Impact of biochar on the antibiotic resistome and associated microbial functions in rhizosphere and bulk soil in water-saving and flooding irrigated paddy fields.

The addition of biochar in paddies under the condition of water-saving irrigation can simultaneously achieve soil improvement and water conservation, but little is known about the role of these two regulations in mediating the fate of antibiotic resistome in paddy soils. Here, metagenomic analysis was conducted to investigate the effects and intrinsic mechanisms of biochar application and irrigation patterns on propagation of antibiotic resistance genes (ARGs) in paddy soils. The addition of biochar in paddy soil resulted in a reduction of approximately 1.32%-8.01% in the total absolute abundance of ARGs and 0.60%-22.09% in the numbers of ARG subtype. Compared with flooding irrigation, the numbers of detected ARG subtype were reduced by 1.60%-22.90%, but the total absolute abundance of ARGs increased by 0.06%-5.79% in water-saving irrigation paddy soils. Moreover, the combined treatments of flooding irrigation and biochar could significantly reduce the abundance of ARGs in paddy soils. The incremental antibiotic resistance in soil induced by water-saving irrigation was likewise mitigated by the addition of biochar. Correlation analyses indicated that, the differences in soil physicochemical properties under biochar addition or irrigation treatments contributed to the corresponding changes in the abundance of ARGs. Moreover, the variations of microbial community diversity, multidrug efflux abundance and transport system-related genes in paddy soil were also important for mediating the corresponding differences in the abundance of ARGs under the conditions of biochar addition or irrigation treatments. The findings of this study demonstrated the effectiveness of biochar application in mitigating antibiotic resistance in paddy soils. However, it also highlighted a potential concern relating to the elevated antibiotic resistance associated with water-saving irrigation in paddy fields. Consequently, these results contribute to a deeper comprehension of the environmental risks posed by ARGs in paddy soils.

Soil nitrogen functional transformation microbial genes response to biochar application in different irrigation paddy field in southern China.

Growing evidence points to the controlled irrigation (CI) and biochar application (BA) having agricultural economic value and ecological benefits, but their synergistic effect and microbial mechanism of nitrogen conversion remain unknown in paddy fields. The effects of different BA (0, 20, 40 t/hm2) on the soil nitrogen functional transformation microbial genes (nifH, AOA-amoA, AOB-amoA) in different irrigation (CI, flooding irrigation) were clarified. After one seasonal growth of paddy, the correlation between the abundance of functional genes OUT and soil nitrogen transformation environment factors during the typical growth period was analyzed. High-throughput sequencing results illustrated that the application of CC (40 t/hm2 biochar) increased the nifH genes bacterial community abundance; the abundance of dominant microorganism increased by 79.68~86.19%. Because biochar can potentially control the rates of N cycling in soil systems by adsorbing ammonia and increasing NH4+ storage, it increased soil NH4+-N and NO3--N content by 60.77% and 26.14%, improving microbial nitrogen fixation. Rare species Nitrosopumilus, Nitrosococcus, and Methylocystis appeared in biochar treatments group, which increased the diversity of microbial in paddy. The combined use of CI and BA affected soil inorganic nitrogen content, temperature (T), pH, Eh, etc., which affected urease, urea hydrolysis, and nitrogen functional transformation microorganism genes. Correlation analysis shows that soil NH4+-N, T, and Eh, respectively, are significant factors for the formation of nifH, AOA-amoA, and AOB-amoA soil bacterial communities, respectively. This study suggests that to maintain the biodiversity of soil and realize the sustainable development of rice cultivation, CI is of great importance in combination with BA.

Can ensemble machine learning be used to predict the groundwater level dynamics of farmland under future climate: a 10-year study on Huaibei Plain.

Accurate and simple prediction of farmland groundwater level (GWL) is an important aspect of agricultural water management. A farmland GWL prediction model, GWPRE, was developed that integrates four machine learning (ML) models (support vector machine regression, random forest, multiple perceptions, and the stacking ensemble model) with weather forecasts. Based on the GWL and meteorological data of five monitoring wells (N1, N2, N3, N4, and N5) in Huaibei plain from 2010 to 2020, the feasibility of predicting GWL by meteorological factors and ML algorithm was tested. In addition, the stacking ensemble model and future meteorological data after Bayesian model averaging were introduced for the first time to predict GWL under future climate conditions. The results showed that GWL showed an increasing trend in the past decade, but it will decrease in the future. The performance of the stacking ensemble model was better than that of any single ML model, with RMSE reduced by 4.26 ~ 96.97% and the running time reduced by 49.25 ~ 99.40%. GWL was most sensitive to rainfall, and the sensitivity index ranged from 0.2547 to 0.4039. The fluctuation range of GWL of N1, N2, and N3 was 1.5 ~ 2.5 m in the next decade. Due to the possible high rainfall, the GWL decreased in 2024 under RCP 2.6 and 2026 under RCP 8.5. It is worth noting that although the stacking ensemble model can improve the accuracy, it is not always the best among ML models in terms of portability. Nevertheless, the stacking ensemble model was recommended for GWL prediction under climate change.

Controlled release urea improves rice production and reduces environmental pollution: a research based on meta-analysis and machine learning.

To reveal the comprehensive impacts of controlled release urea (CRU) on rice production, nitrogen (N) loss, and greenhouse gas (GHG) emissions, a research based on global meta-analysis and machine learning (ML) was conducted. The results revealed that the CRU application instead of conventional fertilizer can increase rice yield, N use efficiency (NUE), and net benefits by 5.24%, 20.18%, and 9.30%, respectively, under the same amount of N. Furthermore, the emission of N2O and CH4, global warming potential (GWP), the loss of N leaching, and NH3 volatilization were respectively reduced by 25.64%, 18.33%, 21.10%, 14.90%, and 35.88%. The enhancing effects of CRU on rice yield and NUE were greater when the nitrogen application rate was 150 kg N ha-1. Nevertheless, the reducing effects of CRU on GHG emission reduction, nitrogen leaching, and NH3 volatilization was greater at high nitrogen application rate (≥150 kg ha-1). Mitigating effects of CRU on N2O and CH4 emission were significant when soil pH ≥ 6, while CRU posed a measurable effect on reducing nitrogen leaching and NH3 volatilization in paddy fields with soil organic carbon lower than 15 g kg-1 and pH lower than 6. Based on the data collected from meta-analysis, the results of ML demonstrated that it was feasible to use soil data and N application rate to predict N losses in rice fields under CRU. The performance of random forest is better than multilayer perceptron regression in predicting N losses from paddy fields. Thus, it is necessary to promote the application of CRU in paddy fields, especially in coarse soil, in which scenario the environmental pollution would be decreased and the rice yields, NUE, and net benefits would be increased. Meanwhile, machine learning models can be used to predict N losses in CRU paddy fields.

Antibiotic resistance genes alternation in soils modified with neutral and alkaline salts: interplay of salinity stress and response strategies of microbes.

Growing evidence points to the pivotal roles of salt accumulation in mediating antibiotic resistance genes (ARGs) spread in soil, whereas how salt mediates ARGs dissemination remains unknown. Herein, the effects of neutral or alkaline (Ne/Al) salt at low, moderate and high levels (Ne/Al-L, Ne/Al-M, Ne/Al-H) on the dissemination of ten typical ARGs in soils were explored, by simultaneously considering the roles of salinity stress and response strategies of microbes. In the soils amended with Ne/Al-L and Al-M salt, the dissemination of ARGs was negligible and the relative abundances of ARGs and mobile genetic elements (MGEs) were decreased. However, Ne-M and Al-H salt contributed to the dissemination of ARGs in soils, with the significantly increased absolute and relative abundances of ARGs and MGEs. In Ne-H soil, although the absolute abundance of ARGs declined drastically due to serious oxidative damage, their relative abundances were promoted. The facilitated ARGs transfer was potentially related to the excessive generation of intracellular reactive oxygen species and increased activities of DNA repair enzymes involved in SOS system. In addition, the activated intracellular protective response including quorum sensing and energy metabolism largely provided essential factors for ARGs dissemination. The co-occurrence of ARGs and over-expressed salt-tolerant genes in specific halotolerant bacteria further suggested the selection of salt stress on ARGs. Moreover, less disturbance of alkaline salt than neutral salt on ARGs evolution was observed, due to the lower abiotic stress and selective pressure on microbes. This study highlights that soil salinity-sodicity could dose-dependently reshape the dissemination of ARGs and community structure of microbes, which may increase the ecological risks of ARGs in agricultural environment.