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Soil hydraulic conductivity (K s ) is a crucial soil physical property that not only influences soil hydrological processes, but also the planning for vegetation recovery, irrigation practice and drainage design. However, K s data are often lacking at large-scale soil database due to difficulties in direct measurement that is often labour intensive, time consuming and cost inefficient. The objective of this study was to compare the performance of different emerging methods [Multiple linear regression (MLR) and artificial neural network (ANN)] of K s prediction. The pedotransfer function (PTF) is one such method that is based on selected factors closely correlated with K s at regional scale. We collected disturbed and undisturbed soil samples in the 0-40 cm soil layer at 243 sites across the entire typical Loess Plateau of China (430,000 km 2 ) and then measured K s and the potentially related factors. The results showed that K s was normally distributed with moderate a spatial variation (CV = 67%). Correlation analysis indicated that bulk density (BD), saturated soil water content (SSWC), clay content (Clay), silt content (Silt) and latitude were closely correlated (p b 0.05) with K s . Although the accuracies of MLR and ANN were equal in terms of estimating K s , the stability of PTF developed via ANN was not as good as that of MLR. Thus PTF developed via MLR, which included BD, Silt and Clay, was considered as the best model for estimating K s . There is a need to closely monitor the stability and repeatability of PTF during comparison and determination of PTF.

Abiotic stresses greatly influenced wheat productivity executed by environmental factors such as drought, salt, water submergence and heavy metals. The effective management at the molecular level is mandatory for a thorough understanding of plant response to abiotic stress. Understanding the molecular mechanism of stress tolerance is complex and requires information at the omic level. In the areas of genomics, transcriptomics and proteomics enormous progress has been made in the omics field. The rising field of ionomics is also being utilized for examining abiotic stress resilience in wheat. Omic approaches produce a huge amount of data and sufficient developments in computational tools have been accomplished for efficient analysis. However, the integration of omic-scale information to address complex genetics and physiological questions is still a challenge. Though, the incorporation of omic-scale data to address complex genetic qualities and physiological inquiries is as yet a challenge. In this review, we have reported advances in omic tools in the perspective of conventional and present day approaches being utilized to dismember abiotic stress tolerance in wheat. Attention was given to methodologies, for example, quantitative trait loci (QTL), genome-wide association studies (GWAS) and genomic selection (GS). Comparative genomics and candidate genes methodologies are additionally talked about considering the identification of potential genomic loci, genes and biochemical pathways engaged with stress resilience in wheat. This review additionally gives an extensive list of accessible online omic assets for wheat and its effective use. We have additionally addressed the significance of genomics in the integrated approach and perceived high-throughput multi-dimensional phenotyping as a significant restricting component for the enhancement of abiotic stress resistance in wheat.

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There is no universally accepted method for evaluating cadmium (Cd) bioavailability in soil. The diffusive gradient in thin films (DGT) technique is a promising tool, but there is considerable debate about its suitability. The ability of this technique to estimate Cd bioavailability in soils was compared with the abilities of other traditional chemical extraction techniques (soil solution, ethylene diamine tetraacetic acid (EDTA), acetic acid (HAc), calcium chloride (CaCl2), and pseudo-total Cd methods) based on a greenhouse experiment using pakchoi (Brassica chinensis) grown in 15 soils from different provinces of China. In addition, we assessed whether these methods were independent of the soil properties. Correlations between the plant and soil Cd concentrations measured with the traditional extraction techniques were dependent on the pH and organic carbon (OC) content, indicating that these methods are influenced by the soil properties. In contrast, the DGT measurements were independent of the soil properties and showed a higher correlation coefficient compared to that of the traditional techniques. Hence, the DGT technique is better and should be preferable for assessing Cd biological effectiveness in different soil types.