National-scale maps for soil aggregate size distribution parameters using pedotransfer functions and digital soil mapping data products

Purushothaman, Naveen K.; Reddy, Nagarjuna N.; Das, Bhabani S.

doi:10.1016/j.geoderma.2022.116006

Cited by 12 publications

(2 citation statements)

References 52 publications

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“…Detailed maps based on high-resolution sampling can act as stepping stones for many conservations policies at the national and international levels. Various attempts have been made to construct soil maps using digital and conventional methods at the global level (Arrouays et al, 2020; Grunwald, 2009; Hengl et al, 2014, 2015; Hengl, Leenaars, et al, 2017; Poggio et al, 2021) national-level (Purushothaman et al, 2022; Reddy et al, 2021) and at the local level (Arora et al, 2021; Dharumarajan et al, 2020, 2021, 2022; Kalambukattu et al, 2018; Kaushal et al, 2021; Santra et al, 2017; Srinivasan et al, 2022). However, most of the maps are at low resolution levels.…”

Section: Introductionmentioning

confidence: 99%

Characterization and classification of fine-resolution soil profile for precision agriculture using random forest and self-organizing map

Elias,

Sharma,

Goel

2024

Preprint

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The availability of high throughput soil profile information is an important component in precision agriculture to perform efficient soil management for sustainable production. We collected 14 soil physiochemical features from Nagpur, Pune, and Haveri, representing target environments of safflower cultivation, and also from our experiment station at Delhi, at fine resolution and created graphical maps to depict the variability. Additionally, we evaluated the predictive ability of two statistical learning models, random forest (RF) and self-organizing maps (SOM) against multinomial regression models for correctly classifying the soil profile. Clustering was performed around the medoids produced from the dissimilarity matrices of these models using partitioning around medoids (PAM) model. The robustness, versatility, and predictive ability of models in correctly classifying the soil profile to clusters were then tested using cross-validation which was repeated 100 times. This study was performed using training data with proportionate size varying from 60 to 95%, and increasing the unit area of observation up to nine times (or decreasing the total number of observations up to a ninth). RF model was found to be the best performing with average prediction accuracy above 85% in all settings which reached close to 100% in some settings. The predictive ability of all the models was maintained even when only the most influencing six variables were used for classification. The optimal training population size for prediction was found to be 70 to 80%. Based on our study, it is recommended to i) collect fine resolution edaphic features from a marginal farm before crop season, ii) use RF or SOM model to identify the most influencing features distinguishing the soil samples iii) expand the area of sample collection, find values for the most influencing features, and use RF model to correctly predict the class to which the new set of the soil belongs to.

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Section: Introductionmentioning

confidence: 99%

Characterization and classification of fine-resolution soil profile for precision agriculture using random forest and self-organizing map

Elias,

Sharma,

Goel

2024

Preprint

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“…Different types of variable selection/feature selection techniques include, (1) Filter methods, (2) wrapper method, (3) embedded methods and (4) ensemble methods, have been implemented in various studies [10], of which the recursive feature elimination has been majorly utilized for selecting the covariate parameters [5,[11][12][13][14][15][16]. Other variable selection measures that have been implemented includes, in-built variable feature importance of Random Forest (RF) [17,18], Boruta [19,20], Stepwise regression, stepwise AIC [21,22], Multicollinearity analysis, Pearsons or Kendall Correlation Analysis [23,24], etc., Iterative principal component analysis were adopted to reduce the high dimensionality of the reflectance and elevation variables for enabling the quantitative prediction of the soil physical properties [25][26][27]. Similarly, most intricate and complex genetic algorithm (GA) have been utilized for selecting the covariate parameters for predicting the soil organic carbon (SOC) [28].…”

Section: Introductionmentioning

confidence: 99%

Identifying Prominent Environmental Covariates Using Variable Selection Methodologies for Digital Soil Mapping of Tamil Nadu, India

Kshatriya,

Kumaraperumal,

Muthumanickam

et al. 2023

IJECC

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High dimensional datasets that depict intricate spatial variations are necessary to predict complex landscape structures and the corresponding soil properties taking into account the size of the research region in addition to the data attributes. The number and quality of the input datasets taken into consideration essentially determine the quantity and quality of the soil properties that may be predicted thanks to data-driven learning algorithms. The use of variable selection strategies both before and after the prediction can have a significant impact on the outcome and can lower the related computing load. The majority of commonly used variable selection techniques such as correlation analysis, stepwise regression and recursive feature elimination, among others perform recursive statistical/mathematical comparison to identify the significant covariates that improve the effectiveness of the algorithm proposed. In order to identify the effective environmental variables in predicting the soil attribute, this article investigated a widely used recursive ranking method called recursive feature elimination. The covariate layer that produced the lowest RMSE was placed first according to the rankings of the covariates provided by recursive feature elimination. The findings showed that among other factors physiography, mean rainfall, rock outcrop difference ratio, elevation and mean temperature will be effective in predicting the soil properties required for digital soil mapping.

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Soil Aggregate Stability Prediction Using a Hybrid Machine Learning Algorithm

Balamurugan

2024

Metaheuristics for Machine Learning

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National-scale maps for soil aggregate size distribution parameters using pedotransfer functions and digital soil mapping data products

Cited by 12 publications

References 52 publications

Characterization and classification of fine-resolution soil profile for precision agriculture using random forest and self-organizing map

Characterization and classification of fine-resolution soil profile for precision agriculture using random forest and self-organizing map

Identifying Prominent Environmental Covariates Using Variable Selection Methodologies for Digital Soil Mapping of Tamil Nadu, India

Soil Aggregate Stability Prediction Using a Hybrid Machine Learning Algorithm

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