Abstract
This study tested and evaluated a suite of nine individual base learners and seven model averaging techniques for predicting the spatial distribution of soil properties in central Iran. Based on the nested-cross validation approach, the results showed that the artificial neural network and Random Forest base learners were the most effective in predicting soil organic matter and electrical conductivity, respectively. However, all seven model averaging techniques performed better than the base learners. For example, the Granger–Ramanathan averaging approach resulted in the highest prediction accuracy for soil organic matter, while the Bayesian model averaging approach was most effective in predicting sand content. These results indicate that the model averaging approaches could improve the predictive accuracy for soil properties. The resulting maps, produced at a 30 m spatial resolution, can be used as valuable baseline information for managing environmental resources more effectively.
Highlights
In recent years, rapid population growth and the increasing demand for food have had undesirable consequences on the environment
Due to the limestone-enriched parent materials, most soils are highly calcareous throughout the region [28], and because of the low precipitation in arid and semiarid regions, calcium carbonates tend to accumulate in the surficial soils [56]
Sand, and electrical conductivity (EC) values were located in the arid parts of the study area with low precipitation and high temperatures
Summary
Rapid population growth and the increasing demand for food have had undesirable consequences on the environment. In Iran, where 85% of the country is arid or semiarid [3], the intrinsic properties of soil, such as SOM, CCE, gypsum content, soil texture, electrical conductivity (EC), soil pH, and soil reactivity have been shown to be related to soil quality and are commonly considered the main factors in soil quality assessments [4]. These properties are highly variable in space and time [5]—especially in agricultural systems, due to the
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