&lt;b&gt;Calibration and testing of an agrometeorological model for the estimation of soybean yields in different Brazilian regions

Monteiro, Leonardo Amaral; Sentelhas, Paulo César

doi:10.4025/actasciagron.v36i3.17485

Cited by 13 publications

(12 citation statements)

References 19 publications

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“…Among the agrometeorological modelling papers for yield estimation, a few are exclusive dedicated to understand the water deficiencies influence at different phenological phases. For annual crops, this relation is more commonly studied, as the model tested by Monteiro and Sentelhas (2014) that used relative water deficiency at phenological phases to the calculation of actual soybean yield at different Brazil regions. For perennial crops, such analysis are not common, for most of "Valência" orange agrometeorological modelling papers search to understand effects of water stress at citrus orchards in order to determine the irrigation deficit, mainly at flowering (Pérez-Pérez et al, 2009; Roccuzzo et al, 2014) and not to estimate yield and quality of the fruits in function of the monthly water deficiency.…”

Section: Introductionmentioning

confidence: 99%

Estimation of annual yield and quality of Valncia orange related to monthly water deficiencies

Moreto¹,

Rolim²

2015

Afr. J. Agric. Res.

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The State of São Paulo, Brazil is the major orange producer in the world. The "Valência" orange is one of the most important cultivars for industry with a high efficiency of processed juice. Climate is the main factor of influence for citrus yield and quality and its study is fundamental for understanding the climatic requirements of the crop. The estimation of yield and quality by agrometeorological models helps to understand the effects of climate on crop cycle, besides being an option for orchards planning activities. Understanding the relationships between water deficiencies (DEF), phenological phases, yield (fruits per box (FRBOX) and quality of "Valência" orange grafted on rangpur lime (VACR) are important to improve the water use in the production areas and to provide information about water stress for plants during its cycle. The present study aimed to investigate the influence of monthly DEF on yield and quality parameters of VACR, in order to develop agrometeorological models for the main four producers regions of State of São Paulo, Brazil. Data of 13 years were used for analysis, being the period from 2001 to 2009 used for calibration and from 2010 to 2012 for validation. Multiple linear regression for model construction was used. All the developed agrometeorological models were accurate, ranging the values of mean absolute percentage error (MAPE) of 5.25 to 9.27% for mean annual yield (FRBOX) and 2.74 to 14.14% for quality (RATIO) among all regions. The angular coefficients indicate which phenological phase of VACR is more sensitive to DEF. Bauru and Limeira FRBOX was related to DEF during bud formation and vegetative dormancy. Yield at Bebedouro were related to DEF between vegetative dormancy and flowering and at Matão bud formation. Fruit quality was more sensitive to DEF during maturity at all regions.

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Section: Introductionmentioning

confidence: 99%

Estimation of annual yield and quality of Valncia orange related to monthly water deficiencies

Moreto¹,

Rolim²

2015

Afr. J. Agric. Res.

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“…In addition to presenting a large difference in phase II, the results of Suyker & Verma [48] also showed a large difference in phase IV-2. The comparison of the K c EEFlux to the K c determined by Monteiro & Sentelhas [49] showed the best agreements (d = 0.90) ( Fig 7D) and also showed a lower K c difference in phase II, which did not occur in the other comparisons.…”

Section: Comparison Of the Eta And Kc Eeflux With The Mfaomentioning

confidence: 71%

Mapping within‑field variability of soybean evapotranspiration and crop coefficient using the Earth Engine Evaporation Flux (EEFlux) application

et al. 2020

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Accurate information about the spatiotemporal variability of actual crop evapotranspiration (ETa), crop coefficient (K c) and water productivity (WP) is crucial for water efficient management in the agriculture. The Earth Engine Evapotranspiration Flux (EEFlux) application has become a popular approach for providing spatiotemporal information on ETa and Kc worldwide. The aim of this study was to quantify the variability of water consumption (ETa) and the K c for an irrigated commercial planting of soybeans based on the EEFlux application in the western region of the state of Bahia, Brazil. The water productivity (WP) for the fields was also obtained. Six cloud-free images from Landsat 7 and 8 satellites, acquired during the 2016/17 soybean growing season were used and processed on the EEFlux platform. The ETa from EEFlux was compared to that of the modified FAO (MFAO) approach using the following statistical metrics: Willmot's index of agreement (d-index), root mean square error (RMSE), mean absolute error (MAE) and mean bias error (MBE). The K c from EEFlux was compared to the K c used in the soybean field (K c FAO-based) and to the K c values obtained in different scientific studies using the d-index. A similar procedure was performed for WP. Our results reveal that EEFlux is able to provide accurate information about the variability of ETa and the K c of soybean fields. The comparison between ETa EEFlux and ETa MFAO showed good agreement based on the d-index, with values of 0.85, 0.83 and 0.89 for central pivots 1, 2 and 3, respectively. However, EEFlux tends to slightly underestimate ETa. The K c EEFlux showed good accordance with the K c values considered in this study, except in phase II, where a larger difference was observed; the average WP of the three fields (1.14 kg m-3) was higher than that in the majority of the previous studies, which is a strong indicator of the efficient use of water in the studied soybean fields. The study showed that EEFlux, an innovative and free tool for access spatiotemporal variability of ETa and Kc at global scale is very efficient to estimate the ETa and Kc on different growth stages of soybean crop.

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“…This author obtained a correlation of 0.85 between observed and estimated values using the Jensen model modified to estimate the soybean crop yield in the State of Rio Grande do Sul. Using an agrometeorological model, Monteiro and Sentelhas (2014) obtained an R 2 of 0.64 when estimating soybean productivity in different regions. The use of artificial neural networks to estimate soybean yield is viable, since the back propagation training technique allowed the relationship between the independent variables (soybean agronomic characteristics, growth habit and population density) and soybean yield to be identified with high precision (72%).…”

Section: Resultsmentioning

confidence: 99%

“…Soybean productivity was successfully estimated in studies by Monteiro and Sentelhas (2014) using an agrometeorological model. The main agronomic characteristics that are influenced by the different behaviors of each cultivar include the number of branches produced per plant, number of pods and seeds per plant, number of internodes, insertion of the first pod, stem diameter, plant height and, obviously, grain production (Liu et al, 2010;Passos et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

<b>Estimating soybean yields with artificial neural networks

et al. 2018

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ABSTRACT. The complexity of the statistical models used to estimate the productivity of many crops, including soybeans, restricts the use of this practice, but an alternative is the use of artificial neural networks (ANNs). This study aimed to estimate soybean productivity based on growth habit, sowing density and agronomic characteristics using an ANN multilayer perceptron (MLP). Agronomic data from experiments conducted during the 2013/2014 soybean harvest in Anápolis, Goiás State, B razil, were used to conduct this study after being normalized to an ANN-compatible range. Then, several ANNs were trained to choose the best-performing one. After training the network, a performance analysis was conducted to select the ANN with a performance most appropriate for the problem, and the selected network had a 98% success rate with training data and a 72% data validation accuracy. The application of the MLP to the data used in the experiment shows that it is possible to estimate soybean productivity based on agronomic characteristics, growth habit and population density through AI. Estimativa da produtividade de soja com redes neurais artificiais RESUMO. Para estimar a produtividade de muitas culturas, incluído a soja, são utilizados modelos estatísticos complexos, que torna restrito o acesso a essa prática. Uma alternativa a estes modelos é a utilização de sistemas computacionais empregando Redes Neurais Artificiais (RNA). Este trabalho teve por objetivo estimar a produtividade da soja baseada nos hábitos de crescimento, densidade de semeadura e características agronômicas usando RNA do tipo Multilayer Perceptron (MLP). Para isto foram utilizados dados agronômicos da cultura da soja obtidos em experimento conduzido na safra 2013/2014 em Anápolis, Estado de Goiás, Brasil, cujos dados foram normalizados em intervalo compatível para trabalho com RNA e em seguida feito o treinamento de várias RNAs para a escolha da RNA com melhor performance. Após o treinamento das redes, foi realizada a análise de performance para seleção da RNA com a performance mais adequada ao problema. A RNA selecionada apresentou um índice de acerto de 98% com os dados do treinamento e um acerto de 72% com dados de validação. A aplicação das RNAs do tipo MLP nos dados do experimento conduzido demonstram que é possível estimar a produtividade da soja baseando-se nas características agronômicas, hábito de crescimento e densidade populacional por meio da IA. Palavras-chave:Glycine max (L.) Merrill; características agronômicas; modelagem; MLP; perceptron.

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<b>Calibration and testing of an agrometeorological model for the estimation of soybean yields in different Brazilian regions

Cited by 13 publications

References 19 publications

Estimation of annual yield and quality of Valncia orange related to monthly water deficiencies

Estimation of annual yield and quality of Valncia orange related to monthly water deficiencies

Mapping within‑field variability of soybean evapotranspiration and crop coefficient using the Earth Engine Evaporation Flux (EEFlux) application

<b>Estimating soybean yields with artificial neural networks

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