In order to efficiently detect total nitrogen content in Korla fragrant pear leaves, near-infrared spectroscopy technology was utilized to develop a detection model. The collected spectra underwent various preprocessing techniques including first-order derivative, second-order derivative, Savitzky–Golay + second-order derivative, multivariate scattering correction, multivariate scattering correction + first-order derivative, and standard normal variable transformation + second-order derivative. A competitive adaptive reweighted sampling algorithm was employed to extract characteristic wavelengths, and a prediction model for the total nitrogen content of fragrant pear leaves was established by combining the random forest algorithm, genetic algorithm-based random forest algorithm, radial basis neural network algorithm, and extreme learning machine algorithm. The study found that spectral preprocessing of SNV + SD along with the radial basis neural network algorithm yielded better predictions for total nitrogen content of fragrant pear leaves. The validation set results showed an R² of 0.8547, RMSE of 0.291%, and RPD of 2.699. Therefore, the SNV + SD + CARS + RBF algorithm combination model proved to offer optimal comprehensive performance in predicting the total nitrogen content of fragrant pear leaves.

In order to formulate a reasonable water input model for cotton fields in southern Xinjiang for scientific and rational fertilization, to reduce soil carbon leaching, and to improve soil carbon sequestration capacity, an undisturbed soil column leaching test was used to simulate the current field management method in the study area. Two methods, drip irrigation and flood irrigation, were set up, and three irrigation and three nitrogen fertilizer levels were used to carry out the undisturbed soil column leaching test. The results showed that the amount and mode of water and nitrogen input affected the distribution and leaching loss of organic carbon and inorganic carbon in the soil. When the nitrogen application rate increased from 270 kg·hm⁻² to 450 kg·hm⁻², the leaching loss of soluble organic carbon and soluble inorganic carbon increased significantly. When the water input increased from 6000 m³·hm⁻² to 9000 m³·hm⁻², the leaching loss of DOC and DIC increased significantly. The carbon leaching loss under drip irrigation was higher than that under flood irrigation. The leaching rates of DOC and DIC were fastest under the conditions of high water (9000 m³·hm⁻²) and high fertilizer (450 kg·hm⁻²). This shows that water and nitrogen input and irrigation methods are important factors affecting soil carbon leaching. In the case of excessive water input, long-term high-frequency irrigation is the main factor affecting carbon leaching.

This study researched the effects of using various nitrogen (N) conservation measures on the residual characteristics of nitrate and ammonium N in soil and the associated N uptake by cotton plants. A field experiment with six treatments was conducted, as follows, no N application (DT1), conventional N application (DT2), 60% conventional N application combined with DCD (DT3), 60% conventional N application combined with NBPT (DT4), 60% conventional N application combined with cotton straw returning (DT5), and 60% conventional N application combined with DCD, NBPT, and cotton straw returning (DT6). The results showed that the cotton straws in the DT5 treatment were beneficial for the vegetative growth of cotton seedlings. However, it was observed that the later performance of the plants in this sample was poor in terms of height, biomass, and yield of cotton. The plant height in the DT6 sample increased by 15 cm compared with those in DT1, and the soil and plant analyzer development (SPAD) values of the fourth leaf from the top of the DT6 plants were higher than those in the DT1 and DT4 samples. The DT6 plants (60% Urea + DCD + NBPT + cotton straw) increased N use efficiency by up to 47%, and no significant decrease in biomass and cotton yield was observed compared to the DT2 sample. The residual content of nitrate N in the tillage layer increased gradually over time between two rounds of drip irrigation treatment applications. Compared with the DT2 treatment, the other treatments resulted in lower residual nitrate N contents. In summary, the application of N fertilizers at a reduced rate combined with N conservation measures may increase N use efficiency and decrease the risk of non-point source N fertilizer pollution, while maintaining the cotton yield.