Soybean yield modeling using bootstrap methods for small samples

Dalposso, Gustavo Henrique; Uribe-Opazo, Miguel Ángel; Johann, Jerry Adriani

doi:10.5424/sjar/2016143-8635

Cited by 13 publications

(8 citation statements)

References 40 publications

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“…These conclusions are also supported by the confidence intervals constructed for the average of similarity measures using the bootstrap method and 95% confidence level (Dalposso et al, 2016). These confidence intervals are shown in Table 1 for all simu lations grouped according to the anisotropic ratio, the samp ling configuration and type of comparison performed.…”

Section: Analysis Of Simul Ated Datasupporting

confidence: 63%

Relationship Between Sample Design and Geometric Anisotropy in the Preparation of Thematic Maps of Chemical Soil Attributes

et al. 2018

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Spatial variability depends on the sampling configuration and characteristics associated with the georeferenced phenomenon, such as geometric anisotropy. This study aimed to determine the influence of the sampling design on parameter estimation in an anisotropic geostatistical model and the spatial estimation of a georeferenced variable at unsampled locations. Datasets were simulated with geometric anisotropy, considering five values for the anisotropic ratio (), and three sampling designs: lattice, random and lattice plus close pairs. The simulation results were used as a reference to select anisotropic models to describe the spatial dependence structure in chemical soil properties. For each dataset (with either simulated or chemical soil properties), the values of the georeferenced variables at unsampled locations were estimated by kriging, considering estimated isotropic and anisotropic geostatistical models. The choice of the sampling design influenced the spatial estimation of the georeferenced variable and the quality of the estimation of the geostatistical anisotropic model. The incorporation of geometric anisotropy in the spatial estimation of simulated data sets and soil chemical properties produced differences in the spatial estimation and improved the level of detail of subregions in thematic maps.

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Section: Analysis Of Simul Ated Datasupporting

confidence: 63%

Relationship Between Sample Design and Geometric Anisotropy in the Preparation of Thematic Maps of Chemical Soil Attributes

et al. 2018

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Section: Discussionmentioning

confidence: 99%

“…As we had a large number of estimated coefficients (eight) and a small number of sampling units (47 plots), we performed model selection using bootstrapping, because normal AIC can be biased when sample sizes are small (Dalposso, Uribe‐Opazo, & Johann, ). Data were resampled 1500 times and predictors were selected by stepwise procedure, using the boot.stepAIC() function of the package bootStepAIC (R Core Team), accepting predictors selected in ≥60% of bootstrapped samples (Austin & Tu, ).…”

Section: Methodsmentioning

confidence: 99%

Wood density, growth and mortality relationships of lianas on environmental gradients in fragmented forests of montane landscapes

Roeder

Pasion

et al. 2019

J Vegetation Science

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Aim A better understanding of plant communities can be achieved by incorporating data of traits and dynamics into surveys. Wood density is a good predictor for growth and mortality in trees, but to date, no studies of lianas include all three. We examine how liana communities respond to environmental gradients and forest fragmentation in terms of abundance, diversity, size structure, mortality, relative growth rate and wood density, and tested how the latter three are related to each other in liana species. Location Xishuangbanna, SW China. Methods We repeated a survey of lianas (stems >0.5 cm diameter) in 47 plots, distributed in forest fragments of various size, on different bedrock (limestone or sandstone), over an elevational range and across different topographic elements (ridge, slope, valley). We gathered wood density data for 116 of 166 species, covering 90% of all surveyed stems. We also determined relative growth rate, mortality, stem diameter and basal area. Results At the species level, liana mortality and relative growth rate were lower at higher wood density, and mortality was higher with greater relative growth rate. At the plot level, liana communities in valleys had high relative growth and mortality rates as well as high abundance and diversity. Forest on limestone hosted few species but more large‐stemmed liana individuals, and communities had higher wood density weighted by basal area. Liana abundance, relative growth rates and mortality were greater and average wood density lower towards fragment edges, but the explanatory power of these models was low. Conclusion Habitat was the major factor shaping liana communities, whereas fragmentation was not an important predictor in our study. Resource‐rich environments such as valleys harbour diverse liana communities with high mortality and relative growth rates. This pattern matches earlier studies on survival/growth trade‐offs among plant species. The relationship between growth rates, mortality and wood density in lianas follows the same trade‐offs as found in trees.

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“…The bootstrap method is well known and has been used in studies involving independent samples of soybean yield (Dalposso et al, 2016;Gupta & Manjaya, 2016). The bootstrap methods for spatially dependent data have been highlighted in the literature, due to the importance of uncertainty modeling in the analyzes, as can be observed in the works of Kang et al (2008), Schelin & Sjöstedt-De Luna (2010), Olea & Pardo-Igúzquiza (2011) and Pardo-Igúzquiza & Olea (2012).…”

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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Soybean yield modeling using bootstrap methods for small samples

Cited by 13 publications

References 40 publications

Relationship Between Sample Design and Geometric Anisotropy in the Preparation of Thematic Maps of Chemical Soil Attributes

Relationship Between Sample Design and Geometric Anisotropy in the Preparation of Thematic Maps of Chemical Soil Attributes

Wood density, growth and mortality relationships of lianas on environmental gradients in fragmented forests of montane landscapes

Gaussian Spatial Linear Model of Soybean Yield Using Bootstrap Methods

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