ABSTRACT
BACKGROUND: The availability of soil nitrogen (N) decreases as the structure of agricultural soils degrades. Traditional methods focus on organic amendments that indirectly affect the porosity and N content of soil. Due to the low efficiency of such amendments, new materials, particularly highly porous materials, are needed to improve the quality of soil, which has opened new directions. RESULTS: The addition of 2 to 7 mm of porous clay ceramic (PLC) significantly increased the fresh weight of Brassica chinensis. The soil aeration porosity (>50 µm) increased by 0.69% on average in response to 1% PLC application. Soil NO3 - -N, NH4 + -N and mineral N increased by 3.3, 1.3 and 4.6 mg kg-1 on average, respectively, following a 1% PLC application rate. The initial N content of the high PLC treatments was the lowest in the incubation experiment. The parameters of soil N mineralization, i.e. potentially mineralizable N (N0 ), the first-order rate constant (k) and the mineralization composite index (N0 × k), increased obviously as the amount of PLC increased. Porosities larger than 1000 µm were significantly more positively correlated with the parameters of soil N mineralization than those <500 µm. The Pearson correlation coefficients suggested that high porosity, mineral N and N0 values had significant positive relationships with the fresh weights in double seasons. CONCLUSION: The application of PLC increased soil aeration and enhanced the availability of soil N, which yielded large vegetable harvests in clayey soils in the short term. © 2022 Society of Chemical Industry.
Subject(s)
Nitrogen , Soil , Clay , Minerals , Nitrogen/metabolism , Porosity , Soil/chemistryABSTRACT
Soil nitrogen (N) plays a central role in soil quality and biogeochemical cycles. However, little is known about the distribution and spatial variability of the different fractions of soil N within entire soil profiles. This study aimed to investigate the potential of laboratory-based hyperspectral imaging (HSI) spectroscopy to retrieve and map total N (TN), available N (AvailN), ammonium N (NH4-N), nitrate N (NO3-N), and microbial biomass N (MBN) in soil profiles at a high resolution. HSI images of eleven intact soil profiles of 100 ± 5 cm depth from three typical soil types were recorded. A variety of nonlinear machine learning techniques, such as artificial neural networks (ANN), cubist regression tree (Cubist), k-nearest neighbour (KNN), support vector machine regression (SVMR) and extreme gradient boosting (XGBoost), were compared with a partial least squares regression (PLSR) to determine the most suitable model for the prediction of the various soil N fractions. Overall, the results showed that nonlinear techniques performed better than PLSR in most cases, with a high coefficient of determination (R2) and low root mean square error (RMSE). Among the models, SVMR was found to be superior to the other tested models for TN (R2P = 0.94, RMSEP = 0.17 g kg-1), AvailN (R2P = 0.94, RMSEP = 13.35 mg kg-1), NO3-N (R2P = 0.82, RMSEP = 7.31 mg kg-1), and NH4-N (R2P = 0.70, RMSEP = 1.51 mg kg-1) based on independent validation, whereas MBN (R2P = 0.63, RMSEP = 6.62 mg kg-1) was predicted best by KNN. In addition, SVMR required less computational time and was less sensitive to spectral noise. It can therefore be concluded that HSI spectroscopy combined with SVMR is suitable for the high-resolution mapping of various soil N fractions in soil profiles.
ABSTRACT
Recently, some global cases of 2019 novel coronavirus (COVID-19) pneumonia have been caused by second- or third-generation transmission of the viral infection, resulting in no traceable epidemiological history. Owing to the complications of COVID-19 pneumonia, the first symptom and imaging features of patients can be very atypical and early diagnosis of COVID-19 infections remains a challenge. It would aid radiologists and clinicians to be aware of the early atypical symptom and imaging features of the disease and contribute to the prevention of infected patients being missed.
