Relationships among soil moisture at various depths under diverse climate, land cover and soil texture.

Soil moisture is an important component of the hydrological cycle and a key mediator between land surface and atmospheric interactions. Although substantial progress has been made in remote sensing of soil moisture at different spatial scales, the shallow penetration depth of remote sensors greatly limits their utility for applications in meteorological modelling and hydrological studies where the critical variable of interest is the root-zone soil moisture content. Therefore, this study assesses the relationship between soil moisture at the surface (10 cm) and in lower soil layers (20, 40, 60, 80, 100, and 120 cm) under varying climates, soils, and crop types. Cross-correlation analysis is applied to daily in-situ soil moisture measurements from 4712 locations in agricultural lands across the contiguous United States. Our analysis demonstrates that zero-day lag always produced the highest correlation between 10 cm soil moisture and soil moisture in the lower layers. In addition, a positive and strong relationship between 10 and 20 cm soil moisture (r = 0.84) was observed, while the relationships between 10 and 40 cm soil moisture were moderate (r = 0.52). The decline in cross-correlation continued to the deeper soil layers, which indicated that, on a daily timescale, the surface soil moisture gradually becomes decoupled with soil moisture at greater depths. Therefore, our research suggests that the estimation of soil moisture in the soil layers up to 40 cm based on surface soil moisture is most promising. However, the influence of climate, crop type, and soil texture on the strength of relationships between surface and lower layers makes the prediction difficult. The comparatively weak relationship between precipitation and soil moisture (0.09-0.32), as well as the relationship between reference evapotranspiration (ETo) and soil moisture (-0.19-0.18), in this study can be attributed to scale mismatching from different data sources.

Multitemporal satellite imagery analysis for soil organic carbon assessment in an agricultural farm in southeastern Brazil.

Soil organic carbon (SOC) plays a crucial role for soil health. However, large datasets needed to accurately assess SOC at high resolution across scales are labor-intensive, time-consuming, and expensive. Ancillary geodata, including remote sensing spectral indices (RS-SIs) and topographic indicators (TIs), have been proposed as spatial covariates. Reported relationships between SOC and RS-SIs are erratic, possibly because single-date RS-SIs do not accurately capture SOC spatial variability due to transient confounding factors in the soil (e.g., moisture). However, multitemporal RS-SI data analysis may lead to noise reduction in SOC versus RS-SI relationships. This study aimed at: i) comparing single-date versus multitemporal RS-Sis derived from Sentinel-2 imagery for assessment of topsoil (0-0.2 m) SOC in two agricultural fields located in south-eastern Brazil; ii) comparing the performance of RS-SIs and TIs; iii) using adequate RS-SIs and TIs to compare sampling schemes defined on different collection grids; and iv) studying the temporal changes of SOC (0-0.2 m and 0.2-0.4 m). Results showed that: i) single-date RS-SIs were not reliable proxies for topsoil SOC at the study sites. For most of the tested RS-SIs, multitemporal data analysis produced accurate proxies for SOC; e.g., for the Normalized Difference Vegetation Index, the 4.5th multitemporal percentile predicted SOC with an R2 of 0.64; ii) The best TI was elevation (ranging from 643 to 684 m) with an R2 of 0.70; iii) The multitemporal SI and elevation maps indicated that the different sampling schemes were equally representative of the topsoil SOC's distribution across the entire area; and iv) From 2012 through 2019, topsoil SOC increased from 19.3 to 24.1 g kg-1. The ratio between SOC in the topsoil and subsoil (0.2-0.4 m) decreased from 1.7 to 1.1. Further testing of the proposed multitemporal RS-SI analysis is necessary to confirm its dependability for SOC assessment in Brazil and elsewhere.

Modeling of xylem vessel occlusion in grapevine.

Morphological traits of the plant vascular system such as xylem vessel diameter have been implicated in many physiological processes including resistance to drought-induced xylem cavitation and vessel occlusion during infection with vascular wilt diseases. In both events, xylem vessels lose function because they become filled with air or tyloses and gels. Xylem cavitation has been well studied, whereas vessel occlusion remains purely descriptive even though it is a critical response to wounding injuries and compartmentalization of vascular pathogens. The timing of vessel occlusion is a key determinant to a successful compartmentalization of pathogens within the plant vascular system and we hypothesized that xylem vessel diameter is the driving variable. Using a dye injection method coupled with automated image analysis, we parameterized a model to investigate how xylem vessel diameter affects the speed of vessel occlusion in Vitis vinifera L. cv. Cabernet Sauvignon in response to wounding. Our dataset contains observations from 6,646 vessels at five kinetic points following stem pruning, over a time course of 1 week. Using this approach we provide evidence that the diameter of vessels is a key determinant of the timing of their occlusion. We discuss how these findings impact resistance to vascular wilt diseases in perennial woody hosts.

Evaluating Oilseed Biofuel Production Feasibility in California's San Joaquin Valley Using Geophysical and Remote Sensing Techniques.

Though more costly than petroleum-based fuels and a minor component of overall military fuel sources, biofuels are nonetheless strategically valuable to the military because of intentional reliance on multiple, reliable, secure fuel sources. Significant reduction in oilseed biofuel cost occurs when grown on marginally productive saline-sodic soils plentiful in California's San Joaquin Valley (SJV). The objective is to evaluate the feasibility of oilseed production on marginal soils in the SJV to support a 115 ML yr-1 biofuel conversion facility. The feasibility evaluation involves: (1) development of an Ida Gold mustard oilseed yield model for marginal soils; (2) identification of marginally productive soils; (3) development of a spatial database of edaphic factors influencing oilseed yield and (4) performance of Monte Carlo simulations showing potential biofuel production on marginally productive SJV soils. The model indicates oilseed yield is related to boron, salinity, leaching fraction, and water content at field capacity. Monte Carlo simulations for the entire SJV fit a shifted gamma probability density function: Q = 68.986 + gamma (6.134,5.285), where Q is biofuel production in ML yr-1. The shifted gamma cumulative density function indicates a 0.15-0.17 probability of meeting the target biofuel-production level of 115 ML yr-1, making adequate biofuel production unlikely.

Xylem Vessel Diameter Affects the Compartmentalization of the Vascular Pathogen Phaeomoniella chlamydospora in Grapevine.

Fungal wilt diseases are a threat to global food safety. Previous studies in perennial crops showed that xylem vessel diameter affects disease susceptibility. We tested the hypothesis that xylem vessel diameter impacts occlusion processes and pathogen compartmentalization in Vitis vinifera L. We studied the interaction between four grape commercial cultivars with the vascular wilt pathogen Phaeomoniella chlamydospora. We used qPCR and wood necrotic lesion length to measure fungal colonization coupled with histological studies to assess differences in xylem morphology, pathogen compartmentalization, and fungal colonization strategy. We provided evidence that grape cultivar with wide xylem vessel diameter showed increased susceptibility to P. chlamydospora. The host response to pathogen included vessel occlusion with tyloses and gels, deposition of non-structural phenolic compounds and suberin in vessel walls and depletion of starch in parenchyma cells. Pathogen compartmentalization was less efficient in wide xylem vessels than in narrow diameter vessels. Large vessels displayed higher number of tyloses and gel pockets, which provided substrate for P. chlamydospora growth and routes to escape occluded vessels. We discuss in which capacity xylem vessel diameter is a key determinant of the compartmentalization process and in turn grape cultivar resistance to disease caused by P. chlamydospora.

Simplifying field-scale assessment of spatiotemporal changes of soil salinity.

Monitoring soil salinity (ECe) is important for planning and implementing agronomic and irrigation practices. Salinity can be measured through soil sampling directed by geospatial measurements of apparent soil electrical conductivity (ECa). Using data from a long-term (1999-2012) monitoring study at a 32.4-ha saline field located in California, USA, two established field-scale approaches to map and monitor soil salinity using ECa are reviewed: one that relies on a single ECa survey to identify locations that can be repeatedly sampled to infer the frequency distribution of ECe; and another based on repeated ECa surveys that are calibrated, each time, to ECe estimation using ground-truth data from soil samples. The reviewed approaches are very accurate and reliable, but require extensive soil sampling. Subsequently, we propose a novel approach - temporal analysis of covariance (t-ANOCOVA) modeling - that results in accurate spatiotemporal salinity estimations using ECa surveys with a significant reduction in the number of soil samples needed for calibration of ECa to ECe. In this modeling framework, the ECe-ECa relationship is described with a log-transformed linear function. The regression slope indicates the magnitude of the contribution of ECe to ECa and is assumed to remain constant over time, while the intercept represents the secondary factors influencing ECa that are not related to ECe (e.g., soil tillage). Once the t-ANOCOVA slope is established for a field, in subsequent surveys as few as three soil samples are used to estimate a time-specific t-ANOCOVA intercept so that ECa measurements can be converted to ECe estimations. Our results suggest that this approach is reliable at low salinity values (i.e., where common crops can grow). The t-ANOCOVA approach requires further validation before real-world implementations, but represents a significant step towards the use of ECa mobile sensor technology for inexpensive soil salinity monitoring at high temporal resolution.

Validation of Sensor-Directed Spatial Simulated Annealing Soil Sampling Strategy.

Soil spatial variability has a profound influence on most agronomic and environmental processes at field and landscape scales, including site-specific management, vadose zone hydrology and transport, and soil quality. Mobile sensors are a practical means of mapping spatial variability because their measurements serve as a proxy for many soil properties, provided a sensor-soil calibration is conducted. A viable means of calibrating sensor measurements over soil properties is through linear regression modeling of sensor and target property data. In the present study, two sensor-directed, model-based, sampling scheme delineation methods were compared to validate recent applications of soil apparent electrical conductivity (EC)-directed spatial simulated annealing against the more established EC-directed response surface sampling design (RSSD) approach. A 6.8-ha study area near San Jacinto, CA, was surveyed for EC, and 30 soil sampling locations per sampling strategy were selected. Spatial simulated annealing and RSSD were compared for sensor calibration to a target soil property (i.e., salinity) and for evenness of spatial coverage of the study area, which is beneficial for mapping nontarget soil properties (i.e., those not correlated with EC). The results indicate that the linear modeling EC-salinity calibrations obtained from the two sampling schemes provided salinity maps characterized by similar errors. The maps of nontarget soil properties show similar errors across sampling strategies. The Spatial Simulated Annealing methodology is, therefore, validated, and its use in agronomic and environmental soil science applications is justified.

Time course of biochemical, physiological, and molecular responses to field-mimicked conditions of drought, salinity, and recovery in two maize lines.

Drought and salinity stresses will have a high impact on future crop productivity, due to climate change and the increased competition for land, water, and energy. The response to drought (WS), salinity (SS), and the combined stresses (WS+SS) was monitored in two maize lines: the inbred B73 and an F1 commercial stress-tolerant hybrid. A protocol mimicking field progressive stress conditions was developed and its effect on plant growth analyzed at different time points. The results indicated that the stresses limited growth in the hybrid and arrested it in the inbred line. In SS, the two genotypes had different ion accumulation and translocation capacity, particularly for Na(+) and Cl(-). Moreover, the hybrid perceived the stress, reduced all the analyzed physiological parameters, and kept them reduced until the recovery. B73 decreased all physiological parameters more gradually, being affected mainly by SS. Both lines recovered better from WS than the other stresses. Molecular analysis revealed a diverse modulation of some stress markers in the two genotypes, reflecting their different response to stresses. Combining biochemical and physiological data with expression analyses yielded insight into the mechanisms regulating the different stress tolerance of the two lines.

Simultaneous monitoring of soil water content and salinity with a low-cost capacitance-resistance probe.

Capacitance and resistivity sensors can be used to continuously monitor soil volumetric water content (θ) and pore-water electrical conductivity (EC(p)) with non-destructive methods. However, dielectric readings of capacitance sensors operating at low frequencies are normally biased by high soil electrical conductivity. A procedure to calibrate capacitance-resistance probes in saline conditions was implemented in contrasting soils. A low-cost capacitance-resistance probe (ECH2O-5TE, 70 MHz, Decagon Devices, Pullman, WA, USA) was used in five soils at four water contents (i.e., from dry conditions to saturation) and four salinity levels of the wetting solution (0, 5, 10, and 15 dS · m-1). θ was accurately predicted as a function of the dielectric constant, apparent electrical conductivity (EC(a)), texture and organic carbon content, even in high salinity conditions. Four models to estimate pore-water electrical conductivity were tested and a set of empirical predicting functions were identified to estimate the model parameters based on easily available soil properties (e.g., texture, soil organic matter). The four models were reformulated to estimate EC(p) as a function of EC(a), dielectric readings, and soil characteristics, improving their performances with respect to the original model formulation. Low-cost capacitance-resistance probes, if properly calibrated, can be effectively used to monitor water and solute dynamics in saline soils.