Agricultural Research Service - Plains Area

Abstract Peanut smut, caused by the fungus Thecaphora frezzii , is a significant disease of peanuts in Argentina. Infected plants have seeds replaced by a mass of dark teliospores, reducing yield and seed quality. To prevent the spread of the pathogen, several countries have limited import of raw peanuts from Argentina, a major grower and exporter. Following successful in vitro culture of the fungus in its haploid stage, we produced a chromosome-level genome assembly of the species for the first time. We compare this genome with those of 49 other species of true smut fungi, or Ustilaginomycetes, including species of medical, agricultural, and industrial importance, some of which are known as pathogens and others only as saprotrophic yeasts. At almost 39 Mb, T. frezzii has the largest genome of the smut fungi sequenced to date and the highest repetitive content. While it shares some core effectors with species of the distantly related and better studied Ustilago and related fungi, predicted effectors only found in T. frezzii or in Thecaphora suggest unique infection strategies. Comparisons among the 50 smut genomes also show that the 14 smut fungi observed only as yeasts share genomic traits such as low repeat content and generally smaller genomes. This supports the hypothesis that some smut fungi are adapted to saprotrophic growth as yeasts. The high-quality, annotated genome for T. frezzii will be a valuable resource for investigating the population dynamics and evolution of an economically important pathogen, as well as illuminating an understudied clade of smut fungi. Article summary Peanut smut is a destructive and costly disease of peanuts in Argentina. For the first time, a high quality, annotated genome is presented for the causal agent, Thecaphora frezzii . This fungus has the largest and most repetitive genome of the true smut fungi, thus prompting comparison with 49 other species of smut fungi with available genomes, including non-pathogenic ones. While it shares some likely pathogenicity factors with well-studied smuts, it has many unique genes, a trait reflective of its evolutionary distance and likely novel mechanism for infection. This important genomic resource will benefit research regarding the evolution and adaption of T. frezzii , the development of diagnostic tools that enable rapid detection of it, and the study of smut fungi broadly.

Rangelands are extensive ecosystems, providing important ecosystem services while undergoing continuous change. As a result, improved monitoring technologies can help better characterize vegetation change. Satellite remote sensing has proven effective in this regard, tracking vegetation dynamics at broad and fine scales. We leveraged the spatial, spectral, and temporal resolution of Sentinel-2 satellites to estimate fractional cover and canopy gap across rangelands of the western United States. We produced annual, 10 m spatial resolution estimates of fractional cover and canopy gap size class for years 2018 to 2024. Fractional cover estimates include that of common plant functional types (annual forb and grass, bareground, littler, perennial forb and grass, shrub, tree) and select genera (including invasive annual grass species, pinyon-juniper species, and sagebrush species); canopy gap size classes include gap sizes 25 to 50, 51 to 100, 101 to 200, and greater than 200 cm. We make these data available as Cloud Optimized GeoTIFFs, organized as 75 × 75 km tiles covering the 17 western states of the United States.

We examined the spatial movement behavior, growth rates, and enteric CH4 emissions of yearling beef cattle in response to spatial distribution management with virtual fencing (VF) in extensive shortgrass steppe pastures. Over the 110-d grazing season (mid-May to early September), 120 British-breed stocker steers (~12 months of age; mean body weight [BW] 382 kg ± 35) were grazed with VF management (active VF collars) or free-range (non-active VF collars) in two pairs of ~130 ha physically fenced rangeland pastures (i.e., VF-managed vs. control). One pair was associated with a diverse mosaic of soil types supporting alkalai sacaton (Sporobolus airoides [Torr.] Torr.), blue grama (Bouteloua gracilis [Willd. Ex Kunth] Lag. Ex Griffiths), and needle-and-thread (Hesperostipa comata [Trin. &Rupr.] Barkworth), while the other pasture-pair was associated with the Sandy Plains ecological site, primarily hosting western wheatgrass (Pascopyrum smithii [Rydb.] Á. Löve), needle-and-thread, and blue grama. Within each pair of pastures, one herd was rotated among sub-pastures using the VF system, which focused grazing on varying native plant communities over the growing season. In control pastures, steers had access to the entire pasture for the grazing season. Spatial distribution management with VF maintained steers within desired grazing areas occurred 94–99% of the time, even though five of the 60 VF-managed steers consistently made short daily excursions outside the VF boundary. In all four pastures, an automated head-chamber system (AHCS, i.e., GreenFeed) measured the enteric CH4 emissions of individual steers. Steers that met the criteria of a minimum of 15 AHCS visits in each of at least two VF rotation intervals were analyzed for spatial behavior, growth performance, and enteric CH4 emissions. Screening based on AHCS visitation requirements resulted in 15 steers (nine VF, six control) in the diverse mosaic pasture pair, and 39 (17 VF, 22 control) in the Sandy Plains pasture pair. VF management significantly reduced growth rates for all steers across both pasture pairs by an average of 9%, resulting in steers that were 7.3 kg lighter than unmanaged steers at the end of the grazing season. VF management effects on enteric CH4 emissions varied among rotation intervals and pasture type. In the diverse mosaic pair, VF management significantly reduced CH4 emissions during the first rotation interval, when VF steers were concentrated on the C3 grass-dominated plant community, but increased emissions in the second and third intervals when VF steers were concentrated on C4 grass-dominated areas. In the Sandy Plains pasture pair, where cattle were rotated between sub-pastures with and without palatable four-wing saltbush (Atriplex canescens [Pursh] Nutt.) shrubs, VF management reduced CH4 emissions in three of four rotations as well as over the full grazing season. CH4 emissions intensity increased with VF management in the diverse mosaic, but not in the Sandy Plains pastures. Overall, our findings show VF management (1) controlled animals spatially within sub-pastures, (2) did not improve growth performance but rather decreased it, (3) did not consistently reduce enteric CH4 emissions, and (4) tended to increase emissions per kg of product via lowering steer growth performance. While some have posited that VF is a potential tool to reduce enteric emissions, our findings suggest VF management is not a straightforward solution for mediating the relationships between forage resources, growth performance, and enteric CH4 emissions of stocker steers on extensive rangeland. Furthermore, our fusion of animal GPS tracking, growth rates and AHCS data indicated that differences in spatial behavior and weight gain were consistent between VF-managed and control steers irrespective of their AHCS-acclimation status, supporting the perspective that AHCS-based gas flux measurements are a valid means of estimating enteric emissions in extensive rangelands.

The USDA-Agricultural Research Service conducted deficit irrigation and water productivity field trials for irrigated sunflower (Helianthus annuus) in northeastern Colorado from 2008 to 2016. The dataset from these field trials, composed of 10 Excel spreadsheet workbooks, is available online from the USDA National Agricultural Library “Ag Data Commons”. The dataset includes measurements of irrigation, precipitation, soil water storage, and periodic plant responses; daily estimates of crop evapotranspiration; and seasonal crop water use, biomass, and yield data. Soil parameters, hourly and daily weather data, management practices, and photo images are also provided. Each data file includes a data dictionary. The dataset can be useful to develop and evaluate sunflower crop growth and water use models under a wide range of water availability.

Machine learning (ML) models are widely used to analyze the spatiotemporal impacts of agricultural practices on environmental sustainability, including the contribution to global greenhouse gas (GHG) emissions. Management practices, such as organic amendment applications, are critical pillars of Climate-smart agriculture (CSA) strategies that mitigate GHG emissions while maintaining adequate crop yields. This study investigated the critical threshold of soil moisture level associated with soil CO2 emissions from organically amended plots using the classification and regression tree (CART) algorithm. Also, the study predicted the short-term soil CO2 emissions from organically amended systems using soil moisture and weather variables (i.e., air temperature, relative humidity, and solar radiation) using multilinear regression (MLR) and generalized additive models (GAMs). The different organic amendments considered in this study are biochar (2268 and 4536 kg ha−1) and chicken and dairy manure (0, 224, and 448 kg N/ha) under a sweet corn crop in the greater Houston area, Texas. The results of the CART analysis indicated a direct link between soil moisture level and the magnitude of CO2 flux emission from the amended plots. A threshold of 0.103 m3m−3 was calculated for treatment amended by biochar level I (2268 kg ha−1) and chicken manure at the N recommended rate (CXBX), indicating that if the soil moisture is less than the 0.103 m3m−3 threshold, then the median soil CO2 emission is 142 kg ha−1 d−1. Furthermore, applying biochar at a rate of 4536 kg ha−1 reduced the soil CO2 emissions by 14.5% compared to the control plots. Additionally, the results demonstrate that GAMs outperformed MLR, exhibiting the highest performance under the combined effect of chicken and biochar. We conclude that quantifying soil moisture thresholds will provide valuable information for the sustainable mitigation of soil CO2 emissions.

ObjectiveThe fungus Rhizoctonia solani is an important seedling pathogen to many plant hosts including cotton (Gossypium). For multiple annual crops there have been relatively few screenings of germplasm conducted to identify potential sources of resistance to R. solani, and for cotton we have not been able to identify any recent germplasm screenings for resistance to this seedling pathogen. Therefore, the objective of this study was to screen historical as well as more recently developed Upland (Gossypium hirsutum L.) and Pima (Gossypium barbadense L.) cotton germplasm for resistance/susceptibility to R. solani.ResultsThe results of the R. solani screening found no significant differences among 85 Upland and 10 Pima cotton genotypes, which were all similarly susceptible to R. solani based on data for root rot and fresh root weight. While Upland and Pima cotton make up the majority of cotton grown worldwide, the lack of resistance identified in both historical and newer Upland and Pima germplasm suggests a pressing need for further exploration and selection of novel sources of resistance within the vast genetic diversity of different domesticated and wild cotton species.

Las garrapatas de la fiebre del ganado (GFG) representan una carga económica importante para la industria ganadera en todo el mundo y se necesitan nuevas metodologías de tratamiento para su control. Dos pesticidas botánicos similares: Stop the Bites®, y Arkion® Fly and Tick Spray se evaluaron en un ensayo en corral para el control de las GFG en ganado vacuno utilizando el rociador 3-D Quik Hand™ activado por movimiento. El ganado fue tratado pasivamente repetidamente mientras cruzaba el rociador al moverse entre los compartimentos del corral y acceder a la comida y agua y sombra. Ambas formulaciones de los pesticidas botánicos causaron una mortalidad significativa de las GFG en comparación con el control de agua sin pesticida. El pulverizador 3-D Quik Hand™ es una herramienta novedosa para la aplicación de pesticidas con alta frecuencia y bajo volumen de aplicación para el control de las GFG. Esta herramienta podría proporcionar una nueva opción además del tratamiento mediante baños garrapaticidas, cajas de aspersión, o rociadores montados en camiones para uso del programa de erradicación de GTG, por los productores de ganado. También puede ser útil para el tratamiento del antílope nilgai y del venado de cola blanca infestados de fiebre del ganado cuando se mueven a través de huecos en las cercas del ganado o embudos naturales o artificiales que conducen al agua u otros señuelos. Se justifica realizar más investigaciones para optimizar las tasas de pesticidas botánicos y los métodos para la implementación en el campo del rociador 3-D Quik Hand™

When plant-available phosphorus (P) is lost from a soil solution, it often accumulates in the soil as a pool of unavailable legacy P. To acquire legacy P, plants employ recovery strategies, such as forming associations with soil microbes. However, the degree to which plants rely on microbial associations for this purpose varies with crop domestication and subsequent breeding. Here, by generating microbial co-occurrence networks, we sought to explore rhizosphere bacterial interactions in low-P conditions and how they change with tomato domestication and breeding. We grew wild tomato, traditional tomato (developed circa 1900), and modern tomato (developed circa 2020) in high-P and low-P soil throughout their vegetative developmental stage. Co-occurrence network analysis revealed that as the tomatoes progressed along the stages of domestication, the rhizosphere microbiome increased in complexity in a P deficit. However, with the addition of P fertilizer, the wild tomato group became more complex, surpassing the complexity of traditional and modern tomato, suggesting a high degree of responsiveness in the rhizosphere microbiome to P fertilizer by wild tomato relatives. By illustrating these changing patterns of network complexity in the tomato rhizosphere microbiome, we can further understand how plant domestication and breeding have shaped plant–microbe interactions.