Soybean yield maps using regular and optimized sample with different configurations by simulated annealing

Guedes, Luciana Pagliosa Carvalho; Ribeiro, Paulo Justiniano; Uribe-Opazo, Miguel Ángel; Bastiani, Fernanda De

doi:10.1590/1809-4430-eng.agric.v36n1p114-125/2016

Cited by 4 publications

(3 citation statements)

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“…Among the studies involving soybean yield, we highlight the geostatistics methods used to detect the spatial variability in the crops (Borssoi et al, 2011;Kestring et al, 2015;Guedes et al, 2016). Although geostatistics allows the understanding of spatial variability of soybean yield, the samples used in the analyzes are generally few and sparse (Pardo-Igúzquiza & Olea, 2012), then there are uncertainties associated with the results obtained.…”

Section: Introductionmentioning

confidence: 99%

Gaussian Spatial Linear Model of Soybean Yield Using Bootstrap Methods

et al. 2018

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This study aims to quantify the uncertainties associated to the parameters of a Gaussian spatial linear model (GSLM) and the assumption of normality residuals in the modeling of the spatial dependence of the soybean yield as a function of soil chemical attributes. The spatial bootstrap methods were used to determine the point and interval estimators associated with the model parameters. Hypothesis tests were carried out on the significance of the model parameters and the quantile-quantile probability plot was elaborated to verify the data normality. The uncertainties associated to the parameters of the spatial dependence structure were quantified and the potassium content, phosphorus content and soil pH covariates were significant to explain the soybean yield mean. These covariates were used in the elaboration of a new model, which provided the elaboration of a contour map of soybean yield. Analysis of the quantile-quantile plot indicated that soybean yield data follow a normal probability distribution.

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Section: Introductionmentioning

confidence: 99%

Gaussian Spatial Linear Model of Soybean Yield Using Bootstrap Methods

et al. 2018

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“…The costs of collecting and analyzing samples of soil attributes have led to the development of many studies within the scope of sample resizing, aiming to reduce sampling costs, considering a minimal loss of information in spatial prediction. Among these, we can mention from the optimization algorithms (Guedes et al, 2016;Wadoux et al, 2017;Maltauro et al, 2019) to the use of the Effective Sample Size (ESS) . The calculation of the effective sample size considers the effect of spatial autocorrelation between the sampled points collected.…”

Section: Introductionmentioning

confidence: 99%

Reduction of Sample Size in the Soil Physical-Chemical Attributes Using the Multivariate Effective Sample Size

Canton

Guedes

Uribe-Opazo

2021

JAS

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Financial investment with collection and laboratory analysis of soil samples is an important factor to be considered when mapping agricultural areas with soybean planting. One of the alternatives is to use the spatial autocorrelation between the sample points to reduce the number of elements sampled, thus restricting the collection of redundant information. This work aimed to reduce the sample size of this agricultural area, composed of 102 sample points, and use it to analyze the spatial dependence of soil macro- and micro- nutrients, as well as the soil penetration resistance. The agricultural area used in this study has 167.35 ha, cultivated with soybean, which the soil is Red Dystroferric Latosol, and the sampling design has used in this agricultural area is the lattice plus close pairs. The reduction of the sample size was made by the multivariate effective sample size (ESSmulti) methodology. The studies with the simulation data and the soil attributes showed an inverse relationship between the practical range and the estimated value of the univariate effective sample size. With the calculation of ESSmulti, the sample configuration was reduced to 53 points. The Overall Accuracy and Tau concordance index showed differences between the thematic maps elaborated with the original and reduced sampling designs. However, the analysis of the variance inflation factor and the standard error of the spatial dependence parameters showed efficient results with the resized sample size.

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“…There are criteria of optimization efficiency based on spatial prediction (mean or weighted variance, sum of the quadratic error, measure of accuracy, overall accuracy, etc.) (Guedes et al, 2011;Guedes et al, 2016;Szatmári et al, 2018), as well as criteria that consider the efficiency as the geostatistical model estimation, such as the objective function based on the inverse-Fisher information matrix (Zhu & Stein, 2005).…”

Section: Introductionmentioning

confidence: 99%

Reduction of Sample Size in the Analysis of Spatial Variability of Nonstationary Soil Chemical Attributes

2019

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In the study of spatial variability of soil attributes, it is essential to define a sampling plan with adequate sample size. This study aimed to evaluate, through simulated data, the influence of parameters of the geostatistical model and sampling configuration on the optimization process, and resize and reduce the sample size of a sampling configuration of a commercial area composed of 102 points. For this, an optimization process called genetic algorithm (GA) was used to optimize the efficiency of the geostatistical model estimation based on the Fisher information matrix. The simulated data evidenced that the variation of the nugget effect or practical range did not significantly alter the sample size. GA was efficient in reducing the sample size, determining for soil chemical attributes a sample size between 30 and 40 points (29.41 to 39.22% of the initial sampling grid). The presence of spatial dependence was observed for all soil chemical attributes in the two sampling configurations (initial and optimized). The optimized sampling configuration evidenced an increase in trend intensity in the north direction and a more efficient estimation of parameters of the linear spatial regression model.

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Soybean yield maps using regular and optimized sample with different configurations by simulated annealing

Cited by 4 publications

References 11 publications

Gaussian Spatial Linear Model of Soybean Yield Using Bootstrap Methods

Gaussian Spatial Linear Model of Soybean Yield Using Bootstrap Methods

Reduction of Sample Size in the Soil Physical-Chemical Attributes Using the Multivariate Effective Sample Size

Reduction of Sample Size in the Analysis of Spatial Variability of Nonstationary Soil Chemical Attributes

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