Soil Explorer is an interactive mapping tool built on publicly available soil data from a variety of sources. Viewers can zoom in on a number of states and global regions, and explore data on soil moisture, temperature, drainage and many other facets. The...
The soil landscape rule-based model was used to disaggregate the soil map units by exploiting information in the Busia soil map legend and the map unit descriptions. These descriptions were used to generate rules that were applied to a fuzzy soil class map...
Agronomy Busia Area Digital Soil Mapping Disaggregate K-Means Clustering Kenya Multiresolution Ridgetop Flatness Multiresolution Valley Bottom Flatness Planform Curvature Profile Curvature Topographic Position Index
To determine specific crop suitability ratings for Busia area, decision matrices developed by Rachilo and Michieka (1991) were used to generate crop suitability classes for each soil map unit. Land suitability is the fitness of a given tract of land for a...
Agronomy Beans Busia Area Cabbage Cassava Coffee Corn Cotton Crop Suitability Map Decision Matrix Forage Crops Grazing Guavas Kale Kenya Land Suitability Map Unit Millets Onions Peanuts Rice Sorghum Sugarcane Sunflower Tomato
A raster stack of 23 environmental covariates primarily based on remote sensing for predicting soil properties. These covariates were selected to represent the factors of soil formation and included the following. (1) The 1 Arc-Second (30 m) NASA shuttle R...
How can soil classes within a soil map unit be disaggregated? To answer this question, a soil landscape-rule based approach was used to disaggregate the soil map units by exploiting information in the map legend and the map unit descriptions. The descripti...
Soil landscape inference model (SoLIM) maps soil type, not soil mapping units, under the assumption that soil properties are fairly homogeneous over a small spatial extent. Thus, typically it takes a raster-based approach which means that it divides the ar...
Six terrain attributes were subjected to K-means clustering performed in SAGA-GIS using the hill-climbing method (Rubin, 1967) using a terrain attribute combination of multiresolution valley bottom flatness, multiresolution ridgetop flatness, planform curv...
Agronomy Geographic Information Systems (GIS) K-Means Clustering Multiresolution Ridgetop Flatness Multiresolution Valley Bottom Flatness Planform Curvature Profile Curvature Slope Position Terrain Attributes Topographic Position Index
Although in many systems of land evaluation, single or land characteristics such as drainage condition or texture are used as a basis for diagnosis and for establishing suitability class-determining specifications, it is most useful and ideal to consider t...
To ensure that multivariate covariates are independent of each other, Gobin (2000) used principal components instead of the original environmental covariates as predictors to improve on the prediction for soil-landscape modelling. Therefore, all the origin...
The use of legacy soil data for digital soil mapping is determined by the type of data available. When soil profile data is available, it can be used as inputs to predict soil properties at unsampled locations (McBratney et al., 2003). This dataset contain...
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