetivo deste trabalho foi avaliar o efeito do atraso da colheita sobre umidade dos grãos, massa de mil grãos e produtividade da soja, assim como identificar as variáveis agrometeorológicas mais importantes durante o período pré-colheita e propor algoritmos empíricos para a estimativa das perdas de produtividade. Os experimentos foram realizados em Castro, PR, e Itaberá, SP, nas safras de 2011/2012 a 2013/2014, da cultivar de soja 'NA5909RG', em sete ambientes. Os tratamentos consistiram de oito momentos de colheita de soja a partir do estádio fenológico R8.2. A análise de grupos de experimentos mostrou interação significativa entre ambientes e momentos de colheita. A umidade e a produção de grãos são influenciadas diretamente pelo número de dias com precipitação pluvial igual ou superior a 3 mm. No estádio fenológico R8.2, são obtidos os maiores valores de produtividade; no entanto, após esse estádio, há declínio da produtividade em consequência do atraso da colheita. As maiores perdas acumuladas de produtividade ocorrem em ambientes com alta frequência de precipitações pluviais e com temperatura e radiação solar global elevadas. Dois algoritmos com base em variáveis agrometeorológicas são propostos para a estimativa da perda de produtividade de soja.Termos para indexação: Glycine max, pré-colheita, precipitação pluvial, radiação solar. Soybean yield as a consequence of harvest delay and environmental conditionsAbstract -The objective of this work was to evaluate the effect of harvest delay on grain moisture, thousand grain weight, and soybean productivity, as well as to identify the most important meteorological variables during pre-harvest and to propose empirical algorithms to estimate soybean yield losses. The experiments were carried out in the municipalities of Castro, in the state of Paraná, and of Itaberá, in the state of São Paulo, Brazil, in the seasons from 2011/2012 to 2013/2014, with the soybean cultivar 'NA5909RG', in seven environments. Treatments consisted of eight harvest times of soybean after phenological stage R8.2. The analysis of group of experiments showed a significant interaction between environments and harvest times. Grain moisture and grain yield are directly affected by the number of days with rainfall equal to or higher than 3 mm. At the R8.2 phenological stage, the highest values of productivity are obtained; however, after this stage, there is a decline of productivity as a consequence of harvest delay. The greatest, accumulated losses of productivity occur in environments with high frequency of rainfall, and high temperature and solar radiation. Two algorithms based on agrometeorological variables are proposed to estimate soybean yield loss.Index terms: Glycine max, pre-harvest, rainfall, solar radiation. IntroduçãoO período pré-colheita, que se inicia na maturação fisiológica dos grãos e se estende até a operação de colheita, pode ser considerado como uma das etapas mais críticas dentro do processo de produção, em razão da suscetibilidade dos aspectos quantitativos, ...

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“…However, this method uses several parameters not always observed or measured in weather stations, thus encouraging studies to establish more simplified alternative methods for evapotranspiration estimation (Borges Júnior et al, 2012;Pilau et al, 2012;Bezerra et al, 2014;Oliveira et al, 2020). Some mathematical (current agrometeorological) models may act as efficiency indicators, since they consider the action of each meteorological element on crop yield, which can be described (Araujo et al, 2011;Gomes et al, 2014;Sentelhas et al, 2015;Silva et al, 2020). Thus, agrometeorological modeling involves the action of meteorological elements observed on crop traits, aggregating biomass accumulation or loss over time.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of alternative methods to calculate evapotranspiration and their impact on soybean yield estimation

Ferreira

Abi-Saab

Silva

et al. 2020

AgroM

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Despite the several studies comparing methods for evapotranspiration (ETo) estimation, scientific reports demonstrating their use and evaluation when coupled to agrometeorological models have not been analyzed, particularly in regard to the comparison of the estimated results obtained in the modeling with the real production data experimentally obtained in the field. The present study evaluated nine alternative methods to calculate ETo for estimating soybean yield, associated with actual yields obtained in irrigated and non-irrigated fields, at three sowing periods during the 2013/14 crop season in Southern Brazil. All methods were evaluated in relation to the standard Penman-Monteith method. Their performance was measured through regression analysis and statistical coefficients submitted to the Tukey test. ETo values obtained through the alternative methods were used to calculate water balances for soybean, considering irrigated and non-irrigated environments. Theoretical and real potential yields were higher in later sowings. The Priestley-Taylor method was the best to estimate daily ETo alternatively to that recommended by FAO (Penman-Monteith). On the other hand, the alternative method of Thornthwaite-Camargo was the best for estimations at 10-day periods, in all sowing dates. Furthermore, the methods of Thornthwaite-Camargo, Benevides-Lopez, Camargo, and Thornthwaite showed the smallest deviations to estimate ETo (10-day periods) for calculating actual yields (Ya).

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Modelos agro-meteorológicos na estimativa da produtividade da cultura da soja na região de Ponta Grossa, Estado do Paraná

Cited by 3 publications

References 12 publications

Crop Modeling With Less Data: The Fao Model for Soybean Yield Estimation

Crop Modeling With Less Data: The Fao Model for Soybean Yield Estimation

Produtividade de soja em consequência do atraso da colheita e de condições ambientais

Evaluation of alternative methods to calculate evapotranspiration and their impact on soybean yield estimation

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