Simulating maize yield in sub‑tropical conditions of southern Brazil using Glam model

Bergamaschi, Homero; Costa, Susanga; Challinor, Andrew J.

doi:10.1590/s0100-204x2013000200002

Cited by 10 publications

(7 citation statements)

References 17 publications

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“…Bergamaschi et al (2013), using the GLAM-Maize model, also obtained a better correlation (r>0.8; p<0.01) with larger spatial scales than with small ones (r varied from 0.65 to 0.87; p<0.1). However, in the present study, the cause for this result may be the unbalanced distribution of yield classes per region, since the number of municipalities in each yield class differed from one region, or state, to another.…”

Section: Resultsmentioning

confidence: 86%

“…The yield from each of these dates accounted for 10.0, 22.5, 35.0, 22.5, and 10.0% of the total yield in the evaluated period, respectively. According to Bergamaschi et al (2013), a wide range of sowing dates adopted by farmers is problematic for large-area crop modeling. The authors evaluated the GLAM-Maize model in northwestern Rio Grande do (Prasad et al, 2006).…”

Section: Regionmentioning

confidence: 99%

“…In Cropgro-Soybean model, Jagtap & Jones (2002) used the correction factor after crop simulation, reducing the bias from 57 to 11%. In Glam model, Bergamaschi et al (2013) and Challinor et al (2004) applied a correction factor during the simulation process for reducing LAI, in order to account for unknown stresses.…”

Section: Regionmentioning

confidence: 99%

“…Among these models, those incorporated in the platforms Decision Support System for Agrotechnology Transfer (DSSAT) and Agricultural Production Systems Simulator (APSIM) are widely studied (Tojo Soler et al, 2007;Knörzer et al, 2011). The general large area model, proposed by Challinor et al (2004) and adapted by Bergamaschi et al (2013) for corn (GLAM-Maize), is another example. However, most of the methods demand data and parameterization that are not easily available (Zhao et al, 2014), specially for large and heterogeneous areas (Reidsma et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

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Modeling of corn yield in Brazil as a function of meteorological conditions and technological level

Monteiro

Assad

Sentelhas

et al. 2017

Pesq. agropec. bras.

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-The objective of this work was to develop and evaluate a method for estimating corn yield using a minimum number of parameters and limited information about crop management. The proposed method estimates potential and attainable yields based on the technological level of the production systems and on relatively simple agrometeorological models. Corn yield was estimated for the crop seasons from 2000/2001 to 2007/2008, considering several locations and regions in Brazil, and was compared with the actual yield data from official surveys. There was a high correlation between the estimated and observed yield (0.76≤R 2 <0.92; p<0.01), with model efficiency (E1') ranging from 0.45 to 0.73; mean relative error (MRE) between -0.9 and 2.4%; and mean absolute error (MAE) of less than 70 kg ha -1 , depending on the technological level adopted. Based on these results, the proposed yield model can be recommended to forecast yields all over the country, contributing to make this process more precise and accurate.Index terms: Zea mays, large-area crop modeling, paremetrization, risk analysis, technological potential yield, yield forecast. Modelagem da produtividade de milho no Brasil em função das condições meteorológicas e do nível tecnológicoResumo -O objetivo deste trabalho foi desenvolver e avaliar um método para estimar a produtividade de milho com uso de um número mínimo de parâmetros e de informações limitadas sobre o manejo da cultura. O método proposto estima rendimentos potenciais e atingíveis com base no nível tecnológico dos sistemas de produção e em modelos agrometeorológicos relativamente simples. A produtividade de milho foi estimada para as safras de 2000/2001 a 2007/2008, tendo-se considerado vários locais e regiões do Brasil, e comparada aos dados de produção reais de levantamentos oficiais. A produtividade estimada apresentou alta correlação com a observada (0,76≤R 2 <0,92; p<0,01), com eficiência do modelo (E1') entre 0,45 e 0,73; erro médio relativo (MRE) entre -0,9 e 2,4%; e erro médio absoluto (MAE) inferior a 70 kg ha -1 , de acordo com o nível tecnológico considerado. Com base nestes resultados, este modelo pode ser recomendado para estimativas de produtividade em todo o País, e contribuir para tornar este processo mais preciso e exato.Termos para indexação: Zea mays, modelagem de cultura em grandes áreas, parametrização, análise de risco, produtividade potencial tecnológica, previsão de safra.

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Section: Resultsmentioning

confidence: 86%

Section: Regionmentioning

confidence: 99%

Section: Regionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling of corn yield in Brazil as a function of meteorological conditions and technological level

Monteiro

Assad

Sentelhas

et al. 2017

Pesq. agropec. bras.

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“…and January (BERGAMASCHI et al, 2013). The ET c was lower in season 1 than in season 2, although no differences were observed in grain yield.…”

Section: Resultsmentioning

confidence: 76%

Simulation of soil water balance and partitioning of evapotranspiration of maize grown in two growing seasons in Southern Brazil

et al. 2017

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The objective of this study was to simulate the variation of the available soil water during maize crop growth, in two different sowing times (first and second growing season), using a drip irrigation system. The treatments consisted of different irrigation strategies (full to deficit). The SIMDualKc simulation model was used to determine the daily soil water balance and crop evapotranspiration using the dual crop coefficient approach. Soil, climate, crop and irrigation parameters were used as input data. Two experiments were carried out in a rainout shelter composed of two metallic structures (16x10m) in the city of Santa Maria, Rio Grande do Sul, Brazil, during 2010/11 (second crop, season 1) and 2011/12 (first crop, season 2) growing seasons, under no-tillage system. The simulations showed that all the irrigation management strategies used in season 2 resulted in soil water deficit, while only two strategies showed deficit in season 1. Results showed good agreement between observed and simulated soil water data, with an R2 ranging from 0.86 to 0.99 and the root mean square error ranging from 2.7 to 5.6% of the total available water for seasons 1 and 2, respectively. The observed results of water balance showed that maize grown in season 2 presented higher water consumption compared to season 1, due to the higher atmospheric demand of season 2. The SIMDualKc model allowed the partitioning of crop evapotranspiration into soil evaporation and crop transpiration, demonstrating that the vegetative growth subperiod presented the greatest differences between the two seasons compared to the others growth phases.

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Modelling SOC response to land use change and management practices in sugarcane cultivation in South-Central Brazil

et al. 2016

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Simulating maize yield in sub‑tropical conditions of southern Brazil using Glam model

Cited by 10 publications

References 17 publications

Modeling of corn yield in Brazil as a function of meteorological conditions and technological level

Modeling of corn yield in Brazil as a function of meteorological conditions and technological level

Simulation of soil water balance and partitioning of evapotranspiration of maize grown in two growing seasons in Southern Brazil

Modelling SOC response to land use change and management practices in sugarcane cultivation in South-Central Brazil

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