The management of crop residues in farmland is crucial for increasing soil organic matter and reducing soil erosion. Identifying the regional extent of crop residue cover (CRC) is vital for implementing conservation tillage and formulating agricultural subsidy policies. The Google Earth Engine (GEE) and remote sensing images from 2019 to 2023 were used to obtain spectral characteristics before the maize seedling stage in Northeast China, followed by constructing the CRC estimation models using machine learning algorithms. To avoid the impact of multicollinearity among data, three machine learning algorithms—ridge regression (RR), partial least squares regression (PLSR), and least absolute shrinkage and selection operator (LASSO)—were employed. By comparing the accuracy of these methods, the most accurate model was determined and applied to subsequent CRC estimation. Based on the estimated CRC and Conservation Technology Information Center definitions of tillage practices, the conservation tillage mapping was completed, and the spatiotemporal distribution characteristics were thoroughly analyzed. The following findings were demonstrated: (1) the PLSR-based model outperformed RR (Pearson’s correlation coefficient (r) = 0.8875, R² = 0.7877, RMSE = 6.99%) and LASSO (r = 0.8903, R² = 0.7926, RMSE = 6.88%) with higher accuracy (r = 0.9264, R² = 0.8582, RMSE = 4.93%). (2) Over the five years, the average no-tillage (NT) proportion in the study area was 15.9%, reduced tillage (RT) was 17.8%, and conventional tillage (CT) was 66.3%. In 2020 and 2022, NT rates were significantly higher at 27.5% and 15.5%, while RT were 15.7% and 30.0%, respectively. (3) Compared to the Sanjiang and Liaohe Plains (RT = 1907 km² and 1336 km², and NT = 559 km² and 585 km², respectively), the Songnen Plain exhibited higher conservation tillage rates (where RT was 3791 km² and NT was 1265 km²). This provides crucial scientific evidence for the management and planning of conservation tillage, thereby optimizing farmland production planning, enhancing production efficiency, and promoting the development of sustainable agricultural production systems.

Nodulation is the most efficient nitrate assimilation system in the ecosystem, while excessive fertilization has an increased nitrate inhibition effect; deciphering the nitrate signal transduction mechanism in the process is of the utmost importance. In this study, genome-wide analyses of the GmCEP genes were applied to identify nodulation-related CEP genes; 22 GmCEP family members were identified, while GmCEP6 was mainly expressed in nodules and significantly responded to nitrate treatment and rhizobium infection, especially in later stages. Overexpression and CRISPR-Cas9 were used to validate its role in nodulation. We found that GmCEP6 overexpression significantly increased the nodule number, while GmCEP6 knock-out significantly decreased the nodule number, which suggests that GmCEP6 functions as a positive regulator in soybean nodulation. qRT-PCR showed that alterations in the expression of GmCEP6 affected the expression of marker genes in the Nod factor signaling pathway. Lastly, the function of GmCEP6 in nitrate inhibition of nodulation was analyzed; nodule numbers in the GmCEP6-overexpressed roots significantly increased under nitrogen treatments, which suggests that GmCEP6 functions in the resistance to nitrate inhibition. The study helps us understand that GmCEP6 promotes nodulation and participates in the regulation of nitrate inhibition of nodulation, which is of great significance for high efficiency utilization of nitrogen in soybeans.

Remote sensing estimation of maize residue cover (MRC) can rapidly acquire large-scale data on MRC, crucial for monitoring and promoting conservation tillage. Herein, seven tillage indices derived from Sentinel-2 satellite imagery were analyzed alongside measured MRC data to assess their correlation with MRC. MRC estimation models were built using six machine learning algorithms, including back propagation neural network (BPNN), random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost), Stacking1, and Stacking2. Model performance was compared using the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). The potential for conservation tillage was explored. The results showed that the R² values of the seven tillage indices in the study area exceeded 0.5, with particularly high correlations for NDTI and STI, with R² values of 0.755 and 0.751, respectively. When using machine learning algorithms to construct models, the Stacking2 model exhibited the highest estimation accuracy, with an R² of 0.923, RMSE of 3.32%, and MAE of 0.025, while Stacking1 also demonstrated robust performance, with an R² of 0.910, RMSE of 3.45%, and MAE of 0.029. Among the base models, XGBoost achieved the highest estimation performance and the lowest error, with R², RMSE, and MAE values of 0.884, 4.77%, and 0.031, respectively. The R² values of RF, SVR, and BPNN were 0.865, 0.859, and 0.842, respectively, with RMSE values of 4.06%, 4.76%, and 5.91%, and MAE values of 0.039, 0.047, and 0.059, respectively. These results indicate that the Stacking2 model demonstrates a significant advantage in prediction accuracy. Geostatistical analysis of the inversion results of the Stacking2 model revealed that the proportions of farmland with MRC values exceeding 30% in Changchun, Songyuan, and Siping were 81.90%, 77.96%, and 83.58%, respectively. This indicates that Changchun and Siping have greater potential for implementing conservation tillage. This study demonstrates that the stacking ensemble learning model significantly improves the predictive performance by leveraging the strengths of multiple base models and accurately monitoring the spatial distribution of MRC.

Saline–alkaline land serves as a potential arable land reserve for augmenting agricultural productivity and safeguarding food security. However, long-term monitoring of saline–alkaline land conversion remains challenging because of vegetation recovery, surface changes, hydrological modification, and agricultural phenology. Compared with CCDC and LandTrendr, the proposed MK-based framework detects conversion occurrence and timing while reducing dependence on dense observations, parameter tuning, and annual classification. This study examines the spatiotemporal dynamics of saline–alkaline land converted into paddies in Da’an City, utilizing Landsat time-series data (2007–2021) from the Google Earth Engine (GEE) platform. The analysis employed Mann–Kendall (MK) trend and mutation tests to monitor conversion processes and analyze spatiotemporal dynamics. Point-biserial correlation analysis was applied to evaluate the sensitivity of various remote sensing indices in detecting land conversion. The top fifteen indices, including the Land Surface Water Index (LSWI), Salinity Index 4 (SI4), and Salinity Index 5 (SI5), demonstrated strong correlations (|r| = 0.788–0.885) and significant pre- and post-conversion spectral differences (p < 0.01). Validation via confusion matrix confirmed that the June SI5 index attained the highest detection accuracy (overall accuracy: 94.15%; Kappa coefficient: 0.86), supporting the MK trend test’s efficacy in monitoring conversion processes. The MK mutation test achieved 80.36% temporal accuracy in determining conversion timing. The spatiotemporal analyses identified heterogeneity in saline–alkaline land conversion patterns. Spatially, large contiguous paddy fields dominated the eastern region, whereas fragmented conversion characterized the west, with minimal activity in the central zone. Temporally, the conversion area expanded rapidly before 2015 and then gradually declined, reaching a cumulative converted area of 276.29 km² by 2021. This study elucidates spatiotemporal conversion dynamics to guide sustainable land use.