Impacto da quantidade e frequência de chuva no rendimento da soja

Radin, Bernadete; Schönhofen, André; Tazzo, Ivonete Fátima

doi:10.31062/agrom.v25i1.26263

Cited by 7 publications

(7 citation statements)

References 11 publications

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“…Araújo et al (2014) identified a spatial association between soybean productivity and agrometeorological variables such as rainfall, temperature and global solar radiation. Radin et al (2017) verified the correlation between soybean productivity and rainfall accumulation during the crop's vegetative cycle in municipalities of Rio Grande do Sul, from 2005 to 2012. The results showed that the variation in productivity could be explained by rainfall, where the occurrence of consecutive days without rain during the crop cycle exerted a negative impact on soybean productivity.…”

Section: Introductionmentioning

confidence: 59%

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Soybean Productivity and Agrometeorological Variables Assessed from the perspective of Spatial and Circular Statistics

Silva

Gasparin

Paludo

et al. 2021

JAS

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Climate change can affect the development of soybean cultivation, impacting your productivity. Thus, agrometeorological information is essential in order to improve productivity strategies. The objective of the paper was to analyze the influence and occurrence of seasonality of the following agrometeorological variables on soybean productivity: mean air temperature [TMean] (ºC), accumulated rainfall value [Rain] (mm), global solar radiation [Sr] (MJ m-2 day-1), and potential evapotranspiration [ETp] (mm), in ten-day variations of the the maximum vegetative development date (MVDD), in the 2011/2012 and 2013/2014 harvest years in the state of Paraná. The study was based on spatial distribution of variables, using univariate and bivariate Global Moran’s Indexes, and multivariate clustering analysis. To verify seasonality in the time distribution of the agrometeorological variables in the ten-day variations close to soybean MVDD, we used the circular statistics, through the mean vector length (R). Result it was identified regions of the state that have higher and lower rainfall and seasonality, also have higher and lowest productivity, respectively. That the variation in soybean productivity between harvest years was correlated with the agrometeorological variables, and rainfall volume is an important factor in productivity. The other agrometeorological variables occurred uniformly, especially in 2011/2012 harvest year, in the Northwest, Central-northern and West mesoregions. Furthermore, there was clustering of regions with similar spatial distribution of the evapotranspiration and rainfall variables in 2aDMDV2d in the 2011/2012 and 2013/2014 harvest year, showed the same spatial distribution of the agrometeorological variables and the productivity variable.

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Section: Introductionmentioning

confidence: 59%

“…Several studies have evaluated the influence of agrometeorological variables on soybean productivity (Araújo et al, 2014;Radin et al, 2017;Grzegozewski et al, 2017). Araújo et al (2014) identified a spatial association between soybean productivity and agrometeorological variables such as rainfall, temperature and global solar radiation.…”

Section: Introductionmentioning

confidence: 99%

Soybean Productivity and Agrometeorological Variables Assessed from the perspective of Spatial and Circular Statistics

Silva

Gasparin

Paludo

et al. 2021

JAS

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“…2d). This means that the crop management applied in this region could explore more the environmental conditions than other regions, including the interaction with climate (Radin et al 2017;Hampf et al 2020).…”

Section: Resultsmentioning

confidence: 99%

Assessment of agricultural efficiency and yield gap for soybean in the Brazilian Central Cerrado biome

et al. 2021

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Soybean is the main crop grown in Brazil and is important to quantify the capacity of increase in yield by crop production intensification and management. This study aims to quantify yield gap and agricultural efficiency (AE) for soybean in Central Cerrado biome, Brazil. Yield gap by management (YG MG) and water deficit (YG WD) and AE and climate efficiency (CE) were calculated based on potential (PoY), attainable (AtY) and actual (AcY) yields. Potential yields and AtY were simulated for the main sowing date, ranging from Oct 11 to Nov 01, using the agroecological zone crop model, from 1980/81 to 2016/17 growing seasons. The sites were Rio Verde (GO), Cristalina (GO), Barreiras (BA) and Peixe (TO). Actual yield was obtained from official statistics for each site. Agricultural efficiency rate for this period and its relationship with CE were analyzed. The YG MG were 4,345, 3,446, 4,531 and 2,649 kg•ha-1 for Rio Verde, Cristalina, Peixe and Barreiras, respectively. Barreiras had a higher YG WD when compared with other sites, reaching a mean of 6,526 kg•ha-1. However, Barreiras had a higher AE (51%), indicating that crop management applied in this region can explore the environmental conditions more than other sites. Agricultural efficiency showed an increasing rate from 0.70 to 1.21% per year, while the relation between AE and CE fitted an exponential negative function, by which higher water volume available reduced AE. Thus, it is possible to double soybean yield through improvements in management based on AtY (7,409 kg•ha-1) and AcY (3,260 kg•ha-1) in Central Cerrado biome, Brazil.

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“…Mesmo com a evolução tecnológica da agricultura e das diferentes técnicas de manejo (rotação de culturas, cobertura permanente do solo com material vegetal), que buscam minimizar os impactos das variações dos estresses hídricos no rendimento das culturas, períodos prolongados de estiagens ou precipitações excessivas podem impactar fortemente uma safra agrícola (Radin et al, 2017;Zanon et al, 2018). Sendo a soja altamente dependente da precipitação registrada durante o seu ciclo de desenvolvimento, a identificação de mecanismos de teleconexão que impactam a precipitação e, consequentemente a produtividade de soja, faz-se necessária para aprimorar as estimativas de safra, fornecendo mais subsídios no momento em que é realizado o planejamento das lavouras de soja.…”

Section: Introductionunclassified

Indicadores Climáticos e a Produtividade de Soja no Rio Grande do Sul

Arsego

Ferraz

Streck

et al. 2019

Rev. bras. meteorol.

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Resumo O objetivo deste trabalho foi identificar padrões de teleconexão que influenciem na variabilidade da produtividade de soja no Rio Grande do Sul de forma a encontrar potenciais variáveis preditoras da produtividade agrícola no Estado. Foram utilizados dados de produtividade de soja de 87 municípios fornecidos pelo Instituto Brasileiro de Geografia e Estatística. Estas séries foram reunidas em três grupos homogêneos de produtividade mediante análise de agrupamento. Correlações defasadas entre índices climáticos associados a padrões de teleconexão e a produtividade média da soja de cada um dos três grupos evidenciaram a importância de cada índice para a cultura no Rio Grande do Sul. Dentre os padrões de teleconexão, os que apresentaram correlações mais significativas, confiança superior a 90%, e persistentes com a produtividade de soja foram a Oscilação ártica (negativa entre outubro e dezembro), Oscilação do Atlântico Norte (negativa entre outubro e janeiro) e anomalias de Temperatura da Superfície do Mar no Oceano Atlântico, entre 20°S-30°S e 20°O-40°O, (positivas entre dezembro e março). Estes índices também apresentaram elevada correlação com a precipitação compreendida entre dezembro a março para cada grupo homogêneo evidenciando a importância do seu monitoramento para o planejamento da safra de soja no Rio Grande do Sul.

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Impacto da quantidade e frequência de chuva no rendimento da soja

Cited by 7 publications

References 11 publications

Soybean Productivity and Agrometeorological Variables Assessed from the perspective of Spatial and Circular Statistics

Soybean Productivity and Agrometeorological Variables Assessed from the perspective of Spatial and Circular Statistics

Assessment of agricultural efficiency and yield gap for soybean in the Brazilian Central Cerrado biome

Indicadores Climáticos e a Produtividade de Soja no Rio Grande do Sul

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