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 vegetation 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 <40 cm based on surface soil moisture is most promising. However, the influence of climate, land cover, and soil textures 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.

Next Generation Sequencing, and Development of a Pipeline as a Tool for the Detection and Discovery of Citrus Pathogens to Facilitate Safer Germplasm Exchange.

Citrus is affected by many diseases, and hence, the movement of citrus propagative materials is highly regulated in the USA. Currently used regulatory pathogen detection methods include biological and laboratory-based technologies, which are time-consuming, expensive, and have many limitations. There is an urgent need to develop alternate, rapid, economical, and reliable testing methods for safe germplasm exchange. Citrus huanglongbing (HLB) has devastated citrus industries leading to an increased need for germplasm exchanges between citrus growing regions for evaluating many potentially valuable hybrids for both HLB resistance and multilocational performance. In the present study, Next-Generation Sequencing (NGS) methods were used to sequence the transcriptomes of 21 test samples, including 15 well-characterized pathogen-positive plants. A workflow was designed in the CLC Genomics Workbench software, v 21.0.5 for bioinformatics analysis of the sequence data for the detection of pathogens. NGS was rapid and found to be a valuable technique for the detection of viral and bacterial pathogens, and for the discovery of new citrus viruses, complementary to the existing array of biological and laboratory assays. Using NGS methods, we detected beet western yellows virus, a newly reported citrus virus, and a variant of the citrus yellow vein-associated virus associated with the "fatal yellows" disease.

Plant phase extraction: A method for enhanced discovery of the RNA-binding proteome and its dynamics in plants.

RNA-binding proteins (RBPs) play critical roles in posttranscriptional gene regulation. Current methods of systematically profiling RBPs in plants have been predominantly limited to proteins interacting with polyadenylated (poly(A)) RNAs. We developed a method called plant phase extraction (PPE), which yielded a highly comprehensive RNA-binding proteome (RBPome), uncovering 2,517 RBPs from Arabidopsis (Arabidopsis thaliana) leaf and root samples with a highly diverse array of RNA-binding domains. We identified traditional RBPs that participate in various aspects of RNA metabolism and a plethora of nonclassical proteins moonlighting as RBPs. We uncovered constitutive and tissue-specific RBPs essential for normal development and, more importantly, revealed RBPs crucial for salinity stress responses from a RBP-RNA dynamics perspective. Remarkably, 40% of the RBPs are non-poly(A) RBPs that were not previously annotated as RBPs, signifying the advantage of PPE in unbiasedly retrieving RBPs. We propose that intrinsically disordered regions contribute to their nonclassical binding and provide evidence that enzymatic domains from metabolic enzymes have additional roles in RNA binding. Taken together, our findings demonstrate that PPE is an impactful approach for identifying RBPs from complex plant tissues and pave the way for investigating RBP functions under different physiological and stress conditions at the posttranscriptional level.

Morphological, physiological, biochemical, and transcriptome studies reveal the importance of transporters and stress signaling pathways during salinity stress in Prunus.

The almond crop has high economic importance on a global scale, but its sensitivity to salinity stress can cause severe yield losses. Salt-tolerant rootstocks are vital for crop economic feasibility under saline conditions. Two commercial rootstocks submitted to salinity, and evaluated through different parameters, had contrasting results with the survival rates of 90.6% for 'Rootpac 40' (tolerant) and 38.9% for 'Nemaguard' (sensitive) under salinity (Electrical conductivity of water = 3 dS m-1). Under salinity, 'Rootpac 40' accumulated less Na and Cl and more K in leaves than 'Nemaguard'. Increased proline accumulation in 'Nemaguard' indicated that it was highly stressed by salinity compared to 'Rootpac 40'. RNA-Seq analysis revealed that a higher degree of differential gene expression was controlled by genotype rather than by treatment. Differentially expressed genes (DEGs) provided insight into the regulation of salinity tolerance in Prunus. DEGs associated with stress signaling pathways and transporters may play essential roles in the salinity tolerance of Prunus. Some additional vital players involved in salinity stress in Prunus include CBL10, AKT1, KUP8, Prupe.3G053200 (chloride channel), and Prupe.7G202700 (mechanosensitive ion channel). Genetic components of salinity stress identified in this study may be explored to develop new rootstocks suitable for salinity-affected regions.

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.

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.

Diurnal variation of diazinon volatilization: soil moisture effects.

Diurnal variations in diazinon volatilization were monitored in three field experiments conducted with differing soil moisture contents. The highest flux rates in all experiments were recorded just after diazinon application, but the magnitudes of those initial rates differed according to the soil moisture content, with wetter soil producing a higher rate: 5.6 × 10(-4) µg cm(-2) min(-1) for initial soil moisture above field capacity, 8.3 × 10(-5) µg cm(-2) min(-1) for initial soil moisture at field capacity, and 2.5 × 10(-5) µg cm(-2) min(-1) for initially very dry soil. Volatilization decreased during the first day in the two experiments with initially wet soils but remained relatively constant in the experiment with initially dry soil. The volatilization rate during the first night for the wettest soil remained about an order-of-magnitude higher than that observed for driest soil. When the surface dried in the experiment conducted at the intermediate water content, the volatilization rate and temporal pattern transitioned and became similar to that observed for the initially dry soil. Around noon of the second day, a daily maximum value was observed in the volatilization rate for wet soil, whereas a minimum value was observed for the dry soil, resulting in an order-of magnitude difference. This study demonstrates the importance of soil moisture on emissions of pesticides to the atmosphere.