Spatial and multivariate analysis of soybean productivity and soil physical-chemical attributes

Buss, Ricardo Niehues; Silva, Raimunda Alves; Siqueira, Glécio Machado; Leiva, Jairo Osvaldo Rodriguez; Oliveira, Osmann Cid Conde; França, Victor Lamarão de

doi:10.1590/1807-1929/agriambi.v23n6p446-453

Cited by 5 publications

(8 citation statements)

References 19 publications

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“…In this context, methods of spatial area statistics are used, which simultaneously consider information regarding the value and geographical location of variables. Buss et al (2019) verified the spatial variability of soybean productivity in relation to soil attributes using multivariate and geostatistical techniques. The result showed the efficiency of the statistical methods covered in the study.…”

Section: Introductionmentioning

confidence: 86%

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: Introductionmentioning

confidence: 86%

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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“…Several studies have examined the spatial variability in soybean yield (CAMBARDELLA et al, 1994;SILVA et al, 2010;VIEIRA et al, 2010;LIMA et al, 2013;SIQUEIRA et al, 2015a;GAVIOLI et al, 2016;BUTTAFUOCO et al, 2017;FREDDI et al, 2017;BUSS et al, 2019;JIANSHU, 2019). According to Vieira (2000), some soil natural variability always exists but may be altered by soil use and management.…”

Section: Introductionmentioning

confidence: 99%

“…Multivariate analysis describes the variance and covariance structure of variable groups by constructing linear combinations and reducing the number of dimensions (JEFFERS, 1978;SILVA et al, 2010;LIMA et al, 2013;BUSS et al, 2019). Therefore, studies involving the application of geostatistical and multivariate techniques provide a multidimensional understanding and higher accuracy in defining management zones.…”

Section: Introductionmentioning

confidence: 99%

“…Multiple approaches (geostatistical and multivariate analysis) have been used by researchers to describe the spatial variability in plants and soils (SILVA et al, 2010;SILVA;LIMA, 2012;LIMA et al, 2013;GAVIOLI et al, 2016;BUTTAFUOCO et al, 2017;FREDDI et al, 2017;MASOUD et al, 2018;OUMENSKOU et al, 2018;URIBEETXEBARRIA et al, 2018;BUSS et al, 2019), demonstrating its importance.…”

Section: Introductionmentioning

confidence: 99%

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Management Zones Design for Soybean Crop Using Principal Components and Geostatistics

et al. 2022

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In precision agriculture, determining management zones for soil and plant attributes is a complex process that requires knowledge of several variables, which complicates management and decisionmaking processes. This study evaluated the spatial variability of soybean yield and soil chemical properties using geostatistical and multivariate analyses to define management zones in an Oxisol. The soybean yield and soil chemical properties between 0 to 0.2 and 0.2 to 0.4 m soil depths were sampled at 70 points. Geostatistical and multivariate analyses were then performed on these data. The soil chemical properties showed higher variability at 0.2 to 0.4 m soil depth. The semivariogram parameters of the principal component analysis (PCA) data (PCA 1, PCA 2, and PCA 3) for both depths were more homogeneous than the original data. The maps of soil chemical properties showed high similarity to the soybean yield map. The PCA explained 65.34% (0 to 0.2 m) and 70.50% (0.2 to 0.4 m) of data variability, grouping the soybean yield, organic matter, pH, phosphorous, potassium, calcium, magnesium, and sodium. PCA spatialization allowed for the definition of management zones indicated by PCA 1, PCA 2, and PCA 3 for both depths. The result indicates that the area must be managed using different strategies of soil fertility management to increase soybean yield.

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“…Despite the supply of nutrients, the water reuse and sewage sludge might impact the agronomic soil quality [8] and the agricultural sustainability, depending on its physicalchemical characteristics. Thus, proper management in agricultural production requires knowledge about the uniformity of crop yield and soil attributes, and their spatial variability, thus enabling the adoption of specific management zones [9]. Geostatistical analysis has been an adequate technique for the analysis of spatial dependencies [10], allowing the identification of variables that show spatial correlation under different management conditions [11].…”

Section: Introductionmentioning

confidence: 99%

Soil and crop spatial variability in maize irrigated with domestic effluent

Almeida

Montenegro

Carvalho

et al. 2021

DYNA

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Wastewater use in agriculture is a high-potential alternative for coping with water scarcity. The present study aimed to evaluate the impact of using wastewater and sewage sludge on soil attributes in an irrigated maize plantation in the Brazilian northeastern semi-arid region, at an experimental reuse unit. Two experimental plots were installed with the following treatments: wastewater and sludge, and irrigation with wastewater only. The presence of sludge reduced the salt content and promoted an increase in the soil organic matter content, reducing the salinization risk. Biometric parameters showed no spatial dependence. On the other hand, soil moisture, electrical conductivity, sand content, clay content and sodium and potassium contents showed strong spatial dependence for the treatment with sludge application.

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Spatial and multivariate analysis of soybean productivity and soil physical-chemical attributes

Cited by 5 publications

References 19 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

Management Zones Design for Soybean Crop Using Principal Components and Geostatistics

Soil and crop spatial variability in maize irrigated with domestic effluent

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