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Understanding and monitoring soil organic carbon (SOC) stocks is crucial for ecosystem carbon cycling, services, and addressing global environmental challenges. This study employs the BERTopic model and bibliometric trend analysis exploration to comprehensively analyze global SOC estimates. BERTopic, a topic modeling technique based on BERT (bidirectional encoder representatives from transformers), integrates recent advances in natural language processing. The research analyzed 1761 papers on SOC and remote sensing (RS), in addition to 490 related papers on machine learning (ML) techniques. BERTopic modeling identified nine research themes for SOC estimation using RS, emphasizing spectral prediction models, carbon cycle dynamics, and agricultural impacts on SOC. In contrast, for the literature on RS and ML it identified five thematic clusters: spatial forestry analysis, hyperspectral soil analysis, agricultural deep learning, the multitemporal imaging of farmland SOC, and RS platforms (Sentinel-2 and synthetic aperture radar, SAR). From 1991 to 2023, research on SOC estimation using RS and ML has evolved from basic mapping to topics like carbon sequestration and modeling with Sentinel-2A and big data. In summary, this study traces the historical growth and thematic evolution of SOC research, identifying synergies between RS and ML and focusing on SOC estimation with advanced ML techniques. These findings are critical to global ecosystem SOC assessments and environmental policy formulation. [ABSTRACT FROM AUTHOR]

Terahertz (THz) spectroscopy has demonstrated significant potential for substance detection due to its low destructiveness and due to the abundance of molecular fingerprint absorption signatures that it contains. However, there is limited research on the fingerprint detection of substances at different temperatures. Here, we propose a THz metamaterial slit array sensor that exploits localized surface plasmons to enhance the electric field within the slit. The transmission peak frequency can be modulated via temperature adjustments. This method enables the detection of molecular absorption characteristics at multiple spectral frequency points, thereby achieving a specific and highly sensitive detection of characteristic analyte fingerprint spectra. Additionally, the sensor supports the detection of substances at multiple temperatures and sensitively identifies changes in their absorption properties as a function of temperature. Our research has employed temperature variation to achieve a highly sensitive and specific detection of trace analytes, offering a new solution for THz molecular detection. [ABSTRACT FROM AUTHOR]

Conventional fertiliser nitrogen (N) inputs to sugarcane farming promote gaseous losses of the greenhouse gas nitrous oxide (N2O). This study investigated the effects of a nitrification inhibitor coated urea (NICU) and a 50:50 blend (N wt%) of polymer coated urea and conventional urea (PCU + U), both at a sub-recommended rate (112 kg N ha−1), on N2O emissions and productivity in a sugarcane crop. Three rates of conventional urea (70%, 100% and 130% of the recommended rate at 160 kg N ha−1) were also assessed. Nitrous oxide emissions were measured over a 7.5-month sugarcane crop using automatic chambers. High N2O emissions (> 50 g N2O–N ha−1 d−1) occurred in the first 2 months after fertiliser application, and the variability in daily emissions was best described by a combination of pH, soil nitrate concentration, soil temperature, water filled pore space and soil ammonium concentration. The blended PCU + U resulted in 62% higher, but non-significant, net fertiliser-induced N2O emissions, while NICU significantly reduced net emissions by 81%, compared to conventional urea at the same rate (112 kg N ha−1). Net emissions from conventional urea increased linearly with increasing rate, with a mean emission factor of 2.6%. Thus, applying NICU at 70% of the recommended rate achieved the greatest N2O emission reduction compared to a PCU + U blend or conventional urea at the same N rate. There was no significant reduction in yield when the fertiliser N rate was reduced to 70%. Further field trials are required to ascertain whether the use of reduced N rates and/or enhanced efficiency fertilisers can mitigate N2O emissions while maintaining or increasing productivity in the long term. [ABSTRACT FROM AUTHOR]

Soil protease plays a fundamental role in soil nitrogen (N) transformations. Soil N and phosphorus (P) management significantly influence soil protease activity. However, the impacts of N and P combined modification on soil protease remain unclear. A better understanding of the activity and dynamic of soil protease could provide new insights into soil N cycling and available N supply. This study aimed to quantify the influences of combined effects of N and P managements on soil protease activities and decipher the potential mechanism from the perspectives of soil chemical properties, functional microbes, and functional genes. The nitrification inhibitor 3, 4‐dimethylpyrazole phosphate (DMPP) application or phosphate‐solubilizing bacteria (PSB) Klebsiella inoculation significantly increased soil protease activity (23.39% and 70.99%, respectively) and ammonium N (NH4+–N) contents, relative to the blank control. However, compared with the DMPP or PSB alone application, the combined applications of DMPP and PSB significantly decreased protease activity, implying that an antagonistic effect on soil protease activity was generated. The abundances of genus Klebsiella were stimulated by the DMPP or PSB but significantly inhibited by the combined additions of DMPP and PSB. The DMPP and PSB applications also significantly changed soil microbial communities and led to more complicated soil microbial co‐occurrence networks. Soil protease activity had a significantly positive correlation with the normalized abundances of tri and clpX genes. Our findings suggested that the combined additions of DMPP and PSB generated an antagonistic effect on soil protease activity and that the antagonistic effect was directly associated with soil NH4+–N and NO3−–N contents, P fractions, and functional gene abundances. [ABSTRACT FROM AUTHOR]