Soil aggregates highly regulate nitrogen (N) turnover, yet their functions in regulating N retention under long-term crop rotation remain unclear.  This study used ¹⁵N-labeled fertilizer N to investigate how different-sized soil aggregates regulate fertilizer N retention and redistribution under long-term crop rotation systems.  The results revealed that large macroaggregates exhibited a more pronounced depletion of fertilizer N (enrichment factor, Ef: 0.73−0.95) than of total N (Ef: 0.93−1.00).  In contrast, macroaggregates and microaggregates enriched fertilizer N (Ef: 1.00−1.16).  Crucially, we found a temporal divergence: after harvest (with new ¹⁵N-labeled fertilizer application), macroaggregates preferentially sequestered new N (current-season fertilizer N), whereas microaggregates dominated the stabilization of old N (previous-year fertilizer N) in the absence of ¹⁵N-labeled fertilizer in 2024.  This functional specialization is driven by aggregate turnover, whereby disintegrating macroaggregates release old N for stabilization in microaggregates, while reforming macroaggregates encapsulate new N.  The accelerated release of fertilizer N from these macroaggregates during crop cultivation supplies N to crops, while their reformation concurrently enriches new N.  This process underscores the dual functions of macroaggregates in both supplying N to crops and enriching new N.  Ultimately, crop rotations reinforce this beneficial dynamic by restructuring soil aggregation, leading to a marked expansion of the soil N pool, with fertilizer N storage increasing by 27.11−111.68% and total N storage by 2.94−14.22% compared to continuous cropping.  Our findings establish the functional heterogeneity of soil aggregates as a key mechanism for long-term fertilizer N retention and stabilization.  This provides a mechanistic basis for optimizing N management under crop rotations.

Soil organic carbon (SOC) plays a crucial role as a nutrient trigger and directly impacts soil health and agricultural productivity.  In China, the Well-facilitated Farmland Construction (WFC) Project is a comprehensive agricultural management strategy, changing the soil environment and then influencing the SOC dynamics.  However, the long-term trajectory of SOC under the implementation of the WFC Project remains unclear.  To address this knowledge gap, this study focused on farmland in southeastern China that completed the WFC Project in 2022.  A total of 202 topsoil samples (0–20 cm) were collected from the regional paddy soil in 2023.  Using digital soil mapping (DSM) and the CENTURY model, we delineated key soil properties and simulated the spatio-temporal changes of SOC density (SOCD).  The results revealed that the SOCD ranged from 1.23 to 6.35 kg m^–2, with an average value of 3.68 kg m^–2 in 2023.  Soil pH, clay, and sand content were primary factors influencing SOCD distribution.  According to CENTURY model simulations, SOCD exhibited a declining trend from 2010 to 2021, while it was projected to increase from 2022 to 2030 following the WFC implementation, which could be attributed to enhancements in irrigation and straw incorporation.  Besides, the scenario without WFC results shows that SOCD would decline from 2022 to 2030, underscoring the project’s effectiveness in preventing SOC loss for paddy soil.  The spatial patterns of SOCD in 2021 and 2030 were similar, and the low-value areas showed faster increase rates than the areas with high SOCD levels, indicating that the specific field plots with lower SOCD levels could sequester more carbon with improved soil management.  In conclusion, the WFC Project can potentially increase SOC sequestration in the paddy soil and grain yield, ensuring food security and addressing climate change.

Soil organic matter (SOM) is a core indicator of soil fertility and ecosystem function.  However, in regions where Mollisol and non-Mollisol coexist, high-precision spatial mapping faces significant challenges due to pronounced terrain heterogeneity and redundancy in high-dimensional covariates.  This study proposes a “remote sensing zoning-feature selection optimization-random forest (RSZ-FSO-RF)” framework.  By integrating Landsat-8 multi-temporal imagery from 2014–2023 with topographic and climatic factors, and leveraging the Google Earth Engine (GEE) platform, it achieves high-precision remote sensing zoning of Mollisol and non-Mollisol areas (overall accuracy: 92.13%, Kappa coefficient: 0.70).  Subsequently, local Random Forest (RF) regression models were established within each zone for SOM prediction, with predictive variables optimized using recursive feature elimination (RFE).  Results demonstrate that compared to FAO-zone-based modeling, the RSZ-FSO-RF framework significantly enhances prediction accuracy (R²=0.619, RMSE=6.849 g kg^–1).  And further feature optimization continued to enhance model performance (R²=0.627, RMSE=6.781 g kg^–1).  Notably, optimal predictor combinations varied significantly across zones, with SOM spatial variability generally higher in non-Mollisol areas than in Mollisol regions.  By organically integrating remote sensing zoning with feature selection, this framework effectively mitigates covariate redundancy while accounting for local heterogeneity, significantly enhancing the accuracy and stability of high-resolution SOM mapping.  Furthermore, this study provides scientific basis and decision support for soil resource management and sustainable agricultural development under complex topographic conditions.

Cropland parcels are the basic unit for agricultural production, and their size and shape may change due to human activities, e.g., land consolidation.  Remote sensing has been increasingly used for mapping cropland parcel, yet detecting changes in cropland parcels by wall-to-wall mapping is time-consuming.  This paper proposes a new algorithm to identify whether and where cropland parcel changes have been undertaken without generating complete parcel maps.  We use the number of edge pixels derived from remote sensing imagery as a proxy indicator for cropland parcel changes.  First, we apply a Sobel operator to delineate the total edge pixels of parcels from dual-time images.  Second, we apply the connected-components labeling to remove pseudo-edges arising from non-cropland built structures and transmission towers.  We then perform topological optimization, including morphological dilation and skeleton extraction, to eliminate redundant edge pixels for parcel structure.  Finally, we detect whether parcel changes have been undertaken by counting and comparing the number of edge pixels derived from dual-time images.  We applied this innovative framework in five regions in East Asia where land consolidation has significantly changed cropland parcels.  Our method demonstrated robust detection results, with stable accuracy, precision, recall, and F1-score, all exceeding 0.85.  Screening redundant edge pixels reduced noise and permitted efficient detection of changes in cropland parcels.  Our method extends the traditional detection of semantic change to structural change and can quickly detect cropland parcel changes with high accuracy.  This capability offers the potential to identify hotspot areas of cropland changes on a larger scale without the need to produce full cropland maps, which is particularly useful for monitoring land consolidation programs.

Nitrogen limitation has been well documented in grasslands on the Qinghai-Tibet Plateau (QTP), significantly affecting predictions of plant growth and carbon sequestration potential under future climate change scenario.  Beside atmospheric deposition, asymbiotic nitrogen fixation (ANF) may be crucial for nitrogen input in QTP grasslands, due to the lack of artificial fertilization and legume plants.  However, little is known about the ANF’s contribution to nitrogen input on the QTP.  To fill this knowledge gap, we studied the composition, diversity and activity of ANF diazotrophs across the QTP grasslands by using multiple methods of transect sampling, ¹⁵N-labeling and DNA stable isotope probing (SIP), amplicon sequencing, Random Forest algorithm modelling and digital mapping.  We found that Skermanella and Mesorhizobium were the most abundant diazotrophic genera.  Soil pH and total phosphorus concentration were the dominant driving factors for their composition and diversity.  DNA stable isotope probing with ¹⁵N₂ revealed that Mesorhizobium were the most active nitrogen-fixing microorganisms.  The potential N-fixation rates of these diazotrophs ranged from 0 to 18.1 kg N ha^–1 yr^–1, resulting in an estimated annual input of approximately 0.50 Tg N across the entire QTP’s alpine grasslands (i.e., ~25% of annual nitrogen input).  The most important factor affecting the ANF rate was soil micronutrient molybdenum, a cofactor in the nitrogen-fixing nitrogenase, accounting for 24.64% of the variance.  These findings suggested that ANF diazotrophs play important roles in maintaining nitrogen balance in the QTP grasslands and expand our understanding of Mesorhizobium’s ecological roles beyond traditional symbiotic interactions.

Plant–pathogen interactions are complex, multifaceted processes involving various participants, including insect vectors and parasitic plants, that play a crucial role in the spread of plant diseases.  This review explores the intricate relationships between plants, pathogens, and insect vectors, emphasizing these interactions’ ecological and epidemiological significance.  Insect vectors, such as aphids, leafhoppers, whiteflies, and beetles, transmit various plant pathogens, including viruses, bacteria, fungi, and phytoplasmas, through different mechanisms.  The transmission mode can be direct or indirect, continuous or discontinuous, depending on the biology of the pathogen and the insect vector.  We differentiate between noncirculative and circulative pathogen transmission pathways and describe how pathogen movement within insect bodies influences their ability to spread diseases to new plant hosts.  The impacts of these interactions extend beyond agricultural productivity to encompass significant economic losses, environmental challenges, and potential human health risks due to excessive use of chemical controls.  Understanding these complex dynamics is essential for designing effective disease management strategies and developing environmentally sustainable control measures.  This review synthesizes current knowledge on the transmission mechanisms, types of plant pathogens, and the consequences of insect-mediated disease spread, providing insights crucial for advancing plant protection and integrated pest management practices.