Quantifying salinity in calcareous soils through advanced spectroscopic models: A comparative study of random forests and regression techniques across diverse land use systems.

Precise prediction of soil salinity using visible, and near-infrared (vis-NIR) spectroscopy is crucial for ensuring food security and effective environmental management. This paper focuses on the precise prediction of soil salinity utilizing visible and near-infrared (vis-NIR) spectroscopy, a critical factor for food security and effective environmental management. The objective is to utilize vis-NIR spectra alongside a multiple regression model (MLR) and a random forest (RF) modeling approach to predict soil salinity across various land use types, such as farmlands, bare lands, and rangelands accurately.

A comprehensive model for predicting the development of defense system of Capparis spinosa L.: a novel approach to assess the physiological indices.

Capparis spinosa L. (caper) is a halophytic plant that grows in semi-arid or arid environments. The current study used an integrated experimental and computational approach to investigate the network of inter-correlated effective variables on the activity of antioxidant enzymes, proline, and photosynthetic pigments in stressed caper.

New non-ureolytic heterotrophic microbial induced carbonate precipitation for suppression of sand dune wind erosion.

The detrimental effects of sand storms on agriculture, human health, transportation network, and infrastructures pose serious threats in many countries worldwide. Hence, wind erosion is considered a global challenge. An environmental-friendly method to suppress wind erosion is to employ microbially induced carbonate precipitation (MICP).