Zhang, ShixiuLiu, PingZhang, ShaoqingMcLaughlin, Neil B....
10页
查看更多>>摘要：Rhizodeposition plays an important role in soil carbon (C) cycling, and the process of rhizodeposition-C transfer and conservation in soil has been considered to be mainly regulated by microbes. In this study, a pulse labeling approach combined with 13C-phospholipid fatty acids (PLFA) and 13C-nuclear magnetic resonance (NMR) techniques were used to test how the recently synthesized photosynthate-C was sequestered into soil by microbial processes during two growth stages of maize (Zea mays L.) under field conditions. The results showed that saprophytic fungi were important in the incorporation of labeled C at the vegetative stage, while actinomycetes were dominant at the reproductive stage. Additionally, the contribution of labeled C fixed in microbial biomass C (13C-MBC) to the total labeled C fixed in soil organic C (13C-SOC) increased from 5% at the vegetative stage to 25% at the reproductive stage, although the amount of 13C-MBC was almost the same at both growth stages. The difference in soil C chemical composition between vegetative and reproductive stages was primarily revealed in aryl C and O-alkyl C, with a higher proportion of aryl C at the vegetative stage and a higher proportion of O-alkyl C at the reproductive stage. Furthermore, saprophytic fungi and actinomycetes were linked to the higher proportions of aryl C and O-alkyl C in SOC at respective maize growth stages. Our results highlight the important role of soil microbes in the continuous processing and sequestration of rhizodeposition-C into the soil during the growth of maize. Further research should pay more attention to the microbial processes involved in the formation of SOC in different crop species and management practices under field conditions to clarify the importance of rhizodeposition for C sequestration and soil fertility.
查看更多>>摘要：Soil biodiversity is of key importance to many essential ecosystem functions, but currently it is severely threatened by both intensive agriculture and climate changes. Ecological intensification, including organic amendments and less disturbance, is expected to buffer the degradation of biodiversity and ecosystem functioning induced by intensive agriculture, but its effects in the context of climate changes are poorly understood. In the present work, we studied the responses of agricultural soil biodiversity to ecological intensification under different natural rainfall intensities in a subtropical field. We focused on a numerically dominant group of soil microarthropods, the Collembola, and three conservation managements, i.e., straw, manure, and no-tillage. The experimental site was established with a full-factorial design of different managements. Soil physiochemical parameters and the density, taxonomic diversity, and morpho-functional traits of the collembolan community were measured over three consecutive years. Results showed that rainfall intensification markedly reduced collembolan density and had severe impact on large euedaphic species. Straw amendment buffered the detrimental effect of dense rainfall on collembolan density, but aggravated the body size reduction caused by the rainfall. Manure input and no-tillage mainly affected the community functional composition, in which manure favours more active and mobile species characterised by a well-developed furca, whereas no-tillage favoured surface-dwelling species. These results support the hypothesis that external resource enrichment and reduced disturbance would shape the functional traits of soil fauna, and further modified their response to climate change events. Our findings call for more attention on the functional consequences of ecological intensification and the interactions among soil biodiversity, agricultural managements and climate changes.
查看更多>>摘要：Within field variations of plant available water capacity (PAWC) of soil is one of the major causes of spatial yield variability in dryland agriculture systems, as PAWC interacts with pre-season and in-season rainfall and other climatic variables to determine crop growth and final yield. Quantification of such variations helps to better understand the changes in soil texture and subsoil constraints to inform spatially explicit management practice. An inverse modelling approach to estimate PAWC from crop yields was developed as a more cost-effective alternative to traditional soil sampling methods. In this study, we further extend this approach to predict and map in-field variations of PAWC from yield maps of single and multiple crops. Soil PAWC maps were produced based on inversely predicted PAWC using crop yield maps together with in-field management information, and compared with: 1) available water capacity derived using laboratory-measured soil properties, and 2) soil types derived from proximally sensed soil spectra and ground geophysics for four representative farms in Australia. The results show that the predicted PAWC maps matched well with within-field spatial variation of soil types, and well reflected the impact of soil constraints (e.g. salinity), and soil classifications from soil survey and local experience. This demonstrates that the predicted PAWC from crop yield using inverse modelling can reflect the soil physicochemical variations within-field. The generated PAWC maps can be combined with process-based modelling to predict crop yield and yield zones and to inform spatial field management and soil sampling.
查看更多>>摘要：Soil cadmium (Cd) contamination has emerged as an alarming environmental issue worldwide. Chemical reactivity and bioavailability of Cd in soils are highly dependent on soil properties. However, the sorption mechanism of Cd(II) on soils is not yet fully explored. For this purpose, batch sorption experiments, statistical analysis, and extended X-ray absorption fine structure (EXAFS) spectroscopy were integrated to elucidate the Cd(II) sorption mechanism by investigating the Cd(II) sorption behaviors in 49 soils with contrasting physicochemical characteristics and to figure out the influence of soil properties on Cd(II) sorption in soils. The outcomes of this study revealed that the soils from different areas had large differences in their abilities to adsorb Cd(II), while the differences were closely related to the physicochemical properties of soils. Cd(II) sorption capacities were controlled together by pH, metal (hydro)oxides, organic matter, and cation exchange capacity. The soil pH was the most critical factor in connecting Qmax with soil properties. EXAFS results indicated that Cd(II) adsorbed on soils was mainly bound with metal (hydro)oxides or existed as CdCO3 precipitate. The findings of this study provide a theoretical basis for predicting the risk of soil Cd contamination.
查看更多>>摘要：Remote sensing indices have been proposed to characterize soil salinity. However, the sensitivity of these indicators is unstable owing to differences in geographic environment and vegetation type. This study investigated the performance of several existing indices to estimate the salinity of topsoil with residues in southern Xinjiang, China. The results showed that these indices were not satisfactory. In order to construct an index that can be used to directly indicate soil salinity in a specific area, novel salinity indices were calculated using optical bands (blue, green, red, vegetation red edge, and shortwave infrared bands) derived from Sentinel-2 multispectral data and Sentinel-1 radar data (backscattering coefficient VV, VH). To enhance the sensitivity of the optical bands, five transformation methods (logarithmic, reciprocal, first-, second-, and third-derivative) were applied to the original spectra. Based on previous studies, statistical methods were used to construct two-, three-, and four-bands indices. One constructed three-bands index with the second-derivative transformation, called the Enhanced Residues Soil Salinity Index (ERSSI), showed the highest correlation with topsoil salinity (r = 0.65 and 0.68 in training and testing). ERSSI establishes a linear relationship in soil salinity estimation with an R-2 of 0.53 and a LCCC of 0.65 in training dataset, with an R-2 of 0.51 and a LCCC of 0.73 in testing dataset. And it shows contribution in random forest regression with an R-2 of 0.80 and a LCCC of 0.86 in training dataset, with an R-2 of 0.77 and a LCCC of 0.81 in testing dataset. The ERSSI consisted of the B, G, and SWIR1 bands, and was sensitive to salinity variations in the residues remaining in farmland soils. This study provides a novel index and method for the accurate and robust assessment and mapping of salinity in farmland covered by crop residues.
Sturrock, Craig J.Mooney, Sacha J.Wardak, D. Luke R.Padia, Faheem N....
11页
查看更多>>摘要：Following the adoption of zero-tillage (ZT) from conventional tillage (CT), the soil pore network undergoes immediate and significant changes. As soil remains undisturbed for an extended period, a soil structure emerges that is primarily generated and stabilised by both biotic and abiotic processes. There is limited understanding concerning how the adoption of ZT influences the soil porous architecture and associated soil hydraulic prop-erties, and specifically over what timeframe these changes occur. Since a previous synthesis of such information over 20-years ago, there has been a substantial number of new investigations aimed at addressing this knowledge gap. Here we review 34 papers that illustrate ZT can influence porosity depending on soil texture, pore size class, depth and time, and also influence important transport mechanisms likely to impact the fate of agrochemicals in soils. We found decreased macroporosity in surface layers of soil under ZT when compared with CT. In addition, soil pore connectivity tended to increase in soil under ZT though the associated effects on hydraulic transport were less clear. Our investigation reveals the value of a prospective examination of an evolving ZT pore network both visually and functionally across temporal and spatial scales. We also highlight the necessity for standardised methodology to aid in future data compatibility and quantitative analysis.
查看更多>>摘要：Understanding the spatial variation of soil properties is central to many sub-disciplines of soil science. Commonly in soil mapping studies, a soil map is constructed through prediction by a statistical or non-statistical model calibrated with measured values of the soil property and environmental covariates of which maps are available. In recent years, the field has gradually shifted attention towards more complex statistical and algorithmic tools from the field of machine learning. These models are particularly useful for their predictive capabilities and are often more accurate than classical models, but they lack interpretability and their functioning cannot be readily visualized. There is a need to understand how these models can be used for purposes other than making accurate prediction and whether it is possible to extract information on the relationships among variables found by the models. In this paper we describe and evaluate a set of methods for the interpretation of complex models of soil variation. An overview is presented of how model-independent methods can serve the purpose of interpreting and visualizing different aspects of the model. We illustrate the methods with the interpretation of two mapping models in a case study mapping topsoil organic carbon in France. We reveal the importance of each driver of soil variation, their interaction, as well as the functional form of the association between environmental covariate and the soil property. Interpretation is also conducted locally for an area and two spatial locations with distinct land use and climate. We show that in all cases important insights can be obtained, both into the overall model functioning and into the decision made by the model for a prediction at a location. This underpins the importance of going beyond accurate prediction in soil mapping studies. Interpretation of mapping models reveal how the predictions are made and can help us formulating hypotheses on the underlying soil processes and mechanisms driving soil variation.
查看更多>>摘要：Dust emission is one of the important segments of the circulation of materials between lithosphere, atmosphere, and ocean systems. However, studies on dust emission have mainly concentrated on arid and semiarid regions with few studies focused on dust emission in alpine areas, where having lower air temperatures and lower air pressure environments. The lower air temperature and lower air pressure could significantly impact the air density and make the entrainment and transport of dust different from those at lower altitudes. The lack of accurate studies on dust emission in alpine regions has resulted in large uncertainties in the global dust budget and brought large challenges in controlling aeolian hazards in alpine regions. To bridge this gap, we took the air density as an important parameter to simulate dust emission in the Qinghai-Tibetan Plateau (QTP), where has typical low air-temperature and low air-pressure environments. The measured data showed that the lower air temperature and low air pressure affect the particle size distributions (PSDs) of aeolian sediment, the threshold velocity (TFV) as well as the dust emission rate. Herein, we introduced air density as an important factor to improve a dust emission model, and the validated results showed that the deviations of the simulated horizontal sand flux and vertical dust flux were less than 30% and 35%, respectively. Using the improved model, we could exactly depict the spatial and temporal distribution of dust emission in the QTP. The results shown, our model could exactly depict the spatial and temporal distribution of dust emission in the QTP. We believe that the improvement of the model could offer a new perspective on dust emission in environments with lower air temperature and lower air pressure, and the simulated results could provide valuable data for global dust budget estimation.
查看更多>>摘要：Although the effect of different mycorrhizal types on the soil carbon (C) and nitrogen (N) cycles has been studied intensively, the mismatch between the C:N stoichiometry of microorganisms and their resources (i.e., C:N imbalance) and its impact on microbial physiology remain unclear. In this study, we measured the C:N imbalance, microbial C use efficiency (CUE), and C-and N-acquisition enzyme activities in four tree plantations associated with ectomycorrhizal (ECM) or arbuscular mycorrhizal (AM) fungi in subtropical China. We found that the C:N imbalance in ECM plantations was 44% higher than that in AM plantations, which was associated with the lower microbial CUE and higher specific C-and N-acquisition enzyme activities. Collectively, from a novel perspective of ecological stoichiometry, our results demonstrate that the C:N imbalance and the microbial physiological activities it mediated differ between ECM-and AM plantations which have important knock-on consequences for soil C sequestration in the subtropical forests.
查看更多>>摘要：Atmospheric C sequestration in agricultural soils is viewed as one of the most promising negative emission technologies currently available. Nonetheless, it remains unclear how strongly soil organic carbon (SOC) stocks respond to agricultural practices, especially for subsoil. Here, we assess the SOC storage potential in croplands and how the presence of temporary grasslands (TG) in the crop rotation affects SOC stocks. We developed a new approach to correct for bias in bulk density (BD) induced by sampling conditions and land-use effects with a data-driven model to predict the BD of fine soil (< 2 mm) for reference condition. Using 54 permanent grassland and cropland sites with various proportions of TG from a monitoring network in Switzerland, we showed that SOC stock differences down to 50-cm depth between cropland and permanent grasslands (maximum: 3.0 +/- 0.8 kg C m(-2)) depend on the TG proportion in the crop rotation, regardless of clay content and pH. An increase of the TG proportion by 10% would induce a SOC gain of 0.40 +/- 0.13 kg C m(-2). The responses of topsoil (0-20 cm) and subsoil (20-50 cm) SOC stocks to TG proportion were linear and equivalent. The effect of TG on SOC storage would have been underestimated by 58% without accounting for subsoil stocks response and by 16% without BD corrections. The conversion of all croplands to permanent grasslands in the study region would potentially store a quantity of SOC equivalent to the anthropogenic greenhouse gas emissions generated by the same region during one year. Although the potential of agricultural soils as negative emission technology is relatively modest compared to former expectations, the findings demonstrate the potential to manage SOC and its associated ecosystem services at large scales and down to deep soil layers.
NSTL
Elsevier