Soil erosion results in dissolved organic matter (DOM) loss and is one of the main paths of soil carbon loss. Bacteria affect the generation and transformation of DOM. However, the effect of bacteria on the composition and slope distribution of DOM has rarely been investigated under field conditions. Based on a long-term experiment of three gradients (3°, 5°, 8°) in a black soil erosion area of Northeast China, the content, composition, and source of DOM were studied. The results showed that the DOM of the 3° and 5° slope was enriched midslope, and the DOM of the 8° slope was enriched downslope. Parallel factor (PARAFAC) analysis indicated that the main substances in DOM were fulvic-like acid, humic-like acid, tryptophan-like protein, and soluble microbial metabolites. The upslope and downslope soils of 3° and 5° slopes showed high DOM bioavailability, while the downslope soil of the 8° slope showed high DOM bioavailability. The content of new DOM in downslope soil increased with the gradient. Bacteria played an important role in the synthesis and transformation of DOM and affected its composition and slope distribution. Verrucomicrobiota, Firmicutes, Planctomycetota, and Gemmatimonadota were the main factors affecting soil DOM. The results could be helpful in understanding the loss mechanism of DOM in eroded black soil and provide support for soil carbon sequestration.

The conventional method of raising seedlings with plastic trays has drawbacks, including large plastic tray consumption, difficulty in recycling and potential farmland white pollution, along with high labor input and production costs. To clarify the effects of tray-free simplified rice seedling raising technology, which uses biodegradable biomass seedling films to replace traditional plastic trays on rice grain yield and its components, large-scale comparative field experiments were conducted in seven major rice-producing counties of Heilongjiang Province from 2022 to 2024. Independent-sample t-tests, linear mixed-effects models and structural equation modeling were applied to systematically evaluate the application effects of this technology. The results showed that the average grain yield of the treatment group reached 9032 kg/ha, which was a significant increase of 5.93% compared with the 8526.0 kg/ha of the control group. Compared with the conventional method, this technology markedly increased the effective panicle number, the filled grain number per panicle and 1000-grain weight, and reduced the percentage of unfilled grains, presenting positive yield performance across different regions, although the magnitude of increase varied (2.7–9.5%). Based on the previous literature, this technology has the potential to reduce plastic use and its associated environmental impacts, although direct measurements of carbon emissions and degradation rates were not conducted in this study. Featuring potential ecological benefits and simplified cultivation with prominent yield-increasing effects, it is worthy of popularization and application in cold-region rice planting areas.

The Northeast black soil region is a major grain-producing area in China, where county-scale assessment of topsoil quality is essential for soil conservation and provides a potential indicator framework for regional soil quality monitoring. In this study, 500 composite cultivated-layer samples were collected from dryland croplands in Keshan County, Heilongjiang Province. Soil physical properties, basic chemical properties, and macro-, secondary, and micronutrient contents were measured to establish a total data set (TDS). Three hierarchical total data sets (TDS1, TDS2, and TDS3) were established from the original TDS according to the progressive incorporation of soil physical properties, basic chemical properties, macronutrients, secondary nutrients, and micronutrients. Within each hierarchical TDS, key indicators were selected using random forest (RF), mutual information (MI), principal component analysis (PCA), and minimum spanning tree (Tree) methods to construct algorithm-specific minimum data sets (MDSs). TDS1 included soil physical properties, basic chemical properties, and macronutrients; TDS2 further incorporated secondary nutrients; and TDS3 additionally included micronutrients to represent progressively comprehensive soil nutrient information. The soils exhibited considerable soil organic matter and cation exchange capacity, with mean values of 51.45 g kg⁻¹ and 34.85 cmolc kg⁻¹, respectively, and a mean pH of 6.04. Across the four algorithms, commonly retained indicators included SOM, TN, pH, CEC, and Silt, while nutrient-related indicators such as AP, AK, S, Zn, and Fe were additionally selected under different hierarchical MDSs, reflecting the importance of multi-nutrient information in soil quality characterization. Available phosphorus, available potassium, sulfur, and zinc showed greater spatial variability than basic physicochemical properties. The four algorithms differed in indicator selection, reflecting their distinct sensitivities to linear variation, information gain, multilevel contributions, and network structure. RF_MDS1 showed the highest agreement with the TDS (R² = 0.924), indicating its strong capability in preserving overall soil quality information using a simplified indicator set. RF_MDS3 maintained a high consistency with the TDS (R² = 0.836) while incorporating additional secondary nutrients and micronutrients. Therefore, RF_MDS3 was considered a more comprehensive MDS framework when multi-nutrient representation and potential nutrient constraint identification were prioritized, whereas RF_MDS1 remained an efficient option for simplified soil quality assessment. This framework may provide a transferable approach for soil quality assessment and nutrient management in comparable black-soil regions and dryland farming systems.