Todd. H. Skaggs

High‐resolution geospatial soil moisture measurements are needed to inform hydrological modeling and to guide water management in agriculture, especially in highly heterogeneous systems such as micro‐irrigated orchards. In this research, we used a Portable L‐band Radiometer (PoLRa) to map very high‐resolution (Prunus dulcis Mill.), olive ( Olea europaea L.), and orange ( Citrus × sinensis Osbeck) orchards grown on Monserate sandy‐loam soil were surveyed from the Summer through the Fall of 2022. The sensor was mounted on an all‐terrain vehicle and paired with a centimeter‐level positioning system. PoLRa measurements were compared with ground‐truth volumetric water content determined from soil cores collected at the study sites. The sensor data were calibrated to estimate surface soil moisture with an analysis of covariance linear regression approach. The lowest estimation errors were observed in the almond orchard, which had flat soil and no canopy interference. There, the root mean square error of the tested linear models ranged between 3.9% and 4.1%. Over the entire dataset, the root mean square error was 5.9%. This new sensor technology may be a means for improving understanding of water dynamics in complex and heterogeneous agricultural systems. Nevertheless, further research is needed to refine calibration models and address environmental variability and its effects on the sensor's measurements.

Yield maps and in-season forecasts help optimize agricultural practices. The traditional approaches to predicting yield during the growing season often rely on ground-based observations, which are time-consuming and labor-intensive. Remote sensing offers a promising alternative by providing frequent and spatially extensive information on crop development. In this study, we evaluated the feasibility of high-resolution satellite imagery for the early yield prediction of an under-investigated crop, Japanese squash (Cucurbita maxima), in a small farm in Hollister, California, over the growing seasons of 2022 and 2023 using vegetation indices, including the Normalized Difference Vegetation Index (NDVI) and the Soil-Adjusted Vegetation Index (SAVI). We identified the optimal time for yield prediction and compared the performances across satellite platforms (Sentinel-2: 10 m; PlanetScope: 3 m; SkySat: 0.5 m). Pearson’s correlation coefficient (r) was employed to determine the dependencies between the yield and vegetation indices measured at various stages throughout the squash growing season. The results showed that SkySat-derived vegetation indices outperformed those of Sentinel-2 and PlanetScope in explaining the squash yields (R² = 0.75–0.76; RMSE = 0.8–1.9 tons/ha). Remote sensing showed very strong correlations with yield as early as 29 days after planting in 2022 and 37 and 76 days in 2023 for the NDVI and the SAVI, respectively. These early dates corresponded with the vegetative stages when the crop canopy became denser before fruit development. These findings highlight the utility of high-resolution imagery for in-season yield estimation and within-field variability detection. Detecting yield variability early enables timely management interventions to optimize crop productivity and resource efficiency, a critical advantage for small-scale farms, where marginal yield changes impact economic outcomes.

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.

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.

RNA-binding proteins (RBPs) are versatile effectors in posttranscriptional gene regulation. Systematic profiling of RBPs in plants has been limited to proteins interacting with polyadenylated (poly(A)) RNAs due to the lack of an efficient method of isolating RBPs associated with non-poly(A) RNAs. Here we reported the establishment and application of plant phase extraction (PPE) as a novel method to comprehensively discover the RNA-binding proteome in Arabidopsis leaf tissues, leading to the isolation of 1,169 RBPs, of which 673 corroborate with previously reported RBPs and 496 are novel RBPs. PPE showed unmatched ability in capturing 374 diverse RNA-binding domains (RBDs), while 44% of the RBPs lack recognized RBDs. PPE recovered far more ribosomal and tRNA-related RBPs and metabolic enzymes with moonlighting RBP functions than any other individual report from plants, signifying its advantage in unbiasedly retrieving non-poly(A) and unconventional RBPs. We proposed that intrinsically disordered regions (IDRs) contributed to the non-classical binding of some novel RBPs, and provided the first evidence that enzymatic domains from metabolic enzymes have additional roles in RNA-binding. Taken together, our findings demonstrated that PPE is an effective approach to identifying a wide range of RBPs in complex plant tissues and may have broad biological implications.

California is increasingly experiencing drought conditions that restrict irrigation deliveries to perennial nut crops such as almonds and pistachios. During drought, poorer quality groundwater is often used to maintain these crops, but this use often results in secondary salinization that requires skilled management. Process-based models can help improve management guidelines under these challenging circumstances. The main objective of this work was to assess seasonal soil salinity and root water uptake as a function of irrigation water salinity and annual rain amounts. The manuscript presents a comparison of three-year experimental and numerically simulated root zone salinities in and below the root zone of almond and pistachio drip-irrigated orchards at multiple locations in the San Joaquin Valley (SJV), California, with different meteorological characteristics. The HYDRUS-1D numerical model was calibrated and validated using field measurements of soil water contents and soil solute bulk electrical conductivities at four root zone depths and measured soil hydraulic conductivities. The remaining soil hydraulic parameters were estimated inversely. Observations and simulations showed that the effects of rain on root zone salinity were higher in fields with initially low salinities than in fields with high salinities. The maximum reduction in simulated root water uptake (7%) occurred in response to initially high soil salinity conditions and saline irrigation water. The minimum reduction in simulated water uptake (2.5%) occurred in response to initially low soil salinity conditions and a wet rain year. Simulated water uptake reductions and leaching fractions varied at early and late times of the growing season, depending on irrigation water salinity. Root water uptake reduction was highly correlated with the cumulative effects of using saline waters in prior years, more than salt leaching during a particular season, even when rain was sufficient to leach salts during a wet year.

Soil salinity increases when growers are forced to use higher salinity irrigation waters due to water shortages. It is necessary to estimate the impact of irrigation water on soil properties and conditions for crop growth to manage the effects of salinity on perennial crops. Therefore, in this study, we monitored root zone salinity in five almond and pistachio orchards in eastern and western San Joaquin Valley (SJV), California (CA). Volumetric soil water contents and bulk electrical conductivities were measured at four root-zone depths. Evapotranspiration was measured by eddy covariance along with three other types of data. The first is seasonal precipitation and irrigation patterns, including the temporal distribution of rains, irrigation events, and irrigation water salinity. The second is soil chemistry, including the initial sodium adsorption ratio (SAR) and soil solute electrical conductivity (ECe). The third type is the physical properties, including soil type, hydraulic conductivity, and bulk density. As expected, we found low salinity at the eastern sites and higher salinity at the western sites. The western sites have finer textured soils and lower quality irrigation water; measured actual ET was about 90% of modeled crop ET. Across the three western sites, the annual average apparent leaching fraction ranged from 11 to 28%. At the eastern sites, measured ET almost exactly matched modeled crop ET each year. Apparent leaching fractions in the eastern sites were approximately 20%.