In this study, the potentiality of visible and near-infrared reflectance spectroscopy to estimate soil organic matter was assessed. Six preprocessing methods were implemented to process the original spectra. The partial least-squares regression approach was also applied to construct predictive models and evaluate the optimal spectral preprocessing method. The significant wavelengths of soil organic matter were determined by using the correlation analysis and the partial least-squares regression analysis. The results were: (i) visible and near-infrared reflectance spectroscopy was proved to be an ideal approach in the soil organic matter estimation; (ii) different preprocessed spectra could improve their correlation with soil organic matter; the combination of first-order derivative and Savitzky–Golay smoothing method outperformed other preprocessing methods; (iii) the soil organic matter predictive models based on spectra processed by derivatives and Savitzky–Golay smoothing together presented a satisfactory accuracy, yielding the determination coefficient and root mean square error values of 0.986 and 0.077, respectively, for first-order derivative; and 0.973 and 0.105, respectively, for second-order derivative. The combination of first-order derivative and Savitzky–Golay smoothing was ultimately recommended the preferable preprocessing method; and (iv) the wavelengths of 417, 1853, 1000, and 2412 nm were determined as the significant wavelengths associated with soil organic matter. The study will provide a reference for the site specific management of agricultural inputs by using the visible and near-infrared reflectance spectroscopy technology.

The saturation problem associated with the use of normalized difference vegetation index for crop variable estimation is well known. However, its physical mechanism is not systemically explored. The wavebands computing the vegetation indices also suffered saturation when the leaf area index and aboveground biomass reached to 2.5 and 1 kg m⁻², respectively. We thought that the saturation might be not only referred to normalized difference vegetation index, but also to certain wavebands. Furthermore, the performances of seven different vegetation indices were assessed on overcoming the saturation. The findings will improve our understanding of the spectral saturation.

To extract the sensitive bands for estimating the winter wheat growth status and yields, field experiments were conducted. The crop variables including aboveground biomass (AGB), soil and plant analyzer development (SPAD) value, yield, and canopy spectra were determined. Statistical methods of correlation analysis, partial least squares (PLS), and stepwise multiple linear regression (SMLR) were used to extract sensitive bands and estimate the crop variables with calibration set. The predictive model based on the selected bands was tested with validation set. The results showed that the crop variables were significantly correlated with spectral reflectance. The major spectral regions were selected with the B-coefficient and variable importance on projection (VIP) parameter derived from the PLS analysis. The calibrated SMLR model based on the selected wavelengths demonstrated an excellent performance as the R², TC, and RMSE were 0.634, 0.055, and 843.392 for yield; 0.671, 0.017, and 1.798 for SPAD; and 0.760, 0.081, and 1.164 for AGB. These models also performed accurately and robustly by using the field validation data set. It indicated that these wavelengths retained in models were important. The determined wavelengths for yield, SPAD, and AGB were 350, 410, 730, 1015, 1185 and 1245 nm; 355, 400, 515, 705, 935, 1090, and 1365 nm; and 470, 570, 895, 1170, 1285, and 1355 nm, respectively. This study illustrated that it was feasible to predict the crop variables by using the multivariate method. The step-by-step procedure to select the significant bands and optimize the prediction model of crop variables may serve as a valuable approach. The findings of this study may provide a theoretical and practical reference for rapidly and accurately monitoring the crop growth status and predicting the yield of winter wheat.

Fertilization practices change soil organic carbon content and distribution, which is relevant to crop rotation and soil aggregates. However, how fertilization management under corn–soybean rotation affects soil organic carbon and aggregate stability at different soil depths in Mollisols is unclear. The effects of 6–yr fertilization under corn–soybean rotation on aggregate stability, soil organic carbon content and storage, and size distribution in soil aggregates were investigated. Five different fertilization practices were carried out in 2013: corn and soybean without fertilizer; corn with chemical fertilizer, soybean without fertilizer; corn with chemical fertilizer, soybean without fertilizer, returning the corn and soybean residues; corn and soybean with chemical fertilizer; and corn with chemical fertilizer, soybean with farmyard manure. Compared with corn and soybean without fertilizer, returning the corn and soybean residues increased bulk SOC content, and enhanced mean weight diameter and geometric mean diameter values at 0–10 cm because of increased water–stable aggregates (WSA) larger than 2 mm proportion and decreased WSA_<0.053mm proportion. Simultaneously, corn with chemical fertilizer and soybean with farmyard manure increased bulk soil organic carbon content but reduced mean weight diameter and geometric mean diameter values at 0–20 cm due to increased WSA_<0.053mm proportion and decreased WSA_>2mm proportion. Altogether, the application of consecutive returning crop residues and chemical fertilizer in alternate years is the most favorable approach for soil organic carbon accumulation and aggregate stability at 0–10 cm under corn–soybean rotation in Mollisols.

The aims of this study were to examine interrelationships between microbial biomass and enzyme activities of soil quality and to determine their suitability for differentiating areas. The study included five simulated erosion soil depths (0, 5, 10, 20, and 30 cm), which had received contrasting fertilizer treatments over a 5-year period in an eroded black soil of northeastern China. Our results indicated that soil microbial carbon, nitrogen, urease, phosphatase, and invertase activities declined as the erosion depth increased. On the five erosion depths, soil microbial carbon, phosphatase, and invertase variation ranged as follows: 35.4–53.3, 39.8–45.2, and 55.9–67.1%, respectively. In the fertilizer + manure treatment, soil microbial carbon, nitrogen, urease, phosphatase, and invertase activities were significantly greater (P < 0.05) than those of fertilizer treatment in all five different erosion depths. Overall, this study may be considered as the foundation for soil quality evaluation and fertility restoration in northeastern China and similar regions.