Estimation of soil organic matter content in Yinchuan Plain based on fractional derivative combined with spectral indices.

Soil organic matter (SOM) is a crucial indicator of soil fertility. Field hyperspectral reflectance and laboratory SOM data of soil samples from the Yinchuan Plain were used to explore the performance of models based on fractional derivative combined with different spectral indices. Following reciprocal and logarithmic transformation, the reflectance data were processed using fractional derivative from 0 to 2 orders (interval 0.20). Then, the difference index (DI), ratio index (RI), brightness index (BI), normalized difference index (NDI), renormalized difference index (RDI), and generalized difference index (GDI) were constructed. The two-dimensional correlation between the six indices and SOM content were analyzed. The optimal spectral indices were selected to establish SOM estimation models with principal component regression (PCR), partial least square regression (PLSR), back propagation neural network (BPNN), support vector machine (SVM), and geographically weighted regression (GWR). Results showed that the maximum absolute correlation coefficient (MACC) values between DI, RI, NDI, BI, GDI, RDI, and SOM contents increased firstly and then decreased, with the highest values observed at 1.0, 0.6, 1.4, and 1.6 orders. The 0.2-2.0 order RDI under fractional derivative variation could be used for subsequent model construction, in which the optimal combinations of bands for MACC values were mainly concentrated at 400-600 nm and 1300-1700 nm. Among the different models based on the single spectral index RDI, the model based on SVM achieved the highest estimation accuracy, whose modeling determination coefficient, verification determination coefficient and relative percentage difference reached 0.86, 0.87 and 2.32. Our results would provide a scientific reference for quick and accurate SOM assessment and mapping in areas with relatively low SOM content.

[Distribution Characteristics of Antibiotics and Antibiotic Resistance Genes in Manure and Surrounding Soil of Poultry Farm in Ningxia].

Antibiotics and antibiotic resistance genes (ARGs) in livestock and poultry manure pose potential ecological risks. In order to understand the distribution characteristic of antibiotics and ARGs in manure and surrounding soils of poultry farms in Ningxia, the poultry manure and relative soil samples were collected from 12 layers of different poultry breeding farms. The compositions of antibiotics and ARGs in the samples were analyzed using UPLC-MS/MS and HT-qPCR. The results showed that:① tetracycline, aminoglycoside, and sulfonamide were the dominant antibiotics in poultry manure. The types and contents of antibiotics in poultry manure were different in different breeding periods. There were more types of antibiotics in the brooding period, the average content was high, and the initial stage showed the opposite trend. ② A small amount of antibiotics was detected in the surrounding soil only 20 m away from the poultry farm, and the poultry farm had little effect on the distribution of antibiotics in the surrounding soil. The content of quinolone in the soils with poultry manure application was significantly higher than that in the control and surrounding soil. ③ We detected 132-168 ARGs in poultry manure, and the number of aminoglycosides and tetracycline was higher. The relative abundance of ARGs in the rearing period was highest, and the initial stage showed the opposite trend. The total relative abundance of ARGs in the brooding period was highest, but the terminal period showed the opposite. There were 110 ARGs in poultry manure during all breeding periods. ④ There were 23-105 ARGs in the soils, and the number of aminoglycoside was highest, followed by multidrug ARGs. The poultry farm had a great effect on the number and relative abundance of ARGs in the surrounding soil. For example, the number and relative abundance of ARGs in the surrounding soil of poultry farms gradually decreased with the increase in the distance from the poultry farms. The number and relative abundance of ARGs in the soil with applied poultry manure were significantly increased; however, these values were lower than those in the soil 20 m away from the poultry farm. ⑤ ß-lactamases, aminoglycosides, and macrolide lincosamide-streptogramin B (MLSB) ARGs were all at risk of horizontal movement in manure, and chloramphenicol ARGs were at risk of horizontal movement in soil. Correlation analysis showed that the relative abundance of aminoglycoside, tetracycline, sulfonamide, ß-lactamase, and MLSB were not significantly correlated with their contents. ⑥ Different types of ARGs had related co-occurrence phenomena, such as the positive correlation between the relative abundance of ARGs in poultry manure, and aminoglycoside and ß-lactamases, MGEs, multidrugs and vancomycins. The relative abundances of ARGs in soil, aminoglycoside and tetracyclines, vancomycins, sulfonamides, and MLSBs; tetracyclines and MLSBs; etc., all showed a significant positive correlation. In short, the co-occurrence among the relative abundance of ARGs in soil was significantly stronger than that in poultry manure. These results could provide the theoretical basis for the site selection of poultry farms, the selection of antibiotic types and dosages for large-scale breeding of laying hens, and the application of poultry manure.

[Distribution Characteristics of Antibiotics and Antibiotic Resistance Genes in Manure and Surrounding Soil of Cattle Farms in Ningxia].

Livestock breeding intensively uses veterinary antibiotics in concentrated feeding operations to improve growth and control disease. Consequently, livestock and poultry manure is an important repository of antibiotics and antibiotic resistance genes (ARGs). To understanding the distribution of antibiotics and ARGs in manure and surrounding soils of cattle farms in Ningxia, cattle manure from five breeding periods (lactation, calving, growing, pre-fattening, and post-fattening periods) and comparative soil samples were collected from the largest beef-breeding area in Ningxia. The compositions of ARGs in the samples were analyzed by UPLC-MS/MS and HT-qPCR. The results showed that:① Tetracycline, quinolone, and sulfonamide were the dominant antibiotics in cattle manure. The content of antibiotics in the manure varied greatly between different breeding periods. High amounts of antibiotics were detected during the pre-fattening and lactation periods, and the lowest amounts were detected during the calving period. ② Quinolone and tetracycline were the dominant antibiotics in the soils, and the detection rate and content of quinolone were highest. The cattle farms did not affect the distribution of antibiotics in the surrounding soils. The content of quinolone and tetracycline in the soils with cattle manure application were significantly higher than control and surrounding soils. ③ We detected 79-142 ARGs in cattle manure, with aminoglycosides the most common form. The number and relative abundance of ARGs were highest during the growing period and lowest during the calving period. The dominant ARGs were tetQ, ermF, and tetO-01 across all the breeding periods. ④ There were 35-79 ARGs in the tested soils, and multidrug and aminoglycoside ARGs were dominant. The cattle farms did not affect the number and relative abundance of ARGs in the surrounding soils; however, manure application significantly affected the number and relative abundance of ARGs. ⑤ Sulfonamide and chloramphenicol ARGs are at risk of mobilization and horizontal transport. A correlation analysis showed that the relative abundances of aminoglycoside and tetracycline in cattle manure were significantly positively correlated with their contents. ⑥ Aminoglycoside, sulfonamide, chloramphenicol, and mobile genetic elements (MGEs) in manure were significantly or extremely significantly positively correlated with aminoglycosides and sulfonamides in the soils, whereas macrolides were negatively correlated with vancomycin. These results provide baseline data to inform controls on the variety and dosages of feed and veterinary drugs in cattle farms and the application of organic fertilizers in agriculture.

Estimation of soil Na + content based on measured hyperspectral and Sentinel-2B data in northern Ningxia, China.

To explore the ability of different sensors to estimate soil Na+ content, we got the mea-sured soil spectra and Sentinel-2B image spectra of the typical soil samples from the northern area of Ningxia. We filtered the sensitive parameters from the spectra data by means of stepwise regression (SR) and principal component regression analysis (PCA). We established the models to estimate soil Na+ content based on the measured spectra and image data using partial least square regression (PLSR), support vector machine (SVM) and back propagation neural network model (BPNN). The results showed that, except for Band9, there was significant correlation between the resampling data and the image data. The estimation accuracy of models based on SR-screening was generally higher than the PCA (excluding SVM model). The PCA-SVM model was the best image estimation model for soil Na+ content, with a prediction accuracy of 0.792. The SR-BPNN model was the best measured estimation model, with a prediction accuracy of 0.908. The estimating accuracy of the SR-PLSR image-spectra-based model increased from 0.481 to 0.798 after calibrated by the resampled measured spectrum model, which effectively enhanced the accuracy in estimating the soil Na+ content at large scale. We successfully made the spatial transformation of soil Na+ content from point to surface. Our results provided a scientific reference for Sentinel-2B image to monitor Na+ content in salinized soil.

[Characteristics of heavy metals and bacterial community in manure and surrounding soil of cattle farm in Xiji, Ningxia, China]. / å®å¤è¥¿å‰åŽ¿å »ç‰›åœºç²ªæ±¡å’Œå‘¨è¾¹åœŸå£¤é‡é‡‘å±žåŠç»†èŒç¾¤è½ç‰¹å¾.

To understand the contamination status of heavy metals and bacterial community in the manure and surrounding soils of cattle farm in Ningxia, we collected cattle manure at different breeding periods (lactation, calf, growing, pre-fattening and post-fattening periods) and soil samples from the largest beef breeding area in the region by different distances, with the waste land far away as control. We measured heavy metal contents and the composition and diversity of bacterial community in manure and soil samples. The results showed that: 1) The contents of Cu, Zn, Cd, Pb and Hg in cattle manure were 33.8%-95.8% lower than the national average, while As content was 94.7% higher than the national average. During the breeding periods, the change tendencies of seven kinds of heavy metals in cattle manure were different. The contents of Cu, Cd, Hg and Cr of cattle manure in the pre-fattening period were the highest across all periods. The comprehensive pollution index of heavy metals in pre-fattening and post-fattening periods of cattle manure was highest in all periods. 2) The contents of Cu and Zn in the soils with cattle manure application were higher than control, and the content of Zn were significantly higher than surrounding soil samples. All single and comprehensive soil pollution indices of soil were safe. 3) During the breeding periods, Sequence, OTU and Chao1 index of cattle manure had no specific change. Sequence, OTU and Chao1 of soil with cattle manure application were higher than the soil around the farm. 4) The types of dominant phylum were less in the manure. The relative abundance of Firmicutes was the highest, followed by Bacteroidetes and Proteobacteria. The relative abundance of Firmicutes in the growing period was significantly lower than other breeding periods, and that of other phylum had little variation among periods. The cattle farm did not affect bacterial community in the surrounding soil. The cattle manure application didn't change the relative abundance of bacteria at the phyla level. The abundance of Gp, Gemmatimona, Lysobacte, Subdivision3_genera_incertae_sedis were significantly higher than control and surrounding soil. 5) Organic matter, pH, EC, total nitrogen, total potas-sium, Cd and As significantly affected bacterial diversity and component abundance in cattle manure. Soil pH, total phosphorus and Hg significantly influenced soil bacterial diversity and component abundance. On the whole, the effects of physicochemical properties in cattle manure and soil on bacterial community were more significant than heavy metals. Our results could provide scientific basis for selecting the variety and dosage of feed and veterinary drugs in local cattle farms, as well as the rational application of organic fertilizers.