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. To this end, we selected 150 sampling points representatives of these diverse land uses. At each point, we collected soil samples to measure the soil salinity (ECe) and employed a portable spectrometer to capture the spectral reflectance across the full wavelength range of 400 to 2400 nm. The methodology involved using both individual spectral reflectance values and combinations of reflectance values from different wavelengths as input variables for developing the MLR and RF models. The results indicated that the RF model (RMSE = 4.85 dS m-1, R2 = 0.87, and RPD = 3.15), utilizing combined factors as input variables, outperformed others. Furthermore, our analysis across different land uses revealed that models incorporating combined input variables yielded significantly better results, particularly for farmlands and rangelands. This study underscores the potential of combining vis-NIR spectroscopy with advanced modeling techniques to enhance the accuracy of soil salinity predictions, thereby supporting more informed agricultural and environmental management decisions.

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. To investigate the possible relationships among intercorrelated variables and understand the possible mechanisms, predictive regression modelling, principal component analysis (PCA), Pearson's correlation, and path analysis were implemented. PCA successfully discerned different salt ratio- and drought-specific effects in data in the current study, and treatments with higher growth indices are easily recognizable. Different salt ratios did not have a significant effect on the activity of four antioxidant enzymes, proline and photosynthesis pigments content of caper. While at the mean level, the activity of four antioxidant enzymes of SOD, POD, CAT, and APX significantly increased under drought stress by 54.0%, 71.2%, 79.4%, and 117.6%, respectively, compared to 100% FC. The drought stress also significantly increased the content of carotemoid (29.3%) and proline (by 117.7%). Predictive equation models with highly significant R2 were developed for the estimation of antioxidant enzyme activity and proline content (> 0.94) as well as pigments (> 0.58) were developed. Path analysis studies revealed that proline is the most important regressor in four antioxidant enzyme activities, while leaf tissue density was the most effective variable in the case of chlorophylls. Furthermore, the network of intercorrelated variables demonstrated a close relationship between caper's antioxidant defence system, pigments, and morphological parameters under stress conditions. The findings of this study will be a useful guide to caper producers as well as plant ecophysiological researchers.

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). However, the by-products of ureolysis-based MICP, such as ammonia, are not favorable when produced in large volumes. This study introduces two calcium formate-bacteria compositions for non-ureolytic MICP and comprehensively compares their performance with two calcium acetate-bacteria compositions, all of which do not produce ammonia. The considered bacteria are Bacillus subtilis and Bacillus amyloliquefaciens. First, the optimized values of factors controlling CaCO3 production were determined. Then, wind tunnel tests were performed on sand dune samples treated with the optimized compositions, where wind erosion resistance, threshold detachment velocity, and sand bombardment resistance were measured. An optical microscope, scanning electron microscope (SEM), and X-ray diffraction analysis were employed to evaluate the CaCO3 polymorph. Calcium formate-based compositions performed much better than the acetate-based compositions in producing CaCO3. Moreover, B. subtilis produced more CaCO3 than B. amyloliquefaciens. SEM micrographs clearly illustrated precipitation-induced active and inactive bounds and imprints of bacteria on CaCO3. All compositions considerably reduced wind erosion.