In silico assessment of the potential of basalt amendments to reduce N2O emissions from bioenergy crops

The potential of large‐scale deployment of basalt to reduce N2O emissions from cultivated soils may contribute to climate stabilization beyond the CO2‐removal effect from enhanced weathering. We used 3 years of field observations from maize (Zea mays) and miscanthus (Miscanthus × giganteus) to improve the nitrogen (N) module of the DayCent model and evaluate the potential of basalt amendments to reduce N losses and increase yields from two bioenergy crops. We found 20%–60% improvement in our N2O flux estimates over previous model descriptions. Model results predict that the application of basalt would reduce N2O emissions by 16% in maize and 9% in miscanthus. Lower N2O emissions responded to increases in the N2:N2O ratio of denitrification with basalt‐induced increases in soil pH, with minor contributions from the impact of P additions (a minor component of some basalts) on N immobilization. The larger reduction of N2O emissions in maize than in miscanthus was likely explained by a synergistic effect between soil pH and N content, leading to a higher sensitivity of the N2:N2O ratio to changes in pH in heavily fertilized maize. Basalt amendments led to modest increases in modeled yields and the nitrogen use efficiency (i.e., fertilizer‐N recover in crop production) of maize but did not affect the productivity of miscanthus. However, enhanced soil P availability maintained the long‐term productivity of crops with high nutrient requirements. The alleviation of plant P limitation led to enhanced plant N uptake, thereby contributing to lower microbial N availability and N2O emissions from crops with high nutrient requirements. Our results from the improved model suggest that the large‐scale deployment of basalt, by reducing N2O fluxes of cropping systems, could contribute to the sustainable intensification of agriculture and enhance the climate mitigation potential of bioenergy with carbon capture and storage strategies

Carbon allocation and carbon isotope fluxes in the plant-soil-atmosphere continuum: A review

The terrestrial carbon (C) cycle has received increasing interest over the past few decades, however, there is still a lack of understanding of the fate of newly assimilated C allocated within plants and to the soil, stored within ecosystems and lost to the atmosphere. Stable carbon isotope studies can give novel insights into these issues. In this review we provide an overview of an emerging picture of plant-soil-atmosphere C fluxes, as based on C isotope studies, and identify processes determining related C isotope signatures. The first part of the review focuses on isotopic fractionation processes within plants during and after photosynthesis. The second major part elaborates on plant-internal and plant-rhizosphere C allocation patterns at different time scales (diel, seasonal, interannual), including the speed of C transfer and time lags in the coupling of assimilation and respiration, as well as the magnitude and controls of plant-soil C allocation and respiratory fluxes. Plant responses to changing environmental conditions, the functional relationship between the physiological and phenological status of plants and C transfer, and interactions between C, water and nutrient dynamics are discussed. The role of the C counterflow from the rhizosphere to the aboveground parts of the plants, e.g. via CO2 dissolved in the xylem water or as xylem-transported sugars, is highlighted. The third part is centered around belowground C turnover, focusing especially on above- and belowground litter inputs, soil organic matter formation and turnover, production and loss of dissolved organic C, soil respiration and CO2 fixation by soil microbes. Furthermore, plant controls on microbial communities and activity via exudates and litter production as well as microbial community effects on C mineralization are reviewed. A further part of the paper is dedicated to physical interactions between soil CO2 and the soil matrix, such as CO2 diffusion and dissolution processes within the soil profile. Finally, we highlight state-of-the-art stable isotope methodologies and their latest developments. From the presented evidence we conclude that there exists a tight coupling of physical, chemical and biological processes involved in C cycling and C isotope fluxes in the plant-soil-atmosphere system. Generally, research using information from C isotopes allows an integrated view of the different processes involved. However, complex interactions among the range of processes complicate or currently impede the interpretation of isotopic signals in CO2 or organic compounds at the plant and ecosystem level. This review tries to identify present knowledge gaps in correctly interpreting carbon stable isotope signals in the plant-soil-atmosphere system and how future research approaches could contribute to closing these gaps

Deep-sequencing reveals broad subtype-specific HCV resistance mutations associated with treatment failure

A percentage of hepatitis C virus (HCV)-infected patients fail direct acting antiviral (DAA)-based treatment regimens, often because of drug resistance-associated substitutions (RAS). The aim of this study was to characterize the resistance profile of a large cohort of patients failing DAA-based treatments, and investigate the relationship between HCV subtype and failure, as an aid to optimizing management of these patients. A new, standardized HCV-RAS testing protocol based on deep sequencing was designed and applied to 220 previously subtyped samples from patients failing DAA treatment, collected in 39 Spanish hospitals. The majority had received DAA-based interferon (IFN) a-free regimens; 79% had failed sofosbuvir-containing therapy. Genomic regions encoding the nonstructural protein (NS) 3, NS5A, and NS5B (DAA target regions) were analyzed using subtype-specific primers. Viral subtype distribution was as follows: genotype (G) 1, 62.7%; G3a, 21.4%; G4d, 12.3%; G2, 1.8%; and mixed infections 1.8%. Overall, 88.6% of patients carried at least 1 RAS, and 19% carried RAS at frequencies below 20% in the mutant spectrum. There were no differences in RAS selection between treatments with and without ribavirin. Regardless of the treatment received, each HCV subtype showed specific types of RAS. Of note, no RAS were detected in the target proteins of 18.6% of patients failing treatment, and 30.4% of patients had RAS in proteins that were not targets of the inhibitors they received. HCV patients failing DAA therapy showed a high diversity of RAS. Ribavirin use did not influence the type or number of RAS at failure. The subtype-specific pattern of RAS emergence underscores the importance of accurate HCV subtyping. The frequency of “extra-target” RAS suggests the need for RAS screening in all three DAA target regions

Improving Estimations of Spatial Distribution of Soil Respiration Using the Bayesian Maximum Entropy Algorithm and Soil Temperature as Auxiliary Data

This study was supported by the NSF China Programs (Grant No. 31300539 and 31570629) and the Public Welfare Technology Application Research Program of Zhejiang province (Grant No. 2015C31004).Soil respiration inherently shows strong spatial variability. It is difficult to obtain an accurate characterization of soil respiration with an insufficient number of monitoring points. However, it is expensive and cumbersome to deploy many sensors. To solve this problem, we proposed employing the Bayesian Maximum Entropy (BME) algorithm, using soil temperature as auxiliary information, to study the spatial distribution of soil respiration. The BME algorithm used the soft data (auxiliary information) effectively to improve the estimation accuracy of the spatiotemporal distribution of soil respiration. Based on the functional relationship between soil temperature and soil respiration, the BME algorithm satisfactorily integrated soil temperature data into said spatial distribution. As a means of comparison, we also applied the Ordinary Kriging (OK) and Co-Kriging (Co-OK) methods. The results indicated that the root mean squared errors (RMSEs) and absolute values of bias for both Day 1 and Day 2 were the lowest for the BME method, thus demonstrating its higher estimation accuracy. Further, we compared the performance of the BME algorithm coupled with auxiliary information, namely soil temperature data, and the OK method without auxiliary information in the same study area for 9, 21, and 37 sampled points. The results showed that the RMSEs for the BME algorithm (0.972 and 1.193) were less than those for the OK method (1.146 and 1.539) when the number of sampled points was 9 and 37, respectively. This indicates that the former method using auxiliary information could reduce the required number of sampling points for studying spatial distribution of soil respiration. Thus, the BME algorithm, coupled with soil temperature data, can not only improve the accuracy of soil respiration spatial interpolation but can also reduce the number of sampling points.Yeshttp://www.plosone.org/static/editorial#pee

Are we approaching a water ceiling to maize yields in the United States?

While annual precipitation in much of the US Corn Belt is likely to remain constant, atmospheric vapor pressure deficit (VPD), the driver of crop water loss (evapotranspiration; ET), is projected to increase from ~2.2 kPa today to ~2.7 kPa by mid-century primarily due to the temperature increase. Without irrigation, it has been hypothesized that the increase in VPD will create a ceiling to future increases in maize yields. We calculated current and future growing season ET based on biomass, water use efficiency, and the amount of yield these levels of ET would support for maize production in the Midwest USA. We assumed that the production of more grain will necessitate a proportional increase in the production of biomass, with a corresponding increase in ET. Here we show that as VPD increases, maintaining current maize yields (2013–2016) will require a large expansion of irrigation, greater than threefold, in areas currently supported by rain. The average predicted yield for the region of 244 ± 4 bushels/acre (15,316 ± 251 kg/ha) projected for 2050, assuming yield increases observed for the past 60 yr continue, would not be possible with projected increases in VPD, creating a water ceiling to maize yields. Substantial increases in maize yields and the production of high yielding grasses for bioenergy will require developing cultivars with greater water use efficiency, a trait that has not been a priority for breeders in the past. © 2019 The Authors