The rhizosphere is a critical interface supporting the exchange of resources between plants and their associated soil environment. Rhizosphere microbial diversity is influenced by the physical and ...chemical properties of the rhizosphere, some of which are determined by the genetics of the host plant. However, within a plant species, the impact of genetic variation on the composition of the microbiota is poorly understood. Here, we characterized the rhizosphere bacterial diversity of 27 modern maize inbreds possessing exceptional genetic diversity grown under field conditions. Randomized and replicated plots of the inbreds were planted in five field environments in three states, each with unique soils and management conditions. Using pyrosequencing of bacterial 16S rRNA genes, we observed substantial variation in bacterial richness, diversity, and relative abundances of taxa between bulk soil and the maize rhizosphere, as well as between fields. The rhizospheres from maize inbreds exhibited both a small but significant proportion of heritable variation in total bacterial diversity across fields, and substantially more heritable variation between replicates of the inbreds within each field. The results of this study should facilitate expanded studies to identify robust heritable plant–microbe interactions at the level of individual polymorphisms by genome wide association, so that plant-microbiome interactions can ultimately be incorporated into plant breeding.
Mangroves sequester large quantities of carbon (C) that become significant sources of greenhouse gases when disturbed through land-use change. Thus, they are of great value to incorporate into ...climate change adaptation and mitigation strategies. In response, a global network of mangrove plots was established to provide policy-relevant ecological data relating to interactions of mangrove C stocks with climatic, tidal, plant community, and geomorphic factors. Mangroves from 190 sites were sampled across five continents encompassing large biological, physical, and climatic gradients using consistent methodologies for the quantification of total ecosystem C stocks (TECS). Carbon stock data were collected along with vegetation, physical, and climatic data to explore potential predictive relationships. There was a 28-fold range in TECS (79–2,208 Mg C/ha) with a mean of 856 ± 32 Mg C/ha. Belowground C comprised an average 85% of the TECS. Mean soil depth was 216 cm, ranging from 22 to >300 cm, with 68 sites (35%) exceeding a depth of 300 cm. TECS were weakly correlated with metrics of forest structure, suggesting that aboveground forest structure alone cannot accurately predict TECS. Similarly, precipitation was not a strong predictor of TECS. Reasonable estimates of TECS were derived via multiple regression analysis using precipitation, soil depth, tree mass, and latitude (𝑅² = 0.54) as variables. Soil carbon to a 1 m depth averaged 44% of the TECS. Limiting analyses of soil C stocks to the top 1 m of soils result in large underestimates of TECS as well as in the greenhouse gas emissions that would arise from their conversion to other land uses. The current IPCC Tier 1 default TECS value for mangroves is 511 Mg C/ha, which is only 60% of our calculated global mean. This study improves current assessments of mangrove C stocks providing a foundation necessary for C valuation related to climate change mitigation. We estimate mangroves globally store about 11.7 Pg C: an aboveground carbon stock of 1.6 Pg C and a belowground carbon stock of 10.2 Pg C). The differences in the estimates of total ecosystem carbon stocks based on climate, salinity, forest structure, geomorphology, or geopolitical boundaries are not as much of an influence as the choice of soil depth included in the estimate. Choosing to limit soils to a 1 m depth resulted in estimates of <5 Pg whereas those that included the soil profile >1 m depth resulted in global carbon stock estimates that exceeded 11.2 Pg C.
Satellite remote sensing provides unprecedented information on near-surface soil moisture at a global scale, enabling a wide range of studies such as drought monitoring and forecasting. Data ...Assimilation (DA) has been recognized as an effective means to incorporate such observations into hydrologic models to better predict and forecast hydroclimatic variables. In this study, we use a recently developed Evolutionary Particle Filter with Markov Chain Monte Carlo (EPFM) approach to assimilate Soil Moisture Active Passive (SMAP) soil moisture data into Variable Infiltration Capacity (VIC) hydrologic model to provide more reliable topsoil layer moisture (0~–5 cm) over the entire Continental United States (CONUS). The EPFM outperformed an Ensemble Kalman filter (EnKF) in terms of correlations and the unbiased root mean square error (ubRMSE) with in situ measurements from the Soil Climate Analysis Network (SCAN) and the United States Climate Reference Network (USCRN). Also, we used a multivariate probability distribution based on a Copula function to integrate the posterior soil moisture, precipitation (from the North American Land Data Assimilation System (NLDAS)) and evapotranspiration (from the Moderate Resolution Imaging Spectroradiometer (MODIS)) information to develop a new integrated drought index, i.e. SPESMI. To validate the usefulness of the developed integrated drought index, we compared the drought events detected by this index with those reported by the United States Drought Monitor (USDM). The results indicated a strong temporal consistency of the drought areas detected by our approach and the USDM over the entire period of study (April 2015 to June 2018). In addition to such promising results, we noticed that our approach could capture the flash drought in 2017 in the U.S. Northern Plains earlier than the USDM, and could identify some severe to extreme drought events that had been underestimated by the USDM. Moreover, the SPESMI has a high correlation with the yield loss of spring and winter wheat in the United States. This novel drought monitoring framework can serve as an independent and potentially complementary drought monitoring system.
•SMAP soil moisture is assimilated by the Evolutionary Particle Filter with MCMC (EPFM).•The EPFM outperforms the EnKF method in soil moisture assimilation for most in-situ stations.•A multivariate drought index (SPESMI) is developed based on precipitation, PET and soil moisture.•The SPESMI drought index can detect some drought events that were underestimated by the USDM.
The Soil Moisture Active Passive (SMAP) satellite can no longer directly deliver high-resolution (9 km) soil moisture products with the failure of the onboard L-band radar. Thus, an appropriate ...replacement sensor and new algorithms are urgently needed to compensate for the lost radar and estimate soil moisture at high-resolution. This paper presents a new downscaling approach, the Downscaling basEd oN gradient boosting deciSion trEe (DENSE) method to solve this problem. We analyzed 26 soil moisture related indices, derived from MODIS and a digital elevation model, to identify proxy variables for soil moisture variability. A gradient boosting decision tree regression links the aggregated soil moisture proxies and SMAP observations at a coarse scale to express the nonlinear relationships between them. High-resolution soil moisture products were generated by applying this built regression model to the optimal soil moisture proxies at a fine scale over the entire Tibetan Plateau during the years 2015–2017. In situ measurements were collected from the Ngari, Naqu, and Maqu networks, which represent different climatic and vegetation conditions. We evaluated the downscaled soil moisture against ground observations at the daily, point, and network scales. The results indicated that the DENSE method effectively infers the spatio-temporal variability of soil moisture; and at the same time, preserves SMAP soil moisture accuracy. The performance of the proposed method failed in the Maqu network as this area is covered by dense vegetation. Although further improvements are still needed to correct vegetation effects, nevertheless, the DENSE method improved the spatial resolution of SMAP soil moisture estimates, from 36 km to 1 km over the Tibetan plateau where high-resolution soil moisture products are necessary to support local and global hydrological applications.
•A novel method named DENSE is proposed to downscale SMAP soil moisture (SM).•Nine SM proxies is used for the downscaling scheme.•High-resolution SM maps are generated for the entire Tibetan Plateau.•Downscaled SM was evaluated against in situ measurements at three scales.
Summary
Soil water‐holding capacity is an important component of the water and energy balances of the terrestrial biosphere. It controls the rate of evapotranspiration, and is a key to crop ...production. It is widely accepted that the available water capacity in soil can be improved by increasing organic matter content. However, the increase in amount of water that is available to plants with an increase in organic matter is still uncertain and may be overestimated. To clarify this issue, we carried out a meta‐analysis from 60 published studies and analysed large databases (more than 50 000 measurements globally) to seek relations between organic carbon (OC) and water content at saturation, field capacity, wilting point and available water capacity. We show that the increase in organic carbon in soil has a small effect on soil water content. A 1% mass increase in soil OC (or 10 g C kg−1 soil mineral), on average, increases water content at saturation, field capacity, wilting point and available water capacity by: 2.95, 1.61, 0.17 and 1.16 mm H2O 100 mm soil−1, respectively. The increase is larger in sandy soils, followed by loams and is least in clays. Overall the increase in available water capacity is very small; 75% of the studies reported had values between 0.7 and 2 mm 100 mm−1 with an increase of 10 g C kg−1 soil. Compared with reported annual rates of carbon sequestration after the adoption of conservation agricultural systems, the effect on soil available water is negligible. Thus, arguments for sequestering carbon to increase water storage are questionable. Conversely, global warming may cause losses in soil carbon, but the effects on soil water storage and its consequent impact on hydrological cycling might be less than thought previously.
Highlights
We investigated how available water capacity can be increased with a 1% increase in soil organic carbon.
We analysed data from 60 published studies and global databases with more than 50 000 measurements.
The increase in organic carbon in soil has a small effect on soil water retention.
A 1% mass increase in soil OC on average increased available water capacity by 1.16%, volumetrically.
Land aridity has been projected to increase with global warming. Such projections are mostly based on off‐line aridity and drought metrics applied to climate model outputs but also are supported by ...climate‐model projections of decreased surface soil moisture. Here we comprehensively analyze soil moisture projections from the Coupled Model Intercomparison Project phase 5, including surface, total, and layer‐by‐layer soil moisture. We identify a robust vertical gradient of projected mean soil moisture changes, with more negative changes near the surface. Some regions of the northern middle to high latitudes exhibit negative annual surface changes but positive total changes. We interpret this behavior in the context of seasonal changes in the surface water budget. This vertical pattern implies that the extensive drying predicted by off‐line drought metrics, while consistent with the projected decline in surface soil moisture, will tend to overestimate (negatively) changes in total soil water availability.
Key Points
We identify a robust vertical gradient of projected soil moisture changes under global warming with more negative changes near the surface
We interpret this gradient as resulting from the physical asymmetry between winter precipitation/infiltration and summer evaporation
These results shed light on the discrepancy between projected land aridity increase and more modest changes in the surface water budget
•Cost-effective method is proposed for quantification of pesticides in soil.•Polymeric ionic liquid-based coatings designed and used for HS-SPME of analytes.•These fibers provide high extraction ...effectiveness at elevated temperatures.•The HS-SPME at 90 °C for 60 min provided the optimum extraction of pesticides.•The coating containing vinylbenzyl moieties provided greatest accuracy.
In this work, a green approach utilizing novel polymeric ionic liquid (PIL) coatings for headspace solid-phase microextraction (HS-SPME) of four current-use pesticides from soil samples was studied for the first time. Epoxiconazole, fluroxypyr, metribuzin, and oxyfluorfen were the target pesticides. Three PIL coatings containing 1-vinylbenzyl-3-hexadecylimidazolium bis(trifluoromethyl)sulfonylimide (PIL1 and PIL2) and 1-vinyl-3-(10-hydroxydecyl)imidazolium bis(trifluoromethyl)sulfonylimide (PIL3) monomers, and 1,12-di(3-vinylbenzylimidazolium)dodecane bis(trifluoromethyl)sulfonylimide (PIL1) and 1,12-di(3-vinylbenzimidazolium)dodecane bis(trifluoromethyl)sulfonylimide (PIL2 and PIL3) crosslinkers were employed in this study. The performance of these PIL coatings was evaluated and compared with commercial SPME coatings based on polydimethylsiloxane/divinylbenzene (PDMS/DVB) and polydimethylsiloxane (PDMS) at the different extraction temperatures (50–90 °C) and sampling times (15–60 min). HS-SPME at 90 °C for 60 min provided the highest sensitivity and adequate reproducibility for the majority of analytes. Despite having a lower thickness, PIL2 and PIL3 coatings provided similar extraction effectiveness of analytes, and 24–247% higher coating volume-normalized responses compared to the commercial PDMS/DVB coating. The use of the PIL1 sorbent coating resulted in excellent linearity (R2 = 0.995–0.999) and lower detection limits (0.06–0.4 ng g−1) for all analytes. The optimized method provides acceptable recoveries of spiked concentrations with better performance (84–112%) achieved with the PIL1 coating. Compared to other known methods for target pesticides in soil, the proposed method provides the highest compliance with the principles of green analytical chemistry evaluated using Analytical Eco-Scale and Green Analytical Procedure Index tools.
Remote sensing (RS) platforms such as unmanned aerial vehicles (UAVs) represent an essential source of information in precision agriculture (PA) as they are able to provide images on a daily basis ...and at a very high resolution. In this framework, this study aims to identify the optimal level of nitrogen (N)-based nutrients for improved productivity in an onion field of “Cipolla Rossa di Tropea” (Tropea red onion). Following an experiment that involved the arrangement of nine plots in the onion field in a randomized complete block design (RCBD), with three replications, three different levels of N fertilization were compared: N150 (150 kg N ha−1), N180 (180 kg N ha−1), and e N210 (210 kg N ha−1). The crop cycle was monitored using multispectral (MS) UAV imagery, producing vigor maps and taking into account the yield of data. The soil-adjusted vegetation index (SAVI) was used to monitor the vigor of the crop. In addition, the coverage’s class onion was spatially identified using geographical object-based image classification (GEOBIA), observing differences in SAVI values obtained in plots subjected to differentiated N fertilizer treatment. The information retrieved from the analysis of soil properties (electrical conductivity, ammonium and nitrate nitrogen), yield performance and mean SAVI index data from each field plot showed significant relationships between the different indicators investigated. A higher onion yield was evident in plot N180, in which SAVI values were higher based on the production data.
Iron (Fe)-bearing mineral phases contribute disproportionately to adsorption of soil organic matter (SOM) due to their elevated chemical reactivity and specific surface area (SSA). However, the ...spectrum of Fe solid-phase speciation present in oxidation–reduction-active soils challenges analysis of SOM–mineral interactions and may induce differential molecular fractionation of dissolved organic matter (DOM). This work used paired selective dissolution experiments and batch sorption of postextraction residues to (1) quantify the contributions of Fe-bearing minerals of varying crystallinity to DOM sorption, and (2) characterize molecular fractionation using Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS). A substantial proportion of soil SSA was derived from extracted Fe-bearing phases, and FT-ICR-MS analysis of extracted DOM revealed distinct chemical signatures across Fe-OM associations. Sorbed carbon (C) was highly correlated with Fe concentrations, suggesting that Fe-bearing phases are strong drivers of sorption in these soils. Molecular fractionation was observed across treatments, particularly those dominated by short-range-order (SRO) mineral phases, which preferentially adsorbed aromatic and lignin-like formulas, and higher-crystallinity phases, associated with aliphatic DOM. These findings suggest Fe speciation-mediated complexation acts as a physicochemical filter of DOM moving through the critical zone, an important observation as predicted changes in precipitation may dynamically alter Fe crystallinity and C stability.
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