Spatial Correlation of Soybean Productivity, Enhanced Vegetation Index (Evi) and Agrometeorological Variables

Grzegozewski, Denise Maria; Uribe-Opazo, Miguel Ángel; Johann, Jerry Adriani; Guedes, Luciana Pagliosa Carvalho

doi:10.1590/1809-4430-eng.agric.v37n3p541-555/2017

Cited by 6 publications

(10 citation statements)

References 21 publications

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“…The study area comprises the 399 municipalities of the state of Paraná , Southern Brazil, limited by the coordinates 22º 29' S and 26º 43' S and 48º 2' W and 54º 38' W. This area was divided into 10 mesoregions of according to IBGE (Brazilian Institute of Geography and Statistics); these mesoregions are presented in Grzegozewski et al (2017).…”

Section: Determination Of the Study Area And Data Acquisitionmentioning

confidence: 99%

“…The mapping of soybean cultivated areas for crop years 2010/2011 and 2011/2012 was provided by Souza et al (2015), and for crop year 2012/2013 was provided by Grzegozewski et al (2016). From these mappings, the pixel values of the average EVI vegetation index of the crop years studied were extracted for each municipality, and the details of the procedure adopted to calculate this average EVI for each municipality are described in Grzegozewski et al (2017).…”

Section: Determination Of the Study Area And Data Acquisitionmentioning

confidence: 99%

“…Studies using these indicators have shown for the state of Paraná : the soybean crop profile at different seeding dates (Dalposso et al, 2013, Cima et al, 2018; how municipalities relate spatially to soybean production; which are the main municipalities producing soy (Prudente et al, 2014); and the analysis of the spatial relationship of soybean yield with agrometeorological characteristics (Grzegozewski et al, 2017) In this context, regarding the simultaneous analysis of a set of variables, the multivariate analysis techniques can be helpful in finding patterns generated by the set of variables. In the Western region of Paraná , Araújo et al (2013) carried out a cluster analysis using information on the local Moran index (LISA) applied to agrometeorological and soybean yield data in crop year 2005/2006, and the formation of groups of similar municipalities was identified regarding their spatial distribution in relation to soybean yield and all agrometeorological elements analyzed.…”

Section: Introductionmentioning

confidence: 99%

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Spatial and Multivariate Analysis of Soybean Yield in the State of Paraná-Brazil

Grzegozewski

Cima

Uribe-Opazo

et al. 2020

JAS

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In this work, the aim was to evaluate the existence of spatial association of the municipal average official soybean yield (t ha-1) with agrometeorological data and vegetation indices. The information was observed by ten-day periods, in crop years 2010/2011, 2011/2012 and 2012/2013 in the State of Paraná. Local univariate spatial correlation (LISA index), as well as global bivariate correlation (L statistics) were calculated. With this study, we identified neighboring municipalities with high yield in the West as well as municipalities that are located with low-low yield Northwestern, showing positive spatial autocorrelation (IMG=1), significative (p-value < 0.05). In addition, there were differences between seeding times in different regions, and climate irregularity during flowering periods and grain filling in crop year 2011/2012 throughout the state, which caused a large drop in production in all municipalities of the state of Paraná. The analysis of local spatial association showed that in the three crop years, the Northwest region presented a significant low yield potential of soybean (p-value < 0.05). In addition, it was observed that the period from the 3rd ten-day period of October to the 2nd ten-day period of January was essential for the soybean cycle in the different regions of the state, since this period encompasses the critical phases of crop. Differences were also observed between the crop years studied, regarding the agrometeorological variables, which affected soybean yield mainly in the Western region of Paraná – Brazil.

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Section: Determination Of the Study Area And Data Acquisitionmentioning

confidence: 99%

Section: Determination Of the Study Area And Data Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatial and Multivariate Analysis of Soybean Yield in the State of Paraná-Brazil

Grzegozewski

Cima

Uribe-Opazo

et al. 2020

JAS

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“…Crop data can be estimated using acquired earth observation (EO) data, along the crop growth cycle, at time intervals suitable for the detection of changes in crop phenology (D'Urso & Calera Belmonte, 2006;Vilar, 2015;Vilar et al, 2015;Navarro et al, 2016;Rolim et al, 2016). Presently, the availability of free and open access to high spatial resolution EO data with a short revisit time allows for accurate crop parameter estimation as well as crop growth cycle characterization, improving the identification of each growth cycle stage, which is often imperceptible when lower temporal resolution data are used (El Hajj et al, 2009;D'Urso et al, 2010;Ramme et al, 2010;Johann et al, 2013;Johann et al, 2016;Navarro et al, 2016;Rolim et al, 2016;Grzegozewski et al, 2017;Toureiro et al, 2017). EO methodologies have been widely used for crop evapotranspiration (ETc) and IWR estimation because of the reflective properties of vegetation that allow one to estimate crop biophysical parameters and plant processes such as transpiration (Neale et al, 1989;Calera Belmonte et al, 2005;D'Urso et al, 2010;Paço et al, 2014;Vuolo et al, 2015;Ferreira et al, 2016;Oliveira et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Crop Data Retrieval Using Earth Observation Data to Support Agricultural Water Management

et al. 2019

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Accurate crop data are essential for reliable irrigation water requirements (IWR) calculations, which can be acquired during the crop growth season for a given region using earth observation (EO) satellite time series. In addition, a relationship between crop coefficients and the NDVI can be established to allow for crop evapotranspiration estimation. The main objective of the present work was to develop a methodology, and its implementation in an application software, to extract crop parameters from EO image time series for a set of parcels of different types of crops, to be used as input data for a soil water balance model to compute IWR. The methodology was tested at two distinct test sites, the Vila Franca de Xira (site I) and Vila Velha de Ródão (site II) municipalities, Portugal. Landsat-7 and-8 images acquired from April to October 2013 were used for site I, while SPOT-5 Take-5 images from April to September 2015 were considered for site II. EO data were used to estimate the basal crop coefficients, planting dates, and crops growth stage lengths. Based on crop, soil and meteorological data, the IWR for the main crops of both test regions were estimated using the IrrigRotation model. The crop coefficient curves obtained from the EO data proved to be reliable for IWR estimation.

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“…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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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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Spatial Correlation of Soybean Productivity, Enhanced Vegetation Index (Evi) and Agrometeorological Variables

Cited by 6 publications

References 21 publications

Spatial and Multivariate Analysis of Soybean Yield in the State of Paraná-Brazil

Spatial and Multivariate Analysis of Soybean Yield in the State of Paraná-Brazil

Crop Data Retrieval Using Earth Observation Data to Support Agricultural Water Management

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

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