Identifying an Appropriate Extractant for Assessing the Presence of Available Potassium in Soils with High Mica Content: A Comprehensive Statistical Analysis

Authors: Ghosh, S., Banerjee, S., Basak, K., Prajapati, J., Majumder, S. and Bhattacharyya, P.

Journal: Communications in Soil Science and Plant Analysis

eISSN: 1532-2416

ISSN: 0010-3624

DOI: 10.1080/00103624.2025.2452162

Abstract:

The choice of extractant for estimating plant-available potassium (K) in mica-rich soil has not been universally established. In order to evaluate and monitor the complex chemical performance of K in K-rich soil, and subsequently, its availability in crops, a suitable extractant protocol that takes into consideration soil properties in relation to rice plant parts (root, shoot, and grain) uptake becomes necessary. Nine laboratory soil K extraction methods to estimate the K content using 63 soil samples collected from nearly 21 mica mines, encompassing a wide range of properties across different agricultural lands. While extraction using ammonium acetate is the widely used for soil K testing, other methods have also been proposed for estimating crop-available K. Among the various extractants tested, 0.5 M sodium bicarbonate at pH 8.5 (mean 132.24 mg/kg) gave the best results for estimating the K content in mica-rich soil from agricultural areas near mica mines. Additionally, the soil water-soluble K extractant showed promise for rice plant parts such as root, shoot, and grain K uptake, as indicated by a correlation coefficient of 0.94, 0.94, and 0.93 respectively. Based on the artificial neural network (ANN) and Sobol (variance-based method for determining how different input variables contribute to the output variance of a model) methods, water-soluble K and the Mehlich 3 extraction procedure showed the best results for suitable extractable forms of K and a positive impact on K uptake by rice grain. Taylor (a graphical tool used to compare various models) diagram used for comparing multiple machine learning algorithms, multivariate adaptive regression spline and extreme gradient boosting (XGBoost) emerged as the best-fit models for determining the contribution of each extractant to rice K uptake. Considering soils derived from weathered parent materials often have negative K balances, which could lead to negative of plant-available K.

Source: Scopus