Mahaveer Nogiya

ABSTRACT Soil pH plays a crucial role in crop production, directly influencing nutrient availability and microbial activity, thereby impacting environmental and ecosystem functions. High-resolution spatial data are essential for sustainable agricultural practices. This study represents the first attempt to generate a spatial distribution prediction map for soil pH across Rajasthan using 893 legacy soil profiles, along with digital auxiliary data and topographic variables.

ABSTRACT Soil depth is essential for eco-hydrological modeling, carbon storage estimation, and land evaluation, yet its spatial variation is often poorly understood and rarely mapped, particularly in complex landscapes with limited sample sizes. Digital soil mapping, employing various machine learning methods, has become crucial in predicting and mapping soil attributes. This study investigates soil depth distribution in Rajasthan and Gujarat, India, using machine learning techniques.