• This study quantified global soil C research trends and the proportional focus on SOC and SIC pools. • Soil inorganic C is an overlooked pool with only 4% share in global soil C research. • Approximately 40% of the soil C research was dedicated to climate change. • Soil C research is concentrated in few countries with 70% research contributed by 9 countries. • It is imperative to expand research focus on SIC to gain a comprehensive understanding of global C cycle.

The Xinjiang Region in Northwest China is known as the “dust center” of the Eurasian mainland. Dust on the leaf surface affects overall plant development. While emphasis was on studying the impacts of industrial dust particles on crop development, the effect of natural dust deposition on the physiological parameters of cotton had not been studied before. The objective of this study was to examine the effects of dust deposits on cotton leaves and to estimate their impact on crop development and yield. For this purpose, an experiment was set up having two treatments and a control. In Treatment 1, cotton leaves were cleaned with water at three-day intervals or after a natural dust fall. In Treatment 2, 100 g·m⁻² of dust was applied at 10-day intervals. The control received neither additional dust nor cleaning. In all of the treatments, stomatal conductance, leaf temperature, biomass and yield were measured. The results show a 28% reduction in yield and 30% reduction in stomatal conductance of the dust treatment compared to the control treatment. This indicates blocking of the stomata on the top of the leaf surface. In addition, the canopy temperature of the dust-applied leaves was always higher than the control and treatment.

The long-term overuse of fertilizers negatively affects soil chemical properties and health, causing unsustainable agricultural development. Although many studies have focused on the effects of long-term fertilization on soil properties, few comparative and comprehensive studies have been conducted on fertilization management over the past 35 years in China. This meta-analysis (2058 data) evaluated the effects of the fertilizer, climate, crop types, cultivation duration and soil texture on the soil chemical properties of Chinese croplands. NPKM (NPK fertilizers + manure) led to the highest increase in pH (−0.1), soil organic carbon (SOC) (+67%), total nitrogen (TN) (+63%), alkali-hydrolysable nitrogen (AN) (+70%), total phosphorus (TP) (+149%) and available potassium (AK) (+281%) compared to the unfertilized control, while the sole nitrogen fertilizer (N) led to the lowest increase. The SOC (+115%) and TN (+84%) showed the highest increase under the influence of NPKM in an arid region. The increase in the chemical properties was higher in unflooded crops, with the maximum increase in the wheat–maize rotation, compared to rice, under NPKM. The SOC and TN increased faster under the influence of organic fertilizers (manure or straw) compared to mineral fertilization. Fertilizers produced faster effects on the change in the SOC and TN in sandy loam compared to the control. Fertilizers showed the highest and lowest effects on change in pH, organic C to total N ratio (C/N), TP and TK in clay loam with the cultivation duration. NPKM greatly increased the C/N compared to NPK in an arid region by 1.74 times and in wheat by 1.86 times. Reaching the same SOC increase, the lowest TN increase was observed in wheat, and the lowest increase in TP and AK was observed in rice, compared to the other crops. These results suggest that organic fertilizers (manure or straw) play important roles in improving soil fertility and in acidification. NPKM greatly increased the potential for soil C sequestration in wheat and in the arid region. The small increases in TP and TK can increase the SOC in rice and in the humid region. Therefore, considering the crop types and climatic conditions, reduced fertilization and the combination of mineral fertilizers with manure may be the best ways to avoid agricultural soil deterioration and increase soil carbon sequestration.

An accurate estimation of soil electrical conductivity (EC) using hyperspectral techniques is of great significance for understanding the spatial distribution of solutes and soil salinization. Although spectral transformation has been widely used in data pre-processing, the performance of different pre-processing techniques (or combination methods) on different models of the same data set is still ambiguous. Moreover, extremely randomized trees (ERT) and light gradient boosting machine (LightGBM) models are new learning algorithms with good generalization performance (soil moisture and above-ground biomass), but are less studied in estimating soil salinity in the visible and near-infrared spectra. In this study, 130 soil EC data, soil measured hyperspectral data, topographic factors, conventional salinity indices such as Salinity Index 1, and two-band (2D) salinity indices such as ratio indices, were introduced. The five spectral pre-processing methods of standard normal variate (SNV), standard normal variate and detrend (SNV-DT), inverse (1/OR) (OR is original spectrum), inverse-log (Log(1/OR) and fractional order derivative (FOD) (range 0–2, with intervals of 0.25) were performed. A gradient boosting machine (GBM) was used to select sensitive spectral parameters. Models (extreme gradient boosting (XGBoost), LightGBM, random forest (RF), ERT, classification and regression tree (CART), and ridge regression (RR)) were used for inversion soil EC and model validation. The results reveal that the two-dimensional correlation coefficient highlighted EC more effectively than the one-dimensional. Under SNV and the second order derivative, the two-dimensional correlation coefficient increased by 0.286 and 0.258 compared to the one-dimension, respectively. The 13 characteristic factors of slope, NDI, SI-T, RI, profile curvature, DOA, plane curvature, SI (conventional), elevation, Int2, aspect, S1 and TWI provided 90% of the cumulative importance for EC using GBM. Among the six machine models, the ERT model performed the best for simulation (R² = 0.98) and validation (R² = 0.96). The ERT model showed the best performance among the EC estimation models from the reference data. The kriging map based on the ERT simulation showed a close relationship with the measured data. Our study selected the effective pre-processing methods (SNV and the 2 order derivative) using one- and two-dimensional correlation, 13 important factors and the ERT model for EC hyperspectral inversion. This provides a theoretical support for the quantitative monitoring of soil salinization on a larger scale using remote sensing techniques.