Black soils, which play an important role in agricultural production and food security, are well known for their relatively high content of soil organic matter (SOM). SOM has a significant impact on the sustainability of farmland and provides nutrients for plants. Hyperspectral imaging (HSI) in the visible and near-infrared region has shown the potential to detect soil nutrient levels in the laboratory. However, using portable spectrometers directly in the field remains challenging due to variations in soil moisture (SM). The current study used spectral data captured by a handheld spectrometer outdoors to predict SOM, available nitrogen (AN), available phosphorus (AP) and available potassium (AK) with different SM levels. Partial least squares regression (PLSR) models were established to compare the predictive performance of air-dried soil samples with SMs around 20%, 30% and 40%. The results showed that the model established using dry sample data had the best performance (RMSE = 4.47 g/kg) for the prediction of SOM, followed by AN (RMSE = 20.92 mg/kg) and AK (RMSE = 22.67 mg/kg). The AP was better predicted by the model based on 30% SM (RMSE = 8.04 mg/kg). In general, model performance deteriorated with an increase in SM, except for the case of AP. Feature wavelengths for predicting four kinds of soil properties were recommended based on variable importance in the projection (VIP), which offered useful guidance for the development of portable hyperspectral sensors based on discrete wavebands to reduce cost and save time for on-site data collection.

Ammonia−oxidizing archaea (AOA), ammonia−oxidizing bacteria (AOB), and Comammox are key drivers of soil nitrification and play crucial roles in soil nitrogen cycling, with distinct functional contributions. AOA and AOB primarily drive the first step of nitrification by oxidizing ammonia to nitrite, while comammox bacteria can directly oxidize ammonia to nitrate. In this study, quantitative real−time polymerase chain reaction (qPCR) and Illumina MiSeq high−throughput sequencing were employed to determine the absolute abundance, diversity, and community structure of AOA, AOB, and Comammox in alkaline soybean farmland soil under single biochar application. Combined with soil chemical properties, the driving factors shaping ammonia−oxidizing microbial community structure were analyzed to clarify the impact of biochar on ammonia−oxidizing microorganisms from the perspective of soil microbial ecology. The results showed that biochar application effectively modified soil physicochemical properties, increased the absolute abundance of AOA, AOB, and Comammox, influenced their α–diversity, and reshaped community structure. Redundancy analysis revealed that after biochar application, AOA, AOB, and Comammox exhibited positive correlations with soil total phosphorus (TP), total nitrogen (TN), available nitrogen (AN), available phosphorus (AP), total organic carbon (TOC), and available potassium (AK). In conclusion, biochar application regulates soil nutrient cycling, particularly by modulating the ammonia oxidation and nitrite oxidation processes, thereby regulating soil nitrogen cycling and ammonia−oxidizing microbial communities, with potential benefits for sustaining soil quality in agricultural systems.